cell type assignment

[elengss]

Dear LucasESBS, thank you very much for creating this.
I have been trying to assign cell types but it seems to produce NaN. Do you have any ideas why? Thanks.

celltype_assign = ct.assign_celltypes(cluster_assignment=cluster_assign, ct_pval_df=ct_pval, ct_score_df=ct_score, cutoff=0.1)

Add to anndata object

adata2.obs['scorect'] = celltype_assign
celltype_assign
AAACCTGCAAAGGCGT-1-02_CARTP00G1LI_AMP_ch2_GEX-5pr-v2-0 NaN
AAACCTGCATTTGCCC-1-02_CARTP00G1LI_AMP_ch2_GEX-5pr-v2-0 NaN
AAACCTGGTAGCAAAT-1-02_CARTP00G1LI_AMP_ch2_GEX-5pr-v2-0 NaN
AAACCTGTCATTGCCC-1-02_CARTP00G1LI_AMP_ch2_GEX-5pr-v2-0 NaN
AAACCTGTCTGCAGTA-1-02_CARTP00G1LI_AMP_ch2_GEX-5pr-v2-0 NaN
..
TTTGCGCTCATAACCG-1-09_CARTP00G1LI_BDR_ch2_GEX-5pr-v2-0 NaN
TTTGGTTCAAGCGATG-1-09_CARTP00G1LI_BDR_ch2_GEX-5pr-v2-0 NaN
TTTGGTTCAAGTCATC-1-09_CARTP00G1LI_BDR_ch2_GEX-5pr-v2-0 NaN
TTTGGTTTCGGCGGTT-1-09_CARTP00G1LI_BDR_ch2_GEX-5pr-v2-0 NaN
TTTGTCATCGTCACGG-1-09_CARTP00G1LI_BDR_ch2_GEX-5pr-v2-0 NaN
Name: leiden_res_0.7, Length: 4818, dtype: float64
cluster_assign
AAACCTGCAAAGGCGT-1-02_CARTP00G1LI_AMP_ch2_GEX-5pr-v2-0 11
AAACCTGCATTTGCCC-1-02_CARTP00G1LI_AMP_ch2_GEX-5pr-v2-0 7
AAACCTGGTAGCAAAT-1-02_CARTP00G1LI_AMP_ch2_GEX-5pr-v2-0 11
AAACCTGTCATTGCCC-1-02_CARTP00G1LI_AMP_ch2_GEX-5pr-v2-0 11
AAACCTGTCTGCAGTA-1-02_CARTP00G1LI_AMP_ch2_GEX-5pr-v2-0 3
..
TTTGCGCTCATAACCG-1-09_CARTP00G1LI_BDR_ch2_GEX-5pr-v2-0 0
TTTGGTTCAAGCGATG-1-09_CARTP00G1LI_BDR_ch2_GEX-5pr-v2-0 7
TTTGGTTCAAGTCATC-1-09_CARTP00G1LI_BDR_ch2_GEX-5pr-v2-0 2
TTTGGTTTCGGCGGTT-1-09_CARTP00G1LI_BDR_ch2_GEX-5pr-v2-0 12
TTTGTCATCGTCACGG-1-09_CARTP00G1LI_BDR_ch2_GEX-5pr-v2-0 13
Name: leiden_res_0.7, Length: 4818, dtype: category
Categories (17, int64): [0, 1, 2, 3, ..., 13, 14, 15, 16]
ct_pval
BCells plasma myeloid fibroendo myofibro lamina_propria ... tuft endocrine erythrocytes NK_cells monocytes CustomSet
0 1.204387e-02 4.070538e-10 1.000000e+00 1.000000e+00 1.000000 1.000000e+00 ... 6.347508e-02 1.0 1.0 1.0 1.00000 1.0
1 2.306778e-02 1.000000e+00 1.000000e+00 1.000000e+00 1.000000 1.000000e+00 ... 1.000000e+00 1.0 1.0 1.0 1.00000 1.0
10 2.306778e-02 1.000000e+00 1.515579e-12 1.000000e+00 1.000000 1.000000e+00 ... 1.385330e-02 1.0 1.0 1.0 0.00491 1.0
11 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000 1.000000e+00 ... 1.000000e+00 1.0 1.0 1.0 1.00000 1.0
12 1.000000e+00 1.000000e+00 1.000000e+00 3.435101e-12 1.000000 3.402924e-02 ... 1.000000e+00 1.0 1.0 1.0 1.00000 1.0
13 1.000000e+00 1.000000e+00 1.000000e+00 1.536033e-28 1.000000 8.539887e-06 ... 1.000000e+00 1.0 1.0 1.0 1.00000 1.0
14 3.402924e-02 1.000000e+00 7.647075e-09 1.000000e+00 1.000000 1.000000e+00 ... 2.666548e-04 1.0 1.0 1.0 1.00000 1.0
15 1.000000e+00 1.000000e+00 1.000000e+00 4.871892e-15 0.019492 5.576572e-02 ... 1.000000e+00 1.0 1.0 1.0 1.00000 1.0
16 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000 1.000000e+00 ... 1.031787e-17 1.0 1.0 1.0 1.00000 1.0
2 2.306778e-02 1.000000e+00 1.000000e+00 1.000000e+00 1.000000 1.000000e+00 ... 1.000000e+00 1.0 1.0 1.0 1.00000 1.0
3 2.306778e-02 1.000000e+00 1.000000e+00 1.000000e+00 1.000000 1.000000e+00 ... 1.000000e+00 1.0 1.0 1.0 1.00000 1.0
4 1.000000e+00 1.000000e+00 1.000000e+00 3.901415e-06 1.000000 1.000000e+00 ... 1.000000e+00 1.0 1.0 1.0 1.00000 1.0
5 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000 1.000000e+00 ... 1.000000e+00 1.0 1.0 1.0 1.00000 1.0
6 1.339561e-17 1.000000e+00 1.000000e+00 1.000000e+00 1.000000 1.000000e+00 ... 2.637989e-02 1.0 1.0 1.0 1.00000 1.0
7 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000 1.000000e+00 ... 1.000000e+00 1.0 1.0 1.0 1.00000 1.0
8 1.000000e+00 1.000000e+00 1.000000e+00 1.692935e-35 1.000000 2.657604e-13 ... 1.000000e+00 1.0 1.0 1.0 1.00000 1.0
9 1.000000e+00 1.000000e+00 1.000000e+00 2.309834e-20 0.024291 1.000000e+00 ... 1.000000e+00 1.0 1.0 1.0 1.00000 1.0

