diff --git a/01_overview.ipynb b/01_overview.ipynb index f475da9..edd2d74 100644 --- a/01_overview.ipynb +++ b/01_overview.ipynb @@ -15,7 +15,7 @@ "Seaborn and pandas are \"libraries\" of Python. Think of them as books of instructions. If you have them available, you can call these instructions through the Python language to tell the computer how to accomplish very specialized tasks.\n", "\n", "Structure of the course\n", - "- Prior course on Oct 3rd: (custom study)\n", + "- Prior to class on November 22nd: (custom study)\n", " - Download Anaconda\n", " - Follow 02_homework.ipynb" ] @@ -29,7 +29,7 @@ "## Download this tutorial\n", "Github, which stores the code for this tutorial, is used (and required by some journals) to exchange computational code. However, you can not execute code on github itself, and editing code on github is difficult. \n", "\n", - "Instead, you will need to download a copy of this code, and store it on your computer. Click the above link, https://github.com/morimotolab/systems_class, and then click on the large green button that says \"Clone or download\" and download the code as a ZIP file to your desktop. In case that after the download you only see a ZIP file on the desktop, rather than a folder containing the individual files of the code of the tutorial, you will need to uncompress the ZIP file. On many computers this will be possible by double-clicking on the ZIP file. This can also be accomplished on the (unix) command line by running: `unzip [filepath]`.\n", + "Instead, you will need to download a copy of this code, and store it on your computer. Click the above link, https://github.com/morimotolab/systems_class, and then click on the large green button that says \"Clone or download\" and download the code as a ZIP file to your desktop. In case that after the download you only see a ZIP file on the desktop, rather than a folder containing the individual files of the code of the tutorial, you will need to uncompress the ZIP file. On many computers this will be possible by double-clicking on the ZIP file. This can also be accomplished on the (unix) command line by running: `unzip [filepath]`. Note that you can also run unix commands on Windows using the [Windows Subsystem for Linux (WSL)](https://docs.microsoft.com/en-us/windows/wsl/install).\n", "\n", "\n", "## Installing Anaconda\n", @@ -77,7 +77,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.8" } }, "nbformat": 4, diff --git a/02_homework.ipynb b/02_homework.ipynb index a0174f4..aa6a7c3 100644 --- a/02_homework.ipynb +++ b/02_homework.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -56,17 +56,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "On the next line you will need to adjust the code, to point it toward the location on your computer, which contains \"chaperome_expression_rpkm_1_1_orthologs.csv\", a file, which you can download from canvas (Class-3-Thursday, Oct3)" + "On the next line you will need to adjust the code, to point it toward the location on your computer, which contains \"chaperome_expression_rpkm_1_1_orthologs.csv\", a file, which you can download from canvas (Class-3-Monday, November 22nd)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "my_table = pd.read_csv(\n", - " filepath_or_buffer='/Users/tstoeger/Box/chaperome_student_course/2020/material/chaperome_developmental_expression_rpkm_1_1_orthologs.csv' # <- adapt code here`\n", + " filepath_or_buffer='C://Users/rogan/Downloads/chaperome_student_course/chaperome_student_course/2020/material/chaperome_developmental_expression_rpkm_1_1_orthologs.csv' # <- adapt code here`\n", ")" ] }, @@ -81,9 +81,164 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0organismtissueagegenemedian_RPKMdevelopmentalIndexmouseStageratStagerabbitStagehumanStagerhesusStageopossumStagechickenStage
01ChickenBraine10AGR20.1804597.0e16.5e19e2112wpcNaN6.0e10
12ChickenBraine10AHSA161.0754527.0e16.5e19e2112wpcNaN6.0e10
23ChickenBraine10ANAPC513.1964357.0e16.5e19e2112wpcNaN6.0e10
34ChickenBraine10APPBP223.8240297.0e16.5e19e2112wpcNaN6.0e10
45ChickenBraine10BAG121.4637967.0e16.5e19e2112wpcNaN6.0e10
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 organism tissue age gene median_RPKM developmentalIndex \\\n", + "0 1 Chicken Brain e10 AGR2 0.180459 7.0 \n", + "1 2 Chicken Brain e10 AHSA1 61.075452 7.0 \n", + "2 3 Chicken Brain e10 ANAPC5 13.196435 7.0 \n", + "3 4 Chicken Brain e10 APPBP2 23.824029 7.0 \n", + "4 5 Chicken Brain e10 BAG1 21.463796 7.0 \n", + "\n", + " mouseStage ratStage rabbitStage humanStage rhesusStage opossumStage \\\n", + "0 e16.5 e19 e21 12wpc NaN 6.0 \n", + "1 e16.5 e19 e21 12wpc NaN 6.0 \n", + "2 e16.5 e19 e21 12wpc NaN 6.0 \n", + "3 e16.5 e19 e21 12wpc NaN 6.0 \n", + "4 e16.5 e19 e21 12wpc NaN 6.0 \n", + "\n", + " chickenStage \n", + "0 e10 \n", + "1 e10 \n", + "2 e10 \n", + "3 e10 \n", + "4 e10 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Let us look at content, using the .head function\n", "# Btw, # marks \"comments\", which will not be executed\n", @@ -99,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -117,9 +272,151 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
organismtissueagegenemedian_RPKMdevelopmentalIndexmouseStageratStagerabbitStagehumanStagerhesusStageopossumStagechickenStage
0ChickenBraine10AGR20.1804597.0e16.5e19e2112wpcNaN6.0e10
1ChickenBraine10AHSA161.0754527.0e16.5e19e2112wpcNaN6.0e10
2ChickenBraine10ANAPC513.1964357.0e16.5e19e2112wpcNaN6.0e10
3ChickenBraine10APPBP223.8240297.0e16.5e19e2112wpcNaN6.0e10
4ChickenBraine10BAG121.4637967.0e16.5e19e2112wpcNaN6.0e10
\n", + "
" + ], + "text/plain": [ + " organism tissue age gene median_RPKM developmentalIndex mouseStage \\\n", + "0 Chicken Brain e10 AGR2 0.180459 7.0 e16.5 \n", + "1 Chicken Brain e10 AHSA1 61.075452 7.0 e16.5 \n", + "2 Chicken Brain e10 ANAPC5 13.196435 7.0 e16.5 \n", + "3 Chicken Brain e10 APPBP2 23.824029 7.0 e16.5 \n", + "4 Chicken Brain e10 BAG1 21.463796 7.0 e16.5 \n", + "\n", + " ratStage rabbitStage humanStage rhesusStage opossumStage chickenStage \n", + "0 e19 e21 12wpc NaN 6.0 e10 \n", + "1 e19 e21 12wpc NaN 6.0 e10 \n", + "2 e19 e21 12wpc NaN 6.0 e10 \n", + "3 e19 e21 12wpc NaN 6.0 e10 \n", + "4 e19 e21 12wpc NaN 6.0 e10 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "my_table.head()" ] @@ -144,19 +441,43 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Let us at first inspect, which organisms are present\n", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Chicken', 'Human', 'Mouse', 'Opossum', 'Rabbit', 'Rat', 'Rhesus'],\n", + " dtype=object)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let us at first inspect which organisms are present\n", "my_table['organism'].unique()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Brain', 'Cerebellum', 'Heart', 'Kidney', 'Liver', 'Ovary',\n", + " 'Testis'], dtype=object)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Which tissues are present?\n", "my_table['tissue'].unique()" @@ -164,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -184,7 +505,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -193,16 +514,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 True\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + "Name: tissue, dtype: bool" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "is_tissue_of_interest.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -211,9 +548,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "Name: organism, dtype: bool" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "is_organism_of_interest.head()" ] @@ -231,9 +584,151 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
organismtissueagegenemedian_RPKMdevelopmentalIndexmouseStageratStagerabbitStagehumanStagerhesusStageopossumStagechickenStage
0ChickenBraine10AGR20.1804597.0e16.5e19e2112wpcNaN6.0e10
1ChickenBraine10AHSA161.0754527.0e16.5e19e2112wpcNaN6.0e10
2ChickenBraine10ANAPC513.1964357.0e16.5e19e2112wpcNaN6.0e10
3ChickenBraine10APPBP223.8240297.0e16.5e19e2112wpcNaN6.0e10
4ChickenBraine10BAG121.4637967.0e16.5e19e2112wpcNaN6.0e10
\n", + "
" + ], + "text/plain": [ + " organism tissue age gene median_RPKM developmentalIndex mouseStage \\\n", + "0 Chicken Brain e10 AGR2 0.180459 7.0 e16.5 \n", + "1 Chicken Brain e10 AHSA1 61.075452 7.0 e16.5 \n", + "2 Chicken Brain e10 ANAPC5 13.196435 7.0 e16.5 \n", + "3 Chicken Brain e10 APPBP2 23.824029 7.0 e16.5 \n", + "4 Chicken Brain e10 BAG1 21.463796 7.0 e16.5 \n", + "\n", + " ratStage rabbitStage humanStage rhesusStage opossumStage chickenStage \n", + "0 e19 e21 12wpc NaN 6.0 e10 \n", + "1 e19 e21 12wpc NaN 6.0 e10 \n", + "2 e19 e21 12wpc NaN 6.0 e10 \n", + "3 e19 e21 12wpc NaN 6.0 e10 \n", + "4 e19 e21 12wpc NaN 6.0 e10 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "my_table.head()" ] @@ -258,7 +753,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -267,9 +762,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "dtype: bool" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "use_these_records.head()" ] @@ -283,23 +794,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(90831, 13)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "my_table.shape" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -308,18 +823,178 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3014, 13)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "filtered_table.shape" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
organismtissueagegenemedian_RPKMdevelopmentalIndexmouseStageratStagerabbitStagehumanStagerhesusStageopossumStagechickenStage
8631HumanBrain10wpcAGR20.204796NaNNaNNaNNaNNaNNaNNaNNaN
8632HumanBrain10wpcAHSA135.900024NaNNaNNaNNaNNaNNaNNaNNaN
8633HumanBrain10wpcANAPC513.215176NaNNaNNaNNaNNaNNaNNaNNaN
8634HumanBrain10wpcAPPBP212.089572NaNNaNNaNNaNNaNNaNNaNNaN
8635HumanBrain10wpcBAG17.810598NaNNaNNaNNaNNaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " organism tissue age gene median_RPKM developmentalIndex \\\n", + "8631 Human Brain 10wpc AGR2 0.204796 NaN \n", + "8632 Human Brain 10wpc AHSA1 35.900024 NaN \n", + "8633 Human Brain 10wpc ANAPC5 13.215176 NaN \n", + "8634 Human Brain 10wpc APPBP2 12.089572 NaN \n", + "8635 Human Brain 10wpc BAG1 7.810598 NaN \n", + "\n", + " mouseStage ratStage rabbitStage humanStage rhesusStage opossumStage \\\n", + "8631 NaN NaN NaN NaN NaN NaN \n", + "8632 NaN NaN NaN NaN NaN NaN \n", + "8633 NaN NaN NaN NaN NaN NaN \n", + "8634 NaN NaN NaN NaN NaN NaN \n", + "8635 NaN NaN NaN NaN NaN NaN \n", + "\n", + " chickenStage \n", + "8631 NaN \n", + "8632 NaN \n", + "8633 NaN \n", + "8634 NaN \n", + "8635 NaN " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "filtered_table.head()" ] @@ -344,37 +1019,282 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
organismtissueagegenemedian_RPKMdevelopmentalIndexmouseStageratStagerabbitStagehumanStagerhesusStageopossumStagechickenStage
8631HumanBrain10wpcAGR20.204796NaNNaNNaNNaNNaNNaNNaNNaN
8632HumanBrain10wpcAHSA135.900024NaNNaNNaNNaNNaNNaNNaNNaN
8633HumanBrain10wpcANAPC513.215176NaNNaNNaNNaNNaNNaNNaNNaN
8634HumanBrain10wpcAPPBP212.089572NaNNaNNaNNaNNaNNaNNaNNaN
8635HumanBrain10wpcBAG17.810598NaNNaNNaNNaNNaNNaNNaNNaN
..........................................
11640HumanBrainyoungMidAgeTXNDC115.05304314.0P63P112P186youngMidAgeP3285120.0P155
11641HumanBrainyoungMidAgeTXNDC50.01176414.0P63P112P186youngMidAgeP3285120.0P155
11642HumanBrainyoungMidAgeUNC45B0.08055914.0P63P112P186youngMidAgeP3285120.0P155
11643HumanBrainyoungMidAgeURI112.52748114.0P63P112P186youngMidAgeP3285120.0P155
11644HumanBrainyoungMidAgeWDTC114.44779614.0P63P112P186youngMidAgeP3285120.0P155
\n", + "

