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/immicro/fewshot_n5/draw4_scsemi5rt/per_donor.csv

@@ -0,0 +1,103 @@
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paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/immicro/fewshot_n5/draw4_scsemi5rt/summary.json

@@ -0,0 +1,290 @@
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+ },
+ "deg_definition": {
+  "enabled": false,
+  "reason": "contrast off"
+ },
+ "headline": {
+  "scsemi5rt": {
+   "comp_l1": 0.2509816471329434,
+   "comp_l1_obs_loo_null": 0.15939575114092489,
+   "precision": 0.5277124183006536,
+   "coverage": 0.03405228758169935,
+   "precision_dm": 0.8352941176470589,
+   "coverage_dm": 0.26418300653594773,
+   "pb_r_median": 0.6563029046771015,
+   "pb_top1": 0.0392156862745098,
+   "mono_pct_gen": 12.112418300653596,
+   "mono_pct_obs": 16.58568855464869,
+   "median_umi_gen": 1920.0,
+   "median_umi_obs": 1963.0,
+   "seconds": 22.5
+  }
+ },
+ "per_arm": {
+  "scsemi5rt": {
+   "arm": "scsemi5rt",
+   "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/immicro/fewshot_n5/draw4/scsemi5rt__raw.npz",
+   "npz_sha256": "c9fe82e114a7a4a0f76a5b8d7ec0a10593173d8f433c897a5d909d55cf18ba6c",
+   "receipt_verified": true,
+   "generation": {
+    "checkpoint": null,
+    "checkpoint_sha256": null,
+    "input_condition_path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro/fewshot_n5/draw4/test_raw.npz",
+    "input_condition_sha256": "e864e90de96eaf6cec21e14dda14021922f00e4c7033786a422b6707ce5808f9",
+    "seed": 20260927,
+    "steps": null,
+    "support_readout": "upstream (values <= 10 normalized units zeroed), re-read from the saved per-target model; control of the read-out re-run (equals the official output up to z sampling noise)",
+    "weights": null
+   },
+   "condition_for_broadcast": null,
+   "poisson": {
+    "seed": 20260916,
+    "rule": "whole NPZ, rows in NPZ order",
+    "nnz_whole_npz": 19352628,
+    "median_library_whole_npz_panel": 1920.0,
+    "n_rows_whole_npz": 153000
+   },
+   "annotation": {
+    "model_pt_sha256": "bf3655899e5abee435340fc008656c4a17342da0f30b70cfd43de8e9223e1930",
+    "zero_padded_model_genes": 1,
+    "label_counts": {
+     "T cell": 104297,
+     "Monocyte": 18532,
+     "B cell": 14094,
+     "DC": 8634,
+     "NK cell": 6398,
+     "Other": 1045
+    },
+    "mean_l2_max_prob": 0.9141641007681298,
+    "predict_seconds": 5.3
+   },
+   "n_donors": 102,
+   "n_generated_cells": 153000,
+   "composition": {
+    "l1_mean": 0.2509816471329434,
+    "l1_median": 0.23523694191985603,
+    "l1_obs_loo_null_mean": 0.15939575114092489,
+    "l1_generated_loo_mean_vs_observed_mean": 0.22609317951483973,
+    "composition_top1": 0.0196078431372549,
+    "mean_fraction_generated": {
+     "T cell": 0.68168,
+     "B cell": 0.092118,
+     "Monocyte": 0.121124,
+     "NK cell": 0.041817,
+     "DC": 0.056431,
+     "Other": 0.00683
+    },
+    "mean_fraction_observed": {
+     "T cell": 0.640183,
+     "B cell": 0.074202,
+     "Monocyte": 0.165857,
+     "NK cell": 0.091479,
+     "DC": 0.022745,
+     "Other": 0.005534
+    }
+   },
+   "pseudobulk": {
+    "n_genes": 5508,
+    "r_median": 0.6563029046771015,
+    "r_mean": 0.6514990764245431,
+    "top1": 0.0392156862745098,
+    "n_top1": 4,
+    "own_rank_median": 37.0,
+    "chance_top1": 0.00980392156862745
+   },
+   "precision_coverage": {
+    "precision": 0.5277124183006536,
+    "coverage": 0.03405228758169935,
+    "density": 0.10340522875816993,
+    "n_query": 15300,
+    "n_reference": 15300
+   },
+   "precision_coverage_depth_matched": {
+    "precision": 0.8352941176470589,
+    "coverage": 0.26418300653594773,
+    "density": 0.6606732026143791,
+    "detected_median_before": {
+     "R1": 362.0,
+     "Q": 123.0
+    },
+    "detected_median_after": {
+     "R1": 123.0,
+     "Q": 123.0
+    }
+   },
+   "monocyte_pct": {
+    "generated_mean_over_donors": 12.112418300653596,
+    "observed_mean_over_donors": 16.58568855464869,
+    "generated_pooled_cells": 12.112418300653594
+   },
+   "median_umi_per_cell_scoring_genes": {
+    "generated_pooled": 1920.0,
+    "observed_pooled": 1963.0,
+    "generated_mean_of_donor_medians": 1919.740234375,
+    "observed_mean_of_donor_medians": 1999.362745098039
+   },
+   "seconds": 22.5
+  }
+ },
+ "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": 28.6
+}

