Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261015T095445Z-all-width-results/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/immicro/healthy/fold3/n10/score/README.md

@@ -0,0 +1,60 @@
+# score_ft.py output: immicro, 21 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/immicro/reference.npz` (sha c5e2547be3ec), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro;
+  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/utility_studies_final6k_20260925/immicro/labels/observed_immicro/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 (5508 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 21 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) (3150 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 0.9940, 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
+   0.9927, 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): not computed (--no-contrast).
+   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`): not computed (--no-deg).
+
+## 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/20261015T095445Z-all-width-results/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/immicro/healthy/fold3/n10/score/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
+lora,HS1,DB1,1500,4384,150,0.6846666666666666,0.6332116788321168,0.082,0.05862226277372263,0.076,0.13822992700729927,0.11466666666666667,0.14393248175182483,0.03133333333333333,0.018476277372262775,0.011333333333333334,0.007527372262773723,0.1829914841849148,0.1477341288426135,0.9105450919518464,3,False,0.9866666666666667,0.9933333333333333,0.9866666666666667,0.98,7.6,13.822992700729927,2690.5,1738.0
+lora,HS4,DB4,1500,4167,150,0.6606666666666666,0.54307655387569,0.064,0.04535637149028078,0.19,0.2371010319174466,0.059333333333333335,0.14254859611231102,0.018,0.02423806095512359,0.008,0.007679385649148068,0.2731087113030957,0.2034821822143433,0.9053275601552822,18,False,0.9733333333333334,0.9333333333333333,1.0,0.9666666666666667,19.0,23.71010319174466,2825.0,2111.0
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+lora,HS15,DB4,1500,4592,150,0.44,0.46972996515679444,0.09733333333333333,0.04638501742160279,0.374,0.37282229965156793,0.052,0.08776132404181185,0.029333333333333333,0.0195993031358885,0.007333333333333333,0.0037020905923344947,0.13098257839721256,0.38777794593743353,0.9197570052371082,2,False,1.0,0.88,0.9933333333333333,1.0,37.4,37.282229965156795,2834.5,2302.0
+lora,HS23,DB6,1500,1680,150,0.6086666666666667,0.5630952380952381,0.08066666666666666,0.0994047619047619,0.236,0.2386904761904762,0.05,0.06904761904761905,0.018666666666666668,0.025,0.006,0.004761904761904762,0.09361904761904766,0.16257609013548865,0.9190537055922986,4,False,1.0,0.96,1.0,0.9933333333333333,23.599999999999998,23.86904761904762,2983.0,2296.0
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paper-releases/pbisc-diffusion/updates/20261015T095445Z-all-width-results/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/immicro/healthy/fold3/n10/score/summary.json

@@ -0,0 +1,213 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "immicro",
+ "written_utc": "2026-10-15T09:51:16.859724+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",
+  "immicro",
+  "--out",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/immicro/healthy/fold3/n10/score",
+  "--arm",
+  "lora=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/immicro/healthy/fold3/n10/lora.npz",
+  "--condition",
+  "lora=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/data/immicro/healthy/fold3/n10/test_raw.npz",
+  "--donors",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/immicro/healthy/fold3/n10/test_samples.txt",
+  "--threads",
+  "2",
+  "--no-contrast",
+  "--no-deg"
+ ],
+ "arms": [
+  "lora"
+ ],
+ "donors": [
+  "HS1",
+  "HS4",
+  "HS10",
+  "HS15",
+  "HS23",
+  "HS29",
+  "HS30",
+  "HS40",
+  "HS49",
+  "HS53",
+  "HS61",
+  "HS62",
+  "HS63",
+  "HS69",
+  "HS78",
+  "HS79",
+  "HS89",
+  "HS90",
+  "HS100",
+  "HS106",
+  "HS110"
+ ],
+ "n_donors": 21,
+ "reference": {
+  "cache": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/immicro",
+  "reference_sha256": "c5e2547be3ec3b12cb0c0b404a49e14a046ae59d40be70427f98f083a76bce17",
+  "features_sha256": "735255066ce7ebbac13a1fdfd058e26f31edd60314cc02fd589b0a292dbbc838",
+  "n_r1_cells": 3150,
+  "pca": {
+   "n_components": 50,
+   "explained_variance_ratio_sum": 0.3122588098049164,
+   "n_fit_cells": 3150
+  },
+  "r2_control": {
+   "precision": 0.993968253968254,
+   "coverage": 1.0,
+   "density": 2.678,
+   "n_query": 3150,
+   "depth_matched": {
+    "precision": 0.9926984126984127,
+    "coverage": 1.0,
+    "density": 2.6237142857142857
+   }
+  },
+  "observed_l1_loo_null_mean": 0.20994821515523107
+ },
+ "contrast_definition": {
+  "enabled": false,
+  "reason": "--no-contrast"
+ },
+ "deg_definition": {
+  "enabled": false,
+  "reason": "--no-deg"
+ },
+ "headline": {
+  "lora": {
+   "comp_l1": 0.15999694044416848,
+   "comp_l1_obs_loo_null": 0.20994821515523107,
+   "precision": 0.9873015873015873,
+   "coverage": 0.9571428571428572,
+   "precision_dm": 0.9926984126984127,
+   "coverage_dm": 0.9952380952380953,
+   "pb_r_median": 0.906939416835699,
+   "pb_top1": 0.09523809523809523,
+   "mono_pct_gen": 16.520634920634922,
+   "mono_pct_obs": 18.816613491945674,
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paper-releases/pbisc-diffusion/updates/20261015T095445Z-all-width-results/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/immicro/healthy/fold3/n20/score/arms/lora/labels/labels.manifest.json

@@ -0,0 +1,156 @@
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