3014 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " organism tissue age gene median_RPKM developmentalIndex \\\n", + "8631 Human Brain 10wpc AGR2 0.204796 NaN \n", + "8632 Human Brain 10wpc AHSA1 35.900024 NaN \n", + "8633 Human Brain 10wpc ANAPC5 13.215176 NaN \n", + "8634 Human Brain 10wpc APPBP2 12.089572 NaN \n", + "8635 Human Brain 10wpc BAG1 7.810598 NaN \n", + "... ... ... ... ... ... ... \n", + "11640 Human Brain youngMidAge TXNDC11 5.053043 14.0 \n", + "11641 Human Brain youngMidAge TXNDC5 0.011764 14.0 \n", + "11642 Human Brain youngMidAge UNC45B 0.080559 14.0 \n", + "11643 Human Brain youngMidAge URI1 12.527481 14.0 \n", + "11644 Human Brain youngMidAge WDTC1 14.447796 14.0 \n", + "\n", + " mouseStage ratStage rabbitStage humanStage rhesusStage opossumStage \\\n", + "8631 NaN NaN NaN NaN NaN NaN \n", + "8632 NaN NaN NaN NaN NaN NaN \n", + "8633 NaN NaN NaN NaN NaN NaN \n", + "8634 NaN NaN NaN NaN NaN NaN \n", + "8635 NaN NaN NaN NaN NaN NaN \n", + "... ... ... ... ... ... ... \n", + "11640 P63 P112 P186 youngMidAge P3285 120.0 \n", + "11641 P63 P112 P186 youngMidAge P3285 120.0 \n", + "11642 P63 P112 P186 youngMidAge P3285 120.0 \n", + "11643 P63 P112 P186 youngMidAge P3285 120.0 \n", + "11644 P63 P112 P186 youngMidAge P3285 120.0 \n", + "\n", + " chickenStage \n", + "8631 NaN \n", + "8632 NaN \n", + "8633 NaN \n", + "8634 NaN \n", + "8635 NaN \n", + "... ... \n", + "11640 P155 \n", + "11641 P155 \n", + "11642 P155 \n", + "11643 P155 \n", + "11644 P155 \n", + "\n", + "[3014 rows x 13 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "filtered_table" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -387,9 +1307,244 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
age10wpc11wpc12wpc13wpc16wpc18wpc19wpc20wpc4wpc5wpc...9wpcinfantnewbornolderMidAgeschoolseniorteenagertoddleryoungAdultyoungMidAge
gene
AGR20.2047960.2874130.7296500.1475060.1250680.0000000.0112050.0000001.8168182.032421...0.0525020.0419510.0807420.0385560.0506570.0077480.0000000.0000000.0089810.024298
AHSA135.90002442.45027725.75203238.35084137.26347938.95378637.16694332.56517539.60348533.717186...41.14493152.79909255.85624956.85861565.43533571.43444366.41361965.53262656.47100256.138286
ANAPC513.21517612.79118215.05891612.66494614.11705713.41955413.18014513.72020112.50736611.914390...11.7426238.04458510.97830713.44757819.91955615.81803512.5416688.88924213.02838912.613188
APPBP212.0895727.62931012.3718118.5537438.7195496.7649858.4440818.4291278.47773410.734288...7.3634765.5081936.1894107.3635018.2253106.0363846.1607564.6522836.4432036.190867
BAG17.81059810.0678336.6169037.7197836.5979576.8771958.1446656.9096067.3343277.294337...10.83724114.53530410.3248368.74711715.22181814.38273610.07805916.25982710.2072009.130678
\n", + "

