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/draw1_scsemi5/per_donor.csv

@@ -0,0 +1,11 @@
+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/draw1_scsemi5/summary.json

@@ -0,0 +1,521 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "giroux2022",
+ "written_utc": "2026-10-01T01:53:44.436134+00:00",
+ "command": [
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py",
+  "--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/draw1/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/draw1/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/draw1_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/draw1_scsemi5"
+ ],
+ "arms": [
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+ "donors": [
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+       "n_genes": 3116
+      },
+      "Monocyte": {
+       "r_generated": 0.20501249554757078,
+       "r_broadcast": 0.5186239391714523,
+       "partial_r_generated_given_broadcast": 0.14681932163178835,
+       "r_generated_vs_broadcast": 0.15622684637543005,
+       "n_genes": 3062
+      },
+      "NK cell": {
+       "r_generated": 0.2739290338316517,
+       "r_broadcast": 0.514418668129312,
+       "partial_r_generated_given_broadcast": 0.08408187626281273,
+       "r_generated_vs_broadcast": 0.40430372385817537,
+       "n_genes": 3013
+      }
+     },
+     "mean_r_generated": 0.24770654550790502,
+     "mean_r_broadcast": 0.5246539198397664,
+     "mean_partial_r": 0.09608768941358609
+    }
+   },
+   "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": 50,
+      "gen_top_list_size": 0,
+      "top50_overlap_broadcast": 0,
+      "top50_jaccard_broadcast": 0.0,
+      "n_cells_obs": 58362,
+      "n_cells_gen": 7572
+     },
+     "B 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": 0.0,
+      "n_cells_obs": 6177,
+      "n_cells_gen": 823
+     },
+     "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": 0.0,
+      "n_cells_obs": 18467,
+      "n_cells_gen": 4262
+     },
+     "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": 0.0,
+      "n_cells_obs": 9050,
+      "n_cells_gen": 1895
+     },
+     "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": 0.0,
+      "n_cells_obs": 1271,
+      "n_cells_gen": 82
+     }
+    },
+    "sum": 0,
+    "sum_4type": 0,
+    "max": 50,
+    "mean": 0.0,
+    "sum_broadcast": 0,
+    "sum_broadcast_4type": 0,
+    "broadcast_n_significant": 3,
+    "seconds": 0.02
+   },
+   "seconds": 1.4
+  }
+ },
+ "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": 1.9,
+ "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/draw1_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/draw2/README.md

@@ -0,0 +1,79 @@
+# score_ft.py output: giroux2022, 12 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 12 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) (1800 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 0.9989, 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
+   0.9994, 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 = 6) vs control groups ['mild'] (n = 6); 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 6 + 6
