Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261015T072909Z-disease-width-readiness/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/disease_width_comparison_20261015/ji2025_chili/draw0/BASELINE.json

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+{
+  "summary": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/recoveries/disease_width_comparison_20261015/ji2025_chili/draw0/score/summary.json",
+  "summary_sha256": "14e855aabf6dc6c2cd226f541e1e139204f9f6b9c4cbc05c3ca2018667cb4400",
+  "binding": {
+    "arm": "lora",
+    "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/ji2025_chili/disease/draw0/lora.npz",
+    "npz_sha256": "fba94878b514efea1019e36ad51e83fb58a81305e8b692137775563d44548b09",
+    "receipt": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/ji2025_chili/disease/draw0/lora.log",
+    "receipt_sha256": "92ec9b798db9f855576272e391ecb63c0a55e53bbf394a478383c9feae6daff2",
+    "source_binding": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/runs/ji2025_chili/disease/draw0/lora/RUN.json",
+    "source_binding_sha256": "9a34723dd42eb52a8cee11812d60017762a3c688989435325bba89c649f4d242",
+    "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/data/ji2025_chili/disease/draw0/test_raw.npz",
+    "condition_sha256": "ca5da7eba08208ca5e2ebf107830ad72db4a1e739698e0412fff48edf8d2b2b9",
+    "checkpoint_sha256": "53488bb1133c0b5969d6094112798d7666e055f1dea3f630c8db1064d892a08b",
+    "support_sha256": "b361deccb2e2b5da9fc4fcf8d3b558dc4ebefa14390ebba15e419ff8c351b9b8"
+  },
+  "reused_existing_score": false
+}

paper-releases/pbisc-diffusion/updates/20261015T072909Z-disease-width-readiness/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/disease_width_comparison_20261015/ji2025_chili/draw1/score/README.md

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+# score_ft.py output: ji2025_chili, 26 scored donors
+
+Arms: lora.  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 26 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) (3900 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 0.9972, 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 = 15) vs control groups ['BC', 'AC'] (n = 11); 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 15 + 11
+   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/20261015T072909Z-disease-width-readiness/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/disease_width_comparison_20261015/ji2025_chili/draw1/score/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
+lora,T cell,mean_cp10k,3013,0.2218250278686112,0.17723815953863759,0.19098880347356217,0.21610130350689077,15,11,15,11,49230,17765,,,,,,,,,,,
+lora,T cell,pseudobulk_log1p_cpm,3013,0.23919606411584796,0.2548071458258705,0.20145135898585814,0.18783449195825294,15,11,15,11,49230,17765,,,,,,,,,,,
+lora,B cell,mean_cp10k,3263,0.05611875370455356,0.09853768043196273,0.0488365814548893,0.07780967496844972,15,11,15,11,6059,2190,,,,,,,,,,,
+lora,B cell,pseudobulk_log1p_cpm,3263,0.019918294149347548,0.035183033974984575,0.018982074489246437,0.0271426554940043,15,11,15,11,6059,2190,,,,,,,,,,,
+lora,Monocyte,mean_cp10k,3157,0.5608159254317674,0.3948466424201932,0.5000674077355118,0.31662394078864825,15,11,15,11,30700,11770,,,,,,,,,,,
+lora,Monocyte,pseudobulk_log1p_cpm,3157,0.42949949093107903,0.3639773066663638,0.38307617179013786,0.22483400513827378,15,11,15,11,30700,11770,,,,,,,,,,,
+lora,NK cell,mean_cp10k,3047,0.16919401871178957,0.16105690782451637,0.14296857494381274,0.19045897680752427,15,11,15,11,16047,5345,,,,,,,,,,,
+lora,NK cell,pseudobulk_log1p_cpm,3047,0.07060525811721484,0.1345214411691737,0.05689187703966256,0.10824858421385543,15,11,15,11,16047,5345,,,,,,,,,,,
+lora,T cell,welch_top50_deg,5752,,,,,15,11,15,11,49230,17765,True,0,0.0,50,0,1824,0,0,0.0,0,0
+lora,B cell,welch_top50_deg,5752,,,,,15,11,15,11,6059,2190,True,0,0.0,43,0,43,0,0,0.0,0,0
+lora,Monocyte,welch_top50_deg,5752,,,,,15,11,15,11,30700,11770,True,0,0.0,50,1,113,1,0,0.0,0,0
+lora,NK cell,welch_top50_deg,5752,,,,,15,11,15,11,16047,5345,True,0,0.0,50,0,406,0,0,0.0,0,0
+lora,DC,welch_top50_deg,5752,,,,,13,11,15,11,1448,854,True,0,0.0,50,0,106,0,0,0.0,0,0