5 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + "age 10wpc 11wpc 12wpc 13wpc 16wpc 18wpc \\\n", + "gene \n", + "AGR2 0.204796 0.287413 0.729650 0.147506 0.125068 0.000000 \n", + "AHSA1 35.900024 42.450277 25.752032 38.350841 37.263479 38.953786 \n", + "ANAPC5 13.215176 12.791182 15.058916 12.664946 14.117057 13.419554 \n", + "APPBP2 12.089572 7.629310 12.371811 8.553743 8.719549 6.764985 \n", + "BAG1 7.810598 10.067833 6.616903 7.719783 6.597957 6.877195 \n", + "\n", + "age 19wpc 20wpc 4wpc 5wpc ... 9wpc infant \\\n", + "gene ... \n", + "AGR2 0.011205 0.000000 1.816818 2.032421 ... 0.052502 0.041951 \n", + "AHSA1 37.166943 32.565175 39.603485 33.717186 ... 41.144931 52.799092 \n", + "ANAPC5 13.180145 13.720201 12.507366 11.914390 ... 11.742623 8.044585 \n", + "APPBP2 8.444081 8.429127 8.477734 10.734288 ... 7.363476 5.508193 \n", + "BAG1 8.144665 6.909606 7.334327 7.294337 ... 10.837241 14.535304 \n", + "\n", + "age newborn olderMidAge school senior teenager toddler \\\n", + "gene \n", + "AGR2 0.080742 0.038556 0.050657 0.007748 0.000000 0.000000 \n", + "AHSA1 55.856249 56.858615 65.435335 71.434443 66.413619 65.532626 \n", + "ANAPC5 10.978307 13.447578 19.919556 15.818035 12.541668 8.889242 \n", + "APPBP2 6.189410 7.363501 8.225310 6.036384 6.160756 4.652283 \n", + "BAG1 10.324836 8.747117 15.221818 14.382736 10.078059 16.259827 \n", + "\n", + "age youngAdult youngMidAge \n", + "gene \n", + "AGR2 0.008981 0.024298 \n", + "AHSA1 56.471002 56.138286 \n", + "ANAPC5 13.028389 12.613188 \n", + "APPBP2 6.443203 6.190867 \n", + "BAG1 10.207200 9.130678 \n", + "\n", + "[5 rows x 22 columns]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pivotted_table.head()" ] @@ -403,9 +1558,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['10wpc', '11wpc', '12wpc', '13wpc', '16wpc', '18wpc', '19wpc', '20wpc',\n", + " '4wpc', '5wpc', '7wpc', '8wpc', '9wpc', 'infant', 'newborn',\n", + " 'olderMidAge', 'school', 'senior', 'teenager', 'toddler', 'youngAdult',\n", + " 'youngMidAge'],\n", + " dtype='object', name='age')" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pivotted_table.columns" ] @@ -419,7 +1589,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -450,7 +1620,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -459,9 +1629,244 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
age4wpc5wpc7wpc8wpc9wpc10wpc11wpc12wpc13wpc16wpc...20wpcnewborntoddlerinfantschoolteenageryoungAdultyoungMidAgeolderMidAgesenior
gene
AGR21.8168182.0324210.0448690.1094350.0525020.2047960.2874130.7296500.1475060.125068...0.0000000.0807420.0000000.0419510.0506570.0000000.0089810.0242980.0385560.007748
AHSA139.60348533.71718633.95366932.82718041.14493135.90002442.45027725.75203238.35084137.263479...32.56517555.85624965.53262652.79909265.43533566.41361956.47100256.13828656.85861571.434443
ANAPC512.50736611.91439010.21131011.58534111.74262313.21517612.79118215.05891612.66494614.117057...13.72020110.9783078.8892428.04458519.91955612.54166813.02838912.61318813.44757815.818035
APPBP28.47773410.73428810.9453289.3004437.36347612.0895727.62931012.3718118.5537438.719549...8.4291276.1894104.6522835.5081938.2253106.1607566.4432036.1908677.3635016.036384
BAG17.3343277.2943376.7781857.45988710.8372417.81059810.0678336.6169037.7197836.597957...6.90960610.32483616.25982714.53530415.22181810.07805910.2072009.1306788.74711714.382736
\n", + "

5 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + "age 4wpc 5wpc 7wpc 8wpc 9wpc 10wpc \\\n", + "gene \n", + "AGR2 1.816818 2.032421 0.044869 0.109435 0.052502 0.204796 \n", + "AHSA1 39.603485 33.717186 33.953669 32.827180 41.144931 35.900024 \n", + "ANAPC5 12.507366 11.914390 10.211310 11.585341 11.742623 13.215176 \n", + "APPBP2 8.477734 10.734288 10.945328 9.300443 7.363476 12.089572 \n", + "BAG1 7.334327 7.294337 6.778185 7.459887 10.837241 7.810598 \n", + "\n", + "age 11wpc 12wpc 13wpc 16wpc ... 20wpc newborn \\\n", + "gene ... \n", + "AGR2 0.287413 0.729650 0.147506 0.125068 ... 0.000000 0.080742 \n", + "AHSA1 42.450277 25.752032 38.350841 37.263479 ... 32.565175 55.856249 \n", + "ANAPC5 12.791182 15.058916 12.664946 14.117057 ... 13.720201 10.978307 \n", + "APPBP2 7.629310 12.371811 8.553743 8.719549 ... 8.429127 6.189410 \n", + "BAG1 10.067833 6.616903 7.719783 6.597957 ... 6.909606 10.324836 \n", + "\n", + "age toddler infant school teenager youngAdult youngMidAge \\\n", + "gene \n", + "AGR2 0.000000 0.041951 0.050657 0.000000 0.008981 0.024298 \n", + "AHSA1 65.532626 52.799092 65.435335 66.413619 56.471002 56.138286 \n", + "ANAPC5 8.889242 8.044585 19.919556 12.541668 13.028389 12.613188 \n", + "APPBP2 4.652283 5.508193 8.225310 6.160756 6.443203 6.190867 \n", + "BAG1 16.259827 14.535304 15.221818 10.078059 10.207200 9.130678 \n", + "\n", + "age olderMidAge senior \n", + "gene \n", + "AGR2 0.038556 0.007748 \n", + "AHSA1 56.858615 71.434443 \n", + "ANAPC5 13.447578 15.818035 \n", + "APPBP2 7.363501 6.036384 \n", + "BAG1 8.747117 14.382736 \n", + "\n", + "[5 rows x 22 columns]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pivotted_table.head()" ] @@ -475,7 +1880,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -489,7 +1894,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -507,9 +1912,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sns.clustermap(\n", " data=pivotted_table)" @@ -535,7 +1963,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -544,7 +1972,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -555,7 +1983,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -567,9 +1995,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sns.clustermap(\n", " data=z_scored_table)" @@ -584,9 +2035,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sns.clustermap(\n", " data=z_scored_table,\n", @@ -607,16 +2081,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sns.clustermap(\n", " data=z_scored_table,\n", @@ -646,9 +2136,151 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
organismtissueagegenemedian_RPKMdevelopmentalIndexmouseStageratStagerabbitStagehumanStagerhesusStageopossumStagechickenStage
0ChickenBraine10AGR20.1804597.0e16.5e19e2112wpcNaN6.0e10
1ChickenBraine10AHSA161.0754527.0e16.5e19e2112wpcNaN6.0e10
2ChickenBraine10ANAPC513.1964357.0e16.5e19e2112wpcNaN6.0e10
3ChickenBraine10APPBP223.8240297.0e16.5e19e2112wpcNaN6.0e10
4ChickenBraine10BAG121.4637967.0e16.5e19e2112wpcNaN6.0e10
\n", + "
" + ], + "text/plain": [ + " organism tissue age gene median_RPKM developmentalIndex mouseStage \\\n", + "0 Chicken Brain e10 AGR2 0.180459 7.0 e16.5 \n", + "1 Chicken Brain e10 AHSA1 61.075452 7.0 e16.5 \n", + "2 Chicken Brain e10 ANAPC5 13.196435 7.0 e16.5 \n", + "3 Chicken Brain e10 APPBP2 23.824029 7.0 e16.5 \n", + "4 Chicken Brain e10 BAG1 21.463796 7.0 e16.5 \n", + "\n", + " ratStage rabbitStage humanStage rhesusStage opossumStage chickenStage \n", + "0 e19 e21 12wpc NaN 6.0 e10 \n", + "1 e19 e21 12wpc NaN 6.0 e10 \n", + "2 e19 e21 12wpc NaN 6.0 e10 \n", + "3 e19 e21 12wpc NaN 6.0 e10 \n", + "4 e19 e21 12wpc NaN 6.0 e10 " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "my_table.head()" ] @@ -669,7 +2301,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -701,7 +2333,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -710,16 +2342,251 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
age4wpc5wpc7wpc8wpc9wpc10wpc11wpc12wpc13wpc16wpc...20wpcnewborntoddlerinfantschoolteenageryoungAdultyoungMidAgeolderMidAgesenior
gene
AGR21.8168182.0324210.0448690.1094350.0525020.2047960.2874130.7296500.1475060.125068...0.0000000.0807420.0000000.0419510.0506570.0000000.0089810.0242980.0385560.007748
AHSA139.60348533.71718633.95366932.82718041.14493135.90002442.45027725.75203238.35084137.263479...32.56517555.85624965.53262652.79909265.43533566.41361956.47100256.13828656.85861571.434443
ANAPC512.50736611.91439010.21131011.58534111.74262313.21517612.79118215.05891612.66494614.117057...13.72020110.9783078.8892428.04458519.91955612.54166813.02838912.61318813.44757815.818035
APPBP28.47773410.73428810.9453289.3004437.36347612.0895727.62931012.3718118.5537438.719549...8.4291276.1894104.6522835.5081938.2253106.1607566.4432036.1908677.3635016.036384
BAG17.3343277.2943376.7781857.45988710.8372417.81059810.0678336.6169037.7197836.597957...6.90960610.32483616.25982714.53530415.22181810.07805910.2072009.1306788.74711714.382736
\n", + "

