"
]
},
"execution_count": 19,
@@ -1201,43 +1193,43 @@
" 0 | \n",
" 0 | \n",
" census_schema_version | \n",
- " 2.1.0 | \n",
+ " 2.0.1 | \n",
" \n",
" \n",
" 1 | \n",
" 1 | \n",
" census_build_date | \n",
- " 2024-09-02 | \n",
+ " 2024-05-20 | \n",
"
\n",
" \n",
" 2 | \n",
" 2 | \n",
" dataset_schema_version | \n",
- " 5.1.0 | \n",
+ " 5.0.0 | \n",
"
\n",
" \n",
" 3 | \n",
" 3 | \n",
" total_cell_count | \n",
- " 120108090 | \n",
+ " 115556140 | \n",
"
\n",
" \n",
" 4 | \n",
" 4 | \n",
" unique_cell_count | \n",
- " 64161082 | \n",
+ " 60597966 | \n",
"
\n",
" \n",
" 5 | \n",
" 5 | \n",
" number_donors_homo_sapiens | \n",
- " 19993 | \n",
+ " 17651 | \n",
"
\n",
" \n",
" 6 | \n",
" 6 | \n",
" number_donors_mus_musculus | \n",
- " 4698 | \n",
+ " 4216 | \n",
"
\n",
" \n",
"\n",
@@ -1245,13 +1237,13 @@
],
"text/plain": [
" soma_joinid label value\n",
- "0 0 census_schema_version 2.1.0\n",
- "1 1 census_build_date 2024-09-02\n",
- "2 2 dataset_schema_version 5.1.0\n",
- "3 3 total_cell_count 120108090\n",
- "4 4 unique_cell_count 64161082\n",
- "5 5 number_donors_homo_sapiens 19993\n",
- "6 6 number_donors_mus_musculus 4698"
+ "0 0 census_schema_version 2.0.1\n",
+ "1 1 census_build_date 2024-05-20\n",
+ "2 2 dataset_schema_version 5.0.0\n",
+ "3 3 total_cell_count 115556140\n",
+ "4 4 unique_cell_count 60597966\n",
+ "5 5 number_donors_homo_sapiens 17651\n",
+ "6 6 number_donors_mus_musculus 4216"
]
},
"execution_count": 20,
@@ -1314,7 +1306,6 @@
" collection_id | \n",
" collection_name | \n",
" collection_doi | \n",
- " collection_doi_label | \n",
" dataset_id | \n",
" dataset_version_id | \n",
" dataset_title | \n",
@@ -1330,9 +1321,8 @@
" 44531dd9-1388-4416-a117-af0a99de2294 | \n",
" Single-Cell, Single-Nucleus, and Spatial RNA S... | \n",
" 10.1002/hep4.1854 | \n",
- " Andrews et al. (2022) Hepatology Communications | \n",
" 0895c838-e550-48a3-a777-dbcd35d30272 | \n",
- " aaab3abd-624a-442e-b62b-3f2edb10b45e | \n",
+ " fb76c95f-0391-4fac-9fb9-082ce2430b59 | \n",
" Healthy human liver: B cells | \n",
" 0895c838-e550-48a3-a777-dbcd35d30272.h5ad | \n",
" 146 | \n",
@@ -1344,9 +1334,8 @@
" 3a2af25b-2338-4266-aad3-aa8d07473f50 | \n",
" Single-cell analysis of human B cell maturatio... | \n",
" 10.1126/sciimmunol.abe6291 | \n",
- " King et al. (2021) Sci. Immunol. | \n",
" 00ff600e-6e2e-4d76-846f-0eec4f0ae417 | \n",
- " 50c1d621-995d-4386-9fcb-5c70fcdf8d66 | \n",
+ " b6737a5e-9069-4dd6-9a57-92e17a746df9 | \n",
" Human tonsil nonlymphoid cells scRNA | \n",
" 00ff600e-6e2e-4d76-846f-0eec4f0ae417.h5ad | \n",
" 363 | \n",
@@ -1358,9 +1347,8 @@
" 180bff9c-c8a5-4539-b13b-ddbc00d643e6 | \n",
" Molecular characterization of selectively vuln... | \n",
" 10.1038/s41593-020-00764-7 | \n",
- " Leng et al. (2021) Nat Neurosci | \n",
" bdacc907-7c26-419f-8808-969eab3ca2e8 | \n",
- " e95b54b1-8656-4fe8-9f53-6fdd97f397ba | \n",
+ " 0e02290f-b992-450b-8a19-554f73cd7f09 | \n",
" Molecular characterization of selectively vuln... | \n",
" bdacc907-7c26-419f-8808-969eab3ca2e8.h5ad | \n",
" 3799 | \n",
@@ -1372,9 +1360,8 @@
" bf325905-5e8e-42e3-933d-9a9053e9af80 | \n",
" Single-cell Atlas of common variable immunodef... | \n",
" 10.1038/s41467-022-29450-x | \n",
- " Rodríguez-Ubreva et al. (2022) Nat Commun | \n",
" a5d95a42-0137-496f-8a60-101e17f263c8 | \n",
- " d6e742c5-f6e5-42f4-8064-622783542f6b | \n",
+ " 40832710-d7b1-43fb-b2c2-1cd2255bc3ac | \n",
" Steady-state B cells - scRNA-seq | \n",
" a5d95a42-0137-496f-8a60-101e17f263c8.h5ad | \n",
" 1324 | \n",
@@ -1386,9 +1373,8 @@
" 93eebe82-d8c3-41bc-a906-63b5b5f24a9d | \n",
" Single-cell proteo-genomic reference maps of t... | \n",
" 10.1038/s41590-021-01059-0 | \n",
- " Triana et al. (2021) Nat Immunol | \n",
" d3566d6a-a455-4a15-980f-45eb29114cab | \n",
- " 61f15353-e598-43b5-bb5a-80ac44a0cf0b | \n",
+ " eb6c070c-ff67-4c1f-8d4d-65f9fe2119ee | \n",
" blood and bone marrow from a healthy young donor | \n",
" d3566d6a-a455-4a15-980f-45eb29114cab.h5ad | \n",
" 15502 | \n",
@@ -1405,81 +1391,75 @@
" ... | \n",
" ... | \n",
" ... | \n",
- " ... | \n",
" \n",
" \n",
- " 895 | \n",
- " 895 | \n",
+ " 807 | \n",
+ " 807 | \n",
" Publication: https://doi.org/10.1038/s41586-02... | \n",
" 45d5d2c3-bc28-4814-aed6-0bb6f0e11c82 | \n",
" A single-cell transcriptional timelapse of mou... | \n",
" 10.1038/s41586-024-07069-w | \n",
- " Qiu et al. (2024) Nature | \n",
" 0bce33ed-455c-4e12-93f8-b7b04a2de4a1 | \n",
- " fa49086f-0e18-4f4b-908d-beefdb00ac3e | \n",
+ " ffeb40f8-d4b9-45c4-95cc-5e2674452ef8 | \n",
" Whole dataset: Normalized subset 2 | \n",
" 0bce33ed-455c-4e12-93f8-b7b04a2de4a1.h5ad | \n",
" 2863559 | \n",
"
\n",
" \n",
- " 896 | \n",
- " 896 | \n",
+ " 808 | \n",
+ " 808 | \n",
" Publication: https://doi.org/10.1101/2023.05.0... | \n",
" 1ca90a2d-2943-483d-b678-b809bf464c30 | \n",
" SEA-AD: Seattle Alzheimer’s Disease Brain Cell... | \n",
" 10.1101/2023.05.08.539485 | \n",
- " Gabitto et al. (2023) bioRxiv | \n",
" c2876b1b-06d8-4d96-a56b-5304f815b99a | \n",
- " 291ce735-8d18-4a2f-a6bc-98f75f8d6bc0 | \n",
+ " 77dab54a-f2a8-42fc-8c1b-3fda90622ac7 | \n",
" Whole Taxonomy - MTG: Seattle Alzheimer's Dise... | \n",
" c2876b1b-06d8-4d96-a56b-5304f815b99a.h5ad | \n",
" 1226855 | \n",
"
\n",
" \n",
- " 897 | \n",
- " 897 | \n",
+ " 809 | \n",
+ " 809 | \n",
" Publication: https://doi.org/10.1101/2023.05.0... | \n",
" 1ca90a2d-2943-483d-b678-b809bf464c30 | \n",
" SEA-AD: Seattle Alzheimer’s Disease Brain Cell... | \n",
" 10.1101/2023.05.08.539485 | \n",
- " Gabitto et al. (2023) bioRxiv | \n",
" 6f7fd0f1-a2ed-4ff1-80d3-33dde731cbc3 | \n",
- " e9bffe1d-9f07-4467-9230-c080b362e737 | \n",
+ " b0cbf861-edd3-4add-a09a-c8698ed0cedf | \n",
" Whole Taxonomy - DLPFC: Seattle Alzheimer's Di... | \n",
" 6f7fd0f1-a2ed-4ff1-80d3-33dde731cbc3.h5ad | \n",
" 1309414 | \n",
"
\n",
" \n",
- " 898 | \n",
- " 898 | \n",
+ " 810 | \n",
+ " 810 | \n",
" Publication: https://doi.org/10.1038/s41586-02... | \n",
" 45d5d2c3-bc28-4814-aed6-0bb6f0e11c82 | \n",
" A single-cell transcriptional timelapse of mou... | \n",
" 10.1038/s41586-024-07069-w | \n",
- " Qiu et al. (2024) Nature | \n",
" dcfa2614-7ca7-4d82-814c-350626eccb26 | \n",
- " b8cfe635-1cf6-4a6d-8bc0-5059477b9a8c | \n",