paper-releases/pbisc-diffusion/updates/20261015T072909Z-disease-width-readiness/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/disease_width_comparison_20261015/ji2025_chili/draw1/score/summary.json

@@ -0,0 +1,543 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "ji2025_chili",
+ "written_utc": "2026-10-15T07:26:22.584689+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",
+  "ji2025_chili",
+  "--out",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/recoveries/disease_width_comparison_20261015/ji2025_chili/draw1/score",
+  "--arm",
+  "lora=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/ji2025_chili/disease/draw1/lora.npz",
+  "--condition",
+  "lora=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/data/ji2025_chili/disease/draw1/test_raw.npz",
+  "--donors",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/recoveries/disease_width_comparison_20261015/ji2025_chili/draw1/test_samples.txt",
+  "--threads",
+  "2",
+  "--case-groups",
+  "TOL,FU1",
+  "--control-groups",
+  "BC,AC"
+ ],
+ "arms": [
+  "lora"
+ ],
+ "donors": [
+  "N064_TOL",
+  "N064_FU1",
+  "N032_AC",
+  "N038_BC",
+  "N038_AC",
+  "N055_AC",
+  "N138_TOL",
+  "N138_FU1",
+  "N129_BC",
+  "N129_AC",
+  "N147_TOL",
+  "N158_AC",
+  "N001_TOL",
+  "N001_FU1",
+  "N002_TOL",
+  "N002_FU1",
+  "N005_BC",
+  "N005_AC",
+  "N012_TOL",
+  "N012_FU1",
+  "N018_BC",
+  "N018_AC",
+  "N022_TOL",
+  "N022_FU1",
+  "N043_TOL",
+  "N043_FU1"
+ ],
+ "n_donors": 26,
+ "reference": {
+  "cache": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/ji2025_chili",
+  "reference_sha256": "19c30f56db699eb4f5a642927c1cd6e173212b877c29ff20d84e60a5ecccef6d",
+  "features_sha256": "049795e3a2470a223347015c0c965f56ca90c21143aad6f72d12e39f3e95e58b",
+  "n_r1_cells": 3900,
+  "pca": {
+   "n_components": 50,
+   "explained_variance_ratio_sum": 0.281139612197876,
+   "n_fit_cells": 3900
+  },
+  "r2_control": {
+   "precision": 0.9971794871794872,
+   "coverage": 1.0,
+   "density": 2.5245384615384614,
+   "n_query": 3900,
+   "depth_matched": {
+    "precision": 0.9971794871794872,
+    "coverage": 1.0,
+    "density": 2.4581282051282054
+   }
+  },
+  "observed_l1_loo_null_mean": 0.4723223144793971
+ },
+ "contrast_definition": {
+  "enabled": true,
+  "rule": "explicit --case-groups / --control-groups",
+  "group_source": "features groups",
+  "case_groups": [
+   "TOL",
+   "FU1"
+  ],
+  "control_groups": [
+   "BC",
+   "AC"
+  ],
+  "n_case_donors": 15,
+  "n_control_donors": 11,
+  "n_donors_in_neither": 0,
+  "case": [
+   true,
+   true,
+   false,
+   false,
+   false,
+   false,
+   true,
+   true,
+   false,
+   false,
+   true,
+   false,
+   true,
+   true,
+   true,
+   true,
+   false,
+   false,
+   true,
+   true,
+   false,
+   false,
+   true,
+   true,
+   true,
+   true
+  ],
+  "ctrl": [