5 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + "age 4wpc 5wpc 7wpc 8wpc 9wpc 10wpc \\\n", + "gene \n", + "AGR2 1.816818 2.032421 0.044869 0.109435 0.052502 0.204796 \n", + "AHSA1 39.603485 33.717186 33.953669 32.827180 41.144931 35.900024 \n", + "ANAPC5 12.507366 11.914390 10.211310 11.585341 11.742623 13.215176 \n", + "APPBP2 8.477734 10.734288 10.945328 9.300443 7.363476 12.089572 \n", + "BAG1 7.334327 7.294337 6.778185 7.459887 10.837241 7.810598 \n", + "\n", + "age 11wpc 12wpc 13wpc 16wpc ... 20wpc newborn \\\n", + "gene ... \n", + "AGR2 0.287413 0.729650 0.147506 0.125068 ... 0.000000 0.080742 \n", + "AHSA1 42.450277 25.752032 38.350841 37.263479 ... 32.565175 55.856249 \n", + "ANAPC5 12.791182 15.058916 12.664946 14.117057 ... 13.720201 10.978307 \n", + "APPBP2 7.629310 12.371811 8.553743 8.719549 ... 8.429127 6.189410 \n", + "BAG1 10.067833 6.616903 7.719783 6.597957 ... 6.909606 10.324836 \n", + "\n", + "age toddler infant school teenager youngAdult youngMidAge \\\n", + "gene \n", + "AGR2 0.000000 0.041951 0.050657 0.000000 0.008981 0.024298 \n", + "AHSA1 65.532626 52.799092 65.435335 66.413619 56.471002 56.138286 \n", + "ANAPC5 8.889242 8.044585 19.919556 12.541668 13.028389 12.613188 \n", + "APPBP2 4.652283 5.508193 8.225310 6.160756 6.443203 6.190867 \n", + "BAG1 16.259827 14.535304 15.221818 10.078059 10.207200 9.130678 \n", + "\n", + "age olderMidAge senior \n", + "gene \n", + "AGR2 0.038556 0.007748 \n", + "AHSA1 56.858615 71.434443 \n", + "ANAPC5 13.447578 15.818035 \n", + "APPBP2 7.363501 6.036384 \n", + "BAG1 8.747117 14.382736 \n", + "\n", + "[5 rows x 22 columns]" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pivotted_table.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -728,7 +2595,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -740,21 +2607,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'RPKM of CCT4')" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.plot(x_axis, y_axis)\n", "plt.xlabel('Age (days)', fontsize=20)\n", "plt.ylabel('RPKM of '+ gene_of_interest, fontsize=20)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -773,7 +2656,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.8" } }, "nbformat": 4, diff --git a/03_optional_visualization_exercises.ipynb b/03_optional_visualization_exercises.ipynb index 398368d..5794d44 100644 --- a/03_optional_visualization_exercises.ipynb +++ b/03_optional_visualization_exercises.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -52,12 +52,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# import expression data\n", - "counts = pd.read_csv(\"/Users/tstoeger/Box/chaperome_student_course/2020/material/Human.RPKM.txt\",\n", + "counts = pd.read_csv(\"C://Users/rogan/Downloads/chaperome_student_course/chaperome_student_course/2020/material/Human.RPKM.txt\",\n", " sep = \" \") # <- adapt code here" ] }, @@ -70,9 +70,230 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
indexBrain.4wpc.1Brain.4wpc.2Brain.4wpc.3Brain.5wpc.4Brain.5wpc.5Brain.7wpc.6Brain.7wpc.7Brain.7wpc.8Brain.8wpc.9...Testis.youngTeenager.32Testis.youngTeenager.33Testis.oldTeenager.34Testis.oldTeenager.35Testis.youngAdult.36Testis.youngAdult.37Testis.youngAdult.38Testis.youngMidAge.39Testis.olderMidAge.40Testis.Senior.41
0ENSG0000000000344.20056648.58355254.30391252.56301051.69286632.02211532.41588228.24298617.923539...16.22448212.12721621.49376753.86000030.28317947.02872420.95292932.21146629.94535646.004925
1ENSG000000000050.0348220.0000000.0291860.4921490.0277580.3614600.0310590.0264290.058898...0.5467910.1941430.1001090.0365880.0425990.0697950.1592840.0812550.0497780.023723
2ENSG0000000041930.12163821.98182330.17139034.59846130.88015622.48322433.97216822.17438927.339891...30.08597532.84857932.68923737.28606036.73580746.57999030.32953446.17194636.69592436.674850
3ENSG000000004573.3388273.6586173.3622513.4741433.6852582.4677783.2762392.4320082.565086...1.9204492.4547342.2315251.9596822.3839132.2170962.3356552.0357472.9915442.044109
4ENSG000000004609.3438239.30093910.5900188.31351010.9723115.5049205.7489655.6788053.208603...2.1613493.2959013.8353523.4208943.9344014.9664805.1559732.6641765.4613813.396201
\n", + "

