Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261008T121550Z-deck-fig4-rules-no-broadcast/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/fig3_time_days_slide_text.json

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+{
+ "section": "fig3-time",
+ "scripts": [
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/tools/fig3_time_days.py"
+ ],
+ "generated_from": "tools/fig3_time_days.py (rerun to refresh; do not edit by hand)",
+ "slides": [
+  {
+   "title": "Time: days for scSemiProfiler, minutes for LoRA",
+   "layout": "full",
+   "figures": [
+    "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/figures/fig3_time_days.png"
+   ],
+   "keywords": [
+    "5 donors, 5 draws: 5–20 h vs 13–54 min",
+    "5-fold: up to 1.6 days vs 37 min (Sepsis)",
+    "All runs: 7.7 days vs 5.7 h"
+   ],
+   "footnote_short": "One GB10 GPU, job alone on it. GPU time from receipts; waits for the GPU excluded.",
+   "footnote": "GPU time per cohort on one GB10 GPU with every step alone on the GPU, from each run's receipts; waits for the GPU (our environment) are excluded. Main comparison: 5 donors, i.e. LoRA–5 vs scSemiProfiler with the same 5 pairs as representatives, all 5 draws per cohort. 5–fold CV (the five fold models, every usable sample tested once) is shown separately. scSemiProfiler = pretraining of every representative (two stages) + one model per test sample (upstream c2aea02, default settings); PBISC LoRA = fine–tune + sampling 1,500 cells per test sample. Ratio per cohort: 5 donors 17–59x, 5–fold 19–62x. scSemiProfiler 5–fold running time including its CPU steps: ImMicro (healthy) 27 h; Sepsis 41 h; Imperial 33 h; Giroux 15 h; Ji (whole blood) 8 h. All runs = 25 five–donor draws + 25 fold models: scSemiProfiler 184 h, LoRA 5.7 h. Not counted on either side: data preparation, scoring, and what each method provides (PBISC's pretrained model, scSemiProfiler's code). The equal–pair–budget scSemiProfiler runs are not included (they reuse the 5–fold run's representative models). Sources: data/sec09_same_pairs.csv, data/sec09_time_to_apply.csv, scsemi/work/<cohort>/cv/fold<k>/events.jsonl.",
+   "numbers": [
+    {
+     "value": "ImMicro (healthy): 5 donors (5 draws) scSemiProfiler 11.7 h vs LoRA 0.70 h; 5-fold 23.7 h (0.99 days) vs 1.22 h (19x); Sepsis: 5 donors (5 draws) scSemiProfiler 20.3 h vs LoRA 0.56 h; 5-fold 38.5 h (1.60 days) vs 0.62 h (62x); Imperial: 5 donors (5 draws) scSemiProfiler 19.1 h vs LoRA 0.90 h; 5-fold 30.9 h (1.29 days) vs 0.52 h (60x); Giroux: 5 donors (5 draws) scSemiProfiler 13.0 h vs LoRA 0.22 h; 5-fold 14.4 h (0.60 days) vs 0.30 h (49x); Ji (whole blood): 5 donors (5 draws) scSemiProfiler 4.8 h vs LoRA 0.28 h; 5-fold 7.4 h (0.31 days) vs 0.37 h (20x)",
+     "source": "data/fig3_time_days.csv"
+    },
+    {
+     "value": "all runs: scSemiProfiler 183.8 GPU h (7.66 days); PBISC LoRA 5.68 h",
+     "source": "data/fig3_time_days.csv"
+    }
+   ]
+  }
+ ]
+}

paper-releases/pbisc-diffusion/updates/20261008T121550Z-deck-fig4-rules-no-broadcast/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/fig4_disease_slide_text.json

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+{
+ "section": "fig4-disease",
+ "scripts": [
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/tools/fig4_disease.py"
+ ],
+ "generated_from": "tools/fig4_disease.py (rerun to refresh; do not edit by hand)",
+ "slides": [
+  {
+   "title": "Disease signal with 5 pairs: LoRA-5 vs others",
+   "layout": "full",
+   "figures": [
