Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20260930T065633Z-summary-deck-scsemi-final/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw4_scsemi5/README.md

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+# score_ft.py output: ji2025_chili, 24 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/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 24 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) (3600 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 0.9975, 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
+   0.9972, 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 = 14) 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 14 + 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/20260930T065633Z-summary-deck-scsemi-final/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw4_scsemi5/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
+scsemi5,T cell,mean_cp10k,3051,0.13310925915878705,0.22058892790303236,0.04882095315316486,0.40616565045047565,14,10,14,10,38484,24094,,,,,,,,,,,
+scsemi5,T cell,pseudobulk_log1p_cpm,3051,0.07768694209438909,0.22354974300191646,0.019652510060015324,0.2648897449520748,14,10,14,10,38484,24094,,,,,,,,,,,
+scsemi5,B cell,mean_cp10k,3274,-0.07159808624147453,0.1250583519693801,-0.12775646569226326,0.3693637754063409,14,10,14,9,5290,1475,,,,,,,,,,,
+scsemi5,B cell,pseudobulk_log1p_cpm,3274,0.006114677182887554,0.03179791067265472,-0.00015577219691138941,0.19709837375141367,14,10,14,9,5290,1475,,,,,,,,,,,
+scsemi5,Monocyte,mean_cp10k,3154,0.2901329528268927,0.3600824414237215,0.1713350781309486,0.39863101387408584,14,10,14,10,24894,4792,,,,,,,,,,,
+scsemi5,Monocyte,pseudobulk_log1p_cpm,3154,0.18939670234182787,0.34831134154512483,0.12195777919500816,0.22387361338800324,14,10,14,10,24894,4792,,,,,,,,,,,
+scsemi5,NK cell,mean_cp10k,3068,0.07079127461487278,0.19608672297995844,-0.011915772054252427,0.41522859723272854,14,10,14,9,13678,3592,,,,,,,,,,,
+scsemi5,NK cell,pseudobulk_log1p_cpm,3068,0.04717691432364568,0.07191205216182345,0.033331551192614946,0.2033956282370046,14,10,14,9,13678,3592,,,,,,,,,,,
+scsemi5,T cell,welch_top50_deg,5752,,,,,14,10,14,10,38484,24094,True,0,0.0,50,0,296,0,0,0.0,2,2
+scsemi5,B cell,welch_top50_deg,5752,,,,,14,10,14,9,5290,1475,True,0,0.0,21,0,21,0,0,0.0,2,2
+scsemi5,Monocyte,welch_top50_deg,5752,,,,,14,10,14,10,24894,4792,True,0,0.0,50,0,238,0,0,0.0,2,2
+scsemi5,NK cell,welch_top50_deg,5752,,,,,14,10,14,9,13678,3592,True,0,0.0,18,0,18,0,0,0.0,2,2
+scsemi5,DC,welch_top50_deg,5752,,,,,12,10,14,9,1186,952,True,0,0.0,50,2,216,2,0,0.0,2,2

paper-releases/pbisc-diffusion/updates/20260930T065633Z-summary-deck-scsemi-final/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw4_scsemi5/per_donor.csv

@@ -0,0 +1,25 @@
+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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+scsemi5,N038_AC,AC,1500,3976,150,0.724,0.6830985915492958,0.03933333333333333,0.00829979879275654,0.07266666666666667,0.1290241448692153,0.13533333333333333,0.12072434607645875,0.024666666666666667,0.03194164989939638,0.004,0.026911468812877263,0.17308786049631117,0.544696017292601,0.6925174043934037,4,False,0.9866666666666667,0.3,0.6533333333333333,0.25333333333333335,7.2666666666666675,12.90241448692153,922.0,910.5
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+scsemi5,N136_FU1,FU1,1500,2927,150,0.794,0.4246668944311582,0.023333333333333334,0.14724974376494704,0.068,0.19337205329689103,0.09333333333333334,0.09531943969935087,0.018,0.0023915271609156134,0.0033333333333333335,0.13700034164673727,0.7698831568158524,0.4066704938920718,0.5235626130958062,3,False,0.8,0.11333333333333333,0.96,0.18,6.800000000000001,19.3372053296891,919.0,1501.0
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+scsemi5,N128_BC,BC,1500,3902,150,0.7233333333333334,0.5158892875448488,0.04666666666666667,0.06817016914402871,0.074,0.29420809841107126,0.112,0.07842132239876987,0.03133333333333333,0.03331624807790876,0.012666666666666666,0.00999487442337263,0.4873890312660175,0.2621662754087492,0.636546702332147,5,False,0.9466666666666667,0.35333333333333333,0.7266666666666667,0.18,7.3999999999999995,29.420809841107125,922.0,1088.0
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+scsemi5,N150_BC,BC,1500,2433,150,0.7266666666666667,0.802301685162351,0.042,0.02301685162351007,0.076,0.1385121249486231,0.12,0.02178380600082203,0.02266666666666667,0.005754212905877518,0.012666666666666666,0.008631319358816275,0.27629428688861485,0.742163251171083,0.7064529206524948,2,False,0.9866666666666667,0.02666666666666667,0.7533333333333333,0.43333333333333335,7.6,13.851212494862311,920.0,1629.0
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+scsemi5,N001_TOL,TOL,1500,5658,150,0.414,0.51926475786497,0.048,0.03375751148815836,0.37533333333333335,0.21067515022976316,0.12533333333333332,0.2163308589607635,0.02666666666666667,0.003711558854718982,0.010666666666666666,0.016260162601626018,0.40371155885471904,0.23122529866547606,0.5858062437517861,17,False,0.9466666666666667,0.2733333333333333,0.29333333333333333,0.06,37.53333333333334,21.067515022976316,920.0,787.5
+scsemi5,N001_FU1,FU1,1500,4547,150,0.7706666666666667,0.29601935341983726,0.04,0.06575764240158347,0.096,0.48823400043985044,0.052,0.13591378931163403,0.018666666666666668,0.0010996261271167802,0.02266666666666667,0.012975588299978007,1.003810864306136,0.45731464040137537,0.5292404983891713,17,False,0.9266666666666666,0.12,0.5066666666666667,0.03333333333333333,9.6,48.823400043985046,922.0,1165.0
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+scsemi5,N012_FU1,FU1,1500,4369,150,0.6473333333333333,0.35889219501029984,0.05733333333333333,0.19249256122682537,0.11,0.3126573586633097,0.09733333333333333,0.10849164568551156,0.006666666666666667,0.000686655985351339,0.08133333333333333,0.02677958342870222,0.69794979781796,0.3826182560206709,0.6026752528237097,21,False,0.7866666666666666,0.04,0.21333333333333335,0.07333333333333333,11.0,31.265735866330967,921.0,1299.0
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paper-releases/pbisc-diffusion/updates/20260930T065633Z-summary-deck-scsemi-final/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw4_scsemi5/summary.json

@@ -0,0 +1,533 @@
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