5 rows × 298 columns

\n", + "
" + ], + "text/plain": [ + " index Brain.4wpc.1 Brain.4wpc.2 Brain.4wpc.3 Brain.5wpc.4 \\\n", + "0 ENSG00000000003 44.200566 48.583552 54.303912 52.563010 \n", + "1 ENSG00000000005 0.034822 0.000000 0.029186 0.492149 \n", + "2 ENSG00000000419 30.121638 21.981823 30.171390 34.598461 \n", + "3 ENSG00000000457 3.338827 3.658617 3.362251 3.474143 \n", + "4 ENSG00000000460 9.343823 9.300939 10.590018 8.313510 \n", + "\n", + " Brain.5wpc.5 Brain.7wpc.6 Brain.7wpc.7 Brain.7wpc.8 Brain.8wpc.9 ... \\\n", + "0 51.692866 32.022115 32.415882 28.242986 17.923539 ... \n", + "1 0.027758 0.361460 0.031059 0.026429 0.058898 ... \n", + "2 30.880156 22.483224 33.972168 22.174389 27.339891 ... \n", + "3 3.685258 2.467778 3.276239 2.432008 2.565086 ... \n", + "4 10.972311 5.504920 5.748965 5.678805 3.208603 ... \n", + "\n", + " Testis.youngTeenager.32 Testis.youngTeenager.33 Testis.oldTeenager.34 \\\n", + "0 16.224482 12.127216 21.493767 \n", + "1 0.546791 0.194143 0.100109 \n", + "2 30.085975 32.848579 32.689237 \n", + "3 1.920449 2.454734 2.231525 \n", + "4 2.161349 3.295901 3.835352 \n", + "\n", + " Testis.oldTeenager.35 Testis.youngAdult.36 Testis.youngAdult.37 \\\n", + "0 53.860000 30.283179 47.028724 \n", + "1 0.036588 0.042599 0.069795 \n", + "2 37.286060 36.735807 46.579990 \n", + "3 1.959682 2.383913 2.217096 \n", + "4 3.420894 3.934401 4.966480 \n", + "\n", + " Testis.youngAdult.38 Testis.youngMidAge.39 Testis.olderMidAge.40 \\\n", + "0 20.952929 32.211466 29.945356 \n", + "1 0.159284 0.081255 0.049778 \n", + "2 30.329534 46.171946 36.695924 \n", + "3 2.335655 2.035747 2.991544 \n", + "4 5.155973 2.664176 5.461381 \n", + "\n", + " Testis.Senior.41 \n", + "0 46.004925 \n", + "1 0.023723 \n", + "2 36.674850 \n", + "3 2.044109 \n", + "4 3.396201 \n", + "\n", + "[5 rows x 298 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# move ensembl IDs into a column for merging\n", "counts = counts.reset_index()\n", @@ -81,22 +302,312 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ensembl_gene_idexternal_gene_name
0ENSG00000210049MT-TF
1ENSG00000211459MT-RNR1
2ENSG00000210077MT-TV
3ENSG00000210082MT-RNR2
4ENSG00000209082MT-TL1
\n", + "
" + ], + "text/plain": [ + " ensembl_gene_id external_gene_name\n", + "0 ENSG00000210049 MT-TF\n", + "1 ENSG00000211459 MT-RNR1\n", + "2 ENSG00000210077 MT-TV\n", + "3 ENSG00000210082 MT-RNR2\n", + "4 ENSG00000209082 MT-TL1" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Conversion\n", "# import conversion table\n", - "gene_conversions = pd.read_csv(\"/Users/tstoeger/Box/chaperome_student_course/2020/material/human_gene_name_conversions.csv\") # <- adapt code here\n", + "gene_conversions = pd.read_csv(\"C://Users/rogan/Downloads/chaperome_student_course/chaperome_student_course/2020/material/human_gene_name_conversions.csv\") # <- adapt code here\n", "name_conversions = gene_conversions[[\"ensembl_gene_id\", \"external_gene_name\"]]\n", "name_conversions.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
indexBrain.4wpc.1Brain.4wpc.2Brain.4wpc.3Brain.5wpc.4Brain.5wpc.5Brain.7wpc.6Brain.7wpc.7Brain.7wpc.8Brain.8wpc.9...Testis.oldTeenager.34Testis.oldTeenager.35Testis.youngAdult.36Testis.youngAdult.37Testis.youngAdult.38Testis.youngMidAge.39Testis.olderMidAge.40Testis.Senior.41ensembl_gene_idexternal_gene_name
0ENSG0000000000344.20056648.58355254.30391252.56301051.69286632.02211532.41588228.24298617.923539...21.49376753.86000030.28317947.02872420.95292932.21146629.94535646.004925ENSG00000000003TSPAN6
1ENSG000000000050.0348220.0000000.0291860.4921490.0277580.3614600.0310590.0264290.058898...0.1001090.0365880.0425990.0697950.1592840.0812550.0497780.023723ENSG00000000005TNMD
2ENSG0000000041930.12163821.98182330.17139034.59846130.88015622.48322433.97216822.17438927.339891...32.68923737.28606036.73580746.57999030.32953446.17194636.69592436.674850ENSG00000000419DPM1
3ENSG000000004573.3388273.6586173.3622513.4741433.6852582.4677783.2762392.4320082.565086...2.2315251.9596822.3839132.2170962.3356552.0357472.9915442.044109ENSG00000000457SCYL3
4ENSG000000004609.3438239.30093910.5900188.31351010.9723115.5049205.7489655.6788053.208603...3.8353523.4208943.9344014.9664805.1559732.6641765.4613813.396201ENSG00000000460C1orf112
\n", + "

5 rows × 300 columns

\n", + "
" + ], + "text/plain": [ + " index Brain.4wpc.1 Brain.4wpc.2 Brain.4wpc.3 Brain.5wpc.4 \\\n", + "0 ENSG00000000003 44.200566 48.583552 54.303912 52.563010 \n", + "1 ENSG00000000005 0.034822 0.000000 0.029186 0.492149 \n", + "2 ENSG00000000419 30.121638 21.981823 30.171390 34.598461 \n", + "3 ENSG00000000457 3.338827 3.658617 3.362251 3.474143 \n", + "4 ENSG00000000460 9.343823 9.300939 10.590018 8.313510 \n", + "\n", + " Brain.5wpc.5 Brain.7wpc.6 Brain.7wpc.7 Brain.7wpc.8 Brain.8wpc.9 ... \\\n", + "0 51.692866 32.022115 32.415882 28.242986 17.923539 ... \n", + "1 0.027758 0.361460 0.031059 0.026429 0.058898 ... \n", + "2 30.880156 22.483224 33.972168 22.174389 27.339891 ... \n", + "3 3.685258 2.467778 3.276239 2.432008 2.565086 ... \n", + "4 10.972311 5.504920 5.748965 5.678805 3.208603 ... \n", + "\n", + " Testis.oldTeenager.34 Testis.oldTeenager.35 Testis.youngAdult.36 \\\n", + "0 21.493767 53.860000 30.283179 \n", + "1 0.100109 0.036588 0.042599 \n", + "2 32.689237 37.286060 36.735807 \n", + "3 2.231525 1.959682 2.383913 \n", + "4 3.835352 3.420894 3.934401 \n", + "\n", + " Testis.youngAdult.37 Testis.youngAdult.38 Testis.youngMidAge.39 \\\n", + "0 47.028724 20.952929 32.211466 \n", + "1 0.069795 0.159284 0.081255 \n", + "2 46.579990 30.329534 46.171946 \n", + "3 2.217096 2.335655 2.035747 \n", + "4 4.966480 5.155973 2.664176 \n", + "\n", + " Testis.olderMidAge.40 Testis.Senior.41 ensembl_gene_id \\\n", + "0 29.945356 46.004925 ENSG00000000003 \n", + "1 0.049778 0.023723 ENSG00000000005 \n", + "2 36.695924 36.674850 ENSG00000000419 \n", + "3 2.991544 2.044109 ENSG00000000457 \n", + "4 5.461381 3.396201 ENSG00000000460 \n", + "\n", + " external_gene_name \n", + "0 TSPAN6 \n", + "1 TNMD \n", + "2 DPM1 \n", + "3 SCYL3 \n", + "4 C1orf112 \n", + "\n", + "[5 rows x 300 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# perform merge\n", "counts = pd.merge(counts,\n", @@ -109,9 +620,276 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Brain.4wpc.1Brain.4wpc.2Brain.4wpc.3Brain.5wpc.4Brain.5wpc.5Brain.7wpc.6Brain.7wpc.7Brain.7wpc.8Brain.8wpc.9Brain.8wpc.10...Testis.youngTeenager.32Testis.youngTeenager.33Testis.oldTeenager.34Testis.oldTeenager.35Testis.youngAdult.36Testis.youngAdult.37Testis.youngAdult.38Testis.youngMidAge.39Testis.olderMidAge.40Testis.Senior.41
external_gene_name
TSPAN644.20056648.58355254.30391252.56301051.69286632.02211532.41588228.24298617.92353927.117528...16.22448212.12721621.49376753.86000030.28317947.02872420.95292932.21146629.94535646.004925
TNMD0.0348220.0000000.0291860.4921490.0277580.3614600.0310590.0264290.0588980.093092...0.5467910.1941430.1001090.0365880.0425990.0697950.1592840.0812550.0497780.023723
DPM130.12163821.98182330.17139034.59846130.88015622.48322433.97216822.17438927.33989131.540324...30.08597532.84857932.68923737.28606036.73580746.57999030.32953446.17194636.69592436.674850
SCYL33.3388273.6586173.3622513.4741433.6852582.4677783.2762392.4320082.5650862.826391...1.9204492.4547342.2315251.9596822.3839132.2170962.3356552.0357472.9915442.044109
C1orf1129.3438239.30093910.5900188.31351010.9723115.5049205.7489655.6788053.2086034.190815...2.1613493.2959013.8353523.4208943.9344014.9664805.1559732.6641765.4613813.396201
\n", + "

