Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261001T023742Z-deck-calibration-giroux-sexgenes/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores_nochrY/giroux2022/fewshot_n5/draw3_scsemi5/per_donor.csv

@@ -0,0 +1,12 @@
+arm,donor,group,n_generated_cells,n_observed_cells,n_reference_cells,gen_T cell,obs_T cell,gen_B cell,obs_B cell,gen_Monocyte,obs_Monocyte,gen_NK cell,obs_NK cell,gen_DC,obs_DC,gen_Other,obs_Other,comp_l1,comp_l1_obs_loo_null,pb_r,pb_own_rank,pb_top1,precision,coverage,precision_depth_matched,coverage_depth_matched,mono_pct_gen,mono_pct_obs,median_umi_gen,median_umi_obs
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paper-releases/pbisc-diffusion/updates/20261001T023742Z-deck-calibration-giroux-sexgenes/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores_nochrY/giroux2022/fewshot_n5/draw3_scsemi5/summary.json

@@ -0,0 +1,525 @@
+{
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+ "written_utc": "2026-10-01T01:54:36.782124+00:00",
+ "command": [
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+  "--dataset",
+  "giroux2022",
+  "--threads",
+  "6",
+  "--donors",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/fewshot_n5/draw3/test_samples.txt",
+  "--case-groups",
+  "severe",
+  "--control-groups",
+  "mild",
+  "--arm",
+  "scsemi5=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/giroux2022/fewshot_n5/draw3/scsemi5__raw.npz",
+  "--out",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nochrY/giroux2022/fewshot_n5/draw3_scsemi5",
+  "--exclude-genes",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/sex_linked_genes/chrY_nonPAR_genes.csv",
+  "--reuse-labels",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/giroux2022/fewshot_n5/draw3_scsemi5"
+ ],
+ "arms": [
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+  "0BF51C_d0",
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+ "deg_definition": {
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+       "r_generated": 0.37357703012284427,
+       "r_broadcast": 0.4089393184513365,
+       "partial_r_generated_given_broadcast": 0.24426288992389913,
+       "r_generated_vs_broadcast": 0.41846710125178377,
+       "n_genes": 2944
+      }
+     },
+     "mean_r_generated": 0.3609923379806533,
+     "mean_r_broadcast": 0.46788561695151265,
+     "mean_partial_r": 0.2152050236161209
+    },
+    "pseudobulk_log1p_cpm": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.3670962419319874,
+       "r_broadcast": 0.5981602115733774,
+       "partial_r_generated_given_broadcast": 0.1696278740524292,
+       "r_generated_vs_broadcast": 0.4060281332957381,
+       "n_genes": 2902
+      },
+      "B cell": {
+       "r_generated": 0.1363946848621054,
+       "r_broadcast": 0.5480227438496978,
+       "partial_r_generated_given_broadcast": 0.018749344034823492,
+       "r_generated_vs_broadcast": 0.22097484186598987,
+       "n_genes": 3036
+      },
+      "Monocyte": {
+       "r_generated": 0.23994962402051254,
+       "r_broadcast": 0.6473562052917851,
+       "partial_r_generated_given_broadcast": 0.05437097550838561,
+       "r_generated_vs_broadcast": 0.30979464734144196,
+       "n_genes": 3056
+      },
+      "NK cell": {
+       "r_generated": 0.22896308231541926,
+       "r_broadcast": 0.5086947172287124,
+       "partial_r_generated_given_broadcast": 0.06813292419632191,
+       "r_generated_vs_broadcast": 0.3417287092863526,
+       "n_genes": 2944
+      }
+     },
+     "mean_r_generated": 0.24310090828250616,
+     "mean_r_broadcast": 0.5755584694858933,
+     "mean_partial_r": 0.07772027944799006
+    }
+   },
+   "deg_top50": {
+    "types_scored": [
+     "T cell",
+     "B cell",
+     "Monocyte",
+     "NK cell",
+     "DC"
+    ],
+    "types_not_scored": {},
+    "per_type": {
+     "T cell": {
+      "deg_scored": true,
+      "top50_overlap": 0,
+      "top50_jaccard": 0.0,
+      "obs_top_list_size": 28,
+      "gen_top_list_size": 0,
+      "top50_overlap_broadcast": 0,
+      "top50_jaccard_broadcast": 0.0,
+      "n_cells_obs": 69840,
+      "n_cells_gen": 9625
+     },
+     "B cell": {
+      "deg_scored": true,
