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/draw2_scsemi5rt/per_donor.csv

@@ -0,0 +1,143 @@
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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/draw2_scsemi5rt/summary.json

@@ -0,0 +1,1003 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "imperial_eskd",
+ "written_utc": "2026-10-03T17:13:09.853087+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",
+  "imperial_eskd",
+  "--out",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/imperial_eskd/fewshot_n5/draw2_scsemi5rt",
+  "--threads",
+  "6",
+  "--donors",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/imperial_eskd/fewshot_n5/draw2/test_samples.txt",
+  "--case-groups",
+  "covid_positive",
+  "--control-groups",
+  "covid_negative",
+  "--arm",
+  "scsemi5rt=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/imperial_eskd/fewshot_n5/draw2/scsemi5rt__raw.npz"
+ ],
+ "arms": [
+  "scsemi5rt"
+ ],
+ "donors": [
+  "C20_1004",
+  "C20_1404",
+  "C21_1004",
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+    "density": 0.05857667671439337,
+    "n_query": 21232,
+    "n_reference": 21232
+   },
+   "precision_coverage_depth_matched": {
+    "precision": 0.4262434061793519,
+    "coverage": 0.17162773172569706,
+    "density": 0.12989826676714394,
+    "detected_median_before": {
+     "R1": 347.0,
+     "Q": 131.0
+    },
+    "detected_median_after": {
+     "R1": 131.0,
+     "Q": 131.0
+    }
+   },
+   "monocyte_pct": {
+    "generated_mean_over_donors": 19.76948356807512,
+    "observed_mean_over_donors": 16.205826518972017,
+    "generated_pooled_cells": 19.769483568075117
+   },
+   "median_umi_per_cell_scoring_genes": {
+    "generated_pooled": 1013.0,
+    "observed_pooled": 1372.0,
+    "generated_mean_of_donor_medians": 1013.29931640625,
+    "observed_mean_of_donor_medians": 1311.5598591549297
+   },
+   "contrast": {
+    "mean_cp10k": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.6329086202522154,
+       "r_broadcast": 0.6311553277299028,
+       "partial_r_generated_given_broadcast": 0.5777502567284429,
+       "r_generated_vs_broadcast": 0.3333695090050492,
+       "n_genes": 3036
+      },
+      "B cell": {
+       "r_generated": 0.48900049406167184,
+       "r_broadcast": 0.6245632309702648,
+       "partial_r_generated_given_broadcast": 0.3911872669587693,
+       "r_generated_vs_broadcast": 0.3194197241632821,
+       "n_genes": 3187
+      },
+      "Monocyte": {
+       "r_generated": 0.6070431926329957,
+       "r_broadcast": 0.6837051334204964,
+       "partial_r_generated_given_broadcast": 0.3688275922577626,
+       "r_generated_vs_broadcast": 0.562345015667859,
+       "n_genes": 2886
+      },
+      "NK cell": {
+       "r_generated": 0.520914212124315,
+       "r_broadcast": 0.6340215947478178,
+       "partial_r_generated_given_broadcast": 0.38445391040895727,
+       "r_generated_vs_broadcast": 0.38977122998120733,
+       "n_genes": 2961
+      }
+     },
+     "mean_r_generated": 0.5624666297677995,
+     "mean_r_broadcast": 0.6433613217171205,
+     "mean_partial_r": 0.430554756588483
+    },
+    "pseudobulk_log1p_cpm": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.35558244819481016,
+       "r_broadcast": 0.6350373450250355,
+       "partial_r_generated_given_broadcast": 0.2711544852697006,
+       "r_generated_vs_broadcast": 0.23971479267223214,
+       "n_genes": 3036
+      },
+      "B cell": {
+       "r_generated": 0.01805357859035772,
+       "r_broadcast": 0.4816610289030315,
+       "partial_r_generated_given_broadcast": -0.08624988013256002,
+       "r_generated_vs_broadcast": 0.19150470483064824,
+       "n_genes": 3187
+      },
+      "Monocyte": {
+       "r_generated": 0.3953675020844538,
+       "r_broadcast": 0.6802082395314715,
+       "partial_r_generated_given_broadcast": 0.1611569031297768,
+       "r_generated_vs_broadcast": 0.42395579121847565,
+       "n_genes": 2886
+      },
+      "NK cell": {
+       "r_generated": 0.2629533205966495,
+       "r_broadcast": 0.5753918871563406,
+       "partial_r_generated_given_broadcast": 0.14316513740728767,
+       "r_generated_vs_broadcast": 0.2605273293126705,
+       "n_genes": 2961
+      }
+     },
+     "mean_r_generated": 0.2579892123665678,
+     "mean_r_broadcast": 0.5930746251539698,
+     "mean_partial_r": 0.12230666141855126
+    }
+   },
+   "deg_top50": {
+    "types_scored": [
+     "T cell",
+     "B cell",
+     "Monocyte",
+     "NK cell",
+     "DC"
+    ],
+    "types_not_scored": {},
+    "per_type": {
+     "T cell": {
+      "deg_scored": true,
+      "top50_overlap": 12,
+      "top50_jaccard": 0.13636363636363635,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 13,
+      "top50_jaccard_broadcast": 0.14942528735632185,
+      "n_cells_obs": 249798,
+      "n_cells_gen": 101302
+     },
+     "B cell": {
+      "deg_scored": true,
+      "top50_overlap": 5,
+      "top50_jaccard": 0.05263157894736842,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 10,
+      "top50_jaccard_broadcast": 0.1111111111111111,
+      "n_cells_obs": 46668,
+      "n_cells_gen": 10485
+     },
+     "Monocyte": {
+      "deg_scored": true,
+      "top50_overlap": 7,
+      "top50_jaccard": 0.07526881720430108,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 18,
+      "top50_jaccard_broadcast": 0.21951219512195122,
+      "n_cells_obs": 75478,
+      "n_cells_gen": 39841
+     },
+     "NK cell": {
+      "deg_scored": true,
+      "top50_overlap": 7,
+      "top50_jaccard": 0.07526881720430108,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 13,
+      "top50_jaccard_broadcast": 0.14942528735632185,
+      "n_cells_obs": 66389,
+      "n_cells_gen": 36399
+     },
+     "DC": {
+      "deg_scored": true,
+      "top50_overlap": 9,
+      "top50_jaccard": 0.0989010989010989,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 11,
+      "top50_jaccard_broadcast": 0.12359550561797752,
+      "n_cells_obs": 12425,
+      "n_cells_gen": 9251
+     }
+    },
+    "sum": 40,
+    "sum_4type": 31,
+    "max": 250,
+    "mean": 8.0,
+    "sum_broadcast": 65,
+    "sum_broadcast_4type": 54,
+    "broadcast_n_significant": 2026,
+    "seconds": 0.27
+   },
+   "seconds": 32.5
+  }
+ },
+ "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": 41.2
+}