+    "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/figures/fig4_disease_fewpair.png"
+   ],
+   "keywords": [
+    "vs scSemiProfiler-5: logFC r 13/20, partial r 14/20 draws",
+    "Beyond bulk: partial r 0.60 vs 0.43 (Imperial)",
+    "Above zero-shot and calibration in 4/4 cohorts"
+   ],
+   "footnote_short": "5 donors, 5 draws; bars = mean, dots = draws. Partial r = cell-type signal beyond the bulk.",
+   "footnote": "Main comparison: 5 donors per draw, LoRA–5 vs scSemiProfiler with the same 5 donors as representatives (and the two zero–shot PBISC versions), 5 draws per cohort, every other usable sample tested. logFC r = Pearson r of case–vs–control logFC (mean CP10K) with the observed cells, mean over T, B, monocyte and NK. Partial r = the same after both logFCs are residualised on the input bulk's logFC: the cell–type signal the bulk does not carry. Top–50 DEG: LoRA–5 ahead in 12/20 draws, tied in 8 (Giroux and Ji: few DEG in the observed cells, mostly 0 for every method). Giroux's 5–donor test sets hold 10–12 samples from 4 subjects (2 vs 2), so its disease metrics are noisy; there scSemiProfiler–5 is ahead. Giroux: sex–linked genes excluded (severity is confounded with sex). Per cohort, mean over draws (r / partial r / DEG): Sepsis LoRA–5 0.35 / 0.30 / 16.4, scSemiProfiler–5 0.40 / 0.33 / 5.2; Imperial LoRA–5 0.75 / 0.60 / 113.6, scSemiProfiler–5 0.56 / 0.43 / 46.8; Giroux LoRA–5 0.30 / 0.19 / 0.2, scSemiProfiler–5 0.40 / 0.24 / 0.0; Ji LoRA–5 0.33 / 0.30 / 0.2, scSemiProfiler–5 0.15 / 0.09 / 0.0. Source: data/fig4_disease.csv (scores[_nosex]/<cohort>/fewshot_n5/draw<j>[_scsemi5]).",
+   "refs": [
+    "Wang 2024, Nat Commun",
+    "Viñas Torné 2026, Nat Biotechnol"
+   ],
+   "refs_full": [
+    "Wang et al. scSemiProfiler: advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning. Nat Commun 15:5989 (2024). doi:10.1038/s41467-024-50150-1",
+    "Viñas Torné et al. Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation. Nat Biotechnol 44:1050-1059 (2026). doi:10.1038/s41587-025-02777-8"
+   ],
+   "numbers": [
+    {
+     "value": "Sepsis LoRA–5 0.35 / 0.30 / 16.4, scSemiProfiler–5 0.40 / 0.33 / 5.2; Imperial LoRA–5 0.75 / 0.60 / 113.6, scSemiProfiler–5 0.56 / 0.43 / 46.8; Giroux LoRA–5 0.30 / 0.19 / 0.2, scSemiProfiler–5 0.40 / 0.24 / 0.0; Ji LoRA–5 0.33 / 0.30 / 0.2, scSemiProfiler–5 0.15 / 0.09 / 0.0",
+     "source": "data/fig4_disease.csv"
+    }
+   ]
+  },
+  {
+   "title": "5-fold CV: 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/fig4_disease_fivefold.png"
+   ],
+   "keywords": [
+    "logFC r up to 0.82 (Imperial)",
+    "vs scSemiProfiler: r 3/4, partial r 3/4, DEG 4/4 cohorts",
+    "Top PBISC version on logFC r in 4/4 cohorts"
+   ],
+   "footnote_short": "Donor-level 5-fold CV, pooled; reported separately from the 5-pair comparison.",
+   "footnote": "Reported separately from the 5-pair comparison: donor-level 5-fold CV, each sample generated by the fold model that never trained on it, one pooled scoring call per method; scSemiProfiler uses exactly LoRA's training pairs as representatives. Per cohort LoRA vs scSemiProfiler (r / partial r / DEG): Sepsis 0.53 / 0.47 / 23 vs 0.44 / 0.36 / 6; Imperial 0.82 / 0.69 / 143 vs 0.71 / 0.54 / 87; Giroux 0.46 / 0.32 / 5 vs 0.54 / 0.39 / 0; Ji 0.48 / 0.45 / 15 vs 0.27 / 0.23 / 0. Giroux: sex-linked genes excluded. Source: data/fig4_disease.csv (scores[_nosex]/<cohort>/pooled, scsemi_cv_20261002/pooled; sepsis DEG from pooled_deg).",
+   "refs": [
+    "Wang 2024, Nat Commun",
+    "Viñas Torné 2026, Nat Biotechnol"
