diff --git a/A/A1_Load_Cellranger_Data.ipynb b/A/A1_Load_Cellranger_Data.ipynb index 61eb30d..38aa3ff 100644 --- a/A/A1_Load_Cellranger_Data.ipynb +++ b/A/A1_Load_Cellranger_Data.ipynb @@ -1056,52 +1056,6 @@ "##### For each library check out names of features / hashtags" ] }, - { - "cell_type": "code", - "execution_count": 37, - "id": "87f62c22-3c28-4d52-b267-cd29a06cc6e3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['1.1', '2.1', 'S3', '4.1', '5.1', '6.1', '7.1', '8.1', '9.1', '10.1'], dtype='object')\n", - "Index(['12.1', '13.1', '14.1', '15.1', '16.1', '17.1', '18.1', '19.1'], dtype='object')\n", - "Index(['21.1', '22.1', '23.1', '25.1', '26.1', '27.1', '6.1'], dtype='object')\n", - "Index(['S1', '2.2', '3.2', '4.2', 'S5', '6.2', 'S7', '8.2', '9.2', '10.2'], dtype='object')\n", - "Index(['11.2', '12.2', '13.2', '14.2', '15.2', '16.2', '7.2', '18.2', '19.2',\n", - " '20.2'],\n", - " dtype='object')\n", - "Index(['21.2', '22.2', '23.2', '24.2', '25.2', '26.2', '27.2', '28.2', 'S9',\n", - " '13.2'],\n", - " dtype='object')\n", - "Index(['11.3', '2.3', '3.3', '4.3', '5.3', '6.3', '7.3', '8.3', '9.3', 'S10'], dtype='object')\n", - "Index(['13.3', '15.3', '18.3', '19.3', '20.3', '21.3', '22.3', '23.3', '24.3',\n", - " '25.3'],\n", - " dtype='object')\n", - "Index(['26.3', '27.3', '28.3', '26', '28', '33', '34', '10.3', 'S9', 'S10'], dtype='object')\n", - "Index(['2.4', '3.4', 'S3', '5.4', '6.4', '7.4', '8.4', '11.4', '13.4', '15.4'], dtype='object')\n", - "Index(['Ch-CCS-2', 'Ch-CCS-4', 'Ch-CCS-5', 'Ch-CCS-7', 'Ch-CCS-8', 'Ch-CCS-10',\n", - " 'Ch-CCS-14', 'Ch-CCS-15', 'Ch-CCS-16', 'Ch-CCS-21'],\n", - " dtype='object')\n", - "Index(['Ch-CCS-22', 'Ch-CCS-23', 'Ch-CCS-27', 'Ch-CCS-29', 'Ch-CCS-30',\n", - " 'Ch-CCS-32', 'C-CCS-17', 'C-CCS-19', 'C-CCS-24', 'C-CCS-25'],\n", - " dtype='object')\n", - "Index(['No-CCS-1', 'No-CCS-3', 'No-CCS-6', 'No-CCS-9', 'No-CCS-11',\n", - " 'No-CCS-12', 'No-CCS-13', 'No-CCS-18', 'No-CCS-20', 'No-CCS-31'],\n", - " dtype='object')\n", - "Index(['20.4', '21.4', '22.4', '23.4', '24.4', '26.4', '28.4', '4.4', '6.4',\n", - " 'S10'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "for key in protein_dict:\n", - " print(protein_dict[key].var_names)" - ] - }, { "cell_type": "code", "execution_count": 38, diff --git a/B/.ipynb_checkpoints/B3_Combine_Concat_Scanorama-checkpoint.ipynb b/B/.ipynb_checkpoints/B3_Combine_Concat_Scanorama-checkpoint.ipynb index f30eea2..c84ef4f 100644 --- a/B/.ipynb_checkpoints/B3_Combine_Concat_Scanorama-checkpoint.ipynb +++ b/B/.ipynb_checkpoints/B3_Combine_Concat_Scanorama-checkpoint.ipynb @@ -590,221 +590,6 @@ "library_sample_group_mapping['sample'] = library_sample_group_mapping['sample'].str.replace('(\\.0)', '')" ] }, - { - "cell_type": "code", - "execution_count": 114, - "id": "67085051-9610-481e-b5cd-84e1bb8b1d21", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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library.hashtagagesexmclassificationgroupmeasurementsamplelibrary
0<NA>55fK1vollstaendiger_ausschlussno_ccsTP01<NA>
11.262mM2acs_w_o_infectionacsTP12.1L1
21.476mM4acs_w_o_infectionacsTP14.1L1
33.950mM6acs_w_o_infectionacsTP16.1L3
41.766mM7acs_w_o_infectionacsTP17.1L1
..............................
11712.958mK24koronarskleroseno_ccsTP024L12
11812.166mK25koronarskleroseno_ccsTP025L12
1199.464mK26koronarskleroseno_ccsTP026L9
1209.676fK33koronarskleroseno_ccsTP033L9
1219.766mK34koronarskleroseno_ccsTP034L9
\n", - "

122 rows × 9 columns

\n", - "
" - ], - "text/plain": [ - " library.hashtag age sex m classification group \\\n", - "0 55 f K1 vollstaendiger_ausschluss no_ccs \n", - "1 1.2 62 m M2 acs_w_o_infection acs \n", - "2 1.4 76 m M4 acs_w_o_infection acs \n", - "3 3.9 50 m M6 acs_w_o_infection acs \n", - "4 1.7 66 m M7 acs_w_o_infection acs \n", - ".. ... ... .. ... ... ... \n", - "117 12.9 58 m K24 koronarsklerose no_ccs \n", - "118 12.1 66 m K25 koronarsklerose no_ccs \n", - "119 9.4 64 m K26 koronarsklerose no_ccs \n", - "120 9.6 76 f K33 koronarsklerose no_ccs \n", - "121 9.7 66 m K34 koronarsklerose no_ccs \n", - "\n", - " measurement sample library \n", - "0 TP0 1 \n", - "1 TP1 2.1 L1 \n", - "2 TP1 4.1 L1 \n", - "3 TP1 6.1 L3 \n", - "4 TP1 7.1 L1 \n", - ".. ... ... ... \n", - "117 TP0 24 L12 \n", - "118 TP0 25 L12 \n", - "119 TP0 26 L9 \n", - "120 TP0 33 L9 \n", - "121 TP0 34 L9 \n", - "\n", - "[122 rows x 9 columns]" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "library_sample_group_mapping" - ] - }, { "cell_type": "markdown", "id": "c41338a4-1d48-48ea-98bd-8c52d2bb7358", @@ -833,197 +618,6 @@ "print('Last modified' + time.ctime(os.path.getmtime(dataset_path)))" ] }, - { - "cell_type": "code", - "execution_count": 116, - "id": "780f0cec-8bb8-4c23-af65-e249fa75607d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Unnamed: 0library.hashtagmmeasurementdelta_ef_value_groupdelta_ef_valuesample
011.2M2 (1,15)TP1x_greater_11.152.1
121.4M4 (3,1)TP1x_greater_13.104.1
231.9M9 (14,3)TP1x_greater_114.309.1
342.5M15 (5,2)TP1x_greater_15.2015.1
452.9M19 (1,15)TP1x_greater_11.1519.1
........................
9394-M12TP3NaNNaN12.3
9495-M16TP3NaNNaN16.3
959610.8M11TP4NaNNaN11.4
9697-M12TP4NaNNaN12.4
9798-M16TP4NaNNaN16.4
\n", - "

98 rows × 7 columns

\n", - "
" - ], - "text/plain": [ - " Unnamed: 0 library.hashtag m measurement delta_ef_value_group \\\n", - "0 1 1.2 M2 (1,15) TP1 x_greater_1 \n", - "1 2 1.4 M4 (3,1) TP1 x_greater_1 \n", - "2 3 1.9 M9 (14,3) TP1 x_greater_1 \n", - "3 4 2.5 M15 (5,2) TP1 x_greater_1 \n", - "4 5 2.9 M19 (1,15) TP1 x_greater_1 \n", - ".. ... ... ... ... ... \n", - "93 94 - M12 TP3 NaN \n", - "94 95 - M16 TP3 NaN \n", - "95 96 10.8 M11 TP4 NaN \n", - "96 97 - M12 TP4 NaN \n", - "97 98 - M16 TP4 NaN \n", - "\n", - " delta_ef_value sample \n", - "0 1.15 2.1 \n", - "1 3.10 4.1 \n", - "2 14.30 9.1 \n", - "3 5.20 15.1 \n", - "4 1.15 19.1 \n", - ".. ... ... \n", - "93 NaN 12.3 \n", - "94 NaN 16.3 \n", - "95 NaN 11.4 \n", - "96 NaN 12.4 \n", - "97 NaN 16.4 \n", - "\n", - "[98 rows x 7 columns]" - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "library_sample_ef_mapping" - ] - }, { "cell_type": "code", "execution_count": 117, diff --git a/B/B3_Combine_Concat_Scanorama.ipynb b/B/B3_Combine_Concat_Scanorama.ipynb index f30eea2..c84ef4f 100644 --- a/B/B3_Combine_Concat_Scanorama.ipynb +++ b/B/B3_Combine_Concat_Scanorama.ipynb @@ -590,221 +590,6 @@ "library_sample_group_mapping['sample'] = library_sample_group_mapping['sample'].str.replace('(\\.0)', '')" ] }, - { - "cell_type": "code", - "execution_count": 114, - "id": "67085051-9610-481e-b5cd-84e1bb8b1d21", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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library.hashtagagesexmclassificationgroupmeasurementsamplelibrary
0<NA>55fK1vollstaendiger_ausschlussno_ccsTP01<NA>
11.262mM2acs_w_o_infectionacsTP12.1L1
21.476mM4acs_w_o_infectionacsTP14.1L1
33.950mM6acs_w_o_infectionacsTP16.1L3
41.766mM7acs_w_o_infectionacsTP17.1L1
..............................
11712.958mK24koronarskleroseno_ccsTP024L12
11812.166mK25koronarskleroseno_ccsTP025L12
1199.464mK26koronarskleroseno_ccsTP026L9
1209.676fK33koronarskleroseno_ccsTP033L9
1219.766mK34koronarskleroseno_ccsTP034L9
\n", - "

122 rows × 9 columns

\n", - "
" - ], - "text/plain": [ - " library.hashtag age sex m classification group \\\n", - "0 55 f K1 vollstaendiger_ausschluss no_ccs \n", - "1 1.2 62 m M2 acs_w_o_infection acs \n", - "2 1.4 76 m M4 acs_w_o_infection acs \n", - "3 3.9 50 m M6 acs_w_o_infection acs \n", - "4 1.7 66 m M7 acs_w_o_infection acs \n", - ".. ... ... .. ... ... ... \n", - "117 12.9 58 m K24 koronarsklerose no_ccs \n", - "118 12.1 66 m K25 koronarsklerose no_ccs \n", - "119 9.4 64 m K26 koronarsklerose no_ccs \n", - "120 9.6 76 f K33 koronarsklerose no_ccs \n", - "121 9.7 66 m K34 koronarsklerose no_ccs \n", - "\n", - " measurement sample library \n", - "0 TP0 1 \n", - "1 TP1 2.1 L1 \n", - "2 TP1 4.1 L1 \n", - "3 TP1 6.1 L3 \n", - "4 TP1 7.1 L1 \n", - ".. ... ... ... \n", - "117 TP0 24 L12 \n", - "118 TP0 25 L12 \n", - "119 TP0 26 L9 \n", - "120 TP0 33 L9 \n", - "121 TP0 34 L9 \n", - "\n", - "[122 rows x 9 columns]" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "library_sample_group_mapping" - ] - }, { "cell_type": "markdown", "id": "c41338a4-1d48-48ea-98bd-8c52d2bb7358", @@ -833,197 +618,6 @@ "print('Last modified' + time.ctime(os.path.getmtime(dataset_path)))" ] }, - { - "cell_type": "code", - "execution_count": 116, - "id": "780f0cec-8bb8-4c23-af65-e249fa75607d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Unnamed: 0library.hashtagmmeasurementdelta_ef_value_groupdelta_ef_valuesample
011.2M2 (1,15)TP1x_greater_11.152.1
121.4M4 (3,1)TP1x_greater_13.104.1
231.9M9 (14,3)TP1x_greater_114.309.1
342.5M15 (5,2)TP1x_greater_15.2015.1
452.9M19 (1,15)TP1x_greater_11.1519.1
........................
9394-M12TP3NaNNaN12.3
9495-M16TP3NaNNaN16.3
959610.8M11TP4NaNNaN11.4
9697-M12TP4NaNNaN12.4
9798-M16TP4NaNNaN16.4
\n", - "

98 rows × 7 columns

\n", - "
" - ], - "text/plain": [ - " Unnamed: 0 library.hashtag m measurement delta_ef_value_group \\\n", - "0 1 1.2 M2 (1,15) TP1 x_greater_1 \n", - "1 2 1.4 M4 (3,1) TP1 x_greater_1 \n", - "2 3 1.9 M9 (14,3) TP1 x_greater_1 \n", - "3 4 2.5 M15 (5,2) TP1 x_greater_1 \n", - "4 5 2.9 M19 (1,15) TP1 x_greater_1 \n", - ".. ... ... ... ... ... \n", - "93 94 - M12 TP3 NaN \n", - "94 95 - M16 TP3 NaN \n", - "95 96 10.8 M11 TP4 NaN \n", - "96 97 - M12 TP4 NaN \n", - "97 98 - M16 TP4 NaN \n", - "\n", - " delta_ef_value sample \n", - "0 1.15 2.1 \n", - "1 3.10 4.1 \n", - "2 14.30 9.1 \n", - "3 5.20 15.1 \n", - "4 1.15 19.1 \n", - ".. ... ... \n", - "93 NaN 12.3 \n", - "94 NaN 16.3 \n", - "95 NaN 11.4 \n", - "96 NaN 12.4 \n", - "97 NaN 16.4 \n", - "\n", - "[98 rows x 7 columns]" - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "library_sample_ef_mapping" - ] - }, { "cell_type": "code", "execution_count": 117, diff --git a/B/B4_Distribution_Analysis_Comparisons.ipynb b/B/B4_Distribution_Analysis_Comparisons.ipynb index 733d250..cdb7298 100644 --- a/B/B4_Distribution_Analysis_Comparisons.ipynb +++ b/B/B4_Distribution_Analysis_Comparisons.ipynb @@ -2444,325 +2444,6 @@ "unique(data_for_analysis[,c('m_x', 'group_y')]) %>% group_by(group_y) %>% count()" ] }, - { - "cell_type": "code", - "execution_count": 90, - "id": "0845cd81-92c8-4014-b3ad-f5174d078a97", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A grouped_df: 6 × 2
classificationn
<chr><int>
acs_subacute 4
acs_w_infection 5
acs_w_o_infection 19
ccs 16
koronarsklerose 7
vollstaendiger_ausschluss10
\n" - ], - "text/latex": [ - "A grouped\\_df: 6 × 2\n", - "\\begin{tabular}{ll}\n", - " classification & n\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t acs\\_subacute & 4\\\\\n", - "\t acs\\_w\\_infection & 5\\\\\n", - "\t acs\\_w\\_o\\_infection & 19\\\\\n", - "\t ccs & 16\\\\\n", - "\t koronarsklerose & 7\\\\\n", - "\t vollstaendiger\\_ausschluss & 10\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A grouped_df: 6 × 2\n", - "\n", - "| classification <chr> | n <int> |\n", - "|---|---|\n", - "| acs_subacute | 4 |\n", - "| acs_w_infection | 5 |\n", - "| acs_w_o_infection | 19 |\n", - "| ccs | 16 |\n", - "| koronarsklerose | 7 |\n", - "| vollstaendiger_ausschluss | 10 |\n", - "\n" - ], - "text/plain": [ - " classification n \n", - "1 acs_subacute 4\n", - "2 acs_w_infection 5\n", - "3 acs_w_o_infection 19\n", - "4 ccs 16\n", - "5 koronarsklerose 7\n", - "6 vollstaendiger_ausschluss 10" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unique(data_for_analysis[,c('m_x', 'classification')]) %>% group_by(classification) %>% count()" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "fa7e8d7d-0087-4cf6-a18b-69d28cbd3fca", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A grouped_df: 6 × 3
group_ymeasurement_xn
<chr><chr><int>
acs TP122
acs TP224
acs TP323
acs TP417
ccs TP016
no_ccsTP017
\n" - ], - "text/latex": [ - "A grouped\\_df: 6 × 3\n", - "\\begin{tabular}{lll}\n", - " group\\_y & measurement\\_x & n\\\\\n", - " & & \\\\\n", - "\\hline\n", - "\t acs & TP1 & 22\\\\\n", - "\t acs & TP2 & 24\\\\\n", - "\t acs & TP3 & 23\\\\\n", - "\t acs & TP4 & 17\\\\\n", - "\t ccs & TP0 & 16\\\\\n", - "\t no\\_ccs & TP0 & 17\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A grouped_df: 6 × 3\n", - "\n", - "| group_y <chr> | measurement_x <chr> | n <int> |\n", - "|---|---|---|\n", - "| acs | TP1 | 22 |\n", - "| acs | TP2 | 24 |\n", - "| acs | TP3 | 23 |\n", - "| acs | TP4 | 17 |\n", - "| ccs | TP0 | 16 |\n", - "| no_ccs | TP0 | 17 |\n", - "\n" - ], - "text/plain": [ - " group_y measurement_x n \n", - "1 acs TP1 22\n", - "2 acs TP2 24\n", - "3 acs TP3 23\n", - "4 acs TP4 17\n", - "5 ccs TP0 16\n", - "6 no_ccs TP0 17" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unique(data_for_analysis[,c('m_x', 'group_y', 'measurement_x')]) %>% group_by(group_y, measurement_x) %>% count()" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "id": "2df9277a-f3d4-4596-9241-4aea69bbc2d1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "61.1475409836066" - ], - "text/latex": [ - "61.1475409836066" - ], - "text/markdown": [ - "61.1475409836066" - ], - "text/plain": [ - "[1] 61.14754" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mean(unique(data_for_analysis[,c('m_x','age')])$age) # Average Age" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "bf31fb81-8e5a-40e6-b44d-914ce550988b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "31" - ], - "text/latex": [ - "31" - ], - "text/markdown": [ - "31" - ], - "text/plain": [ - "[1] 31" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "min(unique(data_for_analysis[,c('m_x','age')])$age) # Minimum Age" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "10b154fb-393f-49c2-847a-fb1e51f0198e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "81" - ], - "text/latex": [ - "81" - ], - "text/markdown": [ - "81" - ], - "text/plain": [ - "[1] 81" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "max(unique(data_for_analysis[,c('m_x','age')])$age) # Maximum Age" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "95471bdf-b98a-4ed0-bd0d-717c38b1e4ba", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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A grouped_df: 2 × 2
sexn
<chr><int>
f14
m47
\n" - ], - "text/latex": [ - "A grouped\\_df: 2 × 2\n", - "\\begin{tabular}{ll}\n", - " sex & n\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t f & 14\\\\\n", - "\t m & 47\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A grouped_df: 2 × 2\n", - "\n", - "| sex <chr> | n <int> |\n", - "|---|---|\n", - "| f | 14 |\n", - "| m | 47 |\n", - "\n" - ], - "text/plain": [ - " sex n \n", - "1 f 14\n", - "2 m 47" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unique(data_for_analysis[,c('m_x', 'sex')]) %>% group_by(sex) %>% count() # 14 female, 47 male" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2bd9ff5d-1417-4d16-8809-27dbacc7df88", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "064943a9-efe1-422d-b18b-95aff00b5e2d", diff --git a/E/E3_2_MOFA_w_o_clinical.ipynb b/E/E3_2_MOFA_w_o_clinical.ipynb index 92558ac..a79109e 100644 --- a/E/E3_2_MOFA_w_o_clinical.ipynb +++ b/E/E3_2_MOFA_w_o_clinical.ipynb @@ -4389,73 +4389,6 @@ "merged_data_long = melt(merged_data)" ] }, - { - "cell_type": "code", - "execution_count": 186, - "id": "39284ece-9de3-4d2e-96af-21f36b51d1e3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 25
sample_idsamplemeasurementlibraryid.ynamereadpatternsequencefeature_type⋯delta_ef_value_groupdelta_ef_valuedelta_ef_value_classclassmeasurement2measurement_classdelta_ef_value_class_summarizedtp_outcomevariablevalue
<chr><chr><chr><chr><chr><chr><chr><chr><chr><chr>⋯<chr><chr><chr><chr><chr><chr><chr><chr><fct><dbl>
1k1 K1 TP0L13HTO_B0251No-CCS-1 R25PNNNNNNNNNN(BC)GTCAACTCTTTAGCGAntibody Capture⋯NANANAno_ccsTP0_no_ccsTP0_vollstaendiger_ausschlussNATP0_NAFactor10.2399255
2k10K10TP0L11HTO_B0256Ch-CCS-10R25PNNNNNNNNNN(BC)GGTTGCCAGATGTCAAntibody Capture⋯NANANAccs TP0_ccs TP0_ccs NATP0_NAFactor10.9120340
\n" - ], - "text/latex": [ - "A data.frame: 2 × 25\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & sample\\_id & sample & measurement & library & id.y & name & read & pattern & sequence & feature\\_type & ⋯ & delta\\_ef\\_value\\_group & delta\\_ef\\_value & delta\\_ef\\_value\\_class & class & measurement2 & measurement\\_class & delta\\_ef\\_value\\_class\\_summarized & tp\\_outcome & variable & value\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & k1 & K1 & TP0 & L13 & HTO\\_B0251 & No-CCS-1 & R2 & 5PNNNNNNNNNN(BC) & GTCAACTCTTTAGCG & Antibody Capture & ⋯ & NA & NA & NA & no\\_ccs & TP0\\_no\\_ccs & TP0\\_vollstaendiger\\_ausschluss & NA & TP0\\_NA & Factor1 & 0.2399255\\\\\n", - "\t2 & k10 & K10 & TP0 & L11 & HTO\\_B0256 & Ch-CCS-10 & R2 & 5PNNNNNNNNNN(BC) & GGTTGCCAGATGTCA & Antibody Capture & ⋯ & NA & NA & NA & ccs & TP0\\_ccs & TP0\\_ccs & NA & TP0\\_NA & Factor1 & 0.9120340\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 25\n", - "\n", - "| | sample_id <chr> | sample <chr> | measurement <chr> | library <chr> | id.y <chr> | name <chr> | read <chr> | pattern <chr> | sequence <chr> | feature_type <chr> | ⋯ ⋯ | delta_ef_value_group <chr> | delta_ef_value <chr> | delta_ef_value_class <chr> | class <chr> | measurement2 <chr> | measurement_class <chr> | delta_ef_value_class_summarized <chr> | tp_outcome <chr> | variable <fct> | value <dbl> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | k1 | K1 | TP0 | L13 | HTO_B0251 | No-CCS-1 | R2 | 5PNNNNNNNNNN(BC) | GTCAACTCTTTAGCG | Antibody Capture | ⋯ | NA | NA | NA | no_ccs | TP0_no_ccs | TP0_vollstaendiger_ausschluss | NA | TP0_NA | Factor1 | 0.2399255 |\n", - "| 2 | k10 | K10 | TP0 | L11 | HTO_B0256 | Ch-CCS-10 | R2 | 5PNNNNNNNNNN(BC) | GGTTGCCAGATGTCA | Antibody Capture | ⋯ | NA | NA | NA | ccs | TP0_ccs | TP0_ccs | NA | TP0_NA | Factor1 | 0.9120340 |\n", - "\n" - ], - "text/plain": [ - " sample_id sample measurement library id.y name read\n", - "1 k1 K1 TP0 L13 HTO_B0251 No-CCS-1 R2 \n", - "2 k10 K10 TP0 L11 HTO_B0256 Ch-CCS-10 R2 \n", - " pattern sequence feature_type ⋯ delta_ef_value_group\n", - "1 5PNNNNNNNNNN(BC) GTCAACTCTTTAGCG Antibody Capture ⋯ NA \n", - "2 5PNNNNNNNNNN(BC) GGTTGCCAGATGTCA Antibody Capture ⋯ NA \n", - " delta_ef_value delta_ef_value_class class measurement2\n", - "1 NA NA no_ccs TP0_no_ccs \n", - "2 NA NA ccs TP0_ccs \n", - " measurement_class delta_ef_value_class_summarized tp_outcome\n", - "1 TP0_vollstaendiger_ausschluss NA TP0_NA \n", - "2 TP0_ccs NA TP0_NA \n", - " variable value \n", - "1 Factor1 0.2399255\n", - "2 Factor1 0.9120340" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(merged_data_long,2)" - ] - }, { "cell_type": "code", "execution_count": 187, diff --git a/E/E3_Integration_MOFA.ipynb b/E/E3_Integration_MOFA.ipynb index c7a23bf..18f8aa9 100644 --- a/E/E3_Integration_MOFA.ipynb +++ b/E/E3_Integration_MOFA.ipynb @@ -2967,47 +2967,6 @@ "outfile = file.path( paste0(result_path, '/E-Analysis/', model_name) )" ] }, - { - "cell_type": "code", - "execution_count": 119, - "id": "5d926cea-2a3f-4925-9fcc-781299e37dd0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "'/groups/umcg-franke-scrna/tmp01/users/umcg-closert/stemi/data/results//E-Analysis/MOFA_MODELV_FINAL_INTEGRATED-FALSE.hdf5'" - ], - "text/latex": [ - "'/groups/umcg-franke-scrna/tmp01/users/umcg-closert/stemi/data/results//E-Analysis/MOFA\\_MODELV\\_FINAL\\_INTEGRATED-FALSE.hdf5'" - ], - "text/markdown": [ - "'/groups/umcg-franke-scrna/tmp01/users/umcg-closert/stemi/data/results//E-Analysis/MOFA_MODELV_FINAL_INTEGRATED-FALSE.hdf5'" - ], - "text/plain": [ - "[1] \"/groups/umcg-franke-scrna/tmp01/users/umcg-closert/stemi/data/results//E-Analysis/MOFA_MODELV_FINAL_INTEGRATED-FALSE.hdf5\"" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "outfile" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "id": "2a8a5645-3e04-403e-8fd8-565dec359e27", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "#outfile" - ] - }, { "cell_type": "code", "execution_count": 121, @@ -4398,70 +4357,6 @@ "unique(sample_data$tp_outcome)" ] }, - { - "cell_type": "code", - "execution_count": 162, - "id": "13323592-53e4-498a-83c4-82618fbe813f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 36
X.1sample_idsampleidmeasurementlibraryid.ynamereadpattern⋯CK_MBTroponinCRPclinical_dataCK_rawclassmeasurement2measurement_classdelta_ef_value_class_summarizedtp_outcome
<int><chr><chr><dbl><chr><chr><chr><chr><chr><chr>⋯<dbl><dbl><dbl><int><int><chr><chr><chr><chr><chr>
11k1 K1 1TP0L13HTO_B0251No-CCS-1 R25PNNNNNNNNNN(BC)⋯NA0.018634170.4854268143no_ccsTP0_no_ccsTP0_vollstaendiger_ausschlussNATP0_NA
22k10K1010TP0L11HTO_B0256Ch-CCS-10R25PNNNNNNNNNN(BC)⋯NA NA0.26303441NAccs TP0_ccs TP0_ccs NATP0_NA
\n" - ], - "text/latex": [ - "A data.frame: 2 × 36\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & X.1 & sample\\_id & sample & id & measurement & library & id.y & name & read & pattern & ⋯ & CK\\_MB & Troponin & CRP & clinical\\_data & CK\\_raw & class & measurement2 & measurement\\_class & delta\\_ef\\_value\\_class\\_summarized & tp\\_outcome\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & 1 & k1 & K1 & 1 & TP0 & L13 & HTO\\_B0251 & No-CCS-1 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & NA & 0.01863417 & 0.4854268 & 1 & 43 & no\\_ccs & TP0\\_no\\_ccs & TP0\\_vollstaendiger\\_ausschluss & NA & TP0\\_NA\\\\\n", - "\t2 & 2 & k10 & K10 & 10 & TP0 & L11 & HTO\\_B0256 & Ch-CCS-10 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & NA & NA & 0.2630344 & 1 & NA & ccs & TP0\\_ccs & TP0\\_ccs & NA & TP0\\_NA\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 36\n", - "\n", - "| | X.1 <int> | sample_id <chr> | sample <chr> | id <dbl> | measurement <chr> | library <chr> | id.y <chr> | name <chr> | read <chr> | pattern <chr> | ⋯ ⋯ | CK_MB <dbl> | Troponin <dbl> | CRP <dbl> | clinical_data <int> | CK_raw <int> | class <chr> | measurement2 <chr> | measurement_class <chr> | delta_ef_value_class_summarized <chr> | tp_outcome <chr> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | 1 | k1 | K1 | 1 | TP0 | L13 | HTO_B0251 | No-CCS-1 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | NA | 0.01863417 | 0.4854268 | 1 | 43 | no_ccs | TP0_no_ccs | TP0_vollstaendiger_ausschluss | NA | TP0_NA |\n", - "| 2 | 2 | k10 | K10 | 10 | TP0 | L11 | HTO_B0256 | Ch-CCS-10 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | NA | NA | 0.2630344 | 1 | NA | ccs | TP0_ccs | TP0_ccs | NA | TP0_NA |\n", - "\n" - ], - "text/plain": [ - " X.1 sample_id sample id measurement library id.y name read\n", - "1 1 k1 K1 1 TP0 L13 HTO_B0251 No-CCS-1 R2 \n", - "2 2 k10 K10 10 TP0 L11 HTO_B0256 Ch-CCS-10 R2 \n", - " pattern ⋯ CK_MB Troponin CRP clinical_data CK_raw class \n", - "1 5PNNNNNNNNNN(BC) ⋯ NA 0.01863417 0.4854268 1 43 no_ccs\n", - "2 5PNNNNNNNNNN(BC) ⋯ NA NA 0.2630344 1 NA ccs \n", - " measurement2 measurement_class delta_ef_value_class_summarized\n", - "1 TP0_no_ccs TP0_vollstaendiger_ausschluss NA \n", - "2 TP0_ccs TP0_ccs NA \n", - " tp_outcome\n", - "1 TP0_NA \n", - "2 TP0_NA " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(sample_data,2)" - ] - }, { "cell_type": "markdown", "id": "705fd53f-47f4-4b44-9436-f88023fd40c1", diff --git a/E/E4_MOFA_Factor_Amount_Test.ipynb b/E/E4_MOFA_Factor_Amount_Test.ipynb index 73efb99..abfd80d 100644 --- a/E/E4_MOFA_Factor_Amount_Test.ipynb +++ b/E/E4_MOFA_Factor_Amount_Test.ipynb @@ -1476,61 +1476,6 @@ "final_data_long$X = NULL" ] }, - { - "cell_type": "code", - "execution_count": 50, - "id": "ab15d009-6494-41d4-b740-5a374108cf15", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 4
sample_idvariablevaluetype
<chr><chr><dbl><chr>
1k1 CK-2.397022clinical_data
2k10CK NAclinical_data
\n" - ], - "text/latex": [ - "A data.frame: 2 × 4\n", - "\\begin{tabular}{r|llll}\n", - " & sample\\_id & variable & value & type\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t1 & k1 & CK & -2.397022 & clinical\\_data\\\\\n", - "\t2 & k10 & CK & NA & clinical\\_data\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 4\n", - "\n", - "| | sample_id <chr> | variable <chr> | value <dbl> | type <chr> |\n", - "|---|---|---|---|---|\n", - "| 1 | k1 | CK | -2.397022 | clinical_data |\n", - "| 2 | k10 | CK | NA | clinical_data |\n", - "\n" - ], - "text/plain": [ - " sample_id variable value type \n", - "1 k1 CK -2.397022 clinical_data\n", - "2 k10 CK NA clinical_data" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(final_data_long,2)" - ] - }, { "cell_type": "code", "execution_count": 51, @@ -2084,82 +2029,6 @@ "names(data_list)" ] }, - { - "cell_type": "code", - "execution_count": 69, - "id": "6e236407-9843-4293-b413-2ca5f3aa1609", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A matrix: 4 × 128 of type dbl
k1k10k11k12k13k14k15k16k17k18⋯m7.2m7.3m7.4m8.1m8.2m8.3m8.4m9.1m9.2m9.3
CK-2.3970221 NA-1.443924-0.8775918-1.5053606-0.3268231-0.6941258-1.648873-1.335178-0.9402147⋯1.2402687 0.4383163-0.6941258-0.5921965-0.9897138-1.078094 NA 0.87759181.0417650 0.05181301
CK_MB NA NA NA NA NA NA NA NA NA NA⋯0.9766002-0.5602008 NA NA NA NA NA 0.52046470.6853063-0.50090447
CRP-0.3186394-0.8871466-1.574445 NA-0.3186394 0.2683089-1.5744450 NA-1.574445 NA⋯ NA 0.9051525 0.3827258 0.7050659 1.2760918 1.6637931.426077-0.5024022 NA 0.38272581
Troponin-1.2864792 NA-1.286479-1.2864792-1.2864792-0.8172368-1.2864792-1.286479-1.286479-1.2864792⋯0.6744898 0.4585578 NA-0.6211776-0.5951785 NA NA-0.51944810.6211776 0.21779838
\n" - ], - "text/latex": [ - "A matrix: 4 × 128 of type dbl\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & k1 & k10 & k11 & k12 & k13 & k14 & k15 & k16 & k17 & k18 & ⋯ & m7.2 & m7.3 & m7.4 & m8.1 & m8.2 & m8.3 & m8.4 & m9.1 & m9.2 & m9.3\\\\\n", - "\\hline\n", - "\tCK & -2.3970221 & NA & -1.443924 & -0.8775918 & -1.5053606 & -0.3268231 & -0.6941258 & -1.648873 & -1.335178 & -0.9402147 & ⋯ & 1.2402687 & 0.4383163 & -0.6941258 & -0.5921965 & -0.9897138 & -1.078094 & NA & 0.8775918 & 1.0417650 & 0.05181301\\\\\n", - "\tCK\\_MB & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & ⋯ & 0.9766002 & -0.5602008 & NA & NA & NA & NA & NA & 0.5204647 & 0.6853063 & -0.50090447\\\\\n", - "\tCRP & -0.3186394 & -0.8871466 & -1.574445 & NA & -0.3186394 & 0.2683089 & -1.5744450 & NA & -1.574445 & NA & ⋯ & NA & 0.9051525 & 0.3827258 & 0.7050659 & 1.2760918 & 1.663793 & 1.426077 & -0.5024022 & NA & 0.38272581\\\\\n", - "\tTroponin & -1.2864792 & NA & -1.286479 & -1.2864792 & -1.2864792 & -0.8172368 & -1.2864792 & -1.286479 & -1.286479 & -1.2864792 & ⋯ & 0.6744898 & 0.4585578 & NA & -0.6211776 & -0.5951785 & NA & NA & -0.5194481 & 0.6211776 & 0.21779838\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A matrix: 4 × 128 of type dbl\n", - "\n", - "| | k1 | k10 | k11 | k12 | k13 | k14 | k15 | k16 | k17 | k18 | ⋯ | m7.2 | m7.3 | m7.4 | m8.1 | m8.2 | m8.3 | m8.4 | m9.1 | m9.2 | m9.3 |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| CK | -2.3970221 | NA | -1.443924 | -0.8775918 | -1.5053606 | -0.3268231 | -0.6941258 | -1.648873 | -1.335178 | -0.9402147 | ⋯ | 1.2402687 | 0.4383163 | -0.6941258 | -0.5921965 | -0.9897138 | -1.078094 | NA | 0.8775918 | 1.0417650 | 0.05181301 |\n", - "| CK_MB | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | ⋯ | 0.9766002 | -0.5602008 | NA | NA | NA | NA | NA | 0.5204647 | 0.6853063 | -0.50090447 |\n", - "| CRP | -0.3186394 | -0.8871466 | -1.574445 | NA | -0.3186394 | 0.2683089 | -1.5744450 | NA | -1.574445 | NA | ⋯ | NA | 0.9051525 | 0.3827258 | 0.7050659 | 1.2760918 | 1.663793 | 1.426077 | -0.5024022 | NA | 0.38272581 |\n", - "| Troponin | -1.2864792 | NA | -1.286479 | -1.2864792 | -1.2864792 | -0.8172368 | -1.2864792 | -1.286479 | -1.286479 | -1.2864792 | ⋯ | 0.6744898 | 0.4585578 | NA | -0.6211776 | -0.5951785 | NA | NA | -0.5194481 | 0.6211776 | 0.21779838 |\n", - "\n" - ], - "text/plain": [ - " k1 k10 k11 k12 k13 k14 \n", - "CK -2.3970221 NA -1.443924 -0.8775918 -1.5053606 -0.3268231\n", - "CK_MB NA NA NA NA NA NA\n", - "CRP -0.3186394 -0.8871466 -1.574445 NA -0.3186394 0.2683089\n", - "Troponin -1.2864792 NA -1.286479 -1.2864792 -1.2864792 -0.8172368\n", - " k15 k16 k17 k18 ⋯ m7.2 m7.3 \n", - "CK -0.6941258 -1.648873 -1.335178 -0.9402147 ⋯ 1.2402687 0.4383163\n", - "CK_MB NA NA NA NA ⋯ 0.9766002 -0.5602008\n", - "CRP -1.5744450 NA -1.574445 NA ⋯ NA 0.9051525\n", - "Troponin -1.2864792 -1.286479 -1.286479 -1.2864792 ⋯ 0.6744898 0.4585578\n", - " m7.4 m8.1 m8.2 m8.3 m8.4 m9.1 \n", - "CK -0.6941258 -0.5921965 -0.9897138 -1.078094 NA 0.8775918\n", - "CK_MB NA NA NA NA NA 0.5204647\n", - "CRP 0.3827258 0.7050659 1.2760918 1.663793 1.426077 -0.5024022\n", - "Troponin NA -0.6211776 -0.5951785 NA NA -0.5194481\n", - " m9.2 m9.3 \n", - "CK 1.0417650 0.05181301\n", - "CK_MB 0.6853063 -0.50090447\n", - "CRP NA 0.38272581\n", - "Troponin 0.6211776 0.21779838" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(data_list[[1]])" - ] - }, { "cell_type": "code", "execution_count": 70, diff --git a/E/E6_Pathway_Enrichment_MOFA_approach.ipynb b/E/E6_Pathway_Enrichment_MOFA_approach.ipynb index 6ee20ab..19d3364 100644 --- a/E/E6_Pathway_Enrichment_MOFA_approach.ipynb +++ b/E/E6_Pathway_Enrichment_MOFA_approach.ipynb @@ -1458,61 +1458,6 @@ "print(file.info(path)$mtime)" ] }, - { - "cell_type": "code", - "execution_count": 14, - "id": "c8a67c22-c40e-42e6-87d7-8f9a34167c38", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 5
Xsample_idvariablevaluetype
<int><chr><chr><dbl><chr>
