Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/imperial_eskd/locoL_fullsat_s20000_K1000/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
+locoL_fullsat_s20000_K1000,T cell,mean_cp10k,3031,0.5611301318821678,0.6352016868529187,0.3980970435492882,0.4514813536717134,113,34,113,34,262546,96672,,,,,,,,,,,
+locoL_fullsat_s20000_K1000,T cell,pseudobulk_log1p_cpm,3031,0.34059386482112125,0.6294466207665587,0.053923653116259546,0.48280497210369105,113,34,113,34,262546,96672,,,,,,,,,,,
+locoL_fullsat_s20000_K1000,B cell,mean_cp10k,3184,0.5432113643910083,0.6290331452237995,0.405652508899826,0.4052600881217884,113,34,113,34,48625,14205,,,,,,,,,,,
+locoL_fullsat_s20000_K1000,B cell,pseudobulk_log1p_cpm,3184,0.2660332598259233,0.4870645233040134,0.13399088469225398,0.3184427245551302,113,34,113,34,48625,14205,,,,,,,,,,,
+locoL_fullsat_s20000_K1000,Monocyte,mean_cp10k,2877,0.6129956723884897,0.7184291928055079,0.34665394894429663,0.5797736207923812,112,34,113,34,80851,84022,,,,,,,,,,,
+locoL_fullsat_s20000_K1000,Monocyte,pseudobulk_log1p_cpm,2877,0.5117797976740269,0.7066219687885489,0.16044003777800098,0.5951541817503695,112,34,113,34,80851,84022,,,,,,,,,,,
+locoL_fullsat_s20000_K1000,NK cell,mean_cp10k,2964,0.41120233562396963,0.6597572279517877,0.1509956496105915,0.47160254614550207,113,34,113,34,72125,14707,,,,,,,,,,,
+locoL_fullsat_s20000_K1000,NK cell,pseudobulk_log1p_cpm,2964,0.2677614576883179,0.5964102520780005,0.029509881524264727,0.4127806819804313,113,34,113,34,72125,14707,,,,,,,,,,,
+locoL_fullsat_s20000_K1000,T cell,welch_top50_deg,5927,,,,,113,34,113,34,262546,96672,True,23,0.2987012987012987,50,50,1057,691,17,0.20481927710843373,50,2242
+locoL_fullsat_s20000_K1000,B cell,welch_top50_deg,5927,,,,,113,34,113,34,48625,14205,True,18,0.21951219512195122,50,50,298,233,14,0.16279069767441862,50,2242
+locoL_fullsat_s20000_K1000,Monocyte,welch_top50_deg,5927,,,,,112,34,113,34,80851,84022,True,25,0.3333333333333333,50,50,349,910,22,0.28205128205128205,50,2242
+locoL_fullsat_s20000_K1000,NK cell,welch_top50_deg,5927,,,,,113,34,113,34,72125,14707,True,24,0.3157894736842105,50,50,427,266,16,0.19047619047619047,50,2242
+locoL_fullsat_s20000_K1000,DC,welch_top50_deg,5927,,,,,113,34,109,34,13572,1175,True,8,0.08695652173913043,50,50,341,76,15,0.17647058823529413,50,2242

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/imperial_eskd/locoL_fullsat_s20000_K1000/per_donor.csv

@@ -0,0 +1,158 @@
+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
+locoL_fullsat_s20000_K1000,C20_1004,covid_positive,1500,1810,150,0.236,0.43425414364640885,0.08933333333333333,0.3994475138121547,0.586,0.03867403314917127,0.050666666666666665,0.07790055248618785,0.007333333333333333,0.04751381215469613,0.030666666666666665,0.0022099447513812156,1.1515653775322283,0.6005338406000345,0.8366697519718957,128,False,0.66,0.2866666666666667,0.6933333333333334,0.5066666666666667,58.599999999999994,3.867403314917127,1419.5,1373.5
+locoL_fullsat_s20000_K1000,C20_1404,covid_positive,1500,5836,150,0.23333333333333334,0.4885195339273475,0.07533333333333334,0.2705620287868403,0.5866666666666667,0.015421521590130226,0.07466666666666667,0.204592186429061,0.002,0.016620973269362578,0.028,0.004283755997258396,1.1899227781585562,0.377519259206171,0.8502929249233658,124,False,0.7133333333333334,0.4666666666666667,0.7466666666666667,0.6866666666666666,58.666666666666664,1.5421521590130227,1492.5,1473.0
