Archive 4 checkpoints (1-4)
Browse files- paper-releases/20260928/diffusion/data/annotator/aifi_scanvi_f01common_v1/MODEL_MANIFEST.json +268 -0
- paper-releases/20260928/diffusion/data/annotator/aifi_scanvi_f01common_v1/README.md +393 -0
- paper-releases/20260928/diffusion/data/annotator/aifi_scanvi_f01common_v1/config_f01common.json +68 -0
- paper-releases/20260928/diffusion/data/annotator/aifi_scanvi_f01common_v1/genes.csv +1006 -0
paper-releases/20260928/diffusion/data/annotator/aifi_scanvi_f01common_v1/MODEL_MANIFEST.json
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| 1 |
+
{
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| 2 |
+
"schema_version": "aifi_scanvi_rebuild.model_manifest.v1",
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| 3 |
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"run_name": "aifi_scanvi_f01common_v1",
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| 4 |
+
"built_at_utc": "2026-09-23T17:31:10.405930+00:00",
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| 5 |
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"smoke": false,
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| 6 |
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"device": "cuda",
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| 7 |
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"contract": {
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| 8 |
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"layer": "counts",
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| 9 |
+
"labels_key": "aifi_l2_train",
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| 10 |
+
"unlabeled_category": "Unknown",
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| 11 |
+
"batch_key": "batch_key",
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| 12 |
+
"query_train_epochs": 0
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| 13 |
+
},
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| 14 |
+
"reference": {
|
| 15 |
+
"h5ad": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/reference/aifi_reference_f01common.h5ad",
|
| 16 |
+
"sha256": "e90de26e48ec905215e6f33057fb4f57d0cecc7f4c820b6bbf26497b0e45e1e1",
|
| 17 |
+
"manifest": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/reference/REFERENCE_MANIFEST.json"
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| 18 |
+
},
|
| 19 |
+
"n_train_cells": 360310,
|
| 20 |
+
"n_val_cells": 39690,
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| 21 |
+
"n_genes": 1005,
|
| 22 |
+
"genes_csv": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/runs/aifi_scanvi_f01common_v1/genes.csv",
|
| 23 |
+
"classes": [
|
| 24 |
+
"ASDC",
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| 25 |
+
"CD14 monocyte",
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| 26 |
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"CD16 monocyte",
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| 27 |
+
"CD56bright NK cell",
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| 28 |
+
"CD56dim NK cell",
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| 29 |
+
"CD8aa",
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| 30 |
+
"DN T cell",
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| 31 |
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"Effector B cell",
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| 32 |
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"Erythrocyte",
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| 33 |
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"ILC",
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| 34 |
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"Intermediate monocyte",
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| 35 |
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"MAIT",
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| 36 |
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"Memory B cell",
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| 37 |
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"Memory CD4 T cell",
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| 38 |
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"Memory CD8 T cell",
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| 39 |
+
"Naive B cell",
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| 40 |
+
"Naive CD4 T cell",
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| 41 |
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"Naive CD8 T cell",
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| 42 |
+
"Plasma cell",
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| 43 |
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"Platelet",
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| 44 |
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"Progenitor cell",
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| 45 |
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"Proliferating NK cell",
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| 46 |
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"Proliferating T cell",
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| 47 |
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"Transitional B cell",
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| 48 |
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"Treg",
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| 49 |
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"cDC1",
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| 50 |
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"cDC2",
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| 51 |
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"gdT",
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| 52 |
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"pDC"
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| 53 |
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],
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| 54 |
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"n_batches": 34,
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| 55 |
+
"train_donors": [
|
| 56 |
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"BR1001",
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| 57 |
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"BR1002",
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| 58 |
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"BR1003",
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| 59 |
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"BR1004",
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| 60 |
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"BR1005",
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| 61 |
+
"BR1006",
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| 62 |
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"BR1008",
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| 63 |
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"BR1009",
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| 64 |
+
"BR1010",
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| 65 |
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"BR1011",
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| 66 |
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"BR1012",
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| 67 |
+
"BR1013",
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| 68 |
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"BR1014",
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| 69 |
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"BR1015",
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| 70 |
+
"BR1017",
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| 71 |
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"BR1018",
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| 72 |
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"BR1019",
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| 73 |
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"BR1021",
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| 74 |
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"BR1023",
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| 75 |
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"BR1024",
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| 76 |
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"BR1025",
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| 77 |
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"BR1026",
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| 78 |
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"BR1028",
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| 79 |
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"BR1030",
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| 80 |
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"BR1031",
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| 81 |
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"BR1032",
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| 82 |
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"BR1037",
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| 83 |
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"BR1040",
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| 84 |
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"BR1041",
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| 85 |
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"BR1043",
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| 86 |
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"BR1044",
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| 87 |
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"BR1045",
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| 88 |
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"BR1047",
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"BR1048",
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"BR1049",
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| 91 |
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"BR1050",
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"BR1051",
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| 93 |
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"BR1052",
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| 94 |
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"BR1054",
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| 95 |
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"BR1056",
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"BR1057",
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| 97 |
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"BR1058",
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| 98 |
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"BR2002",
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| 99 |
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"BR2003",
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| 100 |
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"BR2005",
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| 101 |
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"BR2008",
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"BR2009",
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| 103 |
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"BR2010",
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| 104 |
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"BR2012",
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| 105 |
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"BR2014",
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| 106 |
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"BR2015",
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| 107 |
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"BR2016",
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| 108 |
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"BR2018",
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| 109 |
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"BR2019",
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| 110 |
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"BR2020",
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| 111 |
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"BR2021",
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| 112 |
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"BR2022",
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| 113 |
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"BR2023",
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| 114 |
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"BR2025",
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| 115 |
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"BR2026",
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| 116 |
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"BR2028",
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| 117 |
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"BR2029",
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| 118 |
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"BR2030",
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| 119 |
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"BR2031",
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| 120 |
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"BR2032",
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| 121 |
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"BR2033",
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| 122 |
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"BR2034",
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| 123 |
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"BR2035",
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| 124 |
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"BR2036",
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| 125 |
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"BR2038",
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| 126 |
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"BR2039",
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| 127 |
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"BR2040",
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| 128 |
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"BR2041",
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| 129 |
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"BR2042",
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| 130 |
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"BR2043",
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| 131 |
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"BR2044",
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| 132 |
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"BR2045",
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| 133 |
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"BR2046",
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| 134 |
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"BR2047",
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| 135 |
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"BR2048",
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| 136 |
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"BR2050",
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| 137 |
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"BR2051",
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| 138 |
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"BR2052",
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| 139 |
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"UP1008",
|
| 140 |
+
"UP1011",
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| 141 |
+
"UP1012",
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| 142 |
+
"UP1014",
|
| 143 |
+
"UP1015",
|
| 144 |
+
"UP1017",
|
| 145 |
+
"UP1018",
|
| 146 |
+
"UP1021",
|
| 147 |
+
"UP1023",
|
| 148 |
+
"UP1024",
|
| 149 |
+
"UP1026",
|
| 150 |
+
"UP1031",
|
| 151 |
+
"UP1032",
|
| 152 |
+
"UP1034"
|
| 153 |
+
],
|
| 154 |
+
"val_donors": [
|
| 155 |
+
"BR1007",
|
| 156 |
+
"BR1016",
|
| 157 |
+
"BR1033",
|
| 158 |
+
"BR1042",
|
| 159 |
+
"BR1059",
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| 160 |
+
"BR2013",
|
| 161 |
+
"BR2024",
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| 162 |
+
"BR2037",
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| 163 |
+
"BR2053",
|
| 164 |
+
"UP1022",
|
| 165 |
+
"UP1025"
|
| 166 |
+
],
|
| 167 |
+
"scvi_model_dir": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/runs/aifi_scanvi_f01common_v1/scvi_model",
|
| 168 |
+
"scanvi_model_dir": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/runs/aifi_scanvi_f01common_v1/scanvi_model",
|
| 169 |
+
"scanvi_model_pt_sha256": "bf3655899e5abee435340fc008656c4a17342da0f30b70cfd43de8e9223e1930",
|
| 170 |
+
"config": {
|
| 171 |
+
"config_path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/config_f01common.json",
|
| 172 |
+
"config_sha256": "84e9389eb63f7ebf45bb42eadf36704d919251bcb7e8d7330e654e0833f60038",
|
| 173 |
+
"cli_overrides": {},
|
| 174 |
+
"resolved": {
|
| 175 |
+
"anndata": {
|
| 176 |
+
"batch_key": "batch_key",
|
| 177 |
+
"labels_key": "aifi_l2_train",
|
| 178 |
+
"layer": "counts",
|
| 179 |
+
"split_key": "split",
|
| 180 |
+
"train_split": "train",
|
| 181 |
+
"unlabeled_category": "Unknown",
|
| 182 |
+
"val_split": "val"
|
| 183 |
+
},
|
| 184 |
+
"eval": {
|
| 185 |
+
"batch_size": 2048,
|
| 186 |
+
"in_sample_cells": 20000,
|
| 187 |
+
"other_label": "Other",
|
| 188 |
+
"pbmc5_labels": [
|
| 189 |
+
"T cell",
|
| 190 |
+
"B cell",
|
| 191 |
+
"Monocyte",
|
| 192 |
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"NK cell",
|
| 193 |
+
"DC"
|
| 194 |
+
]
|
| 195 |
+
},
|
| 196 |
+
"l2_to_l1_module": "/home/sj_server_1/PBISC/immun-pbmc-fm/model/vae_bulk2sc/scripts/aifi_celltype_mapping.py",
|
| 197 |
+
"output_dir": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/runs/aifi_scanvi_f01common_v1",
|
| 198 |
+
"reference_h5ad": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/reference/aifi_reference_f01common.h5ad",
|
| 199 |
+
"reference_manifest": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/reference/REFERENCE_MANIFEST.json",
|
| 200 |
+
"run_name": "aifi_scanvi_f01common_v1",
|
| 201 |
+
"scanvi_model": {
|
| 202 |
+
"classifier_parameters": {
|
| 203 |
+
"dropout_rate": 0.1,
|
| 204 |
+
"n_hidden": 128,
|
| 205 |
+
"n_layers": 2
|
| 206 |
+
},
|
| 207 |
+
"linear_classifier": false
|
| 208 |
+
},
|
| 209 |
+
"scanvi_train": {
|
| 210 |
+
"batch_size": 1024,
|
| 211 |
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"check_val_every_n_epoch": 1,
|
| 212 |
+
"max_epochs": 25,
|
| 213 |
+
"n_samples_per_label": 100,
|
| 214 |
+
"plan_kwargs": {
|
| 215 |
+
"classification_ratio": 50,
|
| 216 |
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"lr": 0.001
|
| 217 |
+
},
|
| 218 |
+
"train_size": 0.9
|
| 219 |
+
},
|
| 220 |
+
"scvi_model": {
|
| 221 |
+
"dispersion": "gene",
|
| 222 |
+
"dropout_rate": 0.1,
|
| 223 |
+
"gene_likelihood": "nb",
|
| 224 |
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"n_hidden": 256,
|
| 225 |
+
"n_latent": 30,
|
| 226 |
+
"n_layers": 2
|
| 227 |
+
},
|
| 228 |
+
"scvi_train": {
|
| 229 |
+
"batch_size": 1024,
|
| 230 |
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"check_val_every_n_epoch": 1,
|
| 231 |
+
"early_stopping": true,
|
| 232 |
+
"early_stopping_monitor": "elbo_validation",
|
| 233 |
+
"early_stopping_patience": 8,
|
| 234 |
+
"max_epochs": 40,
|
| 235 |
+
"plan_kwargs": {
|
| 236 |
+
"lr": 0.001
|
| 237 |
+
},
|
| 238 |
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"train_size": 0.9
|
| 239 |
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},
|
| 240 |
+
"seed": 20260916
|
| 241 |
+
},
|
| 242 |
+
"device": "cuda"
|
| 243 |
+
},
|
| 244 |
+
"versions": {
|
| 245 |
+
"scvi-tools": "1.4.1",
|
| 246 |
+
"torch": "2.11.0+cu130",
|
| 247 |
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"anndata": "0.12.19",
|
| 248 |
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"numpy": "2.5.3",
|
| 249 |
+
"python": "3.12.14"
|
| 250 |
+
},
|
| 251 |
+
"timings_s": {
|
| 252 |
+
"scvi": 115.1,
|
| 253 |
+
"scanvi": 292.7,
|
| 254 |
+
"total": 414.1
|
| 255 |
+
},
|
| 256 |
+
"epochs_run": {
|
| 257 |
+
"scvi": 40,
|
| 258 |
+
"scanvi": 25
|
| 259 |
+
},
|
| 260 |
+
"held_out_summary": {
|
| 261 |
+
"l2_accuracy": 0.8692869740488788,
|
| 262 |
+
"l2_macro_f1": 0.8500925599737216,
|
| 263 |
+
"l1_accuracy": 0.9918367346938776,
|
| 264 |
+
"l1_macro_f1": 0.9715232379009036,
|
| 265 |
+
"pbmc5_other_accuracy": 0.9920886873267826,
|
| 266 |
+
"pbmc5_other_macro_f1": 0.9812088972148597
|
| 267 |
+
}
|
| 268 |
+
}
|
paper-releases/20260928/diffusion/data/annotator/aifi_scanvi_f01common_v1/README.md
ADDED
|
@@ -0,0 +1,393 @@
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|
| 1 |
+
# AIFI scANVI annotator rebuild (2026-09-16)
|
| 2 |
+
|
| 3 |
+
Everything needed to **re-train** the AIFI-reference scANVI cell-type annotator whose
|
| 4 |
+
original run (`training/annotation/runs/aifi_scanvi_new_blocks_v1`) and hyper-parameters were
|
| 5 |
+
lost. The reference data and the query-side contract survived and are honoured exactly.
