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  1. paper-releases/pbisc-diffusion/updates/20261015T095445Z-all-width-results/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/HEALTHY.csv +19 -0
  2. paper-releases/pbisc-diffusion/updates/20261015T095445Z-all-width-results/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/REPORT.md +79 -0
  3. paper-releases/pbisc-diffusion/updates/20261015T095445Z-all-width-results/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/SOURCES.json +240 -0
  4. paper-releases/pbisc-diffusion/updates/20261015T095445Z-all-width-results/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/healthy.py +41 -0
paper-releases/pbisc-diffusion/updates/20261015T095445Z-all-width-results/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/HEALTHY.csv ADDED
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+ job,n_pairs,fold,method,comp_l1,precision,coverage,precision_dm,coverage_dm,pb_r_median
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paper-releases/pbisc-diffusion/updates/20261015T095445Z-all-width-results/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/REPORT.md ADDED
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1
+ # Trunk ×1 versus ×4: currently matched results
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+
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+ Snapshot: 2026-10-15T18:49:28.864754+09:00
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+
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+ Same current6000-gene panel. Existing outputs reused; missing healthy baseline tables computed on CPU only. No new GPU operation.
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+
7
+ SLE favors×1 for composition and disease metrics. Giroux favors×4 for expression reconstruction/coverage but not mean disease logFC. ImMicro shows consistent sample-expression improvements, but raw-coverage gains become much smaller after depth matching; precision is mixed. No cross-disease superiority claim is established.
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+
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+ ## SLE
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+
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+ | Setting | Metric | ×1 | ×4 | N |
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+ |---|---|---:|---:|---:|
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+ | pseudobulk_axis1006 | composition_mae | 0.0301 | 0.0410 | 5 |
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+ | pseudobulk_axis1006 | composition_l1 | 0.1808 | 0.2459 | 5 |
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+ | pseudobulk_axis1006 | composition_pearson | 0.9818 | 0.9662 | 5 |
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+ | pseudobulk_axis1006 | mean_logFC_pearson | 0.3713 | 0.3297 | 5 |
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+ | pseudobulk_axis1006 | mean_top50_overlap | 12.7000 | 9.9667 | 5 |
18
+ | pseudobulk_axis1006 | total_top50_overlap | 76.2000 | 59.8000 | 5 |
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+ | pseudobulk_axis1006 | mean_top50_jaccard | 0.1615 | 0.1277 | 5 |
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+ | pseudobulk_axis2279 | composition_mae | 0.0301 | 0.0410 | 5 |
21
+ | pseudobulk_axis2279 | composition_l1 | 0.1808 | 0.2459 | 5 |
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+ | pseudobulk_axis2279 | composition_pearson | 0.9818 | 0.9662 | 5 |
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+ | pseudobulk_axis2279 | mean_logFC_pearson | 0.3327 | 0.3028 | 5 |
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+ | pseudobulk_axis2279 | mean_top50_overlap | 13.7000 | 11.1333 | 5 |