+locoL_fullsat_s20000_K1000,C21_1004,covid_positive,1500,5552,150,0.716,0.8982348703170029,0.027333333333333334,0.08609510086455331,0.168,0.0,0.021333333333333333,0.0111671469740634,0.0026666666666666666,0.0005403458213256485,0.06466666666666666,0.003962536023054755,0.4819932756964458,0.813335352446266,0.8259136579426225,147,False,0.6066666666666667,0.43333333333333335,0.72,0.7333333333333333,16.8,0.0,1640.0,1563.5
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+locoL_fullsat_s20000_K1000,C23_2204,covid_positive,1500,3088,150,0.5506666666666666,0.685880829015544,0.042666666666666665,0.10362694300518134,0.3006666666666667,0.06703367875647669,0.05466666666666667,0.11269430051813471,0.005333333333333333,0.025906735751295335,0.046,0.0048575129533678756,0.5495509499136443,0.37947949920467566,0.8865388331239934,29,False,0.7733333333333333,0.6733333333333333,0.7533333333333333,0.82,30.06666666666667,6.703367875647668,1671.5,1936.5
+locoL_fullsat_s20000_K1000,C31_1304,covid_positive,1500,3525,150,0.7633333333333333,0.782695035460993,0.062,0.1075177304964539,0.11533333333333333,0.007659574468085106,0.021333333333333333,0.07716312056737588,0.009333333333333334,0.02070921985815603,0.028666666666666667,0.00425531914893617,0.26417021276595753,0.5810203700567228,0.8780660389105304,58,False,0.84,0.7866666666666666,0.8866666666666667,0.8466666666666667,11.533333333333333,0.7659574468085106,1573.0,1623.0
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+locoL_fullsat_s20000_K1000,C31_2704,covid_positive,1500,4200,150,0.6966666666666667,0.7042857142857143,0.046,0.08023809523809523,0.17933333333333334,0.08714285714285715,0.058,0.09857142857142857,0.005333333333333333,0.025952380952380952,0.014666666666666666,0.0038095238095238095,0.2060952380952381,0.42420172770616554,0.8983106916703123,6,False,0.8333333333333334,0.7,0.8466666666666667,0.82,17.933333333333334,8.714285714285715,1685.0,1704.0
+locoL_fullsat_s20000_K1000,C33_1401,covid_positive,1500,3983,150,0.434,0.38890283705749434,0.02666666666666667,0.016570424303288978,0.39066666666666666,0.11172483052975145,0.116,0.464474014561888,0.004,0.014310820989204118,0.028666666666666667,0.004017072558373086,0.7175696711021843,0.6015977414820979,0.8790010894499747,61,False,0.82,0.47333333333333333,0.8133333333333334,0.4866666666666667,39.06666666666666,11.172483052975144,1256.5,909.0
+locoL_fullsat_s20000_K1000,C33_1601,covid_positive,1500,395,150,0.408,0.14430379746835442,0.029333333333333333,0.11645569620253164,0.37333333333333335,0.2,0.168,0.5240506329113924,0.0033333333333333335,0.007594936708860759,0.018,0.007594936708860759,0.8948691983122362,0.781467701292667,0.8635224669696656,111,False,0.8,0.7333333333333333,0.78,0.6933333333333334,37.333333333333336,20.0,1294.0,644.0
+locoL_fullsat_s20000_K1000,C33_1901,covid_positive,1500,1947,150,0.6046666666666667,0.3754494093477144,0.024666666666666667,0.042116076014381096,0.12533333333333332,0.12223934257832562,0.22266666666666668,0.43656908063687727,0.0006666666666666666,0.01078582434514638,0.022,0.012840267077555213,0.4829419619928095,0.5457878736320764,0.8895367806817613,21,False,0.6466666666666666,0.56,0.74,0.6333333333333333,12.533333333333333,12.223934257832562,1112.5,897.0
+locoL_fullsat_s20000_K1000,C34_1404,covid_positive,1500,2483,150,0.2853333333333333,0.8771647200966572,0.029333333333333333,0.01973419250906162,0.5953333333333334,0.016915022150624244,0.025333333333333333,0.07047925896093435,0.008666666666666666,0.011679420056383408,0.056,0.004027386226339106,1.2799801315612835,0.7709669223771655,0.8310081062110627,124,False,0.7066666666666667,0.6266666666666667,0.76,0.7533333333333333,59.53333333333334,1.6915022150624244,1817.0,1720.0
