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/giroux2022/fewshot_n5/draw2_scsemi5rt/README.md

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+# score_ft.py output: giroux2022, 12 scored donors
+
+Arms: scsemi5rt.  Every arm is scored on the same donors against the same observed reference.
+
+## Inputs and fixed contracts
+- Observed reference: `/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/giroux2022/reference.npz` (sha dd0152f7bde4), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022;
+  observed cells sum exactly to the features pseudobulk of every donor. Observed labels: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/giroux2022/observed_labels/labels/labels.parquet.
+- Generated cells: `generated_counts` read out once with Poisson(seed 20260916) over the **whole NPZ in row order**
+  (`poisson_readout.poisson_integerise`), then the scored donors' rows. These integer counts are used for labels, embedding,
+  depth and the contrast. Pseudobulk uses the summed `generated_counts` (rates), as `score_generated.py`.
+- Labels: frozen f01common scANVI (`predict_scanvi.py`, CPU, zero query epochs), six-type gate ['T cell', 'B cell', 'Monocyte', 'NK cell', 'DC', 'Other']; query genes =
+  the 1,006-gene annotation list minus genes the dataset's sc assay did not measure in every sample (1006 genes).
+- Scoring genes: 6k panel genes measured by both assays in every sample of the dataset (5563 genes).
+
+## Definitions
+1. **Composition**: fraction of each donor's cells per six-type label (all generated cells; all observed QC cells).
+   `comp_l1` = sum over the six labels of |generated - observed| (immicro_common.l1). `comp_l1_obs_loo_null` = L1 of the
+   donor's observed composition to the mean of the *other scored donors'* observed compositions. `composition_top1` =
+   fraction of donors whose generated composition is closest (L1) to their own observed composition.
+2. **Pseudobulk**: `pb_r` = Pearson r of CPM-log1p(summed generated_counts) vs CPM-log1p(observed pseudobulk) on the scoring
+   genes (evaluate_realbulk.cpm_log1p / paired_r). `pb_top1` = the donor's generated pseudobulk is closest (Pearson) to its own
+   observed pseudobulk among the 12 scored donors (immicro_common.retrieval = evaluate_realbulk.top1 arithmetic).
+3. **Precision / coverage / density** (stage_b_embed.py functions): per-cell CP10K over the scoring genes + log1p; PCA-50
+   (randomized, random_state 20260925) and k = 10 real-real k-NN balls fitted on **R1 of the scored donors only**.
+   Reference rule (independent of fold and arm): per donor, cells in canonical order (library_id+barcode), one
+   permutation from `default_rng([20260925, uint64(sha256(donor)[:8])])`; R1 = first n, R2 = next n,
+   n = min(150, n_cells // 2) (1800 R1 cells here). Generated query: per donor n cells (the donor's R1 size),
+   without replacement, `default_rng([20260926, same key])`. Precision = fraction of generated cells inside any R1 ball
+   (Kynkaanniemi 2019); coverage = fraction of R1 cells whose ball holds a generated cell; density = Naeem 2020.
+   Per-donor precision = that donor's generated cells; per-donor coverage = that donor's R1 cells (the pooled values are
+   their n-weighted means; plain means when every donor has 150 reference cells). R2 (held-out real cells of the same donors) is the
+   perfect-generator control: precision 0.9989, coverage 1.0000.
+   **Depth matched**: stage_b_embed depth matching run pairwise (R1 vs this arm): per donor and within-donor rank of detected
+   genes, both cells are thinned by UMI sampling without replacement to the smaller detected-gene count; PCA kept from
+   un-thinned R1, radii recomputed on thinned R1; fresh rng(20260929) per arm. R2 control: precision
+   0.9994, coverage 1.0000.
+4. **Monocyte %** = 100 x Monocyte fraction (all cells) per donor; **median UMI per cell** = median over cells of the
+   Poisson-read (generated) / observed counts summed over the scoring genes (per donor, and pooled).