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+ | pseudobulk_axis2279 | total_top50_overlap | 82.2000 | 66.8000 | 5 |
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+ | pseudobulk_axis2279 | mean_top50_jaccard | 0.1771 | 0.1450 | 5 |
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+
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+ ## Giroux
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+
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+ | Setting | Metric | ×1 | ×4 | N |
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+ |---|---|---:|---:|---:|
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+ | strict5_LoRA16 | composition_l1 | 0.2704 | 0.2606 | 5 |
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+ | strict5_LoRA16 | coverage | 0.9552 | 0.9703 | 5 |
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+ | strict5_LoRA16 | precision | 0.9997 | 0.9999 | 5 |
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+ | strict5_LoRA16 | expression_r | 0.8617 | 0.9414 | 5 |
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+ | strict5_LoRA16 | logfc_r | 0.2617 | 0.2506 | 5 |
37
+ | strict5_LoRA16 | partial_r | 0.1502 | 0.1513 | 5 |
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+ | strict5_LoRA16 | deg_overlap | 0.0000 | 0.0000 | 5 |
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+ | strict5_LoRA16 | precision_dm | 0.9965 | 0.9989 | 5 |
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+ | strict5_LoRA16 | coverage_dm | 0.9686 | 0.9801 | 5 |
41
+
42
+ ## ImMicro
43
+
44
+ | Setting | Metric | ×1 | ×4 | N |
45
+ |---|---|---:|---:|---:|
46
+ | strict5_LoRA16 | comp_l1 | 0.2009 | 0.1974 | 3 |
47
+ | strict5_LoRA16 | precision | 0.9915 | 0.9921 | 3 |
48
+ | strict5_LoRA16 | coverage | 0.8968 | 0.9716 | 3 |
49
+ | strict5_LoRA16 | precision_dm | 0.9948 | 0.9922 | 3 |
50
+ | strict5_LoRA16 | coverage_dm | 0.9868 | 0.9902 | 3 |
51
+ | strict5_LoRA16 | pb_r_median | 0.8999 | 0.9583 | 3 |
52
+ | strict10_LoRA16 | comp_l1 | 0.1582 | 0.1566 | 2 |
53
+ | strict10_LoRA16 | precision | 0.9887 | 0.9917 | 2 |
54
+ | strict10_LoRA16 | coverage | 0.9557 | 0.9919 | 2 |
55
+ | strict10_LoRA16 | precision_dm | 0.9929 | 0.9925 | 2 |
56
+ | strict10_LoRA16 | coverage_dm | 0.9952 | 0.9959 | 2 |
57
+ | strict10_LoRA16 | pb_r_median | 0.9063 | 0.9615 | 2 |
58
+ | strict20_LoRA16 | comp_l1 | 0.1682 | 0.1517 | 2 |
59
+ | strict20_LoRA16 | precision | 0.9892 | 0.9886 | 2 |
60
+ | strict20_LoRA16 | coverage | 0.9644 | 0.9959 | 2 |
61
+ | strict20_LoRA16 | precision_dm | 0.9917 | 0.9881 | 2 |
62
+ | strict20_LoRA16 | coverage_dm | 0.9970 | 0.9967 | 2 |
63
+ | strict20_LoRA16 | pb_r_median | 0.9074 | 0.9642 | 2 |
64
+ | strict40_LoRA16 | comp_l1 | 0.1790 | 0.1514 | 2 |
65
+ | strict40_LoRA16 | precision | 0.9929 | 0.9881 | 2 |
66
+ | strict40_LoRA16 | coverage | 0.9454 | 0.9943 | 2 |
67
+ | strict40_LoRA16 | precision_dm | 0.9921 | 0.9911 | 2 |
68
+ | strict40_LoRA16 | coverage_dm | 0.9948 | 0.9976 | 2 |
69
+ | strict40_LoRA16 | pb_r_median | 0.9076 | 0.9640 | 2 |
70
+
71
+ ## Interpretation limits
72
+
73
+ SLE has5 sampling seeds of each fixed pretrained model, not5 independently trained models. Giroux draws overlap in donors; these are descriptive means, no independent-replicate significance. Healthy rows use available matched folds:5pairs folds0/1/2;10/20/40pairs folds2/3. Compare x1 versus x4 within a row; different fold sets prevent a complete budget learning curve. Means of fold P/C are not pooled CV P/C.
74
+
75
+ Healthy disease logFC/DEG are not applicable in this no-contrast evaluation. Giroux top50 DEG overlap is zero in both models; this does not cover every possible DEG test. Reference-depth matched P/C measures differ from raw P/C and are retained to avoid overstating gains.