+   ],
+   "refs_full": [
+    "Wang et al. scSemiProfiler: advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning. Nat Commun 15:5989 (2024). doi:10.1038/s41467-024-50150-1",
+    "Viñas Torné et al. Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation. Nat Biotechnol 44:1050-1059 (2026). doi:10.1038/s41587-025-02777-8"
+   ],
+   "numbers": [
+    {
+     "value": "Sepsis 0.53 / 0.47 / 23 vs 0.44 / 0.36 / 6; Imperial 0.82 / 0.69 / 143 vs 0.71 / 0.54 / 87; Giroux 0.46 / 0.32 / 5 vs 0.54 / 0.39 / 0; Ji 0.48 / 0.45 / 15 vs 0.27 / 0.23 / 0",
+     "source": "data/fig4_disease.csv"
+    }
+   ]
+  },
+  {
+   "title": "Sepsis: fine-tuning variants",
+   "layout": "full",
+   "figures": [
+    "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/figures/fig4_disease_variants.png"
+   ],
+   "keywords": [
+    "LoRA r16 ≈ full fine-tune: r 0.53 vs 0.53",
+    "Rank 64: r 0.55, DEG 26 vs 23",
+    "Multi-K and recipes: no gain"
+   ],
+   "footnote_short": "Sepsis, 82 donors, pooled 5-fold. LoRA r = 16 is the model used throughout.",
+   "footnote": "Sepsis, 82 donors, pooled 5–fold CV, same scorer and donors for every variant. LoRA r = 16 is the model used throughout the deck. Full fine–tune trains all 54.2 M weights, LoRA r16 1.30 M. Multi–K: LoRA trained on K = 250–4,000 conditions; recipes: half of each batch is pseudobulk from the training donors' cells. LoRA r = 4: r 0.476, partial r 0.416, DEG 18; LoRA r = 16 (used): r 0.527, partial r 0.469, DEG 23; LoRA r = 64: r 0.553, partial r 0.492, DEG 26; Full fine–tune: r 0.534, partial r 0.468, DEG 24; LoRA, multi–K: r 0.509, partial r 0.447, DEG 23; LoRA + recipes: r 0.496, partial r 0.431, DEG 23. Source: data/fig4_disease.csv (scores/ye2026_sepsis/{pooled_deg, lora_rank_20261001/pooled}).",
+   "refs": [
+    "Hu 2022, ICLR"
+   ],
+   "refs_full": [
+    "Hu et al. LoRA: Low-Rank Adaptation of Large Language Models. ICLR 2022"
+   ],
+   "numbers": [
+    {
+     "value": "LoRA r = 4: r 0.476, partial r 0.416, DEG 18; LoRA r = 16 (used): r 0.527, partial r 0.469, DEG 23; LoRA r = 64: r 0.553, partial r 0.492, DEG 26; Full fine–tune: r 0.534, partial r 0.468, DEG 24; LoRA, multi–K: r 0.509, partial r 0.447, DEG 23; LoRA + recipes: r 0.496, partial r 0.431, DEG 23",
+     "source": "data/fig4_disease.csv"
+    }
+   ]
+  }
+ ]
+}

paper-releases/pbisc-diffusion/updates/20261008T121550Z-deck-fig4-rules-no-broadcast/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/figure_plan.json

@@ -0,0 +1,368 @@
+{
+ "source": "Nature Style Manuscript Writing.pdf (paper-release-20260928/reference-papers): six main figures, up to 17 supplementary. User decisions of 2026-10-06: no PBISC-VAE; scSemiProfiler, a VAE-based method, is the comparison. User decisions of 2026-10-08 (supersede the earlier figure split): Fig. 2 shows no disease metrics; Fig. 3 compares with scSemiProfiler on healthy samples, with time as a main result; Figs 4-6 use the disease cohorts: Fig. 4 disease benchmarks (PBISC versions, scSemiProfiler, and BayesPrism / CIBERSORTx labelled as cell-type deconvolution), Fig. 5 rare-cell fractions and detection, Fig. 6 new findings from the bulk-only sepsis and COVID samples (K = 1,000 and 4,000).",
+ "status_legend": {
+  "Ready": "slide exists and fits the panel",
+  "Adapt": "slide exists; change its scope or style",
+  "Draft": "new draft exists, not yet approved",
+  "Build": "results exist; no slide yet",
+  "Run": "needs a new analysis or experiment",
+  "Plan": "not planned yet"
+ },
+ "figures": [
