Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261012T130738Z-strictpairs-implementation/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/archive/20261012T130738Z-strictpairs-implementation/snapshot/scsemi/ye2026_sepsis/disease/draw3/geneset_feature_qc.json

@@ -0,0 +1,36 @@
+{
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+  "Res-S139": {
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+   "median_nonzero_genes": 168.0,
+   "zero_score_pathways": 129
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+  "Res-S174": {
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+   "median_thresholded_gene_library": 8645.4365234375,
+   "median_nonzero_genes": 131.0,
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+  "Skin-S142": {
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+   "median_thresholded_gene_library": 8642.5947265625,
+   "median_nonzero_genes": 154.0,
+   "zero_score_pathways": 121
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+  "Abd-S57": {
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+   "median_thresholded_gene_library": 10000.0,
+   "median_nonzero_genes": 194.0,
+   "zero_score_pathways": 91
+  }
+ }
+}

paper-releases/pbisc-diffusion/updates/20261012T130738Z-strictpairs-implementation/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/archive/20261012T130738Z-strictpairs-implementation/snapshot/scsemi/ye2026_sepsis/disease/draw3/prepared.json

@@ -0,0 +1,24 @@
+{
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+ "seconds": 101.7,
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+  "status/init_cluster_labels.txt": "246bc8b35beb0862aec51eecb90426eb8a6df85fbdcef2bd32e2a0da2f2fc30d",
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+  "sample_sc/Res-S174.h5ad": "ac1901c548c52f24f0ed0c8f5e066c7363343d15c305635f5e2c9894a78bc61d",
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+  "geneset_scores/PHC05.npy": "9e51fc9b334f7b9c4520e1c788b1e152992441944a0e30d5a1dad3b1feb29cca",
+  "geneset_scores/Res-S139.npy": "661a31df1d6cfd5207c130962c154b20c3c5cab43670e006e643e434a8529a3f",
+  "geneset_scores/Res-S174.npy": "d8c1c3a281568c912dee39c7e3f20a3d239b598a605b33a29bf59f23b729558d",
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+ },
+ "genesets_gmt_sha256": "9c4386ac1ff666fc88301e126f1a1c6452122c6b63f311131a640bffe9c4d84b"
+}

paper-releases/pbisc-diffusion/updates/20261012T130738Z-strictpairs-implementation/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/archive/20261012T130738Z-strictpairs-implementation/snapshot/scsemi/ye2026_sepsis/disease/draw3/run_manifest.json

@@ -0,0 +1,103 @@
+{
+ "run_signature": "5da993f903f971d1e91d88ab76fd17fb322ef6de7a014a95d08c5deb329397ac",
+ "protocol": {
+  "method": "scSemiProfiler",
+  "setting": "paired_fixed_representatives_realbulk_genesets",
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+  "initsetup": {
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+   "geneset": true,
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+  "representatives": "fixed: every usable row of the split's training donors (no active learning)",
+  "assignment": "upstream representative_selection.update_membership: nearest representative by Euclidean distance in processed_bulkdata.obsm['X_pca'] (initsetup PCA of log1p CP10K real bulk)",
+  "pretrain1": {
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+   "epochs": 150,
+   "ministages": 5,
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+   "diagw": 1.0
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+  "seeds": {
+   "base": 20260927,
+   "pretrain1": "base + 1000 + bulk row index",
+   "pretrain2": "base + 2000 + bulk row index",
+   "target": "base + bulk row index (frozen campaign rule)"
+  },
+  "output_unit": "normalized_expression (upstream fast_semi output: ZINB mean, rep-cell library, values <= 10 zeroed)",
+  "cellgraph_impl": "tools/lean_cellgraph.fast_cellgraph_lean replaces upstream singlecell_process.fast_cellgraph inside scprocess: same arithmetic, chunked (16 GiB CPU cap); bit-identical outputs verified (validation/lean_cellgraph_*.json)",
+  "scprocess_orchestration": "cohort hvset first (upstream scprocess's own steps: sorted sample_ids, normalize_total 1e4, dense, X*(X>10), getGeneSetMatrix score formula, concatenate, log1p, highly_variable_genes) -> hvset.npy; then upstream scprocess once per representative (it skips hvset when hvset.npy exists). Bit-identical to one scprocess call over all representatives (validation/prepared_*.json); peak memory is one representative",
+  "hvset_impl": "tools/lean_hvset.cohort_hvset_lean: streamed, one representative in memory; output-identical to cohort_hvset (validation/hvset_dependence_20261002.json)"
+ },
+ "inputs": {
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+  "rep_sc.h5ad": "32ecf3260de92a3b178e1f5004d3075fa9125692665d48e2c03242dff443cef4"
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+  "commit": "c2aea021188759e56ecf14578915c0237aaa75d2",
+  "compatibility_patch_sha256": "314d353002ffb482b4ed4b61a32b280a50ff8b6a9933512ada9fdc74c17b1711",
+  "changes": [
+   "remove unused scvi.nn.one_hot import",
+   "translate legacy use_gpu to accelerator/devices",
+   "avoid empty slice when geneset_len is zero",
+   "drop a size-1 trailing training batch: scvi DataSplitter(drop_last=True) only when n_obs % batch_size == 1 (upstream's own commented-out remedy); all other cell counts unchanged"
+  ],
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+}