5 rows × 297 columns

\n", + "
" + ], + "text/plain": [ + " Brain.4wpc.1 Brain.4wpc.2 Brain.4wpc.3 Brain.5wpc.4 \\\n", + "external_gene_name \n", + "TSPAN6 44.200566 48.583552 54.303912 52.563010 \n", + "TNMD 0.034822 0.000000 0.029186 0.492149 \n", + "DPM1 30.121638 21.981823 30.171390 34.598461 \n", + "SCYL3 3.338827 3.658617 3.362251 3.474143 \n", + "C1orf112 9.343823 9.300939 10.590018 8.313510 \n", + "\n", + " Brain.5wpc.5 Brain.7wpc.6 Brain.7wpc.7 Brain.7wpc.8 \\\n", + "external_gene_name \n", + "TSPAN6 51.692866 32.022115 32.415882 28.242986 \n", + "TNMD 0.027758 0.361460 0.031059 0.026429 \n", + "DPM1 30.880156 22.483224 33.972168 22.174389 \n", + "SCYL3 3.685258 2.467778 3.276239 2.432008 \n", + "C1orf112 10.972311 5.504920 5.748965 5.678805 \n", + "\n", + " Brain.8wpc.9 Brain.8wpc.10 ... Testis.youngTeenager.32 \\\n", + "external_gene_name ... \n", + "TSPAN6 17.923539 27.117528 ... 16.224482 \n", + "TNMD 0.058898 0.093092 ... 0.546791 \n", + "DPM1 27.339891 31.540324 ... 30.085975 \n", + "SCYL3 2.565086 2.826391 ... 1.920449 \n", + "C1orf112 3.208603 4.190815 ... 2.161349 \n", + "\n", + " Testis.youngTeenager.33 Testis.oldTeenager.34 \\\n", + "external_gene_name \n", + "TSPAN6 12.127216 21.493767 \n", + "TNMD 0.194143 0.100109 \n", + "DPM1 32.848579 32.689237 \n", + "SCYL3 2.454734 2.231525 \n", + "C1orf112 3.295901 3.835352 \n", + "\n", + " Testis.oldTeenager.35 Testis.youngAdult.36 \\\n", + "external_gene_name \n", + "TSPAN6 53.860000 30.283179 \n", + "TNMD 0.036588 0.042599 \n", + "DPM1 37.286060 36.735807 \n", + "SCYL3 1.959682 2.383913 \n", + "C1orf112 3.420894 3.934401 \n", + "\n", + " Testis.youngAdult.37 Testis.youngAdult.38 \\\n", + "external_gene_name \n", + "TSPAN6 47.028724 20.952929 \n", + "TNMD 0.069795 0.159284 \n", + "DPM1 46.579990 30.329534 \n", + "SCYL3 2.217096 2.335655 \n", + "C1orf112 4.966480 5.155973 \n", + "\n", + " Testis.youngMidAge.39 Testis.olderMidAge.40 \\\n", + "external_gene_name \n", + "TSPAN6 32.211466 29.945356 \n", + "TNMD 0.081255 0.049778 \n", + "DPM1 46.171946 36.695924 \n", + "SCYL3 2.035747 2.991544 \n", + "C1orf112 2.664176 5.461381 \n", + "\n", + " Testis.Senior.41 \n", + "external_gene_name \n", + "TSPAN6 46.004925 \n", + "TNMD 0.023723 \n", + "DPM1 36.674850 \n", + "SCYL3 2.044109 \n", + "C1orf112 3.396201 \n", + "\n", + "[5 rows x 297 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#remove unnecessary columns\n", "counts = counts.set_index(\"external_gene_name\")\n", @@ -148,9 +926,223 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
external_gene_nameTSPAN6TNMDDPM1SCYL3C1orf112FGRCFHFUCA2GCLCNFYA...BX649601.1AL157392.4AL133215.3AL391069.3AL354760.1OR6R2PAL139339.2AC104836.1AC008264.2AC098479.1
Brain.4wpc.144.2005660.03482230.1216383.3388279.3438230.1613790.11014316.6355415.27639529.267179...0.5390670.1625010.0000000.0000000.3487590.00.3839930.0433590.0225390.546068
Brain.4wpc.248.5835520.00000021.9818233.6586179.3009390.1404860.09261513.8552184.01057726.765158...0.7756640.1286030.2971070.0000000.4830110.00.7428430.0343140.0118920.000000
Brain.4wpc.354.3039120.02918630.1713903.36225110.5900180.2028910.05769816.8885024.48629625.214784...0.8214950.1362010.1048870.0000000.2192360.00.2145640.1090240.0377830.915380
Brain.5wpc.452.5630100.49214934.5984613.4741438.3135100.1520550.72790216.0235384.83389924.164240...0.6669700.1701260.0000000.0000000.3651220.00.6253480.0907860.0235970.762252
Brain.5wpc.551.6928660.02775830.8801563.68525810.9723110.1029160.15365316.6069075.04312528.461090...0.7031810.2590790.2992710.1858260.4170240.00.4761610.0691280.0119780.580403
\n", + "