+ " 4ef3a829-b36e-413f-9a32-56f5a91b1041 | \n",
" Major cell cluster: Mesoderm | \n",
" dcfa2614-7ca7-4d82-814c-350626eccb26.h5ad | \n",
" 3267338 | \n",
"
\n",
" \n",
- " 899 | \n",
- " 899 | \n",
+ " 811 | \n",
+ " 811 | \n",
" Publication: https://doi.org/10.1038/s41586-02... | \n",
" 45d5d2c3-bc28-4814-aed6-0bb6f0e11c82 | \n",
" A single-cell transcriptional timelapse of mou... | \n",
" 10.1038/s41586-024-07069-w | \n",
- " Qiu et al. (2024) Nature | \n",
" dcfd4feb-18a3-4b30-81d7-1b0c544a8ab3 | \n",
- " 2d4c483c-0495-4747-8ab5-c21a955852f8 | \n",
+ " 9da4d19f-f6ac-4bf0-a47e-2935b1164569 | \n",
" Whole dataset: Raw counts only | \n",
" dcfd4feb-18a3-4b30-81d7-1b0c544a8ab3.h5ad | \n",
" 11441407 | \n",
"
\n",
" \n",
"\n",
- "900 rows × 11 columns
\n",
+ "812 rows × 10 columns
\n",
""
],
"text/plain": [
@@ -1490,11 +1470,11 @@
"3 3 Publication: https://doi.org/10.1038/s41467-02... \n",
"4 4 Publication: https://doi.org/10.1038/s41590-02... \n",
".. ... ... \n",
- "895 895 Publication: https://doi.org/10.1038/s41586-02... \n",
- "896 896 Publication: https://doi.org/10.1101/2023.05.0... \n",
- "897 897 Publication: https://doi.org/10.1101/2023.05.0... \n",
- "898 898 Publication: https://doi.org/10.1038/s41586-02... \n",
- "899 899 Publication: https://doi.org/10.1038/s41586-02... \n",
+ "807 807 Publication: https://doi.org/10.1038/s41586-02... \n",
+ "808 808 Publication: https://doi.org/10.1101/2023.05.0... \n",
+ "809 809 Publication: https://doi.org/10.1101/2023.05.0... \n",
+ "810 810 Publication: https://doi.org/10.1038/s41586-02... \n",
+ "811 811 Publication: https://doi.org/10.1038/s41586-02... \n",
"\n",
" collection_id \\\n",
"0 44531dd9-1388-4416-a117-af0a99de2294 \n",
@@ -1503,11 +1483,11 @@
"3 bf325905-5e8e-42e3-933d-9a9053e9af80 \n",
"4 93eebe82-d8c3-41bc-a906-63b5b5f24a9d \n",
".. ... \n",
- "895 45d5d2c3-bc28-4814-aed6-0bb6f0e11c82 \n",
- "896 1ca90a2d-2943-483d-b678-b809bf464c30 \n",
- "897 1ca90a2d-2943-483d-b678-b809bf464c30 \n",
- "898 45d5d2c3-bc28-4814-aed6-0bb6f0e11c82 \n",
- "899 45d5d2c3-bc28-4814-aed6-0bb6f0e11c82 \n",
+ "807 45d5d2c3-bc28-4814-aed6-0bb6f0e11c82 \n",
+ "808 1ca90a2d-2943-483d-b678-b809bf464c30 \n",
+ "809 1ca90a2d-2943-483d-b678-b809bf464c30 \n",
+ "810 45d5d2c3-bc28-4814-aed6-0bb6f0e11c82 \n",
+ "811 45d5d2c3-bc28-4814-aed6-0bb6f0e11c82 \n",
"\n",
" collection_name \\\n",
"0 Single-Cell, Single-Nucleus, and Spatial RNA S... \n",
@@ -1516,63 +1496,37 @@
"3 Single-cell Atlas of common variable immunodef... \n",
"4 Single-cell proteo-genomic reference maps of t... \n",
".. ... \n",
- "895 A single-cell transcriptional timelapse of mou... \n",
- "896 SEA-AD: Seattle Alzheimer’s Disease Brain Cell... \n",
- "897 SEA-AD: Seattle Alzheimer’s Disease Brain Cell... \n",
- "898 A single-cell transcriptional timelapse of mou... \n",
- "899 A single-cell transcriptional timelapse of mou... \n",
+ "807 A single-cell transcriptional timelapse of mou... \n",
+ "808 SEA-AD: Seattle Alzheimer’s Disease Brain Cell... \n",
+ "809 SEA-AD: Seattle Alzheimer’s Disease Brain Cell... \n",
+ "810 A single-cell transcriptional timelapse of mou... \n",
+ "811 A single-cell transcriptional timelapse of mou... \n",
"\n",
- " collection_doi \\\n",
- "0 10.1002/hep4.1854 \n",
- "1 10.1126/sciimmunol.abe6291 \n",
- "2 10.1038/s41593-020-00764-7 \n",
- "3 10.1038/s41467-022-29450-x \n",
- "4 10.1038/s41590-021-01059-0 \n",
- ".. ... \n",
- "895 10.1038/s41586-024-07069-w \n",
- "896 10.1101/2023.05.08.539485 \n",
- "897 10.1101/2023.05.08.539485 \n",
- "898 10.1038/s41586-024-07069-w \n",
- "899 10.1038/s41586-024-07069-w \n",
- "\n",
- " collection_doi_label \\\n",
- "0 Andrews et al. (2022) Hepatology Communications \n",
- "1 King et al. (2021) Sci. Immunol. \n",
- "2 Leng et al. (2021) Nat Neurosci \n",
- "3 Rodríguez-Ubreva et al. (2022) Nat Commun \n",
- "4 Triana et al. (2021) Nat Immunol \n",
- ".. ... \n",
- "895 Qiu et al. (2024) Nature \n",
- "896 Gabitto et al. (2023) bioRxiv \n",
- "897 Gabitto et al. (2023) bioRxiv \n",
- "898 Qiu et al. (2024) Nature \n",
- "899 Qiu et al. (2024) Nature \n",
- "\n",
- " dataset_id \\\n",
- "0 0895c838-e550-48a3-a777-dbcd35d30272 \n",
- "1 00ff600e-6e2e-4d76-846f-0eec4f0ae417 \n",
- "2 bdacc907-7c26-419f-8808-969eab3ca2e8 \n",
- "3 a5d95a42-0137-496f-8a60-101e17f263c8 \n",
- "4 d3566d6a-a455-4a15-980f-45eb29114cab \n",
- ".. ... \n",
- "895 0bce33ed-455c-4e12-93f8-b7b04a2de4a1 \n",
- "896 c2876b1b-06d8-4d96-a56b-5304f815b99a \n",
- "897 6f7fd0f1-a2ed-4ff1-80d3-33dde731cbc3 \n",
- "898 dcfa2614-7ca7-4d82-814c-350626eccb26 \n",
- "899 dcfd4feb-18a3-4b30-81d7-1b0c544a8ab3 \n",
+ " collection_doi dataset_id \\\n",
+ "0 10.1002/hep4.1854 0895c838-e550-48a3-a777-dbcd35d30272 \n",
+ "1 10.1126/sciimmunol.abe6291 00ff600e-6e2e-4d76-846f-0eec4f0ae417 \n",
+ "2 10.1038/s41593-020-00764-7 bdacc907-7c26-419f-8808-969eab3ca2e8 \n",
+ "3 10.1038/s41467-022-29450-x a5d95a42-0137-496f-8a60-101e17f263c8 \n",
+ "4 10.1038/s41590-021-01059-0 d3566d6a-a455-4a15-980f-45eb29114cab \n",
+ ".. ... ... \n",
+ "807 10.1038/s41586-024-07069-w 0bce33ed-455c-4e12-93f8-b7b04a2de4a1 \n",
+ "808 10.1101/2023.05.08.539485 c2876b1b-06d8-4d96-a56b-5304f815b99a \n",
+ "809 10.1101/2023.05.08.539485 6f7fd0f1-a2ed-4ff1-80d3-33dde731cbc3 \n",
+ "810 10.1038/s41586-024-07069-w dcfa2614-7ca7-4d82-814c-350626eccb26 \n",
+ "811 10.1038/s41586-024-07069-w dcfd4feb-18a3-4b30-81d7-1b0c544a8ab3 \n",
"\n",
" dataset_version_id \\\n",
- "0 aaab3abd-624a-442e-b62b-3f2edb10b45e \n",
- "1 50c1d621-995d-4386-9fcb-5c70fcdf8d66 \n",
- "2 e95b54b1-8656-4fe8-9f53-6fdd97f397ba \n",
- "3 d6e742c5-f6e5-42f4-8064-622783542f6b \n",
- "4 61f15353-e598-43b5-bb5a-80ac44a0cf0b \n",
+ "0 fb76c95f-0391-4fac-9fb9-082ce2430b59 \n",
+ "1 b6737a5e-9069-4dd6-9a57-92e17a746df9 \n",
+ "2 0e02290f-b992-450b-8a19-554f73cd7f09 \n",
+ "3 40832710-d7b1-43fb-b2c2-1cd2255bc3ac \n",
+ "4 eb6c070c-ff67-4c1f-8d4d-65f9fe2119ee \n",
".. ... \n",
- "895 fa49086f-0e18-4f4b-908d-beefdb00ac3e \n",
- "896 291ce735-8d18-4a2f-a6bc-98f75f8d6bc0 \n",
- "897 e9bffe1d-9f07-4467-9230-c080b362e737 \n",
- "898 b8cfe635-1cf6-4a6d-8bc0-5059477b9a8c \n",
- "899 2d4c483c-0495-4747-8ab5-c21a955852f8 \n",