+locoL_fullsat_s20000_K1000,C34_2104,covid_positive,1500,2808,150,0.33066666666666666,0.7916666666666666,0.025333333333333333,0.024216524216524215,0.5673333333333334,0.041666666666666664,0.023333333333333334,0.12428774928774929,0.008666666666666666,0.013888888888888888,0.04466666666666667,0.004273504273504274,1.1343532763532764,0.5999708155171841,0.861741877650191,83,False,0.76,0.6933333333333334,0.8266666666666667,0.8733333333333333,56.733333333333334,4.166666666666666,1968.0,2067.5
+locoL_fullsat_s20000_K1000,C34_2504,covid_positive,1500,1945,150,0.44466666666666665,0.6961439588688946,0.02666666666666667,0.024678663239074552,0.432,0.12544987146529563,0.035333333333333335,0.12904884318766066,0.012666666666666666,0.02416452442159383,0.048666666666666664,0.0005141388174807198,0.7133813196229648,0.40892539992164,0.8883786910060709,18,False,0.78,0.7533333333333333,0.8,0.94,43.2,12.544987146529563,1887.0,2111.0
+locoL_fullsat_s20000_K1000,C36_1504,covid_positive,1500,4765,150,0.4633333333333333,0.7643231899265478,0.048666666666666664,0.14144805876180483,0.386,0.03546694648478489,0.03866666666666667,0.030640083945435468,0.007333333333333333,0.01951731374606506,0.056,0.008604407135362015,0.8119104582021688,0.5893382201890965,0.8652448399956881,88,False,0.7533333333333333,0.7666666666666667,0.8,0.8933333333333333,38.6,3.546694648478489,1848.0,1747.0
+locoL_fullsat_s20000_K1000,C36_1704,covid_positive,1500,1554,150,0.5786666666666667,0.842985842985843,0.04733333333333333,0.07335907335907337,0.24333333333333335,0.014800514800514801,0.07266666666666667,0.0546975546975547,0.0026666666666666666,0.010296010296010296,0.05533333333333333,0.003861003861003861,0.59594851994852,0.7027427978748242,0.8655666161740587,92,False,0.8266666666666667,0.6866666666666666,0.84,0.8533333333333334,24.333333333333336,1.4800514800514801,1587.5,2044.5
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+locoL_fullsat_s20000_K1000,C40_1504,covid_positive,1500,5482,150,0.374,0.7433418460415907,0.029333333333333333,0.09649762860269974,0.5106666666666667,0.05600145932141554,0.038,0.09157241882524626,0.004666666666666667,0.009668004377964246,0.043333333333333335,0.002918642831083546,0.9901597956950019,0.4967961170529107,0.8732385218650017,40,False,0.7333333333333333,0.6533333333333333,0.8,0.8066666666666666,51.06666666666667,5.6001459321415545,1673.0,1253.5
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paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/imperial_eskd/locoL_fullsat_s20000_K1000/summary.json

@@ -0,0 +1,1063 @@
+{
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+ "written_utc": "2026-10-03T07:38:45.194162+00:00",
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+  "--case-groups",
+  "covid_positive",
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+       "r_generated_vs_broadcast": 0.3184427245551302,
+       "n_genes": 3184
+      },
+      "Monocyte": {
+       "r_generated": 0.5117797976740269,
+       "r_broadcast": 0.7066219687885489,
+       "partial_r_generated_given_broadcast": 0.16044003777800098,
+       "r_generated_vs_broadcast": 0.5951541817503695,
+       "n_genes": 2877
+      },
+      "NK cell": {
+       "r_generated": 0.2677614576883179,
+       "r_broadcast": 0.5964102520780005,
+       "partial_r_generated_given_broadcast": 0.029509881524264727,
+       "r_generated_vs_broadcast": 0.4127806819804313,
+       "n_genes": 2964
+      }
+     },
+     "mean_r_generated": 0.3465420950023473,
+     "mean_r_broadcast": 0.6048858412342804,
+     "mean_partial_r": 0.0944661142776948
+    }
+   },
+   "deg_top50": {
+    "types_scored": [
+     "T cell",
+     "B cell",
+     "Monocyte",
+     "NK cell",
+     "DC"
+    ],
+    "types_not_scored": {},