+   false,
+   false,
+   true,
+   true,
+   true,
+   true,
+   false,
+   false,
+   true,
+   true,
+   false,
+   true,
+   false,
+   false,
+   false,
+   false,
+   true,
+   true,
+   false,
+   false,
+   true,
+   true,
+   false,
+   false,
+   false,
+   false
+  ],
+  "groups": [
+   "TOL",
+   "FU1",
+   "AC",
+   "BC",
+   "AC",
+   "AC",
+   "TOL",
+   "FU1",
+   "BC",
+   "AC",
+   "TOL",
+   "AC",
+   "TOL",
+   "FU1",
+   "TOL",
+   "FU1",
+   "BC",
+   "AC",
+   "TOL",
+   "FU1",
+   "BC",
+   "AC",
+   "TOL",
+   "FU1",
+   "TOL",
+   "FU1"
+  ]
+ },
+ "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"
+  ],
+  "reproduces": "utility_studies_final6k_20260925/sle top-50 DEG 81 (1,006 genes) / 84 (2,279 genes) = sum over the six gated types of top50_overlap_count",
+  "top_n": 50,
+  "fdr": 0.05,
+  "n_genes": 5752,
+  "types": [
+   "T cell",
+   "B cell",
+   "Monocyte",
+   "NK cell",
+   "DC"
+  ],
+  "aggregates_source": "/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",
+  "aggregates_sha256": "4f5eb8c8389ef1308ce3ec44be8be024cedb97de37351d109d9070c818df5a9e",
+  "aggregates_note": null,
+  "types_not_scored_observed": [],
+  "observed": {
+   "T cell": {
+    "n_significant": 1824,
+    "n_tested": 5685,
+    "n_case_donors": 15,
+    "n_control_donors": 11,
+    "n_cells": 49230,
+    "top_list_size": 50
+   },
+   "B cell": {
+    "n_significant": 43,
+    "n_tested": 5377,
+    "n_case_donors": 15,
+    "n_control_donors": 11,
+    "n_cells": 6059,
+    "top_list_size": 43
+   },
+   "Monocyte": {
+    "n_significant": 113,
+    "n_tested": 5696,
+    "n_case_donors": 15,
+    "n_control_donors": 11,
+    "n_cells": 30700,
+    "top_list_size": 50
+   },
+   "NK cell": {
+    "n_significant": 406,
+    "n_tested": 5494,
+    "n_case_donors": 15,
+    "n_control_donors": 11,
+    "n_cells": 16047,
+    "top_list_size": 50
+   },
+   "DC": {
+    "n_significant": 106,
+    "n_tested": 5096,
+    "n_case_donors": 13,
+    "n_control_donors": 11,
+    "n_cells": 1448,
+    "top_list_size": 50
+   }
+  },
+  "broadcast": "welch_bh_deg + top_deg_genes on the arm's condition bulk_counts (scoring genes, case|control donors), one list compared with every type's observed list (sle_broadcast.py broadcast_input_bulk); not part of the published benchmark"
+ },
+ "headline": {
+  "lora": {
+   "comp_l1": 0.4032600385662839,
+   "comp_l1_obs_loo_null": 0.4723223144793971,
+   "precision": 0.9887179487179487,
+   "coverage": 0.8746153846153846,
+   "precision_dm": 0.985897435897436,
+   "coverage_dm": 0.9117948717948718,
+   "pb_r_median": 0.879900442491584,
+   "pb_top1": 0.11538461538461539,
+   "mono_pct_gen": 30.179487179487175,
+   "mono_pct_obs": 27.72606893087301,
+   "median_umi_gen": 1485.0,
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