11k1 CK-2.397022clinical_data
22k10CK NAclinical_data
\n" - ], - "text/latex": [ - "A data.frame: 2 × 5\n", - "\\begin{tabular}{r|lllll}\n", - " & X & sample\\_id & variable & value & type\\\\\n", - " & & & & & \\\\\n", - "\\hline\n", - "\t1 & 1 & k1 & CK & -2.397022 & clinical\\_data\\\\\n", - "\t2 & 2 & k10 & CK & NA & clinical\\_data\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 5\n", - "\n", - "| | X <int> | sample_id <chr> | variable <chr> | value <dbl> | type <chr> |\n", - "|---|---|---|---|---|---|\n", - "| 1 | 1 | k1 | CK | -2.397022 | clinical_data |\n", - "| 2 | 2 | k10 | CK | NA | clinical_data |\n", - "\n" - ], - "text/plain": [ - " X sample_id variable value type \n", - "1 1 k1 CK -2.397022 clinical_data\n", - "2 2 k10 CK NA clinical_data" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(data_long,2)" - ] - }, { "cell_type": "code", "execution_count": 15, @@ -1604,57 +1549,6 @@ "unique(data_long$type[data_long$sample_id == 'm.20.1'])" ] }, - { - "cell_type": "code", - "execution_count": 19, - "id": "159c8d22-0040-4ef3-84ca-11ef58c91756", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\n", - "
A data.frame: 1 × 4
sample_idvariablevaluetype
<chr><chr><dbl><chr>
117m6.4CK-1.735543clinical_data
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A data.frame: 2 × 30
X.1sample_idsampleidmeasurementlibraryid.ynamereadpattern⋯meta_datadelta_ef_value_groupdelta_ef_valuedelta_ef_value_classef_classification_dataCKCK_MBTroponinCRPclinical_data
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136136m6.4M66.4TP4L14HTO_B02596.4R25PNNNNNNNNNN(BC)⋯1x_smaller_10.875intermediate1590.4581.31
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\n" - ], - "text/latex": [ - "A data.frame: 2 × 30\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & X.1 & sample\\_id & sample & id & measurement & library & id.y & name & read & pattern & ⋯ & meta\\_data & delta\\_ef\\_value\\_group & delta\\_ef\\_value & delta\\_ef\\_value\\_class & ef\\_classification\\_data & CK & CK\\_MB & Troponin & CRP & clinical\\_data\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t136 & 136 & m6.4 & M6 & 6.4 & TP4 & L14 & HTO\\_B0259 & 6.4 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & 1 & x\\_smaller\\_1 & 0.875 & intermediate & 1 & 59 & & 0.458 & 1.3 & 1\\\\\n", - "\t137 & 137 & m6.4 & M6 & 6.4 & TP4 & L10 & HTO\\_B0255 & 6.4 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & 1 & x\\_smaller\\_1 & 0.875 & intermediate & 1 & 59 & & 0.458 & 1.3 & 1\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 30\n", - "\n", - "| | X.1 <int> | sample_id <chr> | sample <chr> | id <dbl> | measurement <chr> | library <chr> | id.y <chr> | name <chr> | read <chr> | pattern <chr> | ⋯ ⋯ | meta_data <int> | delta_ef_value_group <chr> | delta_ef_value <dbl> | delta_ef_value_class <chr> | ef_classification_data <int> | CK <int> | CK_MB <chr> | Troponin <dbl> | CRP <chr> | clinical_data <int> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 136 | 136 | m6.4 | M6 | 6.4 | TP4 | L14 | HTO_B0259 | 6.4 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | 1 | x_smaller_1 | 0.875 | intermediate | 1 | 59 | | 0.458 | 1.3 | 1 |\n", - "| 137 | 137 | m6.4 | M6 | 6.4 | TP4 | L10 | HTO_B0255 | 6.4 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | 1 | x_smaller_1 | 0.875 | intermediate | 1 | 59 | | 0.458 | 1.3 | 1 |\n", - "\n" - ], - "text/plain": [ - " X.1 sample_id sample id measurement library id.y name read\n", - "136 136 m6.4 M6 6.4 TP4 L14 HTO_B0259 6.4 R2 \n", - "137 137 m6.4 M6 6.4 TP4 L10 HTO_B0255 6.4 R2 \n", - " pattern ⋯ meta_data delta_ef_value_group delta_ef_value\n", - "136 5PNNNNNNNNNN(BC) ⋯ 1 x_smaller_1 0.875 \n", - "137 5PNNNNNNNNNN(BC) ⋯ 1 x_smaller_1 0.875 \n", - " delta_ef_value_class ef_classification_data CK CK_MB Troponin CRP\n", - "136 intermediate 1 59 0.458 1.3\n", - "137 intermediate 1 59 0.458 1.3\n", - " clinical_data\n", - "136 1 \n", - "137 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sample_data[(sample_data$sample_id == 'm6.4'),] " - ] - }, { "cell_type": "code", "execution_count": 22, @@ -1827,120 +1657,6 @@ "sample_data$sample_id[(sample_data$sample_id == 'm6.1') & (sample_data$library %in% c('L3'))] = 'm6.12'" ] }, - { - "cell_type": "code", - "execution_count": 29, - "id": "463c8ebf-5e07-4ab2-a201-ee477b40d225", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\n", - "
A data.frame: 1 × 30
X.1sample_idsampleidmeasurementlibraryid.ynamereadpattern⋯meta_datadelta_ef_value_groupdelta_ef_valuedelta_ef_value_classef_classification_dataCKCK_MBTroponinCRPclinical_data
<int><chr><chr><dbl><chr><chr><chr><chr><chr><chr>⋯<int><chr><dbl><chr><int><dbl><dbl><dbl><dbl><int>
136136m6.4M66.4TP4L14HTO_B02596.4R25PNNNNNNNNNN(BC)⋯1x_smaller_10.875intermediate15.906891NA0.54399071.2016341
\n" - ], - "text/latex": [ - "A data.frame: 1 × 30\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & X.1 & sample\\_id & sample & id & measurement & library & id.y & name & read & pattern & ⋯ & meta\\_data & delta\\_ef\\_value\\_group & delta\\_ef\\_value & delta\\_ef\\_value\\_class & ef\\_classification\\_data & CK & CK\\_MB & Troponin & CRP & clinical\\_data\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t136 & 136 & m6.4 & M6 & 6.4 & TP4 & L14 & HTO\\_B0259 & 6.4 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & 1 & x\\_smaller\\_1 & 0.875 & intermediate & 1 & 5.906891 & NA & 0.5439907 & 1.201634 & 1\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 1 × 30\n", - "\n", - "| | X.1 <int> | sample_id <chr> | sample <chr> | id <dbl> | measurement <chr> | library <chr> | id.y <chr> | name <chr> | read <chr> | pattern <chr> | ⋯ ⋯ | meta_data <int> | delta_ef_value_group <chr> | delta_ef_value <dbl> | delta_ef_value_class <chr> | ef_classification_data <int> | CK <dbl> | CK_MB <dbl> | Troponin <dbl> | CRP <dbl> | clinical_data <int> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 136 | 136 | m6.4 | M6 | 6.4 | TP4 | L14 | HTO_B0259 | 6.4 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | 1 | x_smaller_1 | 0.875 | intermediate | 1 | 5.906891 | NA | 0.5439907 | 1.201634 | 1 |\n", - "\n" - ], - "text/plain": [ - " X.1 sample_id sample id measurement library id.y name read\n", - "136 136 m6.4 M6 6.4 TP4 L14 HTO_B0259 6.4 R2 \n", - " pattern ⋯ meta_data delta_ef_value_group delta_ef_value\n", - "136 5PNNNNNNNNNN(BC) ⋯ 1 x_smaller_1 0.875 \n", - " delta_ef_value_class ef_classification_data CK CK_MB Troponin \n", - "136 intermediate 1 5.906891 NA 0.5439907\n", - " CRP clinical_data\n", - "136 1.201634 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sample_data[(sample_data$sample_id == 'm6.4'),] " - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "e8ba3d42-0fec-4dde-8863-bc350d846798", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\n", - "
A data.frame: 1 × 30
X.1sample_idsampleidmeasurementlibraryid.ynamereadpattern⋯meta_datadelta_ef_value_groupdelta_ef_valuedelta_ef_value_classef_classification_dataCKCK_MBTroponinCRPclinical_data
<int><chr><chr><dbl><chr><chr><chr><chr><chr><chr>⋯<int><chr><dbl><chr><int><dbl><dbl><dbl><dbl><int>
137137m6.42M66.4TP4L10HTO_B02556.4R25PNNNNNNNNNN(BC)⋯1x_smaller_10.875intermediate15.906891NA0.54399071.2016341
\n" - ], - "text/latex": [ - "A data.frame: 1 × 30\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & X.1 & sample\\_id & sample & id & measurement & library & id.y & name & read & pattern & ⋯ & meta\\_data & delta\\_ef\\_value\\_group & delta\\_ef\\_value & delta\\_ef\\_value\\_class & ef\\_classification\\_data & CK & CK\\_MB & Troponin & CRP & clinical\\_data\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t137 & 137 & m6.42 & M6 & 6.4 & TP4 & L10 & HTO\\_B0255 & 6.4 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & 1 & x\\_smaller\\_1 & 0.875 & intermediate & 1 & 5.906891 & NA & 0.5439907 & 1.201634 & 1\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 1 × 30\n", - "\n", - "| | X.1 <int> | sample_id <chr> | sample <chr> | id <dbl> | measurement <chr> | library <chr> | id.y <chr> | name <chr> | read <chr> | pattern <chr> | ⋯ ⋯ | meta_data <int> | delta_ef_value_group <chr> | delta_ef_value <dbl> | delta_ef_value_class <chr> | ef_classification_data <int> | CK <dbl> | CK_MB <dbl> | Troponin <dbl> | CRP <dbl> | clinical_data <int> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 137 | 137 | m6.42 | M6 | 6.4 | TP4 | L10 | HTO_B0255 | 6.4 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | 1 | x_smaller_1 | 0.875 | intermediate | 1 | 5.906891 | NA | 0.5439907 | 1.201634 | 1 |\n", - "\n" - ], - "text/plain": [ - " X.1 sample_id sample id measurement library id.y name read\n", - "137 137 m6.42 M6 6.4 TP4 L10 HTO_B0255 6.4 R2 \n", - " pattern ⋯ meta_data delta_ef_value_group delta_ef_value\n", - "137 5PNNNNNNNNNN(BC) ⋯ 1 x_smaller_1 0.875 \n", - " delta_ef_value_class ef_classification_data CK CK_MB Troponin \n", - "137 intermediate 1 5.906891 NA 0.5439907\n", - " CRP clinical_data\n", - "137 1.201634 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sample_data[(sample_data$sample_id == 'm6.42'),] " - ] - }, { "cell_type": "markdown", "id": "4ea9f63b-5dfa-4bea-bf17-9ac925af05da", @@ -3926,6 +3642,7 @@ "execution_count": 118, "id": "3f9fb18c-02f2-4bc5-b37f-85142e3b708c", "metadata": { + "scrolled": true, "tags": [] }, "outputs": [ @@ -3982,85 +3699,6 @@ "head(model@samples_metadata, n=3)" ] }, - { - "cell_type": "code", - "execution_count": 119, - "id": "ed22160c-1e70-4d21-b53c-02cbff6b0419", - "metadata": {}, - "outputs": [], - "source": [ - "#sample_data$sample_id[(sample_data$sample_id == 'm13.2') & (sample_data$library =='L6')] = 'm13.22' #13.2-L5, 13.2-L6\t, 6.4-L10, 6.4-L14\t\n", - "#sample_data$sample_id[(sample_data$sample_id == 'm6.4') & (sample_data$library == 'L14')] = 'm6.42'\n", - "#sample_data$sample_id[(sample_data$sample_id == 'm6.1') & (sample_data$library == 'L3')] = 'm6.12'" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "id": "03bab8fb-9aef-4b47-a90a-e428d2d0861a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\n", - "
A data.frame: 1 × 30
X.1sample_idsampleidmeasurementlibraryid.ynamereadpattern⋯meta_datadelta_ef_value_groupdelta_ef_valuedelta_ef_value_classef_classification_dataCKCK_MBTroponinCRPclinical_data
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136136m6.4M66.4TP4L14HTO_B02596.4R25PNNNNNNNNNN(BC)⋯1x_smaller_10.875intermediate15.906891NA0.54399071.2016341
\n" - ], - "text/latex": [ - "A data.frame: 1 × 30\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & X.1 & sample\\_id & sample & id & measurement & library & id.y & name & read & pattern & ⋯ & meta\\_data & delta\\_ef\\_value\\_group & delta\\_ef\\_value & delta\\_ef\\_value\\_class & ef\\_classification\\_data & CK & CK\\_MB & Troponin & CRP & clinical\\_data\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t136 & 136 & m6.4 & M6 & 6.4 & TP4 & L14 & HTO\\_B0259 & 6.4 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & 1 & x\\_smaller\\_1 & 0.875 & intermediate & 1 & 5.906891 & NA & 0.5439907 & 1.201634 & 1\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 1 × 30\n", - "\n", - "| | X.1 <int> | sample_id <chr> | sample <chr> | id <dbl> | measurement <chr> | library <chr> | id.y <chr> | name <chr> | read <chr> | pattern <chr> | ⋯ ⋯ | meta_data <int> | delta_ef_value_group <chr> | delta_ef_value <dbl> | delta_ef_value_class <chr> | ef_classification_data <int> | CK <dbl> | CK_MB <dbl> | Troponin <dbl> | CRP <dbl> | clinical_data <int> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 136 | 136 | m6.4 | M6 | 6.4 | TP4 | L14 | HTO_B0259 | 6.4 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | 1 | x_smaller_1 | 0.875 | intermediate | 1 | 5.906891 | NA | 0.5439907 | 1.201634 | 1 |\n", - "\n" - ], - "text/plain": [ - " X.1 sample_id sample id measurement library id.y name read\n", - "136 136 m6.4 M6 6.4 TP4 L14 HTO_B0259 6.4 R2 \n", - " pattern ⋯ meta_data delta_ef_value_group delta_ef_value\n", - "136 5PNNNNNNNNNN(BC) ⋯ 1 x_smaller_1 0.875 \n", - " delta_ef_value_class ef_classification_data CK CK_MB Troponin \n", - "136 intermediate 1 5.906891 NA 0.5439907\n", - " CRP clinical_data\n", - "136 1.201634 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(sample_data[sample_data$sample_id == 'm6.4',])" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "id": "ebffb606-13d8-4769-aaf1-5d9d2bcb3106", - "metadata": {}, - "outputs": [], - "source": [ - "#ample_data" - ] - }, { "cell_type": "code", "execution_count": 122, @@ -4119,16 +3757,6 @@ "length(unique(sample_data$sample_id))" ] }, - { - "cell_type": "code", - "execution_count": 124, - "id": "62c3cc03-e583-48fb-b673-060243908177", - "metadata": {}, - "outputs": [], - "source": [ - "#head( sample_data)" - ] - }, { "cell_type": "code", "execution_count": 125, @@ -4139,67 +3767,6 @@ "model@samples_metadata = merge(model@samples_metadata, sample_data, by.x = 'sample', by.y = 'sample_id')" ] }, - { - "cell_type": "code", - "execution_count": 126, - "id": "1d98d3a0-b7b2-4dd7-976f-c5187e9c2b6b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 31
samplegroup.xX.1sample.yidmeasurementlibraryid.ynameread⋯meta_datadelta_ef_value_groupdelta_ef_valuedelta_ef_value_classef_classification_dataCKCK_MBTroponinCRPclinical_data
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1k1 group11K1 1TP0L13HTO_B0251No-CCS-1 R2⋯1NANANA05.459432NA0.018634170.48542681
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\n" - ], - "text/latex": [ - "A data.frame: 2 × 31\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & sample & group.x & X.1 & sample.y & id & measurement & library & id.y & name & read & ⋯ & meta\\_data & delta\\_ef\\_value\\_group & delta\\_ef\\_value & delta\\_ef\\_value\\_class & ef\\_classification\\_data & CK & CK\\_MB & Troponin & CRP & clinical\\_data\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & k1 & group1 & 1 & K1 & 1 & TP0 & L13 & HTO\\_B0251 & No-CCS-1 & R2 & ⋯ & 1 & NA & NA & NA & 0 & 5.459432 & NA & 0.01863417 & 0.4854268 & 1\\\\\n", - "\t2 & k10 & group1 & 2 & K10 & 10 & TP0 & L11 & HTO\\_B0256 & Ch-CCS-10 & R2 & ⋯ & 1 & NA & NA & NA & 0 & NA & NA & NA & 0.2630344 & 1\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 31\n", - "\n", - "| | sample <chr> | group.x <fct> | X.1 <int> | sample.y <chr> | id <dbl> | measurement <chr> | library <chr> | id.y <chr> | name <chr> | read <chr> | ⋯ ⋯ | meta_data <int> | delta_ef_value_group <chr> | delta_ef_value <dbl> | delta_ef_value_class <chr> | ef_classification_data <int> | CK <dbl> | CK_MB <dbl> | Troponin <dbl> | CRP <dbl> | clinical_data <int> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | k1 | group1 | 1 | K1 | 1 | TP0 | L13 | HTO_B0251 | No-CCS-1 | R2 | ⋯ | 1 | NA | NA | NA | 0 | 5.459432 | NA | 0.01863417 | 0.4854268 | 1 |\n", - "| 2 | k10 | group1 | 2 | K10 | 10 | TP0 | L11 | HTO_B0256 | Ch-CCS-10 | R2 | ⋯ | 1 | NA | NA | NA | 0 | NA | NA | NA | 0.2630344 | 1 |\n", - "\n" - ], - "text/plain": [ - " sample group.x X.1 sample.y id measurement library id.y name read ⋯\n", - "1 k1 group1 1 K1 1 TP0 L13 HTO_B0251 No-CCS-1 R2 ⋯\n", - "2 k10 group1 2 K10 10 TP0 L11 HTO_B0256 Ch-CCS-10 R2 ⋯\n", - " meta_data delta_ef_value_group delta_ef_value delta_ef_value_class\n", - "1 1 NA NA NA \n", - "2 1 NA NA NA \n", - " ef_classification_data CK CK_MB Troponin CRP clinical_data\n", - "1 0 5.459432 NA 0.01863417 0.4854268 1 \n", - "2 0 NA NA NA 0.2630344 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(model@samples_metadata,2)" - ] - }, { "cell_type": "code", "execution_count": 127, @@ -4371,82 +3938,6 @@ "weights = get_weights(model, views = \"all\", factors = \"all\")" ] }, - { - "cell_type": "code", - "execution_count": 135, - "id": "8ae27478-52db-4f4a-afa6-253c4775842c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A matrix: 4 × 128 of type dbl
k1k10k11k12k13k14k15k16k17k18⋯m7.2m7.3m7.4m8.1m8.2m8.3m8.4m9.1m9.2m9.3
CK-2.540364 NA-1.530271-0.9300719-1.595381-0.3463673-0.7356347-1.747476-1.415022-0.9964397⋯1.314437 0.4645275-0.7356347-0.6276100-1.0488988-1.142564 NA 0.93007181.1040625 0.05491135
CK_MB NA NA NA NA NA NA NA NA NA NA⋯1.034993-0.5937088 NA NA NA NA NA 0.55158060.7262797-0.53086653
CRP-0.338300-0.940804-1.669203 NA-0.338300 0.2837479-1.6692029 NA-1.669203 NA⋯ NA 0.9586747 0.4050069 0.7466229 1.3517962 1.7626821.51075-0.5330519 NA 0.40500689
Troponin-1.364494 NA-1.364494-1.3644941-1.364494-0.8671909-1.3644941-1.364494-1.364494-1.3644941⋯0.713741 0.4848964 NA-0.6594074-0.6318535 NA NA-0.55159440.6572408 0.22973948
\n" - ], - "text/latex": [ - "A matrix: 4 × 128 of type dbl\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & k1 & k10 & k11 & k12 & k13 & k14 & k15 & k16 & k17 & k18 & ⋯ & m7.2 & m7.3 & m7.4 & m8.1 & m8.2 & m8.3 & m8.4 & m9.1 & m9.2 & m9.3\\\\\n", - "\\hline\n", - "\tCK & -2.540364 & NA & -1.530271 & -0.9300719 & -1.595381 & -0.3463673 & -0.7356347 & -1.747476 & -1.415022 & -0.9964397 & ⋯ & 1.314437 & 0.4645275 & -0.7356347 & -0.6276100 & -1.0488988 & -1.142564 & NA & 0.9300718 & 1.1040625 & 0.05491135\\\\\n", - "\tCK\\_MB & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & ⋯ & 1.034993 & -0.5937088 & NA & NA & NA & NA & NA & 0.5515806 & 0.7262797 & -0.53086653\\\\\n", - "\tCRP & -0.338300 & -0.940804 & -1.669203 & NA & -0.338300 & 0.2837479 & -1.6692029 & NA & -1.669203 & NA & ⋯ & NA & 0.9586747 & 0.4050069 & 0.7466229 & 1.3517962 & 1.762682 & 1.51075 & -0.5330519 & NA & 0.40500689\\\\\n", - "\tTroponin & -1.364494 & NA & -1.364494 & -1.3644941 & -1.364494 & -0.8671909 & -1.3644941 & -1.364494 & -1.364494 & -1.3644941 & ⋯ & 0.713741 & 0.4848964 & NA & -0.6594074 & -0.6318535 & NA & NA & -0.5515944 & 0.6572408 & 0.22973948\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A matrix: 4 × 128 of type dbl\n", - "\n", - "| | k1 | k10 | k11 | k12 | k13 | k14 | k15 | k16 | k17 | k18 | ⋯ | m7.2 | m7.3 | m7.4 | m8.1 | m8.2 | m8.3 | m8.4 | m9.1 | m9.2 | m9.3 |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| CK | -2.540364 | NA | -1.530271 | -0.9300719 | -1.595381 | -0.3463673 | -0.7356347 | -1.747476 | -1.415022 | -0.9964397 | ⋯ | 1.314437 | 0.4645275 | -0.7356347 | -0.6276100 | -1.0488988 | -1.142564 | NA | 0.9300718 | 1.1040625 | 0.05491135 |\n", - "| CK_MB | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | ⋯ | 1.034993 | -0.5937088 | NA | NA | NA | NA | NA | 0.5515806 | 0.7262797 | -0.53086653 |\n", - "| CRP | -0.338300 | -0.940804 | -1.669203 | NA | -0.338300 | 0.2837479 | -1.6692029 | NA | -1.669203 | NA | ⋯ | NA | 0.9586747 | 0.4050069 | 0.7466229 | 1.3517962 | 1.762682 | 1.51075 | -0.5330519 | NA | 0.40500689 |\n", - "| Troponin | -1.364494 | NA | -1.364494 | -1.3644941 | -1.364494 | -0.8671909 | -1.3644941 | -1.364494 | -1.364494 | -1.3644941 | ⋯ | 0.713741 | 0.4848964 | NA | -0.6594074 | -0.6318535 | NA | NA | -0.5515944 | 0.6572408 | 0.22973948 |\n", - "\n" - ], - "text/plain": [ - " k1 k10 k11 k12 k13 k14 \n", - "CK -2.540364 NA -1.530271 -0.9300719 -1.595381 -0.3463673\n", - "CK_MB NA NA NA NA NA NA\n", - "CRP -0.338300 -0.940804 -1.669203 NA -0.338300 0.2837479\n", - "Troponin -1.364494 NA -1.364494 -1.3644941 -1.364494 -0.8671909\n", - " k15 k16 k17 k18 ⋯ m7.2 m7.3 \n", - "CK -0.7356347 -1.747476 -1.415022 -0.9964397 ⋯ 1.314437 0.4645275\n", - "CK_MB NA NA NA NA ⋯ 1.034993 -0.5937088\n", - "CRP -1.6692029 NA -1.669203 NA ⋯ NA 0.9586747\n", - "Troponin -1.3644941 -1.364494 -1.364494 -1.3644941 ⋯ 0.713741 0.4848964\n", - " m7.4 m8.1 m8.2 m8.3 m8.4 m9.1 \n", - "CK -0.7356347 -0.6276100 -1.0488988 -1.142564 NA 0.9300718\n", - "CK_MB NA NA NA NA NA 0.5515806\n", - "CRP 0.4050069 0.7466229 1.3517962 1.762682 1.51075 -0.5330519\n", - "Troponin NA -0.6594074 -0.6318535 NA NA -0.5515944\n", - " m9.2 m9.3 \n", - "CK 1.1040625 0.05491135\n", - "CK_MB 0.7262797 -0.53086653\n", - "CRP NA 0.40500689\n", - "Troponin 0.6572408 0.22973948" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(get_data(model)[[1]][[1]])" - ] - }, { "cell_type": "code", "execution_count": 136, @@ -4761,72 +4252,6 @@ "nrow(factors)" ] }, - { - "cell_type": "code", - "execution_count": 143, - "id": "ac117f20-03b7-40a1-bd46-9627682d0e01", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 30
X.1sample_idsampleidmeasurementlibraryid.ynamereadpattern⋯meta_datadelta_ef_value_groupdelta_ef_valuedelta_ef_value_classef_classification_dataCKCK_MBTroponinCRPclinical_data
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11k1 K1 1TP0L13HTO_B0251No-CCS-1 R25PNNNNNNNNNN(BC)⋯1NANANA05.459432NA0.018634170.48542681
22k10K1010TP0L11HTO_B0256Ch-CCS-10R25PNNNNNNNNNN(BC)⋯1NANANA0 NANA NA0.26303441
\n" - ], - "text/latex": [ - "A data.frame: 2 × 30\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & X.1 & sample\\_id & sample & id & measurement & library & id.y & name & read & pattern & ⋯ & meta\\_data & delta\\_ef\\_value\\_group & delta\\_ef\\_value & delta\\_ef\\_value\\_class & ef\\_classification\\_data & CK & CK\\_MB & Troponin & CRP & clinical\\_data\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & 1 & k1 & K1 & 1 & TP0 & L13 & HTO\\_B0251 & No-CCS-1 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & 1 & NA & NA & NA & 0 & 5.459432 & NA & 0.01863417 & 0.4854268 & 1\\\\\n", - "\t2 & 2 & k10 & K10 & 10 & TP0 & L11 & HTO\\_B0256 & Ch-CCS-10 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & 1 & NA & NA & NA & 0 & NA & NA & NA & 0.2630344 & 1\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 30\n", - "\n", - "| | X.1 <int> | sample_id <chr> | sample <chr> | id <dbl> | measurement <chr> | library <chr> | id.y <chr> | name <chr> | read <chr> | pattern <chr> | ⋯ ⋯ | meta_data <int> | delta_ef_value_group <chr> | delta_ef_value <dbl> | delta_ef_value_class <chr> | ef_classification_data <int> | CK <dbl> | CK_MB <dbl> | Troponin <dbl> | CRP <dbl> | clinical_data <int> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | 1 | k1 | K1 | 1 | TP0 | L13 | HTO_B0251 | No-CCS-1 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | 1 | NA | NA | NA | 0 | 5.459432 | NA | 0.01863417 | 0.4854268 | 1 |\n", - "| 2 | 2 | k10 | K10 | 10 | TP0 | L11 | HTO_B0256 | Ch-CCS-10 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | 1 | NA | NA | NA | 0 | NA | NA | NA | 0.2630344 | 1 |\n", - "\n" - ], - "text/plain": [ - " X.1 sample_id sample id measurement library id.y name read\n", - "1 1 k1 K1 1 TP0 L13 HTO_B0251 No-CCS-1 R2 \n", - "2 2 k10 K10 10 TP0 L11 HTO_B0256 Ch-CCS-10 R2 \n", - " pattern ⋯ meta_data delta_ef_value_group delta_ef_value\n", - "1 5PNNNNNNNNNN(BC) ⋯ 1 NA NA \n", - "2 5PNNNNNNNNNN(BC) ⋯ 1 NA NA \n", - " delta_ef_value_class ef_classification_data CK CK_MB Troponin \n", - "1 NA 0 5.459432 NA 0.01863417\n", - "2 NA 0 NA NA NA\n", - " CRP clinical_data\n", - "1 0.4854268 1 \n", - "2 0.2630344 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(sample_data,2)" - ] - }, { "cell_type": "code", "execution_count": 144, @@ -4885,63 +4310,6 @@ "length(unique(sample_data$sample_id))" ] }, - { - "cell_type": "code", - "execution_count": 146, - "id": "469802b3-5449-423f-9724-9d94132bc1ef", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\n", - "
A data.frame: 1 × 30
X.1sample_idsampleidmeasurementlibraryid.ynamereadpattern⋯meta_datadelta_ef_value_groupdelta_ef_valuedelta_ef_value_classef_classification_dataCKCK_MBTroponinCRPclinical_data
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137137m6.42M66.4TP4L10HTO_B02556.4R25PNNNNNNNNNN(BC)⋯1x_smaller_10.875intermediate15.906891NA0.54399071.2016341
\n" - ], - "text/latex": [ - "A data.frame: 1 × 30\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & X.1 & sample\\_id & sample & id & measurement & library & id.y & name & read & pattern & ⋯ & meta\\_data & delta\\_ef\\_value\\_group & delta\\_ef\\_value & delta\\_ef\\_value\\_class & ef\\_classification\\_data & CK & CK\\_MB & Troponin & CRP & clinical\\_data\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t137 & 137 & m6.42 & M6 & 6.4 & TP4 & L10 & HTO\\_B0255 & 6.4 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & 1 & x\\_smaller\\_1 & 0.875 & intermediate & 1 & 5.906891 & NA & 0.5439907 & 1.201634 & 1\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 1 × 30\n", - "\n", - "| | X.1 <int> | sample_id <chr> | sample <chr> | id <dbl> | measurement <chr> | library <chr> | id.y <chr> | name <chr> | read <chr> | pattern <chr> | ⋯ ⋯ | meta_data <int> | delta_ef_value_group <chr> | delta_ef_value <dbl> | delta_ef_value_class <chr> | ef_classification_data <int> | CK <dbl> | CK_MB <dbl> | Troponin <dbl> | CRP <dbl> | clinical_data <int> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 137 | 137 | m6.42 | M6 | 6.4 | TP4 | L10 | HTO_B0255 | 6.4 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | 1 | x_smaller_1 | 0.875 | intermediate | 1 | 5.906891 | NA | 0.5439907 | 1.201634 | 1 |\n", - "\n" - ], - "text/plain": [ - " X.1 sample_id sample id measurement library id.y name read\n", - "137 137 m6.42 M6 6.4 TP4 L10 HTO_B0255 6.4 R2 \n", - " pattern ⋯ meta_data delta_ef_value_group delta_ef_value\n", - "137 5PNNNNNNNNNN(BC) ⋯ 1 x_smaller_1 0.875 \n", - " delta_ef_value_class ef_classification_data CK CK_MB Troponin \n", - "137 intermediate 1 5.906891 NA 0.5439907\n", - " CRP clinical_data\n", - "137 1.201634 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sample_data[sample_data$sample_id == 'm6.42',]" - ] - }, { "cell_type": "code", "execution_count": 147, @@ -4978,70 +4346,6 @@ "merged_data = merge(factors_merge, sample_data, by.x = 'sample_id', by.y = 'sample_id')" ] }, - { - "cell_type": "code", - "execution_count": 151, - "id": "70df6e35-5e21-4565-9a5b-e39f3e82c43a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 50
sample_idFactor1Factor2Factor3Factor4Factor5Factor6Factor7Factor8Factor9⋯meta_datadelta_ef_value_groupdelta_ef_valuedelta_ef_value_classef_classification_dataCKCK_MBTroponinCRPclinical_data
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1k1 0.4811325-0.27313171.4988169-0.41356781.5052706-0.92308998-0.3904185-0.1944797 0.16486799⋯1NANANA05.459432NA0.018634170.48542681
2k100.8920603-0.75477700.3501023-0.15244780.4074517-0.04596939 0.6276925-1.1656937-0.05003504⋯1NANANA0 NANA NA0.26303441
\n" - ], - "text/latex": [ - "A data.frame: 2 × 50\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & sample\\_id & Factor1 & Factor2 & Factor3 & Factor4 & Factor5 & Factor6 & Factor7 & Factor8 & Factor9 & ⋯ & meta\\_data & delta\\_ef\\_value\\_group & delta\\_ef\\_value & delta\\_ef\\_value\\_class & ef\\_classification\\_data & CK & CK\\_MB & Troponin & CRP & clinical\\_data\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & k1 & 0.4811325 & -0.2731317 & 1.4988169 & -0.4135678 & 1.5052706 & -0.92308998 & -0.3904185 & -0.1944797 & 0.16486799 & ⋯ & 1 & NA & NA & NA & 0 & 5.459432 & NA & 0.01863417 & 0.4854268 & 1\\\\\n", - "\t2 & k10 & 0.8920603 & -0.7547770 & 0.3501023 & -0.1524478 & 0.4074517 & -0.04596939 & 0.6276925 & -1.1656937 & -0.05003504 & ⋯ & 1 & NA & NA & NA & 0 & NA & NA & NA & 0.2630344 & 1\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 50\n", - "\n", - "| | sample_id <chr> | Factor1 <dbl> | Factor2 <dbl> | Factor3 <dbl> | Factor4 <dbl> | Factor5 <dbl> | Factor6 <dbl> | Factor7 <dbl> | Factor8 <dbl> | Factor9 <dbl> | ⋯ ⋯ | meta_data <int> | delta_ef_value_group <chr> | delta_ef_value <dbl> | delta_ef_value_class <chr> | ef_classification_data <int> | CK <dbl> | CK_MB <dbl> | Troponin <dbl> | CRP <dbl> | clinical_data <int> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | k1 | 0.4811325 | -0.2731317 | 1.4988169 | -0.4135678 | 1.5052706 | -0.92308998 | -0.3904185 | -0.1944797 | 0.16486799 | ⋯ | 1 | NA | NA | NA | 0 | 5.459432 | NA | 0.01863417 | 0.4854268 | 1 |\n", - "| 2 | k10 | 0.8920603 | -0.7547770 | 0.3501023 | -0.1524478 | 0.4074517 | -0.04596939 | 0.6276925 | -1.1656937 | -0.05003504 | ⋯ | 1 | NA | NA | NA | 0 | NA | NA | NA | 0.2630344 | 1 |\n", - "\n" - ], - "text/plain": [ - " sample_id Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 \n", - "1 k1 0.4811325 -0.2731317 1.4988169 -0.4135678 1.5052706 -0.92308998\n", - "2 k10 0.8920603 -0.7547770 0.3501023 -0.1524478 0.4074517 -0.04596939\n", - " Factor7 Factor8 Factor9 ⋯ meta_data delta_ef_value_group\n", - "1 -0.3904185 -0.1944797 0.16486799 ⋯ 1 NA \n", - "2 0.6276925 -1.1656937 -0.05003504 ⋯ 1 NA \n", - " delta_ef_value delta_ef_value_class ef_classification_data CK CK_MB\n", - "1 NA NA 0 5.459432 NA \n", - "2 NA NA 0 NA NA \n", - " Troponin CRP clinical_data\n", - "1 0.01863417 0.4854268 1 \n", - "2 NA 0.2630344 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(merged_data,2)" - ] - }, { "cell_type": "code", "execution_count": 152, @@ -7306,34 +6610,6 @@ "slotNames(model)" ] }, - { - "cell_type": "code", - "execution_count": 222, - "id": "56de30b3-1a92-483f-8c40-ab267a2295a1", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "### Debugging if doesn't work:\n", - "# data <- lapply(model_conc@data['complete'], function(x) x['group1'])\n", - "# seq_len(length(data[[1]]))\n", - "# features <- features_names(model_conc)['complete']\n", - "# dim(data$complete$group1)\n", - "# length(features$complete)\n", - "#features <- features_names(model_conc)['proteomics2']\n", - "# data[['complete']][[1]][as.character(features[['complete']]),]\n", - "#get_data(model_conc, views = 'complete', as.data.frame = FALSE)\n", - "\n", - "### Test to do after running reactome part \n", - "#enrichment.parametric = run_enrichment(model_conc,view = 'complete', factors = factor_set,\n", - "# set.statistic = c(use_statistic),\n", - "# feature.sets = feature_set,\n", - "# sign = \"negative\", # alternatives: positive, negative, all\n", - "# statistical.test = use_test, # alternatives: \"parametric\", \"cor.adj.parametric\", \"permutation\".\n", - "# alpha = p_val_cutoff # defines the p-value cutoff for significant pathways\n", - "#)\n" - ] - }, { "cell_type": "code", "execution_count": 223, diff --git a/E/E9_Predictions_with_MOFA_Factors.ipynb b/E/E9_Predictions_with_MOFA_Factors.ipynb index baf24a5..45fc869 100644 --- a/E/E9_Predictions_with_MOFA_Factors.ipynb +++ b/E/E9_Predictions_with_MOFA_Factors.ipynb @@ -1229,10 +1229,7 @@ "source": [ "### Data to load for Factors and Gene Expression\n", "\n", - "version = 'V_FINAL_INTEGRATED_FALSE'\n", - "#version = 'V29_FALSEall'\n", - "#version = 'V29_FALSEall'\n", - "#version = 'V29w_o_clinical_FALSE'" + "version = 'V_FINAL_INTEGRATED_FALSE'" ] }, { @@ -1291,70 +1288,6 @@ "print(file.info(path)$mtime)" ] }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d7ecf985-7a8a-4afd-b575-bdd780941ffb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 30
