Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261005T121346Z-scsemi-cv-budget-interim/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/scsemi_cv_20261002/fold0/per_donor.csv

@@ -0,0 +1,8 @@
+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
+scsemi__raw,N136_TOL,TOL,1500,759,150,0.20666666666666667,0.48616600790513836,0.07,0.10013175230566534,0.36533333333333334,0.19104084321475626,0.12733333333333333,0.0922266139657444,0.2,0.0013175230566534915,0.030666666666666665,0.12911725955204217,0.8161633728590251,0.44391114931037756,0.6703013264190718,2,False,1.0,0.10666666666666667,0.7733333333333333,0.35333333333333333,36.53333333333333,19.104084321475625,1013.0,1837.0
+scsemi__raw,N136_FU1,FU1,1500,2927,150,0.17466666666666666,0.4246668944311582,0.066,0.14724974376494704,0.5146666666666667,0.19337205329689103,0.13666666666666666,0.09531943969935087,0.076,0.0023915271609156134,0.032,0.13700034164673727,0.8725006263523516,0.4371007209375838,0.6624523375845527,1,True,0.9866666666666667,0.25333333333333335,0.8466666666666667,0.58,51.46666666666667,19.3372053296891,1012.0,1501.0
+scsemi__raw,N128_BC,BC,1500,3902,150,0.7193333333333334,0.5158892875448488,0.050666666666666665,0.06817016914402871,0.07266666666666667,0.29420809841107126,0.11533333333333333,0.07842132239876987,0.034,0.03331624807790876,0.008,0.00999487442337263,0.48207961729027865,0.34108490928783697,0.6370956686490399,2,False,1.0,0.4533333333333333,0.6666666666666666,0.25333333333333335,7.2666666666666675,29.420809841107125,1010.0,1088.0
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+scsemi__raw,N012_TOL,TOL,1500,6709,150,0.48933333333333334,0.34759278581010583,0.03666666666666667,0.07884930690117753,0.043333333333333335,0.4672827545088687,0.4186666666666667,0.08570576837084513,0.0,0.0005962140408406618,0.012,0.01997317036816217,0.9494028916380981,0.3166837113206966,0.6598568624352467,5,False,0.96,0.26666666666666666,0.5933333333333334,0.12,4.333333333333334,46.72827545088687,1013.0,1193.0
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paper-releases/pbisc-diffusion/updates/20261005T121346Z-scsemi-cv-budget-interim/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/scsemi_cv_20261002/fold0/summary.json

@@ -0,0 +1,463 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "ji2025_chili",
+ "written_utc": "2026-10-02T08:57:50.788324+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/paired_finetune_intradataset_20260927/scores/ji2025_chili/scsemi_cv_20261002/fold0",
+  "--threads",
+  "8",
+  "--arm",
+  "scsemi__raw=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ji2025_chili/fold0/scsemi__raw.npz",
+  "--case-groups",
+  "TOL,FU1",
+  "--control-groups",
+  "BC,AC"
+ ],
+ "arms": [
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+ ],
+ "donors": [
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+  "N136_FU1",
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+  "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/bulk2sc-paper/scripts/evaluate_generated_vs_truth.py",
+  "functions": [
+   "welch_bh_deg",
+   "top_deg_genes",
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+  "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",
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+      "top50_jaccard": 0.0,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 0,
+      "top50_overlap_broadcast": 0,
+      "top50_jaccard_broadcast": 0.0,
+      "n_cells_obs": 318,
+      "n_cells_gen": 668
+     }
+    },
+    "sum": 0,
+    "sum_4type": 0,
+    "max": 50,
+    "mean": 0.0,
+    "sum_broadcast": 0,
+    "sum_broadcast_4type": 0,
+    "broadcast_n_significant": 0,
+    "seconds": 0.02
+   },
+   "seconds": 4.5
+  }
+ },
+ "code_sha256": {
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py": "ec5f40b4cea6d5062d0138f3b934abb6465e34cbfeac869c8a0906924c033aec",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/ft_common.py": "4a6366b0f22208cd58714fc0261b8840c643b9d4569704d560cfd93534cbc747",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/utility_studies_final6k_20260925/umap/cell_immicro_5arm/tools/stage_b_embed.py": "d056cfdf38fb0e5f478756f370174e993b75d0c973c48e02e21749175a4ca2da",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/bulk2sc-paper/scripts/evaluate_generated_vs_truth.py": "48b12d794782b840838853e93e0fc55fab00532c4b3698f4588e926b985d2881",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/utility_studies_final6k_20260925/immicro/tools/immicro_common.py": "c5c30e917148cc5e664e91ea30c11bbdff9b6d1f53a3e0a13e16b1917eff37e9",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/predict_scanvi.py": "482ee5dcf4d72b54bc36cbb6dcff62e5030bc7afc46cb5fb01eca73a82ee45a5",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/poisson_readout.py": "8bfa080942fbcdc89ae23b276524a1a83266fb8cbc56b9af279e50f82e6cde38"
+ },
+ "seconds": 5.0
+}

