Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261008T050202Z-scsemi-imperial-deck/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/secS_deconvolution_slide_text.json

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+{
+ "section": "S-deconvolution",
+ "scripts": [
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/tools/secS_deconvolution.py"
+ ],
+ "generated_from": "tools/secS_deconvolution.py (rerun to refresh; do not edit by hand)",
+ "slides": [
+  {
+   "title": "Deconvolution with the same pairs: composition",
+   "layout": "full",
+   "figures": [
+    "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/figures/secS_deconv_composition.png"
+   ],
+   "keywords": [
+    "PBISC closer than BayesPrism in 22/25 draws, than CIBERSORTx in 24/25",
+    "Deconvolution misreads real bulk",
+    "5-fold: PBISC lowest in 4 of 5 cohorts"
+   ],
+   "footnote_short": "Same donors, truth and scorer as PBISC. Reference = the paired donors' cells.",
+   "footnote": "Supplementary. Bulk deconvolution given the same paired donors as PBISC LoRA, on the paired benchmark's own splits, observed truth and scorer: 5-donor draws (reference = the draw's 5 training donors' observed cells, mixture = the test samples' real bulk) and 5-fold CV (reference = each fold's LoRA training + inner-validation pairs; pooled). BayesPrism: recommended gene set (protein-coding minus chrX/Y/M and ribosomal genes, 15-19k), reference states = gate | AIFI L2; its better theta (initial or final) in cell units is shown. CIBERSORTx: official Docker images (x86, run under qemu emulation on server_1), single-cell reference mode with S-mode batch correction (up to 400 reference cells per type), fractions in cell units (divided by each type's mean UMI per reference cell). CIBERSORTx HiRes gives per-sample cell-type expression for the genes it imputes per type (others are written as 1 and count as zero here); it imputed no gene of the four contrast types in 1 Ji ChILI and 2 Giroux draws, which therefore have no CIBERSORTx disease score. Composition L1 over the six-type gate, lower is better. 5-fold: PBISC has the lowest L1 except in Giroux (scSemiProfiler). Pipeline check: on pseudobulk mixtures BayesPrism recovers the observed mRNA shares (e.g. T 0.39 vs 0.38), so the error comes from real bulk. Source: plans/bayesprism_paired_20261006/results/RESULTS_full.md, RESULTS_cibersortx.md.",
+   "numbers": [
+    {
+     "value": "ImMicro: 5 donors L1 PBISC 0.176 / BayesPrism 0.537 / CIBERSORTx 0.491; 5-fold PBISC 0.162 / BayesPrism 0.575 / CIBERSORTx 0.507 / scSemiProfiler 0.217; Sepsis: 5 donors L1 PBISC 0.466 / BayesPrism 0.446 / CIBERSORTx 0.582; 5-fold PBISC 0.362 / BayesPrism 0.475 / CIBERSORTx 0.607 / scSemiProfiler 0.502; Ji ChILI: 5 donors L1 PBISC 0.513 / BayesPrism 0.893 / CIBERSORTx 0.827; 5-fold PBISC 0.504 / BayesPrism 0.883 / CIBERSORTx 0.914 / scSemiProfiler 0.575; Imperial: 5 donors L1 PBISC 0.503 / BayesPrism 0.845 / CIBERSORTx 0.646; 5-fold PBISC 0.376 / BayesPrism 0.887 / CIBERSORTx 0.592 / scSemiProfiler 0.520; Giroux: 5 donors L1 PBISC 0.256 / BayesPrism 1.455 / CIBERSORTx 0.669; 5-fold PBISC 0.258 / BayesPrism 0.878 / CIBERSORTx 0.591 / scSemiProfiler 0.243",
+     "source": "data/secS_deconvolution.csv"
+    }
+   ]
+  },
+  {
+   "title": "Deconvolution with the same pairs: disease signal",
+   "layout": "full",
+   "figures": [
+    "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/figures/secS_deconv_disease.png"
+   ],
+   "keywords": [
+    "logFC r: PBISC ahead in 15/20 draws (BayesPrism), 17/17 (CIBERSORTx)",
+    "Beyond bulk: PBISC ahead in 18/20 and 17/17",
+    "Top-50 DEG mixed: PBISC ahead in 6/20 and 6/17"
+   ],
+   "footnote_short": "5-donor draws, mean over draws. Partial r = signal beyond the bulk broadcast.",
+   "footnote": "Supplementary. Case-vs-control per-cell-type logFC (mean CP10K; T, B, Monocyte, NK) from BayesPrism's posterior cell-type expression Z and CIBERSORTx HiRes, scored with the benchmark's own functions (the recomputed bulk broadcast equals the stored one in every split); partial r = the part beyond the bulk broadcast; top-50 DEG = Welch, summed over types (many ties at 0 in Ji and Giroux). 5-donor draws, mean over draws with a score. Giroux excludes sex-linked genes, as everywhere in the deck. CIBERSORTx: official Docker images (x86, run under qemu emulation on server_1), single-cell reference mode with S-mode batch correction (up to 400 reference cells per type), fractions in cell units (divided by each type's mean UMI per reference cell). CIBERSORTx HiRes gives per-sample cell-type expression for the genes it imputes per type (others are written as 1 and count as zero here); it imputed no gene of the four contrast types in 1 Ji ChILI and 2 Giroux draws, which therefore have no CIBERSORTx disease score. Source: plans/bayesprism_paired_20261006/results/RESULTS_full.md, RESULTS_cibersortx.md.",
+   "numbers": [
+    {
+     "value": "Sepsis: logFC r PBISC 0.350 / BayesPrism 0.241 / CIBERSORTx 0.069 / broadcast 0.320; partial r 0.305 / 0.095 / -0.010; DEG 16.4 / 27.0 / 25.4 / 20.0; Ji ChILI: logFC r PBISC 0.333 / BayesPrism 0.193 / CIBERSORTx 0.158 / broadcast 0.234; partial r 0.300 / 0.032 / 0.005; DEG 0.2 / 2.0 / 0.0 / 1.0; Imperial: logFC r PBISC 0.749 / BayesPrism 0.598 / CIBERSORTx 0.283 / broadcast 0.656; partial r 0.603 / 0.187 / 0.061; DEG 113.6 / 72.4 / 30.8 / 74.8; Giroux: logFC r PBISC 0.297 / BayesPrism 0.371 / CIBERSORTx 0.095 / broadcast 0.492; partial r 0.189 / 0.117 / 0.029; DEG 0.2 / 0.2 / 0.0 / 0.0",
+     "source": "data/secS_deconvolution.csv"
+    },
+    {
+     "value": "draws where PBISC LoRA-5 beats BayesPrism: logFC r 15/20, partial r 18/20, top-50 DEG 6 (BayesPrism ahead 8, ties 6); beats CIBERSORTx: logFC r 17/17, partial r 17/17, top-50 DEG 6 (CIBERSORTx ahead 4, ties 7)",
+     "source": "plans/bayesprism_paired_20261006/results/TABLE_fewshot_full.csv, TABLE_fewshot_cibersortx.csv"
+    }
+   ]
+  }
+ ]
+}

paper-releases/pbisc-diffusion/updates/20261008T050202Z-scsemi-imperial-deck/diffusion/metadata/inference_locations.json

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