Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw4_scsemi5rt/per_donor.csv

@@ -0,0 +1,25 @@
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paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw4_scsemi5rt/summary.json

@@ -0,0 +1,533 @@
+{
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+ "dataset": "ji2025_chili",
+ "written_utc": "2026-10-03T14:45:17.169386+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",
+  "ji2025_chili",
+  "--out",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw4_scsemi5rt",
+  "--threads",
+  "6",
+  "--donors",
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+  "--case-groups",
+  "TOL,FU1",
+  "--control-groups",
+  "BC,AC",
+  "--arm",
+  "scsemi5rt=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ji2025_chili/fewshot_n5/draw4/scsemi5rt__raw.npz"
+ ],
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+ "n_donors": 24,
+ "reference": {
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+   "DC": {
+    "n_significant": 216,
+    "n_tested": 5000,
+    "n_case_donors": 12,
+    "n_control_donors": 10,
+    "n_cells": 1186,
+    "top_list_size": 50
+   }
+  },
+  "broadcast": "welch_bh_deg + top_deg_genes on the arm's condition bulk_counts (scoring genes, case|control donors), one list compared with every type's observed list (sle_broadcast.py broadcast_input_bulk); not part of the published benchmark"
+ },
+ "headline": {
+  "scsemi5rt": {
+   "comp_l1": 0.6634949065982759,
+   "comp_l1_obs_loo_null": 0.44729410690921495,
+   "precision": 0.8675,
+   "coverage": 0.15944444444444444,
+   "precision_dm": 0.5427777777777778,
+   "coverage_dm": 0.15555555555555556,
+   "pb_r_median": 0.6006712408411685,
+   "pb_top1": 0.041666666666666664,
+   "mono_pct_gen": 13.238888888888889,
+   "mono_pct_obs": 27.37570630497282,
+   "median_umi_gen": 920.0,
+   "median_umi_obs": 1132.0,
+   "contrast_mean_cp10k_r_gen": 0.10593397348248755,
+   "contrast_pseudobulk_log1p_cpm_r_gen": 0.0751221944996769,
+   "contrast_mean_cp10k_r_broadcast": 0.2254541110690231,
+   "contrast_pseudobulk_log1p_cpm_r_broadcast": 0.16889276184537988,
+   "deg_top50_T": 0,
+   "deg_top50_B": 0,
+   "deg_top50_Mono": 0,
+   "deg_top50_NK": 0,
+   "deg_top50_DC": 0,
+   "deg_top50_sum": 0,
+   "deg_top50_max": 189,
+   "deg_top50_sum_4type": 0,
+   "deg_top50_sum_broadcast": 0,
+   "seconds": 8.0
+  }
+ },
+ "per_arm": {
+  "scsemi5rt": {
+   "arm": "scsemi5rt",
+   "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ji2025_chili/fewshot_n5/draw4/scsemi5rt__raw.npz",
+   "npz_sha256": "0018048727fcc26ded291222a1acd8554807d2e5a7a0ed8c1910c52c2b5b4312",
+   "receipt_verified": true,
+   "generation": {
+    "checkpoint": null,
+    "checkpoint_sha256": null,
+    "input_condition_path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ji2025_chili/fewshot_n5/draw4/test_raw.npz",
+    "input_condition_sha256": "cc4771e4a9c4ec2ada5be09852a568ab87c74b950e63a012ef3159f6a27a9bc5",
+    "seed": 20260927,
+    "steps": null,
+    "support_readout": "upstream (values <= 10 normalized units zeroed), re-read from the saved per-target model; control of the read-out re-run (equals the official output up to z sampling noise)",
+    "weights": null
+   },
+   "condition_for_broadcast": {
+    "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ji2025_chili/fewshot_n5/draw4/test_raw.npz",
+    "sha256": "cc4771e4a9c4ec2ada5be09852a568ab87c74b950e63a012ef3159f6a27a9bc5"
+   },
+   "poisson": {
+    "seed": 20260916,
+    "rule": "whole NPZ, rows in NPZ order",
+    "nnz_whole_npz": 4340441,
+    "median_library_whole_npz_panel": 920.0,
+    "n_rows_whole_npz": 36000
+   },
+   "annotation": {
+    "model_pt_sha256": "bf3655899e5abee435340fc008656c4a17342da0f30b70cfd43de8e9223e1930",
+    "zero_padded_model_genes": 1,
+    "label_counts": {
+     "T cell": 24040,
+     "Monocyte": 4766,
+     "NK cell": 3588,
+     "B cell": 1490,
+     "Other": 1137,
+     "DC": 979
+    },
+    "mean_l2_max_prob": 0.8829975394221644,
+    "predict_seconds": 3.9
+   },
+   "n_donors": 24,
+   "n_generated_cells": 36000,
+   "composition": {
+    "l1_mean": 0.6634949065982759,
+    "l1_median": 0.6003233837574649,
+    "l1_obs_loo_null_mean": 0.44729410690921495,
+    "l1_generated_loo_mean_vs_observed_mean": 0.5643247547600859,
+    "composition_top1": 0.0,
+    "mean_fraction_generated": {
+     "T cell": 0.667778,
+     "B cell": 0.041389,
+     "Monocyte": 0.132389,
+     "NK cell": 0.099667,
+     "DC": 0.027194,
+     "Other": 0.031583
+    },
+    "mean_fraction_observed": {
+     "T cell": 0.446682,
+     "B cell": 0.057734,
+     "Monocyte": 0.273757,
+     "NK cell": 0.170283,
+     "DC": 0.01371,
+     "Other": 0.037834
+    }
+   },
+   "pseudobulk": {
+    "n_genes": 5752,
+    "r_median": 0.6006712408411685,
+    "r_mean": 0.5937809933989605,