+5. **Group contrast** (T, B, Monocyte, NK): case groups ['severe'] (n = 6) vs control groups ['mild'] (n = 6); rule: explicit --case-groups / --control-groups; groups from features groups.
+   Genes per type = scoring genes detected (count > 0) in >= 1 % of the scored donors' observed
+   cells of that type. Primary statistic `mean_cp10k`: per-cell CP10K over the scoring genes; per donor the mean over its
+   cells of the type (donors with >= 1 such cell); group mean = mean over the group's donors (donor-balanced);
+   logFC = log2(case mean + 1) - log2(control mean + 1). Secondary `pseudobulk_log1p_cpm`: the SLE benchmark evaluator's
+   statistic (donor x type summed counts, log1p CPM over the scoring genes, mean case - mean control;
+   evaluate_generated_vs_truth.group_log_fold_change). Both are computed on the generated cells (their frozen labels) and on
+   the observed cells; `r_generated` = Pearson r of the two logFC vectors. Broadcast baseline `r_broadcast`: the same
+   observed vector against the logFC of the input bulk (condition NPZ `bulk_counts` of the arm, scoring genes, per-donor
+   CP10K resp. log1p CPM, group means). `partial_r_generated_given_broadcast`: r of the OLS residuals of observed and
+   generated logFC on the bulk logFC (residual_specificity.py).
+6. **Top-50 DEG overlap** (contrast.csv rows `statistic = welch_top50_deg`; summary `deg_top50`): the SLE benchmark's score (`total_top50_overlap` of
+   `bulk2sc-paper/scripts/evaluate_generated_vs_truth.py`, Welch backend: the "top-50 DEG 81 / 84" of the final model), run
+   with the evaluator's own functions on the same donor x type pseudobulks as the contrast. Per cell type, observed and
+   generated separately: donor x type summed counts on all 5563 scoring genes (donors of the two groups with
+   >= 1 cell of the type) -> `welch_bh_deg` (log1p CPM, two-sided Welch t-test case vs control, Benjamini-Hochberg over the
+   genes with a defined p-value; needs >= 2 donors per group) -> `top_deg_genes` (FDR < 0.05, descending |logFC|,
+   first 50; shorter when fewer genes pass). `top50_overlap` = |observed list & generated list|; `top50_jaccard`
+   the evaluator's Jaccard. Summary `sum` = total over the scored types (the quantity reported as 81), `max` = the sum of
+   the observed list lengths (the "of 262"), `sum_4type` = T + B + Monocyte + NK. A type is scored when its observed test
+   is defined; an arm whose generated test is undefined for a scored type gets overlap 0. Types: T cell, B cell, Monocyte, NK cell, DC
+   (aggregates: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/giroux2022/deg_reference.npz).
+   **Broadcast analogue** (`top50_overlap_broadcast`, as `sle_broadcast.py` `broadcast_input_bulk`; not part of the
+   published benchmark): the same test on the arm's condition bulk_counts (scoring genes, the 6 + 6
+   donors), one whole-PBMC top-50 list, compared with every type's observed list.
+
+## Files
+- `per_donor.csv`: one row per arm x donor (all per-donor quantities above).
+- `summary.json`: per arm the donor means / pooled values, provenance (NPZ sha, receipt check, generation metadata,
+  Poisson nnz of the whole NPZ, annotation contract), and the reference block (PCA, R2 control).
+- `contrast.csv`: per arm x cell type x statistic (`mean_cp10k`, `pseudobulk_log1p_cpm`: logFC r; `welch_top50_deg`:
+  top-50 DEG overlap, T / B / Monocyte / NK / DC; columns of the other kind are empty).
+- `arms/<arm>/labels/` (predict_scanvi.py labels + manifest), `arms/<arm>/generated_cells.parquet` (NPZ row, donor, label,
+  UMI on the scoring genes, P/C subsample flag), `arms/<arm>/deg_top50_lists.csv` (observed, generated and broadcast
+  top-50 gene lists, Ensembl ids, rank order).