|
| 6 |
+
|
| 7 |
+
Status: **CPU-only preparation is complete and smoke-tested end to end. No GPU training has
|
| 8 |
+
been run.** The coordinator launches it with the command in "GPU launch" below.
|
| 9 |
+
|
| 10 |
+
```
|
| 11 |
+
plans/aifi_scanvi_rebuild_20260916/
|
| 12 |
+
├── setup_env.sh # builds /home/sj_server_1/miniforge3/envs/scanvi (done)
|
| 13 |
+
├── ENV_FREEZE.txt # exact `uv pip freeze` of that env (done)
|
| 14 |
+
├── build_reference.py # AIFI atlas raw/X -> canonical-panel reference h5ads (done, ran)
|
| 15 |
+
├── reference/ # 400k-cell references + REFERENCE_MANIFEST.json (built)
|
| 16 |
+
├── poisson_readout.py # NPZ generated cells -> Poisson integer-count query AnnData
|
| 17 |
+
├── tests/test_poisson_readout.py # 10 unit tests (all pass)
|
| 18 |
+
├── scanvi_common.py # shared: L2->L1 collapse, PBMC5+Other, metrics, hashing
|
| 19 |
+
├── train_scanvi.py # scVI pretrain -> scANVI, held-out-donor evaluation
|
| 20 |
+
├── config_6k.json # 5,944-gene model (the benchmark's 6k tier) <- primary
|
| 21 |
+
├── config_20097.json # 19,642-gene model (canonical panel)
|
| 22 |
+
├── predict_scanvi.py # frozen predict(soft=True), zero query epochs -> labels.parquet
|
| 23 |
+
├── launch_train.sh # guarded systemd-run / ml.slice GPU launcher (NOT run)
|
| 24 |
+
├── make_manifest.py # writes MANIFEST.json (config seals) (ran)
|
| 25 |
+
├── MANIFEST.json
|
| 26 |
+
├── runs/smoke_6k_cpu/ # CPU smoke: 3,000 cells x 2 epochs (ran)
|
| 27 |
+
├── runs/smoke_predict_6k_cpu/# CPU smoke: 500 Poisson-readout cells (ran)
|
| 28 |
+
├── logs/
|
| 29 |
+
│ --- 2,980-gene one-annotator line (§10) ---
|
| 30 |
+
├── panel_2980.py # verbatim 2,980-gene list loader + shared paths
|
| 31 |
+
├── build_reference_2980.py # 6k reference -> reference/aifi_reference_2980.h5ad (ran)
|
| 32 |
+
├── config_2980.json # 2,977-gene model config (sealed; NOT trained)
|
| 33 |
+
├── build_benchmark_queries.py# truth / scSemi / VAE / diffusion NPZ -> queries_2980/*.h5ad (ran)
|
| 34 |
+
├── verify_queries_2980.py # real-data check of the queries vs sources (ran, 7/7)
|
| 35 |
+
├── tests/test_build_benchmark_queries.py # 9 unit tests (pass)
|
| 36 |
+
├── annotate_all_2980.sh # predict_scanvi.py on every query (after training)
|
| 37 |
+
└── evaluate_all_2980.sh # evaluate_generated_vs_truth.py with scANVI truth labels
|
| 38 |
+
```
|
| 39 |
+
|
| 40 |
+
## 1. Environment (deliverable 1)
|
| 41 |
+
|
| 42 |
+
`/home/sj_server_1/miniforge3/envs/scanvi/bin/python` (Python 3.12.14), built by
|
| 43 |
+
`setup_env.sh` with conda (python) + `uv pip`:
|
| 44 |
+
|
| 45 |
+
| package | version | source |
|
| 46 |
+
|---|---|---|
|
| 47 |
+
| torch | **2.11.0+cu130** | `https://download.pytorch.org/whl/cu130` (manylinux_2_28_aarch64) — same wheel family as the proven `sc-perturb-bench/.venv-baselines` |
|
| 48 |
+
| scvi-tools | **1.4.1** | PyPI (matches `.venv-baselines`; original freeze had 1.4.3) |
|
| 49 |
+
| anndata / scanpy | 0.12.19 / 1.11.5 | PyPI |
|
| 50 |
+
| numpy / pandas / scipy | 2.5.3 / 2.3.3 / 1.18.1 | PyPI |
|
| 51 |
+
| pyarrow / zarr / h5py / scikit-learn | 25.0.1 / 3.3.0 / 3.16.0 / 1.9.1 | PyPI |
|
| 52 |
+
| lightning | 2.6.6 | PyPI |
|
| 53 |
+
|
| 54 |
+
Verification (from `logs/setup_env.log`, run with `CUDA_VISIBLE_DEVICES=""`):
|
| 55 |
+
`import scvi` OK; `torch.version.cuda = 13.0`; `torch.cuda.is_available() == False` (CUDA hidden, as
|
| 56 |
+
required — CUDA was never initialised). Full pin list: `ENV_FREEZE.txt` (116 lines).
|
| 57 |
+
|
| 58 |
+
## 2. Query-side contract (preserved from the original)
|
| 59 |
+
|
| 60 |
+
From `model/vae_bulk2sc/method_1/scripts/aifi_recon_annotation_grid.py` lines 103–128 and the
|
| 61 |
+
surviving L15 QC manifest (`cell_count_readout: poisson`, `query_train_epochs: 0`):
|
| 62 |
+
|
| 63 |
+
| item | value |
|
| 64 |
+
|---|---|
|
| 65 |
+
| `labels_key` | `aifi_l2_train` (= `AIFI_L2` in the reference) |
|
| 66 |
+
| unlabeled category | literal `"Unknown"` |
|
| 67 |
+
| `batch_key` | `batch_key` (reference: `batch_id`; query: one batch per bulk sample, `prefix + sample_id`) |
|
| 68 |
+
| counts | `layer="counts"`, integer counts |
|
| 69 |
+
| gene space | canonical panel (Ensembl IDs, no version) / 6k tier subset |
|
| 70 |
+
| prediction | `SCANVI.prepare_query_anndata` → `SCANVI.load_query_data` → `predict(soft=True)` with **zero** query-training epochs (surgery only) |
|
| 71 |
+
| generated cells | Poisson-integerised first |
|
| 72 |
+
|
| 73 |
+
## 3. Reference AnnData (deliverable 2) — `reference/`
|
| 74 |
+
|
| 75 |
+
Source: AIFI Human Immune Health Atlas h5ad, **`raw/X`** (integer UMI; the scaled `X` is never
|
| 76 |
+
read). Built by `build_reference.py` in one streaming pass (2.6 min, ~10 GB peak RAM).
|
| 77 |
+
|
| 78 |
+
Gene match (join on Ensembl ID without version; absent panel genes are **absent**, never
|
| 79 |
+
zero-filled; per-gene presence masks in `panel_presence_20097.csv`, `panel_presence_6k.csv`,
|
| 80 |
+
`uns["canonical_panel_presence"]`):
|
| 81 |
+
|
| 82 |
+
| panel | matched | absent from atlas |
|
| 83 |
+
|---|---|---|
|
| 84 |
+
| canonical 20,097 | **19,642** | 455 (mostly novel `ENSG000002856xx`-style IDs newer than the atlas annotation) |
|
| 85 |
+
| 6k tier (6,000) | **5,944** | 56 |
|
| 86 |
+
| 2k tier (2,000) | 1,984 | 16 (informational) |
|
| 87 |
+
| broad tier (11,589) | 11,517 | 72 (informational) |
|
| 88 |
+
|
| 89 |
+
Cells: 400,000 selected from 1,821,725 (seed 20260916). Donor hold-out: 11 of 108 donors
|
| 90 |
+
(stratified by cohort: BR1 5, BR2 4, UP1 2) → `val`; every cell of a donor is on one side.
|
| 91 |
+
|
| 92 |
+
| | cells | donors |
|
| 93 |
+
|---|---|---|
|
| 94 |
+
| train | 360,310 | 97 |
|
| 95 |
+
| val (held-out donors) | 39,690 | 11: BR1007 BR1016 BR1033 BR1042 BR1059 BR2013 BR2024 BR2037 BR2053 UP1022 UP1025 |
|
| 96 |
+
|
| 97 |
+
Per-class AIFI_L2 counts (train / val) — all 29 classes present on both sides
|
| 98 |
+
(full table in `REFERENCE_MANIFEST.json`):
|
| 99 |
+
|
| 100 |
+
Naive CD4 T 67,899/7,776 · Memory CD4 T 58,278/6,132 · CD14 mono 48,369/5,540 · Memory CD8 T
|
| 101 |
+
33,084/3,565 · CD56dim NK 24,222/2,576 · Naive CD8 T 21,525/2,728 · Naive B 15,822/1,534 · gdT
|
| 102 |
+
9,130/996 · Memory B 8,594/991 · MAIT 8,306/930 · CD16 mono 8,197/994 · Treg 7,106/718 ·
|
| 103 |
+
cDC2 4,448/552 · Transitional B 4,454/546 · Platelet 4,611/389 · pDC 4,488/512 · Intermediate
|
| 104 |
+
mono 4,522/478 · Effector B 4,531/469 · CD8aa 4,833/167 · CD56bright NK 4,543/457 ·
|
| 105 |
+
Proliferating NK 2,566/259 · DN T 2,096/253 · Proliferating T 2,107/213 · Plasma 1,981/170 ·
|
| 106 |
+
Progenitor 1,360/166 · Erythrocyte 1,165/343 · cDC1 876/67 · ILC 733/111 · ASDC 464/58.
|
| 107 |
+
|
| 108 |
+
AIFI_L1 totals: T cell 237,842 · Monocyte 68,100 · B cell 39,092 · NK cell 34,623 · DC 11,465
|
| 109 |
+
· Platelet 5,000 · Progenitor cell 1,526 · Erythrocyte 1,508 · ILC 844 — the four non-PBMC5
|
| 110 |
+
classes are **kept as model classes** and collapsed to `Other` only downstream.
|
| 111 |
+
|
| 112 |
+
Files (sha256 in `REFERENCE_MANIFEST.json`):
|
| 113 |
+
|
| 114 |
+
| file | shape | nnz | size | median library |
|
| 115 |
+
|---|---|---|---|---|
|
| 116 |
+
| `aifi_reference_20097.h5ad` | 400,000 × 19,642 | 658.2 M | 5.3 GB | 4,560 |
|
| 117 |
+
| `aifi_reference_6k.h5ad` | 400,000 × 5,944 | 240.8 M | 2.0 GB | 2,885 |
|
| 118 |
+
|
| 119 |
+
`obs`: `aifi_l2_train`, `AIFI_L1`, `AIFI_L2`, `AIFI_L3`, `batch_key`, `batch_id`, `donor_id`,
|
| 120 |
+
`cohort`, `age_group`, `sex`, `cmv`, `split`, `atlas_row`, `n_umis_atlas`, `n_genes_atlas`.
|
| 121 |
+
`var`: `gene_symbol`, `panel_index`, (`tier_rank`), `atlas_col`. `X` = raw counts (float32
|
| 122 |
+
CSR, 100 % integer-valued); `train_scanvi.py` aliases `layers["counts"] = X` in memory (kept out
|
| 123 |
+
of the file so the 5.3 GB is not doubled on disk). Side files: `donor_split.csv`,
|
| 124 |
+
`stratum_allocation.csv`, `cell_selection.parquet`, `build_reference.log`.
|
| 125 |
+
|
| 126 |
+
## 4. Poisson readout (deliverable 3) — `poisson_readout.py`
|
| 127 |
+
|
| 128 |
+
`counts[i,g] ~ Poisson(generated_counts[i,g])`, one seeded draw (`numpy.random.default_rng`),
|
| 129 |
+
chunked over rows in a way that makes the result independent of chunk size.