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11k1 K1 1TP0L13HTO_B0251No-CCS-1 R25PNNNNNNNNNN(BC)⋯1NANANA0430.0130.41
22k10K1010TP0L11HTO_B0256Ch-CCS-10R25PNNNNNNNNNN(BC)⋯1NANANA0NA NA0.21
\n" - ], - "text/latex": [ - "A data.frame: 2 × 30\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & X.1 & sample\\_id & sample & id & measurement & library & id.y & name & read & pattern & ⋯ & meta\\_data & delta\\_ef\\_value\\_group & delta\\_ef\\_value & delta\\_ef\\_value\\_class & ef\\_classification\\_data & CK & CK\\_MB & Troponin & CRP & clinical\\_data\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & 1 & k1 & K1 & 1 & TP0 & L13 & HTO\\_B0251 & No-CCS-1 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & 1 & NA & NA & NA & 0 & 43 & & 0.013 & 0.4 & 1\\\\\n", - "\t2 & 2 & k10 & K10 & 10 & TP0 & L11 & HTO\\_B0256 & Ch-CCS-10 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & 1 & NA & NA & NA & 0 & NA & & NA & 0.2 & 1\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 30\n", - "\n", - "| | X.1 <int> | sample_id <chr> | sample <chr> | id <dbl> | measurement <chr> | library <chr> | id.y <chr> | name <chr> | read <chr> | pattern <chr> | ⋯ ⋯ | meta_data <int> | delta_ef_value_group <chr> | delta_ef_value <dbl> | delta_ef_value_class <chr> | ef_classification_data <int> | CK <int> | CK_MB <chr> | Troponin <dbl> | CRP <chr> | clinical_data <int> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | 1 | k1 | K1 | 1 | TP0 | L13 | HTO_B0251 | No-CCS-1 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | 1 | NA | NA | NA | 0 | 43 | | 0.013 | 0.4 | 1 |\n", - "| 2 | 2 | k10 | K10 | 10 | TP0 | L11 | HTO_B0256 | Ch-CCS-10 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | 1 | NA | NA | NA | 0 | NA | | NA | 0.2 | 1 |\n", - "\n" - ], - "text/plain": [ - " X.1 sample_id sample id measurement library id.y name read\n", - "1 1 k1 K1 1 TP0 L13 HTO_B0251 No-CCS-1 R2 \n", - "2 2 k10 K10 10 TP0 L11 HTO_B0256 Ch-CCS-10 R2 \n", - " pattern ⋯ meta_data delta_ef_value_group delta_ef_value\n", - "1 5PNNNNNNNNNN(BC) ⋯ 1 NA NA \n", - "2 5PNNNNNNNNNN(BC) ⋯ 1 NA NA \n", - " delta_ef_value_class ef_classification_data CK CK_MB Troponin CRP\n", - "1 NA 0 43 0.013 0.4\n", - "2 NA 0 NA NA 0.2\n", - " clinical_data\n", - "1 1 \n", - "2 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(sample_data,2)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1363,171 +1296,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "code", - "execution_count": 11, - "id": "def76611-1e4c-46a9-8ac8-f00c221a0a51", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
  1. 'K1'
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  7. 'K15'
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  12. 'K2'
  13. 'K20'
  14. 'K21'
  15. 'K22'
  16. 'K23'
  17. 'K24'
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  19. 'K26'
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  40. 'M14'
  41. 'M15'
  42. 'M16'
  43. 'M17'
  44. 'M18'
  45. 'M19'
  46. 'M2'
  47. 'M20'
  48. 'M21'
  49. 'M22'
  50. 'M23'
  51. 'M24'
  52. 'M25'
  53. 'M26'
  54. 'M27'
  55. 'M28'
  56. 'M3'
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A grouped_df: 4 × 5
delta_ef_value_class_summarizeddelta_ef_value_classamountmin_dfmax_df
<chr><chr><int><dbl><dbl>
bad bad 7-2.50-0.060
goodgood 7 1.1514.300
goodintermediate 7 0.00 0.875
NA NA 41 NA NA
\n" - ], - "text/latex": [ - "A grouped\\_df: 4 × 5\n", - "\\begin{tabular}{lllll}\n", - " delta\\_ef\\_value\\_class\\_summarized & delta\\_ef\\_value\\_class & amount & min\\_df & max\\_df\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t bad & bad & 7 & -2.50 & -0.060\\\\\n", - "\t good & good & 7 & 1.15 & 14.300\\\\\n", - "\t good & intermediate & 7 & 0.00 & 0.875\\\\\n", - "\t NA & NA & 41 & NA & NA\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A grouped_df: 4 × 5\n", - "\n", - "| delta_ef_value_class_summarized <chr> | delta_ef_value_class <chr> | amount <int> | min_df <dbl> | max_df <dbl> |\n", - "|---|---|---|---|---|\n", - "| bad | bad | 7 | -2.50 | -0.060 |\n", - "| good | good | 7 | 1.15 | 14.300 |\n", - "| good | intermediate | 7 | 0.00 | 0.875 |\n", - "| NA | NA | 41 | NA | NA |\n", - "\n" - ], - "text/plain": [ - " delta_ef_value_class_summarized delta_ef_value_class amount min_df max_df\n", - "1 bad bad 7 -2.50 -0.060\n", - "2 good good 7 1.15 14.300\n", - "3 good intermediate 7 0.00 0.875\n", - "4 NA NA 41 NA NA" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unique(sample_data[,c('sample', 'delta_ef_value_class_summarized', 'delta_ef_value', 'delta_ef_value_class')]) %>% group_by(delta_ef_value_class_summarized, delta_ef_value_class) %>% summarise(amount = n(), min_df = min(delta_ef_value), max_df = max(delta_ef_value))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f8438f4f-e19c-4b23-8683-2ecf35f580a5", - "metadata": {}, - "outputs": [], - "source": [ - "test = unique(sample_data[,c('sample', 'delta_ef_value_class_summarized', 'delta_ef_value', 'delta_ef_value_class')]) " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "63b8dff8-0403-409f-ba86-9af23ab530a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 62 × 4
sampledelta_ef_value_class_summarizeddelta_ef_valuedelta_ef_value_class
<chr><chr><dbl><chr>
72M18bad -2.500bad
51M13bad -2.200bad
56M14bad -1.300bad
84M20bad -1.000bad
112M27bad -0.667bad
39M10bad -0.300bad
120M3 bad -0.060bad
96M23good 0.000intermediate
92M22good 0.067intermediate
128M5 good 0.083intermediate
108M26good 0.300intermediate
104M25good 0.690intermediate
138M7 good 0.750intermediate
132M6 good 0.875intermediate
76M19good 1.150good
80M2 good 1.150good
116M28good 2.250good
124M4 good 3.100good
88M21good 3.550good
60M15good 5.200good
146M9 good14.300good
1K1 NA NANA
2K10NA NANA
3K11NA NANA
4K12NA NANA
5K13NA NANA
6K14NA NANA
7K15NA NANA
8K16NA NANA
9K17NA NANA
⋮⋮⋮⋮⋮
12K2 NANANA
13K20NANANA
14K21NANANA
15K22NANANA
16K23NANANA
17K24NANANA
18K25NANANA
19K26NANANA
20K27NANANA
21K28NANANA
22K29NANANA
23K3 NANANA
24K30NANANA
25K31NANANA
26K32NANANA
27K33NANANA
28K34NANANA
29K4 NANANA
30K5 NANANA
31K6 NANANA
32K7 NANANA
33K8 NANANA
34K9 NANANA
35M1 NANANA
43M11NANANA
47M12NANANA
64M16NANANA
68M17NANANA
100M24NANANA
142M8 NANANA
\n" - ], - "text/latex": [ - "A data.frame: 62 × 4\n", - "\\begin{tabular}{r|llll}\n", - " & sample & delta\\_ef\\_value\\_class\\_summarized & delta\\_ef\\_value & delta\\_ef\\_value\\_class\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t72 & M18 & bad & -2.500 & bad \\\\\n", - "\t51 & M13 & bad & -2.200 & bad \\\\\n", - "\t56 & M14 & bad & -1.300 & bad \\\\\n", - "\t84 & M20 & bad & -1.000 & bad \\\\\n", - "\t112 & M27 & bad & -0.667 & bad \\\\\n", - "\t39 & M10 & bad & -0.300 & bad \\\\\n", - "\t120 & M3 & bad & -0.060 & bad \\\\\n", - "\t96 & M23 & good & 0.000 & intermediate\\\\\n", - "\t92 & M22 & good & 0.067 & intermediate\\\\\n", - "\t128 & M5 & good & 0.083 & intermediate\\\\\n", - "\t108 & M26 & good & 0.300 & intermediate\\\\\n", - "\t104 & M25 & good & 0.690 & intermediate\\\\\n", - "\t138 & M7 & good & 0.750 & intermediate\\\\\n", - "\t132 & M6 & good & 0.875 & intermediate\\\\\n", - "\t76 & M19 & good & 1.150 & good \\\\\n", - "\t80 & M2 & good & 1.150 & good \\\\\n", - "\t116 & M28 & good & 2.250 & good \\\\\n", - "\t124 & M4 & good & 3.100 & good \\\\\n", - "\t88 & M21 & good & 3.550 & good \\\\\n", - "\t60 & M15 & good & 5.200 & good \\\\\n", - "\t146 & M9 & good & 14.300 & good \\\\\n", - "\t1 & K1 & NA & NA & NA \\\\\n", - "\t2 & K10 & NA & NA & NA \\\\\n", - "\t3 & K11 & NA & NA & NA \\\\\n", - "\t4 & K12 & NA & NA & NA \\\\\n", - "\t5 & K13 & NA & NA & NA \\\\\n", - "\t6 & K14 & NA & NA & NA \\\\\n", - "\t7 & K15 & NA & NA & NA \\\\\n", - "\t8 & K16 & NA & NA & NA \\\\\n", - "\t9 & K17 & NA & NA & NA \\\\\n", - "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", - "\t12 & K2 & NA & NA & NA\\\\\n", - "\t13 & K20 & NA & NA & NA\\\\\n", - "\t14 & K21 & NA & NA & NA\\\\\n", - "\t15 & K22 & NA & NA & NA\\\\\n", - "\t16 & K23 & NA & NA & NA\\\\\n", - "\t17 & K24 & NA & NA & NA\\\\\n", - "\t18 & K25 & NA & NA & NA\\\\\n", - "\t19 & K26 & NA & NA & NA\\\\\n", - "\t20 & K27 & NA & NA & NA\\\\\n", - "\t21 & K28 & NA & NA & NA\\\\\n", - "\t22 & K29 & NA & NA & NA\\\\\n", - "\t23 & K3 & NA & NA & NA\\\\\n", - "\t24 & K30 & NA & NA & NA\\\\\n", - "\t25 & K31 & NA & NA & NA\\\\\n", - "\t26 & K32 & NA & NA & NA\\\\\n", - "\t27 & K33 & NA & NA & NA\\\\\n", - "\t28 & K34 & NA & NA & NA\\\\\n", - "\t29 & K4 & NA & NA & NA\\\\\n", - "\t30 & K5 & NA & NA & NA\\\\\n", - "\t31 & K6 & NA & NA & NA\\\\\n", - "\t32 & K7 & NA & NA & NA\\\\\n", - "\t33 & K8 & NA & NA & NA\\\\\n", - "\t34 & K9 & NA & NA & NA\\\\\n", - "\t35 & M1 & NA & NA & NA\\\\\n", - "\t43 & M11 & NA & NA & NA\\\\\n", - "\t47 & M12 & NA & NA & NA\\\\\n", - "\t64 & M16 & NA & NA & NA\\\\\n", - "\t68 & M17 & NA & NA & NA\\\\\n", - "\t100 & M24 & NA & NA & NA\\\\\n", - "\t142 & M8 & NA & NA & NA\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 62 × 4\n", - "\n", - "| | sample <chr> | delta_ef_value_class_summarized <chr> | delta_ef_value <dbl> | delta_ef_value_class <chr> |\n", - "|---|---|---|---|---|\n", - "| 72 | M18 | bad | -2.500 | bad |\n", - "| 51 | M13 | bad | -2.200 | bad |\n", - "| 56 | M14 | bad | -1.300 | bad |\n", - "| 84 | M20 | bad | -1.000 | bad |\n", - "| 112 | M27 | bad | -0.667 | bad |\n", - "| 39 | M10 | bad | -0.300 | bad |\n", - "| 120 | M3 | bad | -0.060 | bad |\n", - "| 96 | M23 | good | 0.000 | intermediate |\n", - "| 92 | M22 | good | 0.067 | intermediate |\n", - "| 128 | M5 | good | 0.083 | intermediate |\n", - "| 108 | M26 | good | 0.300 | intermediate |\n", - "| 104 | M25 | good | 0.690 | intermediate |\n", - "| 138 | M7 | good | 0.750 | intermediate |\n", - "| 132 | M6 | good | 0.875 | intermediate |\n", - "| 76 | M19 | good | 1.150 | good |\n", - "| 80 | M2 | good | 1.150 | good |\n", - "| 116 | M28 | good | 2.250 | good |\n", - "| 124 | M4 | good | 3.100 | good |\n", - "| 88 | M21 | good | 3.550 | good |\n", - "| 60 | M15 | good | 5.200 | good |\n", - "| 146 | M9 | good | 14.300 | good |\n", - "| 1 | K1 | NA | NA | NA |\n", - "| 2 | K10 | NA | NA | NA |\n", - "| 3 | K11 | NA | NA | NA |\n", - "| 4 | K12 | NA | NA | NA |\n", - "| 5 | K13 | NA | NA | NA |\n", - "| 6 | K14 | NA | NA | NA |\n", - "| 7 | K15 | NA | NA | NA |\n", - "| 8 | K16 | NA | NA | NA |\n", - "| 9 | K17 | NA | NA | NA |\n", - "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n", - "| 12 | K2 | NA | NA | NA |\n", - "| 13 | K20 | NA | NA | NA |\n", - "| 14 | K21 | NA | NA | NA |\n", - "| 15 | K22 | NA | NA | NA |\n", - "| 16 | K23 | NA | NA | NA |\n", - "| 17 | K24 | NA | NA | NA |\n", - "| 18 | K25 | NA | NA | NA |\n", - "| 19 | K26 | NA | NA | NA |\n", - "| 20 | K27 | NA | NA | NA |\n", - "| 21 | K28 | NA | NA | NA |\n", - "| 22 | K29 | NA | NA | NA |\n", - "| 23 | K3 | NA | NA | NA |\n", - "| 24 | K30 | NA | NA | NA |\n", - "| 25 | K31 | NA | NA | NA |\n", - "| 26 | K32 | NA | NA | NA |\n", - "| 27 | K33 | NA | NA | NA |\n", - "| 28 | K34 | NA | NA | NA |\n", - "| 29 | K4 | NA | NA | NA |\n", - "| 30 | K5 | NA | NA | NA |\n", - "| 31 | K6 | NA | NA | NA |\n", - "| 32 | K7 | NA | NA | NA |\n", - "| 33 | K8 | NA | NA | NA |\n", - "| 34 | K9 | NA | NA | NA |\n", - "| 35 | M1 | NA | NA | NA |\n", - "| 43 | M11 | NA | NA | NA |\n", - "| 47 | M12 | NA | NA | NA |\n", - "| 64 | M16 | NA | NA | NA |\n", - "| 68 | M17 | NA | NA | NA |\n", - "| 100 | M24 | NA | NA | NA |\n", - "| 142 | M8 | NA | NA | NA |\n", - "\n" - ], - "text/plain": [ - " sample delta_ef_value_class_summarized delta_ef_value delta_ef_value_class\n", - "72 M18 bad -2.500 bad \n", - "51 M13 bad -2.200 bad \n", - "56 M14 bad -1.300 bad \n", - "84 M20 bad -1.000 bad \n", - "112 M27 bad -0.667 bad \n", - "39 M10 bad -0.300 bad \n", - "120 M3 bad -0.060 bad \n", - "96 M23 good 0.000 intermediate \n", - "92 M22 good 0.067 intermediate \n", - "128 M5 good 0.083 intermediate \n", - "108 M26 good 0.300 intermediate \n", - "104 M25 good 0.690 intermediate \n", - "138 M7 good 0.750 intermediate \n", - "132 M6 good 0.875 intermediate \n", - "76 M19 good 1.150 good \n", - "80 M2 good 1.150 good \n", - "116 M28 good 2.250 good \n", - "124 M4 good 3.100 good \n", - "88 M21 good 3.550 good \n", - "60 M15 good 5.200 good \n", - "146 M9 good 14.300 good \n", - "1 K1 NA NA NA \n", - "2 K10 NA NA NA \n", - "3 K11 NA NA NA \n", - "4 K12 NA NA NA \n", - "5 K13 NA NA NA \n", - "6 K14 NA NA NA \n", - "7 K15 NA NA NA \n", - "8 K16 NA NA NA \n", - "9 K17 NA NA NA \n", - "⋮ ⋮ ⋮ ⋮ ⋮ \n", - "12 K2 NA NA NA \n", - "13 K20 NA NA NA \n", - "14 K21 NA NA NA \n", - "15 K22 NA NA NA \n", - "16 K23 NA NA NA \n", - "17 K24 NA NA NA \n", - "18 K25 NA NA NA \n", - "19 K26 NA NA NA \n", - "20 K27 NA NA NA \n", - "21 K28 NA NA NA \n", - "22 K29 NA NA NA \n", - "23 K3 NA NA NA \n", - "24 K30 NA NA NA \n", - "25 K31 NA NA NA \n", - "26 K32 NA NA NA \n", - "27 K33 NA NA NA \n", - "28 K34 NA NA NA \n", - "29 K4 NA NA NA \n", - "30 K5 NA NA NA \n", - "31 K6 NA NA NA \n", - "32 K7 NA NA NA \n", - "33 K8 NA NA NA \n", - "34 K9 NA NA NA \n", - "35 M1 NA NA NA \n", - "43 M11 NA NA NA \n", - "47 M12 NA NA NA \n", - "64 M16 NA NA NA \n", - "68 M17 NA NA NA \n", - "100 M24 NA NA NA \n", - "142 M8 NA NA NA " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "test[order(test$delta_ef_value),]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "0aa96da1-fde6-4661-b2b2-75dbd9d4a9ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 42 × 4
sampledelta_ef_value_class_summarizeddelta_ef_valuedelta_ef_value_class
<chr><chr><dbl><chr>
NANA NA NANA
NA.1NA NA NANA
NA.2NA NA NANA
NA.3NA NA NANA
NA.4NA NA NANA
NA.5NA NA NANA
NA.6NA NA NANA
NA.7NA NA NANA
NA.8NA NA NANA
NA.9NA NA NANA
NA.10NA NA NANA
NA.11NA NA NANA
NA.12NA NA NANA
NA.13NA NA NANA
NA.14NA NA NANA
NA.15NA NA NANA
NA.16NA NA NANA
NA.17NA NA NANA
NA.18NA NA NANA
NA.19NA NA NANA
NA.20NA NA NANA
NA.21NA NA NANA
NA.22NA NA NANA
NA.23NA NA NANA
NA.24NA NA NANA
NA.25NA NA NANA
NA.26NA NA NANA
NA.27NA NA NANA
NA.28NA NA NANA
NA.29NA NA NANA
NA.30NA NA NANA
NA.31NA NA NANA
NA.32NA NA NANA
NA.33NA NA NANA
NA.34NA NA NANA
NA.35NA NA NANA
NA.36NA NA NANA
NA.37NA NA NANA
NA.38NA NA NANA
96M23good 0intermediate
NA.39NA NA NANA
NA.40NA NA NANA
\n" - ], - "text/latex": [ - "A data.frame: 42 × 4\n", - "\\begin{tabular}{r|llll}\n", - " & sample & delta\\_ef\\_value\\_class\\_summarized & delta\\_ef\\_value & delta\\_ef\\_value\\_class\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\tNA & NA & NA & NA & NA \\\\\n", - "\tNA.1 & NA & NA & NA & NA \\\\\n", - "\tNA.2 & NA & NA & NA & NA \\\\\n", - "\tNA.3 & NA & NA & NA & NA \\\\\n", - "\tNA.4 & NA & NA & NA & NA \\\\\n", - "\tNA.5 & NA & NA & NA & NA \\\\\n", - "\tNA.6 & NA & NA & NA & NA \\\\\n", - "\tNA.7 & NA & NA & NA & NA \\\\\n", - "\tNA.8 & NA & NA & NA & NA \\\\\n", - "\tNA.9 & NA & NA & NA & NA \\\\\n", - "\tNA.10 & NA & NA & NA & NA \\\\\n", - "\tNA.11 & NA & NA & NA & NA \\\\\n", - "\tNA.12 & NA & NA & NA & NA \\\\\n", - "\tNA.13 & NA & NA & NA & NA \\\\\n", - "\tNA.14 & NA & NA & NA & NA \\\\\n", - "\tNA.15 & NA & NA & NA & NA \\\\\n", - "\tNA.16 & NA & NA & NA & NA \\\\\n", - "\tNA.17 & NA & NA & NA & NA \\\\\n", - "\tNA.18 & NA & NA & NA & NA \\\\\n", - "\tNA.19 & NA & NA & NA & NA \\\\\n", - "\tNA.20 & NA & NA & NA & NA \\\\\n", - "\tNA.21 & NA & NA & NA & NA \\\\\n", - "\tNA.22 & NA & NA & NA & NA \\\\\n", - "\tNA.23 & NA & NA & NA & NA \\\\\n", - "\tNA.24 & NA & NA & NA & NA \\\\\n", - "\tNA.25 & NA & NA & NA & NA \\\\\n", - "\tNA.26 & NA & NA & NA & NA \\\\\n", - "\tNA.27 & NA & NA & NA & NA \\\\\n", - "\tNA.28 & NA & NA & NA & NA \\\\\n", - "\tNA.29 & NA & NA & NA & NA \\\\\n", - "\tNA.30 & NA & NA & NA & NA \\\\\n", - "\tNA.31 & NA & NA & NA & NA \\\\\n", - "\tNA.32 & NA & NA & NA & NA \\\\\n", - "\tNA.33 & NA & NA & NA & NA \\\\\n", - "\tNA.34 & NA & NA & NA & NA \\\\\n", - "\tNA.35 & NA & NA & NA & NA \\\\\n", - "\tNA.36 & NA & NA & NA & NA \\\\\n", - "\tNA.37 & NA & NA & NA & NA \\\\\n", - "\tNA.38 & NA & NA & NA & NA \\\\\n", - "\t96 & M23 & good & 0 & intermediate\\\\\n", - "\tNA.39 & NA & NA & NA & NA \\\\\n", - "\tNA.40 & NA & NA & NA & NA \\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 42 × 4\n", - "\n", - "| | sample <chr> | delta_ef_value_class_summarized <chr> | delta_ef_value <dbl> | delta_ef_value_class <chr> |\n", - "|---|---|---|---|---|\n", - "| NA | NA | NA | NA | NA |\n", - "| NA.1 | NA | NA | NA | NA |\n", - "| NA.2 | NA | NA | NA | NA |\n", - "| NA.3 | NA | NA | NA | NA |\n", - "| NA.4 | NA | NA | NA | NA |\n", - "| NA.5 | NA | NA | NA | NA |\n", - "| NA.6 | NA | NA | NA | NA |\n", - "| NA.7 | NA | NA | NA | NA |\n", - "| NA.8 | NA | NA | NA | NA |\n", - "| NA.9 | NA | NA | NA | NA |\n", - "| NA.10 | NA | NA | NA | NA |\n", - "| NA.11 | NA | NA | NA | NA |\n", - "| NA.12 | NA | NA | NA | NA |\n", - "| NA.13 | NA | NA | NA | NA |\n", - "| NA.14 | NA | NA | NA | NA |\n", - "| NA.15 | NA | NA | NA | NA |\n", - "| NA.16 | NA | NA | NA | NA |\n", - "| NA.17 | NA | NA | NA | NA |\n", - "| NA.18 | NA | NA | NA | NA |\n", - "| NA.19 | NA | NA | NA | NA |\n", - "| NA.20 | NA | NA | NA | NA |\n", - "| NA.21 | NA | NA | NA | NA |\n", - "| NA.22 | NA | NA | NA | NA |\n", - "| NA.23 | NA | NA | NA | NA |\n", - "| NA.24 | NA | NA | NA | NA |\n", - "| NA.25 | NA | NA | NA | NA |\n", - "| NA.26 | NA | NA | NA | NA |\n", - "| NA.27 | NA | NA | NA | NA |\n", - "| NA.28 | NA | NA | NA | NA |\n", - "| NA.29 | NA | NA | NA | NA |\n", - "| NA.30 | NA | NA | NA | NA |\n", - "| NA.31 | NA | NA | NA | NA |\n", - "| NA.32 | NA | NA | NA | NA |\n", - "| NA.33 | NA | NA | NA | NA |\n", - "| NA.34 | NA | NA | NA | NA |\n", - "| NA.35 | NA | NA | NA | NA |\n", - "| NA.36 | NA | NA | NA | NA |\n", - "| NA.37 | NA | NA | NA | NA |\n", - "| NA.38 | NA | NA | NA | NA |\n", - "| 96 | M23 | good | 0 | intermediate |\n", - "| NA.39 | NA | NA | NA | NA |\n", - "| NA.40 | NA | NA | NA | NA |\n", - "\n" - ], - "text/plain": [ - " sample delta_ef_value_class_summarized delta_ef_value\n", - "NA NA NA NA \n", - "NA.1 NA NA NA \n", - "NA.2 NA NA NA \n", - "NA.3 NA NA NA \n", - "NA.4 NA NA NA \n", - "NA.5 NA NA NA \n", - "NA.6 NA NA NA \n", - "NA.7 NA NA NA \n", - "NA.8 NA NA NA \n", - "NA.9 NA NA NA \n", - "NA.10 NA NA NA \n", - "NA.11 NA NA NA \n", - "NA.12 NA NA NA \n", - "NA.13 NA NA NA \n", - "NA.14 NA NA NA \n", - "NA.15 NA NA NA \n", - "NA.16 NA NA NA \n", - "NA.17 NA NA NA \n", - "NA.18 NA NA NA \n", - "NA.19 NA NA NA \n", - "NA.20 NA NA NA \n", - "NA.21 NA NA NA \n", - "NA.22 NA NA NA \n", - "NA.23 NA NA NA \n", - "NA.24 NA NA NA \n", - "NA.25 NA NA NA \n", - "NA.26 NA NA NA \n", - "NA.27 NA NA NA \n", - "NA.28 NA NA NA \n", - "NA.29 NA NA NA \n", - "NA.30 NA NA NA \n", - "NA.31 NA NA NA \n", - "NA.32 NA NA NA \n", - "NA.33 NA NA NA \n", - "NA.34 NA NA NA \n", - "NA.35 NA NA NA \n", - "NA.36 NA NA NA \n", - "NA.37 NA NA NA \n", - "NA.38 NA NA NA \n", - "96 M23 good 0 \n", - "NA.39 NA NA NA \n", - "NA.40 NA NA NA \n", - " delta_ef_value_class\n", - "NA NA \n", - "NA.1 NA \n", - "NA.2 NA \n", - "NA.3 NA \n", - "NA.4 NA \n", - "NA.5 NA \n", - "NA.6 NA \n", - "NA.7 NA \n", - "NA.8 NA \n", - "NA.9 NA \n", - "NA.10 NA \n", - "NA.11 NA \n", - "NA.12 NA \n", - "NA.13 NA \n", - "NA.14 NA \n", - "NA.15 NA \n", - "NA.16 NA \n", - "NA.17 NA \n", - "NA.18 NA \n", - "NA.19 NA \n", - "NA.20 NA \n", - "NA.21 NA \n", - "NA.22 NA \n", - "NA.23 NA \n", - "NA.24 NA \n", - "NA.25 NA \n", - "NA.26 NA \n", - "NA.27 NA \n", - "NA.28 NA \n", - "NA.29 NA \n", - "NA.30 NA \n", - "NA.31 NA \n", - "NA.32 NA \n", - "NA.33 NA \n", - "NA.34 NA \n", - "NA.35 NA \n", - "NA.36 NA \n", - "NA.37 NA \n", - "NA.38 NA \n", - "96 intermediate \n", - "NA.39 NA \n", - "NA.40 NA " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "test[test$delta_ef_value == 0,]" - ] - }, { "cell_type": "code", "execution_count": 17, @@ -2346,105 +1455,6 @@ "unique(sample_data$tp_outcome)" ] }, - { - "cell_type": "code", - "execution_count": 20, - "id": "e210c486-7d41-44bc-8a15-4210dbc07edd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 7 × 32
X.1sample_idsampleidmeasurementlibraryid.ynamereadpattern⋯delta_ef_valuedelta_ef_value_classef_classification_dataCKCK_MBTroponinCRPclinical_datadelta_ef_value_class_summarizedtp_outcome
<int><chr><chr><dbl><chr><chr><chr><chr><chr><chr>⋯<dbl><chr><int><int><chr><dbl><chr><int><chr><chr>
39 39m10.1M1010.1TP1L1HTO_B026010.1R25PNNNNNNNNNN(BC)⋯-0.300bad15116126224.9000.6 1badTP1_bad
51 51m13.1M1313.1TP1L2HTO_B025313.1R25PNNNNNNNNNN(BC)⋯-2.200bad11446146 1.6900.3 1badTP1_bad
56 56m14.1M1414.1TP1L2HTO_B025414.1R25PNNNNNNNNNN(BC)⋯-1.300bad12440168 9.3500.2 1badTP1_bad
72 72m18.1M1818.1TP1L2HTO_B025818.1R25PNNNNNNNNNN(BC)⋯-2.500bad11341228 4.1900.2 1badTP1_bad
84 84m20.1M2020.1TP1NANA NA NANA ⋯-1.000bad1 66277 0.680<0,11badTP1_bad
112112m27.1M2727.1TP1L3HTO_B025727.1R25PNNNNNNNNNN(BC)⋯-0.667bad1 587 0.6490.2 1badTP1_bad
120120m3.1 M3 3.1TP1NANA NA NANA ⋯-0.060bad1 NA NA- 1badTP1_bad
\n" - ], - "text/latex": [ - "A data.frame: 7 × 32\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & X.1 & sample\\_id & sample & id & measurement & library & id.y & name & read & pattern & ⋯ & delta\\_ef\\_value & delta\\_ef\\_value\\_class & ef\\_classification\\_data & CK & CK\\_MB & Troponin & CRP & clinical\\_data & delta\\_ef\\_value\\_class\\_summarized & tp\\_outcome\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t39 & 39 & m10.1 & M10 & 10.1 & TP1 & L1 & HTO\\_B0260 & 10.1 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & -0.300 & bad & 1 & 5116 & 1262 & 24.900 & 0.6 & 1 & bad & TP1\\_bad\\\\\n", - "\t51 & 51 & m13.1 & M13 & 13.1 & TP1 & L2 & HTO\\_B0253 & 13.1 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & -2.200 & bad & 1 & 1446 & 146 & 1.690 & 0.3 & 1 & bad & TP1\\_bad\\\\\n", - "\t56 & 56 & m14.1 & M14 & 14.1 & TP1 & L2 & HTO\\_B0254 & 14.1 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & -1.300 & bad & 1 & 2440 & 168 & 9.350 & 0.2 & 1 & bad & TP1\\_bad\\\\\n", - "\t72 & 72 & m18.1 & M18 & 18.1 & TP1 & L2 & HTO\\_B0258 & 18.1 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & -2.500 & bad & 1 & 1341 & 228 & 4.190 & 0.2 & 1 & bad & TP1\\_bad\\\\\n", - "\t84 & 84 & m20.1 & M20 & 20.1 & TP1 & NA & NA & NA & NA & NA & ⋯ & -1.000 & bad & 1 & 662 & 77 & 0.680 & <0,1 & 1 & bad & TP1\\_bad\\\\\n", - "\t112 & 112 & m27.1 & M27 & 27.1 & TP1 & L3 & HTO\\_B0257 & 27.1 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & -0.667 & bad & 1 & 587 & & 0.649 & 0.2 & 1 & bad & TP1\\_bad\\\\\n", - "\t120 & 120 & m3.1 & M3 & 3.1 & TP1 & NA & NA & NA & NA & NA & ⋯ & -0.060 & bad & 1 & NA & & NA & - & 1 & bad & TP1\\_bad\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 7 × 32\n", - "\n", - "| | X.1 <int> | sample_id <chr> | sample <chr> | id <dbl> | measurement <chr> | library <chr> | id.y <chr> | name <chr> | read <chr> | pattern <chr> | ⋯ ⋯ | delta_ef_value <dbl> | delta_ef_value_class <chr> | ef_classification_data <int> | CK <int> | CK_MB <chr> | Troponin <dbl> | CRP <chr> | clinical_data <int> | delta_ef_value_class_summarized <chr> | tp_outcome <chr> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 39 | 39 | m10.1 | M10 | 10.1 | TP1 | L1 | HTO_B0260 | 10.1 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | -0.300 | bad | 1 | 5116 | 1262 | 24.900 | 0.6 | 1 | bad | TP1_bad |\n", - "| 51 | 51 | m13.1 | M13 | 13.1 | TP1 | L2 | HTO_B0253 | 13.1 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | -2.200 | bad | 1 | 1446 | 146 | 1.690 | 0.3 | 1 | bad | TP1_bad |\n", - "| 56 | 56 | m14.1 | M14 | 14.1 | TP1 | L2 | HTO_B0254 | 14.1 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | -1.300 | bad | 1 | 2440 | 168 | 9.350 | 0.2 | 1 | bad | TP1_bad |\n", - "| 72 | 72 | m18.1 | M18 | 18.1 | TP1 | L2 | HTO_B0258 | 18.1 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | -2.500 | bad | 1 | 1341 | 228 | 4.190 | 0.2 | 1 | bad | TP1_bad |\n", - "| 84 | 84 | m20.1 | M20 | 20.1 | TP1 | NA | NA | NA | NA | NA | ⋯ | -1.000 | bad | 1 | 662 | 77 | 0.680 | <0,1 | 1 | bad | TP1_bad |\n", - "| 112 | 112 | m27.1 | M27 | 27.1 | TP1 | L3 | HTO_B0257 | 27.1 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | -0.667 | bad | 1 | 587 | | 0.649 | 0.2 | 1 | bad | TP1_bad |\n", - "| 120 | 120 | m3.1 | M3 | 3.1 | TP1 | NA | NA | NA | NA | NA | ⋯ | -0.060 | bad | 1 | NA | | NA | - | 1 | bad | TP1_bad |\n", - "\n" - ], - "text/plain": [ - " X.1 sample_id sample id measurement library id.y name read\n", - "39 39 m10.1 M10 10.1 TP1 L1 HTO_B0260 10.1 R2 \n", - "51 51 m13.1 M13 13.1 TP1 L2 HTO_B0253 13.1 R2 \n", - "56 56 m14.1 M14 14.1 TP1 L2 HTO_B0254 14.1 R2 \n", - "72 72 m18.1 M18 18.1 TP1 L2 HTO_B0258 18.1 R2 \n", - "84 84 m20.1 M20 20.1 TP1 NA NA NA NA \n", - "112 112 m27.1 M27 27.1 TP1 L3 HTO_B0257 27.1 R2 \n", - "120 120 m3.1 M3 3.1 TP1 NA NA NA NA \n", - " pattern ⋯ delta_ef_value delta_ef_value_class\n", - "39 5PNNNNNNNNNN(BC) ⋯ -0.300 bad \n", - "51 5PNNNNNNNNNN(BC) ⋯ -2.200 bad \n", - "56 5PNNNNNNNNNN(BC) ⋯ -1.300 bad \n", - "72 5PNNNNNNNNNN(BC) ⋯ -2.500 bad \n", - "84 NA ⋯ -1.000 bad \n", - "112 5PNNNNNNNNNN(BC) ⋯ -0.667 bad \n", - "120 NA ⋯ -0.060 bad \n", - " ef_classification_data CK CK_MB Troponin CRP clinical_data\n", - "39 1 5116 1262 24.900 0.6 1 \n", - "51 1 1446 146 1.690 0.3 1 \n", - "56 1 2440 168 9.350 0.2 1 \n", - "72 1 1341 228 4.190 0.2 1 \n", - "84 1 662 77 0.680 <0,1 1 \n", - "112 1 587 0.649 0.2 1 \n", - "120 1 NA NA - 1 \n", - " delta_ef_value_class_summarized tp_outcome\n", - "39 bad TP1_bad \n", - "51 bad TP1_bad \n", - "56 bad TP1_bad \n", - "72 bad TP1_bad \n", - "84 bad TP1_bad \n", - "112 bad TP1_bad \n", - "120 bad TP1_bad " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sample_data[sample_data$tp_outcome == 'TP1_bad',]" - ] - }, { "cell_type": "code", "execution_count": 22, @@ -2583,37 +1593,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "id": "6645751a-e169-411e-9f24-50edbcaf7f9d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "'../results/current/E-Analysis/Factor_Data_V_FINAL_INTEGRATED_FALSE.csv'" - ], - "text/latex": [ - "'../results/current/E-Analysis/Factor\\_Data\\_V\\_FINAL\\_INTEGRATED\\_FALSE.csv'" - ], - "text/markdown": [ - "'../results/current/E-Analysis/Factor_Data_V_FINAL_INTEGRATED_FALSE.csv'" - ], - "text/plain": [ - "[1] \"../results/current/E-Analysis/Factor_Data_V_FINAL_INTEGRATED_FALSE.csv\"" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "path" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "226573f0-3feb-4653-8f30-d23626f4c0cf", + "execution_count": 27, + "id": "226573f0-3feb-4653-8f30-d23626f4c0cf", "metadata": {}, "outputs": [ { @@ -2716,61 +1697,6 @@ "print(file.info(path)$mtime)" ] }, - { - "cell_type": "code", - "execution_count": 29, - "id": "93973dad-2585-4d33-8ce6-e66d7234c01a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 5
Xsample_idvariablevaluetype
<int><chr><chr><dbl><chr>
11k1 CK-2.397022clinical_data
22k10CK NAclinical_data
\n" - ], - "text/latex": [ - "A data.frame: 2 × 5\n", - "\\begin{tabular}{r|lllll}\n", - " & X & sample\\_id & variable & value & type\\\\\n", - " & & & & & \\\\\n", - "\\hline\n", - "\t1 & 1 & k1 & CK & -2.397022 & clinical\\_data\\\\\n", - "\t2 & 2 & k10 & CK & NA & clinical\\_data\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 5\n", - "\n", - "| | X <int> | sample_id <chr> | variable <chr> | value <dbl> | type <chr> |\n", - "|---|---|---|---|---|---|\n", - "| 1 | 1 | k1 | CK | -2.397022 | clinical_data |\n", - "| 2 | 2 | k10 | CK | NA | clinical_data |\n", - "\n" - ], - "text/plain": [ - " X sample_id variable value type \n", - "1 1 k1 CK -2.397022 clinical_data\n", - "2 2 k10 CK NA clinical_data" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(data_long,2)" - ] - }, { "cell_type": "code", "execution_count": 30, @@ -2781,99 +1707,6 @@ "data_long$X = NULL" ] }, - { - "cell_type": "markdown", - "id": "fadae648-630f-4e69-90bc-13da36a2cb5e", - "metadata": {}, - "source": [ - "## Score Data for ccs vs no-ccs" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "eb1d10ce-9784-4164-8cf4-98da9eaccd6f", - "metadata": {}, - "outputs": [], - "source": [ - "## TBD" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "461b18e2-3151-4d0f-b0db-c8d85f25929c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] \"2022-11-30 20:47:46 CET\"\n" - ] - } - ], - "source": [ - "path = paste0(data_path, '/preprocessed-data/meta-data/scores_prediction_prepared.csv')\n", - "ccs_scores = read.csv(path)\n", - "print(file.info(path)$mtime)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "a06c7f53-0870-406b-a2c4-f37a0092fbcf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 4