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/giroux2022/fewshot_n5/draw2_scsemi5rt/contrast.csv

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+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
+scsemi5rt,T cell,mean_cp10k,2893,0.44852509020374953,0.45635058627701297,0.32170369310261326,0.41102319347920035,6,6,6,6,67243,10266,,,,,,,,,,,
+scsemi5rt,T cell,pseudobulk_log1p_cpm,2893,0.3651450858542144,0.6892255669260532,0.15084278585410507,0.3833304567196616,6,6,6,6,67243,10266,,,,,,,,,,,
+scsemi5rt,B cell,mean_cp10k,3023,0.25453590098657686,0.4100058931750264,0.15121203342994594,0.2999150009898807,6,6,6,6,10555,827,,,,,,,,,,,
+scsemi5rt,B cell,pseudobulk_log1p_cpm,3023,0.17878161380241375,0.6010434257741435,0.04630305530265919,0.2376460793259705,6,6,6,6,10555,827,,,,,,,,,,,
+scsemi5rt,Monocyte,mean_cp10k,3052,0.4689070449957996,0.605855757517743,0.305501744200196,0.4076343962094268,6,6,6,6,25096,4366,,,,,,,,,,,
+scsemi5rt,Monocyte,pseudobulk_log1p_cpm,3052,0.2598173601685311,0.7367636063610655,0.07863691595200033,0.2834389166856583,6,6,6,6,25096,4366,,,,,,,,,,,
+scsemi5rt,NK cell,mean_cp10k,2925,0.36742084216416243,0.3796040355648556,0.2647004922758764,0.368084646078841,6,6,6,6,16282,2019,,,,,,,,,,,
+scsemi5rt,NK cell,pseudobulk_log1p_cpm,2925,0.21212416668584125,0.6122082213455176,0.05967771859608569,0.2723266870957012,6,6,6,6,16282,2019,,,,,,,,,,,
+scsemi5rt,T cell,welch_top50_deg,5563,,,,,6,6,6,6,67243,10266,True,1,0.02564102564102564,39,1,39,1,6,0.15384615384615385,6,6
+scsemi5rt,B cell,welch_top50_deg,5563,,,,,6,6,6,6,10555,827,True,1,0.024390243902439025,39,3,39,3,6,0.15384615384615385,6,6
+scsemi5rt,Monocyte,welch_top50_deg,5563,,,,,6,6,6,6,25096,4366,True,0,0.0,9,0,9,0,5,0.5,6,6
+scsemi5rt,NK cell,welch_top50_deg,5563,,,,,6,6,6,6,16282,2019,True,1,0.05555555555555555,17,2,17,2,5,0.2777777777777778,6,6
+scsemi5rt,DC,welch_top50_deg,5563,,,,,6,6,6,6,1911,76,True,0,0.0,5,0,5,0,4,0.5714285714285714,6,6

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/giroux2022/fewshot_n5/draw2_scsemi5rt/per_donor.csv

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+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
+scsemi5rt,7085CA_d0,mild,1500,12585,150,0.22266666666666668,0.21660707191100517,0.022,0.024791418355184745,0.6086666666666667,0.5874453714739769,0.12,0.13198251887167262,0.0006666666666666666,0.017719507350019866,0.026,0.021454112038140644,0.0636535558204212,0.8990236963821155,0.6711971052698417,1,True,0.9933333333333333,0.5866666666666667,0.49333333333333335,0.5133333333333333,60.86666666666667,58.74453714739769,447.0,640.0
+scsemi5rt,7085CA_d7,mild,1500,9515,150,0.5253333333333333,0.521492380451918,0.03133333333333333,0.0722017866526537,0.20466666666666666,0.10110352075669994,0.212,0.2725170782974251,0.01,0.019863373620599055,0.016666666666666666,0.012821860220704152,0.22249781047468906,0.3579081617616235,0.6129070052942083,1,True,1.0,0.6333333333333333,0.35333333333333333,0.13333333333333333,20.466666666666665,10.110352075669994,446.0,502.0