76
+
77
+ ## Not yet comparable
78
+
79
+ At the snapshot, the current exact-panel x4 Sepsis/Imperial/CHILI core score tables are absent. X1 ready:Sepsis5/5,Imperial5/5,CHILI2/5. The historical Sepsis legacy-panel pilot is excluded from this exact-panel comparison. See the disease_width_comparison_20261015 worker for automatic subsequent joins.
paper-releases/pbisc-diffusion/updates/20261015T095445Z-all-width-results/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/SOURCES.json ADDED
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+ "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/immicro/healthy/fold2/n20/lora.npz",
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+ "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/immicro/healthy/fold2/n5/lora.npz",
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+ "receipt": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/immicro/healthy/fold2/n5/lora.log",
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+ "source_binding": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/runs/immicro/healthy/fold2/n5/lora/RUN.json",
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+ "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/data/immicro/healthy/fold2/n5/test_raw.npz",
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+ "same_contract": true,
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+ "x1": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/immicro/healthy/fold3/n10/score/summary.json",
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+ "x1_binding": {
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+ "arm": "lora",
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+ "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/immicro/healthy/fold3/n10/lora.npz",
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+ "receipt": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/immicro/healthy/fold3/n10/lora.log",
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+ "source_binding": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/runs/immicro/healthy/fold3/n10/lora/RUN.json",
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+ "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/data/immicro/healthy/fold3/n10/test_raw.npz",
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+ }
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+ },
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+ {
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+ "same_contract": true,
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+ "x1": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/immicro/healthy/fold3/n20/score/summary.json",
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+ "x1_binding": {
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+ "arm": "lora",
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+ "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/immicro/healthy/fold3/n20/lora.npz",
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+ "source_binding_sha256": "7488489b3fd805658231a45b4f1bc2874d03ca50cd1dd3858ff142fa1c3b3ae9",
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+ }
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+ },
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+ {
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+ "job": "strict/immicro/healthy/fold3/n40",
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+ "same_contract": true,
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+ "x1": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/immicro/healthy/fold3/n40/score/summary.json",
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+ "x1_sha256": "34c1b232e514cb7bec10852f75111c4790cd71f71faecbef949f171afb6ecf79",
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+ "x4": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/f01_trunk4x_complete_20261013/payload/paired/strict/immicro/healthy/fold3/n40/evaluation/score/summary.json",
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+ "x4_sha256": "9a34b1c8bd4675af867f67ae14bbe73315e29b32a27f8a30b9d65ea39d17b4b8",
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+ "x1_binding": {
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+ "arm": "lora",
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+ "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/immicro/healthy/fold3/n40/lora.npz",
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+ "npz_sha256": "c2da20abe7f12a7801e5e772324c8d0689016b04b17cf26ea0baf043f947e79a",
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+ "receipt": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/generated/immicro/healthy/fold3/n40/lora.log",
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+ "receipt_sha256": "d114cfb7746188d335450d4f2610bb67600cf0e632cd1530e3c3185a84faaf22",
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+ "source_binding": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/runs/immicro/healthy/fold3/n40/lora/RUN.json",