+    "top1": 0.041666666666666664,
+    "n_top1": 1,
+    "own_rank_median": 9.5,
+    "chance_top1": 0.041666666666666664
+   },
+   "precision_coverage": {
+    "precision": 0.8675,
+    "coverage": 0.15944444444444444,
+    "density": 0.40941666666666665,
+    "n_query": 3600,
+    "n_reference": 3600
+   },
+   "precision_coverage_depth_matched": {
+    "precision": 0.5427777777777778,
+    "coverage": 0.15555555555555556,
+    "density": 0.20855555555555555,
+    "detected_median_before": {
+     "R1": 377.0,
+     "Q": 118.0
+    },
+    "detected_median_after": {
+     "R1": 118.0,
+     "Q": 118.0
+    }
+   },
+   "monocyte_pct": {
+    "generated_mean_over_donors": 13.238888888888889,
+    "observed_mean_over_donors": 27.37570630497282,
+    "generated_pooled_cells": 13.238888888888889
+   },
+   "median_umi_per_cell_scoring_genes": {
+    "generated_pooled": 920.0,
+    "observed_pooled": 1132.0,
+    "generated_mean_of_donor_medians": 920.375,
+    "observed_mean_of_donor_medians": 1244.5416666666667
+   },
+   "contrast": {
+    "mean_cp10k": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.1313361277968531,
+       "r_broadcast": 0.22058892790303236,
+       "partial_r_generated_given_broadcast": 0.04689908200029386,
+       "r_generated_vs_broadcast": 0.4058651129691971,
+       "n_genes": 3051
+      },
+      "B cell": {
+       "r_generated": -0.06753940127469833,
+       "r_broadcast": 0.1250583519693801,
+       "partial_r_generated_given_broadcast": -0.12155167696768589,
+       "r_generated_vs_broadcast": 0.35971577348542716,
+       "n_genes": 3274
+      },
+      "Monocyte": {
+       "r_generated": 0.2950512496617287,
+       "r_broadcast": 0.3600824414237215,
+       "partial_r_generated_given_broadcast": 0.17553095660224538,
+       "r_generated_vs_broadcast": 0.40323666970097605,
+       "n_genes": 3154
+      },
+      "NK cell": {
+       "r_generated": 0.06488791774606674,
+       "r_broadcast": 0.19608672297995844,
+       "partial_r_generated_given_broadcast": -0.016183238775601006,
+       "r_generated_vs_broadcast": 0.40491210461558974,
+       "n_genes": 3068
+      }
+     },
+     "mean_r_generated": 0.10593397348248755,
+     "mean_r_broadcast": 0.2254541110690231,
+     "mean_partial_r": 0.021173780714813087
+    },
+    "pseudobulk_log1p_cpm": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.0774543747457402,
+       "r_broadcast": 0.22354974300191646,
+       "partial_r_generated_given_broadcast": 0.019925586114484306,
+       "r_generated_vs_broadcast": 0.2626480514483466,
+       "n_genes": 3051
+      },
+      "B cell": {
+       "r_generated": -0.019470409174669037,
+       "r_broadcast": 0.03179791067265472,
+       "partial_r_generated_given_broadcast": -0.02553368963852234,
+       "r_generated_vs_broadcast": 0.17752719370774805,
+       "n_genes": 3274
+      },
+      "Monocyte": {
+       "r_generated": 0.1948042630823925,
+       "r_broadcast": 0.34831134154512483,
+       "partial_r_generated_given_broadcast": 0.12396467291986936,
+       "r_generated_vs_broadcast": 0.23501067688587954,
+       "n_genes": 3154
+      },
+      "NK cell": {
+       "r_generated": 0.04770054934524398,
+       "r_broadcast": 0.07191205216182345,
+       "partial_r_generated_given_broadcast": 0.0336469701638475,
+       "r_generated_vs_broadcast": 0.20671877560256477,
+       "n_genes": 3068
+      }
+     },
+     "mean_r_generated": 0.0751221944996769,
+     "mean_r_broadcast": 0.16889276184537988,
+     "mean_partial_r": 0.038000884889919706
+    }
+   },
+   "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": 38484,
+      "n_cells_gen": 24040
+     },
+     "B cell": {
+      "deg_scored": true,
+      "top50_overlap": 0,
+      "top50_jaccard": 0.0,
+      "obs_top_list_size": 21,
+      "gen_top_list_size": 0,
+      "top50_overlap_broadcast": 0,
+      "top50_jaccard_broadcast": 0.0,
+      "n_cells_obs": 5290,
+      "n_cells_gen": 1490
+     },
+     "Monocyte": {
+      "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": 24894,
+      "n_cells_gen": 4766
+     },
+     "NK cell": {
+      "deg_scored": true,
+      "top50_overlap": 0,
+      "top50_jaccard": 0.0,
+      "obs_top_list_size": 18,
+      "gen_top_list_size": 0,
+      "top50_overlap_broadcast": 0,
+      "top50_jaccard_broadcast": 0.0,
+      "n_cells_obs": 13678,
+      "n_cells_gen": 3588
+     },
+     "DC": {
+      "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": 1186,
+      "n_cells_gen": 979
+     }
+    },
+    "sum": 0,
+    "sum_4type": 0,
+    "max": 189,
+    "mean": 0.0,
+    "sum_broadcast": 0,
+    "sum_broadcast_4type": 0,
+    "broadcast_n_significant": 2,
+    "seconds": 0.04
+   },
+   "seconds": 8.0
+  }
+ },
+ "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": 9.3
+}