|
| 130 |
+
|
| 131 |
+
**Library size: no rescaling.** `generated_counts` is used as the Poisson rate as-is, so
|
| 132 |
+
`E[library] = generated library` and the Poisson library variance is the sampling noise a real
|
| 133 |
+
UMI library has. The 6k NPZ already has PBMC-scale libraries on its panel (median row sum
|
| 134 |
+
1,844; the lost L15 QC reported median 2,022 on 20,097 genes), and rescaling would change the
|
| 135 |
+
expression shape relative to the diffusion conditioning. A global `--library-scale` exists
|
| 136 |
+
(default 1.0, recorded in `uns`) for explicit experiments only. Zero rates give zero counts
|
| 137 |
+
with probability one, so the generated support (`active_mask`) is preserved exactly.
|
| 138 |
+
|
| 139 |
+
Output AnnData: `X` = `layers["counts"]` = integer counts (float32 CSR), `var_names` = NPZ
|
| 140 |
+
`gene_ids`, `obs`: `sample_id`, `bulk_index`, `npz_row`, `cell_index`, `source`,
|
| 141 |
+
`batch_key = prefix + sample_id`, `aifi_l2_train = "Unknown"`.
|
| 142 |
+
|
| 143 |
+
Tests (`tests/test_poisson_readout.py`, 10/10 pass): zeros stay zeros; per-column mean of
|
| 144 |
+
draws within 4 sd of the rate; library mean preserved (<1 %); integer-valued; seeded and
|
| 145 |
+
chunk-invariant; sparse == dense; negative/NaN refused; `--library-scale` scales expectation;
|
| 146 |
+
NPZ → AnnData contract; missing-key refusal.
|
| 147 |
+
|
| 148 |
+
## 5. Training (deliverable 4) — `train_scanvi.py` + `config_*.json`
|
| 149 |
+
|
| 150 |
+
`SCVI.setup_anndata(layer="counts", batch_key="batch_key", labels_key="aifi_l2_train")` →
|
| 151 |
+
`SCVI.train` → `SCANVI.from_scvi_model(unlabeled_category="Unknown")` → `SCANVI.train` →
|
| 152 |
+
save `scvi_model/`, `scanvi_model/` → evaluate the 11 held-out donors **through the production
|
| 153 |
+
path** (labels hidden as `Unknown`, `prepare_query_anndata` + `load_query_data`, zero epochs,
|
| 154 |
+
`predict(soft=True)`). Writes `metrics.json` (accuracy, macro-F1 at L2 / L1 / PBMC5+Other,
|
| 155 |
+
per-class P/R/F1), `confusion_{l2,l1,pbmc5}.csv`, `per_class_l2.csv`,
|
| 156 |
+
`val_predictions.parquet`, `history_{scvi,scanvi}.csv`, `genes.csv`, `MODEL_MANIFEST.json`,
|
| 157 |
+
`train_config_resolved.json`, `status.json` (state machine for the coordinator), `train.log`.
|
| 158 |
+
Flags: `--device cpu|cuda`, `--max-cells`, `--max-epochs`, `--batch-size`, `--output-dir`,
|
| 159 |
+
`--expected-config-sha256` (seal), `--num-threads`.
|
| 160 |
+
|
| 161 |
+
### CHOICES made where the original is lost (all in the config JSON, all recorded in `MODEL_MANIFEST.json`)
|
| 162 |
+
|
| 163 |
+
| choice | value | why |
|
| 164 |
+
|---|---|---|
|
| 165 |
+
| scVI/scANVI architecture | `n_hidden=256, n_latent=30, n_layers=2, dropout=0.1` | modern default for a 360k-cell, 29-class reference; larger than scvi's 128/10/1 defaults because the classifier must separate 29 fine classes |
|
| 166 |
+
| likelihood / dispersion | `gene_likelihood="nb"`, `dispersion="gene"` | UMI counts; NB is the scvi-tools recommendation over ZINB for 10x data |
|
| 167 |
+
| scVI epochs | 40, early stopping on `elbo_validation` (patience 8, `train_size=0.9`) | scvi's heuristic gives ~22 epochs at 360k cells; 40 with early stopping is a safe upper bound |
|
| 168 |
+
| scANVI epochs | 25, fixed (no early stopping) | classifier convergence; history is saved so the coordinator can inspect |
|
| 169 |
+
| `n_samples_per_label` | 100 | scvi-tools tutorial default for imbalanced references |
|
| 170 |
+
| classifier | 2 layers × 128, dropout 0.1, `linear_classifier=False` | non-linear head needed for L2 granularity |
|
| 171 |
+
| batch size / lr | 1024 / 1e-3 (`classification_ratio=50`) | GPU throughput; scvi defaults otherwise |
|
| 172 |
+
| KL warm-up | scvi default (`n_epochs_kl_warmup=400`, i.e. partial at 40 epochs) | standard scvi behaviour on large atlases; override via `plan_kwargs` if desired |
|
| 173 |
+
| subsample | 400k cells, `AIFI_L2 × donor` strata, classes < 5k kept in full, others `max(5000, 0.2002·n)` | manageable GPU run; rare classes fully represented (ASDC 522, ILC 844, cDC1 943 all kept) |
|
| 174 |
+
| donor hold-out | 10 % of donors, cohort-stratified, seed 20260916 | donor-level (not cell-level) generalisation |
|
| 175 |
+
| batch covariate | `batch_id` (36 batches; each donor sits in exactly one batch) | closest analogue of the original `batch_key`; donors as batch would be 108 categories with no query analogue |
|
| 176 |
+
| training labels | all 29 AIFI_L2 classes incl. Platelet/Erythrocyte/Progenitor/ILC | never dropped; collapsed to `Other` only in the PBMC5 view |
|
| 177 |
+
| seed | 20260916 everywhere (`scvi.settings.seed`, subsampling, Poisson) | reproducibility |
|
| 178 |
+
|
| 179 |
+
## 6. Prediction (deliverable 5) — `predict_scanvi.py`
|
| 180 |
+
|
| 181 |
+
Input `.npz` (Poisson-integerised on the fly, seeded) or `.h5ad` (integer counts). Gene guard:
|
| 182 |
+
all model genes must exist in the query — `prepare_query_anndata` would otherwise zero-pad
|
| 183 |
+
them, which fabricates measurements; refused unless `--allow-missing-genes` (count recorded).
|
| 184 |
+
Query genes outside the model panel are dropped (recorded). **Use the 6k model on the 6k NPZ**
|
| 185 |
+
(5,944 model genes ⊂ 6,000 query genes, 0 padded); the 20,097 model cannot be applied to a 6k
|
| 186 |
+
NPZ without padding 13,698 genes.
|
| 187 |
+
|
| 188 |
+
`labels.parquet` schema = the benchmark centroid transfer (`label_transfer_representative_centroid.py`,
|
| 189 |
+
scheme `pbmc5`) + two extra columns:
|
| 190 |
+
|
| 191 |
+
`sample_id, group, source, cell_index, celltype_l1_pred, label_confidence, aifi_l1, aifi_l2,
|
| 192 |
+
aifi_l2_confidence` (+ `aifi_l1_confidence`, `npz_row`).
|
| 193 |
+
|
| 194 |
+
* `celltype_l1_pred` ∈ PBMC5 ∪ {`Other`} (Platelet, Erythrocyte, Progenitor cell, ILC → `Other`,
|
| 195 |
+
never dropped); `aifi_l1` keeps the full L1 name; `aifi_l2` the 29-class prediction.
|
| 196 |
+
* `label_confidence` = summed L2 probability mass of the predicted L1;
|
| 197 |
+
`aifi_l2_confidence` = max L2 probability.
|
| 198 |
+
* `labels.manifest.json` carries `target_truth_used_for_fitting: false`,
|
| 199 |
+
`training_source` = reference h5ad, `classifier`, `labels` = the 6-way list, gene-space and
|
| 200 |
+
readout provenance, model sha256 — so `evaluate_generated_vs_truth.py` accepts it
|
| 201 |
+
(verified: `resolve_label_scheme("pbmc5", labels=manifest["labels"])` → 6 labels, no
|
| 202 |
+
predicted label outside the scheme). Run the evaluator with `--labels-from-manifest`
|
| 203 |
+
(or `--labels-json` listing the six labels); with the plain 5-label scheme, `Other`
|
| 204 |
+
predictions would be rejected by the evaluator's "labels outside the scheme" check.
|
| 205 |
+
* Without `--max-cells` the parquet rows are 1:1 with the NPZ rows (`sample_id` order equal
|
| 206 |
+
to the generated cells, as the evaluator requires); `--max-cells` outputs are marked
|
| 207 |
+
`evaluator_aligned: false`.
|
| 208 |
+
* Soft outputs: `soft_probabilities_l2.parquet`, `probabilities_l1.parquet`.
|
| 209 |
+
|
| 210 |
+
## 7. CPU smoke (deliverable 6) — real numbers, deliberately tiny
|
| 211 |
+
|
| 212 |
+
`train_scanvi.py --config config_6k.json --device cpu --max-cells 3000 --max-epochs 2 --batch-size 128`
|
| 213 |
+
(`runs/smoke_6k_cpu`, log `logs/smoke_train_6k_cpu.log`): 3,000 train cells (97 donors, 34
|
| 214 |
+
batches, 29 classes) × 5,944 genes, scVI 8.3 s + scANVI 27.2 s, total 40.5 s.
|
| 215 |
+
|
| 216 |
+
| held-out donors, n = 1,000 cells | accuracy | macro-F1 (classes present) |
|
| 217 |
+
|---|---|---|
|
| 218 |
+
| AIFI L2 (29) | 0.386 | 0.166 |
|
| 219 |
+
| AIFI L1 (9) | 0.939 | 0.544 |
|
| 220 |
+
| PBMC5 + Other | 0.942 | 0.822 |
|
| 221 |
+
|
| 222 |
+
(in-sample L2 on the 3,000 training cells: acc 0.384.) `confusion_pbmc5.csv`: T cell
|
| 223 |
+
575/592 correct, B 104/105, Monocyte 161/161, NK 74/86, DC 21/31, Other 7/25. Two epochs on
|
| 224 |
+
3k cells is only a plumbing test; the L2 head is far from converged.
|
| 225 |
+
|
| 226 |
+
`predict_scanvi.py --model-dir runs/smoke_6k_cpu/scanvi_model --query analysis/scsemi_benchmark_diffusion_20260913/generated_6k_e4.npz --max-cells 500 --seed 20260916 --batch-prefix 004_A_GSE135779_SLE_PBMC_`
|
| 227 |
+
(`runs/smoke_predict_6k_cpu`, log `logs/smoke_predict_6k_cpu.log`): 500 seeded Poisson-readout
|
| 228 |
+
cells, 5,944/6,000 genes used, 0 zero-padded, 56 query genes dropped; 2.4 s total.
|
| 229 |
+
Predicted `celltype_l1_pred`: T cell 341, Monocyte 82, B cell 44, NK cell 28, DC 5, Other 0;
|
| 230 |
+
mean L1 mass 0.52, mean L2 max-prob 0.14 (a 2-epoch model — expected to be uncertain).
|
| 231 |
+
Same script on 500 real held-out reference cells via the `.h5ad` path: L2 acc 0.370, L1 acc
|
| 232 |
+
0.938 (consistent with the training-side numbers; scratch output, not kept).
|
| 233 |
+
|
| 234 |
+
## 8. GPU launch (for the coordinator; not run here)
|
| 235 |
+
|
| 236 |
+
Preflight per the host policy, then launch the 6k model (primary) and, optionally, the 20,097
|
| 237 |
+
model afterwards (never concurrently):
|
| 238 |
+
|
| 239 |
+
```bash
|
| 240 |
+
systemctl --user is-active ml-memory-guard.service
|
| 241 |
+
/home/sj_server_1/.local/libexec/ml-memory-guard --check
|
| 242 |
+
nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv,noheader # must be empty
|
| 243 |
+
|
| 244 |
+
PLAN=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916
|
| 245 |
+
"$PLAN/launch_train.sh" 6k # -> runs/aifi_scanvi_6k_v2 (systemd unit pbisc-aifi-scanvi-6k-*)
|
| 246 |
+
# later, after the 6k unit is inactive:
|
| 247 |
+
"$PLAN/launch_train.sh" 20097 # -> runs/aifi_scanvi_20097_v2
|
| 248 |
+
```
|
| 249 |
+
|
| 250 |
+
`launch_train.sh` mirrors `plans/arch_ablation_6k_20260913/launch_ablation.sh`: config seal
|
| 251 |
+
check against `MANIFEST.json`, guard active, `--check` headroom, GPU idle, shared
|
| 252 |
+
`/run/user/1000/ml-gpu0.lock`, `systemd-run --user --slice=ml.slice` with
|
| 253 |
+
`MemoryHigh=40G MemoryMax=48G MemorySwapMax=0 OOMPolicy=kill OOMScoreAdjust=500 Restart=no`,
|
| 254 |
+
`CUDA_VISIBLE_DEVICES=0`, log to `$PLAN/logs/train_<variant>_<host>_<ts>.log`. Monitor with
|
| 255 |
+
`cat runs/aifi_scanvi_6k_v2/status.json` (`starting → training_scvi → training_scanvi →
|
| 256 |
+
evaluating → complete | failed`) and `tail -f` the log. Runtime is unmeasured on this host;
|
| 257 |
+
360k cells × 5,944 genes at batch 1024 is ~350 steps/epoch, so expect tens of minutes for the
|
| 258 |
+
6k model and a few times that for 20,097 (estimate, not a measurement). RAM: the 6k reference
|
| 259 |
+
is 2 GB on disk, the 20,097 one 5.3 GB; both fit the 40 GB unit limit with margin.