+ "807 ffeb40f8-d4b9-45c4-95cc-5e2674452ef8 \n",
+ "808 77dab54a-f2a8-42fc-8c1b-3fda90622ac7 \n",
+ "809 b0cbf861-edd3-4add-a09a-c8698ed0cedf \n",
+ "810 4ef3a829-b36e-413f-9a32-56f5a91b1041 \n",
+ "811 9da4d19f-f6ac-4bf0-a47e-2935b1164569 \n",
"\n",
" dataset_title \\\n",
"0 Healthy human liver: B cells \n",
@@ -1581,11 +1535,11 @@
"3 Steady-state B cells - scRNA-seq \n",
"4 blood and bone marrow from a healthy young donor \n",
".. ... \n",
- "895 Whole dataset: Normalized subset 2 \n",
- "896 Whole Taxonomy - MTG: Seattle Alzheimer's Dise... \n",
- "897 Whole Taxonomy - DLPFC: Seattle Alzheimer's Di... \n",
- "898 Major cell cluster: Mesoderm \n",
- "899 Whole dataset: Raw counts only \n",
+ "807 Whole dataset: Normalized subset 2 \n",
+ "808 Whole Taxonomy - MTG: Seattle Alzheimer's Dise... \n",
+ "809 Whole Taxonomy - DLPFC: Seattle Alzheimer's Di... \n",
+ "810 Major cell cluster: Mesoderm \n",
+ "811 Whole dataset: Raw counts only \n",
"\n",
" dataset_h5ad_path dataset_total_cell_count \n",
"0 0895c838-e550-48a3-a777-dbcd35d30272.h5ad 146 \n",
@@ -1594,13 +1548,13 @@
"3 a5d95a42-0137-496f-8a60-101e17f263c8.h5ad 1324 \n",
"4 d3566d6a-a455-4a15-980f-45eb29114cab.h5ad 15502 \n",
".. ... ... \n",
- "895 0bce33ed-455c-4e12-93f8-b7b04a2de4a1.h5ad 2863559 \n",
- "896 c2876b1b-06d8-4d96-a56b-5304f815b99a.h5ad 1226855 \n",
- "897 6f7fd0f1-a2ed-4ff1-80d3-33dde731cbc3.h5ad 1309414 \n",
- "898 dcfa2614-7ca7-4d82-814c-350626eccb26.h5ad 3267338 \n",
- "899 dcfd4feb-18a3-4b30-81d7-1b0c544a8ab3.h5ad 11441407 \n",
+ "807 0bce33ed-455c-4e12-93f8-b7b04a2de4a1.h5ad 2863559 \n",
+ "808 c2876b1b-06d8-4d96-a56b-5304f815b99a.h5ad 1226855 \n",
+ "809 6f7fd0f1-a2ed-4ff1-80d3-33dde731cbc3.h5ad 1309414 \n",
+ "810 dcfa2614-7ca7-4d82-814c-350626eccb26.h5ad 3267338 \n",
+ "811 dcfd4feb-18a3-4b30-81d7-1b0c544a8ab3.h5ad 11441407 \n",
"\n",
- "[900 rows x 11 columns]"
+ "[812 rows x 10 columns]"
]
},
"execution_count": 21,
@@ -1675,8 +1629,8 @@
" ENSG00000000003 | \n",
" TSPAN6 | \n",
" 4530 | \n",
- " 4811135 | \n",
- " 77128603 | \n",
+ " 4530448 | \n",
+ " 73855064 | \n",
" \n",
" \n",
" 1 | \n",
@@ -1684,8 +1638,8 @@
" ENSG00000000005 | \n",
" TNMD | \n",
" 1476 | \n",
- " 269136 | \n",
- " 64017621 | \n",
+ " 236059 | \n",
+ " 61201828 | \n",
"
\n",
" \n",
" 2 | \n",
@@ -1693,8 +1647,8 @@
" ENSG00000000419 | \n",
" DPM1 | \n",
" 9276 | \n",
- " 18420588 | \n",
- " 77502438 | \n",
+ " 17576462 | \n",
+ " 74159149 | \n",
"
\n",
" \n",
" 3 | \n",
@@ -1702,8 +1656,8 @@
" ENSG00000000457 | \n",
" SCYL3 | \n",
" 6883 | \n",
- " 9268173 | \n",
- " 76952827 | \n",
+ " 9117322 | \n",
+ " 73988868 | \n",
"
\n",
" \n",
" 4 | \n",
@@ -1711,8 +1665,8 @@
" ENSG00000000460 | \n",
" C1orf112 | \n",
" 5970 | \n",
- " 6484239 | \n",
- " 76979490 | \n",
+ " 6287794 | \n",
+ " 73636201 | \n",
"
\n",
" \n",
" ... | \n",
@@ -1724,8 +1678,8 @@
" ... | \n",
"
\n",
" \n",
- " 60532 | \n",
- " 60532 | \n",
+ " 60525 | \n",
+ " 60525 | \n",
" ENSG00000288718 | \n",
" ENSG00000288718.1 | \n",
" 1070 | \n",
@@ -1733,8 +1687,8 @@
" 1248980 | \n",
"
\n",
" \n",
- " 60533 | \n",
- " 60533 | \n",
+ " 60526 | \n",
+ " 60526 | \n",
" ENSG00000288719 | \n",
" ENSG00000288719.1 | \n",
" 4252 | \n",
@@ -1742,8 +1696,8 @@
" 1248980 | \n",
"
\n",
" \n",
- " 60534 | \n",
- " 60534 | \n",
+ " 60527 | \n",
+ " 60527 | \n",
" ENSG00000288724 | \n",
" ENSG00000288724.1 | \n",
" 625 | \n",
@@ -1751,26 +1705,26 @@
" 1248980 | \n",
"
\n",
" \n",
- " 60535 | \n",
- " 60535 | \n",
- " ENSG00000290735 | \n",
- " ENSG00000290735.1 | \n",
- " 4103 | \n",
- " 128 | \n",
- " 49359 | \n",
+ " 60528 | \n",
+ " 60528 | \n",
+ " ENSG00000290791 | \n",
+ " ENSG00000290791.1 | \n",
+ " 3612 | \n",
+ " 1642 | \n",
+ " 43485 | \n",
"
\n",
" \n",
- " 60536 | \n",
- " 60536 | \n",
- " ENSG00000289810 | \n",
- " ENSG00000289810.1 | \n",
- " 1205 | \n",
- " 82 | \n",
- " 48478 | \n",
+ " 60529 | \n",
+ " 60529 | \n",
+ " ENSG00000290146 | \n",
+ " ENSG00000290146.1 | \n",
+ " 1292 | \n",
+ " 7958 | \n",
+ " 43485 | \n",
"
\n",
" \n",
"\n",
- "60537 rows × 6 columns
\n",
+ "60530 rows × 6 columns
\n",
""
],
"text/plain": [
@@ -1781,26 +1735,26 @@
"3 3 ENSG00000000457 SCYL3 6883 \n",
"4 4 ENSG00000000460 C1orf112 5970 \n",
"... ... ... ... ... \n",
- "60532 60532 ENSG00000288718 ENSG00000288718.1 1070 \n",
- "60533 60533 ENSG00000288719 ENSG00000288719.1 4252 \n",
- "60534 60534 ENSG00000288724 ENSG00000288724.1 625 \n",
- "60535 60535 ENSG00000290735 ENSG00000290735.1 4103 \n",
- "60536 60536 ENSG00000289810 ENSG00000289810.1 1205 \n",
+ "60525 60525 ENSG00000288718 ENSG00000288718.1 1070 \n",
+ "60526 60526 ENSG00000288719 ENSG00000288719.1 4252 \n",
+ "60527 60527 ENSG00000288724 ENSG00000288724.1 625 \n",
+ "60528 60528 ENSG00000290791 ENSG00000290791.1 3612 \n",
+ "60529 60529 ENSG00000290146 ENSG00000290146.1 1292 \n",
"\n",
" nnz n_measured_obs \n",
- "0 4811135 77128603 \n",
- "1 269136 64017621 \n",
- "2 18420588 77502438 \n",
- "3 9268173 76952827 \n",
- "4 6484239 76979490 \n",
+ "0 4530448 73855064 \n",
+ "1 236059 61201828 \n",
+ "2 17576462 74159149 \n",
+ "3 9117322 73988868 \n",
+ "4 6287794 73636201 \n",
"... ... ... \n",
- "60532 4 1248980 \n",
- "60533 2826 1248980 \n",
- "60534 36 1248980 \n",
- "60535 128 49359 \n",
- "60536 82 48478 \n",
+ "60525 4 1248980 \n",
+ "60526 2826 1248980 \n",
+ "60527 36 1248980 \n",
+ "60528 1642 43485 \n",
+ "60529 7958 43485 \n",
"\n",
- "[60537 rows x 6 columns]"
+ "[60530 rows x 6 columns]"
]
},
"execution_count": 22,
@@ -2043,8 +1997,8 @@
" dc30c3ec-46d6-4cd8-8ec1-b544a3d0f503 | \n",
" 10x 3' v2 | \n",
" EFO:0009899 | \n",
- " neuron | \n",
- " CL:0000540 | \n",
+ " oligodendrocyte | \n",
+ " CL:0000128 | \n",
" 44-year-old human stage | \n",
" HsapDv:0000138 | \n",
" multiple sclerosis | \n",
@@ -2055,10 +2009,10 @@
" tissue | \n",
" brain | \n",
" UBERON:0000955 | \n",
- " 2105.0 | \n",
- " 1357 | \n",
- " 1.551216 | \n",
- " 4.489447 | \n",
+ " 595.0 | \n",
+ " 472 | \n",
+ " 1.260593 | \n",
+ " 0.613476 | \n",
" 20383 | \n",
" \n",
" \n",
@@ -2067,8 +2021,8 @@
" dc30c3ec-46d6-4cd8-8ec1-b544a3d0f503 | \n",
" 10x 3' v2 | \n",
" EFO:0009899 | \n",
- " neuron | \n",
