Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20260928T225702Z-paired-ft-scsemi-delta/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/immicro/fewshot_n5/draw0_scsemi5/README.md

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+# score_ft.py output: immicro, 102 scored donors
+
+Arms: scsemi5.  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/immicro/reference.npz` (sha c5e2547be3ec), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro;
+  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/utility_studies_final6k_20260925/immicro/labels/observed_immicro/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 (5508 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 102 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) (15300 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 0.9668, coverage 0.9998.
+   **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
+   0.9652, coverage 0.9998.
+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): not computed (dataset immicro has no group contrast (use --groups-csv / --case-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`): not computed (contrast off).
+
+## 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/20260928T225702Z-paired-ft-scsemi-delta/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/immicro/fewshot_n5/draw0_scsemi5/per_donor.csv

@@ -0,0 +1,103 @@
+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/20260928T225702Z-paired-ft-scsemi-delta/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/immicro/fewshot_n5/draw0_scsemi5/summary.json

@@ -0,0 +1,290 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "immicro",
+ "written_utc": "2026-09-28T17:03:39.773075+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",
+  "immicro",
+  "--out",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/fewshot_n5/draw0_scsemi5",
+  "--threads",
+  "6",
+  "--donors",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro/fewshot_n5/draw0/test_samples.txt",
+  "--arm",
+  "scsemi5=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/immicro/fewshot_n5/draw0/scsemi5__raw.npz"
+ ],
+ "arms": [
+  "scsemi5"
+ ],
+ "donors": [
+  "HS1",
+  "HS2",
+  "HS3",
+  "HS4",
+  "HS6",
+  "HS7",
+  "HS8",
+  "HS9",
+  "HS10",
+  "HS11",
+  "HS12",
+  "HS13",
+  "HS14",
+  "HS15",
+  "HS16",
+  "HS17",
+  "HS18",
+  "HS19",
+  "HS20",
+  "HS21",
+  "HS23",
+  "HS24",
+  "HS25",
+  "HS26",
+  "HS28",
+  "HS29",
+  "HS30",
+  "HS31",
+  "HS32",
+  "HS34",
+  "HS35",
+  "HS36",
+  "HS37",
+  "HS38",
+  "HS39",
+  "HS40",
+  "HS41",
+  "HS42",
+  "HS43",
+  "HS44",
+  "HS46",
+  "HS47",
+  "HS48",
+  "HS49",
+  "HS50",
+  "HS51",
+  "HS52",
+  "HS53",
+  "HS55",
+  "HS56",
+  "HS57",
+  "HS58",
+  "HS59",
+  "HS60",
+  "HS61",
+  "HS62",
+  "HS63",
+  "HS64",
+  "HS65",
+  "HS66",
+  "HS67",
+  "HS68",
+  "HS69",
+  "HS70",
+  "HS71",
+  "HS72",
+  "HS75",
+  "HS76",
+  "HS77",
+  "HS78",
+  "HS79",
+  "HS80",
+  "HS81",
+  "HS82",
+  "HS83",
+  "HS84",
+  "HS85",
+  "HS86",
+  "HS87",
+  "HS88",
+  "HS89",
+  "HS90",
+  "HS91",
+  "HS92",
+  "HS93",
+  "HS94",
+  "HS95",
+  "HS96",
+  "HS97",
+  "HS98",
+  "HS100",
+  "HS101",
+  "HS102",
+  "HS103",
+  "HS104",
+  "HS105",
+  "HS106",
+  "HS107",
+  "HS108",
+  "HS109",
+  "HS110",
+  "HS111"
+ ],
+ "n_donors": 102,
+ "reference": {
+  "cache": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/immicro",
+  "reference_sha256": "c5e2547be3ec3b12cb0c0b404a49e14a046ae59d40be70427f98f083a76bce17",
+  "features_sha256": "735255066ce7ebbac13a1fdfd058e26f31edd60314cc02fd589b0a292dbbc838",
+  "n_r1_cells": 15300,
+  "pca": {
+   "n_components": 50,
+   "explained_variance_ratio_sum": 0.2884935736656189,
+   "n_fit_cells": 15300
+  },
+  "r2_control": {
+   "precision": 0.966797385620915,
+   "coverage": 0.9998039215686274,
+   "density": 1.3968888888888888,
+   "n_query": 15300,
+   "depth_matched": {
+    "precision": 0.9651633986928104,
+    "coverage": 0.9998039215686274,
+    "density": 1.3858496732026144
+   }
+  },
+  "observed_l1_loo_null_mean": 0.16062088756301035
+ },
+ "contrast_definition": {
+  "enabled": false,
+  "reason": "dataset immicro has no group contrast (use --groups-csv / --case-groups)"
+ },
+ "deg_definition": {
+  "enabled": false,
+  "reason": "contrast off"
+ },
+ "headline": {
+  "scsemi5": {
+   "comp_l1": 0.22879079851017084,
+   "comp_l1_obs_loo_null": 0.16062088756301035,
+   "precision": 0.49549019607843137,
+   "coverage": 0.03601307189542484,
+   "precision_dm": 0.8175816993464052,
+   "coverage_dm": 0.2541830065359477,
+   "pb_r_median": 0.6767345640437741,
+   "pb_top1": 0.0392156862745098,
+   "mono_pct_gen": 9.564705882352943,
+   "mono_pct_obs": 16.948492942022476,
+   "median_umi_gen": 1816.0,