paper-releases/pbisc-diffusion/updates/20261005T121346Z-scsemi-cv-budget-interim/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/scsemi_cv_20261002/fold1/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: ji2025_chili, 6 scored donors
+
+Arms: scsemi__raw.  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 6 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) (900 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 1.0000, 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
+   1.0000, 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 = 3) vs control groups ['BC', 'AC'] (n = 3); 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 3 + 3
+   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/20261005T121346Z-scsemi-cv-budget-interim/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/scsemi_cv_20261002/fold1/contrast.csv

@@ -0,0 +1,14 @@
+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
+scsemi__raw,T cell,mean_cp10k,2886,0.08710899733854235,0.0974832995921796,0.058682989387564256,0.3275078807006967,3,3,3,3,13445,4827,,,,,,,,,,,
+scsemi__raw,T cell,pseudobulk_log1p_cpm,2886,0.09157260875211974,0.12834064515855614,0.057197735406139816,0.2906006774777449,3,3,3,3,13445,4827,,,,,,,,,,,
+scsemi__raw,B cell,mean_cp10k,3179,0.26040977701643203,0.1565939262890353,0.2303479441890876,0.2601411042071766,3,3,3,3,1557,380,,,,,,,,,,,
+scsemi__raw,B cell,pseudobulk_log1p_cpm,3179,0.11074180098026187,0.06269765547587812,0.10138106044202859,0.17835693068730563,3,3,3,3,1557,380,,,,,,,,,,,
+scsemi__raw,Monocyte,mean_cp10k,3152,0.15450473249589533,0.3167845727193329,0.040320926524732285,0.3758533342598298,3,3,3,3,4852,2140,,,,,,,,,,,
+scsemi__raw,Monocyte,pseudobulk_log1p_cpm,3152,0.10542205007598027,0.26881008026051,0.03408140378193626,0.2747608986132983,3,3,3,3,4852,2140,,,,,,,,,,,
+scsemi__raw,NK cell,mean_cp10k,3122,0.07560628018139051,0.10009055515705502,0.049298272810500754,0.2857488251226578,3,3,3,3,3312,1170,,,,,,,,,,,
+scsemi__raw,NK cell,pseudobulk_log1p_cpm,3122,0.0016043570591733796,0.06711575520388033,-0.015368636376876335,0.24538943521053888,3,3,3,3,3312,1170,,,,,,,,,,,
+scsemi__raw,T cell,welch_top50_deg,5752,,,,,3,3,3,3,13445,4827,True,0,,0,0,0,0,0,,0,0
+scsemi__raw,B cell,welch_top50_deg,5752,,,,,3,3,3,3,1557,380,True,0,0.0,1,0,1,0,0,0.0,0,0
+scsemi__raw,Monocyte,welch_top50_deg,5752,,,,,3,3,3,3,4852,2140,True,0,,0,0,0,0,0,,0,0
+scsemi__raw,NK cell,welch_top50_deg,5752,,,,,3,3,3,3,3312,1170,True,0,,0,0,0,0,0,,0,0
+scsemi__raw,DC,welch_top50_deg,5752,,,,,3,3,3,3,313,245,True,0,,0,0,0,0,0,,0,0