- " CL:0000540 | \n",
+ " oligodendrocyte | \n",
+ " CL:0000128 | \n",
" 44-year-old human stage | \n",
" HsapDv:0000138 | \n",
" multiple sclerosis | \n",
@@ -2079,10 +2033,10 @@
" tissue | \n",
" brain | \n",
" UBERON:0000955 | \n",
- " 1791.0 | \n",
- " 1168 | \n",
- " 1.533390 | \n",
- " 2.197684 | \n",
+ " 669.0 | \n",
+ " 506 | \n",
+ " 1.322134 | \n",
+ " 1.145529 | \n",
" 20383 | \n",
"
\n",
" \n",
@@ -2091,8 +2045,8 @@
" dc30c3ec-46d6-4cd8-8ec1-b544a3d0f503 | \n",
" 10x 3' v2 | \n",
" EFO:0009899 | \n",
- " neuron | \n",
- " CL:0000540 | \n",
+ " oligodendrocyte | \n",
+ " CL:0000128 | \n",
" 44-year-old human stage | \n",
" HsapDv:0000138 | \n",
" multiple sclerosis | \n",
@@ -2103,10 +2057,10 @@
" tissue | \n",
" brain | \n",
" UBERON:0000955 | \n",
- " 4989.0 | \n",
- " 2761 | \n",
- " 1.806954 | \n",
- " 4.679024 | \n",
+ " 882.0 | \n",
+ " 675 | \n",
+ " 1.306667 | \n",
+ " 1.856855 | \n",
" 20383 | \n",
"
\n",
" \n",
@@ -2115,8 +2069,8 @@
" dc30c3ec-46d6-4cd8-8ec1-b544a3d0f503 | \n",
" 10x 3' v2 | \n",
" EFO:0009899 | \n",
- " neuron | \n",
- " CL:0000540 | \n",
+ " oligodendrocyte | \n",
+ " CL:0000128 | \n",
" 44-year-old human stage | \n",
" HsapDv:0000138 | \n",
" multiple sclerosis | \n",
@@ -2127,10 +2081,10 @@
" tissue | \n",
" brain | \n",
" UBERON:0000955 | \n",
- " 5201.0 | \n",
- " 2811 | \n",
- " 1.850231 | \n",
- " 4.767241 | \n",
+ " 623.0 | \n",
+ " 509 | \n",
+ " 1.223969 | \n",
+ " 0.433991 | \n",
" 20383 | \n",
"
\n",
" \n",
@@ -2139,8 +2093,8 @@
" dc30c3ec-46d6-4cd8-8ec1-b544a3d0f503 | \n",
" 10x 3' v2 | \n",
" EFO:0009899 | \n",
- " neuron | \n",
- " CL:0000540 | \n",
+ " oligodendrocyte | \n",
+ " CL:0000128 | \n",
" 44-year-old human stage | \n",
" HsapDv:0000138 | \n",
" multiple sclerosis | \n",
@@ -2151,10 +2105,10 @@
" tissue | \n",
" brain | \n",
" UBERON:0000955 | \n",
- " 5850.0 | \n",
- " 3008 | \n",
- " 1.944814 | \n",
- " 8.650096 | \n",
+ " 647.0 | \n",
+ " 516 | \n",
+ " 1.253876 | \n",
+ " 0.931538 | \n",
" 20383 | \n",
"
\n",
" \n",
@@ -2176,18 +2130,18 @@
"107372 286425 dc30c3ec-46d6-4cd8-8ec1-b544a3d0f503 10x 3' v2 \n",
"107373 286426 dc30c3ec-46d6-4cd8-8ec1-b544a3d0f503 10x 3' v2 \n",
"\n",
- " assay_ontology_term_id cell_type cell_type_ontology_term_id \\\n",
- "0 EFO:0008931 naive B cell CL:0000788 \n",
- "1 EFO:0008931 naive B cell CL:0000788 \n",
- "2 EFO:0008931 naive B cell CL:0000788 \n",
- "3 EFO:0008931 naive B cell CL:0000788 \n",
- "4 EFO:0008931 naive B cell CL:0000788 \n",
- "... ... ... ... \n",
- "107369 EFO:0009899 neuron CL:0000540 \n",
- "107370 EFO:0009899 neuron CL:0000540 \n",
- "107371 EFO:0009899 neuron CL:0000540 \n",
- "107372 EFO:0009899 neuron CL:0000540 \n",
- "107373 EFO:0009899 neuron CL:0000540 \n",
+ " assay_ontology_term_id cell_type cell_type_ontology_term_id \\\n",
+ "0 EFO:0008931 naive B cell CL:0000788 \n",
+ "1 EFO:0008931 naive B cell CL:0000788 \n",
+ "2 EFO:0008931 naive B cell CL:0000788 \n",
+ "3 EFO:0008931 naive B cell CL:0000788 \n",
+ "4 EFO:0008931 naive B cell CL:0000788 \n",
+ "... ... ... ... \n",
+ "107369 EFO:0009899 oligodendrocyte CL:0000128 \n",
+ "107370 EFO:0009899 oligodendrocyte CL:0000128 \n",
+ "107371 EFO:0009899 oligodendrocyte CL:0000128 \n",
+ "107372 EFO:0009899 oligodendrocyte CL:0000128 \n",
+ "107373 EFO:0009899 oligodendrocyte CL:0000128 \n",
"\n",
" development_stage development_stage_ontology_term_id \\\n",
"0 26-year-old human stage HsapDv:0000120 \n",
@@ -2235,11 +2189,11 @@
"3 UBERON:0000178 14370.0 371 38.733154 \n",
"4 UBERON:0000178 13738.0 507 27.096647 \n",
"... ... ... ... ... \n",
- "107369 UBERON:0000955 2105.0 1357 1.551216 \n",
- "107370 UBERON:0000955 1791.0 1168 1.533390 \n",
- "107371 UBERON:0000955 4989.0 2761 1.806954 \n",
- "107372 UBERON:0000955 5201.0 2811 1.850231 \n",
- "107373 UBERON:0000955 5850.0 3008 1.944814 \n",
+ "107369 UBERON:0000955 595.0 472 1.260593 \n",
+ "107370 UBERON:0000955 669.0 506 1.322134 \n",
+ "107371 UBERON:0000955 882.0 675 1.306667 \n",
+ "107372 UBERON:0000955 623.0 509 1.223969 \n",
+ "107373 UBERON:0000955 647.0 516 1.253876 \n",
"\n",
" raw_variance_nnz n_measured_vars \n",
"0 624.620960 19149 \n",
@@ -2248,11 +2202,11 @@
"3 4639.990763 19149 \n",
"4 3874.079574 19149 \n",
"... ... ... \n",
- "107369 4.489447 20383 \n",
- "107370 2.197684 20383 \n",
- "107371 4.679024 20383 \n",
- "107372 4.767241 20383 \n",
- "107373 8.650096 20383 \n",
+ "107369 0.613476 20383 \n",
+ "107370 1.145529 20383 \n",
+ "107371 1.856855 20383 \n",
+ "107372 0.433991 20383 \n",
+ "107373 0.931538 20383 \n",
"\n",
"[107374 rows x 28 columns]"
]
@@ -3037,7 +2991,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 29,
@@ -3275,8 +3229,8 @@
" ENSG00000011465 | \n",
" DCN | \n",
" 12941 | \n",
- " 7905731 | \n",
- " 75319655 | \n",
+ " 7598115 | \n",
+ " 72595321 | \n",
" \n",
" \n",
" 1 | \n",
@@ -3284,8 +3238,8 @@
" ENSG00000171401 | \n",
" KRT13 | \n",
" 2913 | \n",
- " 712612 | \n",
- " 62927690 | \n",
+ " 669102 | \n",
+ " 60499663 | \n",
"
\n",
" \n",
" 2 | \n",
@@ -3293,8 +3247,8 @@
" ENSG00000261371 | \n",
" PECAM1 | \n",
" 7741 | \n",
- " 7739030 | \n",
- " 64633082 | \n",
+ " 7167826 | \n",
+ " 61601430 | \n",
"
\n",
" \n",
"\n",
@@ -3302,14 +3256,14 @@
],
"text/plain": [
" soma_joinid feature_id feature_name feature_length nnz \\\n",
- "0 299 ENSG00000011465 DCN 12941 7905731 \n",
- "1 12852 ENSG00000171401 KRT13 2913 712612 \n",
- "2 28763 ENSG00000261371 PECAM1 7741 7739030 \n",
+ "0 299 ENSG00000011465 DCN 12941 7598115 \n",
+ "1 12852 ENSG00000171401 KRT13 2913 669102 \n",
+ "2 28763 ENSG00000261371 PECAM1 7741 7167826 \n",
"\n",
" n_measured_obs \n",
- "0 75319655 \n",
- "1 62927690 \n",
- "2 64633082 "
+ "0 72595321 \n",
+ "1 60499663 \n",
+ "2 61601430 "
]
},
"execution_count": 32,
@@ -3486,31 +3440,31 @@
" \n",
" \n",
" 0 | \n",
- " 6763612 | \n",
+ " 6387752 | \n",
" epithelial cell | \n",
" tongue | \n",
"
\n",
" \n",
" 1 | \n",
- " 6763613 | \n",
+ " 6387753 | \n",
" epithelial cell | \n",
" tongue | \n",
"
\n",
" \n",
" 2 | \n",
- " 6763614 | \n",
+ " 6387754 | \n",
" epithelial cell | \n",
" tongue | \n",
"
\n",
" \n",
" 3 | \n",
- " 6763615 | \n",
+ " 6387755 | \n",
" epithelial cell | \n",