+   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/draw2` (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/draw2/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,2888,0.14628325024801378,0.3655194961458824,-0.029497181868793052,0.4666418278691388,6,6,6,6,67243,8474,,,,,,,,,,,
+zs_raw,T cell,pseudobulk_log1p_cpm,2888,0.1757309087181335,0.48277319445489014,0.0025898702106024484,0.3596193397975818,6,6,6,6,67243,8474,,,,,,,,,,,
+zs_raw,B cell,mean_cp10k,3018,0.06372747603105325,0.34416431629040306,-0.05252849457806654,0.3208897185543667,6,6,6,6,10555,2297,,,,,,,,,,,
+zs_raw,B cell,pseudobulk_log1p_cpm,3018,0.06487966576124955,0.4452233155685807,-0.03439338820920708,0.2133029150633081,6,6,6,6,10555,2297,,,,,,,,,,,
+zs_raw,Monocyte,mean_cp10k,3047,0.2273595544604648,0.5795391640255549,0.020449503641280606,0.3655450705485278,6,6,6,6,25096,5360,,,,,,,,,,,
+zs_raw,Monocyte,pseudobulk_log1p_cpm,3047,0.16824803691516396,0.5895661684053563,0.022214531311483408,0.2559553659241088,6,6,6,6,25096,5360,,,,,,,,,,,
+zs_raw,NK cell,mean_cp10k,2920,0.09635676681232427,0.30833037919796075,0.003647662285139449,0.30178213664804987,6,6,6,6,16282,1389,,,,,,,,,,,
+zs_raw,NK cell,pseudobulk_log1p_cpm,2920,0.10384380386155202,0.39209933870428326,0.029145190046502727,0.19781281381738694,6,6,6,6,16282,1389,,,,,,,,,,,
+zs_raw,T cell,welch_top50_deg,5556,,,,,6,6,6,6,67243,8474,True,0,0.0,27,0,27,0,0,0.0,0,0
+zs_raw,B cell,welch_top50_deg,5556,,,,,6,6,6,6,10555,2297,True,0,0.0,27,0,27,0,0,0.0,0,0
+zs_raw,Monocyte,welch_top50_deg,5556,,,,,6,6,6,6,25096,5360,True,0,,0,0,0,0,0,,0,0
+zs_raw,NK cell,welch_top50_deg,5556,,,,,6,6,6,6,16282,1389,True,0,0.0,10,0,10,0,0,0.0,0,0
+zs_raw,DC,welch_top50_deg,5556,,,,,6,6,6,6,1911,316,True,0,,0,0,0,0,0,,0,0
+zs_pseudo,T cell,mean_cp10k,2888,0.5295561024321748,0.812256038678895,0.23462873437369475,0.5066953382284706,6,6,6,6,67243,11869,,,,,,,,,,,
+zs_pseudo,T cell,pseudobulk_log1p_cpm,2888,0.3433334917235631,0.8872321028433544,0.08081108646310295,0.34757280706176863,6,6,6,6,67243,11869,,,,,,,,,,,
+zs_pseudo,B cell,mean_cp10k,3018,0.2516296519163338,0.5772993551869245,0.13917921171431138,0.24501910258306384,6,6,6,6,10555,1739,,,,,,,,,,,
+zs_pseudo,B cell,pseudobulk_log1p_cpm,3018,0.13662182269406603,0.6934817178865316,0.04864914626278327,0.14701500159421088,6,6,6,6,10555,1739,,,,,,,,,,,
+zs_pseudo,Monocyte,mean_cp10k,3047,0.2159320593577589,0.6894467290022303,0.07056056205805755,0.2412545464342231,6,6,6,6,25096,2911,,,,,,,,,,,
+zs_pseudo,Monocyte,pseudobulk_log1p_cpm,3047,0.18397541329200956,0.775589702598957,0.04796529164010264,0.19894950007290693,6,6,6,6,25096,2911,,,,,,,,,,,
+zs_pseudo,NK cell,mean_cp10k,2920,0.3643095938222686,0.6852298607540076,0.20409855094265264,0.3266231253108867,6,6,6,6,16282,1246,,,,,,,,,,,
+zs_pseudo,NK cell,pseudobulk_log1p_cpm,2920,0.18147462502957623,0.722122293017217,0.05942220976505604,0.19548134971652198,6,6,6,6,16282,1246,,,,,,,,,,,
+zs_pseudo,T cell,welch_top50_deg,5556,,,,,6,6,6,6,67243,11869,True,0,0.0,27,0,27,0,3,0.10344827586206896,5,5
+zs_pseudo,B cell,welch_top50_deg,5556,,,,,6,6,6,6,10555,1739,True,0,0.0,27,0,27,0,0,0.0,5,5
+zs_pseudo,Monocyte,welch_top50_deg,5556,,,,,6,6,6,6,25096,2911,True,0,,0,0,0,0,0,0.0,5,5
+zs_pseudo,NK cell,welch_top50_deg,5556,,,,,6,6,6,6,16282,1246,True,0,0.0,10,2,10,2,0,0.0,5,5
