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/draw2_scsemi5rt/per_donor.csv

@@ -0,0 +1,27 @@
+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/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw2_scsemi5rt/summary.json

@@ -0,0 +1,541 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "ji2025_chili",
+ "written_utc": "2026-10-03T14:39:29.577640+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/draw2_scsemi5rt",
+  "--threads",
+  "6",
+  "--donors",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ji2025_chili/fewshot_n5/draw2/test_samples.txt",
+  "--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/draw2/scsemi5rt__raw.npz"
+ ],
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+ "n_donors": 26,
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+ "deg_definition": {
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+  "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/bulk2sc-paper/scripts/evaluate_generated_vs_truth.py",
+  "functions": [
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+  ],
+  "aggregates_source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/ji2025_chili/deg_reference.npz",
+  "aggregates_sha256": "4f5eb8c8389ef1308ce3ec44be8be024cedb97de37351d109d9070c818df5a9e",
+  "aggregates_note": null,
+  "types_not_scored_observed": [],
+  "observed": {
+   "T cell": {
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+    "n_tested": 5676,
+    "n_case_donors": 16,
+    "n_control_donors": 10,
+    "n_cells": 42751,
+    "top_list_size": 50
+   },
+   "B cell": {
+    "n_significant": 94,
+    "n_tested": 5364,
+    "n_case_donors": 16,
+    "n_control_donors": 10,
+    "n_cells": 5672,
+    "top_list_size": 50
+   },
+   "Monocyte": {
+    "n_significant": 266,
+    "n_tested": 5691,
+    "n_case_donors": 16,
+    "n_control_donors": 10,
+    "n_cells": 28874,
+    "top_list_size": 50
+   },
+   "NK cell": {
+    "n_significant": 397,
+    "n_tested": 5482,
+    "n_case_donors": 16,
+    "n_control_donors": 10,
+    "n_cells": 14784,
+    "top_list_size": 50
+   },
+   "DC": {
+    "n_significant": 58,
+    "n_tested": 4980,
+    "n_case_donors": 14,
+    "n_control_donors": 10,
+    "n_cells": 1112,
+    "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": 0.6149345050135258,
+   "comp_l1_obs_loo_null": 0.46303386174275474,
+   "precision": 0.8307692307692308,
+   "coverage": 0.10948717948717948,
+   "precision_dm": 0.45487179487179485,
+   "coverage_dm": 0.11743589743589744,
+   "pb_r_median": 0.5498610079750658,
+   "pb_top1": 0.038461538461538464,
+   "mono_pct_gen": 20.97179487179487,
+   "mono_pct_obs": 27.487306723551026,
+   "median_umi_gen": 854.0,
+   "median_umi_obs": 1110.0,
+   "contrast_mean_cp10k_r_gen": 0.18243735023145902,
+   "contrast_pseudobulk_log1p_cpm_r_gen": 0.06766860464952149,
+   "contrast_mean_cp10k_r_broadcast": 0.263723888008903,
+   "contrast_pseudobulk_log1p_cpm_r_broadcast": 0.20893883944397995,
+   "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": 250,
+   "deg_top50_sum_4type": 0,
+   "deg_top50_sum_broadcast": 5,
+   "seconds": 8.3
+  }
+ },
+ "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/draw2/scsemi5rt__raw.npz",
+   "npz_sha256": "66f2940cbf2b36b6d7fb79abe5e85b6ae27dc3a234eddaa1d91d45dde4baebda",
+   "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/draw2/test_raw.npz",
+    "input_condition_sha256": "70953ef3c751fa832f2c8bf184fa1cd9fc6ef2e04329101441f0fce90b64ddc0",
+    "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/draw2/test_raw.npz",
+    "sha256": "70953ef3c751fa832f2c8bf184fa1cd9fc6ef2e04329101441f0fce90b64ddc0"
+   },
+   "poisson": {
+    "seed": 20260916,
+    "rule": "whole NPZ, rows in NPZ order",
+    "nnz_whole_npz": 4456720,
+    "median_library_whole_npz_panel": 854.0,
+    "n_rows_whole_npz": 39000
+   },
+   "annotation": {
+    "model_pt_sha256": "bf3655899e5abee435340fc008656c4a17342da0f30b70cfd43de8e9223e1930",
+    "zero_padded_model_genes": 1,
+    "label_counts": {
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+     "Monocyte": 8179,
+     "NK cell": 5491,
+     "Other": 1460,
+     "DC": 1235,
+     "B cell": 670
+    },
+    "mean_l2_max_prob": 0.882288235746515,