+scsemi5rt,7085CA_d14,mild,1500,11887,150,0.5386666666666666,0.472533019264743,0.03666666666666667,0.07133843694792631,0.216,0.16210986792294102,0.18866666666666668,0.24270211155043325,0.008666666666666666,0.018591738874400604,0.011333333333333334,0.032724825439555814,0.24004755895796526,0.29573495877524286,0.6360552102571668,1,True,1.0,0.56,0.41333333333333333,0.23333333333333334,21.6,16.2109867922941,448.0,536.0
+scsemi5rt,2AD75E_d0,mild,1500,7693,150,0.6666666666666666,0.6034056934875861,0.036,0.06408423241908229,0.16266666666666665,0.14064734173924348,0.11533333333333333,0.15949564539191471,0.002,0.025347718705316522,0.017333333333333333,0.007019368256856883,0.19118852636596034,0.26792313410215146,0.5887611346290674,3,False,1.0,0.5266666666666666,0.3933333333333333,0.17333333333333334,16.266666666666666,14.064734173924348,447.0,532.0
+scsemi5rt,2AD75E_d7,mild,1500,10499,150,0.6953333333333334,0.5808172206876845,0.04066666666666666,0.14372797409277074,0.14,0.10277169254214687,0.106,0.14715687208305553,0.005333333333333333,0.013715591961139155,0.012666666666666666,0.011810648633203162,0.305200876273931,0.3201835922843184,0.6074855429291929,1,True,0.9933333333333333,0.6733333333333333,0.37333333333333335,0.19333333333333333,14.000000000000002,10.277169254214687,448.0,443.0
+scsemi5rt,2AD75E_d14,mild,1500,13692,150,0.694,0.5811422728600643,0.042666666666666665,0.12503651767455448,0.15,0.14198071866783524,0.09066666666666667,0.12496348232544552,0.005333333333333333,0.016579024247735902,0.017333333333333333,0.010297984224364592,0.25582471516213845,0.24443705257466458,0.5915286445177149,2,False,0.9933333333333333,0.7333333333333333,0.38666666666666666,0.18,15.0,14.198071866783524,447.0,428.0
+scsemi5rt,82CCF5_d0,severe,1500,10125,150,0.576,0.5422222222222223,0.11533333333333333,0.11832098765432099,0.18533333333333332,0.1539753086419753,0.10733333333333334,0.09303703703703704,0.004666666666666667,0.016691358024691356,0.011333333333333334,0.07575308641975309,0.1588641975308641,0.23874194415185002,0.6176863344892776,8,False,0.9933333333333333,0.5066666666666667,0.29333333333333333,0.24,18.53333333333333,15.397530864197531,448.0,390.0
+scsemi5rt,82CCF5_d7,severe,1500,10860,150,0.66,0.619060773480663,0.056,0.09972375690607735,0.144,0.14152854511970533,0.08933333333333333,0.09410681399631676,0.0026666666666666666,0.013627992633517496,0.048,0.03195211786372008,0.1189171270718232,0.26485931945363844,0.6829993759709453,1,True,1.0,0.66,0.18,0.2733333333333333,14.399999999999999,14.152854511970533,448.0,497.0
+scsemi5rt,82CCF5_d14,severe,1500,11056,150,0.7066666666666667,0.6330499276410999,0.042666666666666665,0.10121201157742403,0.13066666666666665,0.11369392185238784,0.08733333333333333,0.06828871201157742,0.002,0.014562228654124458,0.030666666666666665,0.0691931982633864,0.219268210323203,0.36985318969456304,0.6691963525416886,2,False,1.0,0.6066666666666667,0.21333333333333335,0.21333333333333335,13.066666666666665,11.369392185238784,447.0,394.0
+scsemi5rt,0BF51C_d0,severe,1500,6510,150,0.276,0.4012288786482335,0.034666666666666665,0.01674347158218126,0.5186666666666667,0.4609831029185868,0.13866666666666666,0.07726574500768049,0.0006666666666666666,0.006144393241167435,0.03133333333333333,0.03763440860215054,0.27401536098310286,0.5709498030244532,0.6485012563582092,1,True,0.98,0.42,0.46,0.3466666666666667,51.866666666666674,46.09831029185868,448.0,628.0