|
| 260 |
+
|
| 261 |
+
If the guard or OOM policy stops a run: keep `runs/…` as evidence, inspect the log and
|
| 262 |
+
`journalctl --user -u ml-memory-guard.service --since today` before any relaunch
|
| 263 |
+
(`launch_train.sh` refuses to overwrite a `complete` run; move a failed run aside).
|
| 264 |
+
|
| 265 |
+
After training, the benchmark step is:
|
| 266 |
+
|
| 267 |
+
```bash
|
| 268 |
+
CUDA_VISIBLE_DEVICES="" /home/sj_server_1/miniforge3/envs/scanvi/bin/python "$PLAN/predict_scanvi.py" \
|
| 269 |
+
--model-dir "$PLAN/runs/aifi_scanvi_6k_v2/scanvi_model" \
|
| 270 |
+
--query "$PLAN/../../analysis/scsemi_benchmark_diffusion_20260913/generated_6k_e4.npz" \
|
| 271 |
+
--out-dir "$PLAN/../../analysis/scsemi_benchmark_diffusion_20260913/labels/diffusion_6k_e4/aifi_scanvi_label_transfer" \
|
| 272 |
+
--method-name diffusion_6k_e4 --dataset-id 004_A_GSE135779_SLE_PBMC --split scsemi_target42 \
|
| 273 |
+
--batch-prefix 004_A_GSE135779_SLE_PBMC_ --seed 20260916 --device cpu
|
| 274 |
+
```
|
| 275 |
+
|
| 276 |
+
(CPU is fine for 63k cells; `--device cuda` only through `ml-run`.) Then point
|
| 277 |
+
`evaluate_generated_vs_truth.py --method … <that>/labels.parquet` at it with
|
| 278 |
+
`--labels-from-manifest` so the 6-way PBMC5+Other scheme is used.
|
| 279 |
+
|
| 280 |
+
## 9. What was not done / caveats
|
| 281 |
+
|
| 282 |
+
* **No GPU training and no CUDA initialisation** (policy). The smoke numbers above are from a
|
| 283 |
+
2-epoch, 3k-cell CPU model and are not an accuracy claim.
|
| 284 |
+
* The original model's hyper-parameters are unrecoverable; §5 lists every substitute. The
|
| 285 |
+
original reference cell selection is also unknown — this one is a fresh seeded subsample.
|
| 286 |
+
* `labels.parquet` provides only the PBMC5(+Other) scheme. The benchmark's `fine` (14-label)
|
| 287 |
+
scheme is a different vocabulary from AIFI L2 and is not emitted; a mapping would be a
|
| 288 |
+
separate, documented choice.
|
| 289 |
+
* CellTypist cross-check (`/home/sj_server_1/gblee/datalake/reference/celltypist/`) is not
|
| 290 |
+
wired in; `celltypist` is not installed in the env.
|
| 291 |
+
* The 455 canonical-panel genes absent from the atlas are absent from the models; a query
|
| 292 |
+
that carries them simply has those columns dropped (recorded in the manifest).
|
| 293 |
+
* `ml-run`/remote launch to spark-0110 is possible but `launch_train.sh` targets this host
|
| 294 |
+
only; the env exists only on server_1.
|
| 295 |
+
|
| 296 |
+
## 10. One annotator for the whole SLE benchmark: the 2,980-gene line (2026-09-16, pass 2)
|
| 297 |
+
|
| 298 |
+
**Problem.** The benchmark compares cells on different gene sets: our diffusion NPZs live on
|
| 299 |
+
the 6,000-gene checkpoint tier panel, while scSemiProfiler / PBISC-VAE exports and the truth
|
| 300 |
+
cells live on the benchmark's 6,000 *working* genes. The 6k model (§5–§8) cannot annotate the
|
| 301 |
+
working-panel sets without zero-padding ~3,000 genes. The earlier label-transfer / metric
|
| 302 |
+
agents restricted every comparison to the **2,980-gene intersection**; so one scANVI trained on
|
| 303 |
+
exactly those genes annotates every set — truth included — with zero padding.
|
| 304 |
+
|
| 305 |
+
**Gene list (verbatim, never re-derived).** `evaluation_gene_ids` of
|
| 306 |
+
`analysis/scsemi_benchmark_diffusion_20260913/metrics/panel_6k_e4_2980genes/pbmc5/evaluation_manifest.json`
|
| 307 |
+
(2,980 Ensembl IDs, benchmark-panel order; identical in the `fine`, `_with4x`, `_with4x_e1`
|
| 308 |
+
manifests — asserted). sha256 of the newline-joined list `f40ee3fd…3b447a`; copied to
|
| 309 |
+
`queries_2980/gene_list_2980.{json,txt}` (`panel_2980.py`). 2,980 ⊂ 6k tier (checked);
|
| 310 |
+
**2,977 are in the AIFI atlas**, 3 absent (`ENSG00000233276`, `ENSG00000168255`,
|
| 311 |
+
`ENSG00000158427`) and therefore absent from the model.
|
| 312 |
+
|
| 313 |
+
| file | role | status |
|
| 314 |
+
|---|---|---|
|
| 315 |
+
| `panel_2980.py` | shared constants + verbatim gene-list loader | done |
|
| 316 |
+
| `build_reference_2980.py` → `reference/aifi_reference_2980.h5ad` | column subset of `aifi_reference_6k.h5ad`: **same 400,000 cells, same order, same obs, same donor split** (asserted row-for-row against `cell_selection.parquet` / `donor_split.csv` and the manifest counts); 400,000 × 2,977, 133.7 M nnz, 1.1 GB, sha256 `e7c8a457…0b39d`, median library 1,361 | built |
|
| 317 |
+
| `reference/REFERENCE_MANIFEST.json` | updated **additively**: `outputs.intersection_2980`, `gene_match.intersection_2980`, `side_files.panel_presence_2980`, `derived_builds[]`; 20097 / 6k entries untouched | done |
|
| 318 |
+
| `config_2980.json` → `runs/aifi_scanvi_2980_v1/` | identical hyper-parameters to `config_6k.json` (§5), only reference / run name differ; sealed in `MANIFEST.json`; `launch_train.sh 2980` | **trained by the coordinator (GPU, 9.1 min)**: held-out L2 macro-F1 0.870, L1 0.973, PBMC5+Other 0.984 |
|
| 319 |
+
| `build_benchmark_queries.py` → `queries_2980/` | annotation-ready queries for every set (table below) + `queries_manifest.json` | built (10 sets) |
|
| 320 |
+
| `verify_queries_2980.py` | independent real-data check of the queries against their sources | passed 10/10 |
|
| 321 |
+
| `tests/test_build_benchmark_queries.py` | 9 synthetic unit tests (shapes, integer counts, Ensembl alignment for all three source kinds, no padding, real-must-be-integer, Poisson decision + seed/chunk reproducibility, obs contract, NPZ readiness guard, verbatim list) | 9/9 pass |
|
| 322 |
+
| `annotate_all_2980.sh` | `predict_scanvi.py` per set (CPU) → `analysis/…/labels_scanvi2980/<set>/labels.parquet` + `labels.manifest.json` | **ran: 10/10 sets, 0 padded genes** |
|
| 323 |
+
| `evaluate_all_2980.sh` | `evaluate_generated_vs_truth.py` per diffusion set + all-in-one → `analysis/…/metrics_scanvi2980/` | **ran: 6 per-set runs + all-diffusion + `summary_tidy.csv`** |
|
| 324 |
+
| `report_scanvi2980.py` | label summary, truth-label agreement, side-by-side vs the centroid tables → `metrics_scanvi2980/RESULTS_scanvi2980_tables.md` (+ CSVs); narrative in `metrics_scanvi2980/RESULTS_scanvi2980.md` | ran |
|
| 325 |
+
| `runs/smoke_2980_cpu/` | 3,000-cell × 2-epoch CPU smoke of `config_2980.json` used to dry-run the whole chain before the real model existed (outputs went to the scratchpad) | ran |
|
| 326 |
+
|
| 327 |
+
**Queries (`queries_2980/`, all 2,980 genes matched, one shared `var`, Ensembl joins only):**
|
| 328 |
+
|
| 329 |
+
| set | cells | source values | readout | median library (in → out) |
|
| 330 |
+
|---|---|---|---|---|
|
| 331 |
+
| `truth_target` | 251,179 | integer UMI | real counts, untouched | 981 |
|
| 332 |
+
| `truth_representative` | 92,489 | integer UMI | real counts, untouched | 1,003 |
|
| 333 |
+
| `scSemiProfiler` | 63,000 | **continuous** (float, floor 10.0) | Poisson, seed 20260916 | 5,666 → 5,667 |
|
| 334 |
+
| `PBISC-VAE` | 63,000 | **integer** (checked over the full matrix) | integer passthrough | 1,098 |
|
| 335 |
+
| `diffusion_6k_e4` / `4x_e1` / `4x_e4` | 63,000 each | continuous NPZ `generated_counts` | Poisson, seed 20260916, drawn on the 2,980-column subset | 867 / 1,045 / 953 |
|
| 336 |
+
| `diffusion_4x_e5` / `16x_e1` / `16x_e2` | 63,000 each | continuous NPZ (added when generation finished; sha256 `0e778fb7…` / `f178ae39…` / `f6da4ec1…` verified against the coordinator, `gene_ids`/`bulk_index`/`sample_ids` identical to 6k_e4; 16x logs carry no receipt → `--allow-unverified-npz`, file sha recorded) | Poisson, seed 20260916 | 986 / 847 / 718 |
|
| 337 |
+
|
| 338 |
+
**Results of the pass (2026-09-16):** `analysis/scsemi_benchmark_diffusion_20260913/metrics_scanvi2980/RESULTS_scanvi2980.md`.
|
| 339 |
+
Headline: truth re-annotation agrees with the frozen legacy labels on 97.4 % of cells (scANVI finds
|
| 340 |
+
3× more DC and resolves the 10,339 legacy `unknown`); on the same four cell types the two annotators
|
| 341 |
+
agree to ≤ 0.03 in logFC r for every method (PBISC-VAE > 1x > scSemiProfiler > wide checkpoints,
|
| 342 |
+
unchanged), but **composition flips**: PBISC-VAE is best (L1 0.166, r 0.984) and scSemiProfiler falls
|
| 343 |
+
to 6th because scANVI sees 4.9 % DC / 2.6 % Other in its cells; the centroid's "wide checkpoints
|
| 344 |
+
over-generate NK 2–2.7×" was largely a labelling artefact.
|
| 345 |
+
|
| 346 |
+
Gene joins: working-panel h5ads with `raw_gene_index` resolve to Ensembl through the cohort
|
| 347 |
+
TSV (the evaluator's own function, then asserted equal to the benchmark panel's IDs); the
|
| 348 |
+
PBISC-VAE export has no `raw_gene_index` and is accepted only because its `var_names` equal
|
| 349 |
+
`gene_panel.csv` position for position (the evaluator's own contract) — never a symbol join.
|
| 350 |
+
Poisson draws were re-drawn with a different chunk size and compared bit-for-bit
|
| 351 |
+
(`poisson_reproducible_second_draw: true`). `obs`: `cell_index`, `sample_id`, `donor`,
|
| 352 |
+
`source`, `batch_key = 004_A_GSE135779_SLE_PBMC_<sample_id>`, `aifi_l2_train = "Unknown"`,
|
| 353 |
+
plus `condition` / `celltype_l1_legacy` / `celltype_l2_legacy` / `source_obs_name` where the
|
| 354 |
+
source has them. Row order = source order, so `labels.parquet` is 1:1 with the evaluator's
|
| 355 |
+
cell order. Truth cells will be **re-annotated** by the same model (the legacy `celltype_l1`
|
| 356 |
+
is kept only as `celltype_l1_legacy` for comparison).