+    "predict_seconds": 4.0
+   },
+   "n_donors": 26,
+   "n_generated_cells": 39000,
+   "composition": {
+    "l1_mean": 0.6149345050135258,
+    "l1_median": 0.5656925415505233,
+    "l1_obs_loo_null_mean": 0.46303386174275474,
+    "l1_generated_loo_mean_vs_observed_mean": 0.47608464091931574,
+    "composition_top1": 0.0,
+    "mean_fraction_generated": {
+     "T cell": 0.563205,
+     "B cell": 0.017179,
+     "Monocyte": 0.209718,
+     "NK cell": 0.140795,
+     "DC": 0.031667,
+     "Other": 0.037436
+    },
+    "mean_fraction_observed": {
+     "T cell": 0.463766,
+     "B cell": 0.059678,
+     "Monocyte": 0.274873,
+     "NK cell": 0.157171,
+     "DC": 0.011569,
+     "Other": 0.032943
+    }
+   },
+   "pseudobulk": {
+    "n_genes": 5752,
+    "r_median": 0.5498610079750658,
+    "r_mean": 0.538311516440984,
+    "top1": 0.038461538461538464,
+    "n_top1": 1,
+    "own_rank_median": 11.5,
+    "chance_top1": 0.038461538461538464
+   },
+   "precision_coverage": {
+    "precision": 0.8307692307692308,
+    "coverage": 0.10948717948717948,
+    "density": 0.3402820512820513,
+    "n_query": 3900,
+    "n_reference": 3900
+   },
+   "precision_coverage_depth_matched": {
+    "precision": 0.45487179487179485,
+    "coverage": 0.11743589743589744,
+    "density": 0.15207692307692308,
+    "detected_median_before": {
+     "R1": 368.0,
+     "Q": 116.0
+    },
+    "detected_median_after": {
+     "R1": 116.0,
+     "Q": 116.0
+    }
+   },
+   "monocyte_pct": {
+    "generated_mean_over_donors": 20.97179487179487,
+    "observed_mean_over_donors": 27.487306723551026,
+    "generated_pooled_cells": 20.97179487179487
+   },
+   "median_umi_per_cell_scoring_genes": {
+    "generated_pooled": 854.0,
+    "observed_pooled": 1110.0,
+    "generated_mean_of_donor_medians": 853.673095703125,
+    "observed_mean_of_donor_medians": 1177.9423076923076
+   },
+   "contrast": {
+    "mean_cp10k": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.21083167730353294,
+       "r_broadcast": 0.2701865645650968,
+       "partial_r_generated_given_broadcast": 0.12714359001402664,
+       "r_generated_vs_broadcast": 0.3571200248074734,
+       "n_genes": 3072
+      },
+      "B cell": {
+       "r_generated": 0.17388346107522107,
+       "r_broadcast": 0.15572870002588607,
+       "partial_r_generated_given_broadcast": 0.1331725427806701,
+       "r_generated_vs_broadcast": 0.3148035444287283,
+       "n_genes": 3277
+      },
+      "Monocyte": {
+       "r_generated": 0.18632268103453511,
+       "r_broadcast": 0.37567927431475395,
+       "partial_r_generated_given_broadcast": 0.05502279551607102,
+       "r_generated_vs_broadcast": 0.36985318765194136,
+       "n_genes": 3152
+      },
+      "NK cell": {
+       "r_generated": 0.15871158151254694,
+       "r_broadcast": 0.2533010131298753,
+       "partial_r_generated_given_broadcast": 0.0763175329747979,
+       "r_generated_vs_broadcast": 0.3539784174807198,
+       "n_genes": 3069
+      }
+     },
+     "mean_r_generated": 0.18243735023145902,
+     "mean_r_broadcast": 0.263723888008903,
+     "mean_partial_r": 0.09791411532139141
+    },
+    "pseudobulk_log1p_cpm": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.1069283650899952,
+       "r_broadcast": 0.29511657864157254,
+       "partial_r_generated_given_broadcast": 0.0558079207867003,
+       "r_generated_vs_broadcast": 0.1847541581406423,
+       "n_genes": 3072
+      },
+      "B cell": {
+       "r_generated": -0.002500899315913413,
+       "r_broadcast": 0.06708509712458424,
+       "partial_r_generated_given_broadcast": -0.013395056338604144,
+       "r_generated_vs_broadcast": 0.1593961676074656,
+       "n_genes": 3277
+      },
+      "Monocyte": {
+       "r_generated": 0.09314314767602234,
+       "r_broadcast": 0.33780407869585316,
+       "partial_r_generated_given_broadcast": 0.02727631636733598,
+       "r_generated_vs_broadcast": 0.20128737831844637,
+       "n_genes": 3152
+      },
+      "NK cell": {
+       "r_generated": 0.0731038051479818,
+       "r_broadcast": 0.1357496033139099,
+       "partial_r_generated_given_broadcast": 0.046967304451457643,
+       "r_generated_vs_broadcast": 0.20286491796147704,
