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/ye2026_sepsis/fewshot_n5/draw3_scsemi5nt/per_donor.csv

@@ -0,0 +1,78 @@
+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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paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/fewshot_n5/draw3_scsemi5nt/summary.json

@@ -0,0 +1,747 @@
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+     "T cell": 0.482638,
+     "B cell": 0.180331,
+     "Monocyte": 0.204733,
+     "NK cell": 0.078339,
+     "DC": 0.005297,
+     "Other": 0.048661
+    }
+   },
+   "pseudobulk": {
+    "n_genes": 5975,
+    "r_median": 0.8476395778429903,
+    "r_mean": 0.8385956391528299,
+    "top1": 0.012987012987012988,
+    "n_top1": 1,
+    "own_rank_median": 26.0,
+    "chance_top1": 0.012987012987012988
+   },
+   "precision_coverage": {
+    "precision": 0.7314285714285714,
+    "coverage": 0.2420779220779221,
+    "density": 0.25955844155844154,
+    "n_query": 11550,
+    "n_reference": 11550
+   },
+   "precision_coverage_depth_matched": {
+    "precision": 0.7907359307359307,
+    "coverage": 0.439047619047619,
+    "density": 0.34594805194805195,
+    "detected_median_before": {
+     "R1": 412.0,
+     "Q": 177.0
+    },
+    "detected_median_after": {
+     "R1": 177.0,
+     "Q": 177.0
+    }
+   },
+   "monocyte_pct": {
+    "generated_mean_over_donors": 19.954112554112555,
+    "observed_mean_over_donors": 20.47332071333849,
+    "generated_pooled_cells": 19.954112554112555
+   },
+   "median_umi_per_cell_scoring_genes": {
+    "generated_pooled": 1493.0,
+    "observed_pooled": 1998.0,
+    "generated_mean_of_donor_medians": 1492.7857666015625,
+    "observed_mean_of_donor_medians": 2137.7077922077924
+   },
+   "contrast": {
+    "mean_cp10k": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.528873611978838,
+       "r_broadcast": 0.2674058776426976,
+       "partial_r_generated_given_broadcast": 0.4753988326672964,
+       "r_generated_vs_broadcast": 0.5686400628425159,
+       "n_genes": 3267
+      },
+      "B cell": {
+       "r_generated": 0.6189408274666053,
+       "r_broadcast": 0.2947135505142224,
+       "partial_r_generated_given_broadcast": 0.5727158775794631,
+       "r_generated_vs_broadcast": 0.3909562021643367,
+       "n_genes": 3307
+      },
+      "Monocyte": {
+       "r_generated": 0.5419669517291292,
+       "r_broadcast": 0.5404162642066815,
+       "partial_r_generated_given_broadcast": 0.4144632418269385,
+       "r_generated_vs_broadcast": 0.41608523716333895,
+       "n_genes": 3299
+      },
+      "NK cell": {
+       "r_generated": 0.6218961914639862,
+       "r_broadcast": 0.2032508698563803,
+       "partial_r_generated_given_broadcast": 0.6115646218992621,
+       "r_generated_vs_broadcast": 0.4876067203575686,
+       "n_genes": 3251
+      }
+     },
+     "mean_r_generated": 0.5779193956596397,
+     "mean_r_broadcast": 0.3264466405549955,
+     "mean_partial_r": 0.51853564349324
+    },
+    "pseudobulk_log1p_cpm": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.4303354054856284,
+       "r_broadcast": 0.2289561663328621,
+       "partial_r_generated_given_broadcast": 0.3990088276340625,
+       "r_generated_vs_broadcast": 0.2276655806282721,
+       "n_genes": 3267
+      },
+      "B cell": {
+       "r_generated": 0.3875009543095494,
+       "r_broadcast": 0.29321258816714024,
+       "partial_r_generated_given_broadcast": 0.37252571918446714,
+       "r_generated_vs_broadcast": 0.11496906747899811,
+       "n_genes": 3307
+      },
+      "Monocyte": {
+       "r_generated": 0.34905500976930776,
+       "r_broadcast": 0.6043259179152038,
+       "partial_r_generated_given_broadcast": 0.21009117937439406,
+       "r_generated_vs_broadcast": 0.31468349404637314,
+       "n_genes": 3299
+      },
+      "NK cell": {
+       "r_generated": 0.4052831274882818,
+       "r_broadcast": 0.06220697008500626,
+       "partial_r_generated_given_broadcast": 0.4013927098414094,
+       "r_generated_vs_broadcast": 0.17871115736890245,
+       "n_genes": 3251
+      }
+     },
+     "mean_r_generated": 0.39304362426319184,
+     "mean_r_broadcast": 0.29717541062505315,
+     "mean_partial_r": 0.34575460900858324
+    }
+   },
+   "deg_top50": {
+    "types_scored": [
+     "T cell",
+     "B cell",
+     "Monocyte",
+     "NK cell",
+     "DC"
+    ],
+    "types_not_scored": {},
+    "per_type": {
+     "T cell": {
+      "deg_scored": true,
+      "top50_overlap": 6,
+      "top50_jaccard": 0.06382978723404255,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 3,
+      "top50_jaccard_broadcast": 0.030927835051546393,
+      "n_cells_obs": 274345,
+      "n_cells_gen": 68033
+     },
+     "B cell": {
+      "deg_scored": true,
+      "top50_overlap": 1,
+      "top50_jaccard": 0.010101010101010102,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 2,
+      "top50_jaccard_broadcast": 0.02040816326530612,
+      "n_cells_obs": 97853,
+      "n_cells_gen": 5796
+     },
+     "Monocyte": {
+      "deg_scored": true,
+      "top50_overlap": 11,
+      "top50_jaccard": 0.12359550561797752,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 11,
+      "top50_jaccard_broadcast": 0.12359550561797752,
+      "n_cells_obs": 103223,
+      "n_cells_gen": 23047
+     },
+     "NK cell": {
+      "deg_scored": true,
+      "top50_overlap": 4,
+      "top50_jaccard": 0.045454545454545456,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 42,
+      "top50_overlap_broadcast": 1,
+      "top50_jaccard_broadcast": 0.010101010101010102,
+      "n_cells_obs": 43846,
+      "n_cells_gen": 7765
+     },
+     "DC": {
+      "deg_scored": true,
+      "top50_overlap": 3,
+      "top50_jaccard": 0.030927835051546393,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 2,
+      "top50_jaccard_broadcast": 0.02040816326530612,
+      "n_cells_obs": 2961,
+      "n_cells_gen": 2235
+     }
+    },
+    "sum": 25,
+    "sum_4type": 22,
+    "max": 250,
+    "mean": 5.0,
+    "sum_broadcast": 19,
+    "sum_broadcast_4type": 17,
+    "broadcast_n_significant": 1139,
+    "seconds": 0.21
+   },
+   "seconds": 28.1
+  }
+ },
+ "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": 34.1
+}