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/fewshot_n5/draw0_scsemi5nt/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: ye2026_sepsis, 77 scored donors
+
+Arms: scsemi5nt.  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/ye2026_sepsis/reference.npz` (sha bac12da8a241), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ye2026_sepsis;
+  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/ye2026_sepsis/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 (5975 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 77 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) (11550 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 0.9815, coverage 0.9997.
+   **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
+   0.9810, coverage 0.9997.
+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 ['adult_sepsis', 'ped_sepsis'] (n = 57) vs control groups ['adult_HC', 'adult_ICU_ctrl', 'ped_HC', 'ped_ICU_ctrl'] (n = 20); 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 5975 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/ye2026_sepsis/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 57 + 20
+   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).

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/fewshot_n5/draw0_scsemi5nt/contrast.csv

@@ -0,0 +1,14 @@
+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
+scsemi5nt,T cell,mean_cp10k,3259,0.48099793755083303,0.28779071017741836,0.43449991694463314,0.28571598491498723,57,20,57,20,281037,76172,,,,,,,,,,,
+scsemi5nt,T cell,pseudobulk_log1p_cpm,3259,0.5068902386120014,0.23446571402689154,0.5088438698270231,0.05540473217444477,57,20,57,20,281037,76172,,,,,,,,,,,
+scsemi5nt,B cell,mean_cp10k,3304,0.48370892678906485,0.30092059959087647,0.44564514797779925,0.23452144456177676,57,20,57,20,94421,14113,,,,,,,,,,,
+scsemi5nt,B cell,pseudobulk_log1p_cpm,3304,0.35339738245245284,0.2824129554276003,0.3520483333382484,0.057499666470372655,57,20,57,20,94421,14113,,,,,,,,,,,
+scsemi5nt,Monocyte,mean_cp10k,3285,0.5460723998290374,0.5511838088445075,0.4585132972557781,0.3373049180579446,57,20,53,20,106918,9936,,,,,,,,,,,
+scsemi5nt,Monocyte,pseudobulk_log1p_cpm,3285,0.3712526238192985,0.6171521141280452,0.28971261242060253,0.24328362558240196,57,20,53,20,106918,9936,,,,,,,,,,,
+scsemi5nt,NK cell,mean_cp10k,3226,0.45851610521628416,0.20406534279418406,0.42361483938284067,0.32510539901298197,57,20,50,18,42712,8631,,,,,,,,,,,
+scsemi5nt,NK cell,pseudobulk_log1p_cpm,3226,0.3451206499620469,0.06147951634862929,0.3423246932676041,0.06941727900422802,57,20,50,18,42712,8631,,,,,,,,,,,
+scsemi5nt,T cell,welch_top50_deg,5975,,,,,57,20,57,20,281037,76172,True,9,0.11538461538461539,50,37,1558,37,4,0.041666666666666664,50,1101
+scsemi5nt,B cell,welch_top50_deg,5975,,,,,57,20,57,20,94421,14113,True,9,0.0989010989010989,50,50,1022,323,3,0.030927835051546393,50,1101
+scsemi5nt,Monocyte,welch_top50_deg,5975,,,,,57,20,53,20,106918,9936,True,0,0.0,50,26,2259,26,13,0.14942528735632185,50,1101
+scsemi5nt,NK cell,welch_top50_deg,5975,,,,,57,20,50,18,42712,8631,True,0,0.0,50,1,1095,1,2,0.02040816326530612,50,1101
+scsemi5nt,DC,welch_top50_deg,5975,,,,,54,20,49,19,3096,2665,True,0,0.0,50,18,1229,18,1,0.010101010101010102,50,1101