5 rows × 43256 columns

\n", + "
" + ], + "text/plain": [ + "external_gene_name TSPAN6 TNMD DPM1 SCYL3 C1orf112 \\\n", + "Brain.4wpc.1 44.200566 0.034822 30.121638 3.338827 9.343823 \n", + "Brain.4wpc.2 48.583552 0.000000 21.981823 3.658617 9.300939 \n", + "Brain.4wpc.3 54.303912 0.029186 30.171390 3.362251 10.590018 \n", + "Brain.5wpc.4 52.563010 0.492149 34.598461 3.474143 8.313510 \n", + "Brain.5wpc.5 51.692866 0.027758 30.880156 3.685258 10.972311 \n", + "\n", + "external_gene_name FGR CFH FUCA2 GCLC NFYA ... \\\n", + "Brain.4wpc.1 0.161379 0.110143 16.635541 5.276395 29.267179 ... \n", + "Brain.4wpc.2 0.140486 0.092615 13.855218 4.010577 26.765158 ... \n", + "Brain.4wpc.3 0.202891 0.057698 16.888502 4.486296 25.214784 ... \n", + "Brain.5wpc.4 0.152055 0.727902 16.023538 4.833899 24.164240 ... \n", + "Brain.5wpc.5 0.102916 0.153653 16.606907 5.043125 28.461090 ... \n", + "\n", + "external_gene_name BX649601.1 AL157392.4 AL133215.3 AL391069.3 \\\n", + "Brain.4wpc.1 0.539067 0.162501 0.000000 0.000000 \n", + "Brain.4wpc.2 0.775664 0.128603 0.297107 0.000000 \n", + "Brain.4wpc.3 0.821495 0.136201 0.104887 0.000000 \n", + "Brain.5wpc.4 0.666970 0.170126 0.000000 0.000000 \n", + "Brain.5wpc.5 0.703181 0.259079 0.299271 0.185826 \n", + "\n", + "external_gene_name AL354760.1 OR6R2P AL139339.2 AC104836.1 AC008264.2 \\\n", + "Brain.4wpc.1 0.348759 0.0 0.383993 0.043359 0.022539 \n", + "Brain.4wpc.2 0.483011 0.0 0.742843 0.034314 0.011892 \n", + "Brain.4wpc.3 0.219236 0.0 0.214564 0.109024 0.037783 \n", + "Brain.5wpc.4 0.365122 0.0 0.625348 0.090786 0.023597 \n", + "Brain.5wpc.5 0.417024 0.0 0.476161 0.069128 0.011978 \n", + "\n", + "external_gene_name AC098479.1 \n", + "Brain.4wpc.1 0.546068 \n", + "Brain.4wpc.2 0.000000 \n", + "Brain.4wpc.3 0.915380 \n", + "Brain.5wpc.4 0.762252 \n", + "Brain.5wpc.5 0.580403 \n", + "\n", + "[5 rows x 43256 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#transpose\n", "counts_for_pca = counts.transpose()\n", @@ -159,9 +1151,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PC1PC2PC3
0-38.268536-0.966679-97.702824
1-37.890572-1.131437-92.723309
2-33.1399808.716746-85.614197
3-33.501747-2.242143-88.904129
4-39.477144-3.599896-92.192351
\n", + "
" + ], + "text/plain": [ + " PC1 PC2 PC3\n", + "0 -38.268536 -0.966679 -97.702824\n", + "1 -37.890572 -1.131437 -92.723309\n", + "2 -33.139980 8.716746 -85.614197\n", + "3 -33.501747 -2.242143 -88.904129\n", + "4 -39.477144 -3.599896 -92.192351" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#scale from 0 to 1\n", "counts_for_pca_scaled = StandardScaler().fit_transform(counts_for_pca)\n", @@ -185,9 +1252,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " pca_dataframe[\"sample\"][i] = \"Human.\" + pca_dataframe[\"sample\"][i]\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PC1PC2PC3sampleorganismtissueagereplicate
0-38.268536-0.966679-97.702824Human.Brain.4wpc.1HumanBrain4wpc1
1-37.890572-1.131437-92.723309Human.Brain.4wpc.2HumanBrain4wpc2
2-33.1399808.716746-85.614197Human.Brain.4wpc.3HumanBrain4wpc3
3-33.501747-2.242143-88.904129Human.Brain.5wpc.4HumanBrain5wpc4
4-39.477144-3.599896-92.192351Human.Brain.5wpc.5HumanBrain5wpc5
\n", + "
" + ], + "text/plain": [ + " PC1 PC2 PC3 sample organism tissue age \\\n", + "0 -38.268536 -0.966679 -97.702824 Human.Brain.4wpc.1 Human Brain 4wpc \n", + "1 -37.890572 -1.131437 -92.723309 Human.Brain.4wpc.2 Human Brain 4wpc \n", + "2 -33.139980 8.716746 -85.614197 Human.Brain.4wpc.3 Human Brain 4wpc \n", + "3 -33.501747 -2.242143 -88.904129 Human.Brain.5wpc.4 Human Brain 5wpc \n", + "4 -39.477144 -3.599896 -92.192351 Human.Brain.5wpc.5 Human Brain 5wpc \n", + "\n", + " replicate \n", + "0 1 \n", + "1 2 \n", + "2 3 \n", + "3 4 \n", + "4 5 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#add metadata\n", "\n", @@ -197,7 +1387,7 @@ "for i in range(pca_dataframe[\"sample\"].size):\n", " pca_dataframe[\"sample\"][i] = \"Human.\" + pca_dataframe[\"sample\"][i]\n", "\n", - "md = pd.read_csv(\"/Users/tstoeger/Box/chaperome_student_course/2020/material/Kaessman_sample_metadata.csv\") # <- adapt code here\n", + "md = pd.read_csv(\"C://Users/rogan/Downloads/chaperome_student_course/chaperome_student_course/2020/material/Kaessman_sample_metadata.csv\") # <- adapt code here\n", "pca_dataframe = pd.merge(pca_dataframe,\n", " md,\n", " on = \"sample\",\n", @@ -207,9 +1397,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# plot scatter\n", "sns.scatterplot(data = pca_dataframe,\n", @@ -220,9 +1433,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sns.scatterplot(data = pca_dataframe,\n", " x = \"PC2\",\n", @@ -241,9 +1477,123 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PC1PC2
external_gene_name
TSPAN60.135977-0.185401
TNMD0.484861-0.030624
DPM10.348143-0.274536
SCYL30.132537-0.736914
C1orf1120.2855090.300362
.........
AL354760.1-0.0791880.087275
OR6R2P-0.3284930.050069
AL139339.2-0.5093380.657152
AC104836.10.118816-0.153049
AC008264.2-0.2925350.418041
\n", + "