+    "per_type": {
+     "T cell": {
+      "deg_scored": true,
+      "top50_overlap": 23,
+      "top50_jaccard": 0.2987012987012987,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 17,
+      "top50_jaccard_broadcast": 0.20481927710843373,
+      "n_cells_obs": 262546,
+      "n_cells_gen": 96672
+     },
+     "B cell": {
+      "deg_scored": true,
+      "top50_overlap": 18,
+      "top50_jaccard": 0.21951219512195122,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 14,
+      "top50_jaccard_broadcast": 0.16279069767441862,
+      "n_cells_obs": 48625,
+      "n_cells_gen": 14205
+     },
+     "Monocyte": {
+      "deg_scored": true,
+      "top50_overlap": 25,
+      "top50_jaccard": 0.3333333333333333,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 22,
+      "top50_jaccard_broadcast": 0.28205128205128205,
+      "n_cells_obs": 80851,
+      "n_cells_gen": 84022
+     },
+     "NK cell": {
+      "deg_scored": true,
+      "top50_overlap": 24,
+      "top50_jaccard": 0.3157894736842105,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 16,
+      "top50_jaccard_broadcast": 0.19047619047619047,
+      "n_cells_obs": 72125,
+      "n_cells_gen": 14707
+     },
+     "DC": {
+      "deg_scored": true,
+      "top50_overlap": 8,
+      "top50_jaccard": 0.08695652173913043,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 15,
+      "top50_jaccard_broadcast": 0.17647058823529413,
+      "n_cells_obs": 13572,
+      "n_cells_gen": 1175
+     }
+    },
+    "sum": 98,
+    "sum_4type": 90,
+    "max": 250,
+    "mean": 19.6,
+    "sum_broadcast": 84,
+    "sum_broadcast_4type": 69,
+    "broadcast_n_significant": 2242,
+    "seconds": 0.86
+   },
+   "seconds": 53.5
+  }
+ },
+ "code_sha256": {
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py": "ec5f40b4cea6d5062d0138f3b934abb6465e34cbfeac869c8a0906924c033aec",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/ft_common.py": "4a6366b0f22208cd58714fc0261b8840c643b9d4569704d560cfd93534cbc747",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/utility_studies_final6k_20260925/umap/cell_immicro_5arm/tools/stage_b_embed.py": "d056cfdf38fb0e5f478756f370174e993b75d0c973c48e02e21749175a4ca2da",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/bulk2sc-paper/scripts/evaluate_generated_vs_truth.py": "48b12d794782b840838853e93e0fc55fab00532c4b3698f4588e926b985d2881",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/utility_studies_final6k_20260925/immicro/tools/immicro_common.py": "c5c30e917148cc5e664e91ea30c11bbdff9b6d1f53a3e0a13e16b1917eff37e9",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/predict_scanvi.py": "482ee5dcf4d72b54bc36cbb6dcff62e5030bc7afc46cb5fb01eca73a82ee45a5",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/poisson_readout.py": "8bfa080942fbcdc89ae23b276524a1a83266fb8cbc56b9af279e50f82e6cde38"
+ },
+ "seconds": 72.4
+}

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/imperial_eskd/locoL_fullsat_s40000_K1000/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: imperial_eskd, 157 scored donors
+
+Arms: locoL_fullsat_s40000_K1000.  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 157 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) (23482 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 0.9673, 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
+   0.9667, 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 ['covid_positive'] (n = 113) vs control groups ['covid_negative'] (n = 34); 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 113 + 34
+   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).