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/fewshot_n5/draw3_scsemi5rt/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: ye2026_sepsis, 77 scored donors
+
+Arms: scsemi5rt.  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/ye2026_sepsis/reference.npz` (sha bac12da8a241), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ye2026_sepsis;
+  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/ye2026_sepsis/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 (5975 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 77 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) (11550 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 0.9804, coverage 0.9997.
+   **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
+   0.9815, coverage 0.9999.
+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 ['adult_sepsis', 'ped_sepsis'] (n = 57) vs control groups ['adult_HC', 'adult_ICU_ctrl', 'ped_HC', 'ped_ICU_ctrl'] (n = 20); 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 5975 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/ye2026_sepsis/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 57 + 20
+   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/ye2026_sepsis/fewshot_n5/draw3_scsemi5rt/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
+scsemi5rt,T cell,mean_cp10k,3267,0.46224809539977757,0.2674058776426976,0.3930015771496323,0.5131382907639547,57,20,57,20,274345,69406,,,,,,,,,,,
+scsemi5rt,T cell,pseudobulk_log1p_cpm,3267,0.3060256984356808,0.2289561663328621,0.26952918761039496,0.2183168094760062,57,20,57,20,274345,69406,,,,,,,,,,,
+scsemi5rt,B cell,mean_cp10k,3307,0.4590592859970562,0.2947135505142224,0.38902687476290765,0.403500466445023,57,20,56,20,97853,5727,,,,,,,,,,,
+scsemi5rt,B cell,pseudobulk_log1p_cpm,3307,0.22882519054421022,0.29321258816714024,0.21719858605309725,0.07416082899434573,57,20,56,20,97853,5727,,,,,,,,,,,
+scsemi5rt,Monocyte,mean_cp10k,3299,0.49294294292532365,0.5404162642066815,0.39189491842749835,0.33787956995116364,57,20,57,19,103223,21886,,,,,,,,,,,
+scsemi5rt,Monocyte,pseudobulk_log1p_cpm,3299,0.0603910757733515,0.6043259179152038,-0.042180654854303556,0.15487089603117654,57,20,57,19,103223,21886,,,,,,,,,,,
+scsemi5rt,NK cell,mean_cp10k,3251,0.5354239721196902,0.2032508698563803,0.5062190867733924,0.4092996086830872,57,20,55,20,43846,6174,,,,,,,,,,,
+scsemi5rt,NK cell,pseudobulk_log1p_cpm,3251,0.26787622378099507,0.06220697008500626,0.261683213285154,0.16584060857299568,57,20,55,20,43846,6174,,,,,,,,,,,
+scsemi5rt,T cell,welch_top50_deg,5975,,,,,57,20,57,20,274345,69406,True,2,0.02040816326530612,50,50,1478,71,3,0.030927835051546393,50,1139
+scsemi5rt,B cell,welch_top50_deg,5975,,,,,57,20,56,20,97853,5727,True,1,0.010101010101010102,50,50,956,64,2,0.02040816326530612,50,1139
+scsemi5rt,Monocyte,welch_top50_deg,5975,,,,,57,20,57,19,103223,21886,True,3,0.030927835051546393,50,50,2437,143,11,0.12359550561797752,50,1139
+scsemi5rt,NK cell,welch_top50_deg,5975,,,,,57,20,55,20,43846,6174,True,2,0.02040816326530612,50,50,1089,89,1,0.010101010101010102,50,1139
+scsemi5rt,DC,welch_top50_deg,5975,,,,,53,20,56,19,2961,2894,True,4,0.041666666666666664,50,50,1499,143,2,0.02040816326530612,50,1139