43255 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " PC1 PC2\n", + "external_gene_name \n", + "TSPAN6 0.135977 -0.185401\n", + "TNMD 0.484861 -0.030624\n", + "DPM1 0.348143 -0.274536\n", + "SCYL3 0.132537 -0.736914\n", + "C1orf112 0.285509 0.300362\n", + "... ... ...\n", + "AL354760.1 -0.079188 0.087275\n", + "OR6R2P -0.328493 0.050069\n", + "AL139339.2 -0.509338 0.657152\n", + "AC104836.1 0.118816 -0.153049\n", + "AC008264.2 -0.292535 0.418041\n", + "\n", + "[43255 rows x 2 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "loadings = pca.components_.T * np.sqrt(pca.explained_variance_)\n", "loadings_df = pd.DataFrame(data = loadings[1:,1:],\n", @@ -254,9 +1604,156 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PC1PC2
external_gene_name
CFH0.818612-0.037234
CMA10.8378930.303883
UBR50.8216920.270304
PPP1R370.8521860.195225
ENO30.8150310.060714
TNFSF40.8617540.051518
FBP20.8236720.120468
MATN30.8215420.037063
RFXAP0.8575540.076716
YME1L10.834546-0.078773
RGS30.8082080.125733
DYRK30.835035-0.114168
RETREG20.8529640.076944
PRKACG0.8015620.043441
LHFPL10.8180700.156279
MT-CO20.8068850.051935
MFAP3L0.815303-0.248906
\n", + "
" + ], + "text/plain": [ + " PC1 PC2\n", + "external_gene_name \n", + "CFH 0.818612 -0.037234\n", + "CMA1 0.837893 0.303883\n", + "UBR5 0.821692 0.270304\n", + "PPP1R37 0.852186 0.195225\n", + "ENO3 0.815031 0.060714\n", + "TNFSF4 0.861754 0.051518\n", + "FBP2 0.823672 0.120468\n", + "MATN3 0.821542 0.037063\n", + "RFXAP 0.857554 0.076716\n", + "YME1L1 0.834546 -0.078773\n", + "RGS3 0.808208 0.125733\n", + "DYRK3 0.835035 -0.114168\n", + "RETREG2 0.852964 0.076944\n", + "PRKACG 0.801562 0.043441\n", + "LHFPL1 0.818070 0.156279\n", + "MT-CO2 0.806885 0.051935\n", + "MFAP3L 0.815303 -0.248906" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#get most important genes\n", "important = loadings_df[loadings_df[\"PC1\"] > 0.8]\n", @@ -303,9 +1800,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Brain.10wpc', 'Brain.11wpc', 'Brain.12wpc', 'Brain.13wpc',\n", + " 'Brain.16wpc', 'Brain.18wpc', 'Brain.19wpc', 'Brain.20wpc',\n", + " 'Brain.4wpc', 'Brain.5wpc', 'Brain.7wpc', 'Brain.8wpc',\n", + " 'Brain.9wpc', 'Brain.infant', 'Brain.newborn', 'Brain.olderMidAge',\n", + " 'Brain.school', 'Brain.senior', 'Brain.teenager', 'Brain.toddler',\n", + " 'Brain.youngAdult', 'Brain.youngMidAge', 'Cerebellum.10wpc',\n", + " 'Cerebellum.11wpc', 'Cerebellum.12wpc', 'Cerebellum.13wpc',\n", + " 'Cerebellum.16wpc', 'Cerebellum.4wpc', 'Cerebellum.5wpc',\n", + " 'Cerebellum.6wpc', 'Cerebellum.7wpc', 'Cerebellum.8wpc',\n", + " 'Cerebellum.9wpc', 'Cerebellum.infant', 'Cerebellum.newborn',\n", + " 'Cerebellum.olderMidAge', 'Cerebellum.school', 'Cerebellum.senior',\n", + " 'Cerebellum.teenager', 'Cerebellum.toddler',\n", + " 'Cerebellum.youngAdult', 'Cerebellum.youngMidAge', 'Heart.10wpc',\n", + " 'Heart.11wpc', 'Heart.12wpc', 'Heart.13wpc', 'Heart.16wpc',\n", + " 'Heart.18wpc', 'Heart.19wpc', 'Heart.4wpc', 'Heart.5wpc',\n", + " 'Heart.6wpc', 'Heart.7wpc', 'Heart.8wpc', 'Heart.9wpc',\n", + " 'Heart.infant', 'Heart.newborn', 'Heart.olderMidAge',\n", + " 'Heart.teenager', 'Heart.toddler', 'Heart.youngAdult',\n", + " 'Kidney.10wpc', 'Kidney.11wpc', 'Kidney.12wpc', 'Kidney.13wpc',\n", + " 'Kidney.16wpc', 'Kidney.18wpc', 'Kidney.19wpc', 'Kidney.20wpc',\n", + " 'Kidney.4wpc', 'Kidney.5wpc', 'Kidney.6wpc', 'Kidney.7wpc',\n", + " 'Kidney.8wpc', 'Kidney.9wpc', 'Kidney.infant', 'Kidney.newborn',\n", + " 'Kidney.school', 'Kidney.toddler', 'Liver.10wpc', 'Liver.11wpc',\n", + " 'Liver.12wpc', 'Liver.13wpc', 'Liver.16wpc', 'Liver.18wpc',\n", + " 'Liver.19wpc', 'Liver.20wpc', 'Liver.4wpc', 'Liver.5wpc',\n", + " 'Liver.6wpc', 'Liver.7wpc', 'Liver.8wpc', 'Liver.9wpc',\n", + " 'Liver.infant', 'Liver.newborn', 'Liver.olderMidAge',\n", + " 'Liver.school', 'Liver.senior', 'Liver.teenager', 'Liver.toddler',\n", + " 'Liver.youngAdult', 'Ovary.10wpc', 'Ovary.11wpc', 'Ovary.12wpc',\n", + " 'Ovary.13wpc', 'Ovary.16wpc', 'Ovary.18wpc', 'Ovary.4wpc',\n", + " 'Ovary.5wpc', 'Ovary.6wpc', 'Ovary.7wpc', 'Ovary.8wpc',\n", + " 'Ovary.9wpc', 'Testis.10wpc', 'Testis.11wpc', 'Testis.12wpc',\n", + " 'Testis.13wpc', 'Testis.16wpc', 'Testis.18wpc', 'Testis.19wpc',\n", + " 'Testis.4wpc', 'Testis.5wpc', 'Testis.6wpc', 'Testis.7wpc',\n", + " 'Testis.8wpc', 'Testis.9wpc', 'Testis.Senior', 'Testis.infant',\n", + " 'Testis.oldTeenager', 'Testis.olderMidAge', 'Testis.toddler',\n", + " 'Testis.youngAdult', 'Testis.youngMidAge', 'Testis.youngTeenager'],\n", + " dtype='\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Brain.10wpcBrain.11wpcBrain.12wpcBrain.13wpcBrain.16wpcBrain.18wpcBrain.19wpcBrain.20wpcBrain.4wpcBrain.5wpc...Testis.8wpcTestis.9wpcTestis.SeniorTestis.infantTestis.oldTeenagerTestis.olderMidAgeTestis.toddlerTestis.youngAdultTestis.youngMidAgeTestis.youngTeenager
external_gene_name
TSPAN6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
TNMDNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
DPM1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
SCYL3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
C1orf112NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", + "

5 rows × 134 columns

\n", + "" + ], + "text/plain": [ + " Brain.10wpc Brain.11wpc Brain.12wpc Brain.13wpc \\\n", + "external_gene_name \n", + "TSPAN6 NaN NaN NaN NaN \n", + "TNMD NaN NaN NaN NaN \n", + "DPM1 NaN NaN NaN NaN \n", + "SCYL3 NaN NaN NaN NaN \n", + "C1orf112 NaN NaN NaN NaN \n", + "\n", + " Brain.16wpc Brain.18wpc Brain.19wpc Brain.20wpc Brain.4wpc \\\n", + "external_gene_name \n", + "TSPAN6 NaN NaN NaN NaN NaN \n", + "TNMD NaN NaN NaN NaN NaN \n", + "DPM1 NaN NaN NaN NaN NaN \n", + "SCYL3 NaN NaN NaN NaN NaN \n", + "C1orf112 NaN NaN NaN NaN NaN \n", + "\n", + " Brain.5wpc ... Testis.8wpc Testis.9wpc Testis.Senior \\\n", + "external_gene_name ... \n", + "TSPAN6 NaN ... NaN NaN NaN \n", + "TNMD NaN ... NaN NaN NaN \n", + "DPM1 NaN ... NaN NaN NaN \n", + "SCYL3 NaN ... NaN NaN NaN \n", + "C1orf112 NaN ... NaN NaN NaN \n", + "\n", + " Testis.infant Testis.oldTeenager Testis.olderMidAge \\\n", + "external_gene_name \n", + "TSPAN6 NaN NaN NaN \n", + "TNMD NaN NaN NaN \n", + "DPM1 NaN NaN NaN \n", + "SCYL3 NaN NaN NaN \n", + "C1orf112 NaN NaN NaN \n", + "\n", + " Testis.toddler Testis.youngAdult Testis.youngMidAge \\\n", + "external_gene_name \n", + "TSPAN6 NaN NaN NaN \n", + "TNMD NaN NaN NaN \n", + "DPM1 NaN NaN NaN \n", + "SCYL3 NaN NaN NaN \n", + "C1orf112 NaN NaN NaN \n", + "\n", + " Testis.youngTeenager \n", + "external_gene_name \n", + "TSPAN6 NaN \n", + "TNMD NaN \n", + "DPM1 NaN \n", + "SCYL3 NaN \n", + "C1orf112 NaN \n", + "\n", + "[5 rows x 134 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#now create an empty data frame that we will fill with mean values\n", "mean_counts = pd.DataFrame(columns = mean_cols, index = counts.index)\n", @@ -447,7 +2243,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.8" } }, "nbformat": 4,