Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261015T072909Z-disease-width-readiness/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/disease_width_comparison_20261015/imperial_eskd/draw0/BASELINE.json

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+{
+  "summary": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/scores/imperial_eskd/disease/draw0/score/summary.json",
+  "summary_sha256": "d129eeb3ec48f773ce0b2629848507d14e210d7275394cfe33bd32d1b6da9412",
+  "binding": {
+    "arm": "lora",
+    "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/imperial_eskd/disease/draw0/lora.npz",
+    "npz_sha256": "ec73f58c1e0e34d6ab10e83f27156145227987e90f158b35ab78cec17069807a",
+    "receipt": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/imperial_eskd/disease/draw0/lora.log",
+    "receipt_sha256": "01cab6b8e7cfe4a2e09aeca8b314f0532223037a484c8e087c7779cf7bc9e88f",
+    "source_binding": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/runs/imperial_eskd/disease/draw0/lora/RUN.json",
+    "source_binding_sha256": "a408b588a2db5877ea340988855095aa65dbfcacd2c79bcb0e3bca2b6a08c94f",
+    "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/data/imperial_eskd/disease/draw0/test_raw.npz",
+    "condition_sha256": "735672055b9222e45379a85fb0de4994028d33943ba220f048c7b6eafca65e0a",
+    "checkpoint_sha256": "9fc5d60367dc36ef959fbdf00ba9d8decd461905a92133696a1b9e58a19d3bae",
+    "support_sha256": "4733416ac0e4142bc928aa91f4fbe75c852492f55832470684fd9d41b56ef716"
+  },
+  "reused_existing_score": true
+}

paper-releases/pbisc-diffusion/updates/20261015T072909Z-disease-width-readiness/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/disease_width_comparison_20261015/imperial_eskd/draw3/BASELINE.json

@@ -0,0 +1,18 @@
+{
+  "summary": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/scores/imperial_eskd/disease/draw3/score/summary.json",
+  "summary_sha256": "3bfd21f7a518adfd1dcc74cf93683fb1d45afc31b2fa927531e17ceb97a90778",
+  "binding": {
+    "arm": "lora",
+    "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/imperial_eskd/disease/draw3/lora.npz",
+    "npz_sha256": "eb9801073de0650d85a3895af52caba41f9c13c389b3433b8390e53dfff10772",
+    "receipt": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/imperial_eskd/disease/draw3/lora.log",
+    "receipt_sha256": "25f7612e7d11900c33b3c4712c79ad4ce088cbc7c495b6fe84edc68f8a2e7107",
+    "source_binding": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/runs/imperial_eskd/disease/draw3/lora/RUN.json",
+    "source_binding_sha256": "db42f102c1caefe550eb59a4d2e3fdf9e0f27dbc5c075ea16170627be428579a",
+    "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/data/imperial_eskd/disease/draw3/test_raw.npz",
+    "condition_sha256": "1bb5ea46702a37ba35cf81b4e82426c5cfa539ef881c477be37e40621df26b7f",
+    "checkpoint_sha256": "3c60fc60e7e90234ce36a527a4ff9878c05fc11207140eea3a6a4ceb8d815def",
+    "support_sha256": "28fb70b356b7850892eab0f7c543f47d99041c1b6ac91d776d04125d532c08ce"
+  },
+  "reused_existing_score": true
+}

paper-releases/pbisc-diffusion/updates/20261015T072909Z-disease-width-readiness/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/disease_width_comparison_20261015/imperial_eskd/draw4/score/README.md

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+# score_ft.py output: imperial_eskd, 143 scored donors
+
+Arms: lora.  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) (21382 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 0.9696, 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
+   0.9684, 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/20261015T072909Z-disease-width-readiness/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/disease_width_comparison_20261015/imperial_eskd/draw4/score/summary.json