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/imperial_eskd/fewshot_n5/draw3_scsemi5nt/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: imperial_eskd, 145 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 145 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) (21682 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 0.9691, 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
+   0.9693, 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 = 104) 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 104 + 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/draw3_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,3031,0.737724420686492,0.6363131785460265,0.7085206456544535,0.35700658625871573,104,31,104,31,241386,112092,,,,,,,,,,,
+scsemi5nt,T cell,pseudobulk_log1p_cpm,3031,0.6408655751488717,0.6151584600190607,0.5458938385641605,0.40081523001508823,104,31,104,31,241386,112092,,,,,,,,,,,
+scsemi5nt,B cell,mean_cp10k,3186,0.6296998188371284,0.6181604529907424,0.4985133262135762,0.4537835374413586,104,31,104,31,46374,11744,,,,,,,,,,,
+scsemi5nt,B cell,pseudobulk_log1p_cpm,3186,0.22676270525083134,0.4479445109520174,0.10390330806347428,0.30899534690738684,104,31,104,31,46374,11744,,,,,,,,,,,
+scsemi5nt,Monocyte,mean_cp10k,2879,0.6586766737476113,0.7038117222497147,0.3886891525124981,0.6317571445558192,103,31,104,31,74900,34011,,,,,,,,,,,
+scsemi5nt,Monocyte,pseudobulk_log1p_cpm,2879,0.5472697251447488,0.6894081488945825,0.3069451368706395,0.5179433629530379,103,31,104,31,74900,34011,,,,,,,,,,,
+scsemi5nt,NK cell,mean_cp10k,2964,0.7699899209928126,0.6572983397395356,0.690300361895073,0.47493885047069795,104,31,104,31,67818,38977,,,,,,,,,,,
+scsemi5nt,NK cell,pseudobulk_log1p_cpm,2964,0.5447601134748183,0.5733589034230205,0.41428480789562355,0.4102330037011771,104,31,104,31,67818,38977,,,,,,,,,,,
+scsemi5nt,T cell,welch_top50_deg,5927,,,,,104,31,104,31,241386,112092,True,31,0.4492753623188406,50,50,873,2844,16,0.19047619047619047,50,2000
+scsemi5nt,B cell,welch_top50_deg,5927,,,,,104,31,104,31,46374,11744,True,12,0.13636363636363635,50,50,215,1983,13,0.14942528735632185,50,2000
+scsemi5nt,Monocyte,welch_top50_deg,5927,,,,,103,31,104,31,74900,34011,True,24,0.3157894736842105,50,50,270,1233,20,0.25,50,2000
+scsemi5nt,NK cell,welch_top50_deg,5927,,,,,104,31,104,31,67818,38977,True,23,0.2987012987012987,50,50,321,1308,15,0.17647058823529413,50,2000
+scsemi5nt,DC,welch_top50_deg,5927,,,,,104,31,104,31,12959,3939,True,12,0.13636363636363635,50,50,272,1354,12,0.13636363636363635,50,2000