+      "top50_overlap": 0,
+      "top50_jaccard": 0.0,
+      "obs_top_list_size": 5,
+      "gen_top_list_size": 1,
+      "top50_overlap_broadcast": 0,
+      "top50_jaccard_broadcast": 0.0,
+      "n_cells_obs": 11400,
+      "n_cells_gen": 650
+     },
+     "Monocyte": {
+      "deg_scored": true,
+      "top50_overlap": 0,
+      "top50_jaccard": null,
+      "obs_top_list_size": 0,
+      "gen_top_list_size": 0,
+      "top50_overlap_broadcast": 0,
+      "top50_jaccard_broadcast": null,
+      "n_cells_obs": 18916,
+      "n_cells_gen": 3890
+     },
+     "NK cell": {
+      "deg_scored": true,
+      "top50_overlap": 0,
+      "top50_jaccard": null,
+      "obs_top_list_size": 0,
+      "gen_top_list_size": 0,
+      "top50_overlap_broadcast": 0,
+      "top50_jaccard_broadcast": null,
+      "n_cells_obs": 12021,
+      "n_cells_gen": 1769
+     },
+     "DC": {
+      "deg_scored": true,
+      "top50_overlap": 0,
+      "top50_jaccard": null,
+      "obs_top_list_size": 0,
+      "gen_top_list_size": 0,
+      "top50_overlap_broadcast": 0,
+      "top50_jaccard_broadcast": null,
+      "n_cells_obs": 1706,
+      "n_cells_gen": 68
+     }
+    },
+    "sum": 0,
+    "sum_4type": 0,
+    "max": 33,
+    "mean": 0.0,
+    "sum_broadcast": 0,
+    "sum_broadcast_4type": 0,
+    "broadcast_n_significant": 0,
+    "seconds": 0.02
+   },
+   "seconds": 1.5
+  }
+ },
+ "code_sha256": {
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py": "ec5f40b4cea6d5062d0138f3b934abb6465e34cbfeac869c8a0906924c033aec",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/ft_common.py": "4a6366b0f22208cd58714fc0261b8840c643b9d4569704d560cfd93534cbc747",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/utility_studies_final6k_20260925/umap/cell_immicro_5arm/tools/stage_b_embed.py": "d056cfdf38fb0e5f478756f370174e993b75d0c973c48e02e21749175a4ca2da",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/bulk2sc-paper/scripts/evaluate_generated_vs_truth.py": "48b12d794782b840838853e93e0fc55fab00532c4b3698f4588e926b985d2881",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/utility_studies_final6k_20260925/immicro/tools/immicro_common.py": "c5c30e917148cc5e664e91ea30c11bbdff9b6d1f53a3e0a13e16b1917eff37e9",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/predict_scanvi.py": "482ee5dcf4d72b54bc36cbb6dcff62e5030bc7afc46cb5fb01eca73a82ee45a5",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/poisson_readout.py": "8bfa080942fbcdc89ae23b276524a1a83266fb8cbc56b9af279e50f82e6cde38"
+ },
+ "seconds": 2.1,
+ "gene_exclusion": {
+  "file": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/sex_linked_genes/chrY_nonPAR_genes.csv",
+  "sha256": "1962e57ca198323fb47c42bc53926b247086073d9293581b994cb2a2f651bb28",
+  "n_listed": 43,
+  "n_scoring_genes": 5563,
+  "n_dropped_from_scoring_genes": 7,
+  "n_scoring_genes_kept": 5556,
+  "dropped": [
+   {
+    "gene_id": "ENSG00000067646",
+    "symbol": "ZFY"
+   },
+   {
+    "gene_id": "ENSG00000099715",
+    "symbol": "PCDH11Y"
+   },
+   {
+    "gene_id": "ENSG00000114374",
+    "symbol": "USP9Y"
+   },
+   {
+    "gene_id": "ENSG00000067048",
+    "symbol": "DDX3Y"
+   },
+   {
+    "gene_id": "ENSG00000183878",
+    "symbol": "UTY"
+   },
+   {
+    "gene_id": "ENSG00000165246",
+    "symbol": "NLGN4Y"
+   },
+   {
+    "gene_id": "ENSG00000012817",
+    "symbol": "KDM5D"
+   }
+  ],
+  "applies_to": "group contrast (logFC r generated / broadcast / partial: dropped from each type's detected-gene set) and top-50 DEG (observed, generated and broadcast tests: columns dropped before log1p CPM, Welch and BH); all other metrics use every scoring gene"
+ },
+ "labels_reused_from": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/giroux2022/fewshot_n5/draw3_scsemi5"
+}

paper-releases/pbisc-diffusion/updates/20261001T023742Z-deck-calibration-giroux-sexgenes/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores_nochrY/giroux2022/fewshot_n5/draw4/README.md

@@ -0,0 +1,79 @@
+# score_ft.py output: giroux2022, 10 scored donors
+
+Arms: zs_raw, zs_pseudo, zs_cal, lora5.  Every arm is scored on the same donors against the same observed reference.