" tongue | \n",
"
\n",
" \n",
" 4 | \n",
- " 6763616 | \n",
+ " 6387756 | \n",
" epithelial cell | \n",
" tongue | \n",
"
\n",
@@ -3522,31 +3476,31 @@
" \n",
" \n",
" 45055 | \n",
- " 53548544 | \n",
+ " 50259505 | \n",
" keratinocyte | \n",
" tongue | \n",
"
\n",
" \n",
" 45056 | \n",
- " 53548545 | \n",
+ " 50259506 | \n",
" keratinocyte | \n",
" tongue | \n",
"
\n",
" \n",
" 45057 | \n",
- " 53548546 | \n",
+ " 50259507 | \n",
" basal cell | \n",
" tongue | \n",
"
\n",
" \n",
" 45058 | \n",
- " 53548547 | \n",
+ " 50259508 | \n",
" keratinocyte | \n",
" tongue | \n",
"
\n",
" \n",
" 45059 | \n",
- " 53548548 | \n",
+ " 50259509 | \n",
" keratinocyte | \n",
" tongue | \n",
"
\n",
@@ -3557,17 +3511,17 @@
],
"text/plain": [
" soma_joinid cell_type tissue_general\n",
- "0 6763612 epithelial cell tongue\n",
- "1 6763613 epithelial cell tongue\n",
- "2 6763614 epithelial cell tongue\n",
- "3 6763615 epithelial cell tongue\n",
- "4 6763616 epithelial cell tongue\n",
+ "0 6387752 epithelial cell tongue\n",
+ "1 6387753 epithelial cell tongue\n",
+ "2 6387754 epithelial cell tongue\n",
+ "3 6387755 epithelial cell tongue\n",
+ "4 6387756 epithelial cell tongue\n",
"... ... ... ...\n",
- "45055 53548544 keratinocyte tongue\n",
- "45056 53548545 keratinocyte tongue\n",
- "45057 53548546 basal cell tongue\n",
- "45058 53548547 keratinocyte tongue\n",
- "45059 53548548 keratinocyte tongue\n",
+ "45055 50259505 keratinocyte tongue\n",
+ "45056 50259506 keratinocyte tongue\n",
+ "45057 50259507 basal cell tongue\n",
+ "45058 50259508 keratinocyte tongue\n",
+ "45059 50259509 keratinocyte tongue\n",
"\n",
"[45060 rows x 3 columns]"
]
@@ -3631,8 +3585,8 @@
" ENSG00000011465 | \n",
" DCN | \n",
" 12941 | \n",
- " 7905731 | \n",
- " 75319655 | \n",
+ " 7598115 | \n",
+ " 72595321 | \n",
" \n",
" \n",
" 1 | \n",
@@ -3640,8 +3594,8 @@
" ENSG00000171401 | \n",
" KRT13 | \n",
" 2913 | \n",
- " 712612 | \n",
- " 62927690 | \n",
+ " 669102 | \n",
+ " 60499663 | \n",
"
\n",
" \n",
" 2 | \n",
@@ -3649,8 +3603,8 @@
" ENSG00000261371 | \n",
" PECAM1 | \n",
" 7741 | \n",
- " 7739030 | \n",
- " 64633082 | \n",
+ " 7167826 | \n",
+ " 61601430 | \n",
"
\n",
" \n",
"\n",
@@ -3658,14 +3612,14 @@
],
"text/plain": [
" soma_joinid feature_id feature_name feature_length nnz \\\n",
- "0 299 ENSG00000011465 DCN 12941 7905731 \n",
- "1 12852 ENSG00000171401 KRT13 2913 712612 \n",
- "2 28763 ENSG00000261371 PECAM1 7741 7739030 \n",
+ "0 299 ENSG00000011465 DCN 12941 7598115 \n",
+ "1 12852 ENSG00000171401 KRT13 2913 669102 \n",
+ "2 28763 ENSG00000261371 PECAM1 7741 7167826 \n",
"\n",
" n_measured_obs \n",
- "0 75319655 \n",
- "1 62927690 \n",
- "2 64633082 "
+ "0 72595321 \n",
+ "1 60499663 \n",
+ "2 61601430 "
]
},
"execution_count": 36,
@@ -3711,7 +3665,7 @@
"soma_dim_1: int64\n",
"soma_data: float\n",
"----\n",
- "soma_dim_0: [[6763612,6763613,6763614,6763615,6763616,...,53548430,53548466,53548497,53548507,53548548]]\n",
+ "soma_dim_0: [[6387752,6387753,6387754,6387755,6387756,...,50259391,50259427,50259458,50259468,50259509]]\n",
"soma_dim_1: [[299,299,299,299,299,...,28763,28763,28763,28763,28763]]\n",
"soma_data: [[8,6,1,6,3,...,16,1,2,1,1]]"
]
@@ -3753,7 +3707,7 @@
{
"data": {
"text/plain": [
- "<53548549x28764 sparse matrix of type ''\n",
+ "<50259510x28764 sparse matrix of type ''\n",
"\twith 63579 stored elements in COOrdinate format>"
]
},
@@ -3775,7 +3729,7 @@
"id": "RmKQusbrhHzo"
},
"source": [
- "**🚨 NOTE:** The shape of this matrix is 53,548,549 rows by 28,764 columns.\n",
+ "**🚨 NOTE:** The shape of this matrix is 50,259,510 rows by 28,764 columns.\n",
"\n",
"However we know that there are 45K cells and 3 genes in our query. The reason for this discrepancy is that we are using the SOMA IDs as the row and column indices.\n",
"\n",
@@ -3896,27 +3850,27 @@
" \n",
" \n",
" \n",
- " 6763612 | \n",
+ " 6387752 | \n",
" epithelial cell | \n",
" tongue | \n",
"
\n",
" \n",
- " 6763613 | \n",
+ " 6387753 | \n",
" epithelial cell | \n",
" tongue | \n",
"
\n",
" \n",
- " 6763614 | \n",
+ " 6387754 | \n",
" epithelial cell | \n",
" tongue | \n",
"
\n",
" \n",
- " 6763615 | \n",
+ " 6387755 | \n",
" epithelial cell | \n",
" tongue | \n",
"
\n",
" \n",
- " 6763616 | \n",
+ " 6387756 | \n",
" epithelial cell | \n",
" tongue | \n",
"
\n",
@@ -3926,27 +3880,27 @@
" ... | \n",
" \n",
" \n",
- " 53548544 | \n",
+ " 50259505 | \n",
" keratinocyte | \n",
" tongue | \n",
"
\n",
" \n",
- " 53548545 | \n",
+ " 50259506 | \n",
" keratinocyte | \n",
" tongue | \n",
"
\n",
" \n",
- " 53548546 | \n",
+ " 50259507 | \n",
" basal cell | \n",
" tongue | \n",
"
\n",
" \n",
- " 53548547 | \n",
+ " 50259508 | \n",
" keratinocyte | \n",
" tongue | \n",
"
\n",
" \n",
- " 53548548 | \n",
+ " 50259509 | \n",
" keratinocyte | \n",
" tongue | \n",
"
\n",
@@ -3958,17 +3912,17 @@
"text/plain": [
" cell_type tissue_general\n",
"soma_joinid \n",
- "6763612 epithelial cell tongue\n",
- "6763613 epithelial cell tongue\n",
- "6763614 epithelial cell tongue\n",
- "6763615 epithelial cell tongue\n",
- "6763616 epithelial cell tongue\n",
+ "6387752 epithelial cell tongue\n",
+ "6387753 epithelial cell tongue\n",
+ "6387754 epithelial cell tongue\n",
+ "6387755 epithelial cell tongue\n",
+ "6387756 epithelial cell tongue\n",
"... ... ...\n",
- "53548544 keratinocyte tongue\n",
- "53548545 keratinocyte tongue\n",
- "53548546 basal cell tongue\n",
- "53548547 keratinocyte tongue\n",
- "53548548 keratinocyte tongue\n",
+ "50259505 keratinocyte tongue\n",
+ "50259506 keratinocyte tongue\n",
+ "50259507 basal cell tongue\n",
+ "50259508 keratinocyte tongue\n",
+ "50259509 keratinocyte tongue\n",
"\n",
"[45060 rows x 2 columns]"
]
@@ -4037,24 +3991,24 @@
" ENSG00000011465 | \n",
" DCN | \n",
" 12941 | \n",
- " 7905731 | \n",
- " 75319655 | \n",
+ " 7598115 | \n",
+ " 72595321 | \n",
" \n",
" \n",
" 12852 | \n",
" ENSG00000171401 | \n",
" KRT13 | \n",
" 2913 | \n",
- " 712612 | \n",
- " 62927690 | \n",
+ " 669102 | \n",
+ " 60499663 | \n",
"
\n",
" \n",
" 28763 | \n",
" ENSG00000261371 | \n",
" PECAM1 | \n",
" 7741 | \n",
- " 7739030 | \n",
- " 64633082 | \n",
+ " 7167826 | \n",
+ " 61601430 | \n",
"
\n",
" \n",
"\n",
