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/draw0_scsemi5nt/README.md

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+# score_ft.py output: giroux2022, 11 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/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 11 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) (1650 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 0.9988, 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
+   0.9982, 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 = 5); 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 + 5
+   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/draw0_scsemi5nt/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
+scsemi5nt,T cell,mean_cp10k,2884,0.5903542405977541,0.5530162885771299,0.432184883785334,0.5058355226339264,6,5,6,5,59044,8947,,,,,,,,,,,
+scsemi5nt,T cell,pseudobulk_log1p_cpm,2884,0.4846283286891656,0.7491008014749582,0.23104493348478414,0.4661866417107693,6,5,6,5,59044,8947,,,,,,,,,,,
+scsemi5nt,B cell,mean_cp10k,3028,0.2675969364711931,0.46079426611859237,0.1262652748227631,0.35321357830661393,6,5,6,5,9139,766,,,,,,,,,,,
+scsemi5nt,B cell,pseudobulk_log1p_cpm,3028,0.1710259996713062,0.6336532411649639,0.042695632369572474,0.21904419095995076,6,5,6,5,9139,766,,,,,,,,,,,
+scsemi5nt,Monocyte,mean_cp10k,3050,0.47930704650166606,0.6395437766126884,0.26199443339719614,0.4717745308195938,6,5,6,5,19384,4260,,,,,,,,,,,
+scsemi5nt,Monocyte,pseudobulk_log1p_cpm,3050,0.2922768324986982,0.7472442358121685,0.1408748858261678,0.2705263881255322,6,5,6,5,19384,4260,,,,,,,,,,,
+scsemi5nt,NK cell,mean_cp10k,2935,0.4250769543073216,0.4632733189911659,0.24969329175397365,0.5053928463512114,6,5,6,5,11027,1954,,,,,,,,,,,
+scsemi5nt,NK cell,pseudobulk_log1p_cpm,2935,0.27302866623861066,0.6651655681008249,0.07193420676895004,0.33436364781010575,6,5,6,5,11027,1954,,,,,,,,,,,
+scsemi5nt,T cell,welch_top50_deg,5563,,,,,6,5,6,5,59044,8947,True,0,0.0,13,0,13,0,5,0.35714285714285715,6,6
+scsemi5nt,B cell,welch_top50_deg,5563,,,,,6,5,6,5,9139,766,True,0,0.0,11,0,11,0,5,0.4166666666666667,6,6
+scsemi5nt,Monocyte,welch_top50_deg,5563,,,,,6,5,6,5,19384,4260,True,0,0.0,6,0,6,0,5,0.7142857142857143,6,6
+scsemi5nt,NK cell,welch_top50_deg,5563,,,,,6,5,6,5,11027,1954,True,0,0.0,14,0,14,0,5,0.3333333333333333,6,6
+scsemi5nt,DC,welch_top50_deg,5563,,,,,6,5,5,5,1558,66,True,0,0.0,7,0,7,0,5,0.625,6,6