+       "n_genes": 3069
+      }
+     },
+     "mean_r_generated": 0.06766860464952149,
+     "mean_r_broadcast": 0.20893883944397995,
+     "mean_partial_r": 0.02916412131672244
+    }
+   },
+   "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": 4,
+      "top50_jaccard_broadcast": 0.05714285714285714,
+      "n_cells_obs": 42751,
+      "n_cells_gen": 21965
+     },
+     "B cell": {
+      "deg_scored": true,
+      "top50_overlap": 0,
+      "top50_jaccard": 0.0,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 0,
+      "top50_overlap_broadcast": 1,
+      "top50_jaccard_broadcast": 0.0136986301369863,
+      "n_cells_obs": 5672,
+      "n_cells_gen": 670
+     },
+     "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": 28874,
+      "n_cells_gen": 8179
+     },
+     "NK 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": 14784,
+      "n_cells_gen": 5491
+     },
+     "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": 1112,
+      "n_cells_gen": 1235
+     }
+    },
+    "sum": 0,
+    "sum_4type": 0,
+    "max": 250,
+    "mean": 0.0,
+    "sum_broadcast": 5,
+    "sum_broadcast_4type": 5,
+    "broadcast_n_significant": 24,
+    "seconds": 0.04
+   },
+   "seconds": 8.3
+  }
+ },
+ "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": 9.7
+}

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw3_scsemi5nt/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: ji2025_chili, 25 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/ji2025_chili/reference.npz` (sha 19c30f56db69), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ji2025_chili;
+  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/ji2025_chili/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 (1004 genes).
+- Scoring genes: 6k panel genes measured by both assays in every sample of the dataset (5752 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 25 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) (3750 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 0.9965, 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
+   0.9963, 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 ['TOL', 'FU1'] (n = 15) vs control groups ['BC', 'AC'] (n = 10); 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 5752 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/ji2025_chili/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 15 + 10
+   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/ji2025_chili/fewshot_n5/draw3_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,3028,0.5138908013437411,0.2368920077117674,0.4710096927213428,0.3932133375147158,15,10,15,10,44374,18355,,,,,,,,,,,
+scsemi5nt,T cell,pseudobulk_log1p_cpm,3028,0.3275987163803153,0.30559676102300276,0.28750707705134904,0.1930492780935949,15,10,15,10,44374,18355,,,,,,,,,,,
+scsemi5nt,B cell,mean_cp10k,3238,0.3484011959210979,0.16221245678147309,0.32110311140549036,0.263572990275426,15,10,15,9,6285,1153,,,,,,,,,,,
+scsemi5nt,B cell,pseudobulk_log1p_cpm,3238,0.10738654186552962,0.047548729682353,0.10355385146399122,0.0923689344979843,15,10,15,9,6285,1153,,,,,,,,,,,
+scsemi5nt,Monocyte,mean_cp10k,3132,0.44276747770204355,0.4012426962403039,0.33975969564718916,0.38894588604364294,15,10,15,9,26936,11103,,,,,,,,,,,
+scsemi5nt,Monocyte,pseudobulk_log1p_cpm,3132,0.3661491976197999,0.36057989165867327,0.30515372308047517,0.25145277807837774,15,10,15,9,26936,11103,,,,,,,,,,,
+scsemi5nt,NK cell,mean_cp10k,3064,0.3377898896459071,0.23076485505524633,0.2779990766588255,0.3791178470807547,15,10,15,9,13603,4818,,,,,,,,,,,
+scsemi5nt,NK cell,pseudobulk_log1p_cpm,3064,0.20518617533771424,0.11924462462412533,0.1903648289955586,0.1547873599677159,15,10,15,9,13603,4818,,,,,,,,,,,
+scsemi5nt,T cell,welch_top50_deg,5752,,,,,15,10,15,10,44374,18355,True,0,0.0,50,0,813,0,0,0.0,1,1
+scsemi5nt,B cell,welch_top50_deg,5752,,,,,15,10,15,9,6285,1153,True,0,0.0,8,0,8,0,0,0.0,1,1
+scsemi5nt,Monocyte,welch_top50_deg,5752,,,,,15,10,15,9,26936,11103,True,0,,0,0,0,0,0,0.0,1,1
+scsemi5nt,NK cell,welch_top50_deg,5752,,,,,15,10,15,9,13603,4818,True,0,0.0,14,0,14,0,0,0.0,1,1
+scsemi5nt,DC,welch_top50_deg,5752,,,,,13,10,14,9,1144,1132,True,0,0.0,3,0,3,0,0,0.0,1,1