sample_idscorescore_preparedclass
<chr><chr><dbl><chr>
1k14%0.04No CCS
2k2- NACCS
\n" - ], - "text/latex": [ - "A data.frame: 2 × 4\n", - "\\begin{tabular}{r|llll}\n", - " & sample\\_id & score & score\\_prepared & class\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t1 & k1 & 4\\% & 0.04 & No CCS \\\\\n", - "\t2 & k2 & - & NA & CCS \\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 4\n", - "\n", - "| | sample_id <chr> | score <chr> | score_prepared <dbl> | class <chr> |\n", - "|---|---|---|---|---|\n", - "| 1 | k1 | 4% | 0.04 | No CCS |\n", - "| 2 | k2 | - | NA | CCS |\n", - "\n" - ], - "text/plain": [ - " sample_id score score_prepared class \n", - "1 k1 4% 0.04 No CCS \n", - "2 k2 - NA CCS " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(ccs_scores,2)" - ] - }, { "cell_type": "markdown", "id": "06293c8c-9ba0-4def-ad81-0e02774ed7d6", @@ -2912,70 +1745,6 @@ "data = merge(factor_data, unique(sample_data[,c('sample_id', 'classification', 'measurement', 'delta_ef_value_class', 'delta_ef_value_class_summarized', 'tp_outcome', 'CK', 'CK_MB', 'Troponin', 'CRP')]), by.x = 'sample_id', by.y = 'sample_id')" ] }, - { - "cell_type": "code", - "execution_count": 37, - "id": "c8b87fd8-c85e-4ec2-898b-c2a978905ba3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 30
sample_idFactor1Factor2Factor3Factor4Factor5Factor6Factor7Factor8Factor9⋯Factor20classificationmeasurementdelta_ef_value_classdelta_ef_value_class_summarizedtp_outcomeCKCK_MBTroponinCRP
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><chr><chr><chr><chr><chr><int><chr><dbl><chr>
1k1 0.4811325-0.27313171.4988169-0.41356781.5052706-0.92308998-0.3904185-0.1944797 0.16486799⋯-0.03085489vollstaendiger_ausschlussTP0NANATP0_NA430.0130.4
2k100.8920603-0.75477700.3501023-0.15244780.4074517-0.04596939 0.6276925-1.1656937-0.05003504⋯-0.03818913ccs TP0NANATP0_NANA NA0.2
\n" - ], - "text/latex": [ - "A data.frame: 2 × 30\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & sample\\_id & Factor1 & Factor2 & Factor3 & Factor4 & Factor5 & Factor6 & Factor7 & Factor8 & Factor9 & ⋯ & Factor20 & classification & measurement & delta\\_ef\\_value\\_class & delta\\_ef\\_value\\_class\\_summarized & tp\\_outcome & CK & CK\\_MB & Troponin & CRP\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & k1 & 0.4811325 & -0.2731317 & 1.4988169 & -0.4135678 & 1.5052706 & -0.92308998 & -0.3904185 & -0.1944797 & 0.16486799 & ⋯ & -0.03085489 & vollstaendiger\\_ausschluss & TP0 & NA & NA & TP0\\_NA & 43 & & 0.013 & 0.4\\\\\n", - "\t2 & k10 & 0.8920603 & -0.7547770 & 0.3501023 & -0.1524478 & 0.4074517 & -0.04596939 & 0.6276925 & -1.1656937 & -0.05003504 & ⋯ & -0.03818913 & ccs & TP0 & NA & NA & TP0\\_NA & NA & & NA & 0.2\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 30\n", - "\n", - "| | sample_id <chr> | Factor1 <dbl> | Factor2 <dbl> | Factor3 <dbl> | Factor4 <dbl> | Factor5 <dbl> | Factor6 <dbl> | Factor7 <dbl> | Factor8 <dbl> | Factor9 <dbl> | ⋯ ⋯ | Factor20 <dbl> | classification <chr> | measurement <chr> | delta_ef_value_class <chr> | delta_ef_value_class_summarized <chr> | tp_outcome <chr> | CK <int> | CK_MB <chr> | Troponin <dbl> | CRP <chr> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | k1 | 0.4811325 | -0.2731317 | 1.4988169 | -0.4135678 | 1.5052706 | -0.92308998 | -0.3904185 | -0.1944797 | 0.16486799 | ⋯ | -0.03085489 | vollstaendiger_ausschluss | TP0 | NA | NA | TP0_NA | 43 | | 0.013 | 0.4 |\n", - "| 2 | k10 | 0.8920603 | -0.7547770 | 0.3501023 | -0.1524478 | 0.4074517 | -0.04596939 | 0.6276925 | -1.1656937 | -0.05003504 | ⋯ | -0.03818913 | ccs | TP0 | NA | NA | TP0_NA | NA | | NA | 0.2 |\n", - "\n" - ], - "text/plain": [ - " sample_id Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 \n", - "1 k1 0.4811325 -0.2731317 1.4988169 -0.4135678 1.5052706 -0.92308998\n", - "2 k10 0.8920603 -0.7547770 0.3501023 -0.1524478 0.4074517 -0.04596939\n", - " Factor7 Factor8 Factor9 ⋯ Factor20 classification \n", - "1 -0.3904185 -0.1944797 0.16486799 ⋯ -0.03085489 vollstaendiger_ausschluss\n", - "2 0.6276925 -1.1656937 -0.05003504 ⋯ -0.03818913 ccs \n", - " measurement delta_ef_value_class delta_ef_value_class_summarized tp_outcome\n", - "1 TP0 NA NA TP0_NA \n", - "2 TP0 NA NA TP0_NA \n", - " CK CK_MB Troponin CRP\n", - "1 43 0.013 0.4\n", - "2 NA NA 0.2" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(data,2)" - ] - }, { "cell_type": "code", "execution_count": 38, @@ -3089,70 +1858,6 @@ "colnames(gene_expression_data) = paste0('Normalized_', colnames(gene_expression_data))" ] }, - { - "cell_type": "code", - "execution_count": 44, - "id": "59080369-6501-4c61-8bed-c5f627b35234", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 12
Normalized_sample_idNormalized_CKNormalized_CK_MBNormalized_CRPNormalized_TroponinNormalized_X3_NK__CD53Normalized_X3_NK__CST7Normalized_X3_NK__FYNNormalized_X3_NK__GZMBNormalized_X3_NK__LITAFNormalized_X3_NK__PRF1Normalized_X3_NK__TXNIP
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1k1 -2.397022NA-0.3186394-1.286479 NA NA NA NA NA NA NA
2k10 NANA-0.8871466 NA0.58531890.21042840.40791871.7316641.2815520.7554151.330562
\n" - ], - "text/latex": [ - "A data.frame: 2 × 12\n", - "\\begin{tabular}{r|llllllllllll}\n", - " & Normalized\\_sample\\_id & Normalized\\_CK & Normalized\\_CK\\_MB & Normalized\\_CRP & Normalized\\_Troponin & Normalized\\_X3\\_NK\\_\\_CD53 & Normalized\\_X3\\_NK\\_\\_CST7 & Normalized\\_X3\\_NK\\_\\_FYN & Normalized\\_X3\\_NK\\_\\_GZMB & Normalized\\_X3\\_NK\\_\\_LITAF & Normalized\\_X3\\_NK\\_\\_PRF1 & Normalized\\_X3\\_NK\\_\\_TXNIP\\\\\n", - " & & & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & k1 & -2.397022 & NA & -0.3186394 & -1.286479 & NA & NA & NA & NA & NA & NA & NA\\\\\n", - "\t2 & k10 & NA & NA & -0.8871466 & NA & 0.5853189 & 0.2104284 & 0.4079187 & 1.731664 & 1.281552 & 0.755415 & 1.330562\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 12\n", - "\n", - "| | Normalized_sample_id <chr> | Normalized_CK <dbl> | Normalized_CK_MB <dbl> | Normalized_CRP <dbl> | Normalized_Troponin <dbl> | Normalized_X3_NK__CD53 <dbl> | Normalized_X3_NK__CST7 <dbl> | Normalized_X3_NK__FYN <dbl> | Normalized_X3_NK__GZMB <dbl> | Normalized_X3_NK__LITAF <dbl> | Normalized_X3_NK__PRF1 <dbl> | Normalized_X3_NK__TXNIP <dbl> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | k1 | -2.397022 | NA | -0.3186394 | -1.286479 | NA | NA | NA | NA | NA | NA | NA |\n", - "| 2 | k10 | NA | NA | -0.8871466 | NA | 0.5853189 | 0.2104284 | 0.4079187 | 1.731664 | 1.281552 | 0.755415 | 1.330562 |\n", - "\n" - ], - "text/plain": [ - " Normalized_sample_id Normalized_CK Normalized_CK_MB Normalized_CRP\n", - "1 k1 -2.397022 NA -0.3186394 \n", - "2 k10 NA NA -0.8871466 \n", - " Normalized_Troponin Normalized_X3_NK__CD53 Normalized_X3_NK__CST7\n", - "1 -1.286479 NA NA \n", - "2 NA 0.5853189 0.2104284 \n", - " Normalized_X3_NK__FYN Normalized_X3_NK__GZMB Normalized_X3_NK__LITAF\n", - "1 NA NA NA \n", - "2 0.4079187 1.731664 1.281552 \n", - " Normalized_X3_NK__PRF1 Normalized_X3_NK__TXNIP\n", - "1 NA NA \n", - "2 0.755415 1.330562 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(gene_expression_data,2)" - ] - }, { "cell_type": "code", "execution_count": 45, @@ -3270,73 +1975,6 @@ "nrow(data)" ] }, - { - "cell_type": "code", - "execution_count": 51, - "id": "0ced394f-7b63-459e-b42d-468d8550cc21", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 44
sample_idFactor1Factor2Factor3Factor4Factor5Factor6Factor7Factor8Factor9⋯Normalized_X3_NK__CD53Normalized_X3_NK__CST7Normalized_X3_NK__FYNNormalized_X3_NK__GZMBNormalized_X3_NK__LITAFNormalized_X3_NK__PRF1Normalized_X3_NK__TXNIPscorescore_preparedclass
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><chr><dbl><chr>
1k1 0.4811325-0.27313171.4988169-0.41356781.5052706-0.92308998-0.3904185-0.1944797 0.16486799⋯ NA NA NA NA NA NA NA4% 0.04No CCS
2k100.8920603-0.75477700.3501023-0.15244780.4074517-0.04596939 0.6276925-1.1656937-0.05003504⋯0.58531890.21042840.40791871.7316641.2815520.7554151.33056211%0.11CCS
\n" - ], - "text/latex": [ - "A data.frame: 2 × 44\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & sample\\_id & Factor1 & Factor2 & Factor3 & Factor4 & Factor5 & Factor6 & Factor7 & Factor8 & Factor9 & ⋯ & Normalized\\_X3\\_NK\\_\\_CD53 & Normalized\\_X3\\_NK\\_\\_CST7 & Normalized\\_X3\\_NK\\_\\_FYN & Normalized\\_X3\\_NK\\_\\_GZMB & Normalized\\_X3\\_NK\\_\\_LITAF & Normalized\\_X3\\_NK\\_\\_PRF1 & Normalized\\_X3\\_NK\\_\\_TXNIP & score & score\\_prepared & class\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & k1 & 0.4811325 & -0.2731317 & 1.4988169 & -0.4135678 & 1.5052706 & -0.92308998 & -0.3904185 & -0.1944797 & 0.16486799 & ⋯ & NA & NA & NA & NA & NA & NA & NA & 4\\% & 0.04 & No CCS \\\\\n", - "\t2 & k10 & 0.8920603 & -0.7547770 & 0.3501023 & -0.1524478 & 0.4074517 & -0.04596939 & 0.6276925 & -1.1656937 & -0.05003504 & ⋯ & 0.5853189 & 0.2104284 & 0.4079187 & 1.731664 & 1.281552 & 0.755415 & 1.330562 & 11\\% & 0.11 & CCS \\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 44\n", - "\n", - "| | sample_id <chr> | Factor1 <dbl> | Factor2 <dbl> | Factor3 <dbl> | Factor4 <dbl> | Factor5 <dbl> | Factor6 <dbl> | Factor7 <dbl> | Factor8 <dbl> | Factor9 <dbl> | ⋯ ⋯ | Normalized_X3_NK__CD53 <dbl> | Normalized_X3_NK__CST7 <dbl> | Normalized_X3_NK__FYN <dbl> | Normalized_X3_NK__GZMB <dbl> | Normalized_X3_NK__LITAF <dbl> | Normalized_X3_NK__PRF1 <dbl> | Normalized_X3_NK__TXNIP <dbl> | score <chr> | score_prepared <dbl> | class <chr> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | k1 | 0.4811325 | -0.2731317 | 1.4988169 | -0.4135678 | 1.5052706 | -0.92308998 | -0.3904185 | -0.1944797 | 0.16486799 | ⋯ | NA | NA | NA | NA | NA | NA | NA | 4% | 0.04 | No CCS |\n", - "| 2 | k10 | 0.8920603 | -0.7547770 | 0.3501023 | -0.1524478 | 0.4074517 | -0.04596939 | 0.6276925 | -1.1656937 | -0.05003504 | ⋯ | 0.5853189 | 0.2104284 | 0.4079187 | 1.731664 | 1.281552 | 0.755415 | 1.330562 | 11% | 0.11 | CCS |\n", - "\n" - ], - "text/plain": [ - " sample_id Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 \n", - "1 k1 0.4811325 -0.2731317 1.4988169 -0.4135678 1.5052706 -0.92308998\n", - "2 k10 0.8920603 -0.7547770 0.3501023 -0.1524478 0.4074517 -0.04596939\n", - " Factor7 Factor8 Factor9 ⋯ Normalized_X3_NK__CD53\n", - "1 -0.3904185 -0.1944797 0.16486799 ⋯ NA \n", - "2 0.6276925 -1.1656937 -0.05003504 ⋯ 0.5853189 \n", - " Normalized_X3_NK__CST7 Normalized_X3_NK__FYN Normalized_X3_NK__GZMB\n", - "1 NA NA NA \n", - "2 0.2104284 0.4079187 1.731664 \n", - " Normalized_X3_NK__LITAF Normalized_X3_NK__PRF1 Normalized_X3_NK__TXNIP score\n", - "1 NA NA NA 4% \n", - "2 1.281552 0.755415 1.330562 11% \n", - " score_prepared class \n", - "1 0.04 No CCS \n", - "2 0.11 CCS " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(data,2)" - ] - }, { "cell_type": "markdown", "id": "c90e7f63-02b3-4dc2-9ffc-0e87d6012443", @@ -3567,152 +2205,18 @@ }, { "cell_type": "code", - "execution_count": 61, - "id": "c441e3da-ab7e-416b-b17f-c8d4ee55aa16", + "execution_count": 68, + "id": "ec5eefe8-3cfd-4f57-b2e7-6b866fae23ff", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 5 × 6
ytp_outcomeFactor4Normalized_CKNormalized_CRPNormalized_Troponin
<chr><chr><dbl><dbl><dbl><dbl>
361TP1_bad 0.47349970.79612690.24423841.0000000
451TP1_bad 0.85588910.52243720.41823880.3199684
491TP1_bad 0.75604520.62361370.62702650.6369666
530TP1_good1.00000000.60514940.62702650.4206572
601TP1_bad 0.71186770.51340510.62702650.4735581
\n" - ], - "text/latex": [ - "A data.frame: 5 × 6\n", - "\\begin{tabular}{r|llllll}\n", - " & y & tp\\_outcome & Factor4 & Normalized\\_CK & Normalized\\_CRP & Normalized\\_Troponin\\\\\n", - " & & & & & & \\\\\n", - "\\hline\n", - "\t36 & 1 & TP1\\_bad & 0.4734997 & 0.7961269 & 0.2442384 & 1.0000000\\\\\n", - "\t45 & 1 & TP1\\_bad & 0.8558891 & 0.5224372 & 0.4182388 & 0.3199684\\\\\n", - "\t49 & 1 & TP1\\_bad & 0.7560452 & 0.6236137 & 0.6270265 & 0.6369666\\\\\n", - "\t53 & 0 & TP1\\_good & 1.0000000 & 0.6051494 & 0.6270265 & 0.4206572\\\\\n", - "\t60 & 1 & TP1\\_bad & 0.7118677 & 0.5134051 & 0.6270265 & 0.4735581\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 5 × 6\n", - "\n", - "| | y <chr> | tp_outcome <chr> | Factor4 <dbl> | Normalized_CK <dbl> | Normalized_CRP <dbl> | Normalized_Troponin <dbl> |\n", - "|---|---|---|---|---|---|---|\n", - "| 36 | 1 | TP1_bad | 0.4734997 | 0.7961269 | 0.2442384 | 1.0000000 |\n", - "| 45 | 1 | TP1_bad | 0.8558891 | 0.5224372 | 0.4182388 | 0.3199684 |\n", - "| 49 | 1 | TP1_bad | 0.7560452 | 0.6236137 | 0.6270265 | 0.6369666 |\n", - "| 53 | 0 | TP1_good | 1.0000000 | 0.6051494 | 0.6270265 | 0.4206572 |\n", - "| 60 | 1 | TP1_bad | 0.7118677 | 0.5134051 | 0.6270265 | 0.4735581 |\n", - "\n" - ], - "text/plain": [ - " y tp_outcome Factor4 Normalized_CK Normalized_CRP Normalized_Troponin\n", - "36 1 TP1_bad 0.4734997 0.7961269 0.2442384 1.0000000 \n", - "45 1 TP1_bad 0.8558891 0.5224372 0.4182388 0.3199684 \n", - "49 1 TP1_bad 0.7560452 0.6236137 0.6270265 0.6369666 \n", - "53 0 TP1_good 1.0000000 0.6051494 0.6270265 0.4206572 \n", - "60 1 TP1_bad 0.7118677 0.5134051 0.6270265 0.4735581 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "head(prediction_data,5)" + "## Alternative use GGPLOT IMPL" ] }, { "cell_type": "code", - "execution_count": 62, - "id": "93813b9c-5a32-42dd-b1dd-7ded4f5d3df7", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot ROC curves (roc implementation DEPRECATED)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "d0843d7c-94b7-490b-9f93-585f371cf195", - "metadata": {}, - "outputs": [], - "source": [ - "#roc_list = list()" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "dac42c0e-e87c-4ab0-bf5c-12acae564e7f", - "metadata": {}, - "outputs": [], - "source": [ - "#for(i in prediction_vars){\n", - "# roc_list[[i]] = roc(prediction_data$y ~ \n", - "# prediction_data[,i], plot = FALSE, print.auc = TRUE)\n", - "# }" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "24cd30f9-8791-42ed-9329-a018e3742dfe", - "metadata": {}, - "outputs": [], - "source": [ - "#plot(roc_list[[2]])" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "c80fa733-8423-43e9-a4e2-04b82f9d20f5", - "metadata": {}, - "outputs": [], - "source": [ - "## Add all onto each other (ROC implementation)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "f1c42e05-ff6e-427c-8827-2d3f1e01e7fe", - "metadata": {}, - "outputs": [], - "source": [ - "#plot(roc_list[[1]], col = 1, lty = 2, main = \"ROC\")\n", - "#for(i in 2:length(roc_list)){\n", - "# plot(roc_list[[i]], col = 4, lty = 3, add = TRUE)\n", - "# }\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "ec5eefe8-3cfd-4f57-b2e7-6b866fae23ff", - "metadata": {}, - "outputs": [], - "source": [ - "## Alternative use GGPLOT IMPL" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "b9970f53-e916-4c2b-97a2-5957fa3a3d29", + "execution_count": 69, + "id": "b9970f53-e916-4c2b-97a2-5957fa3a3d29", "metadata": {}, "outputs": [ { @@ -4389,61 +2893,6 @@ "add_features = prediction_data %>% group_by(sample) %>% summarise(Mean_CK = mean(Normalized_CK, na.rm = TRUE), Max_CK = max(Normalized_CK, na.rm = TRUE), Mean_Troponin = mean(Normalized_Troponin, na.rm = TRUE), Max_Troponin = max(Normalized_Troponin, na.rm = TRUE))" ] }, - { - "cell_type": "code", - "execution_count": 101, - "id": "240a9c46-3810-47c4-9a6a-7b719df4ad7a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 2 × 5
sampleMean_CKMax_CKMean_TroponinMax_Troponin
<chr><dbl><dbl><dbl><dbl>
k1 -2.397022-2.397022-1.286479-1.286479
k10 NaN -Inf NaN -Inf
\n" - ], - "text/latex": [ - "A tibble: 2 × 5\n", - "\\begin{tabular}{lllll}\n", - " sample & Mean\\_CK & Max\\_CK & Mean\\_Troponin & Max\\_Troponin\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t k1 & -2.397022 & -2.397022 & -1.286479 & -1.286479\\\\\n", - "\t k10 & NaN & -Inf & NaN & -Inf\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 2 × 5\n", - "\n", - "| sample <chr> | Mean_CK <dbl> | Max_CK <dbl> | Mean_Troponin <dbl> | Max_Troponin <dbl> |\n", - "|---|---|---|---|---|\n", - "| k1 | -2.397022 | -2.397022 | -1.286479 | -1.286479 |\n", - "| k10 | NaN | -Inf | NaN | -Inf |\n", - "\n" - ], - "text/plain": [ - " sample Mean_CK Max_CK Mean_Troponin Max_Troponin\n", - "1 k1 -2.397022 -2.397022 -1.286479 -1.286479 \n", - "2 k10 NaN -Inf NaN -Inf " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(add_features,2)" - ] - }, { "cell_type": "code", "execution_count": 102, @@ -4498,64 +2947,6 @@ "prediction_data = merge(prediction_data[,c('sample', 'y', 'tp_outcome', 'Factor4')], add_features)" ] }, - { - "cell_type": "code", - "execution_count": 104, - "id": "822095da-df5e-4094-811e-a48ef67ebeaf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 8
sampleytp_outcomeFactor4Mean_CKMax_CKMean_TroponinMax_Troponin
<chr><chr><chr><dbl><dbl><dbl><dbl><dbl>
1k1 TP0_NATP0_NA-0.4135678-2.397022-2.397022-1.286479-1.286479
2k10TP0_NATP0_NA-0.1524478 NaN -Inf NaN -Inf
\n" - ], - "text/latex": [ - "A data.frame: 2 × 8\n", - "\\begin{tabular}{r|llllllll}\n", - " & sample & y & tp\\_outcome & Factor4 & Mean\\_CK & Max\\_CK & Mean\\_Troponin & Max\\_Troponin\\\\\n", - " & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & k1 & TP0\\_NA & TP0\\_NA & -0.4135678 & -2.397022 & -2.397022 & -1.286479 & -1.286479\\\\\n", - "\t2 & k10 & TP0\\_NA & TP0\\_NA & -0.1524478 & NaN & -Inf & NaN & -Inf\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 8\n", - "\n", - "| | sample <chr> | y <chr> | tp_outcome <chr> | Factor4 <dbl> | Mean_CK <dbl> | Max_CK <dbl> | Mean_Troponin <dbl> | Max_Troponin <dbl> |\n", - "|---|---|---|---|---|---|---|---|---|\n", - "| 1 | k1 | TP0_NA | TP0_NA | -0.4135678 | -2.397022 | -2.397022 | -1.286479 | -1.286479 |\n", - "| 2 | k10 | TP0_NA | TP0_NA | -0.1524478 | NaN | -Inf | NaN | -Inf |\n", - "\n" - ], - "text/plain": [ - " sample y tp_outcome Factor4 Mean_CK Max_CK Mean_Troponin\n", - "1 k1 TP0_NA TP0_NA -0.4135678 -2.397022 -2.397022 -1.286479 \n", - "2 k10 TP0_NA TP0_NA -0.1524478 NaN -Inf NaN \n", - " Max_Troponin\n", - "1 -1.286479 \n", - "2 -Inf " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(prediction_data,2)" - ] - }, { "cell_type": "code", "execution_count": 105, @@ -4569,76 +2960,6 @@ "prediction_data$y[prediction_data$y == prediction_subset[1]] = 0 # good outcome as control" ] }, - { - "cell_type": "code", - "execution_count": 106, - "id": "9cea637f-3496-4f08-83b5-1d7b99da659f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message in mean.default(col, ...):\n", - "“argument is not numeric or logical: returning NA”\n", - "Warning message in mean.default(col, ...):\n", - "“argument is not numeric or logical: returning NA”\n", - "\u001b[1m\u001b[22m`summarise()` has grouped output by 'tp_outcome'. You can override using the\n", - "`.groups` argument.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A grouped_df: 2 × 8
tp_outcomeysampleFactor4Mean_CKMax_CKMean_TroponinMax_Troponin
<chr><chr><dbl><dbl><dbl><dbl><dbl><dbl>
TP1_bad 1NA-1.12592120.48263761.3192910.71359921.3862666
TP1_good0NA-0.62981320.32146611.0881530.31605420.7361031
\n" - ], - "text/latex": [ - "A grouped\\_df: 2 × 8\n", - "\\begin{tabular}{llllllll}\n", - " tp\\_outcome & y & sample & Factor4 & Mean\\_CK & Max\\_CK & Mean\\_Troponin & Max\\_Troponin\\\\\n", - " & & & & & & & \\\\\n", - "\\hline\n", - "\t TP1\\_bad & 1 & NA & -1.1259212 & 0.4826376 & 1.319291 & 0.7135992 & 1.3862666\\\\\n", - "\t TP1\\_good & 0 & NA & -0.6298132 & 0.3214661 & 1.088153 & 0.3160542 & 0.7361031\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A grouped_df: 2 × 8\n", - "\n", - "| tp_outcome <chr> | y <chr> | sample <dbl> | Factor4 <dbl> | Mean_CK <dbl> | Max_CK <dbl> | Mean_Troponin <dbl> | Max_Troponin <dbl> |\n", - "|---|---|---|---|---|---|---|---|\n", - "| TP1_bad | 1 | NA | -1.1259212 | 0.4826376 | 1.319291 | 0.7135992 | 1.3862666 |\n", - "| TP1_good | 0 | NA | -0.6298132 | 0.3214661 | 1.088153 | 0.3160542 | 0.7361031 |\n", - "\n" - ], - "text/plain": [ - " tp_outcome y sample Factor4 Mean_CK Max_CK Mean_Troponin Max_Troponin\n", - "1 TP1_bad 1 NA -1.1259212 0.4826376 1.319291 0.7135992 1.3862666 \n", - "2 TP1_good 0 NA -0.6298132 0.3214661 1.088153 0.3160542 0.7361031 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Check in which direction to adjust\n", - "prediction_data %>% group_by(tp_outcome,y) %>% summarise(across(everything(), mean, na.rm =TRUE))\n", - "\n", - "# predict bad --> higher values for bad" - ] - }, { "cell_type": "code", "execution_count": 107, @@ -4747,73 +3068,6 @@ "head(prediction_data,5)" ] }, - { - "cell_type": "code", - "execution_count": 111, - "id": "d8dbd8e6-7134-4b43-bfef-420d53b2f9ac", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot ROC curves (roc implementation DEPRECATED)" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "fc69c9f1-844a-4dbe-987b-861b59594c37", - "metadata": {}, - "outputs": [], - "source": [ - "#roc_list = list()" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "7054e9ff-d097-450c-b1be-be511c967f4f", - "metadata": {}, - "outputs": [], - "source": [ - "#for(i in prediction_vars){\n", - "# roc_list[[i]] = roc(prediction_data$y ~ \n", - "# prediction_data[,i], plot = FALSE, print.auc = TRUE)\n", - "# }" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "id": "fca2e31d-62cb-42c2-8f1f-18853c201933", - "metadata": {}, - "outputs": [], - "source": [ - "#plot(roc_list[[2]])" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "a32bddd7-cab5-4d7b-aa72-78f2387799f6", - "metadata": {}, - "outputs": [], - "source": [ - "## Add all onto each other (ROC implementation)" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "id": "59996873-04b1-4f82-ab22-ec318b622c25", - "metadata": {}, - "outputs": [], - "source": [ - "#plot(roc_list[[1]], col = 1, lty = 2, main = \"ROC\")\n", - "#for(i in 2:length(roc_list)){\n", - "# plot(roc_list[[i]], col = 4, lty = 3, add = TRUE)\n", - "# }\n", - " " - ] - }, { "cell_type": "code", "execution_count": 117, @@ -5357,118 +3611,49 @@ }, { "cell_type": "code", - "execution_count": 142, - "id": "32cd9e4f-19f5-4709-a1bf-497de0395837", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[1m\u001b[22m`summarise()` has grouped output by 'delta_ef_value_class_summarized'. You can\n", - "override using the `.groups` argument.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A grouped_df: 2 × 6
delta_ef_value_class_summarizedyFactor4Normalized_CKNormalized_CRPNormalized_Troponin
<chr><chr><dbl><dbl><dbl><dbl>
bad 1-0.020449190.53710000.191008530.7732146
good0 0.173490690.33105410.094178530.3364290
\n" - ], - "text/latex": [ - "A grouped\\_df: 2 × 6\n", - "\\begin{tabular}{llllll}\n", - " delta\\_ef\\_value\\_class\\_summarized & y & Factor4 & Normalized\\_CK & Normalized\\_CRP & Normalized\\_Troponin\\\\\n", - " & & & & & \\\\\n", - "\\hline\n", - "\t bad & 1 & -0.02044919 & 0.5371000 & 0.19100853 & 0.7732146\\\\\n", - "\t good & 0 & 0.17349069 & 0.3310541 & 0.09417853 & 0.3364290\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A grouped_df: 2 × 6\n", - "\n", - "| delta_ef_value_class_summarized <chr> | y <chr> | Factor4 <dbl> | Normalized_CK <dbl> | Normalized_CRP <dbl> | Normalized_Troponin <dbl> |\n", - "|---|---|---|---|---|---|\n", - "| bad | 1 | -0.02044919 | 0.5371000 | 0.19100853 | 0.7732146 |\n", - "| good | 0 | 0.17349069 | 0.3310541 | 0.09417853 | 0.3364290 |\n", - "\n" - ], - "text/plain": [ - " delta_ef_value_class_summarized y Factor4 Normalized_CK Normalized_CRP\n", - "1 bad 1 -0.02044919 0.5371000 0.19100853 \n", - "2 good 0 0.17349069 0.3310541 0.09417853 \n", - " Normalized_Troponin\n", - "1 0.7732146 \n", - "2 0.3364290 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Check in which direction to adjust\n", - "prediction_data %>% group_by(delta_ef_value_class_summarized,y) %>% summarise(across(everything(), mean, na.rm =TRUE))\n", - "\n", - "# predict bad --> higher values for bad" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "id": "3993b2d2-f7ae-4375-aac3-6eadaffc6957", - "metadata": {}, - "outputs": [], - "source": [ - "# Adjust sign of Factor values\n", - "prediction_data$Factor4 = -1*prediction_data$Factor4\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53e01402-22c5-4404-b1f6-c356c56447c9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 144, - "id": "76a1020f-b1f4-42f3-b663-a695c405996c", - "metadata": {}, - "outputs": [], - "source": [ - "# Transform and scale scores of prediction variables (to be between 0 and 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "id": "9cd13703-5146-493e-b6b6-879c30c7f01e", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data = cbind( prediction_data[,c('y', prediction_group)],\n", - " apply(prediction_data[,prediction_vars], MARGIN = 2, FUN = function(x){\n", - " (x - min(x, na.rm = TRUE)) / (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))}))" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "id": "9069b255-a1ea-4aab-a178-5743826942e5", + "execution_count": 143, + "id": "3993b2d2-f7ae-4375-aac3-6eadaffc6957", + "metadata": {}, + "outputs": [], + "source": [ + "# Adjust sign of Factor values\n", + "prediction_data$Factor4 = -1*prediction_data$Factor4\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53e01402-22c5-4404-b1f6-c356c56447c9", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 144, + "id": "76a1020f-b1f4-42f3-b663-a695c405996c", + "metadata": {}, + "outputs": [], + "source": [ + "# Transform and scale scores of prediction variables (to be between 0 and 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "id": "9cd13703-5146-493e-b6b6-879c30c7f01e", + "metadata": {}, + "outputs": [], + "source": [ + "prediction_data = cbind( prediction_data[,c('y', prediction_group)],\n", + " apply(prediction_data[,prediction_vars], MARGIN = 2, FUN = function(x){\n", + " (x - min(x, na.rm = TRUE)) / (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))}))" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "id": "9069b255-a1ea-4aab-a178-5743826942e5", "metadata": {}, "outputs": [ { @@ -5538,73 +3723,6 @@ "head(prediction_data,5)" ] }, - { - "cell_type": "code", - "execution_count": 147, - "id": "90d0f842-56a4-42a8-ad18-ffbe2486b737", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot ROC curves (roc implementation DEPRECATED)" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "id": "b71e09db-101d-43cb-9235-42960972fb71", - "metadata": {}, - "outputs": [], - "source": [ - "#roc_list = list()" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "id": "144b630e-0814-4099-a329-e01b132d6eb0", - "metadata": {}, - "outputs": [], - "source": [ - "#for(i in prediction_vars){\n", - "# roc_list[[i]] = roc(prediction_data$y ~ \n", - "# prediction_data[,i], plot = FALSE, print.auc = TRUE)\n", - "# }" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "id": "904c6265-2c9a-4968-9d30-1bb0c48beb32", - "metadata": {}, - "outputs": [], - "source": [ - "#plot(roc_list[[2]])" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "id": "3f226ad6-69d4-4185-8397-fdc7bff1fec4", - "metadata": {}, - "outputs": [], - "source": [ - "## Add all onto each other (ROC implementation)" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "id": "e9078eaa-25b8-4644-857a-df5f612b807c", - "metadata": {}, - "outputs": [], - "source": [ - "#plot(roc_list[[1]], col = 1, lty = 2, main = \"ROC\")\n", - "#for(i in 2:length(roc_list)){\n", - "# plot(roc_list[[i]], col = 4, lty = 3, add = TRUE)\n", - "# }\n", - " " - ] - }, { "cell_type": "code", "execution_count": 153, @@ -5810,2655 +3928,6 @@ "dev.off()" ] }, - { - "cell_type": "markdown", - "id": "9cbc95aa-414a-4754-913d-f5ea3336f588", - "metadata": { - "tags": [] - }, - "source": [ - "# Prediction Factor 1 (ccs vs. no-ccs)" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "id": "5ebcfc40-6a17-42f1-b0bb-9fb6cf036ca2", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_vars = c('Factor1', 'score_prepared')" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "id": "4a3f7b9d-6bd7-4efd-81eb-fb9edbea2157", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
  1. 'No CCS '
  2. 'CCS'
  3. NA
\n" - ], - "text/latex": [ - "\\begin{enumerate*}\n", - "\\item 'No CCS '\n", - "\\item 'CCS'\n", - "\\item NA\n", - "\\end{enumerate*}\n" - ], - "text/markdown": [ - "1. 'No CCS '\n", - "2. 'CCS'\n", - "3. NA\n", - "\n", - "\n" - ], - "text/plain": [ - "[1] \"No CCS \" \"CCS\" NA " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unique(data$class)" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "id": "1b1f35f6-60de-46ea-b697-a53c0067089a", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_group = 'class'\n", - "prediction_subset = c('No CCS ', 'CCS')" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "id": "b83988be-1623-4226-82f0-63d79c01a476", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data = data[,c(prediction_vars, prediction_group)]\n", - "prediction_data$y = prediction_data[,prediction_group]\n", - "prediction_data = prediction_data[prediction_data$y %in% prediction_subset,]\n", - "\n", - "prediction_data$y[prediction_data$y == prediction_subset[2]] = 1 # predict CCS \n", - "prediction_data$y[prediction_data$y == prediction_subset[1]] = 0 # no_ccs as control" - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "id": "026a63da-8be1-4ea5-aa03-94d379c7f552", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[1m\u001b[22m`summarise()` has grouped output by 'class'. You can override using the\n", - "`.groups` argument.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A grouped_df: 2 × 4