+
+## Inputs and fixed contracts
+- Observed reference: `/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/giroux2022/reference.npz` (sha dd0152f7bde4), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022;
+  observed cells sum exactly to the features pseudobulk of every donor. Observed labels: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/giroux2022/observed_labels/labels/labels.parquet.
+- Generated cells: `generated_counts` read out once with Poisson(seed 20260916) over the **whole NPZ in row order**
+  (`poisson_readout.poisson_integerise`), then the scored donors' rows. These integer counts are used for labels, embedding,
+  depth and the contrast. Pseudobulk uses the summed `generated_counts` (rates), as `score_generated.py`.
+- Labels: frozen f01common scANVI (`predict_scanvi.py`, CPU, zero query epochs), six-type gate ['T cell', 'B cell', 'Monocyte', 'NK cell', 'DC', 'Other']; query genes =
+  the 1,006-gene annotation list minus genes the dataset's sc assay did not measure in every sample (1006 genes).
+- Scoring genes: 6k panel genes measured by both assays in every sample of the dataset (5563 genes).
+
+## Definitions
+1. **Composition**: fraction of each donor's cells per six-type label (all generated cells; all observed QC cells).
+   `comp_l1` = sum over the six labels of |generated - observed| (immicro_common.l1). `comp_l1_obs_loo_null` = L1 of the
+   donor's observed composition to the mean of the *other scored donors'* observed compositions. `composition_top1` =
+   fraction of donors whose generated composition is closest (L1) to their own observed composition.
+2. **Pseudobulk**: `pb_r` = Pearson r of CPM-log1p(summed generated_counts) vs CPM-log1p(observed pseudobulk) on the scoring
+   genes (evaluate_realbulk.cpm_log1p / paired_r). `pb_top1` = the donor's generated pseudobulk is closest (Pearson) to its own
+   observed pseudobulk among the 10 scored donors (immicro_common.retrieval = evaluate_realbulk.top1 arithmetic).
+3. **Precision / coverage / density** (stage_b_embed.py functions): per-cell CP10K over the scoring genes + log1p; PCA-50
+   (randomized, random_state 20260925) and k = 10 real-real k-NN balls fitted on **R1 of the scored donors only**.
+   Reference rule (independent of fold and arm): per donor, cells in canonical order (library_id+barcode), one
+   permutation from `default_rng([20260925, uint64(sha256(donor)[:8])])`; R1 = first n, R2 = next n,
+   n = min(150, n_cells // 2) (1500 R1 cells here). Generated query: per donor n cells (the donor's R1 size),
+   without replacement, `default_rng([20260926, same key])`. Precision = fraction of generated cells inside any R1 ball
+   (Kynkaanniemi 2019); coverage = fraction of R1 cells whose ball holds a generated cell; density = Naeem 2020.
+   Per-donor precision = that donor's generated cells; per-donor coverage = that donor's R1 cells (the pooled values are
+   their n-weighted means; plain means when every donor has 150 reference cells). R2 (held-out real cells of the same donors) is the
+   perfect-generator control: precision 0.9993, coverage 1.0000.