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+ "source_binding_sha256": "c478a966621ccc103c258fa482120d2a52ef95419acf3c7116bf830fe385fa30",
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+ "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/data/immicro/healthy/fold3/n40/test_raw.npz",
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+ "condition_sha256": "dce8858d52803f436b448466b5619f696cb6df84bb2051772a78d792f089669b",
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+ "checkpoint_sha256": "0dc339216cbd49ebb6d57ebd802faaa8ba950464073cbfc11e97baa17e172bea",
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+ }
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+ }
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+ ],
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+ "giroux_result": {
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+ "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/recoveries/giroux_width_comparison_20261015/RESULT.json",
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+ "sha256": "77964d8d7c2dd8b50ab2f84d96805ebada92e595c830a0fda5c09402bf1e8017"
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+ },
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+ "sle": [
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+ {
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+ "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/f01_trunk4x_complete_20261013/payload/sle/seed0/metrics_1006/metric_summary_common_celltype_gate.csv",
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+ "sha256": "aff1ae7c961bf3f6cf5e194da6adc395ff48263c039825413c638a58501e88f5"
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+ },
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+ {
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+ "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/f01_trunk4x_complete_20261013/payload/sle/seed1/metrics_1006/metric_summary_common_celltype_gate.csv",
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+ "sha256": "a2794dee822d4f850d8ac0ce87c9ab788f30698a99575611298b8d44b6c5638e"
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+ },
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+ {
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+ "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/f01_trunk4x_complete_20261013/payload/sle/seed2/metrics_1006/metric_summary_common_celltype_gate.csv",
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+ "sha256": "bf6291c977ee6fcca70abfff9857a01c9f983445c4a6c56893cd53211c924001"
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+ },
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+ {
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+ "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/f01_trunk4x_complete_20261013/payload/sle/seed3/metrics_1006/metric_summary_common_celltype_gate.csv",
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+ "sha256": "f9d31892923a570302b65402743df4f5c9ca175695d5513a567a8da0aeedea18"
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+ },
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+ {
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+ "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/f01_trunk4x_complete_20261013/payload/sle/seed4/metrics_1006/metric_summary_common_celltype_gate.csv",
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+ "sha256": "5704327fbcb5a6f5fbde1d130b8bdbfa4f95a053d508fa435373109438153ee1"
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+ },
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+ {
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+ "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/f01_trunk4x_complete_20261013/payload/sle/seed0/metrics_2279/metric_summary_common_celltype_gate.csv",
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+ "sha256": "7a94b5b5bf8801c392a3bb294824dfeaf89947397bcc60ea561f2debcef87f46"
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+ },
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+ {
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+ "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/f01_trunk4x_complete_20261013/payload/sle/seed1/metrics_2279/metric_summary_common_celltype_gate.csv",
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+ "sha256": "e6d092b673379ed299db44c3b83da72dac850ed0196510ea8095ee46d10e4d2d"
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+ },
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+ {
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+ "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/f01_trunk4x_complete_20261013/payload/sle/seed2/metrics_2279/metric_summary_common_celltype_gate.csv",
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+ "sha256": "7830deb24f07da01931316d958de775ee4efd504181ab246af234f5ecc246b6a"
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+ },
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+ {