|
| 357 |
+
|
| 358 |
+
**Evaluator change (`bulk2sc-paper/scripts/evaluate_generated_vs_truth.py`).** New,
|
| 359 |
+
backwards-compatible `--truth-labels-parquet PATH [--truth-labels-column COL]`
|
| 360 |
+
(`EvaluationConfig.truth_labels_parquet/_column`): replaces the truth h5ad's label column
|
| 361 |
+
with a row-aligned annotation so truth and generated cells share one annotator. Fail-closed
|
| 362 |
+
(row count, `sample_id` order, `cell_index`, column presence, and the same
|
| 363 |
+
`target_truth_used_for_fitting=false` manifest check as methods). Manifest gains
|
| 364 |
+
`truth_label_source` / `truth_labels_override`. Six new tests; suite 45/45 (was 39/39).
|
| 365 |
+
Real-data regression: an *identity* override (the truth's own `celltype_l1`) plus the 2,980
|
| 366 |
+
JSON restriction reproduces every table of `metrics/panel_6k_e4_2980genes/pbmc5/` with 0
|
| 367 |
+
relative error and the identical evaluation panel.
|
| 368 |
+
|
| 369 |
+
`predict_scanvi.py`: h5ad queries now carry `uns["query_build"]` / `uns["poisson_readout"]`
|
| 370 |
+
into `labels.manifest.json` (`query_build`, `readout`). Verified with the trained 6k model on
|
| 371 |
+
500 held-out reference cells wrapped as a query (L1 acc 0.996, 0 padded genes) — the 6k model
|
| 372 |
+
cannot serve as a stand-in for the 2,980 queries themselves (5,944 model genes ⊄ 2,980).
|
| 373 |
+
|
| 374 |
+
**Run order (all three steps have now been run once; re-run for new NPZs):**
|
| 375 |
+
|
| 376 |
+
```bash
|
| 377 |
+
PLAN=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916
|
| 378 |
+
# 1. GPU (after the 20097 unit is inactive; never concurrently): preflight per §8, then
|
| 379 |
+
"$PLAN/launch_train.sh" 2980 # -> runs/aifi_scanvi_2980_v1 (expect < the 6k's 15 min)
|
| 380 |
+
# 2. CPU: annotate every set with the one model (truth sets included)
|
| 381 |
+
"$PLAN/annotate_all_2980.sh" # -> analysis/scsemi_benchmark_diffusion_20260913/labels_scanvi2980/<set>/
|
| 382 |
+
# 3. CPU: metrics on the 2,980 panel, scANVI labels for generated AND truth cells
|
| 383 |
+
"$PLAN/evaluate_all_2980.sh" # -> analysis/scsemi_benchmark_diffusion_20260913/metrics_scanvi2980/
|
| 384 |
+
# later, when generated_4x_e5 / 16x_e1 / 16x_e2 NPZs have finished:
|
| 385 |
+
CUDA_VISIBLE_DEVICES="" /home/sj_server_1/miniforge3/envs/scanvi/bin/python "$PLAN/build_benchmark_queries.py" --verify-reproducibility
|
| 386 |
+
"$PLAN/annotate_all_2980.sh" diffusion_4x_e5 ... && "$PLAN/evaluate_all_2980.sh"
|
| 387 |
+
```
|
| 388 |
+
|
| 389 |
+
`evaluate_all_2980.sh` runs each diffusion set against scSemiProfiler and PBISC-VAE
|
| 390 |
+
(3-method common gate, comparable to `metrics/panel_6k_e4_2980genes*`), plus one all-methods
|
| 391 |
+
run; Welch/BH, both `metric_summary.csv` (BIBM-asymmetric) and
|
| 392 |
+
`metric_summary_common_celltype_gate.csv`, label scheme PBMC5 + `Other` via
|
| 393 |
+
`--labels-from-manifest`; tidy table in `metrics_scanvi2980/summary_tidy.csv`.
|
paper-releases/20260928/diffusion/data/annotator/aifi_scanvi_f01common_v1/config_f01common.json
ADDED
|
@@ -0,0 +1,68 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
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|
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|
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|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"anndata": {
|
| 3 |
+
"batch_key": "batch_key",
|
| 4 |
+
"labels_key": "aifi_l2_train",
|
| 5 |
+
"layer": "counts",
|
| 6 |
+
"split_key": "split",
|
| 7 |
+
"train_split": "train",
|
| 8 |
+
"unlabeled_category": "Unknown",
|
| 9 |
+
"val_split": "val"
|
| 10 |
+
},
|
| 11 |
+
"eval": {
|
| 12 |
+
"batch_size": 2048,
|
| 13 |
+
"in_sample_cells": 20000,
|
| 14 |
+
"other_label": "Other",
|
| 15 |
+
"pbmc5_labels": [
|
| 16 |
+
"T cell",
|
| 17 |
+
"B cell",
|
| 18 |
+
"Monocyte",
|
| 19 |
+
"NK cell",
|
| 20 |
+
"DC"
|
| 21 |
+
]
|
| 22 |
+
},
|
| 23 |
+
"l2_to_l1_module": "/home/sj_server_1/PBISC/immun-pbmc-fm/model/vae_bulk2sc/scripts/aifi_celltype_mapping.py",
|
| 24 |
+
"output_dir": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/runs/aifi_scanvi_f01common_v1",
|
| 25 |
+
"reference_h5ad": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/reference/aifi_reference_f01common.h5ad",
|
| 26 |
+
"reference_manifest": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/reference/REFERENCE_MANIFEST.json",
|
| 27 |
+
"run_name": "aifi_scanvi_f01common_v1",
|
| 28 |
+
"scanvi_model": {
|
| 29 |
+
"classifier_parameters": {
|
| 30 |
+
"dropout_rate": 0.1,
|
| 31 |
+
"n_hidden": 128,
|
| 32 |
+
"n_layers": 2
|
| 33 |
+
},
|
| 34 |
+
"linear_classifier": false
|
| 35 |
+
},
|
| 36 |
+
"scanvi_train": {
|
| 37 |
+
"batch_size": 1024,
|
| 38 |
+
"check_val_every_n_epoch": 1,
|
| 39 |
+
"max_epochs": 25,
|
| 40 |
+
"n_samples_per_label": 100,
|
| 41 |
+
"plan_kwargs": {
|
| 42 |
+
"classification_ratio": 50,
|
| 43 |
+
"lr": 0.001
|
| 44 |
+
},
|
| 45 |
+
"train_size": 0.9
|
| 46 |
+
},
|
| 47 |
+
"scvi_model": {
|
| 48 |
+
"dispersion": "gene",
|
| 49 |
+
"dropout_rate": 0.1,
|
| 50 |
+
"gene_likelihood": "nb",
|
| 51 |
+
"n_hidden": 256,
|
| 52 |
+
"n_latent": 30,
|
| 53 |
+
"n_layers": 2
|
| 54 |
+
},
|
| 55 |
+
"scvi_train": {
|
| 56 |
+
"batch_size": 1024,
|
| 57 |
+
"check_val_every_n_epoch": 1,
|
| 58 |
+
"early_stopping": true,
|
| 59 |
+
"early_stopping_monitor": "elbo_validation",
|
| 60 |
+
"early_stopping_patience": 8,
|
| 61 |
+
"max_epochs": 40,
|
| 62 |
+
"plan_kwargs": {
|
| 63 |
+
"lr": 0.001
|
| 64 |
+
},
|
| 65 |
+
"train_size": 0.9
|
| 66 |
+
},
|
| 67 |
+
"seed": 20260916
|
| 68 |
+
}
|
paper-releases/20260928/diffusion/data/annotator/aifi_scanvi_f01common_v1/genes.csv
ADDED
|
@@ -0,0 +1,1006 @@
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|
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| 1 |
+
gene_id,gene_symbol,panel_index,tier_rank,atlas_col,benchmark_working_index
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| 2 |
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ENSG00000165029,ABCA1,9142,2934,15820,0
|
| 3 |
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ENSG00000107331,ABCA2,9478,3040,16279,1
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| 4 |
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ENSG00000085563,ABCB1,7519,2421,12045,2
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| 5 |
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ENSG00000005471,ABCB4,7518,2420,12044,3
|
| 6 |
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ENSG00000173208,ABCD2,11864,3708,19868,4
|
| 7 |
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ENSG00000166016,ABTB2,10610,3381,16845,5
|
| 8 |
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ENSG00000240303,ACAD11,4056,1293,6311,6
|
| 9 |
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ENSG00000078124,ACER3,11192,3490,17630,7
|
| 10 |
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ENSG00000111644,ACRBP,11632,3619,19503,8
|
| 11 |
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ENSG00000151726,ACSL1,5130,1653,8146,9
|
| 12 |
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ENSG00000005187,ACSM3,14434,4527,24615,10
|
| 13 |
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ENSG00000115170,ACVR1,2812,863,4477,11
|
| 14 |
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ENSG00000123612,ACVR1C,2811,862,4475,12
|
| 15 |
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ENSG00000121989,ACVR2A,2782,847,4425,13
|
| 16 |
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ENSG00000170634,ACYP2,2335,688,3578,14
|
| 17 |
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ENSG00000148848,ADAM12,10206,3293,19320,15
|
| 18 |
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ENSG00000135074,ADAM19,5889,1895,9504,16
|
| 19 |
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ENSG00000114948,ADAM23,3046,958,4852,17
|
| 20 |
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ENSG00000042980,ADAM28,8209,2630,14156,18
|
| 21 |
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ENSG00000049192,ADAMTS6,5367,1721,8637,19
|
| 22 |
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ENSG00000184060,ADAP2,15416,4797,26290,20
|
| 23 |
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ENSG00000162104,ADCY9,14315,4489,24389,21
|
| 24 |
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ENSG00000156110,ADK,9850,3179,18794,22
|
| 25 |
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ENSG00000111863,ADTRP,6114,1960,9911,23
|
| 26 |
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ENSG00000196526,AFAP1,4486,1433,7022,24
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| 27 |
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ENSG00000144218,AFF3,2599,785,4076,25
|
| 28 |
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ENSG00000157985,AGAP1,3253,1016,5152,26
|
| 29 |
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ENSG00000026652,AGPAT4,7067,2266,11271,27
|
| 30 |
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ENSG00000135541,AHI1,6942,2211,11046,28
|
| 31 |
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ENSG00000204472,AIF1,6341,2015,10219,29
|
| 32 |
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ENSG00000154027,AK5,816,221,1229,30
|
| 33 |
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ENSG00000196139,AKR1C3,9533,3054,18259,31
|
| 34 |
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ENSG00000117020,AKT3,1988,577,2966,32
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| 35 |
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ENSG00000170017,ALCAM,3877,1223,6051,33
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| 36 |
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ENSG00000012779,ALOX5,9688,3123,18557,34
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| 37 |
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ENSG00000073331,ALPK1,4917,1579,7720,35
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| 38 |
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ENSG00000151743,AMN1,11850,3702,19842,36
|
| 39 |
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ENSG00000029534,ANK1,8304,2668,14328,37
|
| 40 |
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ENSG00000151150,ANK3,9752,3139,18653,38
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| 41 |
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ENSG00000154122,ANKH,5211,1667,8345,39
|
| 42 |
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ENSG00000152766,ANKRD22,9917,3195,18913,40
|
| 43 |
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ENSG00000206560,ANKRD28,3411,1068,5404,41
|
| 44 |
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ENSG00000135976,ANKRD36,2573,775,4035,42
|
| 45 |
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ENSG00000166825,ANPEP,14093,4436,24008,43
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| 46 |
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ENSG00000163297,ANTXR2,4779,1526,7504,44
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| 47 |
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ENSG00000136250,AOAH,7308,2353,11712,45
|
| 48 |
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ENSG00000034053,APBA2,13588,4263,23165,46
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| 49 |
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ENSG00000128383,APOBEC3A,19004,5735,31606,47
|
| 50 |
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ENSG00000178878,APOLD1,11753,3667,19667,48
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| 51 |
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ENSG00000109321,AREG,4743,1512,7451,49
|
| 52 |
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ENSG00000071205,ARHGAP10,5017,1616,7925,50
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| 53 |
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ENSG00000146376,ARHGAP18,6906,2197,10990,51
|
| 54 |
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ENSG00000128805,ARHGAP22,9714,3128,18592,52
|
| 55 |
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ENSG00000138639,ARHGAP24,4805,1538,7540,53
|
| 56 |
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ENSG00000145819,ARHGAP26,5790,1862,9361,54
|
| 57 |
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ENSG00000031081,ARHGAP31,3938,1247,6156,55
|
| 58 |
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ENSG00000134909,ARHGAP32,11545,3584,18159,56
|
| 59 |
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ENSG00000047648,ARHGAP6,19200,5794,12905,57
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| 60 |