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/giroux2022/fewshot_n5/draw0_scsemi5nt/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
+scsemi5nt,450905_d0,mild,1500,5914,150,0.188,0.30943523841731485,0.024,0.036185322962461956,0.6033333333333334,0.4293202570172472,0.17333333333333334,0.12089956036523504,0.004,0.009976327358809604,0.007333333333333333,0.09418329387893135,0.4528936985683689,0.652432804255913,0.9450806290245924,1,True,1.0,0.7333333333333333,0.9466666666666667,0.82,60.333333333333336,42.93202570172472,464.0,574.5
+scsemi5nt,450905_d14,mild,1500,7407,150,0.4653333333333333,0.4422843256379101,0.036,0.02929661131362225,0.232,0.27420008100445525,0.23333333333333334,0.15782368030241664,0.010666666666666666,0.02983664101525584,0.02266666666666667,0.06655866072633995,0.21052409882543538,0.3927979931719725,0.9485476776728116,2,False,1.0,0.7733333333333333,1.0,0.9,23.200000000000003,27.420008100445525,461.0,558.0
+scsemi5nt,2AD75E_d0,mild,1500,7693,150,0.6633333333333333,0.6034056934875861,0.03666666666666667,0.06408423241908229,0.16266666666666665,0.14064734173924348,0.118,0.15949564539191471,0.002,0.025347718705316522,0.017333333333333333,0.007019368256856883,0.1845218596992937,0.3152895182425347,0.9318701824194128,3,False,1.0,0.8533333333333334,1.0,0.9,16.266666666666666,14.064734173924348,462.0,532.0
+scsemi5nt,2AD75E_d7,mild,1500,10499,150,0.686,0.5808172206876845,0.042,0.14372797409277074,0.13933333333333334,0.10277169254214687,0.11466666666666667,0.14715687208305553,0.004,0.013715591961139155,0.014,0.011810648633203162,0.2878675429405976,0.37510476699698597,0.9400882093406331,1,True,1.0,0.88,1.0,0.8933333333333333,13.933333333333334,10.277169254214687,462.0,443.0
+scsemi5nt,2AD75E_d14,mild,1500,13692,150,0.686,0.5811422728600643,0.044,0.12503651767455448,0.15066666666666667,0.14198071866783524,0.09133333333333334,0.12496348232544552,0.007333333333333333,0.016579024247735902,0.020666666666666667,0.010297984224364592,0.24782471516213858,0.29971204356328646,0.937529214262556,1,True,1.0,0.9466666666666667,1.0,0.9466666666666667,15.066666666666666,14.198071866783524,464.0,428.0
+scsemi5nt,82CCF5_d0,severe,1500,10125,150,0.5593333333333333,0.5422222222222223,0.11533333333333333,0.11832098765432099,0.18866666666666668,0.1539753086419753,0.11533333333333333,0.09303703703703704,0.004,0.016691358024691356,0.017333333333333333,0.07575308641975309,0.1481975308641975,0.20143355104463556,0.9508035073851665,4,False,1.0,0.8533333333333334,0.9933333333333333,0.88,18.866666666666667,15.397530864197531,464.0,390.0
+scsemi5nt,82CCF5_d7,severe,1500,10860,150,0.6486666666666666,0.619060773480663,0.05733333333333333,0.09972375690607735,0.14133333333333334,0.14152854511970533,0.09,0.09410681399631676,0.0033333333333333335,0.013627992633517496,0.059333333333333335,0.03195211786372008,0.1139742173112338,0.25871125560168845,0.9489894792203372,2,False,1.0,0.9266666666666666,1.0,0.94,14.133333333333335,14.152854511970533,463.0,497.0
+scsemi5nt,82CCF5_d14,severe,1500,11056,150,0.6773333333333333,0.6330499276410999,0.042,0.10121201157742403,0.13,0.11369392185238784,0.09466666666666666,0.06828871201157742,0.002,0.014562228654124458,0.054,0.0691931982633864,0.17393487698986973,0.3322264186222185,0.9504230550748054,1,True,1.0,0.8533333333333334,1.0,0.8933333333333333,13.0,11.369392185238784,464.0,394.0
+scsemi5nt,0BF51C_d0,severe,1500,6510,150,0.19066666666666668,0.4012288786482335,0.018666666666666668,0.01674347158218126,0.6106666666666667,0.4609831029185868,0.16933333333333334,0.07726574500768049,0.0,0.006144393241167435,0.010666666666666666,0.03763440860215054,0.48734869431643635,0.5474367391909245,0.925277864860875,1,True,1.0,0.6533333333333333,0.9533333333333334,0.6733333333333333,61.06666666666667,46.09831029185868,462.0,628.0
+scsemi5nt,0BF51C_d7,severe,1500,10105,150,0.5606666666666666,0.612370113805047,0.05266666666666667,0.05700148441365661,0.25666666666666665,0.1607125185551707,0.05,0.05294408708560119,0.002,0.0073231073725878275,0.078,0.10964868876793667,0.1919082962229919,0.2744910969706922,0.922157789104648,6,False,1.0,0.8266666666666667,1.0,0.8733333333333333,25.666666666666664,16.071251855517072,464.0,509.0
+scsemi5nt,0BF51C_d14,severe,1500,11419,150,0.6393333333333333,0.6330676941938874,0.042,0.07960416849111131,0.22466666666666665,0.151589456169542,0.05266666666666667,0.07907872843506436,0.004666666666666667,0.010508801120938787,0.03666666666666667,0.04615115158945617,0.15868569927314116,0.18889118339141198,0.9413129310230688,3,False,1.0,0.7733333333333333,1.0,0.9,22.466666666666665,15.1589456169542,464.0,541.0

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/giroux2022/fewshot_n5/draw0_scsemi5nt/summary.json

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+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "giroux2022",
+ "written_utc": "2026-10-03T14:55:53.571923+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/draw0_scsemi5nt",
+  "--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/draw0/test_samples.txt",
+  "--case-groups",
+  "severe",
+  "--control-groups",
+  "mild",
+  "--arm",
+  "scsemi5nt=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/giroux2022/fewshot_n5/draw0/scsemi5nt__raw.npz"
+ ],
+ "arms": [
+  "scsemi5nt"
+ ],
+ "donors": [
+  "450905_d0",
+  "450905_d14",
+  "2AD75E_d0",
+  "2AD75E_d7",
+  "2AD75E_d14",
+  "82CCF5_d0",
+  "82CCF5_d7",
+  "82CCF5_d14",
+  "0BF51C_d0",
+  "0BF51C_d7",
+  "0BF51C_d14"
+ ],
+ "n_donors": 11,
+ "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": 1650,
+  "pca": {
+   "n_components": 50,
+   "explained_variance_ratio_sum": 0.26567620038986206,
+   "n_fit_cells": 1650
+  },
+  "r2_control": {
+   "precision": 0.9987878787878788,
+   "coverage": 1.0,
+   "density": 5.159515151515151,
+   "n_query": 1650,
+   "depth_matched": {
+    "precision": 0.9981818181818182,
+    "coverage": 1.0,
+    "density": 5.011030303030303
+   }
+  },
+  "observed_l1_loo_null_mean": 0.348957033732024
+ },
+ "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": 5,
+  "n_donors_in_neither": 0,
+  "case": [
+   false,
+   false,
+   false,
+   false,
+   false,
+   true,
+   true,
+   true,
+   true,
+   true,
+   true
+  ],
+  "ctrl": [
+   true,
+   true,
+   true,
+   true,
+   true,
+   false,
+   false,
+   false,
+   false,
+   false,
+   false
+  ],
+  "groups": [
+   "mild",
+   "mild",
+   "mild",
+   "mild",
+   "mild",
+   "severe",
+   "severe",
+   "severe",
+   "severe",
+   "severe",
+   "severe"
+  ]
+ },
+ "deg_definition": {
+  "enabled": true,
+  "score": "top-50 DEG overlap per cell type (benchmark total_top50_overlap, Welch backend)",
+  "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/bulk2sc-paper/scripts/evaluate_generated_vs_truth.py",
+  "functions": [
+   "welch_bh_deg",
+   "top_deg_genes",
+   "jaccard"
+  ],
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