classyFactor1score_prepared
<chr><chr><dbl><dbl>
CCS 1 1.64322040.1122222
No CCS 0-0.12379630.0850000
\n" - ], - "text/latex": [ - "A grouped\\_df: 2 × 4\n", - "\\begin{tabular}{llll}\n", - " class & y & Factor1 & score\\_prepared\\\\\n", - " & & & \\\\\n", - "\\hline\n", - "\t CCS & 1 & 1.6432204 & 0.1122222\\\\\n", - "\t No CCS & 0 & -0.1237963 & 0.0850000\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A grouped_df: 2 × 4\n", - "\n", - "| class <chr> | y <chr> | Factor1 <dbl> | score_prepared <dbl> |\n", - "|---|---|---|---|\n", - "| CCS | 1 | 1.6432204 | 0.1122222 |\n", - "| No CCS | 0 | -0.1237963 | 0.0850000 |\n", - "\n" - ], - "text/plain": [ - " class y Factor1 score_prepared\n", - "1 CCS 1 1.6432204 0.1122222 \n", - "2 No CCS 0 -0.1237963 0.0850000 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Check in which direction to adjust\n", - "prediction_data %>% group_by(class,y) %>% summarise(across(everything(), mean, na.rm =TRUE))\n", - "\n", - "# predict CCS --> higher values for CCS" - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "id": "d4f62c0d-170c-40fd-9ce5-57d090a6262e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A grouped_df: 2 × 2
classn
<chr><int>
CCS 16
No CCS 18
\n" - ], - "text/latex": [ - "A grouped\\_df: 2 × 2\n", - "\\begin{tabular}{ll}\n", - " class & n\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t CCS & 16\\\\\n", - "\t No CCS & 18\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A grouped_df: 2 × 2\n", - "\n", - "| class <chr> | n <int> |\n", - "|---|---|\n", - "| CCS | 16 |\n", - "| No CCS | 18 |\n", - "\n" - ], - "text/plain": [ - " class n \n", - "1 CCS 16\n", - "2 No CCS 18" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "prediction_data %>% group_by(class) %>% count()" - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "id": "6802c3b9-6eb6-48ac-a4ac-d30d33dfd02b", - "metadata": {}, - "outputs": [], - "source": [ - "# Transform and scale scores of prediction variables (to be between 0 and 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "id": "f05dbe59-bd77-429f-8b25-7cca0cd4cd1d", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data = cbind( prediction_data[,c('y', prediction_group)],\n", - " apply(prediction_data[,prediction_vars], MARGIN = 2, FUN = function(x){\n", - " (x - min(x, na.rm = TRUE)) / (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))}))" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "id": "6e3c42a9-2228-43c0-9e31-6403e692b21a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 5 × 4
yclassFactor1score_prepared
<chr><chr><dbl><dbl>
10No CCS 0.45992960.0000000
21CCS 0.53657390.4117647
30No CCS 0.13816990.1764706
40No CCS 0.0000000 NA
50No CCS 0.34872200.1176471
\n" - ], - "text/latex": [ - "A data.frame: 5 × 4\n", - "\\begin{tabular}{r|llll}\n", - " & y & class & Factor1 & score\\_prepared\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t1 & 0 & No CCS & 0.4599296 & 0.0000000\\\\\n", - "\t2 & 1 & CCS & 0.5365739 & 0.4117647\\\\\n", - "\t3 & 0 & No CCS & 0.1381699 & 0.1764706\\\\\n", - "\t4 & 0 & No CCS & 0.0000000 & NA\\\\\n", - "\t5 & 0 & No CCS & 0.3487220 & 0.1176471\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 5 × 4\n", - "\n", - "| | y <chr> | class <chr> | Factor1 <dbl> | score_prepared <dbl> |\n", - "|---|---|---|---|---|\n", - "| 1 | 0 | No CCS | 0.4599296 | 0.0000000 |\n", - "| 2 | 1 | CCS | 0.5365739 | 0.4117647 |\n", - "| 3 | 0 | No CCS | 0.1381699 | 0.1764706 |\n", - "| 4 | 0 | No CCS | 0.0000000 | NA |\n", - "| 5 | 0 | No CCS | 0.3487220 | 0.1176471 |\n", - "\n" - ], - "text/plain": [ - " y class Factor1 score_prepared\n", - "1 0 No CCS 0.4599296 0.0000000 \n", - "2 1 CCS 0.5365739 0.4117647 \n", - "3 0 No CCS 0.1381699 0.1764706 \n", - "4 0 No CCS 0.0000000 NA \n", - "5 0 No CCS 0.3487220 0.1176471 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(prediction_data,5)" - ] - }, - { - "cell_type": "code", - "execution_count": 172, - "id": "aa1539fa-7335-4530-86be-fcd491b352b6", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot ROC curves (roc implementation DEPRECATED)" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "id": "413a2c78-61ed-41ff-bc3b-fa293a164263", - "metadata": {}, - "outputs": [], - "source": [ - "#roc_list = list()" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "id": "81b7b754-0feb-4fd2-82d8-1d57553b2ea5", - "metadata": {}, - "outputs": [], - "source": [ - "#for(i in prediction_vars){\n", - "# roc_list[[i]] = roc(prediction_data$y ~ \n", - "# prediction_data[,i], plot = FALSE, print.auc = TRUE)\n", - "# }" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "id": "cd7a3f2f-0e6d-46ed-8629-e34dfadc2732", - "metadata": {}, - "outputs": [], - "source": [ - "#plot(roc_list[[2]])" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "id": "17fa99b9-3ffc-4dd0-a9c2-79896cb24cfa", - "metadata": {}, - "outputs": [], - "source": [ - "## Add all onto each other (ROC implementation)" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "id": "85b2897c-7676-423a-8969-75ace5ec4f27", - "metadata": {}, - "outputs": [], - "source": [ - "#plot(roc_list[[1]], col = 1, lty = 2, main = \"ROC\")\n", - "#for(i in 2:length(roc_list)){\n", - "# plot(roc_list[[i]], col = 4, lty = 3, add = TRUE)\n", - "# }\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "id": "d7ee88d0-f967-4007-8a0c-ac5d1f0537ce", - "metadata": {}, - "outputs": [], - "source": [ - "## Alternative use GGPLOT IMPL" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "id": "5960e250-23ca-4e72-949c-c4cc03c87437", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using y, class as id variables\n", - "\n" - ] - } - ], - "source": [ - "prediction_data_long = melt(prediction_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "id": "8242a67c-2f20-4f43-b015-5e4d73645dea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 4
yclassvariablevalue
<chr><chr><fct><dbl>
10No CCS Factor10.4599296
21CCS Factor10.5365739
\n" - ], - "text/latex": [ - "A data.frame: 2 × 4\n", - "\\begin{tabular}{r|llll}\n", - " & y & class & variable & value\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t1 & 0 & No CCS & Factor1 & 0.4599296\\\\\n", - "\t2 & 1 & CCS & Factor1 & 0.5365739\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 4\n", - "\n", - "| | y <chr> | class <chr> | variable <fct> | value <dbl> |\n", - "|---|---|---|---|---|\n", - "| 1 | 0 | No CCS | Factor1 | 0.4599296 |\n", - "| 2 | 1 | CCS | Factor1 | 0.5365739 |\n", - "\n" - ], - "text/plain": [ - " y class variable value \n", - "1 0 No CCS Factor1 0.4599296\n", - "2 1 CCS Factor1 0.5365739" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(prediction_data_long,2)" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "id": "a47fe1aa-7c4b-43e1-82f4-80a5935fd496", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data_long = prediction_data_long[!is.na(prediction_data_long$value),]" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "id": "c2c917ea-0879-4dcd-a853-300246acabd8", - "metadata": {}, - "outputs": [], - "source": [ - "# Figure Name\n", - "figure_name = 'FIG7_E9_ROC_Factor1_CCS_NO_CCS'" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "id": "405da709-a10b-43f2-befa-a7dba466844d", - "metadata": {}, - "outputs": [], - "source": [ - "# Sizes of the plot\n", - "width_par = 5\n", - "height_par = 3" - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "id": "80ab2256-94c0-4575-bf50-1d7007a1d4ac", - "metadata": {}, - "outputs": [], - "source": [ - "xtitle = xlab( 'False Positive Fraction')\n", - "ytitle = ylab('Sensititity (True Positive Fraction)')" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "id": "9ac38733-8b57-42eb-bfba-02a8272d3428", - "metadata": {}, - "outputs": [], - "source": [ - "## Specify Plot\n", - "rocplot = ggplot(prediction_data_long, aes(m = value, d = as.numeric(y), color = variable))+ geom_roc(n.cuts=0,labels=FALSE)" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "id": "458c25ce-2aad-456f-918e-01f7a2c99996", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 120, - "width": 1800 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "## Configuration + Modification of plot\n", - "roc_plot = rocplot + \n", - " plot_config + \n", - " xtitle + \n", - " ytitle + \n", - " ggtitle('ROC curve ccs vs. no-ccs') +\n", - " geom_abline(slope = 1, intercept = 0)\n", - "\n", - "roc_plot" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "id": "be22513d-5be8-444c-adcc-be93e7c6b7fb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "png: 2" - ], - "text/latex": [ - "\\textbf{png:} 2" - ], - "text/markdown": [ - "**png:** 2" - ], - "text/plain": [ - "png \n", - " 2 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pdf(paste0('figures/Final_Figures/', figure_name, version, '.pdf'), width =width_par, height =height_par)\n", - "print(roc_plot)\n", - "dev.off()" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "id": "b6667a7c-6572-4481-b7a9-d898e0e40757", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data_long_ccs = prediction_data_long" - ] - }, - { - "cell_type": "markdown", - "id": "54248291-cc3d-43c2-be5a-bf6b00007883", - "metadata": { - "tags": [] - }, - "source": [ - "# Prediction Factor 1 (ccs vs. vollstaendiger_ausschluss)" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "id": "2e27a3b8-4160-41ce-9f7e-129648076375", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_vars = c('Factor1', 'score_prepared')" - ] - }, - { - "cell_type": "code", - "execution_count": 190, - "id": "c6bcc425-690f-4d18-b0aa-c19de27887f5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
  1. 'vollstaendiger_ausschluss'
  2. 'ccs'
  3. 'koronarsklerose'
  4. 'acs_subacute'
  5. 'acs_w_infection'
  6. 'acs_w_o_infection'
\n" - ], - "text/latex": [ - "\\begin{enumerate*}\n", - "\\item 'vollstaendiger\\_ausschluss'\n", - "\\item 'ccs'\n", - "\\item 'koronarsklerose'\n", - "\\item 'acs\\_subacute'\n", - "\\item 'acs\\_w\\_infection'\n", - "\\item 'acs\\_w\\_o\\_infection'\n", - "\\end{enumerate*}\n" - ], - "text/markdown": [ - "1. 'vollstaendiger_ausschluss'\n", - "2. 'ccs'\n", - "3. 'koronarsklerose'\n", - "4. 'acs_subacute'\n", - "5. 'acs_w_infection'\n", - "6. 'acs_w_o_infection'\n", - "\n", - "\n" - ], - "text/plain": [ - "[1] \"vollstaendiger_ausschluss\" \"ccs\" \n", - "[3] \"koronarsklerose\" \"acs_subacute\" \n", - "[5] \"acs_w_infection\" \"acs_w_o_infection\" " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unique(data$classification)" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "id": "3d7a6b4e-fbce-4855-b6d5-0242b46fdcf7", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_group = 'classification'\n", - "prediction_subset = c('vollstaendiger_ausschluss', 'ccs')" - ] - }, - { - "cell_type": "code", - "execution_count": 192, - "id": "76992223-425c-4904-9c56-592a71e289d1", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data = data[,c(prediction_vars, prediction_group)]\n", - "prediction_data$y = prediction_data[,prediction_group]\n", - "prediction_data = prediction_data[prediction_data$y %in% prediction_subset,]\n", - "\n", - "prediction_data$y[prediction_data$y == prediction_subset[2]] = 1 # predict CCS \n", - "prediction_data$y[prediction_data$y == prediction_subset[1]] = 0 # no_ccs as control" - ] - }, - { - "cell_type": "code", - "execution_count": 193, - "id": "00102300-ad48-4cbb-8bb2-c685f7ebea8f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[1m\u001b[22m`summarise()` has grouped output by 'classification'. You can override using\n", - "the `.groups` argument.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A grouped_df: 2 × 4
classificationyFactor1score_prepared
<chr><chr><dbl><dbl>
ccs 1 1.64322040.11222222
vollstaendiger_ausschluss0-0.91937430.05714286
\n" - ], - "text/latex": [ - "A grouped\\_df: 2 × 4\n", - "\\begin{tabular}{llll}\n", - " classification & y & Factor1 & score\\_prepared\\\\\n", - " & & & \\\\\n", - "\\hline\n", - "\t ccs & 1 & 1.6432204 & 0.11222222\\\\\n", - "\t vollstaendiger\\_ausschluss & 0 & -0.9193743 & 0.05714286\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A grouped_df: 2 × 4\n", - "\n", - "| classification <chr> | y <chr> | Factor1 <dbl> | score_prepared <dbl> |\n", - "|---|---|---|---|\n", - "| ccs | 1 | 1.6432204 | 0.11222222 |\n", - "| vollstaendiger_ausschluss | 0 | -0.9193743 | 0.05714286 |\n", - "\n" - ], - "text/plain": [ - " classification y Factor1 score_prepared\n", - "1 ccs 1 1.6432204 0.11222222 \n", - "2 vollstaendiger_ausschluss 0 -0.9193743 0.05714286 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Check in which direction to adjust\n", - "prediction_data %>% group_by(classification,y) %>% summarise(across(everything(), mean, na.rm =TRUE))\n", - "\n", - "# predict CCS --> higher values for CCS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e810536-de7f-4327-a1ae-dc340256fd10", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 194, - "id": "67e62bd8-19fa-4b72-9a48-86710f51b7e4", - "metadata": {}, - "outputs": [], - "source": [ - "# Transform and scale scores of prediction variables (to be between 0 and 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 195, - "id": "9331ff5a-b04b-4fb5-ac8e-831466b16f84", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data = cbind( prediction_data[,c('y', prediction_group)],\n", - " apply(prediction_data[,prediction_vars], MARGIN = 2, FUN = function(x){\n", - " (x - min(x, na.rm = TRUE)) / (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))}))" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "id": "27c02fdc-75bf-4f6a-bfee-a56b61c1660c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 5 × 4
yclassificationFactor1score_prepared
<chr><chr><dbl><dbl>
10vollstaendiger_ausschluss0.45992960.0000000
21ccs 0.53657390.4117647
30vollstaendiger_ausschluss0.13816990.1764706
40vollstaendiger_ausschluss0.0000000 NA
50vollstaendiger_ausschluss0.34872200.1176471
\n" - ], - "text/latex": [ - "A data.frame: 5 × 4\n", - "\\begin{tabular}{r|llll}\n", - " & y & classification & Factor1 & score\\_prepared\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t1 & 0 & vollstaendiger\\_ausschluss & 0.4599296 & 0.0000000\\\\\n", - "\t2 & 1 & ccs & 0.5365739 & 0.4117647\\\\\n", - "\t3 & 0 & vollstaendiger\\_ausschluss & 0.1381699 & 0.1764706\\\\\n", - "\t4 & 0 & vollstaendiger\\_ausschluss & 0.0000000 & NA\\\\\n", - "\t5 & 0 & vollstaendiger\\_ausschluss & 0.3487220 & 0.1176471\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 5 × 4\n", - "\n", - "| | y <chr> | classification <chr> | Factor1 <dbl> | score_prepared <dbl> |\n", - "|---|---|---|---|---|\n", - "| 1 | 0 | vollstaendiger_ausschluss | 0.4599296 | 0.0000000 |\n", - "| 2 | 1 | ccs | 0.5365739 | 0.4117647 |\n", - "| 3 | 0 | vollstaendiger_ausschluss | 0.1381699 | 0.1764706 |\n", - "| 4 | 0 | vollstaendiger_ausschluss | 0.0000000 | NA |\n", - "| 5 | 0 | vollstaendiger_ausschluss | 0.3487220 | 0.1176471 |\n", - "\n" - ], - "text/plain": [ - " y classification Factor1 score_prepared\n", - "1 0 vollstaendiger_ausschluss 0.4599296 0.0000000 \n", - "2 1 ccs 0.5365739 0.4117647 \n", - "3 0 vollstaendiger_ausschluss 0.1381699 0.1764706 \n", - "4 0 vollstaendiger_ausschluss 0.0000000 NA \n", - "5 0 vollstaendiger_ausschluss 0.3487220 0.1176471 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(prediction_data,5)" - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "id": "24089d9b-69cd-41d2-af60-30a45f498185", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A grouped_df: 2 × 2
classificationn
<chr><int>
ccs 16
vollstaendiger_ausschluss11
\n" - ], - "text/latex": [ - "A grouped\\_df: 2 × 2\n", - "\\begin{tabular}{ll}\n", - " classification & n\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t ccs & 16\\\\\n", - "\t vollstaendiger\\_ausschluss & 11\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A grouped_df: 2 × 2\n", - "\n", - "| classification <chr> | n <int> |\n", - "|---|---|\n", - "| ccs | 16 |\n", - "| vollstaendiger_ausschluss | 11 |\n", - "\n" - ], - "text/plain": [ - " classification n \n", - "1 ccs 16\n", - "2 vollstaendiger_ausschluss 11" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "prediction_data %>% group_by(classification) %>% count()" - ] - }, - { - "cell_type": "code", - "execution_count": 198, - "id": "7fea015c-d45b-4e67-bf6d-300db2bf7199", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot ROC curves (roc implementation DEPRECATED)" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "id": "238a500c-7422-419d-8787-d36d410045cc", - "metadata": {}, - "outputs": [], - "source": [ - "#roc_list = list()" - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "id": "6e429783-7478-4246-afcf-b05a5c862e0e", - "metadata": {}, - "outputs": [], - "source": [ - "#for(i in prediction_vars){\n", - "# roc_list[[i]] = roc(prediction_data$y ~ \n", - "# prediction_data[,i], plot = FALSE, print.auc = TRUE)\n", - "# }" - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "id": "3511b499-460c-40de-985d-29126410fee7", - "metadata": {}, - "outputs": [], - "source": [ - "#plot(roc_list[[2]])" - ] - }, - { - "cell_type": "code", - "execution_count": 202, - "id": "7f810584-7b3c-4762-a42a-f1251181ee42", - "metadata": {}, - "outputs": [], - "source": [ - "## Add all onto each other (ROC implementation)" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "id": "96145303-dda6-44bf-ab3a-5719e83d1489", - "metadata": {}, - "outputs": [], - "source": [ - "#plot(roc_list[[1]], col = 1, lty = 2, main = \"ROC\")\n", - "#for(i in 2:length(roc_list)){\n", - "# plot(roc_list[[i]], col = 4, lty = 3, add = TRUE)\n", - "# }\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 204, - "id": "4ae5c20b-032f-41e4-b822-ad74897d8823", - "metadata": {}, - "outputs": [], - "source": [ - "## Alternative use GGPLOT IMPL" - ] - }, - { - "cell_type": "code", - "execution_count": 205, - "id": "dfb38786-0732-416d-83a1-29eda6a5116a", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using y, classification as id variables\n", - "\n" - ] - } - ], - "source": [ - "prediction_data_long = melt(prediction_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 206, - "id": "a3c4c586-37ee-41d6-a2cc-15796ed96004", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 4
yclassificationvariablevalue
<chr><chr><fct><dbl>
10vollstaendiger_ausschlussFactor10.4599296
21ccs Factor10.5365739
\n" - ], - "text/latex": [ - "A data.frame: 2 × 4\n", - "\\begin{tabular}{r|llll}\n", - " & y & classification & variable & value\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t1 & 0 & vollstaendiger\\_ausschluss & Factor1 & 0.4599296\\\\\n", - "\t2 & 1 & ccs & Factor1 & 0.5365739\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 4\n", - "\n", - "| | y <chr> | classification <chr> | variable <fct> | value <dbl> |\n", - "|---|---|---|---|---|\n", - "| 1 | 0 | vollstaendiger_ausschluss | Factor1 | 0.4599296 |\n", - "| 2 | 1 | ccs | Factor1 | 0.5365739 |\n", - "\n" - ], - "text/plain": [ - " y classification variable value \n", - "1 0 vollstaendiger_ausschluss Factor1 0.4599296\n", - "2 1 ccs Factor1 0.5365739" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(prediction_data_long,2)" - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "id": "a0d9fa89-ae4b-414e-bf94-0a6e4563b863", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data_long = prediction_data_long[!is.na(prediction_data_long$value),]" - ] - }, - { - "cell_type": "code", - "execution_count": 208, - "id": "1a1664c7-e1f6-48e8-b0cd-cde7dee08fea", - "metadata": {}, - "outputs": [], - "source": [ - "# Figure Name\n", - "figure_name = 'FIG7_E9_ROC_Factor1_CCS_Ausschluss'" - ] - }, - { - "cell_type": "code", - "execution_count": 209, - "id": "596a2cb9-7180-4719-ac03-9a125cf63481", - "metadata": {}, - "outputs": [], - "source": [ - "# Sizes of the plot\n", - "width_par = 5\n", - "height_par = 3" - ] - }, - { - "cell_type": "code", - "execution_count": 210, - "id": "e8b9ec7c-b66e-41c6-8c18-41b66c368ff4", - "metadata": {}, - "outputs": [], - "source": [ - "xtitle = xlab( 'False Positive Fraction')\n", - "ytitle = ylab('Sensititity (True Positive Fraction)')" - ] - }, - { - "cell_type": "code", - "execution_count": 211, - "id": "46ac7f91-170b-4359-991f-b27f1726687b", - "metadata": {}, - "outputs": [], - "source": [ - "## Specify Plot\n", - "rocplot = ggplot(prediction_data_long, aes(m = value, d = as.numeric(y), color = variable))+ geom_roc(n.cuts=0,labels=FALSE)" - ] - }, - { - "cell_type": "code", - "execution_count": 212, - "id": "f70baefb-8bdf-416c-8b77-c946a907f7ba", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 120, - "width": 1800 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "## Configuration + Modification of plot\n", - "roc_plot = rocplot + \n", - " plot_config + \n", - " xtitle + \n", - " ytitle + \n", - " ggtitle('ROC curve ccs vs. vollständiger ausschluss') +\n", - " geom_abline(slope = 1, intercept = 0)\n", - "\n", - "roc_plot" - ] - }, - { - "cell_type": "code", - "execution_count": 213, - "id": "f51e1575-1f3c-4f4c-ba24-4a2c32e9e287", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "png: 2" - ], - "text/latex": [ - "\\textbf{png:} 2" - ], - "text/markdown": [ - "**png:** 2" - ], - "text/plain": [ - "png \n", - " 2 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pdf(paste0('figures/Final_Figures/', figure_name, version, '.pdf'), width =width_par, height =height_par)\n", - "print(roc_plot)\n", - "dev.off()" - ] - }, - { - "cell_type": "code", - "execution_count": 214, - "id": "c0af8619-ff9d-49e9-a433-5c583788910a", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data_ausschluss = prediction_data_long" - ] - }, - { - "cell_type": "markdown", - "id": "3d45f247-1b60-4411-905c-a300901fa2fb", - "metadata": {}, - "source": [ - "# Combine ccs and vollstaendiger ausschluss" - ] - }, - { - "cell_type": "code", - "execution_count": 215, - "id": "82730465-d87e-4c1f-bd30-db80e646eda7", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data_long_ccs$variable = str_replace(prediction_data_long_ccs$variable, 'score_prepared', 'Score')\n", - "colnames(prediction_data_long_ccs) = c('y', 'classification', 'variable', 'value')" - ] - }, - { - "cell_type": "code", - "execution_count": 216, - "id": "6ef927a6-2a0a-4f71-aebc-5a02d6dfa635", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 4
yclassificationvariablevalue
<chr><chr><chr><dbl>
10No CCS Factor10.4599296
21CCS Factor10.5365739
\n" - ], - "text/latex": [ - "A data.frame: 2 × 4\n", - "\\begin{tabular}{r|llll}\n", - " & y & classification & variable & value\\\\\n", - " & & & & \\\\\n", - "\\hline\n", - "\t1 & 0 & No CCS & Factor1 & 0.4599296\\\\\n", - "\t2 & 1 & CCS & Factor1 & 0.5365739\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 4\n", - "\n", - "| | y <chr> | classification <chr> | variable <chr> | value <dbl> |\n", - "|---|---|---|---|---|\n", - "| 1 | 0 | No CCS | Factor1 | 0.4599296 |\n", - "| 2 | 1 | CCS | Factor1 | 0.5365739 |\n", - "\n" - ], - "text/plain": [ - " y classification variable value \n", - "1 0 No CCS Factor1 0.4599296\n", - "2 1 CCS Factor1 0.5365739" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(prediction_data_long_ccs,2)" - ] - }, - { - "cell_type": "code", - "execution_count": 217, - "id": "3363c493-66b0-4f44-b06e-0215f1a9047b", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data_ausschluss$variable = str_replace(prediction_data_ausschluss$variable, 'Factor1', 'Factor1_w_o_cs')\n", - "prediction_data_ausschluss$variable = str_replace(prediction_data_ausschluss$variable, 'score_prepared', 'Score_w_o_cs')" - ] - }, - { - "cell_type": "code", - "execution_count": 218, - "id": "50544bc6-0e9f-4ec9-a0e3-c3c83c509173", - "metadata": {}, - "outputs": [], - "source": [ - "#head( prediction_data_long,2)" - ] - }, - { - "cell_type": "code", - "execution_count": 219, - "id": "a25cf561-6712-41c1-a98b-7591513ad784", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data_long = rbind(prediction_data_long_ccs,prediction_data_ausschluss)" - ] - }, - { - "cell_type": "code", - "execution_count": 220, - "id": "5bd32822-dc72-48d8-ab99-f4b3bf62dd71", - "metadata": {}, - "outputs": [], - "source": [ - "amount_samples = prediction_data_long %>% group_by(variable) %>% count()" - ] - }, - { - "cell_type": "code", - "execution_count": 221, - "id": "4702787a-5ca7-47e5-b070-36d623aacbbc", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data_long = merge(prediction_data_long, amount_samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 222, - "id": "708a0265-618d-4922-9b44-5f0582e0337b", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data_long$variable_w_n = paste0(prediction_data_long$variable, ' (n=', prediction_data_long$n, ')')" - ] - }, - { - "cell_type": "code", - "execution_count": 223, - "id": "a0e238fc-563a-412a-b07a-8e3d2685e7d0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 6
variableyclassificationvaluenvariable_w_n
<chr><chr><chr><dbl><int><chr>
1Factor10No CCS 0.459929634Factor1 (n=34)
2Factor11CCS 0.536573934Factor1 (n=34)
\n" - ], - "text/latex": [ - "A data.frame: 2 × 6\n", - "\\begin{tabular}{r|llllll}\n", - " & variable & y & classification & value & n & variable\\_w\\_n\\\\\n", - " & & & & & & \\\\\n", - "\\hline\n", - "\t1 & Factor1 & 0 & No CCS & 0.4599296 & 34 & Factor1 (n=34)\\\\\n", - "\t2 & Factor1 & 1 & CCS & 0.5365739 & 34 & Factor1 (n=34)\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 6\n", - "\n", - "| | variable <chr> | y <chr> | classification <chr> | value <dbl> | n <int> | variable_w_n <chr> |\n", - "|---|---|---|---|---|---|---|\n", - "| 1 | Factor1 | 0 | No CCS | 0.4599296 | 34 | Factor1 (n=34) |\n", - "| 2 | Factor1 | 1 | CCS | 0.5365739 | 34 | Factor1 (n=34) |\n", - "\n" - ], - "text/plain": [ - " variable y classification value n variable_w_n \n", - "1 Factor1 0 No CCS 0.4599296 34 Factor1 (n=34)\n", - "2 Factor1 1 CCS 0.5365739 34 Factor1 (n=34)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(prediction_data_long,2)" - ] - }, - { - "cell_type": "code", - "execution_count": 224, - "id": "7da7899b-8029-4d17-bde3-fd3941ead6d9", - "metadata": {}, - "outputs": [], - "source": [ - "auc_values = data.frame()" - ] - }, - { - "cell_type": "code", - "execution_count": 225, - "id": "42ba3198-2d1a-4f7e-a0d6-d39b385ece69", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Setting levels: control = 0, case = 1\n", - "\n", - "Setting direction: controls < cases\n", - "\n", - "Setting levels: control = 0, case = 1\n", - "\n", - "Setting direction: controls < cases\n", - "\n", - "Setting levels: control = 0, case = 1\n", - "\n", - "Setting direction: controls < cases\n", - "\n", - "Setting levels: control = 0, case = 1\n", - "\n", - "Setting direction: controls < cases\n", - "\n" - ] - } - ], - "source": [ - "for(i in unique(prediction_data_long$variable)){\n", - " calc_auc = data.frame(variable = i, \n", - " auc = auc(prediction_data_long$y[prediction_data_long$variable == i], prediction_data_long$value[prediction_data_long$variable == i])\n", - " )\n", - " auc_values = rbind(auc_values, calc_auc)\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 226, - "id": "4744f54b-d6b3-419c-bec5-0bbee268e038", - "metadata": {}, - "outputs": [], - "source": [ - "### Save AUC values" - ] - }, - { - "cell_type": "code", - "execution_count": 227, - "id": "0a3a25cf-c382-4bcb-aa9c-76763c65fd08", - "metadata": {}, - "outputs": [], - "source": [ - "write.csv(auc_values, paste0(result_path,'/Supplementary/SUPP_E9_Factor1_ROC_Plot_AUC_values.csv'), row.names = FALSE)" - ] - }, - { - "cell_type": "code", - "execution_count": 228, - "id": "0a167231-1fd4-4bff-a1bb-4a241f734eaf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 4 × 2
variableauc
<chr><auc>
Factor1 0.8402778
Factor1_w_o_cs0.9886364
Score 0.6250000
Score_w_o_cs 0.8015873
\n" - ], - "text/latex": [ - "A data.frame: 4 × 2\n", - "\\begin{tabular}{ll}\n", - " variable & auc\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t Factor1 & 0.8402778\\\\\n", - "\t Factor1\\_w\\_o\\_cs & 0.9886364\\\\\n", - "\t Score & 0.6250000\\\\\n", - "\t Score\\_w\\_o\\_cs & 0.8015873\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 4 × 2\n", - "\n", - "| variable <chr> | auc <auc> |\n", - "|---|---|\n", - "| Factor1 | 0.8402778 |\n", - "| Factor1_w_o_cs | 0.9886364 |\n", - "| Score | 0.6250000 |\n", - "| Score_w_o_cs | 0.8015873 |\n", - "\n" - ], - "text/plain": [ - " variable auc \n", - "1 Factor1 0.8402778\n", - "2 Factor1_w_o_cs 0.9886364\n", - "3 Score 0.6250000\n", - "4 Score_w_o_cs 0.8015873" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "auc_values" - ] - }, - { - "cell_type": "code", - "execution_count": 229, - "id": "cb100389-a768-4aec-afcb-c72e432bbbff", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 6
variableyclassificationvaluenvariable_w_n
<chr><chr><chr><dbl><int><chr>
1Factor10No CCS 0.459929634Factor1 (n=34)
2Factor11CCS 0.536573934Factor1 (n=34)