+zs_pseudo,DC,welch_top50_deg,5556,,,,,6,6,6,6,1911,112,True,0,0.0,0,1,0,1,0,0.0,5,5
+zs_cal,T cell,mean_cp10k,2888,0.14571602396173639,0.3848089198900052,0.03125309906264123,0.3073360954666074,6,6,6,6,67243,9397,,,,,,,,,,,
+zs_cal,T cell,pseudobulk_log1p_cpm,2888,0.16010757359513586,0.476386430779341,0.03773546100581597,0.2690089837294238,6,6,6,6,67243,9397,,,,,,,,,,,
+zs_cal,B cell,mean_cp10k,3018,0.07905709752400548,0.3627876935840993,0.013842923604379533,0.1829582663297796,6,6,6,6,10555,1358,,,,,,,,,,,
+zs_cal,B cell,pseudobulk_log1p_cpm,3018,0.08874002331649256,0.43807419174242207,0.03456110433608111,0.13227139753396414,6,6,6,6,10555,1358,,,,,,,,,,,
+zs_cal,Monocyte,mean_cp10k,3047,0.17493284236048368,0.5866353183042382,0.03453707159008142,0.25205785470609415,6,6,6,6,25096,4362,,,,,,,,,,,
+zs_cal,Monocyte,pseudobulk_log1p_cpm,3047,0.12975854180746133,0.5792637122627445,0.021706352908853196,0.19404136126834445,6,6,6,6,25096,4362,,,,,,,,,,,
+zs_cal,NK cell,mean_cp10k,2920,0.10119795112963145,0.32735406732190514,0.04055590080624776,0.19430636869429094,6,6,6,6,16282,2449,,,,,,,,,,,
+zs_cal,NK cell,pseudobulk_log1p_cpm,2920,0.13827474182732588,0.3874443585584828,0.09542068636836412,0.1318247640668115,6,6,6,6,16282,2449,,,,,,,,,,,
+zs_cal,T cell,welch_top50_deg,5556,,,,,6,6,6,6,67243,9397,True,0,0.0,27,0,27,0,0,0.0,0,0
+zs_cal,B cell,welch_top50_deg,5556,,,,,6,6,6,6,10555,1358,True,0,0.0,27,0,27,0,0,0.0,0,0
+zs_cal,Monocyte,welch_top50_deg,5556,,,,,6,6,6,6,25096,4362,True,0,,0,0,0,0,0,,0,0
+zs_cal,NK cell,welch_top50_deg,5556,,,,,6,6,6,6,16282,2449,True,0,0.0,10,0,10,0,0,0.0,0,0
+zs_cal,DC,welch_top50_deg,5556,,,,,6,6,6,6,1911,151,True,0,,0,0,0,0,0,,0,0
+lora5,T cell,mean_cp10k,2888,0.3991767244086233,0.3655194961458824,0.31012176349999926,0.3532689162584037,6,6,6,6,67243,11799,,,,,,,,,,,
+lora5,T cell,pseudobulk_log1p_cpm,2888,0.23319433697205694,0.48277319445489014,0.1419356660641685,0.2326242997493749,6,6,6,6,67243,11799,,,,,,,,,,,
+lora5,B cell,mean_cp10k,3018,0.11877943956813844,0.34416431629040306,0.0675029973679009,0.16344678931412943,6,6,6,6,10555,1162,,,,,,,,,,,
+lora5,B cell,pseudobulk_log1p_cpm,3018,0.056324134451381916,0.4452233155685807,0.025091194716067466,0.0761916419481864,6,6,6,6,10555,1162,,,,,,,,,,,
+lora5,Monocyte,mean_cp10k,3047,0.3199284242863527,0.5795391640255549,0.18926118231160696,0.2979925743911652,6,6,6,6,25096,2906,,,,,,,,,,,
+lora5,Monocyte,pseudobulk_log1p_cpm,3047,0.13006667484413828,0.5895661684053563,0.04548046914103799,0.15909850664696334,6,6,6,6,25096,2906,,,,,,,,,,,
+lora5,NK cell,mean_cp10k,2920,0.09860020884100211,0.30833037919796075,0.04369067668618316,0.18737797592902147,6,6,6,6,16282,1131,,,,,,,,,,,
+lora5,NK cell,pseudobulk_log1p_cpm,2920,-0.012212924801451605,0.39209933870428326,-0.042699712607944765,0.0687949527145379,6,6,6,6,16282,1131,,,,,,,,,,,
+lora5,T cell,welch_top50_deg,5556,,,,,6,6,6,6,67243,11799,True,0,0.0,27,0,27,0,0,0.0,0,0
+lora5,B cell,welch_top50_deg,5556,,,,,6,6,6,6,10555,1162,True,0,0.0,27,0,27,0,0,0.0,0,0
+lora5,Monocyte,welch_top50_deg,5556,,,,,6,6,6,6,25096,2906,True,0,,0,0,0,0,0,,0,0
+lora5,NK cell,welch_top50_deg,5556,,,,,6,6,6,6,16282,1131,True,0,0.0,10,1,10,1,0,0.0,0,0
+lora5,DC,welch_top50_deg,5556,,,,,6,6,6,6,1911,318,True,0,,0,0,0,0,0,,0,0