+   **Depth matched**: stage_b_embed depth matching run pairwise (R1 vs this arm): per donor and within-donor rank of detected
+   genes, both cells are thinned by UMI sampling without replacement to the smaller detected-gene count; PCA kept from
+   un-thinned R1, radii recomputed on thinned R1; fresh rng(20260929) per arm. R2 control: precision
+   0.9987, coverage 1.0000.
+4. **Monocyte %** = 100 x Monocyte fraction (all cells) per donor; **median UMI per cell** = median over cells of the
+   Poisson-read (generated) / observed counts summed over the scoring genes (per donor, and pooled).
+5. **Group contrast** (T, B, Monocyte, NK): case groups ['severe'] (n = 5) vs control groups ['mild'] (n = 5); rule: explicit --case-groups / --control-groups; groups from features groups.
+   Genes per type = scoring genes detected (count > 0) in >= 1 % of the scored donors' observed
+   cells of that type. Primary statistic `mean_cp10k`: per-cell CP10K over the scoring genes; per donor the mean over its
+   cells of the type (donors with >= 1 such cell); group mean = mean over the group's donors (donor-balanced);
+   logFC = log2(case mean + 1) - log2(control mean + 1). Secondary `pseudobulk_log1p_cpm`: the SLE benchmark evaluator's
+   statistic (donor x type summed counts, log1p CPM over the scoring genes, mean case - mean control;
+   evaluate_generated_vs_truth.group_log_fold_change). Both are computed on the generated cells (their frozen labels) and on
+   the observed cells; `r_generated` = Pearson r of the two logFC vectors. Broadcast baseline `r_broadcast`: the same
+   observed vector against the logFC of the input bulk (condition NPZ `bulk_counts` of the arm, scoring genes, per-donor
+   CP10K resp. log1p CPM, group means). `partial_r_generated_given_broadcast`: r of the OLS residuals of observed and
+   generated logFC on the bulk logFC (residual_specificity.py).
+6. **Top-50 DEG overlap** (contrast.csv rows `statistic = welch_top50_deg`; summary `deg_top50`): the SLE benchmark's score (`total_top50_overlap` of
+   `bulk2sc-paper/scripts/evaluate_generated_vs_truth.py`, Welch backend: the "top-50 DEG 81 / 84" of the final model), run
+   with the evaluator's own functions on the same donor x type pseudobulks as the contrast. Per cell type, observed and
+   generated separately: donor x type summed counts on all 5563 scoring genes (donors of the two groups with
+   >= 1 cell of the type) -> `welch_bh_deg` (log1p CPM, two-sided Welch t-test case vs control, Benjamini-Hochberg over the
+   genes with a defined p-value; needs >= 2 donors per group) -> `top_deg_genes` (FDR < 0.05, descending |logFC|,
+   first 50; shorter when fewer genes pass). `top50_overlap` = |observed list & generated list|; `top50_jaccard`
+   the evaluator's Jaccard. Summary `sum` = total over the scored types (the quantity reported as 81), `max` = the sum of
+   the observed list lengths (the "of 262"), `sum_4type` = T + B + Monocyte + NK. A type is scored when its observed test
+   is defined; an arm whose generated test is undefined for a scored type gets overlap 0. Types: T cell, B cell, Monocyte, NK cell, DC
+   (aggregates: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/giroux2022/deg_reference.npz).
+   **Broadcast analogue** (`top50_overlap_broadcast`, as `sle_broadcast.py` `broadcast_input_bulk`; not part of the
+   published benchmark): the same test on the arm's condition bulk_counts (scoring genes, the 5 + 5
+   donors), one whole-PBMC top-50 list, compared with every type's observed list.
+
+## Files
+- `per_donor.csv`: one row per arm x donor (all per-donor quantities above).
+- `summary.json`: per arm the donor means / pooled values, provenance (NPZ sha, receipt check, generation metadata,
+  Poisson nnz of the whole NPZ, annotation contract), and the reference block (PCA, R2 control).