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ENSG00000074964,ARHGEF10L,235,58,379,58
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| 61 |
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ENSG00000196914,ARHGEF12,11459,3566,18038,59
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| 62 |
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ENSG00000150347,ARID5B,9757,3140,18664,60
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| 63 |
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ENSG00000228696,ARL17B,15751,4861,26764,61
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| 64 |
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ENSG00000113273,ARSB,5448,1761,8765,62
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| 65 |
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ENSG00000153317,ASAP1,8616,2768,14965,63
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| 66 |
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ENSG00000169136,ATF5,17637,5366,30532,64
|
| 67 |
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ENSG00000197548,ATG7,3371,1048,5350,65
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| 68 |
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ENSG00000206190,ATP10A,13576,4260,23142,66
|
| 69 |
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ENSG00000058668,ATP2B4,1730,496,2539,67
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| 70 |
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ENSG00000124406,ATP8A1,4607,1478,7247,68
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| 71 |
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ENSG00000104043,ATP8B4,13774,4326,23463,69
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| 72 |
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ENSG00000054793,ATP9A,18373,5566,28758,70
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| 73 |
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ENSG00000124788,ATXN1,6130,1971,9945,71
|
| 74 |
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ENSG00000158321,AUTS2,7428,2393,11941,72
|
| 75 |
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ENSG00000169857,AVEN,13615,4270,23214,73
|
| 76 |
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ENSG00000109956,B3GAT1,11567,3594,18200,74
|
| 77 |
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ENSG00000158470,B4GALT5,18355,5559,28729,75
|
| 78 |
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ENSG00000164929,BAALC,8537,2742,14800,76
|
| 79 |
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ENSG00000156273,BACH1,18516,5604,31993,77
|
| 80 |
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ENSG00000112182,BACH2,6768,2141,10777,78
|
| 81 |
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ENSG00000153064,BANK1,4870,1562,7641,79
|
| 82 |
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ENSG00000176788,BASP1,5217,1669,8359,80
|
| 83 |
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ENSG00000137936,BCAR3,896,261,1356,81
|
| 84 |
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ENSG00000119866,BCL11A,2352,697,3612,82
|
| 85 |
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ENSG00000127152,BCL11B,13473,4233,22761,83
|
| 86 |
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ENSG00000171791,BCL2,16408,5062,27958,84
|
| 87 |
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ENSG00000121380,BCL2L14,11745,3661,19654,85
|
| 88 |
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ENSG00000110987,BCL7A,12523,3910,20919,86
|
| 89 |
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ENSG00000136573,BLK,8123,2604,14019,87
|
| 90 |
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ENSG00000095585,BLNK,9975,3226,18997,88
|
| 91 |
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ENSG00000138756,BMP2K,4775,1524,7496,89
|
| 92 |
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ENSG00000107779,BMPR1A,9896,3191,18886,90
|
| 93 |
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ENSG00000173068,BNC2,8793,2817,15250,91
|
| 94 |
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ENSG00000183826,BTBD9,6504,2068,10408,92
|
| 95 |
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ENSG00000186265,BTLA,3906,1234,6100,93
|
| 96 |
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ENSG00000173715,C11orf80,11055,3451,17417,94
|
| 97 |
+
ENSG00000179088,C12orf42,12351,3843,20642,95
|
| 98 |
+
ENSG00000166920,C15orf48,13753,4319,23421,96
|
| 99 |
+
ENSG00000154102,C16orf74,14947,4663,25503,97
|
| 100 |
+
ENSG00000173372,C1QA,291,70,455,98
|
| 101 |
+
ENSG00000173369,C1QB,294,71,457,99
|
| 102 |
+
ENSG00000116667,C1orf21,1639,471,2385,100
|
| 103 |
+
ENSG00000143443,C1orf56,1175,358,1774,101
|
| 104 |
+
ENSG00000088854,C20orf194,18006,5461,28172,102
|
| 105 |
+
ENSG00000187699,C2orf88,2962,916,4720,103
|
| 106 |
+
ENSG00000197405,C5AR1,17522,5342,30391,104
|
| 107 |
+
ENSG00000148120,C9orf3,9076,2906,15718,105
|
| 108 |
+
ENSG00000158966,CACHD1,767,204,1137,106
|
| 109 |
+
ENSG00000182985,CADM1,11386,3547,17927,107
|
| 110 |
+
ENSG00000122786,CALD1,7851,2527,12497,108
|
| 111 |
+
ENSG00000152495,CAMK4,5532,1806,8956,109
|
| 112 |
+
ENSG00000198286,CARD11,7139,2288,11424,110
|
| 113 |
+
ENSG00000114423,CBLB,3878,1224,6052,111
|
| 114 |
+
ENSG00000163492,CCDC141,2926,902,4662,112
|
| 115 |
+
ENSG00000135205,CCDC146,7495,2408,12004,113
|
| 116 |
+
ENSG00000115355,CCDC88A,2344,693,3593,114
|
| 117 |
+
ENSG00000108691,CCL2,15439,4806,26348,115
|
| 118 |
+
ENSG00000108700,CCL8,15442,4807,26351,116
|
| 119 |
+
ENSG00000163823,CCR1,3564,1133,5623,117
|
| 120 |
+
ENSG00000183813,CCR4,3458,1095,5480,118
|
| 121 |
+
ENSG00000126353,CCR7,15564,4843,26525,119
|
| 122 |
+
ENSG00000184305,CCSER1,4838,1552,7585,120
|
| 123 |
+
ENSG00000170458,CD14,5712,1859,9260,121
|
| 124 |
+
ENSG00000117281,CD160,1091,327,1666,122
|
| 125 |
+
ENSG00000177575,CD163,11665,3632,19543,123
|
| 126 |
+
ENSG00000177455,CD19,14513,4559,24742,124
|
| 127 |
+
ENSG00000091972,CD200,3905,1233,6098,125
|
| 128 |
+
ENSG00000012124,CD22,17160,5246,29936,126
|
| 129 |
+
ENSG00000150637,CD226,16429,5067,28004,127
|
| 130 |
+
ENSG00000198821,CD247,1522,429,2204,128
|
| 131 |
+
ENSG00000120217,CD274,8768,2806,15200,129
|
| 132 |
+
ENSG00000178562,CD28,3036,951,4834,130
|
| 133 |
+
ENSG00000167851,CD300A,16007,4948,27183,131
|
| 134 |
+
ENSG00000186407,CD300E,16012,4949,27188,132
|
| 135 |
+
ENSG00000135218,CD36,7504,2415,12021,133
|
| 136 |
+
ENSG00000004468,CD38,4532,1444,7097,134
|
| 137 |
+
ENSG00000167286,CD3D,11415,3556,17976,135
|
| 138 |
+
ENSG00000198851,CD3E,11414,3555,17975,136
|
| 139 |
+
ENSG00000010610,CD4,11640,3622,19513,137
|
| 140 |
+
ENSG00000129226,CD68,15192,4731,25885,138
|
| 141 |
+
ENSG00000110848,CD69,11695,3642,19591,139
|
| 142 |
+
ENSG00000173762,CD7,16177,4977,27440,140
|
| 143 |
+
ENSG00000137101,CD72,8901,2842,15413,141
|
| 144 |
+
ENSG00000019582,CD74,5842,1876,9438,142
|
| 145 |
+
ENSG00000105369,CD79A,17362,5308,30196,143
|
| 146 |
+
ENSG00000007312,CD79B,15947,4924,27068,144
|
| 147 |
+
ENSG00000121594,CD80,3942,1250,6162,145
|
| 148 |
+
ENSG00000112149,CD83,6125,1966,9932,146
|
| 149 |
+
ENSG00000153563,CD8A,2523,756,3879,147
|
| 150 |
+
ENSG00000172116,CD8B,2524,757,3880,148
|
| 151 |
+
ENSG00000010278,CD9,11616,3612,19481,149
|
| 152 |
+
ENSG00000079335,CDC14A,925,275,1410,150
|
| 153 |
+
ENSG00000128536,CDHR3,7719,2486,12278,151
|
| 154 |
+
ENSG00000058091,CDK14,7534,2427,12065,152
|
| 155 |
+
ENSG00000105810,CDK6,7551,2433,12083,153
|
| 156 |
+
ENSG00000124762,CDKN1A,6487,2058,10389,154
|
| 157 |
+
ENSG00000129757,CDKN1C,10331,3319,16443,155
|
| 158 |
+
ENSG00000166446,CDYL2,14905,4653,25431,156
|
| 159 |
+
ENSG00000079385,CEACAM1,17385,5313,30219,157
|
| 160 |
+
ENSG00000007129,CEACAM21,17352,5306,30184,158
|
| 161 |
+
ENSG00000221869,CEBPD,8325,2677,14360,159
|
| 162 |
+
ENSG00000117724,CENPF,1833,527,2698,160
|
| 163 |
+
ENSG00000100162,CENPM,19058,5757,31674,161
|
| 164 |
+
ENSG00000188312,CENPP,9053,2898,15685,162
|
| 165 |
+
ENSG00000148019,CEP78,9005,2876,15600,163
|
| 166 |
+
ENSG00000197766,CFD,16486,5078,29004,164
|
| 167 |
+
ENSG00000000971,CFH,1665,483,2444,165
|
| 168 |
+
ENSG00000126759,CFP,19361,5838,13122,166
|
| 169 |
+
ENSG00000128656,CHN1,2898,896,4607,167
|
| 170 |
+
ENSG00000106069,CHN2,7267,2338,11641,168
|
| 171 |
+
ENSG00000171310,CHST11,12362,3846,20661,169
|
| 172 |
+
ENSG00000179583,CIITA,14361,4500,24468,170
|
| 173 |
+
ENSG00000132514,CLEC10A,15149,4724,25838,171
|
| 174 |
+
ENSG00000172322,CLEC12A,11700,3644,19599,172
|
| 175 |
+
ENSG00000187912,CLEC17A,16912,5174,29550,173
|
| 176 |
+
ENSG00000172243,CLEC7A,11705,3645,19605,174
|
| 177 |
+
ENSG00000169583,CLIC3,9477,3039,16278,175
|
| 178 |
+
ENSG00000169504,CLIC4,327,84,504,176
|
| 179 |
+
ENSG00000120885,CLU,8229,2640,14187,177
|
| 180 |
+
ENSG00000187118,CMC1,3441,1085,5456,178
|
| 181 |
+
ENSG00000174600,CMKLR1,12383,3860,20693,179
|
| 182 |
+
ENSG00000134326,CMPK2,2085,593,3134,180
|
| 183 |
+
ENSG00000170293,CMTM8,3452,1092,5473,181
|
| 184 |
+
ENSG00000149970,CNKSR2,19257,5813,12981,182
|
| 185 |
+
ENSG00000188822,CNR2,315,79,486,183
|
| 186 |
+
ENSG00000082438,COBLL1,2839,877,4522,184
|
| 187 |
+
ENSG00000133103,COG6,12712,3978,21277,185
|
| 188 |
+
ENSG00000082293,COL19A1,6684,2108,10647,186
|
| 189 |
+
ENSG00000081052,COL4A4,3173,996,5046,187
|
| 190 |
+
ENSG00000142173,COL6A2,18697,5648,32303,188
|
| 191 |
+
ENSG00000198756,COLGALT2,1637,469,2381,189
|
| 192 |
+
ENSG00000206561,COLQ,3408,1065,5401,190
|
| 193 |
+
ENSG00000145781,COMMD10,5557,1813,9003,191
|
| 194 |
+
ENSG00000107864,CPEB3,9940,3208,18952,192
|
| 195 |
+
ENSG00000106034,CPED1,7773,2502,12366,193
|
| 196 |
+
ENSG00000135678,CPM,12219,3800,20388,194
|
| 197 |
+
ENSG00000104324,CPQ,8503,2730,14733,195
|
| 198 |
+
ENSG00000106066,CPVL,7266,2337,11638,196
|
| 199 |
+
ENSG00000203710,CR1,1790,513,2620,197
|
| 200 |
+
ENSG00000117322,CR2,1789,512,2619,198
|
| 201 |
+
ENSG00000150938,CRIM1,2249,648,3434,199
|
| 202 |
+
ENSG00000213145,CRIP1,13545,4254,22891,200
|
| 203 |
+
ENSG00000008405,CRY1,12377,3857,20683,201
|
| 204 |
+
ENSG00000182578,CSF1R,5835,1874,9431,202
|
| 205 |
+
ENSG00000119535,CSF3R,504,135,743,203
|
| 206 |
+
ENSG00000147408,CSGALNACT1,8162,2613,14083,204
|
| 207 |
+
ENSG00000101439,CST3,18108,5491,28374,205
|
| 208 |
+
ENSG00000077984,CST7,18115,5492,28390,206
|
| 209 |
+
ENSG00000121552,CSTA,3970,1260,6194,207
|
| 210 |
+
ENSG00000175029,CTBP2,10198,3291,19306,208
|
| 211 |
+
ENSG00000144677,CTDSPL,3476,1100,5509,209
|
| 212 |
+
ENSG00000163599,CTLA4,3037,952,4835,210
|
| 213 |
+
ENSG00000164733,CTSB,8127,2605,14026,211
|
| 214 |
+
ENSG00000117984,CTSD,10312,3314,16415,212
|
| 215 |
+
ENSG00000135047,CTSL,9031,2889,15648,213
|
| 216 |
+
ENSG00000163131,CTSS,1165,353,1762,214
|
| 217 |
+
ENSG00000172543,CTSW,11014,3444,17364,215
|
| 218 |
+
ENSG00000107611,CUBN,9594,3080,18366,216
|
| 219 |
+
ENSG00000168329,CX3CR1,3496,1110,5533,217
|
| 220 |
+
ENSG00000169245,CXCL10,4757,1516,7474,218
|
| 221 |
+
ENSG00000169248,CXCL11,4758,1517,7475,219
|
| 222 |
+
ENSG00000081041,CXCL2,4739,1509,7445,220
|
| 223 |
+
ENSG00000163734,CXCL3,4738,1508,7444,221
|
| 224 |
+
ENSG00000138755,CXCL9,4755,1515,7472,222
|
| 225 |
+
ENSG00000121966,CXCR4,2772,840,4390,223
|
| 226 |
+
ENSG00000160683,CXCR5,11428,3560,17994,224
|
| 227 |
+
ENSG00000165168,CYBB,19312,5824,13048,225
|
| 228 |
+
ENSG00000138061,CYP1B1,2264,657,3455,226
|
| 229 |
+
ENSG00000135929,CYP27A1,3113,980,4965,227
|
| 230 |
+
ENSG00000008256,CYTH3,7164,2300,11462,228
|
| 231 |
+
ENSG00000153071,DAB2,5286,1689,8492,229
|
| 232 |
+
ENSG00000196730,DAPK1,9030,2888,15647,230
|
| 233 |
+
ENSG00000035664,DAPK2,13855,4358,23616,231
|
| 234 |
+
ENSG00000168209,DDIT4,9820,3164,18750,232
|
| 235 |
+
ENSG00000137628,DDX60,5086,1640,8046,233
|
| 236 |
+
ENSG00000181381,DDX60L,5087,1641,8047,234
|
| 237 |
+
ENSG00000119522,DENND1A,9262,2980,15988,235
|
| 238 |
+
ENSG00000170456,DENND5B,11847,3701,19839,236
|
| 239 |
+
ENSG00000099958,DERL3,18815,5676,31287,237
|
| 240 |
+
ENSG00000162496,DHRS3,159,45,269,238
|
| 241 |
+
ENSG00000147202,DIAPH2,19613,5899,13418,239
|
| 242 |
+
ENSG00000151240,DIP2C,9520,3046,18210,240
|
| 243 |
+
ENSG00000138463,DIRC2,3980,1267,6205,241
|
| 244 |
+
ENSG00000162946,DISC1,1942,554,2878,242
|
| 245 |
+
ENSG00000170579,DLGAP1,16213,4993,27517,243
|
| 246 |
+
ENSG00000104093,DMXL2,13787,4331,23483,244
|
| 247 |
+
ENSG00000197959,DNM3,1562,443,2263,245
|
| 248 |
+
ENSG00000128512,DOCK4,7743,2494,12315,246
|
| 249 |
+
ENSG00000147459,DOCK5,8214,2632,14166,247
|
| 250 |
+
ENSG00000205683,DPF3,13307,4166,22458,248
|
| 251 |
+
ENSG00000197635,DPP4,2831,874,4509,249