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+ "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/f01_trunk4x_complete_20261013/payload/sle/seed3/metrics_2279/metric_summary_common_celltype_gate.csv",
232
+ "sha256": "467ff10ee31663d70af9fa317b3b0d6aab94550758684abd28bd429f92fc3b97"
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+ },
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+ {
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+ "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/f01_trunk4x_complete_20261013/payload/sle/seed4/metrics_2279/metric_summary_common_celltype_gate.csv",
236
+ "sha256": "794f26779013b997b162f7c44f74ae14d3c6dcb857a531f8514575d97b546f63"
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+ }
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+ ],
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+ "gpu_operations": 0
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+ }
paper-releases/pbisc-diffusion/updates/20261015T095445Z-all-width-results/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/recoveries/all_width_results_20261015/healthy.py ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from pathlib import Path
2
+ import json,sys,os,csv,subprocess,statistics
3
+ assert os.environ.get('CUDA_VISIBLE_DEVICES')==''
4
+ R=Path(__file__).resolve().parent;S=R.parents[1];M=S.parents[1];X=M/'plans/f01_trunk4x_complete_20261013';F=M/'plans/paired_finetune_intradataset_20260927'
5
+ sys.path[:0]=[str(S/'tools'),str(S/'recoveries/giroux_width_comparison_20261015')]
6
+ import score_strict as ST
7
+ from validator_fix import install
8
+ from runtime import sha256,atomic_json
9
+ import numpy as np
10
+ install(ST)
11
+ rows=[];proofs=[]
12
+ for j in json.loads((R/'SNAPSHOT.json').read_text())['jobs']:
13
+ out=Path(j['output']);rel=out.relative_to(X/'payload/paired/strict');sup=ST.read_support(Path(j['support_path']));gen=S/'generated'/rel/'lora.npz';binding=ST.validate_generation_arm('lora',gen,sup)
14
+ old=Path(j['baseline_score']);new=json.loads((out/'evaluation/score/summary.json').read_text())
15
+ folder=R/rel;folder.mkdir(parents=True,exist_ok=True)
16
+ if not old.exists():
17
+ ids=folder/'test_samples.txt';ids.write_text('\n'.join(sup['test_sample_ids'])+'\n');old=folder/'score/summary.json'
18
+ assert not old.exists()
19
+ subprocess.run([sys.executable,str(F/'tools/score_ft.py'),'--dataset','immicro','--out',str(old.parent),'--arm',f'lora={gen}','--condition',f"lora={S/'data'/rel/'test_raw.npz'}",'--donors',str(ids),'--threads','2','--no-contrast','--no-deg'],check=True)
20
+ a=json.loads(old.read_text());r1=json.loads((S/'runs'/rel/'lora/RUN.json').read_text());r4=json.loads((out/'run/RUN.json').read_text())
21
+ for key in ['train_rows','inner_val_rows','test_rows_never_loaded_for_training','seed','lr','warmup','batch_size','ema_decay','fixed_steps','selected_step']:assert r1[key]==r4[key],(j['id'],key)
22
+ for key in ['lora_rank','lora_alpha']:assert r1['trainable'][key]==r4['trainable'][key]==16
23
+ g1=json.loads(gen.with_suffix('.log').read_text());g4=json.loads((out/'generated.log').read_text())
24
+ for key in ['condition_sha256','seed','steps','weights']:assert g1[key]==g4[key]
25
+ with np.load(gen,allow_pickle=False) as b1,np.load(out/'generated.npz',allow_pickle=False) as b4:assert np.array_equal(b1['gene_ids'],b4['gene_ids'])
26
+ for key in ['contrast_definition','gene_exclusion','deg_definition']:assert a.get(key)==new.get(key),(j['id'],key)
27
+ assert a['donors']==new['donors'] and a['reference']['reference_sha256']==new['reference']['reference_sha256']
28
+ assert a['per_arm']['lora']['annotation']['model_pt_sha256']==new['per_arm']['trunk4x']['annotation']['model_pt_sha256']
29
+ for method,d,key in [('x1',a,'lora'),('x4',new,'trunk4x')]:
30
+ h=d['headline'][key];rows.append({'job':j['id'],'n_pairs':sup['n_pairs'],'fold':j.get('fold',str(rel.parent.name)),'method':method,**{k:h[k] for k in ['comp_l1','precision','coverage','precision_dm','coverage_dm','pb_r_median']}})
31
+ proofs.append({'job':j['id'],'same_contract':True,'x1':str(old),'x1_sha256':sha256(old),'x4':str(out/'evaluation/score/summary.json'),'x4_sha256':sha256(out/'evaluation/score/summary.json'),'x1_binding':binding})
32
+ atomic_json(R/'PROGRESS.json',{'completed':len(proofs),'total':9})
33
+ with (R/'HEALTHY.csv').open('w') as f:
34
+ w=csv.DictWriter(f,fieldnames=rows[0]);w.writeheader();w.writerows(rows)
35
+ means={}
36
+ for n in sorted({r['n_pairs'] for r in rows}):
37
+ means[str(n)]={}
38
+ for method in ['x1','x4']:
39
+ q=[r for r in rows if r['n_pairs']==n and r['method']==method];means[str(n)][method]={'n_folds':len(q),**{k:statistics.mean(r[k] for r in q) for k in ['comp_l1','precision','coverage','precision_dm','coverage_dm','pb_r_median']}}
40
+ atomic_json(R/'HEALTHY_RESULT.json',{'means':means,'proofs':proofs,'gpu_operations':0,'scope':'Only matched available folds; descriptive fold means, not pooled CV or significance.'})
41
+ print(json.dumps(means),flush=True)