+- `contrast.csv`: per arm x cell type x statistic (`mean_cp10k`, `pseudobulk_log1p_cpm`: logFC r; `welch_top50_deg`:
+  top-50 DEG overlap, T / B / Monocyte / NK / DC; columns of the other kind are empty).
+- `arms/<arm>/labels/` (predict_scanvi.py labels + manifest), `arms/<arm>/generated_cells.parquet` (NPZ row, donor, label,
+  UMI on the scoring genes, P/C subsample flag), `arms/<arm>/deg_top50_lists.csv` (observed, generated and broadcast
+  top-50 gene lists, Ensembl ids, rank order).
+
+## Gene exclusion (--exclude-genes)
+`/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/sex_linked_genes/chrY_nonPAR_genes.csv` (sha 1962e57ca198): 43 genes listed, 7 of them scoring genes (ZFY, PCDH11Y, USP9Y, DDX3Y, UTY, NLGN4Y, KDM5D). Applies to: group contrast (logFC r generated / broadcast / partial: dropped from each type's detected-gene set) and top-50 DEG (observed, generated and broadcast tests: columns dropped before log1p CPM, Welch and BH); all other metrics use every scoring gene. Definitions 5 and 6 above therefore run on 5556 of the 5563 scoring genes.
+
+## Labels
+Frozen scANVI labels reused from `/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/giroux2022/fewshot_n5/draw4` (same reference, NPZ sha256 and scored donors; checked per arm); predict_scanvi.py was not run.

paper-releases/pbisc-diffusion/updates/20261001T023742Z-deck-calibration-giroux-sexgenes/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores_nochrY/giroux2022/fewshot_n5/draw4/contrast.csv

@@ -0,0 +1,53 @@
+arm,celltype,statistic,n_genes,r_generated,r_broadcast,partial_r_generated_given_broadcast,r_generated_vs_broadcast,n_case_donors_obs,n_control_donors_obs,n_case_donors_gen,n_control_donors_gen,n_cells_obs,n_cells_gen,deg_scored,top50_overlap,top50_jaccard,obs_top_list_size,gen_top_list_size,n_sig_obs,n_sig_gen,top50_overlap_broadcast,top50_jaccard_broadcast,broadcast_top_list_size,n_sig_broadcast
+zs_raw,T cell,mean_cp10k,2887,0.6438847794600048,0.7346998009048207,0.3578488414895262,0.6161375323827175,5,5,5,5,55877,7459,,,,,,,,,,,
+zs_raw,T cell,pseudobulk_log1p_cpm,2887,0.4663935481160765,0.6901560669898527,0.22523108190237487,0.4669417513926715,5,5,5,5,55877,7459,,,,,,,,,,,
+zs_raw,B cell,mean_cp10k,3078,0.242784410681658,0.5668134274216865,0.07249927139784211,0.3288161595461271,5,5,5,5,4890,1459,,,,,,,,,,,
+zs_raw,B cell,pseudobulk_log1p_cpm,3078,0.08651486212072436,0.4711802549903378,-0.014624817704418704,0.21037766189109713,5,5,5,5,4890,1459,,,,,,,,,,,
+zs_raw,Monocyte,mean_cp10k,3035,0.32417670267124765,0.5573001529423961,0.1720066289102094,0.34075952534219195,5,5,5,5,21294,4533,,,,,,,,,,,
+zs_raw,Monocyte,pseudobulk_log1p_cpm,3035,0.16181610665718998,0.6587377955934537,0.012491803933288047,0.23176675857802537,5,5,5,5,21294,4533,,,,,,,,,,,