@@ -4063,15 +4017,15 @@
"text/plain": [
" feature_id feature_name feature_length nnz \\\n",
"soma_joinid \n",
- "299 ENSG00000011465 DCN 12941 7905731 \n",
- "12852 ENSG00000171401 KRT13 2913 712612 \n",
- "28763 ENSG00000261371 PECAM1 7741 7739030 \n",
+ "299 ENSG00000011465 DCN 12941 7598115 \n",
+ "12852 ENSG00000171401 KRT13 2913 669102 \n",
+ "28763 ENSG00000261371 PECAM1 7741 7167826 \n",
"\n",
" n_measured_obs \n",
"soma_joinid \n",
- "299 75319655 \n",
- "12852 62927690 \n",
- "28763 64633082 "
+ "299 72595321 \n",
+ "12852 60499663 \n",
+ "28763 61601430 "
]
},
"execution_count": 43,
@@ -4142,7 +4096,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 45,
@@ -4339,8 +4293,8 @@
" ENSG00000133703 | \n",
" KRAS | \n",
" 9230 | \n",
- " 21891125 | \n",
- " 76922693 | \n",
+ " 21064009 | \n",
+ " 73958734 | \n",
" \n",
" \n",
" 1 | \n",
@@ -4348,8 +4302,8 @@
" ENSG00000171885 | \n",
" AQP4 | \n",
" 5943 | \n",
- " 3274017 | \n",
- " 70337111 | \n",
+ " 3162298 | \n",
+ " 67639727 | \n",
"
\n",
" \n",
"\n",
@@ -4357,12 +4311,12 @@
],
"text/plain": [
" soma_joinid feature_id feature_name feature_length nnz \\\n",
- "0 6706 ENSG00000133703 KRAS 9230 21891125 \n",
- "1 12982 ENSG00000171885 AQP4 5943 3274017 \n",
+ "0 6706 ENSG00000133703 KRAS 9230 21064009 \n",
+ "1 12982 ENSG00000171885 AQP4 5943 3162298 \n",
"\n",
" n_measured_obs \n",
- "0 76922693 \n",
- "1 70337111 "
+ "0 73958734 \n",
+ "1 67639727 "
]
},
"execution_count": 48,
@@ -4717,8 +4671,8 @@
" ENSG00000000003 | \n",
" TSPAN6 | \n",
" 4530 | \n",
- " 4811135 | \n",
- " 77128603 | \n",
+ " 4530448 | \n",
+ " 73855064 | \n",
" \n",
" \n",
" 1 | \n",
@@ -4726,8 +4680,8 @@
" ENSG00000000005 | \n",
" TNMD | \n",
" 1476 | \n",
- " 269136 | \n",
- " 64017621 | \n",
+ " 236059 | \n",
+ " 61201828 | \n",
"
\n",
" \n",
" 2 | \n",
@@ -4735,8 +4689,8 @@
" ENSG00000000419 | \n",
" DPM1 | \n",
" 9276 | \n",
- " 18420588 | \n",
- " 77502438 | \n",
+ " 17576462 | \n",
+ " 74159149 | \n",
"
\n",
" \n",
" 3 | \n",
@@ -4744,8 +4698,8 @@
" ENSG00000000457 | \n",
" SCYL3 | \n",
" 6883 | \n",
- " 9268173 | \n",
- " 76952827 | \n",
+ " 9117322 | \n",
+ " 73988868 | \n",
"
\n",
" \n",
" 4 | \n",
@@ -4753,8 +4707,8 @@
" ENSG00000000460 | \n",
" C1orf112 | \n",
" 5970 | \n",
- " 6484239 | \n",
- " 76979490 | \n",
+ " 6287794 | \n",
+ " 73636201 | \n",
"
\n",
" \n",
" ... | \n",
@@ -4766,8 +4720,8 @@
" ... | \n",
"
\n",
" \n",
- " 60532 | \n",
- " 60532 | \n",
+ " 60525 | \n",
+ " 60525 | \n",
" ENSG00000288718 | \n",
" ENSG00000288718.1 | \n",
" 1070 | \n",
@@ -4775,8 +4729,8 @@
" 1248980 | \n",
"
\n",
" \n",
- " 60533 | \n",
- " 60533 | \n",
+ " 60526 | \n",
+ " 60526 | \n",
" ENSG00000288719 | \n",
" ENSG00000288719.1 | \n",
" 4252 | \n",
@@ -4784,8 +4738,8 @@
" 1248980 | \n",
"
\n",
" \n",
- " 60534 | \n",
- " 60534 | \n",
+ " 60527 | \n",
+ " 60527 | \n",
" ENSG00000288724 | \n",
" ENSG00000288724.1 | \n",
" 625 | \n",
@@ -4793,26 +4747,26 @@
" 1248980 | \n",
"
\n",
" \n",
- " 60535 | \n",
- " 60535 | \n",
- " ENSG00000290735 | \n",
- " ENSG00000290735.1 | \n",
- " 4103 | \n",
- " 128 | \n",
- " 49359 | \n",
+ " 60528 | \n",
+ " 60528 | \n",
+ " ENSG00000290791 | \n",
+ " ENSG00000290791.1 | \n",
+ " 3612 | \n",
+ " 1642 | \n",
+ " 43485 | \n",
"
\n",
" \n",
- " 60536 | \n",
- " 60536 | \n",
- " ENSG00000289810 | \n",
- " ENSG00000289810.1 | \n",
- " 1205 | \n",
- " 82 | \n",
- " 48478 | \n",
+ " 60529 | \n",
+ " 60529 | \n",
+ " ENSG00000290146 | \n",
+ " ENSG00000290146.1 | \n",
+ " 1292 | \n",
+ " 7958 | \n",
+ " 43485 | \n",
"
\n",
" \n",
"\n",
- "60537 rows × 6 columns
\n",
+ "60530 rows × 6 columns
\n",
""
],
"text/plain": [
@@ -4823,26 +4777,26 @@
"3 3 ENSG00000000457 SCYL3 6883 \n",
"4 4 ENSG00000000460 C1orf112 5970 \n",
"... ... ... ... ... \n",
- "60532 60532 ENSG00000288718 ENSG00000288718.1 1070 \n",
- "60533 60533 ENSG00000288719 ENSG00000288719.1 4252 \n",
- "60534 60534 ENSG00000288724 ENSG00000288724.1 625 \n",
- "60535 60535 ENSG00000290735 ENSG00000290735.1 4103 \n",
- "60536 60536 ENSG00000289810 ENSG00000289810.1 1205 \n",
+ "60525 60525 ENSG00000288718 ENSG00000288718.1 1070 \n",
+ "60526 60526 ENSG00000288719 ENSG00000288719.1 4252 \n",
+ "60527 60527 ENSG00000288724 ENSG00000288724.1 625 \n",
+ "60528 60528 ENSG00000290791 ENSG00000290791.1 3612 \n",
+ "60529 60529 ENSG00000290146 ENSG00000290146.1 1292 \n",
"\n",
" nnz n_measured_obs \n",
- "0 4811135 77128603 \n",
- "1 269136 64017621 \n",
- "2 18420588 77502438 \n",
- "3 9268173 76952827 \n",
- "4 6484239 76979490 \n",
+ "0 4530448 73855064 \n",
+ "1 236059 61201828 \n",
+ "2 17576462 74159149 \n",
+ "3 9117322 73988868 \n",
+ "4 6287794 73636201 \n",
"... ... ... \n",
- "60532 4 1248980 \n",
- "60533 2826 1248980 \n",
- "60534 36 1248980 \n",
- "60535 128 49359 \n",
- "60536 82 48478 \n",
+ "60525 4 1248980 \n",
+ "60526 2826 1248980 \n",
+ "60527 36 1248980 \n",
+ "60528 1642 43485 \n",
+ "60529 7958 43485 \n",
"\n",
- "[60537 rows x 6 columns]"
+ "[60530 rows x 6 columns]"
]
},
"execution_count": 53,
@@ -5001,7 +4955,7 @@
{
"data": {
"text/plain": [
- "(15020, 60537)"
+ "(15020, 60530)"
]
},
"execution_count": 56,
@@ -5227,16 +5181,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Epoch 1: Train Loss: 0.0167484 Accuracy 0.3447\n",
- "Epoch 2: Train Loss: 0.0150141 Accuracy 0.4685\n",
- "Epoch 3: Train Loss: 0.0143282 Accuracy 0.6459\n",
- "Epoch 4: Train Loss: 0.0140425 Accuracy 0.7488\n",
- "Epoch 5: Train Loss: 0.0138404 Accuracy 0.8250\n",
- "Epoch 6: Train Loss: 0.0137127 Accuracy 0.8730\n",
- "Epoch 7: Train Loss: 0.0135996 Accuracy 0.8861\n",
- "Epoch 8: Train Loss: 0.0135142 Accuracy 0.8932\n",
- "Epoch 9: Train Loss: 0.0134573 Accuracy 0.9048\n",
- "Epoch 10: Train Loss: 0.0133953 Accuracy 0.9112\n"
+ "Epoch 1: Train Loss: 0.0161338 Accuracy 0.3419\n",
+ "Epoch 2: Train Loss: 0.0145978 Accuracy 0.4462\n",
+ "Epoch 3: Train Loss: 0.0143037 Accuracy 0.4872\n",
+ "Epoch 4: Train Loss: 0.0141506 Accuracy 0.5229\n",
+ "Epoch 5: Train Loss: 0.0140337 Accuracy 0.5500\n",
+ "Epoch 6: Train Loss: 0.0139353 Accuracy 0.5838\n",
+ "Epoch 7: Train Loss: 0.0138609 Accuracy 0.6080\n",
+ "Epoch 8: Train Loss: 0.0137868 Accuracy 0.6230\n",
