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/imperial_eskd/fewshot_n5/draw1_scsemi5nt/per_donor.csv

@@ -0,0 +1,144 @@
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paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/imperial_eskd/fewshot_n5/draw1_scsemi5nt/summary.json

@@ -0,0 +1,1007 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "imperial_eskd",
+ "written_utc": "2026-10-03T17:00:50.560768+00:00",
+ "command": [
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+    "l1_mean": 0.6002274788253121,
+    "l1_median": 0.5220296096904442,
+    "l1_obs_loo_null_mean": 0.5086759403753967,
+    "l1_generated_loo_mean_vs_observed_mean": 0.5367985338526137,
+    "composition_top1": 0.0,
+    "mean_fraction_generated": {
+     "T cell": 0.63938,
+     "B cell": 0.10351,
+     "Monocyte": 0.137142,
+     "NK cell": 0.077758,
+     "DC": 0.028531,
+     "Other": 0.013678
+    },
+    "mean_fraction_observed": {
+     "T cell": 0.490211,
+     "B cell": 0.119516,
+     "Monocyte": 0.170001,
+     "NK cell": 0.176062,
+     "DC": 0.030282,
+     "Other": 0.013929
+    }
+   },
+   "pseudobulk": {
+    "n_genes": 5927,
+    "r_median": 0.8721090101161086,
+    "r_mean": 0.8597956686202883,
+    "top1": 0.02097902097902098,
+    "n_top1": 3,
+    "own_rank_median": 38.0,
+    "chance_top1": 0.006993006993006993
+   },
+   "precision_coverage": {
+    "precision": 0.7059593362935265,
+    "coverage": 0.44762795045571396,
+    "density": 0.3124234634260341,
+    "n_query": 21395,
+    "n_reference": 21395
+   },
+   "precision_coverage_depth_matched": {
+    "precision": 0.839121290021033,
+    "coverage": 0.41645244215938304,
+    "density": 0.4812946950222014,
+    "detected_median_before": {
+     "R1": 344.0,
+     "Q": 190.0
+    },
+    "detected_median_after": {
+     "R1": 190.0,
+     "Q": 190.0
+    }
+   },
+   "monocyte_pct": {
+    "generated_mean_over_donors": 13.714219114219109,
+    "observed_mean_over_donors": 17.000072050147295,
+    "generated_pooled_cells": 13.714219114219114
+   },
+   "median_umi_per_cell_scoring_genes": {
+    "generated_pooled": 1478.0,
+    "observed_pooled": 1338.0,
+    "generated_mean_of_donor_medians": 1477.7901611328125,
+    "observed_mean_of_donor_medians": 1275.0874125874127
+   },
+   "contrast": {
+    "mean_cp10k": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.7521056929346437,
+       "r_broadcast": 0.6393101171199623,
+       "partial_r_generated_given_broadcast": 0.725414641414561,
+       "r_generated_vs_broadcast": 0.36365849901272146,
+       "n_genes": 3031
+      },
+      "B cell": {
+       "r_generated": 0.48594883074899853,
+       "r_broadcast": 0.6236351264700181,
+       "partial_r_generated_given_broadcast": 0.4077963239285833,
+       "r_generated_vs_broadcast": 0.2900246411555376,
+       "n_genes": 3188
+      },
+      "Monocyte": {
+       "r_generated": 0.6278000467278493,
+       "r_broadcast": 0.7132449085093915,
+       "partial_r_generated_given_broadcast": 0.3485338316746407,
+       "r_generated_vs_broadcast": 0.6083692188630848,
+       "n_genes": 2883
+      },
+      "NK cell": {
+       "r_generated": 0.667974264310296,
+       "r_broadcast": 0.6685247181490744,
+       "partial_r_generated_given_broadcast": 0.5789535501823146,
+       "r_generated_vs_broadcast": 0.41246655936034843,
+       "n_genes": 2966
+      }
+     },
+     "mean_r_generated": 0.6334572086804469,
+     "mean_r_broadcast": 0.6611787175621116,
+     "mean_partial_r": 0.5151745868000249
+    },
+    "pseudobulk_log1p_cpm": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.6199468885518131,
+       "r_broadcast": 0.6360064629967949,
+       "partial_r_generated_given_broadcast": 0.5434244867673813,
+       "r_generated_vs_broadcast": 0.35947146504949223,
+       "n_genes": 3031
+      },
+      "B cell": {
+       "r_generated": 0.141107810185296,
+       "r_broadcast": 0.4862172069397853,
+       "partial_r_generated_given_broadcast": 0.0706341183312844,
+       "r_generated_vs_broadcast": 0.16501091501548099,
+       "n_genes": 3188
+      },
+      "Monocyte": {
+       "r_generated": 0.5144714274571375,
+       "r_broadcast": 0.7072577030580933,
+       "partial_r_generated_given_broadcast": 0.29347600923432504,
+       "r_generated_vs_broadcast": 0.46820697105924974,
+       "n_genes": 2883
+      },
+      "NK cell": {
+       "r_generated": 0.3866601174724863,
+       "r_broadcast": 0.5979896628156075,
+       "partial_r_generated_given_broadcast": 0.2665941296205608,
+       "r_generated_vs_broadcast": 0.30647008573986106,
+       "n_genes": 2966
+      }
+     },
+     "mean_r_generated": 0.4155465609166833,
+     "mean_r_broadcast": 0.6068677589525703,
+     "mean_partial_r": 0.2935321859883879
+    }
+   },
+   "deg_top50": {
+    "types_scored": [
+     "T cell",
+     "B cell",
+     "Monocyte",
+     "NK cell",
+     "DC"
+    ],
+    "types_not_scored": {},
+    "per_type": {
+     "T cell": {
+      "deg_scored": true,
+      "top50_overlap": 28,
+      "top50_jaccard": 0.3888888888888889,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 17,
+      "top50_jaccard_broadcast": 0.20481927710843373,
+      "n_cells_obs": 239681,
+      "n_cells_gen": 129058
+     },
+     "B cell": {
+      "deg_scored": true,
+      "top50_overlap": 10,
+      "top50_jaccard": 0.1111111111111111,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 13,
+      "top50_jaccard_broadcast": 0.14942528735632185,
+      "n_cells_obs": 44946,
+      "n_cells_gen": 20294
+     },
+     "Monocyte": {
+      "deg_scored": true,
+      "top50_overlap": 21,
+      "top50_jaccard": 0.26582278481012656,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 24,
+      "top50_jaccard_broadcast": 0.3157894736842105,
+      "n_cells_obs": 76538,
+      "n_cells_gen": 27509
+     },
+     "NK cell": {
+      "deg_scored": true,
+      "top50_overlap": 21,
+      "top50_jaccard": 0.26582278481012656,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 17,
+      "top50_jaccard_broadcast": 0.20481927710843373,
+      "n_cells_obs": 68326,
+      "n_cells_gen": 15716
+     },
+     "DC": {
+      "deg_scored": true,
+      "top50_overlap": 11,
+      "top50_jaccard": 0.12359550561797752,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 15,
+      "top50_jaccard_broadcast": 0.17647058823529413,
+      "n_cells_obs": 12644,
+      "n_cells_gen": 5549
+     }
+    },
+    "sum": 91,
+    "sum_4type": 80,
+    "max": 250,
+    "mean": 18.2,
+    "sum_broadcast": 86,
+    "sum_broadcast_4type": 71,
+    "broadcast_n_significant": 1949,
+    "seconds": 0.31
+   },
+   "seconds": 44.0
+  }
+ },
+ "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": 54.5
+}