+  {
+   "n": 1,
+   "title": "Design and performance of PBISC",
+   "claim": "Diffusion on each gene's value and detection, conditioned on bulk, writes realistic blood cells.",
+   "panels": [
+    {
+     "p": "a",
+     "content": "Backbones compared: scDiffusion, SDD, PBISC-Diffusion",
+     "evidence": [
+      "Backbone: SDD → PBISC-Diffusion"
+     ],
+     "status": "Ready"
+    },
+    {
+     "p": "b",
+     "content": "Overview: pretraining, diffusion, generation",
+     "evidence": [
+      "Overview draft (Nature style)"
+     ],
+     "status": "Draft"
+    },
+    {
+     "p": "c",
+     "content": "What the bulk condition contains",
+     "evidence": [
+      "Bulk conditioning"
+     ],
+     "status": "Ready"
+    },
+    {
+     "p": "d",
+     "content": "Pretraining corpus: assays, conditions, cell types",
+     "evidence": [
+      "Pretraining corpus"
+     ],
+     "status": "Ready"
+    },
+    {
+     "p": "e",
+     "content": "Width and context: lower loss, same biology",
+     "evidence": [
+      "Width & context ablation: loss saturation",
+      "Width & context ablation: benchmark"
+     ],
+     "status": "Ready"
+    },
+    {
+     "p": "f",
+     "content": "Gene panel: 2k, 3k, 6k, 15k",
+     "evidence": [
+      "Gene context: 2k / 3k / 6k / 15k"
+     ],
+     "status": "Ready"
+    }
+   ],
+   "candidates": [],
+   "details": [
+    "PBISC-Diffusion: bulk in, cells out",
+    "Denoiser: joint value + support diffusion"
+   ]
+  },
+  {
+   "n": 2,
+   "title": "Fine-tuning PBISC with LoRA",
+   "claim": "On a healthy cohort, a few pairs and a LoRA of minutes give cells close to the real ones. No disease metrics.",
+   "panels": [
+    {
+     "p": "a",
+     "content": "Real bulk differs from the training input",
+     "evidence": [
+      "Real bulk ≠ pseudobulk (model input)",
+      "Same shift in three paired cohorts"
+     ],
+     "status": "Ready"
+    },
+    {
+     "p": "b",
+     "content": "Without fine-tuning, cells fall off the real manifold",
+     "evidence": [
+      "Consequence: cells off-manifold"
+     ],
+     "status": "Ready"
+    },
+    {
+     "p": "c",
+     "content": "LoRA on paired bulk and cells; how it is tested",
+     "evidence": [
+      "Fine-tuning with paired bulk + sc",
+      "Evaluation design"
+     ],
+     "status": "Adapt",
+     "note": "design example: ImMicro instead of sepsis"
+    },
+    {
+     "p": "d",
+     "content": "How generated cells are scored (cells and composition)",
+     "evidence": [
+      "How we score generated cells"
+     ],
+     "status": "Adapt",
+     "note": "disease rows move to Fig. 4"
+    },
+    {
+     "p": "e",
+     "content": "Healthy cohort: precision, coverage, composition",
+     "evidence": [
+      "5-fold CV: train on the other 4 folds"
+     ],
+     "status": "Adapt",
+     "note": "ImMicro only; drop the disease columns"
+    },
+    {
+     "p": "f",
+     "content": "Healthy cohort: classifier AUROC, MMD, E_N",
+     "evidence": [
+      "5-fold: realism beyond precision/coverage"
+     ],
+     "status": "Adapt",
+     "note": "ImMicro only"
+    },
+    {
+     "p": "g",
+     "content": "Full, LoRA and condition-encoder fine-tuning",
+     "evidence": [
+      "Full vs LoRA vs cond.-encoder-only"
+     ],
+     "status": "Adapt",
+     "note": "ImMicro only; no logFC"
+    },
+    {
+     "p": "h",
+     "content": "How many pairs are needed",
+     "evidence": [
+      "Pairs needed: composition, coverage, training steps"