+zs_raw,NK cell,mean_cp10k,2958,0.45286411596711224,0.6567038156643699,0.2513511102795811,0.42884350743430555,5,5,5,5,12579,1189,,,,,,,,,,,
+zs_raw,NK cell,pseudobulk_log1p_cpm,2958,0.26976613372889,0.5660562958441726,0.1657872303092067,0.24232594675970198,5,5,5,5,12579,1189,,,,,,,,,,,
+zs_raw,T cell,welch_top50_deg,5556,,,,,5,5,5,5,55877,7459,True,0,0.0,0,21,0,21,0,0.0,2,2
+zs_raw,B cell,welch_top50_deg,5556,,,,,5,5,5,5,4890,1459,True,0,0.0,0,1,0,1,0,0.0,2,2
+zs_raw,Monocyte,welch_top50_deg,5556,,,,,5,5,5,5,21294,4533,True,0,0.0,6,3,6,3,1,0.14285714285714285,2,2
+zs_raw,NK cell,welch_top50_deg,5556,,,,,5,5,5,5,12579,1189,True,0,0.0,0,1,0,1,0,0.0,2,2
+zs_raw,DC,welch_top50_deg,5556,,,,,5,5,5,5,1773,260,True,0,,0,0,0,0,0,0.0,2,2
+zs_pseudo,T cell,mean_cp10k,2887,0.5948484117185189,0.8445563544023157,0.2265668113271673,0.5881574600780889,5,5,5,5,55877,9961,,,,,,,,,,,
+zs_pseudo,T cell,pseudobulk_log1p_cpm,2887,0.409650966739736,0.8529213786139679,0.1012004846917943,0.4241999443177861,5,5,5,5,55877,9961,,,,,,,,,,,
+zs_pseudo,B cell,mean_cp10k,3078,0.31860217982442673,0.6849891427645646,0.20031066606419823,0.2593604079460536,5,5,5,5,4890,794,,,,,,,,,,,
+zs_pseudo,B cell,pseudobulk_log1p_cpm,3078,0.12183281906928652,0.611926651115736,0.0700331805867669,0.1091198051777532,5,5,5,5,4890,794,,,,,,,,,,,
+zs_pseudo,Monocyte,mean_cp10k,3035,0.42237954530986,0.6503747386305313,0.19285220967166816,0.44807257751389434,5,5,5,5,21294,2891,,,,,,,,,,,
+zs_pseudo,Monocyte,pseudobulk_log1p_cpm,3035,0.1972020137329317,0.7429488189539187,0.061651665721585466,0.2111395698700088,5,5,5,5,21294,2891,,,,,,,,,,,
+zs_pseudo,NK cell,mean_cp10k,2958,0.3856786862218692,0.7601618331044449,0.2441904040942806,0.30885092465467534,5,5,5,5,12579,1167,,,,,,,,,,,
+zs_pseudo,NK cell,pseudobulk_log1p_cpm,2958,0.15247517173223932,0.6925482834194555,0.08785699682433629,0.12942153557451844,5,5,5,5,12579,1167,,,,,,,,,,,
+zs_pseudo,T cell,welch_top50_deg,5556,,,,,5,5,5,5,55877,9961,True,0,0.0,0,18,0,18,0,0.0,2,2
+zs_pseudo,B cell,welch_top50_deg,5556,,,,,5,5,5,5,4890,794,True,0,,0,0,0,0,0,0.0,2,2
+zs_pseudo,Monocyte,welch_top50_deg,5556,,,,,5,5,5,5,21294,2891,True,0,0.0,6,0,6,0,1,0.14285714285714285,2,2
+zs_pseudo,NK cell,welch_top50_deg,5556,,,,,5,5,5,5,12579,1167,True,0,,0,0,0,0,0,0.0,2,2
+zs_pseudo,DC,welch_top50_deg,5556,,,,,5,5,5,5,1773,105,True,0,0.0,0,1,0,1,0,0.0,2,2
+zs_cal,T cell,mean_cp10k,2887,0.5880944068223731,0.7305100349766219,0.2755184563431639,0.5987563442484267,5,5,5,5,55877,8743,,,,,,,,,,,
+zs_cal,T cell,pseudobulk_log1p_cpm,2887,0.4001288198489918,0.6785993416364607,0.17183462044799946,0.4209269941051481,5,5,5,5,55877,8743,,,,,,,,,,,
+zs_cal,B cell,mean_cp10k,3078,0.3042564965044853,0.601771239123967,0.19651685951138903,0.2532903043832624,5,5,5,5,4890,584,,,,,,,,,,,