\n" - ], - "text/latex": [ - "A data.frame: 2 × 6\n", - "\\begin{tabular}{r|llllll}\n", - " & variable & y & classification & value & n & variable\\_w\\_n\\\\\n", - " & & & & & & \\\\\n", - "\\hline\n", - "\t1 & Factor1 & 0 & No CCS & 0.4599296 & 34 & Factor1 (n=34)\\\\\n", - "\t2 & Factor1 & 1 & CCS & 0.5365739 & 34 & Factor1 (n=34)\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 6\n", - "\n", - "| | variable <chr> | y <chr> | classification <chr> | value <dbl> | n <int> | variable_w_n <chr> |\n", - "|---|---|---|---|---|---|---|\n", - "| 1 | Factor1 | 0 | No CCS | 0.4599296 | 34 | Factor1 (n=34) |\n", - "| 2 | Factor1 | 1 | CCS | 0.5365739 | 34 | Factor1 (n=34) |\n", - "\n" - ], - "text/plain": [ - " variable y classification value n variable_w_n \n", - "1 Factor1 0 No CCS 0.4599296 34 Factor1 (n=34)\n", - "2 Factor1 1 CCS 0.5365739 34 Factor1 (n=34)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(prediction_data_long,2)" - ] - }, - { - "cell_type": "code", - "execution_count": 230, - "id": "97ee1db8-45be-4cfb-8777-0fa7d00102b9", - "metadata": {}, - "outputs": [], - "source": [ - "# Figure Name\n", - "figure_name = 'FIG10_E9_ROC_Factor1'" - ] - }, - { - "cell_type": "code", - "execution_count": 231, - "id": "67901903-710b-4f0a-ab6f-7fca1b23ea3d", - "metadata": {}, - "outputs": [], - "source": [ - "# Sizes of the plot\n", - "width_par = 3.5\n", - "height_par = 1.6" - ] - }, - { - "cell_type": "code", - "execution_count": 232, - "id": "e9907bbe-df45-430c-b35c-6da6cac810e7", - "metadata": {}, - "outputs": [], - "source": [ - "xtitle = xlab( 'False Positive Fraction')\n", - "ytitle = ylab('Sensititity')" - ] - }, - { - "cell_type": "code", - "execution_count": 233, - "id": "b155f871-2fe1-40eb-bc1d-3b271796c466", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
  1. 'Factor1 (n=34)'
  2. 'Factor1_w_o_cs (n=27)'
  3. 'Score (n=21)'
  4. 'Score_w_o_cs (n=16)'
\n" - ], - "text/latex": [ - "\\begin{enumerate*}\n", - "\\item 'Factor1 (n=34)'\n", - "\\item 'Factor1\\_w\\_o\\_cs (n=27)'\n", - "\\item 'Score (n=21)'\n", - "\\item 'Score\\_w\\_o\\_cs (n=16)'\n", - "\\end{enumerate*}\n" - ], - "text/markdown": [ - "1. 'Factor1 (n=34)'\n", - "2. 'Factor1_w_o_cs (n=27)'\n", - "3. 'Score (n=21)'\n", - "4. 'Score_w_o_cs (n=16)'\n", - "\n", - "\n" - ], - "text/plain": [ - "[1] \"Factor1 (n=34)\" \"Factor1_w_o_cs (n=27)\" \"Score (n=21)\" \n", - "[4] \"Score_w_o_cs (n=16)\" " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unique(prediction_data_long$variable_w_n)" - ] - }, - { - "cell_type": "code", - "execution_count": 234, - "id": "1a6675f8-2788-40ec-b933-4f142e07f478", - "metadata": {}, - "outputs": [], - "source": [ - "## Specify Plot\n", - "rocplot = ggplot(prediction_data_long, aes(m = value, d = as.numeric(y), color = variable_w_n, linetype = variable_w_n))+ geom_roc(n.cuts=0,labels=FALSE, size = 0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 235, - "id": "0bac35b2-c2b0-417b-aefe-734670de2c08", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 120, - "width": 1800 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "## Configuration + Modification of plot\n", - "roc_plot = rocplot + \n", - " plot_config + \n", - " xtitle + \n", - " ytitle + \n", - " theme(legend.position = 'right', legend.spacing.x = unit(0, 'mm'), legend.spacing.y = unit(0, 'mm'), axis.text.x = element_text(angle = 0)) + \n", - " ggtitle('ROC curve ccs vs. no ccs') +\n", - " geom_abline(slope = 1, intercept = 0) + \n", - " scale_color_manual(values = c('Factor1 (n=34)' = \"#BC0B58\",\n", - " 'Factor1_w_o_cs (n=27)' = \"#BC0B58\",\n", - " 'Score (n=21)'=\"#726068\",\n", - " 'Score_w_o_cs (n=16)'=\"#726068\"\n", - " ),\n", - " \n", - " ) +\n", - " scale_linetype_manual(values = c('Factor1 (n=34)' = \"solid\",\n", - " 'Factor1_w_o_cs (n=27)' = \"dashed\",\n", - " 'Score (n=21)'=\"solid\",\n", - " 'Score_w_o_cs (n=16)'=\"dashed\"\n", - " ))+\n", - " guides(color=guide_legend(nrow=4, byrow=TRUE))\n", - "\n", - "roc_plot" - ] - }, - { - "cell_type": "code", - "execution_count": 236, - "id": "dccf8139-157b-422d-95c4-d058b5f68adf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "png: 2" - ], - "text/latex": [ - "\\textbf{png:} 2" - ], - "text/markdown": [ - "**png:** 2" - ], - "text/plain": [ - "png \n", - " 2 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pdf(paste0('figures/Final_Figures/', figure_name, version, '.pdf'), width =width_par, height =height_par)\n", - "print(roc_plot)\n", - "dev.off()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "669673c7-85d8-458a-b283-3237ea1c0664", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "fc17e522-d622-4795-881a-40f859d83060", - "metadata": {}, - "source": [ - "# Prediction Factor 4 TP1 (further variables for Testing)" - ] - }, - { - "cell_type": "code", - "execution_count": 237, - "id": "20c7c99a-73f6-481e-bb41-cbdc740cc105", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_vars = c( 'Factor4', 'Normalized_X3_NK__TXNIP','Normalized_X3_NK__PRF1', 'Normalized_X3_NK__LITAF' , 'Normalized_X3_NK__GZMB', 'Normalized_X3_NK__FYN', 'Normalized_X3_NK__CST7', 'Normalized_X3_NK__CD53', 'delta_ef_value_class')" - ] - }, - { - "cell_type": "code", - "execution_count": 238, - "id": "31e60bf7-061f-4d9b-a3f9-45c217a9c070", - "metadata": {}, - "outputs": [], - "source": [ - "#colnames(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 239, - "id": "8b5ee87d-16d8-4b90-b6a3-66306f0b91fc", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_group = 'tp_outcome'\n", - "prediction_subset = c('TP1_good', 'TP1_bad')" - ] - }, - { - "cell_type": "code", - "execution_count": 240, - "id": "c4d59e7e-7d75-4aee-9450-87c60a72e2d3", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data = data[,c(prediction_vars, prediction_group)]\n", - "prediction_data$y = prediction_data[,prediction_group]\n", - "prediction_data = prediction_data[prediction_data$y %in% prediction_subset,]\n", - "\n", - "prediction_data$y[prediction_data$y == prediction_subset[2]] = 1 # predict BAD outcome \n", - "prediction_data$y[prediction_data$y == prediction_subset[1]] = 0 # good outcome as control" - ] - }, - { - "cell_type": "code", - "execution_count": 241, - "id": "721de59a-0bb4-4149-be13-63da6ad3c717", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message in mean.default(col, ...):\n", - "“argument is not numeric or logical: returning NA”\n", - "Warning message in mean.default(col, ...):\n", - "“argument is not numeric or logical: returning NA”\n", - "\u001b[1m\u001b[22m`summarise()` has grouped output by 'tp_outcome'. You can override using the\n", - "`.groups` argument.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A grouped_df: 2 × 11
tp_outcomeyFactor4Normalized_X3_NK__TXNIPNormalized_X3_NK__PRF1Normalized_X3_NK__LITAFNormalized_X3_NK__GZMBNormalized_X3_NK__FYNNormalized_X3_NK__CST7Normalized_X3_NK__CD53delta_ef_value_class
<chr><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
TP1_bad 1-1.12592121.32093901.01644530.70254731.10507280.94319770.67299641.4315875NA
TP1_good0-0.62981320.33883790.61744560.81888570.73088061.09824750.40875850.4996068NA
\n" - ], - "text/latex": [ - "A grouped\\_df: 2 × 11\n", - "\\begin{tabular}{lllllllllll}\n", - " tp\\_outcome & y & Factor4 & Normalized\\_X3\\_NK\\_\\_TXNIP & Normalized\\_X3\\_NK\\_\\_PRF1 & Normalized\\_X3\\_NK\\_\\_LITAF & Normalized\\_X3\\_NK\\_\\_GZMB & Normalized\\_X3\\_NK\\_\\_FYN & Normalized\\_X3\\_NK\\_\\_CST7 & Normalized\\_X3\\_NK\\_\\_CD53 & delta\\_ef\\_value\\_class\\\\\n", - " & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t TP1\\_bad & 1 & -1.1259212 & 1.3209390 & 1.0164453 & 0.7025473 & 1.1050728 & 0.9431977 & 0.6729964 & 1.4315875 & NA\\\\\n", - "\t TP1\\_good & 0 & -0.6298132 & 0.3388379 & 0.6174456 & 0.8188857 & 0.7308806 & 1.0982475 & 0.4087585 & 0.4996068 & NA\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A grouped_df: 2 × 11\n", - "\n", - "| tp_outcome <chr> | y <chr> | Factor4 <dbl> | Normalized_X3_NK__TXNIP <dbl> | Normalized_X3_NK__PRF1 <dbl> | Normalized_X3_NK__LITAF <dbl> | Normalized_X3_NK__GZMB <dbl> | Normalized_X3_NK__FYN <dbl> | Normalized_X3_NK__CST7 <dbl> | Normalized_X3_NK__CD53 <dbl> | delta_ef_value_class <dbl> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|\n", - "| TP1_bad | 1 | -1.1259212 | 1.3209390 | 1.0164453 | 0.7025473 | 1.1050728 | 0.9431977 | 0.6729964 | 1.4315875 | NA |\n", - "| TP1_good | 0 | -0.6298132 | 0.3388379 | 0.6174456 | 0.8188857 | 0.7308806 | 1.0982475 | 0.4087585 | 0.4996068 | NA |\n", - "\n" - ], - "text/plain": [ - " tp_outcome y Factor4 Normalized_X3_NK__TXNIP Normalized_X3_NK__PRF1\n", - "1 TP1_bad 1 -1.1259212 1.3209390 1.0164453 \n", - "2 TP1_good 0 -0.6298132 0.3388379 0.6174456 \n", - " Normalized_X3_NK__LITAF Normalized_X3_NK__GZMB Normalized_X3_NK__FYN\n", - "1 0.7025473 1.1050728 0.9431977 \n", - "2 0.8188857 0.7308806 1.0982475 \n", - " Normalized_X3_NK__CST7 Normalized_X3_NK__CD53 delta_ef_value_class\n", - "1 0.6729964 1.4315875 NA \n", - "2 0.4087585 0.4996068 NA " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Check in which direction to adjust\n", - "prediction_data %>% group_by(tp_outcome,y) %>% summarise(across(everything(), mean, na.rm =TRUE))\n", - "\n", - "# predict bad --> higher values for bad" - ] - }, - { - "cell_type": "code", - "execution_count": 242, - "id": "05272216-b210-40a9-8650-6764490c8f5d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A grouped_df: 2 × 2
tp_outcomen
<chr><int>
TP1_bad 6
TP1_good13
\n" - ], - "text/latex": [ - "A grouped\\_df: 2 × 2\n", - "\\begin{tabular}{ll}\n", - " tp\\_outcome & n\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t TP1\\_bad & 6\\\\\n", - "\t TP1\\_good & 13\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A grouped_df: 2 × 2\n", - "\n", - "| tp_outcome <chr> | n <int> |\n", - "|---|---|\n", - "| TP1_bad | 6 |\n", - "| TP1_good | 13 |\n", - "\n" - ], - "text/plain": [ - " tp_outcome n \n", - "1 TP1_bad 6\n", - "2 TP1_good 13" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "prediction_data %>% group_by(tp_outcome) %>% count()" - ] - }, - { - "cell_type": "code", - "execution_count": 243, - "id": "07924425-1b92-4e7c-8172-90548b131c86", - "metadata": {}, - "outputs": [], - "source": [ - "# Adjust sign of Factor values\n", - "prediction_data$Factor4 = -1*prediction_data$Factor4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0ee2434a-bfcb-488f-ac5a-df244e43c82e", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 244, - "id": "6a097bdc-007c-4d56-9b97-fe0316223fae", - "metadata": {}, - "outputs": [], - "source": [ - "# Transform and scale scores of prediction variables (to be between 0 and 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 245, - "id": "b73f0f65-54fb-4185-8017-373088ea486d", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_vars = c( 'Factor4', 'Normalized_X3_NK__TXNIP','Normalized_X3_NK__PRF1', 'Normalized_X3_NK__LITAF' , 'Normalized_X3_NK__GZMB', 'Normalized_X3_NK__FYN', 'Normalized_X3_NK__CST7', 'Normalized_X3_NK__CD53')" - ] - }, - { - "cell_type": "code", - "execution_count": 246, - "id": "5bf9560c-9316-40e9-80fb-b484e94e66ce", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data = cbind( prediction_data[,c('y', prediction_group, 'delta_ef_value_class')],\n", - " apply(prediction_data[,prediction_vars], MARGIN = 2, FUN = function(x){\n", - " (x - min(x, na.rm = TRUE)) / (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))}))" - ] - }, - { - "cell_type": "code", - "execution_count": 247, - "id": "175a45c8-098b-479e-a4f3-8c4721cf824d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 5 × 11
ytp_outcomedelta_ef_value_classFactor4Normalized_X3_NK__TXNIPNormalized_X3_NK__PRF1Normalized_X3_NK__LITAFNormalized_X3_NK__GZMBNormalized_X3_NK__FYNNormalized_X3_NK__CST7Normalized_X3_NK__CD53
<chr><chr><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
361TP1_bad bad 0.4734997 NA NA NA NA NA NA NA
451TP1_bad bad 0.85588910.55013750.6080980.73174840.65140130.653796170.61025990.2683299
491TP1_bad bad 0.75604520.76140171.0000000.60309720.76908880.614269871.00000001.0000000
530TP1_goodgood1.00000000.87188020.8713660.86960890.31381580.728271990.71874020.9125926
601TP1_bad bad 0.71186771.00000000.6400620.00000000.68389630.063041480.37919840.6504756
\n" - ], - "text/latex": [ - "A data.frame: 5 × 11\n", - "\\begin{tabular}{r|lllllllllll}\n", - " & y & tp\\_outcome & delta\\_ef\\_value\\_class & Factor4 & Normalized\\_X3\\_NK\\_\\_TXNIP & Normalized\\_X3\\_NK\\_\\_PRF1 & Normalized\\_X3\\_NK\\_\\_LITAF & Normalized\\_X3\\_NK\\_\\_GZMB & Normalized\\_X3\\_NK\\_\\_FYN & Normalized\\_X3\\_NK\\_\\_CST7 & Normalized\\_X3\\_NK\\_\\_CD53\\\\\n", - " & & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t36 & 1 & TP1\\_bad & bad & 0.4734997 & NA & NA & NA & NA & NA & NA & NA\\\\\n", - "\t45 & 1 & TP1\\_bad & bad & 0.8558891 & 0.5501375 & 0.608098 & 0.7317484 & 0.6514013 & 0.65379617 & 0.6102599 & 0.2683299\\\\\n", - "\t49 & 1 & TP1\\_bad & bad & 0.7560452 & 0.7614017 & 1.000000 & 0.6030972 & 0.7690888 & 0.61426987 & 1.0000000 & 1.0000000\\\\\n", - "\t53 & 0 & TP1\\_good & good & 1.0000000 & 0.8718802 & 0.871366 & 0.8696089 & 0.3138158 & 0.72827199 & 0.7187402 & 0.9125926\\\\\n", - "\t60 & 1 & TP1\\_bad & bad & 0.7118677 & 1.0000000 & 0.640062 & 0.0000000 & 0.6838963 & 0.06304148 & 0.3791984 & 0.6504756\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 5 × 11\n", - "\n", - "| | y <chr> | tp_outcome <chr> | delta_ef_value_class <chr> | Factor4 <dbl> | Normalized_X3_NK__TXNIP <dbl> | Normalized_X3_NK__PRF1 <dbl> | Normalized_X3_NK__LITAF <dbl> | Normalized_X3_NK__GZMB <dbl> | Normalized_X3_NK__FYN <dbl> | Normalized_X3_NK__CST7 <dbl> | Normalized_X3_NK__CD53 <dbl> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 36 | 1 | TP1_bad | bad | 0.4734997 | NA | NA | NA | NA | NA | NA | NA |\n", - "| 45 | 1 | TP1_bad | bad | 0.8558891 | 0.5501375 | 0.608098 | 0.7317484 | 0.6514013 | 0.65379617 | 0.6102599 | 0.2683299 |\n", - "| 49 | 1 | TP1_bad | bad | 0.7560452 | 0.7614017 | 1.000000 | 0.6030972 | 0.7690888 | 0.61426987 | 1.0000000 | 1.0000000 |\n", - "| 53 | 0 | TP1_good | good | 1.0000000 | 0.8718802 | 0.871366 | 0.8696089 | 0.3138158 | 0.72827199 | 0.7187402 | 0.9125926 |\n", - "| 60 | 1 | TP1_bad | bad | 0.7118677 | 1.0000000 | 0.640062 | 0.0000000 | 0.6838963 | 0.06304148 | 0.3791984 | 0.6504756 |\n", - "\n" - ], - "text/plain": [ - " y tp_outcome delta_ef_value_class Factor4 Normalized_X3_NK__TXNIP\n", - "36 1 TP1_bad bad 0.4734997 NA \n", - "45 1 TP1_bad bad 0.8558891 0.5501375 \n", - "49 1 TP1_bad bad 0.7560452 0.7614017 \n", - "53 0 TP1_good good 1.0000000 0.8718802 \n", - "60 1 TP1_bad bad 0.7118677 1.0000000 \n", - " Normalized_X3_NK__PRF1 Normalized_X3_NK__LITAF Normalized_X3_NK__GZMB\n", - "36 NA NA NA \n", - "45 0.608098 0.7317484 0.6514013 \n", - "49 1.000000 0.6030972 0.7690888 \n", - "53 0.871366 0.8696089 0.3138158 \n", - "60 0.640062 0.0000000 0.6838963 \n", - " Normalized_X3_NK__FYN Normalized_X3_NK__CST7 Normalized_X3_NK__CD53\n", - "36 NA NA NA \n", - "45 0.65379617 0.6102599 0.2683299 \n", - "49 0.61426987 1.0000000 1.0000000 \n", - "53 0.72827199 0.7187402 0.9125926 \n", - "60 0.06304148 0.3791984 0.6504756 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(prediction_data,5)" - ] - }, - { - "cell_type": "code", - "execution_count": 248, - "id": "268fe8bd-77b0-4156-a796-212cb3605034", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using y, tp_outcome, delta_ef_value_class as id variables\n", - "\n" - ] - } - ], - "source": [ - "prediction_data_long = melt(prediction_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 249, - "id": "176918c8-4172-47a6-94fe-ee7695c90a5f", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data_long = prediction_data_long[!is.na(prediction_data_long$value),]" - ] - }, - { - "cell_type": "code", - "execution_count": 250, - "id": "e53dc6c9-3079-4fd9-9312-e3ed58775d4a", - "metadata": {}, - "outputs": [], - "source": [ - "amount_samples = prediction_data_long %>% group_by(variable) %>% count()" - ] - }, - { - "cell_type": "code", - "execution_count": 251, - "id": "b1b4ee3a-c553-4847-b5d2-13a591039935", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data_long = merge(prediction_data_long, amount_samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 252, - "id": "5464613d-5956-49e0-932b-fa4cd72d8b64", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_data_long$variable_w_n = paste0(prediction_data_long$variable, ' (n=', prediction_data_long$n, ')')" - ] - }, - { - "cell_type": "code", - "execution_count": 253, - "id": "16348d31-efb1-4ab0-9e71-52cf43ed6379", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 7
variableytp_outcomedelta_ef_value_classvaluenvariable_w_n
<fct><chr><chr><chr><dbl><int><chr>
1Factor41TP1_badbad0.473499719Factor4 (n=19)
2Factor41TP1_badbad0.855889119Factor4 (n=19)
\n" - ], - "text/latex": [ - "A data.frame: 2 × 7\n", - "\\begin{tabular}{r|lllllll}\n", - " & variable & y & tp\\_outcome & delta\\_ef\\_value\\_class & value & n & variable\\_w\\_n\\\\\n", - " & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & Factor4 & 1 & TP1\\_bad & bad & 0.4734997 & 19 & Factor4 (n=19)\\\\\n", - "\t2 & Factor4 & 1 & TP1\\_bad & bad & 0.8558891 & 19 & Factor4 (n=19)\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 7\n", - "\n", - "| | variable <fct> | y <chr> | tp_outcome <chr> | delta_ef_value_class <chr> | value <dbl> | n <int> | variable_w_n <chr> |\n", - "|---|---|---|---|---|---|---|---|\n", - "| 1 | Factor4 | 1 | TP1_bad | bad | 0.4734997 | 19 | Factor4 (n=19) |\n", - "| 2 | Factor4 | 1 | TP1_bad | bad | 0.8558891 | 19 | Factor4 (n=19) |\n", - "\n" - ], - "text/plain": [ - " variable y tp_outcome delta_ef_value_class value n variable_w_n \n", - "1 Factor4 1 TP1_bad bad 0.4734997 19 Factor4 (n=19)\n", - "2 Factor4 1 TP1_bad bad 0.8558891 19 Factor4 (n=19)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(prediction_data_long,2)" - ] - }, - { - "cell_type": "code", - "execution_count": 254, - "id": "d70f3c95-3288-4d4a-8db4-bf37357e89a2", - "metadata": {}, - "outputs": [], - "source": [ - "test = prediction_data_long[prediction_data_long$variable == 'Normalized_X3_NK__GZMB',]" - ] - }, - { - "cell_type": "code", - "execution_count": 255, - "id": "e004c8bf-e187-4803-9e42-eb6ab10da3f8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 7
variableytp_outcomedelta_ef_value_classvaluenvariable_w_n
<fct><chr><chr><chr><dbl><int><chr>
71Normalized_X3_NK__GZMB1TP1_badbad0.651401317Normalized_X3_NK__GZMB (n=17)
72Normalized_X3_NK__GZMB1TP1_badbad0.769088817Normalized_X3_NK__GZMB (n=17)
\n" - ], - "text/latex": [ - "A data.frame: 2 × 7\n", - "\\begin{tabular}{r|lllllll}\n", - " & variable & y & tp\\_outcome & delta\\_ef\\_value\\_class & value & n & variable\\_w\\_n\\\\\n", - " & & & & & & & \\\\\n", - "\\hline\n", - "\t71 & Normalized\\_X3\\_NK\\_\\_GZMB & 1 & TP1\\_bad & bad & 0.6514013 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t72 & Normalized\\_X3\\_NK\\_\\_GZMB & 1 & TP1\\_bad & bad & 0.7690888 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 7\n", - "\n", - "| | variable <fct> | y <chr> | tp_outcome <chr> | delta_ef_value_class <chr> | value <dbl> | n <int> | variable_w_n <chr> |\n", - "|---|---|---|---|---|---|---|---|\n", - "| 71 | Normalized_X3_NK__GZMB | 1 | TP1_bad | bad | 0.6514013 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 72 | Normalized_X3_NK__GZMB | 1 | TP1_bad | bad | 0.7690888 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "\n" - ], - "text/plain": [ - " variable y tp_outcome delta_ef_value_class value n \n", - "71 Normalized_X3_NK__GZMB 1 TP1_bad bad 0.6514013 17\n", - "72 Normalized_X3_NK__GZMB 1 TP1_bad bad 0.7690888 17\n", - " variable_w_n \n", - "71 Normalized_X3_NK__GZMB (n=17)\n", - "72 Normalized_X3_NK__GZMB (n=17)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(test,2)" - ] - }, - { - "cell_type": "code", - "execution_count": 256, - "id": "132b7d05-fe67-43b1-bc24-feea6d7f251e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 17 × 7
variableytp_outcomedelta_ef_value_classvaluenvariable_w_n
<fct><chr><chr><chr><dbl><int><chr>
83Normalized_X3_NK__GZMB0TP1_goodgood 1.000000017Normalized_X3_NK__GZMB (n=17)
77Normalized_X3_NK__GZMB0TP1_goodgood 0.854512417Normalized_X3_NK__GZMB (n=17)
87Normalized_X3_NK__GZMB0TP1_goodgood 0.802891517Normalized_X3_NK__GZMB (n=17)
72Normalized_X3_NK__GZMB1TP1_bad bad 0.769088817Normalized_X3_NK__GZMB (n=17)
85Normalized_X3_NK__GZMB0TP1_goodintermediate0.738392117Normalized_X3_NK__GZMB (n=17)
78Normalized_X3_NK__GZMB0TP1_goodintermediate0.710149817Normalized_X3_NK__GZMB (n=17)
74Normalized_X3_NK__GZMB1TP1_bad bad 0.683896317Normalized_X3_NK__GZMB (n=17)
71Normalized_X3_NK__GZMB1TP1_bad bad 0.651401317Normalized_X3_NK__GZMB (n=17)
79Normalized_X3_NK__GZMB0TP1_goodintermediate0.621234517Normalized_X3_NK__GZMB (n=17)
82Normalized_X3_NK__GZMB1TP1_bad bad 0.621234517Normalized_X3_NK__GZMB (n=17)
84Normalized_X3_NK__GZMB0TP1_goodintermediate0.427579117Normalized_X3_NK__GZMB (n=17)
75Normalized_X3_NK__GZMB0TP1_goodgood 0.396701317Normalized_X3_NK__GZMB (n=17)
80Normalized_X3_NK__GZMB0TP1_goodintermediate0.396701317Normalized_X3_NK__GZMB (n=17)
73Normalized_X3_NK__GZMB0TP1_goodgood 0.313815817Normalized_X3_NK__GZMB (n=17)
86Normalized_X3_NK__GZMB0TP1_goodintermediate0.253280317Normalized_X3_NK__GZMB (n=17)
81Normalized_X3_NK__GZMB0TP1_goodintermediate0.216617817Normalized_X3_NK__GZMB (n=17)
76Normalized_X3_NK__GZMB0TP1_goodgood 0.000000017Normalized_X3_NK__GZMB (n=17)
\n" - ], - "text/latex": [ - "A data.frame: 17 × 7\n", - "\\begin{tabular}{r|lllllll}\n", - " & variable & y & tp\\_outcome & delta\\_ef\\_value\\_class & value & n & variable\\_w\\_n\\\\\n", - " & & & & & & & \\\\\n", - "\\hline\n", - "\t83 & Normalized\\_X3\\_NK\\_\\_GZMB & 0 & TP1\\_good & good & 1.0000000 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t77 & Normalized\\_X3\\_NK\\_\\_GZMB & 0 & TP1\\_good & good & 0.8545124 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t87 & Normalized\\_X3\\_NK\\_\\_GZMB & 0 & TP1\\_good & good & 0.8028915 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t72 & Normalized\\_X3\\_NK\\_\\_GZMB & 1 & TP1\\_bad & bad & 0.7690888 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t85 & Normalized\\_X3\\_NK\\_\\_GZMB & 0 & TP1\\_good & intermediate & 0.7383921 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t78 & Normalized\\_X3\\_NK\\_\\_GZMB & 0 & TP1\\_good & intermediate & 0.7101498 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t74 & Normalized\\_X3\\_NK\\_\\_GZMB & 1 & TP1\\_bad & bad & 0.6838963 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t71 & Normalized\\_X3\\_NK\\_\\_GZMB & 1 & TP1\\_bad & bad & 0.6514013 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t79 & Normalized\\_X3\\_NK\\_\\_GZMB & 0 & TP1\\_good & intermediate & 0.6212345 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t82 & Normalized\\_X3\\_NK\\_\\_GZMB & 1 & TP1\\_bad & bad & 0.6212345 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t84 & Normalized\\_X3\\_NK\\_\\_GZMB & 0 & TP1\\_good & intermediate & 0.4275791 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t75 & Normalized\\_X3\\_NK\\_\\_GZMB & 0 & TP1\\_good & good & 0.3967013 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t80 & Normalized\\_X3\\_NK\\_\\_GZMB & 0 & TP1\\_good & intermediate & 0.3967013 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t73 & Normalized\\_X3\\_NK\\_\\_GZMB & 0 & TP1\\_good & good & 0.3138158 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t86 & Normalized\\_X3\\_NK\\_\\_GZMB & 0 & TP1\\_good & intermediate & 0.2532803 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t81 & Normalized\\_X3\\_NK\\_\\_GZMB & 0 & TP1\\_good & intermediate & 0.2166178 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\t76 & Normalized\\_X3\\_NK\\_\\_GZMB & 0 & TP1\\_good & good & 0.0000000 & 17 & Normalized\\_X3\\_NK\\_\\_GZMB (n=17)\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 17 × 7\n", - "\n", - "| | variable <fct> | y <chr> | tp_outcome <chr> | delta_ef_value_class <chr> | value <dbl> | n <int> | variable_w_n <chr> |\n", - "|---|---|---|---|---|---|---|---|\n", - "| 83 | Normalized_X3_NK__GZMB | 0 | TP1_good | good | 1.0000000 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 77 | Normalized_X3_NK__GZMB | 0 | TP1_good | good | 0.8545124 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 87 | Normalized_X3_NK__GZMB | 0 | TP1_good | good | 0.8028915 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 72 | Normalized_X3_NK__GZMB | 1 | TP1_bad | bad | 0.7690888 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 85 | Normalized_X3_NK__GZMB | 0 | TP1_good | intermediate | 0.7383921 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 78 | Normalized_X3_NK__GZMB | 0 | TP1_good | intermediate | 0.7101498 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 74 | Normalized_X3_NK__GZMB | 1 | TP1_bad | bad | 0.6838963 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 71 | Normalized_X3_NK__GZMB | 1 | TP1_bad | bad | 0.6514013 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 79 | Normalized_X3_NK__GZMB | 0 | TP1_good | intermediate | 0.6212345 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 82 | Normalized_X3_NK__GZMB | 1 | TP1_bad | bad | 0.6212345 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 84 | Normalized_X3_NK__GZMB | 0 | TP1_good | intermediate | 0.4275791 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 75 | Normalized_X3_NK__GZMB | 0 | TP1_good | good | 0.3967013 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 80 | Normalized_X3_NK__GZMB | 0 | TP1_good | intermediate | 0.3967013 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 73 | Normalized_X3_NK__GZMB | 0 | TP1_good | good | 0.3138158 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 86 | Normalized_X3_NK__GZMB | 0 | TP1_good | intermediate | 0.2532803 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 81 | Normalized_X3_NK__GZMB | 0 | TP1_good | intermediate | 0.2166178 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "| 76 | Normalized_X3_NK__GZMB | 0 | TP1_good | good | 0.0000000 | 17 | Normalized_X3_NK__GZMB (n=17) |\n", - "\n" - ], - "text/plain": [ - " variable y tp_outcome delta_ef_value_class value n \n", - "83 Normalized_X3_NK__GZMB 0 TP1_good good 1.0000000 17\n", - "77 Normalized_X3_NK__GZMB 0 TP1_good good 0.8545124 17\n", - "87 Normalized_X3_NK__GZMB 0 TP1_good good 0.8028915 17\n", - "72 Normalized_X3_NK__GZMB 1 TP1_bad bad 0.7690888 17\n", - "85 Normalized_X3_NK__GZMB 0 TP1_good intermediate 0.7383921 17\n", - "78 Normalized_X3_NK__GZMB 0 TP1_good intermediate 0.7101498 17\n", - "74 Normalized_X3_NK__GZMB 1 TP1_bad bad 0.6838963 17\n", - "71 Normalized_X3_NK__GZMB 1 TP1_bad bad 0.6514013 17\n", - "79 Normalized_X3_NK__GZMB 0 TP1_good intermediate 0.6212345 17\n", - "82 Normalized_X3_NK__GZMB 1 TP1_bad bad 0.6212345 17\n", - "84 Normalized_X3_NK__GZMB 0 TP1_good intermediate 0.4275791 17\n", - "75 Normalized_X3_NK__GZMB 0 TP1_good good 0.3967013 17\n", - "80 Normalized_X3_NK__GZMB 0 TP1_good intermediate 0.3967013 17\n", - "73 Normalized_X3_NK__GZMB 0 TP1_good good 0.3138158 17\n", - "86 Normalized_X3_NK__GZMB 0 TP1_good intermediate 0.2532803 17\n", - "81 Normalized_X3_NK__GZMB 0 TP1_good intermediate 0.2166178 17\n", - "76 Normalized_X3_NK__GZMB 0 TP1_good good 0.0000000 17\n", - " variable_w_n \n", - "83 Normalized_X3_NK__GZMB (n=17)\n", - "77 Normalized_X3_NK__GZMB (n=17)\n", - "87 Normalized_X3_NK__GZMB (n=17)\n", - "72 Normalized_X3_NK__GZMB (n=17)\n", - "85 Normalized_X3_NK__GZMB (n=17)\n", - "78 Normalized_X3_NK__GZMB (n=17)\n", - "74 Normalized_X3_NK__GZMB (n=17)\n", - "71 Normalized_X3_NK__GZMB (n=17)\n", - "79 Normalized_X3_NK__GZMB (n=17)\n", - "82 Normalized_X3_NK__GZMB (n=17)\n", - "84 Normalized_X3_NK__GZMB (n=17)\n", - "75 Normalized_X3_NK__GZMB (n=17)\n", - "80 Normalized_X3_NK__GZMB (n=17)\n", - "73 Normalized_X3_NK__GZMB (n=17)\n", - "86 Normalized_X3_NK__GZMB (n=17)\n", - "81 Normalized_X3_NK__GZMB (n=17)\n", - "76 Normalized_X3_NK__GZMB (n=17)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "test[order(test$value, decreasing = TRUE),]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e2b12df-1130-45be-b523-ae0d823772b1", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 257, - "id": "9b0f9a88-6a59-440a-b254-9012749f9973", - "metadata": {}, - "outputs": [], - "source": [ - "# Figure Name\n", - "#figure_name = 'FIG10_E9_ROC_Factor4_TP1'" - ] - }, - { - "cell_type": "code", - "execution_count": 258, - "id": "c43b09f9-ee67-42e2-bc21-1c252b492cc5", - "metadata": {}, - "outputs": [], - "source": [ - "# Sizes of the plot\n", - "width_par = 2\n", - "height_par = 2" - ] - }, - { - "cell_type": "code", - "execution_count": 259, - "id": "5626c24c-2c52-4454-95b3-a24973a9ae90", - "metadata": {}, - "outputs": [], - "source": [ - "xtitle = xlab( 'False Positive Fraction')\n", - "ytitle = ylab('Sensititity')" - ] - }, - { - "cell_type": "code", - "execution_count": 260, - "id": "bd734240-3554-43a6-967b-3bcb77ac34d0", - "metadata": {}, - "outputs": [], - "source": [ - "auc_values = data.frame()" - ] - }, - { - "cell_type": "code", - "execution_count": 261, - "id": "c1b4565e-4cb2-41c3-bfec-68b391f649df", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Setting levels: control = 0, case = 1\n", - "\n", - "Setting direction: controls < cases\n", - "\n", - "Setting levels: control = 0, case = 1\n", - "\n", - "Setting direction: controls < cases\n", - "\n", - "Setting levels: control = 0, case = 1\n", - "\n", - "Setting direction: controls > cases\n", - "\n", - "Setting levels: control = 0, case = 1\n", - "\n", - "Setting direction: controls < cases\n", - "\n", - "Setting levels: control = 0, case = 1\n", - "\n", - "Setting direction: controls < cases\n", - "\n", - "Setting levels: control = 0, case = 1\n", - "\n", - "Setting direction: controls < cases\n", - "\n", - "Setting levels: control = 0, case = 1\n", - "\n", - "Setting direction: controls < cases\n", - "\n", - "Setting levels: control = 0, case = 1\n", - "\n", - "Setting direction: controls < cases\n", - "\n" - ] - } - ], - "source": [ - "for(i in unique(prediction_data_long$variable)){\n", - " calc_auc = data.frame(variable = i, \n", - " auc = auc(prediction_data_long$y[prediction_data_long$variable == i], prediction_data_long$value[prediction_data_long$variable == i])\n", - " )\n", - " auc_values = rbind(auc_values, calc_auc)\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 262, - "id": "f2efa479-caa9-4699-b9f5-5bcff58bb1a3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 8 × 2