+   "median_umi_obs": 1968.0,
+   "seconds": 20.8
+  }
+ },
+ "per_arm": {
+  "scsemi5": {
+   "arm": "scsemi5",
+   "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/immicro/fewshot_n5/draw0/scsemi5__raw.npz",
+   "npz_sha256": "438e330071dbbc6ef888ecad1bd97115d2ec3291b8230190c3dab02df99bd9cf",
+   "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/immicro/fewshot_n5/draw0/test_raw.npz",
+    "input_condition_sha256": "25588c979cd104e1b950542b66e04b0b1fdc51aea688691c6402f35011b93062",
+    "seed": 20260927,
+    "steps": null,
+    "support_readout": "upstream (values <= 10 normalized units zeroed by fast_semi)",
+    "weights": null
+   },
+   "condition_for_broadcast": null,
+   "poisson": {
+    "seed": 20260916,
+    "rule": "whole NPZ, rows in NPZ order",
+    "nnz_whole_npz": 18763768,
+    "median_library_whole_npz_panel": 1816.0,
+    "n_rows_whole_npz": 153000
+   },
+   "annotation": {
+    "model_pt_sha256": "bf3655899e5abee435340fc008656c4a17342da0f30b70cfd43de8e9223e1930",
+    "zero_padded_model_genes": 1,
+    "label_counts": {
+     "T cell": 108683,
+     "Monocyte": 14634,
+     "B cell": 13272,
+     "NK cell": 9988,
+     "DC": 5418,
+     "Other": 1005
+    },
+    "mean_l2_max_prob": 0.9157999960932077,
+    "predict_seconds": 4.9
+   },
+   "n_donors": 102,
+   "n_generated_cells": 153000,
+   "composition": {
+    "l1_mean": 0.22879079851017084,
+    "l1_median": 0.20621451677935898,
+    "l1_obs_loo_null_mean": 0.16062088756301035,
+    "l1_generated_loo_mean_vs_observed_mean": 0.23104653024366292,
+    "composition_top1": 0.00980392156862745,
+    "mean_fraction_generated": {
+     "T cell": 0.710346,
+     "B cell": 0.086745,
+     "Monocyte": 0.095647,
+     "NK cell": 0.065281,
+     "DC": 0.035412,
+     "Other": 0.006569
+    },
+    "mean_fraction_observed": {
+     "T cell": 0.637324,
+     "B cell": 0.074312,
+     "Monocyte": 0.169485,
+     "NK cell": 0.090305,
+     "DC": 0.022931,
+     "Other": 0.005642
+    }
+   },
+   "pseudobulk": {
+    "n_genes": 5508,
+    "r_median": 0.6767345640437741,
+    "r_mean": 0.6710190791068157,
+    "top1": 0.0392156862745098,
+    "n_top1": 4,
+    "own_rank_median": 31.5,
+    "chance_top1": 0.00980392156862745
+   },
+   "precision_coverage": {
+    "precision": 0.49549019607843137,
+    "coverage": 0.03601307189542484,
+    "density": 0.09535947712418301,
+    "n_query": 15300,
+    "n_reference": 15300
+   },
+   "precision_coverage_depth_matched": {
+    "precision": 0.8175816993464052,
+    "coverage": 0.2541830065359477,
+    "density": 0.6314575163398692,
+    "detected_median_before": {
+     "R1": 363.0,
+     "Q": 117.0
+    },
+    "detected_median_after": {
+     "R1": 117.0,
+     "Q": 117.0
+    }
+   },
+   "monocyte_pct": {
+    "generated_mean_over_donors": 9.564705882352943,
+    "observed_mean_over_donors": 16.948492942022476,
+    "generated_pooled_cells": 9.564705882352943
+   },
+   "median_umi_per_cell_scoring_genes": {
+    "generated_pooled": 1816.0,
+    "observed_pooled": 1968.0,
+    "generated_mean_of_donor_medians": 1816.0391845703125,
+    "observed_mean_of_donor_medians": 2004.171568627451
+   },
+   "seconds": 20.8
+  }
+ },
+ "code_sha256": {
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py": "492e929e9bc7b716f6f9c8bbeedc98e7dec8cc759a20cdc6ec39b1e65194eb74",
+  "/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": 26.3
+}

paper-releases/pbisc-diffusion/updates/20260928T225702Z-paired-ft-scsemi-delta/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/imperial_eskd/fewshot_n5/draw1_scsemi5/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: imperial_eskd, 143 scored donors
+
+Arms: scsemi5.  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/imperial_eskd/reference.npz` (sha 34b4caa3f8a6), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/imperial_eskd;
+  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/imperial_eskd/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 (1005 genes).
+- Scoring genes: 6k panel genes measured by both assays in every sample of the dataset (5927 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 143 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) (21395 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 0.9685, coverage 0.9998.
+   **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
+   0.9689, coverage 0.9996.
+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 ['covid_positive'] (n = 102) vs control groups ['covid_negative'] (n = 32); 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 5927 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/imperial_eskd/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 102 + 32
+   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).