+scsemi5rt,0BF51C_d7,severe,1500,10105,150,0.602,0.612370113805047,0.051333333333333335,0.05700148441365661,0.25066666666666665,0.1607125185551707,0.04666666666666667,0.05294408708560119,0.002,0.0073231073725878275,0.04733333333333333,0.10964868876793667,0.17990829622299193,0.3232914310644849,0.5737659238695397,7,False,1.0,0.5533333333333333,0.4266666666666667,0.14666666666666667,25.066666666666666,16.071251855517072,448.0,509.0
+scsemi5rt,0BF51C_d14,severe,1500,11419,150,0.6806666666666666,0.6330676941938874,0.042,0.07960416849111131,0.19933333333333333,0.151589456169542,0.044,0.07907872843506436,0.006666666666666667,0.010508801120938787,0.027333333333333334,0.04615115158945617,0.19068569927314116,0.23769151748520465,0.627021921368665,4,False,0.9866666666666667,0.5333333333333333,0.38666666666666666,0.22666666666666666,19.933333333333334,15.1589456169542,447.0,541.0

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/giroux2022/fewshot_n5/draw2_scsemi5rt/summary.json

@@ -0,0 +1,483 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "giroux2022",
+ "written_utc": "2026-10-03T15:03:22.209706+00:00",
+ "command": [
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py",
+  "--dataset",
+  "giroux2022",
+  "--out",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/giroux2022/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/giroux2022/fewshot_n5/draw2/test_samples.txt",
+  "--case-groups",
+  "severe",
+  "--control-groups",
+  "mild",
+  "--arm",
+  "scsemi5rt=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/giroux2022/fewshot_n5/draw2/scsemi5rt__raw.npz"
+ ],
+ "arms": [
+  "scsemi5rt"
+ ],
+ "donors": [
+  "7085CA_d0",
+  "7085CA_d7",
+  "7085CA_d14",
+  "2AD75E_d0",
+  "2AD75E_d7",
+  "2AD75E_d14",
+  "82CCF5_d0",
+  "82CCF5_d7",
+  "82CCF5_d14",
+  "0BF51C_d0",
+  "0BF51C_d7",
+  "0BF51C_d14"
+ ],
+ "n_donors": 12,
+ "reference": {
+  "cache": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/giroux2022",
+  "reference_sha256": "dd0152f7bde41da80e1c71dabf54ee561982f36dc99a336b65fca419880a1593",
+  "features_sha256": "fb41b91617e3ff786d5153c4dc7869d66191ca5947ff28161d24215a24f819e5",
+  "n_r1_cells": 1800,
+  "pca": {
+   "n_components": 50,
+   "explained_variance_ratio_sum": 0.2604835629463196,
+   "n_fit_cells": 1800
+  },
+  "r2_control": {
+   "precision": 0.9988888888888889,
+   "coverage": 1.0,
+   "density": 4.929388888888889,
+   "n_query": 1800,
+   "depth_matched": {
+    "precision": 0.9994444444444445,
+    "coverage": 1.0,
+    "density": 4.851333333333334
+   }
+  },
+  "observed_l1_loo_null_mean": 0.36588315006285926
+ },
+ "contrast_definition": {
+  "enabled": true,
+  "rule": "explicit --case-groups / --control-groups",
+  "group_source": "features groups",
+  "case_groups": [
+   "severe"
+  ],
+  "control_groups": [
+   "mild"
+  ],
+  "n_case_donors": 6,
+  "n_control_donors": 6,
+  "n_donors_in_neither": 0,
+  "case": [
+   false,
+   false,
+   false,
+   false,
+   false,
+   false,
+   true,
+   true,
+   true,
+   true,
+   true,
+   true
+  ],
+  "ctrl": [
+   true,
+   true,
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