+ "Epoch 9: Train Loss: 0.0137301 Accuracy 0.6349\n",
+ "Epoch 10: Train Loss: 0.0136863 Accuracy 0.6419\n"
]
}
],
@@ -5310,14 +5264,12 @@
{
"data": {
"text/plain": [
- "tensor([ 7, 8, 5, 1, 7, 7, 5, 1, 5, 1, 1, 1, 7, 1, 11, 7, 1, 1,\n",
- " 5, 1, 7, 5, 1, 7, 8, 1, 1, 1, 5, 1, 7, 1, 5, 1, 7, 1,\n",
- " 8, 8, 1, 7, 1, 5, 5, 5, 8, 1, 1, 7, 1, 8, 1, 1, 1, 1,\n",
- " 1, 7, 1, 5, 1, 11, 1, 7, 7, 1, 1, 7, 1, 7, 5, 1, 1, 1,\n",
- " 7, 1, 1, 11, 1, 7, 8, 1, 1, 8, 5, 8, 8, 7, 1, 5, 1, 7,\n",
- " 8, 1, 1, 1, 1, 1, 1, 8, 7, 8, 1, 8, 7, 1, 1, 1, 1, 1,\n",
- " 1, 8, 5, 1, 6, 8, 1, 8, 7, 1, 1, 8, 7, 1, 1, 1, 1, 7,\n",
- " 7, 1])"
+ "tensor([1, 1, 1, 8, 7, 1, 1, 5, 1, 1, 1, 1, 8, 1, 8, 1, 1, 1, 1, 1, 1, 8, 1, 1,\n",
+ " 8, 8, 8, 1, 1, 1, 1, 1, 8, 1, 1, 1, 8, 1, 1, 7, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+ " 1, 8, 1, 8, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 7, 8, 1, 1, 1, 1, 1, 8, 1, 2,\n",
+ " 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1,\n",
+ " 1, 1, 1, 1, 1, 8, 1, 1, 1, 8, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 7,\n",
+ " 8, 1, 1, 1, 1, 1, 8, 1])"
]
},
"metadata": {},
@@ -5361,39 +5313,38 @@
{
"data": {
"text/plain": [
- "array(['keratinocyte', 'leukocyte', 'epithelial cell', 'basal cell',\n",
- " 'keratinocyte', 'keratinocyte', 'epithelial cell', 'basal cell',\n",
- " 'epithelial cell', 'basal cell', 'basal cell', 'basal cell',\n",
- " 'keratinocyte', 'basal cell', 'vein endothelial cell',\n",
+ "array(['basal cell', 'basal cell', 'basal cell', 'leukocyte',\n",
" 'keratinocyte', 'basal cell', 'basal cell', 'epithelial cell',\n",
- " 'basal cell', 'keratinocyte', 'epithelial cell', 'basal cell',\n",
- " 'keratinocyte', 'leukocyte', 'basal cell', 'basal cell',\n",
- " 'basal cell', 'epithelial cell', 'basal cell', 'keratinocyte',\n",
- " 'basal cell', 'epithelial cell', 'basal cell', 'keratinocyte',\n",
- " 'basal cell', 'leukocyte', 'leukocyte', 'basal cell',\n",
- " 'keratinocyte', 'basal cell', 'epithelial cell', 'epithelial cell',\n",
- " 'epithelial cell', 'leukocyte', 'basal cell', 'basal cell',\n",
- " 'keratinocyte', 'basal cell', 'leukocyte', 'basal cell',\n",
" 'basal cell', 'basal cell', 'basal cell', 'basal cell',\n",
- " 'keratinocyte', 'basal cell', 'epithelial cell', 'basal cell',\n",
- " 'vein endothelial cell', 'basal cell', 'keratinocyte',\n",
- " 'keratinocyte', 'basal cell', 'basal cell', 'keratinocyte',\n",
- " 'basal cell', 'keratinocyte', 'epithelial cell', 'basal cell',\n",
- " 'basal cell', 'basal cell', 'keratinocyte', 'basal cell',\n",
- " 'basal cell', 'vein endothelial cell', 'basal cell',\n",
- " 'keratinocyte', 'leukocyte', 'basal cell', 'basal cell',\n",
- " 'leukocyte', 'epithelial cell', 'leukocyte', 'leukocyte',\n",
- " 'keratinocyte', 'basal cell', 'epithelial cell', 'basal cell',\n",
+ " 'leukocyte', 'basal cell', 'leukocyte', 'basal cell', 'basal cell',\n",
+ " 'basal cell', 'basal cell', 'basal cell', 'basal cell',\n",
+ " 'leukocyte', 'basal cell', 'basal cell', 'leukocyte', 'leukocyte',\n",
+ " 'leukocyte', 'basal cell', 'basal cell', 'basal cell',\n",
+ " 'basal cell', 'basal cell', 'leukocyte', 'basal cell',\n",
+ " 'basal cell', 'basal cell', 'leukocyte', 'basal cell',\n",
+ " 'basal cell', 'keratinocyte', 'basal cell', 'basal cell',\n",
+ " 'basal cell', 'basal cell', 'basal cell', 'basal cell',\n",
+ " 'basal cell', 'basal cell', 'basal cell', 'leukocyte',\n",
+ " 'basal cell', 'leukocyte', 'basal cell', 'basal cell',\n",
+ " 'basal cell', 'leukocyte', 'basal cell', 'basal cell',\n",
+ " 'basal cell', 'basal cell', 'basal cell', 'basal cell',\n",
" 'keratinocyte', 'leukocyte', 'basal cell', 'basal cell',\n",
+ " 'basal cell', 'basal cell', 'basal cell', 'leukocyte',\n",
+ " 'basal cell', 'capillary endothelial cell', 'basal cell',\n",
+ " 'basal cell', 'basal cell', 'basal cell', 'basal cell',\n",
+ " 'leukocyte', 'basal cell', 'basal cell', 'basal cell',\n",
+ " 'basal cell', 'basal cell', 'leukocyte', 'basal cell',\n",
" 'basal cell', 'basal cell', 'basal cell', 'basal cell',\n",
- " 'leukocyte', 'keratinocyte', 'leukocyte', 'basal cell',\n",
- " 'leukocyte', 'keratinocyte', 'basal cell', 'basal cell',\n",
+ " 'basal cell', 'basal cell', 'basal cell', 'leukocyte',\n",
" 'basal cell', 'basal cell', 'basal cell', 'basal cell',\n",
- " 'leukocyte', 'epithelial cell', 'basal cell', 'fibroblast',\n",
- " 'leukocyte', 'basal cell', 'leukocyte', 'keratinocyte',\n",
- " 'basal cell', 'basal cell', 'leukocyte', 'keratinocyte',\n",
" 'basal cell', 'basal cell', 'basal cell', 'basal cell',\n",
- " 'keratinocyte', 'keratinocyte', 'basal cell'], dtype=object)"
+ " 'leukocyte', 'basal cell', 'basal cell', 'basal cell', 'leukocyte',\n",
+ " 'basal cell', 'basal cell', 'leukocyte', 'basal cell',\n",
+ " 'basal cell', 'basal cell', 'basal cell', 'basal cell',\n",
+ " 'basal cell', 'basal cell', 'leukocyte', 'basal cell',\n",
+ " 'basal cell', 'keratinocyte', 'leukocyte', 'basal cell',\n",
+ " 'basal cell', 'basal cell', 'basal cell', 'basal cell',\n",
+ " 'leukocyte', 'basal cell'], dtype=object)"
]
},
"metadata": {},
@@ -5457,23 +5408,23 @@
" \n",
" \n",
" 0 | \n",
- " keratinocyte | \n",
- " keratinocyte | \n",
+ " basal cell | \n",
+ " basal cell | \n",
"
\n",
" \n",
" 1 | \n",
- " leukocyte | \n",
- " leukocyte | \n",
+ " keratinocyte | \n",
+ " basal cell | \n",
"
\n",
" \n",
" 2 | \n",
- " epithelial cell | \n",
- " epithelial cell | \n",
+ " basal cell | \n",
+ " basal cell | \n",
"
\n",
" \n",
" 3 | \n",
- " basal cell | \n",
- " basal cell | \n",
+ " leukocyte | \n",
+ " leukocyte | \n",
"
\n",
" \n",
" 4 | \n",
@@ -5487,7 +5438,7 @@
"
\n",
" \n",
" 123 | \n",
- " basal cell | \n",
+ " keratinocyte | \n",
" basal cell | \n",
"
\n",
" \n",
@@ -5497,17 +5448,17 @@
"
\n",
" \n",
" 125 | \n",
- " keratinocyte | \n",
- " keratinocyte | \n",
+ " basal cell | \n",
+ " basal cell | \n",
"
\n",
" \n",
" 126 | \n",
- " keratinocyte | \n",
- " keratinocyte | \n",
+ " leukocyte | \n",
+ " leukocyte | \n",
"
\n",
" \n",
" 127 | \n",
- " basal cell | \n",
+ " keratinocyte | \n",
" basal cell | \n",
"
\n",
" \n",
@@ -5517,17 +5468,17 @@
],
"text/plain": [
" actual cell type predicted cell type\n",