|
| 252 |
+
ENSG00000188641,DPYD,910,267,1384,250
|
| 253 |
+
ENSG00000136048,DRAM1,12343,3840,20627,251
|
| 254 |
+
ENSG00000111817,DSE,6851,2177,10920,252
|
| 255 |
+
ENSG00000197057,DTHD1,4569,1463,7185,253
|
| 256 |
+
ENSG00000138101,DTNB,2164,623,3311,254
|
| 257 |
+
ENSG00000135144,DTX1,12445,3884,20772,255
|
| 258 |
+
ENSG00000120129,DUSP1,5944,1907,9621,256
|
| 259 |
+
ENSG00000111266,DUSP16,11749,3664,19659,257
|
| 260 |
+
ENSG00000158050,DUSP2,2554,766,4009,258
|
| 261 |
+
ENSG00000120875,DUSP4,8245,2649,14211,259
|
| 262 |
+
ENSG00000139318,DUSP6,12284,3815,20518,260
|
| 263 |
+
ENSG00000135636,DYSF,2428,727,3744,261
|
| 264 |
+
ENSG00000133740,E2F5,8451,2715,14632,262
|
| 265 |
+
ENSG00000164330,EBF1,5896,1897,9520,263
|
| 266 |
+
ENSG00000117298,ECE1,276,66,437,264
|
| 267 |
+
ENSG00000158813,EDA,19514,5866,13299,265
|
| 268 |
+
ENSG00000135960,EDAR,2643,802,4168,266
|
| 269 |
+
ENSG00000186197,EDARADD,1965,566,2925,267
|
| 270 |
+
ENSG00000254772,EEF1G,10885,3425,17205,268
|
| 271 |
+
ENSG00000122547,EEPD1,7305,2352,11706,269
|
| 272 |
+
ENSG00000140025,EFCAB11,13401,4206,22641,270
|
| 273 |
+
ENSG00000178852,EFCAB13,15765,4869,26782,271
|
| 274 |
+
ENSG00000096093,EFHC1,6641,2096,10587,272
|
| 275 |
+
ENSG00000129521,EGLN3,13103,4088,22103,273
|
| 276 |
+
ENSG00000120738,EGR1,5678,1849,9215,274
|
| 277 |
+
ENSG00000179388,EGR3,8193,2622,14129,275
|
| 278 |
+
ENSG00000172071,EIF2AK3,2535,760,3897,276
|
| 279 |
+
ENSG00000184110,EIF3C,14506,4556,24729,277
|
| 280 |
+
ENSG00000205609,EIF3CL,14495,4553,24716,278
|
| 281 |
+
ENSG00000075151,EIF4G3,275,65,436,279
|
| 282 |
+
ENSG00000118985,ELL2,5502,1793,8888,280
|
| 283 |
+
ENSG00000170522,ELOVL6,4911,1578,7708,281
|
| 284 |
+
ENSG00000132205,EMILIN2,16207,4988,27501,282
|
| 285 |
+
ENSG00000134531,EMP1,11761,3669,19677,283
|
| 286 |
+
ENSG00000136960,ENPP2,8577,2751,14874,284
|
| 287 |
+
ENSG00000138185,ENTPD1,9970,3222,18991,285
|
| 288 |
+
ENSG00000163508,EOMES,3440,1084,5452,286
|
| 289 |
+
ENSG00000079819,EPB41L2,6912,2200,10998,287
|
| 290 |
+
ENSG00000082397,EPB41L3,16216,4995,27533,288
|
| 291 |
+
ENSG00000129595,EPB41L4A,5535,1809,8963,289
|
| 292 |
+
ENSG00000086289,EPDR1,7314,2358,11720,290
|
| 293 |
+
ENSG00000116106,EPHA4,3152,986,5017,291
|
| 294 |
+
ENSG00000133216,EPHB2,295,72,458,292
|
| 295 |
+
ENSG00000133106,EPSTI1,12728,3987,21301,293
|
| 296 |
+
ENSG00000124882,EREG,4742,1511,7449,294
|
| 297 |
+
ENSG00000140009,ESR2,13249,4142,22364,295
|
| 298 |
+
ENSG00000139083,ETV6,11744,3660,19653,296
|
| 299 |
+
ENSG00000166979,EVA1C,18561,5608,32053,297
|
| 300 |
+
ENSG00000067208,EVI5,888,256,1348,298
|
| 301 |
+
ENSG00000110723,EXPH5,11332,3532,17850,299
|
| 302 |
+
ENSG00000182197,EXT1,8571,2749,14863,300
|
| 303 |
+
ENSG00000124491,F13A1,6083,1949,9861,301
|
| 304 |
+
ENSG00000181104,F2R,5433,1755,8746,302
|
| 305 |
+
ENSG00000198734,F5,1541,437,2232,303
|
| 306 |
+
ENSG00000165591,FAAH2,19490,5860,13270,304
|
| 307 |
+
ENSG00000189057,FAM111B,10802,3410,17097,305
|
| 308 |
+
ENSG00000138439,FAM117B,3028,944,4825,306
|
| 309 |
+
ENSG00000167483,FAM129C,16991,5185,29661,307
|
| 310 |
+
ENSG00000138640,FAM13A,4833,1549,7574,308
|
| 311 |
+
ENSG00000204677,FAM153C,6001,1924,9716,309
|
| 312 |
+
ENSG00000198780,FAM169A,5422,1748,8727,310
|
| 313 |
+
ENSG00000111879,FAM184A,6876,2185,10949,311
|
| 314 |
+
ENSG00000183662,FAM19A1,3789,1188,5914,312
|
| 315 |
+
ENSG00000198673,FAM19A2,12183,3781,20313,313
|
| 316 |
+
ENSG00000108950,FAM20A,15982,4940,27124,314
|
| 317 |
+
ENSG00000183508,FAM46C,1058,315,1611,315
|
| 318 |
+
ENSG00000197872,FAM49A,2120,608,3228,316
|
| 319 |
+
ENSG00000154511,FAM69A,890,258,1350,317
|
| 320 |
+
ENSG00000064763,FAR2,11838,3697,19817,318
|
| 321 |
+
ENSG00000186431,FCAR,17793,5410,30741,319
|
| 322 |
+
ENSG00000179639,FCER1A,1417,398,2063,320
|
| 323 |
+
ENSG00000158869,FCER1G,1473,411,2132,321
|
| 324 |
+
ENSG00000104921,FCER2,16695,5121,29278,322
|
| 325 |
+
ENSG00000203747,FCGR3A,1483,414,2145,323
|
| 326 |
+
ENSG00000162747,FCGR3B,1486,416,2147,324
|
| 327 |
+
ENSG00000085265,FCN1,9423,3032,16204,325
|
| 328 |
+
ENSG00000163534,FCRL1,1387,392,2031,326
|
| 329 |
+
ENSG00000132704,FCRL2,1386,391,2030,327
|
| 330 |
+
ENSG00000160856,FCRL3,1385,390,2028,328
|
| 331 |
+
ENSG00000143297,FCRL5,1383,389,2026,329
|
| 332 |
+
ENSG00000181036,FCRL6,1425,399,2072,330
|
| 333 |
+
ENSG00000132185,FCRLA,1488,418,2150,331
|
| 334 |
+
ENSG00000139132,FGD4,11855,3704,19850,332
|
| 335 |
+
ENSG00000137441,FGFBP2,4534,1445,7099,333
|
| 336 |
+
ENSG00000127951,FGL2,7496,2409,12005,334
|
| 337 |
+
ENSG00000189283,FHIT,3771,1184,5878,335
|
| 338 |
+
ENSG00000122025,FLT3,12662,3963,21188,336
|
| 339 |
+
ENSG00000119686,FLVCR2,13355,4188,22528,337
|
| 340 |
+
ENSG00000248905,FMN1,13613,4269,23208,338
|
| 341 |
+
ENSG00000157827,FMNL2,2800,858,4457,339
|
| 342 |
+
ENSG00000075420,FNDC3B,4241,1348,6625,340
|
| 343 |
+
ENSG00000052795,FNIP2,5063,1634,8009,341
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ENSG00000111885,MAN1A1,6877,2186,10950,564
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ENSG00000180611,MB21D2,4348,1380,6811,573
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ENSG00000204406,MBD5,2784,849,4428,574
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ENSG00000171444,MCC,5541,1811,8975,575
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ENSG00000153898,MCOLN2,839,232,1278,576
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ENSG00000081189,MEF2C,5481,1781,8842,580
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ENSG00000106780,MEGF9,9220,2966,15941,581
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ENSG00000153208,MERTK,2661,808,4201,582
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ENSG00000169519,METTL15,10581,3372,16792,583
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ENSG00000074416,MGLL,4016,1280,6257,584
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ENSG00000243156,MICAL3,18723,5656,31058,585
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ENSG00000132359,RAP1GAP2,15050,4696,25714,751
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ENSG00000109756,RAPGEF2,5065,1635,8011,752
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ENSG00000138670,RASGEF1B,4785,1529,7512,753
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ENSG00000152689,RASGRP3,2247,647,3425,755
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ENSG00000163694,RBM47,4595,1474,7226,756
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ENSG00000162444,RBP7,122,33,220,757
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ENSG00000104918,RETN,16691,5118,29274,758
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ENSG00000133119,RFC3,12688,3976,21239,759
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ENSG00000102760,RGCC,12722,3983,21290,760
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ENSG00000143344,RGL1,1635,468,2378,761
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ENSG00000090104,RGS1,1656,481,2428,763
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ENSG00000143878,RHOB,2137,613,3258,766
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ENSG00000132669,RIN2,18085,5486,28319,767
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ENSG00000129538,RNASE1,12973,4060,21790,768
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ENSG00000169385,RNASE2,12975,4061,21794,769
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ENSG00000151692,RNF144A,2087,595,3137,771
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ENSG00000137393,RNF144B,6142,1975,9958,772
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ENSG00000141576,RNF157,16070,4961,27258,773
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ENSG00000145428,RNF175,5038,1628,7965,775
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ENSG00000116514,RNF19B,459,120,684,776
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ENSG00000169855,ROBO1,3814,1199,5961,777
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ENSG00000069667,RORA,13841,4354,23574,778
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ENSG00000265681,RPL17,16356,5044,27848,779
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ENSG00000122026,RPL21,12652,3961,21174,780
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ENSG00000156482,RPL30,8508,2732,14740,781
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ENSG00000089009,RPL6,12439,3880,20765,782
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ENSG00000112306,RPS12,6932,2207,11021,783
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ENSG00000134419,RPS15A,14411,4523,24573,784
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ENSG00000186468,RPS23,5471,1774,8799,785
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ENSG00000197728,RPS26,12102,3763,20208,786
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ENSG00000143947,RPS27A,2342,692,3591,787
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ENSG00000145425,RPS3A,5023,1619,7939,788
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ENSG00000129824,RPS4Y1,20039,5982,30931,789
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ENSG00000136643,RPS6KC1,1829,525,2689,790
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ENSG00000133818,RRAS2,10510,3352,16682,791
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ENSG00000134321,RSAD2,2086,594,3135,792
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ENSG00000182010,RTKN2,9758,3141,18665,793
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ENSG00000139970,RTN1,13222,4130,22321,794
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ENSG00000188282,RUFY4,3093,974,4943,795
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ENSG00000163191,S100A11,1207,366,1818,796
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ENSG00000163221,S100A12,1256,368,1870,797
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ENSG00000196154,S100A4,1262,371,1878,798
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ENSG00000197956,S100A6,1260,370,1876,799
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ENSG00000143546,S100A8,1257,369,1871,800
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ENSG00000163220,S100A9,1255,367,1869,801
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ENSG00000160307,S100B,18707,5653,32318,802
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ENSG00000180739,S1PR5,16778,5140,29384,803
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ENSG00000177570,SAMD12,8572,2750,14864,804
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ENSG00000164483,SAMD3,6909,2199,10994,805
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ENSG00000020577,SAMD4A,13189,4116,22265,806
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ENSG00000177409,SAMD9L,7553,2435,12086,807
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ENSG00000111961,SASH1,6994,2235,11146,808
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ENSG00000133812,SBF2,10488,3341,16635,809
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ENSG00000173611,SCAI,9274,2985,16008,810
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ENSG00000151466,SCLT1,4968,1598,7809,811
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ENSG00000010803,SCMH1,560,145,828,812
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ENSG00000146285,SCML4,6806,2157,10845,813
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ENSG00000153253,SCN3A,2841,878,4525,814
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ENSG00000069188,SDK2,15999,4946,27164,815
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ENSG00000091490,SEL1L3,4561,1458,7150,816
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ENSG00000167680,SEMA6B,16614,5100,29173,817
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ENSG00000164300,SERINC5,5458,1767,8778,818
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ENSG00000197249,SERPINA1,13448,4226,22704,819
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ENSG00000197632,SERPINB2,16418,5063,27970,820
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ENSG00000149131,SERPING1,10770,3407,17061,821
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ENSG00000152217,SETBP1,16330,5032,27804,822
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ENSG00000118515,SGK1,6938,2208,11032,824
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ENSG00000080493,SLC4A4,4720,1503,7422,847
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ENSG00000172164,SNTB1,8584,2755,14883,856
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ENSG00000128487,SPECC1,15343,4777,26142,864