[17 rows x 20 columns]

ct_score
BCells plasma myeloid fibroendo myofibro lamina_propria sox6_fibroblasts ... paneth tuft endocrine erythrocytes NK_cells monocytes CustomSet
0 5.0 19.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 0.0
1 4.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0
10 4.0 0.0 28.0 0.0 0.0 0.0 0.0 ... 5.0 5.0 0.0 0.0 0.0 3.0 0.0
11 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0
12 0.0 0.0 0.0 29.0 0.0 3.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0
13 0.0 0.0 0.0 62.0 0.0 11.0 2.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0
14 3.0 0.0 20.0 0.0 0.0 0.0 0.0 ... 0.0 9.0 0.0 0.0 0.0 0.0 0.0
15 0.0 0.0 0.0 35.0 2.0 1.0 5.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0
16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 36.0 0.0 0.0 0.0 0.0 0.0
2 4.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 4.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 0.0 0.0 0.0 15.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0
5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 3.0 0.0 0.0 0.0 0.0 0.0 0.0
6 35.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 4.0 0.0 0.0 0.0 0.0 0.0
7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 3.0 0.0 0.0 0.0 0.0 0.0 0.0
8 0.0 0.0 0.0 75.0 0.0 27.0 4.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0
9 0.0 0.0 0.0 46.0 1.0 0.0 21.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0

[LucasESBS]

Dear elengss,
Sorry for the late reply ! This problem probably arises because the Leiden clusters are formatted as integers. If you want you can just convert this field of the dataframe to a category and it should fix this issue. I will probably add a fix to that caveat soon. Thank you for contributing !