- "0 keratinocyte keratinocyte\n",
- "1 leukocyte leukocyte\n",
- "2 epithelial cell epithelial cell\n",
- "3 basal cell basal cell\n",
+ "0 basal cell basal cell\n",
+ "1 keratinocyte basal cell\n",
+ "2 basal cell basal cell\n",
+ "3 leukocyte leukocyte\n",
"4 keratinocyte keratinocyte\n",
".. ... ...\n",
- "123 basal cell basal cell\n",
+ "123 keratinocyte basal cell\n",
"124 basal cell basal cell\n",
- "125 keratinocyte keratinocyte\n",
- "126 keratinocyte keratinocyte\n",
- "127 basal cell basal cell\n",
+ "125 basal cell basal cell\n",
+ "126 leukocyte leukocyte\n",
+ "127 keratinocyte basal cell\n",
"\n",
"[128 rows x 2 columns]"
]
@@ -5578,11 +5529,11 @@
" actual cell type | \n",
" basal cell | \n",
" capillary endothelial cell | \n",
+ " endothelial cell of lymphatic vessel | \n",
" epithelial cell | \n",
+ " fibroblast | \n",
" keratinocyte | \n",
" leukocyte | \n",
- " pericyte | \n",
- " tongue muscle cell | \n",
" vein endothelial cell | \n",
" \n",
" \n",
@@ -5600,101 +5551,95 @@
"
\n",
" \n",
" basal cell | \n",
- " 63 | \n",
- " | \n",
+ " 58 | \n",
" | \n",
+ " 1 | \n",
+ " 7 | \n",
" 2 | \n",
- " | \n",
- " | \n",
- " | \n",
- " | \n",
+ " 29 | \n",
+ " 1 | \n",
+ " 1 | \n",
"
\n",
" \n",
- " epithelial cell | \n",
- " 2 | \n",
- " | \n",
- " 12 | \n",
+ " capillary endothelial cell | \n",
" | \n",
" 1 | \n",
" | \n",
" | \n",
" | \n",
+ " | \n",
+ " | \n",
+ " | \n",
"
\n",
" \n",
- " fibroblast | \n",
+ " epithelial cell | \n",
" | \n",
" | \n",
" | \n",
+ " 1 | \n",
" | \n",
" | \n",
- " 1 | \n",
" | \n",
" | \n",
"
\n",
" \n",
" keratinocyte | \n",
- " 1 | \n",
" | \n",
" | \n",
- " 25 | \n",
" | \n",
" | \n",
" | \n",
+ " 4 | \n",
+ " | \n",
" | \n",
"
\n",
" \n",
" leukocyte | \n",
" | \n",
- " | \n",
- " | \n",
- " | \n",
- " 17 | \n",
- " | \n",
" 1 | \n",
" | \n",
- "
\n",
- " \n",
- " vein endothelial cell | \n",
- " | \n",
- " 2 | \n",
- " | \n",
- " | \n",
" | \n",
+ " 1 | \n",
" | \n",
+ " 21 | \n",
" | \n",
- " 1 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- "actual cell type basal cell capillary endothelial cell epithelial cell \\\n",
- "predicted cell type \n",
- "basal cell 63 \n",
- "epithelial cell 2 12 \n",
- "fibroblast \n",
- "keratinocyte 1 \n",
- "leukocyte \n",
- "vein endothelial cell 2 \n",
+ "actual cell type basal cell capillary endothelial cell \\\n",
+ "predicted cell type \n",
+ "basal cell 58 \n",
+ "capillary endothelial cell 1 \n",
+ "epithelial cell \n",
+ "keratinocyte \n",
+ "leukocyte 1 \n",
"\n",
- "actual cell type keratinocyte leukocyte pericyte tongue muscle cell \\\n",
- "predicted cell type \n",
- "basal cell 2 \n",
- "epithelial cell 1 \n",
- "fibroblast 1 \n",
- "keratinocyte 25 \n",
- "leukocyte 17 1 \n",
- "vein endothelial cell \n",
+ "actual cell type endothelial cell of lymphatic vessel \\\n",
+ "predicted cell type \n",
+ "basal cell 1 \n",
+ "capillary endothelial cell \n",
+ "epithelial cell \n",
+ "keratinocyte \n",
+ "leukocyte \n",
"\n",
- "actual cell type vein endothelial cell \n",
- "predicted cell type \n",
- "basal cell \n",
- "epithelial cell \n",
- "fibroblast \n",
- "keratinocyte \n",
- "leukocyte \n",
- "vein endothelial cell 1 "
+ "actual cell type epithelial cell fibroblast keratinocyte leukocyte \\\n",
+ "predicted cell type \n",
+ "basal cell 7 2 29 1 \n",
+ "capillary endothelial cell \n",
+ "epithelial cell 1 \n",
+ "keratinocyte 4 \n",
+ "leukocyte 1 21 \n",
+ "\n",
+ "actual cell type vein endothelial cell \n",
+ "predicted cell type \n",
+ "basal cell 1 \n",
+ "capillary endothelial cell \n",
+ "epithelial cell \n",
+ "keratinocyte \n",
+ "leukocyte "
]
},
"execution_count": 66,
@@ -5746,8 +5691,8 @@
"!wget --no-check-certificate -q -O data/pbmc3k_filtered_gene_bc_matrices.tar.gz http://cf.10xgenomics.com/samples/cell-exp/1.1.0/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz\n",
"!tar -xzf data/pbmc3k_filtered_gene_bc_matrices.tar.gz -C data/\n",
"\n",
- "!mkdir -p scvi-human-2024-02-12\n",
- "!wget --no-check-certificate -q -O scvi-human-2024-02-12/model.pt https://cellxgene-contrib-public.s3.us-west-2.amazonaws.com/models/scvi/2024-02-12/homo_sapiens/model.pt"
+ "!mkdir -p scvi-human-{CENSUS_VERSION}\n",
+ "!wget --no-check-certificate -q -O scvi-human-{CENSUS_VERSION}/model.pt https://cellxgene-contrib-public.s3.us-west-2.amazonaws.com/models/scvi/{CENSUS_VERSION}/homo_sapiens/model.pt"
]
},
{
@@ -5831,18 +5776,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001b[34mINFO \u001b[0m File scvi-human-\u001b[1;36m2024\u001b[0m-\u001b[1;36m02\u001b[0m-\u001b[1;36m12\u001b[0m/model.pt already downloaded \n",
- "\u001b[34mINFO \u001b[0m Found \u001b[1;36m62.2\u001b[0m% reference vars in query data. \n",
- "\u001b[34mINFO \u001b[0m File scvi-human-\u001b[1;36m2024\u001b[0m-\u001b[1;36m02\u001b[0m-\u001b[1;36m12\u001b[0m/model.pt already downloaded \n"
+ "\u001b[34mINFO \u001b[0m File scvi-human-\u001b[1;36m2024\u001b[0m-\u001b[1;36m07\u001b[0m-\u001b[1;36m01\u001b[0m/model.pt already downloaded \n",
+ "\u001b[34mINFO \u001b[0m Found \u001b[1;36m54.675\u001b[0m% reference vars in query data. \n",
+ "\u001b[34mINFO \u001b[0m File scvi-human-\u001b[1;36m2024\u001b[0m-\u001b[1;36m07\u001b[0m-\u001b[1;36m01\u001b[0m/model.pt already downloaded \n"
]
}
],
"source": [
- "scvi.model.SCVI.prepare_query_anndata(adata, \"scvi-human-2024-02-12\")\n",
- "vae_q = scvi.model.SCVI.load_query_data(\n",
- " adata,\n",
- " \"scvi-human-2024-02-12\",\n",
- ")\n",
+ "scvi.model.SCVI.prepare_query_anndata(adata, f\"scvi-human-{CENSUS_VERSION}\")\n",
+ "vae_q = scvi.model.SCVI.load_query_data(adata, f\"scvi-human-{CENSUS_VERSION}\")\n",
"\n",
"# This allows for a simple forward pass\n",
"vae_q.is_trained = True\n",
@@ -5883,7 +5825,7 @@
{
"data": {
"text/plain": [
- "AnnData object with n_obs × n_vars = 2700 × 4976\n",
+ "AnnData object with n_obs × n_vars = 2700 × 4374\n",
" obs: 'n_counts', 'joinid', 'batch', '_scvi_batch', '_scvi_labels', 'cell_type', 'tissue_general'\n",
" var: 'ensembl_id'\n",
" uns: '_scvi_uuid', '_scvi_manager_uuid'\n",
@@ -5915,7 +5857,7 @@
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
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