variableauc
<chr><auc>
Factor4 0.8333333
Normalized_X3_NK__CD53 0.8076923
Normalized_X3_NK__CST7 0.4711538
Normalized_X3_NK__FYN 0.4230769
Normalized_X3_NK__GZMB 0.6442308
Normalized_X3_NK__LITAF0.5384615
Normalized_X3_NK__PRF1 0.5673077
Normalized_X3_NK__TXNIP0.7980769
\n" - ], - "text/latex": [ - "A data.frame: 8 × 2\n", - "\\begin{tabular}{ll}\n", - " variable & auc\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t Factor4 & 0.8333333\\\\\n", - "\t Normalized\\_X3\\_NK\\_\\_CD53 & 0.8076923\\\\\n", - "\t Normalized\\_X3\\_NK\\_\\_CST7 & 0.4711538\\\\\n", - "\t Normalized\\_X3\\_NK\\_\\_FYN & 0.4230769\\\\\n", - "\t Normalized\\_X3\\_NK\\_\\_GZMB & 0.6442308\\\\\n", - "\t Normalized\\_X3\\_NK\\_\\_LITAF & 0.5384615\\\\\n", - "\t Normalized\\_X3\\_NK\\_\\_PRF1 & 0.5673077\\\\\n", - "\t Normalized\\_X3\\_NK\\_\\_TXNIP & 0.7980769\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 8 × 2\n", - "\n", - "| variable <chr> | auc <auc> |\n", - "|---|---|\n", - "| Factor4 | 0.8333333 |\n", - "| Normalized_X3_NK__CD53 | 0.8076923 |\n", - "| Normalized_X3_NK__CST7 | 0.4711538 |\n", - "| Normalized_X3_NK__FYN | 0.4230769 |\n", - "| Normalized_X3_NK__GZMB | 0.6442308 |\n", - "| Normalized_X3_NK__LITAF | 0.5384615 |\n", - "| Normalized_X3_NK__PRF1 | 0.5673077 |\n", - "| Normalized_X3_NK__TXNIP | 0.7980769 |\n", - "\n" - ], - "text/plain": [ - " variable auc \n", - "1 Factor4 0.8333333\n", - "2 Normalized_X3_NK__CD53 0.8076923\n", - "3 Normalized_X3_NK__CST7 0.4711538\n", - "4 Normalized_X3_NK__FYN 0.4230769\n", - "5 Normalized_X3_NK__GZMB 0.6442308\n", - "6 Normalized_X3_NK__LITAF 0.5384615\n", - "7 Normalized_X3_NK__PRF1 0.5673077\n", - "8 Normalized_X3_NK__TXNIP 0.7980769" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "auc_values" - ] - }, - { - "cell_type": "code", - "execution_count": 263, - "id": "3a6de357-c472-40f5-a7c6-381a3458c78c", - "metadata": {}, - "outputs": [], - "source": [ - "## Specify Plot\n", - "rocplot = ggplot(prediction_data_long, aes(m = value, d = as.numeric(y), color = variable_w_n, linetype= variable_w_n))+ geom_roc(n.cuts=0,labels=FALSE, size = 0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 264, - "id": "cbcc978b-c3df-4c6d-a5be-c7335ad89565", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
  1. 'Factor4 (n=19)'
  2. 'Normalized_X3_NK__CD53 (n=17)'
  3. 'Normalized_X3_NK__CST7 (n=17)'
  4. 'Normalized_X3_NK__FYN (n=17)'
  5. 'Normalized_X3_NK__GZMB (n=17)'
  6. 'Normalized_X3_NK__LITAF (n=17)'
  7. 'Normalized_X3_NK__PRF1 (n=17)'
  8. 'Normalized_X3_NK__TXNIP (n=17)'
\n" - ], - "text/latex": [ - "\\begin{enumerate*}\n", - "\\item 'Factor4 (n=19)'\n", - "\\item 'Normalized\\_X3\\_NK\\_\\_CD53 (n=17)'\n", - "\\item 'Normalized\\_X3\\_NK\\_\\_CST7 (n=17)'\n", - "\\item 'Normalized\\_X3\\_NK\\_\\_FYN (n=17)'\n", - "\\item 'Normalized\\_X3\\_NK\\_\\_GZMB (n=17)'\n", - "\\item 'Normalized\\_X3\\_NK\\_\\_LITAF (n=17)'\n", - "\\item 'Normalized\\_X3\\_NK\\_\\_PRF1 (n=17)'\n", - "\\item 'Normalized\\_X3\\_NK\\_\\_TXNIP (n=17)'\n", - "\\end{enumerate*}\n" - ], - "text/markdown": [ - "1. 'Factor4 (n=19)'\n", - "2. 'Normalized_X3_NK__CD53 (n=17)'\n", - "3. 'Normalized_X3_NK__CST7 (n=17)'\n", - "4. 'Normalized_X3_NK__FYN (n=17)'\n", - "5. 'Normalized_X3_NK__GZMB (n=17)'\n", - "6. 'Normalized_X3_NK__LITAF (n=17)'\n", - "7. 'Normalized_X3_NK__PRF1 (n=17)'\n", - "8. 'Normalized_X3_NK__TXNIP (n=17)'\n", - "\n", - "\n" - ], - "text/plain": [ - "[1] \"Factor4 (n=19)\" \"Normalized_X3_NK__CD53 (n=17)\" \n", - "[3] \"Normalized_X3_NK__CST7 (n=17)\" \"Normalized_X3_NK__FYN (n=17)\" \n", - "[5] \"Normalized_X3_NK__GZMB (n=17)\" \"Normalized_X3_NK__LITAF (n=17)\"\n", - "[7] \"Normalized_X3_NK__PRF1 (n=17)\" \"Normalized_X3_NK__TXNIP (n=17)\"" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unique(prediction_data_long$variable_w_n)" - ] - }, - { - "cell_type": "code", - "execution_count": 265, - "id": "04ba77dd-ebc5-4e67-9a07-f6ffb3e4779d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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BHRnnqsaOJjRRNT3cUBLxgM7kHKs+vEPXdyNrVxT6K9xMAoERERERERERHRPlMf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\nALx+X/5VP2tD0f1bm39yEQB43/zWtTf/qiEC+7V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- "text/plain": [ - "plot without title" - ] - }, - "metadata": { - "image/png": { - "height": 120, - "width": 1800 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "## Configuration + Modification of plot\n", - "roc_plot = rocplot + \n", - " plot_config + \n", - " xtitle + \n", - " ytitle + \n", - " ggtitle('ROC curve TP1 good vs. bad') +\n", - " geom_abline(slope = 1, intercept = 0) +\n", - " theme(legend.position = 'none')\n", - "\n", - "#legend_roc = get_legend(roc_plot)\n", - " \n", - "\n", - "roc_plot" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/F/F4_1_Ligand_Receptor_Interactions_with_NicheNet_Correlations.ipynb b/F/F4_1_Ligand_Receptor_Interactions_with_NicheNet_Correlations.ipynb index da10bb4..8e164cc 100644 --- a/F/F4_1_Ligand_Receptor_Interactions_with_NicheNet_Correlations.ipynb +++ b/F/F4_1_Ligand_Receptor_Interactions_with_NicheNet_Correlations.ipynb @@ -1251,26 +1251,6 @@ "## Sample Data" ] }, - { - "cell_type": "code", - "execution_count": 11, - "id": "df7d79cb-365f-422b-9a18-e4ffb25d72c9", - "metadata": {}, - "outputs": [], - "source": [ - "#head(merged_data$CK,6)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "9aee88af-8147-4836-b2cc-86371952e5d7", - "metadata": {}, - "outputs": [], - "source": [ - "#head(sample_data$CK, 5)" - ] - }, { "cell_type": "code", "execution_count": 13, @@ -1281,70 +1261,6 @@ "sample_data = read.csv(paste0(result_path, '/00_Data_Overview/Merged_Sample_Meta_Data.csv'))" ] }, - { - "cell_type": "code", - "execution_count": 14, - "id": "0a5cc241-55ec-4539-a75b-989dfb8ad5b3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 30
X.1sample_idsampleidmeasurementlibraryid.ynamereadpattern⋯meta_datadelta_ef_value_groupdelta_ef_valuedelta_ef_value_classef_classification_dataCKCK_MBTroponinCRPclinical_data
<int><chr><chr><dbl><chr><chr><chr><chr><chr><chr>⋯<int><chr><dbl><chr><int><int><chr><dbl><chr><int>
136136m6.4M66.4TP4L14HTO_B02596.4R25PNNNNNNNNNN(BC)⋯1x_smaller_10.875intermediate1590.4581.31
137137m6.4M66.4TP4L10HTO_B02556.4R25PNNNNNNNNNN(BC)⋯1x_smaller_10.875intermediate1590.4581.31
\n" - ], - "text/latex": [ - "A data.frame: 2 × 30\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & X.1 & sample\\_id & sample & id & measurement & library & id.y & name & read & pattern & ⋯ & meta\\_data & delta\\_ef\\_value\\_group & delta\\_ef\\_value & delta\\_ef\\_value\\_class & ef\\_classification\\_data & CK & CK\\_MB & Troponin & CRP & clinical\\_data\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t136 & 136 & m6.4 & M6 & 6.4 & TP4 & L14 & HTO\\_B0259 & 6.4 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & 1 & x\\_smaller\\_1 & 0.875 & intermediate & 1 & 59 & & 0.458 & 1.3 & 1\\\\\n", - "\t137 & 137 & m6.4 & M6 & 6.4 & TP4 & L10 & HTO\\_B0255 & 6.4 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & 1 & x\\_smaller\\_1 & 0.875 & intermediate & 1 & 59 & & 0.458 & 1.3 & 1\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 30\n", - "\n", - "| | X.1 <int> | sample_id <chr> | sample <chr> | id <dbl> | measurement <chr> | library <chr> | id.y <chr> | name <chr> | read <chr> | pattern <chr> | ⋯ ⋯ | meta_data <int> | delta_ef_value_group <chr> | delta_ef_value <dbl> | delta_ef_value_class <chr> | ef_classification_data <int> | CK <int> | CK_MB <chr> | Troponin <dbl> | CRP <chr> | clinical_data <int> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 136 | 136 | m6.4 | M6 | 6.4 | TP4 | L14 | HTO_B0259 | 6.4 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | 1 | x_smaller_1 | 0.875 | intermediate | 1 | 59 | | 0.458 | 1.3 | 1 |\n", - "| 137 | 137 | m6.4 | M6 | 6.4 | TP4 | L10 | HTO_B0255 | 6.4 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | 1 | x_smaller_1 | 0.875 | intermediate | 1 | 59 | | 0.458 | 1.3 | 1 |\n", - "\n" - ], - "text/plain": [ - " X.1 sample_id sample id measurement library id.y name read\n", - "136 136 m6.4 M6 6.4 TP4 L14 HTO_B0259 6.4 R2 \n", - "137 137 m6.4 M6 6.4 TP4 L10 HTO_B0255 6.4 R2 \n", - " pattern ⋯ meta_data delta_ef_value_group delta_ef_value\n", - "136 5PNNNNNNNNNN(BC) ⋯ 1 x_smaller_1 0.875 \n", - "137 5PNNNNNNNNNN(BC) ⋯ 1 x_smaller_1 0.875 \n", - " delta_ef_value_class ef_classification_data CK CK_MB Troponin CRP\n", - "136 intermediate 1 59 0.458 1.3\n", - "137 intermediate 1 59 0.458 1.3\n", - " clinical_data\n", - "136 1 \n", - "137 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sample_data[(sample_data$sample_id == 'm6.4'),] " - ] - }, { "cell_type": "code", "execution_count": 15, @@ -2272,36 +2188,6 @@ "## Available ligand receptor potential + transcription factor datasets from nichenet" ] }, - { - "cell_type": "code", - "execution_count": 57, - "id": "7247e04c-d63d-4d16-aac8-0f620a8867a1", - "metadata": {}, - "outputs": [], - "source": [ - "## Networks from data sources without weighting" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "7f0f8850-1b4a-4cd6-9e62-5426007f5c90", - "metadata": {}, - "outputs": [], - "source": [ - "#gr_network = readRDS(url(\"https://zenodo.org/record/3260758/files/gr_network.rds\")) # gene regulatory interactions" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "1fd0c627-8bbe-4378-b193-37369edf9311", - "metadata": {}, - "outputs": [], - "source": [ - "#sig_network = readRDS(url(\"https://zenodo.org/record/3260758/files/signaling_network.rds\")) # signaling interactions, protein-protein interactions" - ] - }, { "cell_type": "code", "execution_count": 60, @@ -2332,28 +2218,6 @@ "## Prepared influential matrices" ] }, - { - "cell_type": "code", - "execution_count": 63, - "id": "5a16b2aa-1372-4f56-8422-0a5ab625903c", - "metadata": {}, - "outputs": [], - "source": [ - "#ligand_tf_matrix = readRDS(url(\"https://zenodo.org/record/3260758/files/ligand_tf_matrix.rds\")) # ligand gene signaling scores\n", - "# gives the PPR(i,k) the importance of gene k in the signaling of ligand i as determined in the ligand signaling network (e.g. determined via personalized page rank or simply directe)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "97c9a5c9-a3e7-424f-a720-1d196509815b", - "metadata": {}, - "outputs": [], - "source": [ - "#tf_target_matrix = readRDS(url(\"https://zenodo.org/record/3260758/files/tf_target_matrix.rds\")) # containing ligand-target gene regulatory potential scores\n", - "# gives the GRN(k,j) weight - second part for calculation of ligand regulatory potential --> is the weight of the gr network in matrix form" - ] - }, { "cell_type": "code", "execution_count": 65, @@ -3644,97 +3508,6 @@ "final_data = dcast(data_long, sample_id ~ ident , value.var = \"value\") # ! with this merging there might be NA values for some samples on some data types" ] }, - { - "cell_type": "code", - "execution_count": 111, - "id": "25a06bad-3646-47b6-87bc-d0619c123803", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 13284
sample_idclinical_data_0_CKclinical_data_0_CK_MBclinical_data_0_CRPclinical_data_0_delta_ef_valueclinical_data_0_Troponincytokine_0_BCA1__CXCL13cytokine_0_CTACK__CCL27cytokine_0_EGF__EGFcytokine_0_ENA78__CXCL5⋯single_cell_0_X9_Monocytes...CD16_FCGR3A__ZEB2single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFAND5single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFAS1single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFP36single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFP36L1single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFP36L2single_cell_0_X9_Monocytes...CD16_FCGR3A__ZNF106single_cell_0_X9_Monocytes...CD16_FCGR3A__ZNF207single_cell_0_X9_Monocytes...CD16_FCGR3A__ZNF706single_cell_0_X9_Monocytes...CD16_FCGR3A__ZYX
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>⋯<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1k1 5.459432NA0.4854268NA0.018634175.6965509.8216625.04526810.562366⋯ NA NA NA NA NA NA NA NA NA NA
2k10 NANA0.2630344NA NA4.6976639.7012201.922198 7.724173⋯2.4610542.1571691.9753263.1395952.0784171.8433551.3431360.65401531.1906770.8572279
\n" - ], - "text/latex": [ - "A data.frame: 2 × 13284\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & sample\\_id & clinical\\_data\\_0\\_CK & clinical\\_data\\_0\\_CK\\_MB & clinical\\_data\\_0\\_CRP & clinical\\_data\\_0\\_delta\\_ef\\_value & clinical\\_data\\_0\\_Troponin & cytokine\\_0\\_BCA1\\_\\_CXCL13 & cytokine\\_0\\_CTACK\\_\\_CCL27 & cytokine\\_0\\_EGF\\_\\_EGF & cytokine\\_0\\_ENA78\\_\\_CXCL5 & ⋯ & single\\_cell\\_0\\_X9\\_Monocytes...CD16\\_FCGR3A\\_\\_ZEB2 & single\\_cell\\_0\\_X9\\_Monocytes...CD16\\_FCGR3A\\_\\_ZFAND5 & single\\_cell\\_0\\_X9\\_Monocytes...CD16\\_FCGR3A\\_\\_ZFAS1 & single\\_cell\\_0\\_X9\\_Monocytes...CD16\\_FCGR3A\\_\\_ZFP36 & single\\_cell\\_0\\_X9\\_Monocytes...CD16\\_FCGR3A\\_\\_ZFP36L1 & single\\_cell\\_0\\_X9\\_Monocytes...CD16\\_FCGR3A\\_\\_ZFP36L2 & single\\_cell\\_0\\_X9\\_Monocytes...CD16\\_FCGR3A\\_\\_ZNF106 & single\\_cell\\_0\\_X9\\_Monocytes...CD16\\_FCGR3A\\_\\_ZNF207 & single\\_cell\\_0\\_X9\\_Monocytes...CD16\\_FCGR3A\\_\\_ZNF706 & single\\_cell\\_0\\_X9\\_Monocytes...CD16\\_FCGR3A\\_\\_ZYX\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & k1 & 5.459432 & NA & 0.4854268 & NA & 0.01863417 & 5.696550 & 9.821662 & 5.045268 & 10.562366 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n", - "\t2 & k10 & NA & NA & 0.2630344 & NA & NA & 4.697663 & 9.701220 & 1.922198 & 7.724173 & ⋯ & 2.461054 & 2.157169 & 1.975326 & 3.139595 & 2.078417 & 1.843355 & 1.343136 & 0.6540153 & 1.190677 & 0.8572279\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 13284\n", - "\n", - "| | sample_id <chr> | clinical_data_0_CK <dbl> | clinical_data_0_CK_MB <dbl> | clinical_data_0_CRP <dbl> | clinical_data_0_delta_ef_value <dbl> | clinical_data_0_Troponin <dbl> | cytokine_0_BCA1__CXCL13 <dbl> | cytokine_0_CTACK__CCL27 <dbl> | cytokine_0_EGF__EGF <dbl> | cytokine_0_ENA78__CXCL5 <dbl> | ⋯ ⋯ | single_cell_0_X9_Monocytes...CD16_FCGR3A__ZEB2 <dbl> | single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFAND5 <dbl> | single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFAS1 <dbl> | single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFP36 <dbl> | single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFP36L1 <dbl> | single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFP36L2 <dbl> | single_cell_0_X9_Monocytes...CD16_FCGR3A__ZNF106 <dbl> | single_cell_0_X9_Monocytes...CD16_FCGR3A__ZNF207 <dbl> | single_cell_0_X9_Monocytes...CD16_FCGR3A__ZNF706 <dbl> | single_cell_0_X9_Monocytes...CD16_FCGR3A__ZYX <dbl> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | k1 | 5.459432 | NA | 0.4854268 | NA | 0.01863417 | 5.696550 | 9.821662 | 5.045268 | 10.562366 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n", - "| 2 | k10 | NA | NA | 0.2630344 | NA | NA | 4.697663 | 9.701220 | 1.922198 | 7.724173 | ⋯ | 2.461054 | 2.157169 | 1.975326 | 3.139595 | 2.078417 | 1.843355 | 1.343136 | 0.6540153 | 1.190677 | 0.8572279 |\n", - "\n" - ], - "text/plain": [ - " sample_id clinical_data_0_CK clinical_data_0_CK_MB clinical_data_0_CRP\n", - "1 k1 5.459432 NA 0.4854268 \n", - "2 k10 NA NA 0.2630344 \n", - " clinical_data_0_delta_ef_value clinical_data_0_Troponin\n", - "1 NA 0.01863417 \n", - "2 NA NA \n", - " cytokine_0_BCA1__CXCL13 cytokine_0_CTACK__CCL27 cytokine_0_EGF__EGF\n", - "1 5.696550 9.821662 5.045268 \n", - "2 4.697663 9.701220 1.922198 \n", - " cytokine_0_ENA78__CXCL5 ⋯ single_cell_0_X9_Monocytes...CD16_FCGR3A__ZEB2\n", - "1 10.562366 ⋯ NA \n", - "2 7.724173 ⋯ 2.461054 \n", - " single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFAND5\n", - "1 NA \n", - "2 2.157169 \n", - " single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFAS1\n", - "1 NA \n", - "2 1.975326 \n", - " single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFP36\n", - "1 NA \n", - "2 3.139595 \n", - " single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFP36L1\n", - "1 NA \n", - "2 2.078417 \n", - " single_cell_0_X9_Monocytes...CD16_FCGR3A__ZFP36L2\n", - "1 NA \n", - "2 1.843355 \n", - " single_cell_0_X9_Monocytes...CD16_FCGR3A__ZNF106\n", - "1 NA \n", - "2 1.343136 \n", - " single_cell_0_X9_Monocytes...CD16_FCGR3A__ZNF207\n", - "1 NA \n", - "2 0.6540153 \n", - " single_cell_0_X9_Monocytes...CD16_FCGR3A__ZNF706\n", - "1 NA \n", - "2 1.190677 \n", - " single_cell_0_X9_Monocytes...CD16_FCGR3A__ZYX\n", - "1 NA \n", - "2 0.8572279 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(final_data,2)" - ] - }, { "cell_type": "code", "execution_count": 112, @@ -3801,132 +3574,6 @@ "### Extract genes" ] }, - { - "cell_type": "code", - "execution_count": 117, - "id": "778ff63c-9081-486f-b11a-31ee8e95d38e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 9
typevariableXsample_idvalueconfigexpressionidentgene
<chr><chr><int><chr><dbl><chr><lgl><chr><chr>
1clinical_dataCK1557210k1 5.459432FALSE-FALSE-TRUE-0.2-FALSE-TRUE-TRUEclinical_data_0_CKCK
2clinical_dataCK1557211k10 NAFALSE-FALSE-TRUE-0.2-FALSE-TRUE-TRUEclinical_data_0_CKCK
\n" - ], - "text/latex": [ - "A data.frame: 2 × 9\n", - "\\begin{tabular}{r|lllllllll}\n", - " & type & variable & X & sample\\_id & value & config & expression & ident & gene\\\\\n", - " & & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & clinical\\_data & CK & 1557210 & k1 & 5.459432 & FALSE-FALSE-TRUE-0.2-FALSE-TRUE- & TRUE & clinical\\_data\\_0\\_CK & CK\\\\\n", - "\t2 & clinical\\_data & CK & 1557211 & k10 & NA & FALSE-FALSE-TRUE-0.2-FALSE-TRUE- & TRUE & clinical\\_data\\_0\\_CK & CK\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 9\n", - "\n", - "| | type <chr> | variable <chr> | X <int> | sample_id <chr> | value <dbl> | config <chr> | expression <lgl> | ident <chr> | gene <chr> |\n", - "|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | clinical_data | CK | 1557210 | k1 | 5.459432 | FALSE-FALSE-TRUE-0.2-FALSE-TRUE- | TRUE | clinical_data_0_CK | CK |\n", - "| 2 | clinical_data | CK | 1557211 | k10 | NA | FALSE-FALSE-TRUE-0.2-FALSE-TRUE- | TRUE | clinical_data_0_CK | CK |\n", - "\n" - ], - "text/plain": [ - " type variable X sample_id value \n", - "1 clinical_data CK 1557210 k1 5.459432\n", - "2 clinical_data CK 1557211 k10 NA\n", - " config expression ident gene\n", - "1 FALSE-FALSE-TRUE-0.2-FALSE-TRUE- TRUE clinical_data_0_CK CK \n", - "2 FALSE-FALSE-TRUE-0.2-FALSE-TRUE- TRUE clinical_data_0_CK CK " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(data_long,2)" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "id": "be2552af-6d02-471f-a9c6-0d927663de79", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A tibble: 5 × 2
typerange_values
<chr><dbl>
clinical_data NA
cytokine 18.018551
neutrophil 8.412481
proteomics 18.321847
single_cell 8.458305
\n" - ], - "text/latex": [ - "A tibble: 5 × 2\n", - "\\begin{tabular}{ll}\n", - " type & range\\_values\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t clinical\\_data & NA\\\\\n", - "\t cytokine & 18.018551\\\\\n", - "\t neutrophil & 8.412481\\\\\n", - "\t proteomics & 18.321847\\\\\n", - "\t single\\_cell & 8.458305\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A tibble: 5 × 2\n", - "\n", - "| type <chr> | range_values <dbl> |\n", - "|---|---|\n", - "| clinical_data | NA |\n", - "| cytokine | 18.018551 |\n", - "| neutrophil | 8.412481 |\n", - "| proteomics | 18.321847 |\n", - "| single_cell | 8.458305 |\n", - "\n" - ], - "text/plain": [ - " type range_values\n", - "1 clinical_data NA \n", - "2 cytokine 18.018551 \n", - "3 neutrophil 8.412481 \n", - "4 proteomics 18.321847 \n", - "5 single_cell 8.458305 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "### Inspect different expression ranges\n", - "data_long %>% group_by(type) %>% summarise(range_values = max(abs(value)) - min(abs(value)))" - ] - }, { "cell_type": "code", "execution_count": 119, @@ -16552,1476 +16199,6 @@ "metadata": {}, "outputs": [], "source": [] - }, - { - "cell_type": "markdown", - "id": "15fd5b7f-af8b-405f-b988-cbe7a0c97a1a", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "# Add Transcription Factors and complete pathway analysis (TBD)" - ] - }, - { - "cell_type": "code", - "execution_count": 338, - "id": "f69f3b66-5700-4e19-b148-917998ffed40", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 5 × 12
Ligand_GeneTarget_GeneclusterLigandTargetcorrelationTarget_TypeLigand_Typeregulatory_potentialregulatory_potential_scaledTarget_Cell_TypeLigand_Cell_Type
<chr><chr><chr><fct><fct><dbl><chr><chr><dbl><dbl><chr><chr>
1ADAM17A1BGproteomics single_cell_0_X6_Monocytes...CD14__ADAM17proteomics_0_A1BG_M0R009 -0.184173885proteomics single_cell_0_X6_Monocytes...CD140.00046228940.01667641proteomics Monocytes...CD14
2ADAM17A1BGproteomics single_cell_0_X4_Monocytes...CD14__ADAM17proteomics_0_A1BG_M0R009 -0.138766393proteomics single_cell_0_X4_Monocytes...CD140.00046228940.01667641proteomics Monocytes...CD14
3ADAM17A1BGproteomics single_cell_0_X6_Monocytes...CD14__ADAM17proteomics_0_A1BG_P04217.2 -0.006539046proteomics single_cell_0_X6_Monocytes...CD140.00046228940.01667641proteomics Monocytes...CD14
4ADAM17A1BGproteomics single_cell_0_X4_Monocytes...CD14__ADAM17proteomics_0_A1BG_P04217.2 0.050564953proteomics single_cell_0_X4_Monocytes...CD140.00046228940.01667641proteomics Monocytes...CD14
5ADAM17AAK1X12_Monocytes...CD16_FCGR3Asingle_cell_0_X4_Monocytes...CD14__ADAM17single_cell_0_X12_Monocytes...CD16_FCGR3A__AAK1-0.046869214single_cell_0_X12_Monocytes...CD16_FCGR3Asingle_cell_0_X4_Monocytes...CD140.00063361370.02285668Monocytes...CD16_FCGR3AMonocytes...CD14
\n" - ], - "text/latex": [ - "A data.frame: 5 × 12\n", - "\\begin{tabular}{r|llllllllllll}\n", - " & Ligand\\_Gene & Target\\_Gene & cluster & Ligand & Target & correlation & Target\\_Type & Ligand\\_Type & regulatory\\_potential & regulatory\\_potential\\_scaled & Target\\_Cell\\_Type & Ligand\\_Cell\\_Type\\\\\n", - " & & & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & ADAM17 & A1BG & proteomics & single\\_cell\\_0\\_X6\\_Monocytes...CD14\\_\\_ADAM17 & proteomics\\_0\\_A1BG\\_M0R009 & -0.184173885 & proteomics & single\\_cell\\_0\\_X6\\_Monocytes...CD14 & 0.0004622894 & 0.01667641 & proteomics & Monocytes...CD14\\\\\n", - "\t2 & ADAM17 & A1BG & proteomics & single\\_cell\\_0\\_X4\\_Monocytes...CD14\\_\\_ADAM17 & proteomics\\_0\\_A1BG\\_M0R009 & -0.138766393 & proteomics & single\\_cell\\_0\\_X4\\_Monocytes...CD14 & 0.0004622894 & 0.01667641 & proteomics & Monocytes...CD14\\\\\n", - "\t3 & ADAM17 & A1BG & proteomics & single\\_cell\\_0\\_X6\\_Monocytes...CD14\\_\\_ADAM17 & proteomics\\_0\\_A1BG\\_P04217.2 & -0.006539046 & proteomics & single\\_cell\\_0\\_X6\\_Monocytes...CD14 & 0.0004622894 & 0.01667641 & proteomics & Monocytes...CD14\\\\\n", - "\t4 & ADAM17 & A1BG & proteomics & single\\_cell\\_0\\_X4\\_Monocytes...CD14\\_\\_ADAM17 & proteomics\\_0\\_A1BG\\_P04217.2 & 0.050564953 & proteomics & single\\_cell\\_0\\_X4\\_Monocytes...CD14 & 0.0004622894 & 0.01667641 & proteomics & Monocytes...CD14\\\\\n", - "\t5 & ADAM17 & AAK1 & X12\\_Monocytes...CD16\\_FCGR3A & single\\_cell\\_0\\_X4\\_Monocytes...CD14\\_\\_ADAM17 & single\\_cell\\_0\\_X12\\_Monocytes...CD16\\_FCGR3A\\_\\_AAK1 & -0.046869214 & single\\_cell\\_0\\_X12\\_Monocytes...CD16\\_FCGR3A & single\\_cell\\_0\\_X4\\_Monocytes...CD14 & 0.0006336137 & 0.02285668 & Monocytes...CD16\\_FCGR3A & Monocytes...CD14\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 5 × 12\n", - "\n", - "| | Ligand_Gene <chr> | Target_Gene <chr> | cluster <chr> | Ligand <fct> | Target <fct> | correlation <dbl> | Target_Type <chr> | Ligand_Type <chr> | regulatory_potential <dbl> | regulatory_potential_scaled <dbl> | Target_Cell_Type <chr> | Ligand_Cell_Type <chr> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | ADAM17 | A1BG | proteomics | single_cell_0_X6_Monocytes...CD14__ADAM17 | proteomics_0_A1BG_M0R009 | -0.184173885 | proteomics | single_cell_0_X6_Monocytes...CD14 | 0.0004622894 | 0.01667641 | proteomics | Monocytes...CD14 |\n", - "| 2 | ADAM17 | A1BG | proteomics | single_cell_0_X4_Monocytes...CD14__ADAM17 | proteomics_0_A1BG_M0R009 | -0.138766393 | proteomics | single_cell_0_X4_Monocytes...CD14 | 0.0004622894 | 0.01667641 | proteomics | Monocytes...CD14 |\n", - "| 3 | ADAM17 | A1BG | proteomics | single_cell_0_X6_Monocytes...CD14__ADAM17 | proteomics_0_A1BG_P04217.2 | -0.006539046 | proteomics | single_cell_0_X6_Monocytes...CD14 | 0.0004622894 | 0.01667641 | proteomics | Monocytes...CD14 |\n", - "| 4 | ADAM17 | A1BG | proteomics | single_cell_0_X4_Monocytes...CD14__ADAM17 | proteomics_0_A1BG_P04217.2 | 0.050564953 | proteomics | single_cell_0_X4_Monocytes...CD14 | 0.0004622894 | 0.01667641 | proteomics | Monocytes...CD14 |\n", - "| 5 | ADAM17 | AAK1 | X12_Monocytes...CD16_FCGR3A | single_cell_0_X4_Monocytes...CD14__ADAM17 | single_cell_0_X12_Monocytes...CD16_FCGR3A__AAK1 | -0.046869214 | single_cell_0_X12_Monocytes...CD16_FCGR3A | single_cell_0_X4_Monocytes...CD14 | 0.0006336137 | 0.02285668 | Monocytes...CD16_FCGR3A | Monocytes...CD14 |\n", - "\n" - ], - "text/plain": [ - " Ligand_Gene Target_Gene cluster \n", - "1 ADAM17 A1BG proteomics \n", - "2 ADAM17 A1BG proteomics \n", - "3 ADAM17 A1BG proteomics \n", - "4 ADAM17 A1BG proteomics \n", - "5 ADAM17 AAK1 X12_Monocytes...CD16_FCGR3A\n", - " Ligand \n", - "1 single_cell_0_X6_Monocytes...CD14__ADAM17\n", - "2 single_cell_0_X4_Monocytes...CD14__ADAM17\n", - "3 single_cell_0_X6_Monocytes...CD14__ADAM17\n", - "4 single_cell_0_X4_Monocytes...CD14__ADAM17\n", - "5 single_cell_0_X4_Monocytes...CD14__ADAM17\n", - " Target correlation \n", - "1 proteomics_0_A1BG_M0R009 -0.184173885\n", - "2 proteomics_0_A1BG_M0R009 -0.138766393\n", - "3 proteomics_0_A1BG_P04217.2 -0.006539046\n", - "4 proteomics_0_A1BG_P04217.2 0.050564953\n", - "5 single_cell_0_X12_Monocytes...CD16_FCGR3A__AAK1 -0.046869214\n", - " Target_Type Ligand_Type \n", - "1 proteomics single_cell_0_X6_Monocytes...CD14\n", - "2 proteomics single_cell_0_X4_Monocytes...CD14\n", - "3 proteomics single_cell_0_X6_Monocytes...CD14\n", - "4 proteomics single_cell_0_X4_Monocytes...CD14\n", - "5 single_cell_0_X12_Monocytes...CD16_FCGR3A single_cell_0_X4_Monocytes...CD14\n", - " regulatory_potential regulatory_potential_scaled Target_Cell_Type \n", - "1 0.0004622894 0.01667641 proteomics \n", - "2 0.0004622894 0.01667641 proteomics \n", - "3 0.0004622894 0.01667641 proteomics \n", - "4 0.0004622894 0.01667641 proteomics \n", - "5 0.0006336137 0.02285668 Monocytes...CD16_FCGR3A\n", - " Ligand_Cell_Type\n", - "1 Monocytes...CD14\n", - "2 Monocytes...CD14\n", - "3 Monocytes...CD14\n", - "4 Monocytes...CD14\n", - "5 Monocytes...CD14" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(ligand_target_correlations_matched,5)" - ] - }, - { - "cell_type": "code", - "execution_count": 339, - "id": "6db63006-4c6d-4dec-ac0d-8ee8e5e1ce0b", - "metadata": {}, - "outputs": [ - { - "ename": "ERROR", - "evalue": "Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'up_features' not found\n", - "output_type": "error", - "traceback": [ - "Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'up_features' not found\nTraceback:\n", - "1. head(up_features)", - "2. .handleSimpleError(function (cond) \n . .Internal(C_tryCatchHelper(addr, 1L, cond)), \"object 'up_features' not found\", \n . base::quote(head(up_features)))", - "3. h(simpleError(msg, call))" - ] - } - ], - "source": [ - "head(up_features)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "15ffe8b9-99df-4435-ace8-31466b84e1b1", - "metadata": {}, - "outputs": [], - "source": [ - "### Look only on selected target genes (Top MOFA features)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a72564c1-6d56-4fe8-98cc-9929214af0ce", - "metadata": {}, - "outputs": [], - "source": [ - "ligand_target_correlations_filtered = ligand_target_correlations_matched[ligand_target_correlations_matched$Target %in% c(up_features, down_features),]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca79bb20-be2f-4dda-8d0c-a2b35a146b61", - "metadata": {}, - "outputs": [], - "source": [ - "unique(ligand_target_correlations_filtered$Ligand_Type)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "05b827e4-70f2-4ec4-8431-63e0297df5b9", - "metadata": {}, - "outputs": [], - "source": [ - "length(unique(ligand_target_correlations_filtered$Target)) # amont of target genes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a48ec6f7-3c2f-4ce8-a147-7f97766fd7d1", - "metadata": {}, - "outputs": [], - "source": [ - "### Look only on a selected cell type as target" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "df5897b4-2190-4fcf-9ff2-c50b82945e08", - "metadata": {}, - "outputs": [], - "source": [ - "unique(ligand_target_correlations_filtered$Target_Type)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0a2e192-7570-4879-afe0-486d876ac260", - "metadata": {}, - "outputs": [], - "source": [ - "target_cluster = 'single_cell_0_X0_T.cell.CD4'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e973525d-09da-4be3-9f3e-ad816f7bb969", - "metadata": {}, - "outputs": [], - "source": [ - "ligand_target_correlations_filtered = ligand_target_correlations_filtered[ligand_target_correlations_filtered$Target_Type == target_cluster,]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "460cfc3c-a2ec-4db3-b09f-1b63fe0c8c0c", - "metadata": {}, - "outputs": [], - "source": [ - "unique(ligand_target_correlations_filtered$Ligand_Type)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "43c4e3f0-a68c-4ee4-9a79-0b8cedddfd2e", - "metadata": {}, - "outputs": [], - "source": [ - "### Summarize target genes of selected type" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "951099ab-547a-4a9b-b48c-6992cea5765e", - "metadata": {}, - "outputs": [], - "source": [ - "targets = unique(ligand_target_correlations_filtered$Target_Gene)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16b745d5-cdce-4b01-b7c4-21f66d262904", - "metadata": {}, - "outputs": [], - "source": [ - "length(targets)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5ce5d946-103e-4bf2-b4e1-0ffaa01ecc4b", - "metadata": {}, - "outputs": [], - "source": [ - "### Identify potential expressed transcription factors" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d1f200b4-ec9b-44d0-bd02-4cc3febaffa8", - "metadata": {}, - "outputs": [], - "source": [ - "amount_tfs = 4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "43d09723-ce37-4539-81ae-1593edcb67e8", - "metadata": {}, - "outputs": [], - "source": [ - "gene_regulatory_network = weighted_networks$gr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c32bbb77-9f48-4524-bfb0-856b747ad6a5", - "metadata": {}, - "outputs": [], - "source": [ - "head(gene_regulatory_network,2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eae80d2e-515f-427d-a892-5868b19b76c7", - "metadata": {}, - "outputs": [], - "source": [ - "length(unique(gene_regulatory_network$from))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8b8214f-b316-400c-ba59-7db4ddadadb5", - "metadata": {}, - "outputs": [], - "source": [ - "### Only consider transcription factors that might regulate one of the selected targets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55ec1659-3b61-4f8b-9787-a84a38555f5f", - "metadata": {}, - "outputs": [], - "source": [ - "nrow(gene_regulatory_network)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "741304ea-2ce1-4d93-99c3-2ed69b06e81f", - "metadata": {}, - "outputs": [], - "source": [ - "gene_regulatory_network = gene_regulatory_network[gene_regulatory_network$to %in% targets,]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9594e156-824f-4fa3-baf1-bbc6d117ebf8", - "metadata": {}, - "outputs": [], - "source": [ - "length(unique(gene_regulatory_network$from))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2161705-2273-4ed5-8e94-12e1d36efc27", - "metadata": {}, - "outputs": [], - "source": [ - "transcription_factors = unique(weighted_networks$gr$from)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc932717-094a-4dac-ba06-92ad1076501d", - "metadata": {}, - "outputs": [], - "source": [ - "nrow(gene_regulatory_network)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aeae8ef4-464d-436e-a0b5-bc669b101338", - "metadata": {}, - "outputs": [], - "source": [ - "options(repr.plot.width=30, repr.plot.height=5)\n", - "hist(gene_regulatory_network$weight)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e54acf86-e31a-4015-8f86-e7f711d38bdf", - "metadata": {}, - "outputs": [], - "source": [ - "### Transcription factors need to be expressed in cell-type? - TBD!