@@ -0,0 +1,1009 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "imperial_eskd",
+ "written_utc": "2026-10-15T07:25:58.792404+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/paper_figures_strictpairs_20261012/recoveries/disease_width_comparison_20261015/imperial_eskd/draw4/score",
+  "--arm",
+  "lora=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/imperial_eskd/disease/draw4/lora.npz",
+  "--condition",
+  "lora=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/data/imperial_eskd/disease/draw4/test_raw.npz",
+  "--donors",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/recoveries/disease_width_comparison_20261015/imperial_eskd/draw4/test_samples.txt",
+  "--threads",
+  "2",
+  "--case-groups",
+  "covid_positive",
+  "--control-groups",
+  "covid_negative"
+ ],
+ "arms": [
+  "lora"
+ ],
+ "donors": [
+  "C20_1004",
+  "C20_1404",
+  "C21_1004",
+  "C21_1704",
+  "C21_2504",
+  "C23_2204",
+  "C31_1304",
+  "C31_2004",
+  "C31_2704",
+  "C34_1404",
+  "C34_2104",
+  "C34_2504",
+  "C40_1504",
+  "C40_2204",
+  "C40_0105",
+  "C42_1504",
+  "C42_2204",
+  "C42_2704",
+  "C58_1604",
+  "C58_2501",
+  "C58_2901",
+  "C58_0102",
+  "C58_0402",
+  "C58_1003",
+  "C60_2504",
+  "C63_1704",
+  "C63_2404",
+  "C63_2904",
+  "C65_1704",
+  "C65_2104",
+  "C65_2504",
+  "C69_1804",
+  "C82_2504",
+  "C85_2004",
+  "C93_2104",
+  "C93_2704",
+  "C93_0405",
+  "C101_0601",
+  "C101_1101",
+  "C101_1301",
+  "C101_1801",
+  "C101_2001",
+  "C101_2502",
+  "C103_2204",
+  "C104_2204",
+  "C106_2204",
+  "C108_2204",
+  "C108_1501",
+  "C108_1801",
+  "C108_2201",
+  "C113_0605",
+  "C123_2404",
+  "C123_2904",
+  "C123_0505",
+  "C124_2404",
+  "C124_2704",
+  "C124_0105",
+  "C126_2504",
+  "C126_3004",
+  "C126_0605",
+  "C127_2504",
+  "C127_0405",
+  "C127_1105",
+  "C128_2504",
+  "C128_0405",
+  "C128_1105",
+  "C132_2804",
+  "C132_0205",
+  "C132_1105",
+  "C138_3004",
+  "C138_2301",
+  "C138_3001",
+  "C138_0202",
+  "C138_0402",
+  "C140_3004",
+  "C140_2801",
+  "C140_3001",
+  "C140_0202",
+  "C140_0402",
+  "C140_0602",
+  "C140_0902",
+  "C140_1103",
+  "C141_2001",
+  "C141_2201",
+  "C141_3001",
+  "C141_0202",
+  "C142_3004",
+  "C145_3004",
+  "C145_0701",
+  "C145_1201",
+  "C145_1401",
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+  "C146_3004",
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+  "C161_1105",
+  "C164_1105",
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+  "C168_1205",
+  "C168_2001",
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+  "C168_0403",
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+  "C169_2301",
+  "C169_0202",
+  "C169_0403",
+  "C187_2605",
+  "C187_1201",
+  "C187_1401",
+  "C187_1901",
+  "C187_2301",
+  "C187_2502",
+  "C190_2605",
+  "C190_2901",
+  "C190_0102",
+  "C190_0302",
+  "C190_0502",
+  "C190_0802",
+  "C190_1002",
+  "C190_0903",
+  "C193_2605",
+  "C195_2605",
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+  "C212_2705",
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+  "C237_2805",
+  "C238_2905",
+  "C240_2905",
+  "C246_2905",
+  "C251_2905"
+ ],
+ "n_donors": 143,
+ "reference": {
+  "cache": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/imperial_eskd",
+  "reference_sha256": "34b4caa3f8a60b46d5faf58f09cdaa5622bcce19e4c45d9902a711fead96375e",
+  "features_sha256": "e46e5bb1a24eae045efe75125d20e3accaa8f68a1006eb2f60d24159013d4aa7",
+  "n_r1_cells": 21382,
+  "pca": {
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+   "explained_variance_ratio_sum": 0.26274892687797546,
+   "n_fit_cells": 21382
+  },
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+  },
+  "observed_l1_loo_null_mean": 0.5095285177538514
+ },
+ "contrast_definition": {
+  "enabled": true,
+  "rule": "explicit --case-groups / --control-groups",
+  "group_source": "features groups",
+  "case_groups": [
+   "covid_positive"
+  ],
+  "control_groups": [
+   "covid_negative"
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+  "n_control_donors": 31,
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