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/imperial_eskd/fewshot_n5/draw0_scsemi5nt/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: imperial_eskd, 142 scored donors
+
+Arms: scsemi5nt.  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/imperial_eskd/reference.npz` (sha 34b4caa3f8a6), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/imperial_eskd;
+  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/imperial_eskd/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 (1005 genes).
+- Scoring genes: 6k panel genes measured by both assays in every sample of the dataset (5927 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 142 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) (21250 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 0.9688, coverage 0.9998.
+   **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
+   0.9698, coverage 0.9997.
+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 ['covid_positive'] (n = 102) vs control groups ['covid_negative'] (n = 31); 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 5927 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/imperial_eskd/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 102 + 31
+   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/imperial_eskd/fewshot_n5/draw0_scsemi5nt/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
+scsemi5nt,T cell,mean_cp10k,3037,0.7345227387431118,0.6380552424293308,0.6972448957798938,0.36922533277261876,102,31,102,31,230847,109445,,,,,,,,,,,
+scsemi5nt,T cell,pseudobulk_log1p_cpm,3037,0.5912020199644832,0.6369550836029715,0.5244266885751149,0.32873625727146677,102,31,102,31,230847,109445,,,,,,,,,,,
+scsemi5nt,B cell,mean_cp10k,3192,0.5415016897514894,0.6193771360577306,0.46473492163407915,0.31522461846957384,102,31,102,31,45466,10318,,,,,,,,,,,
+scsemi5nt,B cell,pseudobulk_log1p_cpm,3192,0.17879922323517355,0.481685993632798,0.12541910608419266,0.14544260671760326,102,31,102,31,45466,10318,,,,,,,,,,,
+scsemi5nt,Monocyte,mean_cp10k,2882,0.42639843032889774,0.7641972589048694,0.2655529825485644,0.34783798166912205,101,31,102,31,68170,40702,,,,,,,,,,,
+scsemi5nt,Monocyte,pseudobulk_log1p_cpm,2882,0.3500892396772375,0.7195560908581415,0.21948731419834358,0.28339473063887877,101,31,102,31,68170,40702,,,,,,,,,,,
+scsemi5nt,NK cell,mean_cp10k,2972,0.6592841593050508,0.6484527334122577,0.5621447525368668,0.4168364666149557,102,31,102,31,64913,24854,,,,,,,,,,,
+scsemi5nt,NK cell,pseudobulk_log1p_cpm,2972,0.4123556543812046,0.5900041444062439,0.2979055763182824,0.3115148314298576,102,31,102,31,64913,24854,,,,,,,,,,,
+scsemi5nt,T cell,welch_top50_deg,5927,,,,,102,31,102,31,230847,109445,True,30,0.42857142857142855,50,50,1029,2696,14,0.16279069767441862,50,2247
+scsemi5nt,B cell,welch_top50_deg,5927,,,,,102,31,102,31,45466,10318,True,14,0.16279069767441862,50,50,306,1307,11,0.12359550561797752,50,2247
+scsemi5nt,Monocyte,welch_top50_deg,5927,,,,,101,31,102,31,68170,40702,True,9,0.0989010989010989,50,50,778,705,21,0.26582278481012656,50,2247
+scsemi5nt,NK cell,welch_top50_deg,5927,,,,,102,31,102,31,64913,24854,True,22,0.28205128205128205,50,50,411,1127,13,0.14942528735632185,50,2247
+scsemi5nt,DC,welch_top50_deg,5927,,,,,102,31,102,31,11578,6488,True,7,0.07526881720430108,50,50,489,853,12,0.13636363636363635,50,2247