+zs_cal,B cell,pseudobulk_log1p_cpm,3078,0.08586338831034195,0.46955489194368033,0.05295224959230488,0.08364412035147077,5,5,5,5,4890,584,,,,,,,,,,,
+zs_cal,Monocyte,mean_cp10k,3035,0.3887168859772075,0.59829028456418,0.20871724090407415,0.39262878494255743,5,5,5,5,21294,3516,,,,,,,,,,,
+zs_cal,Monocyte,pseudobulk_log1p_cpm,3035,0.2734934990044443,0.6567885559295008,0.18795694540344368,0.20520474394871518,5,5,5,5,21294,3516,,,,,,,,,,,
+zs_cal,NK cell,mean_cp10k,2958,0.4753532899896054,0.6735323878652922,0.3564411797121845,0.33754939929703426,5,5,5,5,12579,1903,,,,,,,,,,,
+zs_cal,NK cell,pseudobulk_log1p_cpm,2958,0.20171731624827963,0.5575757671747017,0.14227819642869127,0.15242516554193172,5,5,5,5,12579,1903,,,,,,,,,,,
+zs_cal,T cell,welch_top50_deg,5556,,,,,5,5,5,5,55877,8743,True,0,0.0,0,49,0,49,0,0.0,1,1
+zs_cal,B cell,welch_top50_deg,5556,,,,,5,5,5,5,4890,584,True,0,,0,0,0,0,0,0.0,1,1
+zs_cal,Monocyte,welch_top50_deg,5556,,,,,5,5,5,5,21294,3516,True,0,0.0,6,0,6,0,0,0.0,1,1
+zs_cal,NK cell,welch_top50_deg,5556,,,,,5,5,5,5,12579,1903,True,0,,0,0,0,0,0,0.0,1,1
+zs_cal,DC,welch_top50_deg,5556,,,,,5,5,5,5,1773,97,True,0,0.0,0,1,0,1,0,0.0,1,1
+lora5,T cell,mean_cp10k,2887,0.5872152768455552,0.7346998009048207,0.31891751816598995,0.554127981410676,5,5,5,5,55877,9403,,,,,,,,,,,
+lora5,T cell,pseudobulk_log1p_cpm,2887,0.31848210823036205,0.6901560669898527,0.14402836682402764,0.31829795462331034,5,5,5,5,55877,9403,,,,,,,,,,,
+lora5,B cell,mean_cp10k,3078,0.27713921749447246,0.5668134274216865,0.18951835818484034,0.22024754352278772,5,5,5,5,4890,732,,,,,,,,,,,
+lora5,B cell,pseudobulk_log1p_cpm,3078,0.07476254918944449,0.4711802549903378,0.050318094172744215,0.06467386435255329,5,5,5,5,4890,732,,,,,,,,,,,
+lora5,Monocyte,mean_cp10k,3035,0.3890040368622435,0.5573001529423961,0.23185226666713077,0.37824692356400147,5,5,5,5,21294,2906,,,,,,,,,,,
+lora5,Monocyte,pseudobulk_log1p_cpm,3035,0.16397259843872253,0.6587377955934537,0.0586596894085546,0.18305370004079055,5,5,5,5,21294,2906,,,,,,,,,,,
+lora5,NK cell,mean_cp10k,2958,0.39347586657352746,0.6567038156643699,0.22865427328568327,0.35354293033833906,5,5,5,5,12579,1310,,,,,,,,,,,
+lora5,NK cell,pseudobulk_log1p_cpm,2958,0.11348781339097123,0.5660562958441726,0.053820831229602864,0.12269975886135255,5,5,5,5,12579,1310,,,,,,,,,,,
+lora5,T cell,welch_top50_deg,5556,,,,,5,5,5,5,55877,9403,True,0,0.0,0,1,0,1,0,0.0,2,2
+lora5,B cell,welch_top50_deg,5556,,,,,5,5,5,5,4890,732,True,0,,0,0,0,0,0,0.0,2,2
+lora5,Monocyte,welch_top50_deg,5556,,,,,5,5,5,5,21294,2906,True,0,0.0,6,0,6,0,1,0.14285714285714285,2,2
+lora5,NK cell,welch_top50_deg,5556,,,,,5,5,5,5,12579,1310,True,0,,0,0,0,0,0,0.0,2,2
+lora5,DC,welch_top50_deg,5556,,,,,5,5,5,5,1773,299,True,0,,0,0,0,0,0,0.0,2,2