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46841f4b-4b20-4fb5-b058-bdf8be58d813", - "metadata": {}, - "outputs": [], - "source": [ - "#head(data_long,2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "64826904-da00-42d0-9082-cac6cae66475", - "metadata": {}, - "outputs": [], - "source": [ - "head(data_long)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "064e98f6-2374-4cfb-a3da-b4b445e5b5f0", - "metadata": {}, - "outputs": [], - "source": [ - "data_long$cluster = paste0(data_long$type, '_0_', str_replace(data_long$variable, '__.*', ''))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "38657143-0039-41ff-8370-dfde3fe62c5b", - "metadata": {}, - "outputs": [], - "source": [ - "data_long$cluster[data_long$type != 'single_cell'] = data_long$type[data_long$type != 'single_cell']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3e9ec3a5-9946-4aa4-b364-71108eb8c301", - "metadata": {}, - "outputs": [], - "source": [ - "unique(data_long$cluster)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "849fa5a0-9b2c-4182-b1ce-0a452e85d4a6", - "metadata": {}, - "outputs": [], - "source": [ - "target_cluster" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d2446c82-bdd4-4d0d-b0a6-a08a0a475f04", - "metadata": {}, - "outputs": [], - "source": [ - "unique(data_long$type)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5e870a38-7777-4d78-abc0-cf2406afa8b4", - "metadata": {}, - "outputs": [], - "source": [ - "expressed_genes = data_long[data_long$cluster == target_cluster,] %>% group_by(type, variable, ident) %>% summarise(mean_expr = mean(value, na.rm = TRUE), var_expression = var(value, na.rm = TRUE))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce3547a4-537a-40b3-b56a-c8c4bbbf41f7", - "metadata": {}, - "outputs": [], - "source": [ - "expressed_genes$gene = str_replace(expressed_genes$variable, '.*__', '')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f47c4663-7a96-435b-9322-241010e026ec", - "metadata": {}, - "outputs": [], - "source": [ - "head(expressed_genes,2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "afa0a5a7-5c0d-4f03-8ba3-afe38bbeda65", - "metadata": {}, - "outputs": [], - "source": [ - "nrow(expressed_genes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "512c58c9-56d4-46cd-b575-6dfc7ac5d851", - "metadata": {}, - "outputs": [], - "source": [ - "length(unique(expressed_genes$gene))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "14c7c967-6a87-4e5f-8515-bdff4dcae312", - "metadata": {}, - "outputs": [], - "source": [ - "head(gene_regulatory_network,2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "43c62a4c-1941-4ef1-9baa-4569d05beb05", - "metadata": {}, - "outputs": [], - "source": [ - "nrow(gene_regulatory_network)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b94a2a5-60bf-4e82-8263-f67f4e4ae052", - "metadata": {}, - "outputs": [], - "source": [ - "gene_regulatory_network = merge(gene_regulatory_network, expressed_genes, by.x = c('from'), by.y = c('gene'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e63c019-7a04-45a9-9095-64f87783d129", - "metadata": {}, - "outputs": [], - "source": [ - "nrow(gene_regulatory_network)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6187956d-56cd-437d-a7ea-c856911a1188", - "metadata": {}, - "outputs": [], - "source": [ - "head(gene_regulatory_network,2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30651479-f052-43bd-bca9-264707ae5f18", - "metadata": {}, - "outputs": [], - "source": [ - "gene_regulatory_network = gene_regulatory_network[,c('from', 'to', 'weight', 'mean_expr')]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "101e8a5d-ef77-45af-b395-774e2731f78b", - "metadata": {}, - "outputs": [], - "source": [ - "options(repr.plot.width=30, repr.plot.height=5)\n", - "hist(gene_regulatory_network$weight)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55e38e11-e056-4404-8e6c-c12357951128", - "metadata": {}, - "outputs": [], - "source": [ - "#### Filter on which transcription factors to take for ligand target combination (based on ligand- tf scores and tf-target scores)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2c371c10-3280-466c-8c9a-ed47032eba82", - "metadata": {}, - "outputs": [], - "source": [ - "colnames(gene_regulatory_network) = c('tf', 'target_gene', 'tf_target_weight', 'mean_expr')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "602dc8eb-73ab-4da3-a107-d96c74e526ae", - "metadata": {}, - "outputs": [], - "source": [ - "nrow(gene_regulatory_network)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3452b369-ec25-46fe-88a5-470e1de559d4", - "metadata": {}, - "outputs": [], - "source": [ - "head(gene_regulatory_network)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a75cd62c-8d3e-4f7e-853b-7f343ff0e368", - "metadata": {}, - "outputs": [], - "source": [ - "ligand_tf_scores = melt(ligand_tf_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ff7b38c-bc36-4dbc-8bd3-c3f87f8c4e1d", - "metadata": {}, - "outputs": [], - "source": [ - "colnames(ligand_tf_scores) = c('tf', 'ligand', 'ligand_tf_score')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e9177d29-9492-46c3-9c58-9c13d0f8a357", - "metadata": {}, - "outputs": [], - "source": [ - "head(ligand_tf_scores)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9dadc1a7-f18b-494f-8b31-345aa38b7227", - "metadata": {}, - "outputs": [], - "source": [ - "length(unique(ligand_tf_scores$ligand))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "88bc66d5-d0ca-4423-a9df-09d3841e61ea", - "metadata": {}, - "outputs": [], - "source": [ - "gene_regulatory_network = merge(gene_regulatory_network, ligand_tf_scores)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe768560-92a1-4d12-b032-88abe28b2925", - "metadata": {}, - "outputs": [], - "source": [ - "nrow(gene_regulatory_network)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65a8bbc0-3970-49ef-8008-5b005ec9c54f", - "metadata": {}, - "outputs": [], - "source": [ - "head(gene_regulatory_network,2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "56d6864c-6e1c-4528-974d-78c9023dbad8", - "metadata": {}, - "outputs": [], - "source": [ - "### Combine both scores" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "182b3084-943b-47eb-9cde-297635652dcf", - "metadata": {}, - "outputs": [], - "source": [ - "gene_regulatory_network$combined_score = gene_regulatory_network$tf_target_weight * gene_regulatory_network$ligand_tf_score" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f7c7399-3a18-4cb8-a722-4d70a039df09", - "metadata": {}, - "outputs": [], - "source": [ - "options(repr.plot.width=30, repr.plot.height=5)\n", - "hist(gene_regulatory_network$combined_score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "025ea6a9-d253-4f5f-aa93-5b368c91d42d", - "metadata": {}, - "outputs": [], - "source": [ - "### Select top transcription factors per ligand, target combination" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "17aba87f-7cf3-484c-a9e6-83d4b3dfd725", - "metadata": {}, - "outputs": [], - "source": [ - "top_tfs = gene_regulatory_network %>% group_by(ligand, target_gene) %>% top_n(amount_tfs, combined_score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e11e106d-70ea-4772-85e5-ffd729486a36", - "metadata": {}, - "outputs": [], - "source": [ - "nrow(top_tfs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "43051348-5a96-4023-ad83-9bd80ee4b57c", - "metadata": {}, - "outputs": [], - "source": [ - "head(top_tfs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "933a9085-4bf3-45e1-b997-ebd73987751a", - "metadata": {}, - "outputs": [], - "source": [ - "options(repr.plot.width=30, repr.plot.height=5)\n", - "hist(top_tfs$combined_score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "21cac84e-f8e4-4704-80b6-e449e39edc0e", - "metadata": {}, - "outputs": [], - "source": [ - "#top_tfs[top_tfs$tf == 'HMGB2',]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc449855-08f3-4148-8f5e-b28b4ad266b4", - "metadata": {}, - "outputs": [], - "source": [ - "#### Filter ligand target correlations on highest correlation and potential" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4eeea86a-c323-4744-8aac-64d414d869a7", - "metadata": {}, - "outputs": [], - "source": [ - "head(ligand_target_correlations_filtered,2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b9f9f4b2-297c-4107-92f8-e62a30d99a87", - "metadata": {}, - "outputs": [], - "source": [ - "nrow(ligand_target_correlations_filtered)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ffea5e79-5578-4dc5-8f08-365b1dfe742d", - "metadata": {}, - "outputs": [], - "source": [ - "ligand_target_correlations_filtered = head(ligand_target_correlations_filtered[order(abs(ligand_target_correlations_filtered$cor_pot_combined), decreasing = TRUE),], 100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dbd5b029-2bec-441b-9a2b-ad3fda78146f", - "metadata": {}, - "outputs": [], - "source": [ - "nrow(ligand_target_correlations_filtered) # afterwards - multiple potential TFS mapped to ligand targets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f35d22ef-3804-498a-baf0-46e9cb849a1d", - "metadata": {}, - "outputs": [], - "source": [ - "### Add potential transcription factors to ligand_target_correlations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "de525f14-bdb4-4b6e-b18d-c1d93793717a", - "metadata": {}, - "outputs": [], - "source": [ - "ligand_tf_targets = merge(ligand_target_correlations_filtered, top_tfs, by.x = c('Target_Gene', 'Ligand_Gene'), by.y = c('target_gene', 'ligand'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d6dbd81-f89c-4425-82ba-094890c168ed", - "metadata": {}, - "outputs": [], - "source": [ - "nrow(ligand_tf_targets)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f738c9f8-0214-4b6d-934e-38658b35d5c7", - "metadata": {}, - "outputs": [], - "source": [ - "head(ligand_tf_targets,3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b9f71c6e-b884-4706-92a0-d397f96ac03a", - "metadata": {}, - "outputs": [], - "source": [ - "ligand_tf_targets$tf_id = paste0(ligand_tf_targets$Target_Type, '__', ligand_tf_targets$tf)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "12d445d8-b1f4-40aa-b584-3e81014beab4", - "metadata": {}, - "outputs": [], - "source": [ - "##### Links" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4ed4a85-94c7-4fa3-a367-90eb81bac97c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "82d33e1f-0b83-4b43-8abf-c5274be1d86d", - "metadata": {}, - "outputs": [], - "source": [ - "ligand_tf_links = unique(ligand_tf_targets[,c('Ligand', 'tf_id', 'ligand_tf_score')])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d64f6dd4-a9bc-4eb5-9718-7ac6dfb8a415", - "metadata": {}, - "outputs": [], - "source": [ - "colnames(ligand_tf_links) = c('from', 'to', 'width')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e887e589-0e48-45b3-86e1-fbb05f8510ae", - "metadata": {}, - "outputs": [], - "source": [ - "ligand_tf_links$width = ligand_tf_links$width/ (max(ligand_tf_links$width) - min(ligand_tf_links$width))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "102c65e1-3f4a-41d1-a4b1-d48e0e79c475", - "metadata": {}, - "outputs": [], - "source": [ - "ligand_tf_links$from = paste0(ligand_tf_links$from, '_', 'L')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d2bdf0bf-da81-4b23-847c-1e2ad1f7e5b0", - "metadata": {}, - "outputs": [], - "source": [ - "ligand_tf_links$to = paste0(ligand_tf_links$to, '_', 'TF')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f744fb93-8854-4cca-aa8c-dcade4f69fd4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0a22c27-9742-4446-9e68-5f3bd3eec8c1", - "metadata": {}, - "outputs": [], - "source": [ - "tf_target_links = unique(ligand_tf_targets[,c('tf_id', 'Target', 'tf_target_weight')])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "310d38ab-e341-4342-b79b-39afd71ba3e8", - "metadata": {}, - "outputs": [], - "source": [ - "colnames(tf_target_links) = c('from', 'to', 'width')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3368fd2f-1bd2-43a9-b6a8-5dd9ffdc511d", - "metadata": {}, - "outputs": [], - "source": [ - "tf_target_links$width = tf_target_links$width/ (max(tf_target_links$width) - min(tf_target_links$width))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30a26dc2-0a29-4d21-9638-c5de241f3730", - "metadata": {}, - "outputs": [], - "source": [ - "tf_target_links$from = paste0(tf_target_links$from, '_', 'TF')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a120a63-fa07-45c7-91d1-a68224f13146", - "metadata": {}, - "outputs": [], - "source": [ - "tf_target_links$to = paste0(tf_target_links$to, '_', 'T')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8cebfe76-8750-454f-8781-79ec923af97c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "519fdd52-739a-439c-938b-a40238c4cb54", - "metadata": {}, - "outputs": [], - "source": [ - "links = rbind( ligand_tf_links, tf_target_links)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f3015794-f685-4dcc-9f91-ca67e7efcf72", - "metadata": {}, - "outputs": [], - "source": [ - "head(links,2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "067986d9-ee59-4ccd-9c2b-8d00eddb90d9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e372b934-8e2e-43aa-8a10-1b118cecb28c", - "metadata": {}, - "outputs": [], - "source": [ - "### Set threshold" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0dada902-df97-46fb-b981-b643785ea4ab", - "metadata": {}, - "outputs": [], - "source": [ - "#### Filter ! -- minimum interaction score in order to display link and nodes\n", - "#links = links[links$width > 2,]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd75a5c6-d40b-492b-9f4f-8a580fe92e13", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "50147136-02c5-4f02-a48b-c2ce929b7abe", - "metadata": {}, - "outputs": [], - "source": [ - "#### Nodes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3341a8c3-09a8-4498-8b1f-6b139d8ba2a3", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5ecd411e-a04c-4f5d-8b52-f6fc25a4924d", - "metadata": {}, - "outputs": [], - "source": [ - "ligand_nodes = unique(ligand_tf_targets[,c('Ligand', 'Ligand_Type')])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b90cb7d6-d997-4c4a-b96c-3ef4975a8e1f", - "metadata": {}, - "outputs": [], - "source": [ - "colnames(ligand_nodes) = c('id', 'cluster')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "daf8c0ee-ee2b-4387-acd8-bab73b4e1671", - "metadata": {}, - "outputs": [], - "source": [ - "ligand_nodes$type = 'Ligand'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "09dbaab3-e0b3-4f51-a077-957bc8d4a09f", - "metadata": {}, - "outputs": [], - "source": [ - "ligand_nodes$id = paste0(ligand_nodes$id, '_', 'L')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a55695f-260c-4724-b100-fef5e657c6d4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35e323e6-49b8-4011-9c55-631e091940dd", - "metadata": {}, - "outputs": [], - "source": [ - "tf_nodes = unique(ligand_tf_targets[,c('tf_id', 'Target_Type')])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "67b7344b-0d57-4920-97d6-d1c15b5e29cd", - "metadata": {}, - "outputs": [], - "source": [ - "colnames(tf_nodes) = c('id', 'cluster')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7b40d7a7-7e6f-46ff-a2ab-161f80f607b6", - "metadata": {}, - "outputs": [], - "source": [ - "tf_nodes$type = 'TF'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a70c28a-9fb8-4831-aa54-f3787899f26a", - "metadata": {}, - "outputs": [], - "source": [ - "tf_nodes$id = paste0(tf_nodes$id, '_', 'TF')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "389a4725-f3a3-4479-9e82-131d790c6410", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e18f78d3-b23a-49e8-92d4-fd69dbf4fc5f", - "metadata": {}, - "outputs": [], - "source": [ - "target_nodes = unique(ligand_tf_targets[,c('Target', 'Target_Type')])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "94d6e001-83d6-4e89-b6ee-986e958121b4", - "metadata": {}, - "outputs": [], - "source": [ - "colnames(target_nodes) = c('id', 'cluster')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f96b742e-b508-4b4d-bc63-e93db194ed17", - "metadata": {}, - "outputs": [], - "source": [ - "target_nodes$type = 'Target'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "135967f0-99f0-4040-a9aa-0199f5abf920", - "metadata": {}, - "outputs": [], - "source": [ - "target_nodes$id = paste0(target_nodes$id, '_', 'T')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8496c2aa-2b36-49f5-aaa2-1350a954adba", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "394f246d-a957-4df7-82ac-e234a63d716e", - "metadata": {}, - "outputs": [], - "source": [ - "nodes = rbind(ligand_nodes, tf_nodes, target_nodes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4399dad0-2e30-49b9-bc1e-165fa6109995", - "metadata": {}, - "outputs": [], - "source": [ - "head(nodes,2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1b65348d-b3d1-4fbd-a725-7bfde99f0e39", - "metadata": {}, - "outputs": [], - "source": [ - "nodes$cluster = str_replace(nodes$cluster, 'single_cell_0_', '')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b5edd78-a415-44b2-b94c-880f7ce3a8c9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d07ef8c9-0fb4-48dd-bd2a-34485eb469ad", - "metadata": {}, - "outputs": [], - "source": [ - "nodes$color[nodes$cluster == 'proteomics'] = '#2FE6EC'\n", - "nodes$color[nodes$cluster == 'cytokine'] = '#0E2D9F'\n", - "nodes$color[nodes$cluster == 'neutrophil'] = '#CA580E'\n", - "nodes$color[nodes$cluster %in% c('X0_T.cell.CD4','X11_T.cell.CD4','X2_T.cell.CD4', 'X5_T.cell.CD4') ] = '#E38DE5'\n", - "nodes$color[nodes$cluster %in% c('X4_Monocytes...CD14','X6_Monocytes...CD14','X7_Monocytes...CD14') ] = '#F1E984'\n", - "nodes$color[nodes$cluster %in% c('X10_B.cell','X8_B.cell') ] = '#8C75AD'\n", - "nodes$color[nodes$cluster %in% c('X13_Dendritic') ] = '#E6B17F'\n", - "nodes$color[nodes$cluster %in% c('X1_T.cell.CD8') ] = '#EC349B'\n", - "nodes$color[nodes$cluster %in% c('X12_Monocytes...CD16_FCGR3A','X9_Monocytes...CD16_FCGR3A' ) ] = '#DCF39D'\n", - "nodes$color[nodes$cluster %in% c('X3_NK' ) ] = '#9DB8F3'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77f4d4f2-746c-4e32-9ade-53e48b99c774", - "metadata": {}, - "outputs": [], - "source": [ - "unique(nodes$color)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19b4424d-fc5f-4dd8-aabd-6e2c0d35a636", - "metadata": {}, - "outputs": [], - "source": [ - "nodes$shape = 'dot'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0cee55c8-77ab-4d15-a288-ab0fc5b51f9a", - "metadata": {}, - "outputs": [], - "source": [ - "nodes$shape[nodes$type == 'TF'] = 'triangle'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "410ecdae-db7f-4350-811b-f853731d45d0", - "metadata": {}, - "outputs": [], - "source": [ - "nodes$shape[nodes$type == 'Target'] = 'square'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "549baa98-fd2e-421a-83b2-51fe649d331f", - "metadata": {}, - "outputs": [], - "source": [ - "head(nodes,2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4139f2f8-455c-4c03-ab32-4226ac1499a0", - "metadata": {}, - "outputs": [], - "source": [ - "length(unique(nodes$id))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0074f320-6b49-4d73-b600-69d5645e8c59", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a501ca6-d7f8-46d2-bc7a-535ec9c61d11", - "metadata": {}, - "outputs": [], - "source": [ - "#nodes$type = TRUE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "34582b3e-d788-45a7-8870-f3e6ef7f36d7", - "metadata": {}, - "outputs": [], - "source": [ - "#nodes$type[nodes$type == 'Receptor'] = FALSE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "14812f6c-f841-4079-887c-83663dae3257", - "metadata": {}, - "outputs": [], - "source": [ - "nrow(nodes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d277b65e-be41-45b5-bc46-bc97b0b451fb", - "metadata": {}, - "outputs": [], - "source": [ - "nodes[nodes$id == 'single_cell_0_X0_T.cell.CD4__HMGB2',]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "21c15aac-6530-4f99-ac18-ae94024ab867", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e888ac7-2efb-4e43-995e-2fe5b9b5b011", - "metadata": {}, - "outputs": [], - "source": [ - "#head(nodes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77926901-7e33-4f2f-801a-f1e800e1302f", - "metadata": {}, - "outputs": [], - "source": [ - "#### Plot Graph" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4cf4caf3-bef6-41d8-88ca-050bbe4e3a67", - "metadata": {}, - "outputs": [], - "source": [ - "library('visNetwork')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7be356bf-e6bd-4ac6-aef2-b4020ae39659", - "metadata": {}, - "outputs": [], - "source": [ - "graph = visNetwork(nodes, links, width=\"100%\", height=\"800px\") %>% visEdges(arrows = 'to') %>%\n", - " visOptions(highlightNearest = list(enabled = TRUE, degree = 1, labelOnly = FALSE, hover = FALSE, algorithm = 'hierarchical', hideColor = '#0000FF00',labelOnly =TRUE ), \n", - " #selectedBy = list(variable = \"gene\", selected = ligand, style = 'width: 200px; height: 26px;\n", - " # background: #ffffff;\n", - " # color: darkblue;\n", - " # border:none;\n", - " # outline:none;', hideColor = '#0000FF00', highlight = TRUE, multiple = TRUE)\n", - " ) %>%\n", - "\n", - " #nodesIdSelection = list(enabled = TRUE,\n", - " # selected = \"1\",\n", - " # style = 'width: 200px; height: 26px;\n", - " # background: #ffffff;\n", - " # color: darkblue;\n", - " # border:none;\n", - " # outline:none;')) %>%\n", - " #visLayout(randomSeed = 123, improvedLayout = TRUE) %>% \n", - " #visPhysics(enabled =TRUE, stabilization = list(itearations = 100)) %>% \n", - " #visClusteringByGroup(groups = unique(nodes$group), label = 'Grouped: ', color = 'mediumaquamarine', shape = 'triangle') %>% # define which nodes should be collapsed\n", - " #visLegend(addNodes = legend, width = 0.1, position = \"right\", main = \"Type\", useGroups = FALSE) %>% \n", - " visIgraphLayout(layout = 'layout_nicely') # 'layout_with_lgl', #layout_with_fr #layout_nicely #layout_in_circle #layout_on_sphere #layout_as bipartite? #layout_as_tree\n", - "\n", - "visSave(graph, paste0('figures/F_Ligand_Receptors/Ligand_TF_Target_Network_.html'), selfcontained = TRUE, background = \"white\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e984eadf-4b02-4409-8947-a0699794764c", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/F/F4_2_Ligand_Receptor_Interactions_Visualization.ipynb b/F/F4_2_Ligand_Receptor_Interactions_Visualization.ipynb index 37bd913..1e4acd7 100644 --- a/F/F4_2_Ligand_Receptor_Interactions_Visualization.ipynb +++ b/F/F4_2_Ligand_Receptor_Interactions_Visualization.ipynb @@ -1345,70 +1345,6 @@ "print(file.info(path)$mtime)" ] }, - { - "cell_type": "code", - "execution_count": 32, - "id": "e9655a83-be20-4c67-88ff-10b2e74bcde9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 2 × 30
X.1sample_idsampleidmeasurementlibraryid.ynamereadpattern⋯meta_datadelta_ef_value_groupdelta_ef_valuedelta_ef_value_classef_classification_dataCKCK_MBTroponinCRPclinical_data
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136136m6.4M66.4TP4L14HTO_B02596.4R25PNNNNNNNNNN(BC)⋯1x_smaller_10.875intermediate1590.4581.31
137137m6.4M66.4TP4L10HTO_B02556.4R25PNNNNNNNNNN(BC)⋯1x_smaller_10.875intermediate1590.4581.31
\n" - ], - "text/latex": [ - "A data.frame: 2 × 30\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & X.1 & sample\\_id & sample & id & measurement & library & id.y & name & read & pattern & ⋯ & meta\\_data & delta\\_ef\\_value\\_group & delta\\_ef\\_value & delta\\_ef\\_value\\_class & ef\\_classification\\_data & CK & CK\\_MB & Troponin & CRP & clinical\\_data\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t136 & 136 & m6.4 & M6 & 6.4 & TP4 & L14 & HTO\\_B0259 & 6.4 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & 1 & x\\_smaller\\_1 & 0.875 & intermediate & 1 & 59 & & 0.458 & 1.3 & 1\\\\\n", - "\t137 & 137 & m6.4 & M6 & 6.4 & TP4 & L10 & HTO\\_B0255 & 6.4 & R2 & 5PNNNNNNNNNN(BC) & ⋯ & 1 & x\\_smaller\\_1 & 0.875 & intermediate & 1 & 59 & & 0.458 & 1.3 & 1\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 30\n", - "\n", - "| | X.1 <int> | sample_id <chr> | sample <chr> | id <dbl> | measurement <chr> | library <chr> | id.y <chr> | name <chr> | read <chr> | pattern <chr> | ⋯ ⋯ | meta_data <int> | delta_ef_value_group <chr> | delta_ef_value <dbl> | delta_ef_value_class <chr> | ef_classification_data <int> | CK <int> | CK_MB <chr> | Troponin <dbl> | CRP <chr> | clinical_data <int> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 136 | 136 | m6.4 | M6 | 6.4 | TP4 | L14 | HTO_B0259 | 6.4 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | 1 | x_smaller_1 | 0.875 | intermediate | 1 | 59 | | 0.458 | 1.3 | 1 |\n", - "| 137 | 137 | m6.4 | M6 | 6.4 | TP4 | L10 | HTO_B0255 | 6.4 | R2 | 5PNNNNNNNNNN(BC) | ⋯ | 1 | x_smaller_1 | 0.875 | intermediate | 1 | 59 | | 0.458 | 1.3 | 1 |\n", - "\n" - ], - "text/plain": [ - " X.1 sample_id sample id measurement library id.y name read\n", - "136 136 m6.4 M6 6.4 TP4 L14 HTO_B0259 6.4 R2 \n", - "137 137 m6.4 M6 6.4 TP4 L10 HTO_B0255 6.4 R2 \n", - " pattern ⋯ meta_data delta_ef_value_group delta_ef_value\n", - "136 5PNNNNNNNNNN(BC) ⋯ 1 x_smaller_1 0.875 \n", - "137 5PNNNNNNNNNN(BC) ⋯ 1 x_smaller_1 0.875 \n", - " delta_ef_value_class ef_classification_data CK CK_MB Troponin CRP\n", - "136 intermediate 1 59 0.458 1.3\n", - "137 intermediate 1 59 0.458 1.3\n", - " clinical_data\n", - "136 1 \n", - "137 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sample_data[(sample_data$sample_id == 'm6.4'),] " - ] - }, { "cell_type": "code", "execution_count": 33,