+     ],
+     "status": "Adapt",
+     "note": "ImMicro only; scSemiProfiler diamonds go to Fig. 3"
+    },
+    {
+     "p": "i",
+     "content": "LoRA rank and K choices",
+     "evidence": [
+      "LoRA rank: r = 4, 16, 64",
+      "K diversification: no gain"
+     ],
+     "status": "Adapt",
+     "note": "ImMicro only; supplementary candidate"
+    }
+   ],
+   "candidates": [],
+   "details": []
+  },
+  {
+   "n": 3,
+   "title": "PBISC vs scSemiProfiler on healthy donors",
+   "claim": "On healthy donors, LoRA cells are closer to the real ones than scSemiProfiler's, in minutes instead of days.",
+   "panels": [
+    {
+     "p": "a",
+     "content": "Five donors: generated cells and realism",
+     "evidence": [
+      "5 donors: LoRA vs scSemiProfiler — cells",
+      "5 donors: realism beyond precision/coverage",
+      "5 donors: precision/coverage at k = 3, 5, 10"
+     ],
+     "status": "Adapt",
+     "note": "ImMicro only"
+    },
+    {
+     "p": "b",
+     "content": "Same training pairs (5-fold) and equal pair budget (5-40)",
+     "evidence": [
+      "Same training pairs: LoRA vs scSemiProfiler"
+     ],
+     "status": "Adapt",
+     "note": "ImMicro only; add the n = 5-40 budget runs"
+    },
+    {
+     "p": "c",
+     "content": "scSemiProfiler copies its representative",
+     "evidence": [
+      "scSemiProfiler copies its representative"
+     ],
+     "status": "Adapt",
+     "note": "ImMicro only"
+    },
+    {
+     "p": "d",
+     "content": "Time: GPU hours and days per experiment",
+     "evidence": [
+      "Time: days for scSemiProfiler, minutes for LoRA",
+      "5 donors: time to apply"
+     ],
+     "status": "Ready",
+     "note": "all cohorts shown: time is a property of the method"
+    }
+   ],
+   "candidates": [],
+   "details": []
+  },
+  {
+   "n": 4,
+   "title": "Disease signal on paired cohorts",
+   "claim": "LoRA cells recover the paired single cells' disease signal; compared with other PBISC versions, scSemiProfiler and deconvolution.",
+   "panels": [
+    {
+     "p": "a",
+     "content": "Five paired cohorts",
+     "evidence": [
+      "Five paired cohorts"
+     ],
+     "status": "Ready"
+    },
+    {
+     "p": "b",
+     "content": "With 5 pairs: LoRA-5 vs PBISC versions and scSemiProfiler-5",
+     "evidence": [
+      "Disease signal with 5 pairs: LoRA-5 vs others"
+     ],
+     "status": "Ready",
+     "note": "main comparison"
+    },
+    {
+     "p": "c",
+     "content": "5-fold CV, reported separately",
+     "evidence": [
+      "5-fold CV: disease signal"
+     ],
+     "status": "Ready"
+    },
+    {
+     "p": "d",
+     "content": "Disease genes at every list length (CAT)",
+     "evidence": [
+      "Disease genes at every list length (CAT)"
+     ],
+     "status": "Ready"
+    },
+    {
+     "p": "e",
+     "content": "vs BayesPrism and CIBERSORTx (cell-type deconvolution)",
+     "evidence": [
+      "Cell-type deconvolution, same pairs: disease signal"
+     ],
+     "status": "Ready"
+    },
+    {
+     "p": "f",
+     "content": "Lupus benchmark (sc-pseudobulk input, zero-shot)",
+     "evidence": [
+      "SLE benchmark: PBISC vs scSemiProfiler",
+      "SLE benchmark: per cell type"
+     ],
+     "status": "Ready"
+    }
+   ],
+   "candidates": [],
+   "details": [
+    "Sepsis: fine-tuning variants",
+    "Pairs needed: donor and disease signal",
+    "Cell-type deconvolution, same pairs: composition"
+   ]
+  },
+  {
+   "n": 5,
+   "title": "Rare cell states in disease cohorts",