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ENSG00000269404,SPIB,17649,5367,30547,867
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ENSG00000164808,SPIDR,8324,2676,14359,868
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ENSG00000128040,SPINK2,4666,1495,7337,869
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ENSG00000159674,SPON2,4419,1413,6935,870
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ENSG00000166068,SPRED1,13636,4277,23251,871
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ENSG00000196220,SRGAP3,3335,1039,5301,872
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ENSG00000184005,ST6GALNAC3,813,219,1223,875
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ENSG00000111728,ST8SIA1,11809,3682,19745,876
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ENSG00000010327,STAB1,3716,1166,5797,877
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ENSG00000138134,STAMBPL1,9918,3196,18914,878
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ENSG00000035720,STAP1,4675,1497,7373,879
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ENSG00000159433,STARD9,13705,4302,23357,880
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ENSG00000115415,STAT1,2970,922,4730,881
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ENSG00000138378,STAT4,2971,923,4732,882
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ENSG00000105889,STEAP1B,7223,2321,11566,883
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ENSG00000104375,STK3,8513,2734,14746,884
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ENSG00000117632,STMN1,342,90,529,885
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ENSG00000140022,STON2,13389,4199,22588,886
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ENSG00000165209,STRBP,9260,2979,15986,887
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ENSG00000175600,SUGCT,7325,2362,11761,888
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ENSG00000196562,SULF2,18346,5554,28707,889
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ENSG00000196284,SUPT3H,6602,2088,10530,890
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ENSG00000131018,SYNE1,7023,2246,11192,893
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ENSG00000054654,SYNE2,13248,4141,22363,894
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ENSG00000182253,SYNM,14131,4447,24112,895
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ENSG00000137501,SYTL2,11226,3502,17693,896
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ENSG00000164674,SYTL3,7048,2259,11235,897
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ENSG00000109680,TBC1D19,4565,1460,7157,899
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ENSG00000204634,TBC1D8,2606,787,4090,900
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ENSG00000109436,TBC1D9,4988,1606,7872,901
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ENSG00000073861,TBX21,15769,4871,26789,902
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ENSG00000059377,TBXAS1,7883,2537,12540,903
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ENSG00000165929,TC2N,13414,4214,22666,904
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ENSG00000196628,TCF4,16380,5051,27884,905
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ENSG00000081059,TCF7,5631,1837,9144,906
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ENSG00000148737,TCF7L2,10116,3268,19191,907
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ENSG00000100721,TCL1A,13462,4229,22727,908
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ENSG00000009694,TENM1,19791,5931,13617,910
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ENSG00000187605,TET3,2449,732,3769,911
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ENSG00000121101,TEX14,15887,4901,26983,912
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ENSG00000151575,TEX9,13815,4341,23527,913
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ENSG00000105967,TFEC,7756,2500,12336,914
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ENSG00000003436,TFPI,2949,912,4704,915
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ENSG00000120708,TGFBI,5658,1843,9185,916
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ENSG00000069702,TGFBR3,879,251,1338,917
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ENSG00000137801,THBS1,13639,4280,23265,918
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ENSG00000172673,THEMIS,6903,2195,10983,919
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ENSG00000151090,THRB,3432,1081,5440,920
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ENSG00000146426,TIAM2,7034,2250,11209,921
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ENSG00000181847,TIGIT,3928,1244,6131,922
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ENSG00000007350,TKTL1,19980,5962,13848,923
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ENSG00000137462,TLR2,5037,1627,7964,924
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ENSG00000057704,TMCC3,12304,3825,20568,925
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ENSG00000121895,TMEM156,4581,1467,7206,926
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ENSG00000205269,TMEM170B,6113,1959,9909,927
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ENSG00000106565,TMEM176B,7986,2571,12750,928
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ENSG00000135048,TMEM2,8982,2865,15566,929
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ENSG00000151715,TMEM45B,11547,3585,18163,930
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ENSG00000171729,TMEM51,193,47,311,931
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ENSG00000133687,TMTC1,11841,3698,19826,932
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ENSG00000179104,TMTC2,12271,3812,20495,933
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ENSG00000232810,TNF,6337,2011,10215,934
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ENSG00000185215,TNFAIP2,13506,4243,22829,935
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ENSG00000118503,TNFAIP3,6954,2216,11072,936
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ENSG00000240505,TNFRSF13B,15296,4763,26055,937
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ENSG00000159958,TNFRSF13C,19057,5756,31673,938
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ENSG00000121858,TNFSF10,4243,1349,6627,939
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ENSG00000154310,TNIK,4238,1346,6620,940
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ENSG00000050730,TNIP3,4940,1588,7763,941
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ENSG00000079308,TNS1,3092,973,4941,942
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ENSG00000136205,TNS3,7368,2374,11827,943
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ENSG00000198846,TOX,8363,2687,14441,944
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ENSG00000124191,TOX2,18271,5537,28616,945
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ENSG00000076554,TPD52,8435,2710,14596,946
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ENSG00000188001,TPRG1,4335,1377,6789,947
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ENSG00000169902,TPST1,7420,2388,11923,948
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ENSG00000162722,TRIM58,2027,588,3024,949
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ENSG00000038382,TRIO,5208,1666,8341,950
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ENSG00000066651,TRMT11,6894,2192,10973,951
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ENSG00000104447,TRPS1,8564,2748,14852,952
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ENSG00000102804,TSC22D1,12735,3990,21318,953
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ENSG00000135951,TSGA10,2587,782,4063,954
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ENSG00000182463,TSHZ2,18376,5568,28766,955
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ENSG00000106537,TSPAN13,7203,2313,11530,956
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ENSG00000157570,TSPAN18,10639,3390,16908,957
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ENSG00000168785,TSPAN5,4849,1557,7611,958
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ENSG00000100154,TTC28,18854,5686,31375,959
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ENSG00000155158,TTC39B,8789,2815,15245,960
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ENSG00000155657,TTN,2925,901,4654,961
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ENSG00000101162,TUBB1,18413,5578,28834,962
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ENSG00000175106,TVP23C,15276,4754,26020,963
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ENSG00000074966,TXK,4624,1484,7277,964
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ENSG00000025708,TYMP,19144,5780,31821,965
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ENSG00000011600,TYROBP,17194,5252,29978,966
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ENSG00000182247,UBE2E2,3427,1077,5433,967
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ENSG00000174607,UGT8,4924,1582,7733,968
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ENSG00000168038,ULK4,3510,1116,5550,969
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ENSG00000184979,USP18,18727,5659,31064,970
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ENSG00000145390,USP53,4933,1586,7748,971
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ENSG00000148429,USP6NL,9559,3064,18319,972
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ENSG00000114374,USP9Y,20052,5984,30966,973
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ENSG00000183878,UTY,20054,5986,30968,974
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ENSG00000134215,VAV3,942,281,1441,975
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ENSG00000038427,VCAN,5474,1776,8811,976
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ENSG00000128218,VPREB3,18810,5675,31282,977
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ENSG00000028116,VRK2,2350,695,3604,978
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ENSG00000163625,WDFY3,4804,1537,7538,979
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ENSG00000128815,WDFY4,9715,3129,18594,980
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ENSG00000132530,XAF1,15139,4722,25827,981
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ENSG00000143184,XCL1,1534,434,2220,982
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ENSG00000143185,XCL2,1533,433,2219,983
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ENSG00000152422,XRCC4,5473,1775,8810,984
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ENSG00000103489,XYLT1,14407,4521,24561,985
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ENSG00000176105,YES1,16202,4985,27486,986
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ENSG00000100027,YPEL1,18792,5669,31163,987
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ENSG00000109906,ZBTB16,11377,3546,17916,988
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ENSG00000178199,ZC3H12D,6998,2238,11155,989
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ENSG00000204186,ZDBF2,3045,957,4851,990
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ENSG00000175048,ZDHHC14,7040,2253,11223,991
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ENSG00000169554,ZEB2,2781,846,4413,992
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ENSG00000128016,ZFP36,17287,5289,30108,993
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ENSG00000159733,ZFYVE28,4438,1418,6959,994
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ENSG00000157077,ZFYVE9,689,173,1003,995
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ENSG00000178764,ZHX2,8586,2756,14895,996
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ENSG00000130844,ZNF331,17750,5397,30685,997
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ENSG00000183621,ZNF438,9651,3103,18462,998
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ENSG00000101493,ZNF516,16445,5071,28049,999
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ENSG00000168916,ZNF608,5577,1820,9056,1000
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ENSG00000176083,ZNF683,359,97,546,1001
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ENSG00000140548,ZNF710,14097,4438,24012,1002
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ENSG00000151612,ZNF827,5008,1612,7909,1003
|
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ENSG00000124203,ZNF831,18416,5579,28837,1004
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ENSG00000130449,ZSWIM6,5355,1718,8623,1005
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