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/imperial_eskd/fewshot_n5/draw1_scsemi5rt/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: imperial_eskd, 143 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/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 143 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) (21395 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 0.9685, 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
+   0.9689, coverage 0.9996.
+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 = 32); 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 + 32
+   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/draw1_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,3031,0.6205978465424583,0.6393101171199623,0.5878182769866462,0.29522898811626946,102,32,102,32,239681,133079,,,,,,,,,,,
+scsemi5rt,T cell,pseudobulk_log1p_cpm,3031,0.354571050547333,0.6360064629967949,0.3018765616456218,0.19851035806643874,102,32,102,32,239681,133079,,,,,,,,,,,
+scsemi5rt,B cell,mean_cp10k,3188,0.3977582385727074,0.6236351264700181,0.3404943665341438,0.22161518680325037,102,32,101,32,44946,18781,,,,,,,,,,,
+scsemi5rt,B cell,pseudobulk_log1p_cpm,3188,-0.0007052824725393922,0.4862172069397853,-0.08267797991751184,0.14555723597157025,102,32,101,32,44946,18781,,,,,,,,,,,
+scsemi5rt,Monocyte,mean_cp10k,2883,0.557325331558875,0.7132449085093915,0.31484820512902506,0.5164437017900922,101,32,102,32,76538,26568,,,,,,,,,,,
+scsemi5rt,Monocyte,pseudobulk_log1p_cpm,2883,0.34461209916817165,0.7072577030580933,0.11884723735428866,0.37723139778863607,101,32,102,32,76538,26568,,,,,,,,,,,
+scsemi5rt,NK cell,mean_cp10k,2966,0.5566186070054286,0.6685247181490744,0.4859751194878561,0.3205124541957794,102,32,101,32,68326,12937,,,,,,,,,,,
+scsemi5rt,NK cell,pseudobulk_log1p_cpm,2966,0.2226898334045005,0.5979896628156075,0.12741626296225628,0.20525366083609642,102,32,101,32,68326,12937,,,,,,,,,,,
+scsemi5rt,T cell,welch_top50_deg,5927,,,,,102,32,102,32,239681,133079,True,9,0.0989010989010989,50,50,827,1779,17,0.20481927710843373,50,1949
+scsemi5rt,B cell,welch_top50_deg,5927,,,,,102,32,101,32,44946,18781,True,3,0.030927835051546393,50,50,255,1028,13,0.14942528735632185,50,1949
+scsemi5rt,Monocyte,welch_top50_deg,5927,,,,,101,32,102,32,76538,26568,True,7,0.07526881720430108,50,50,260,742,24,0.3157894736842105,50,1949
+scsemi5rt,NK cell,welch_top50_deg,5927,,,,,102,32,101,32,68326,12937,True,7,0.07526881720430108,50,50,386,873,17,0.20481927710843373,50,1949
+scsemi5rt,DC,welch_top50_deg,5927,,,,,102,32,100,32,12644,6588,True,6,0.06382978723404255,50,50,242,871,15,0.17647058823529413,50,1949