+   "claim": "LoRA cells carry rare disease states near their observed fractions, in the donors that have them.",
+   "panels": [
+    {
+     "p": "a",
+     "content": "Rare-state fractions, generated vs observed, four paired cohorts",
+     "evidence": [],
+     "status": "Run",
+     "note": "LoRA, scSemiProfiler, zero-shot; BayesPrism states where defined"
+    },
+    {
+     "p": "b",
+     "content": "Finding rare states: per-donor detection",
+     "evidence": [],
+     "status": "Run",
+     "note": "donors with vs without the state"
+    },
+    {
+     "p": "c",
+     "content": "Behçet: C1Q-high monocytes from real bulk",
+     "evidence": [
+      "Consequence: disease direction flips"
+     ],
+     "status": "Adapt",
+     "note": "add the LoRA-3 result (C1Q-high BD > HC)"
+    },
+    {
+     "p": "d",
+     "content": "Diseases absent from the corpus: Kawasaki MS1, SLE ISG-high T",
+     "evidence": [],
+     "status": "Build",
+     "note": "rare_states_screen_20261004 results"
+    }
+   ],
+   "candidates": [],
+   "details": []
+  },
+  {
+   "n": 6,
+   "title": "Discovery from public bulk-only data",
+   "claim": "New findings from LoRA cells of bulk-only sepsis and COVID dialysis samples (K = 1,000 and 4,000).",
+   "panels": [
+    {
+     "p": "a",
+     "content": "Bulk-only samples generated: sepsis 122, COVID dialysis 164",
+     "evidence": [
+      "Next: cohorts with a paired subset (planned)"
+     ],
+     "status": "Adapt",
+     "note": "update: K = 1,000 and 4,000 done"
+    },
+    {
+     "p": "b",
+     "content": "New findings",
+     "evidence": [],
+     "status": "Run",
+     "note": "earlier screen: 0 confirmed beyond the bulk"
+    }
+   ],
+   "candidates": [],
+   "details": []
+  }
+ ],
+ "supplementary": {
+  "slides": [
+   "Pairs from other cohorts do not transfer",
+   "Longer training on other cohorts: gap stays",
+   "No pretraining: same cohort, from scratch",
+   "No pretraining: other cohorts, from scratch",
+   "Rare states: from scratch vs pretrained"
+  ],
+  "planned": [
+   {
+    "p": "S",
+    "content": "Influenza exposure from raw clinical bulk (Randolph)",
+    "evidence": [],
+    "status": "Build",
+    "note": "manuscript result; placement open"
+   }
+  ],
+  "notes": {
+   "Pairs from other cohorts do not transfer": "moved from Fig. 5; placement open",
+   "Longer training on other cohorts: gap stays": "moved from Fig. 5; placement open"
+  }
+ },
+ "appendix": [
+  "Appendix: scSemiProfiler without output threshold"
+ ],
+ "discussion": [
+  "Next: other tissues from a few dozen pairs"
+ ]
+}

paper-releases/pbisc-diffusion/updates/20261008T121550Z-deck-fig4-rules-no-broadcast/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/secS_deconvolution.csv

@@ -0,0 +1,6 @@
+cohort,f_lora_l1,f_bp_l1,f_cx_l1,c_lora_l1,c_bp_l1,c_cx_l1,c_scsemi_l1,f_lora_r,f_lora_partial,f_lora_deg,f_bp_r,f_bp_partial,f_bp_deg,f_cx_r,f_cx_partial,f_cx_deg
+ImMicro,0.1755595893035838,0.5374450557106532,0.49140827856730807,0.1617289035078621,0.5745628784374615,0.5065033643252109,0.2170087237986556,,,,,,,,,
+Sepsis,0.4661935856958568,0.4461497190936295,0.5821898971787263,0.3618246783833512,0.4750523423944086,0.607149641152929,0.5022299158951089,0.3501183786066874,0.30469762374899834,16.4,0.24118586866067449,0.09459917525726311,27.0,0.0691472644717398,-0.00988830062408914,25.4
+Ji ChILI,0.5130875970575781,0.8932363204937289,0.8267482537726828,0.5044992874562503,0.8832571878464304,0.914073829946989,0.575388570715078,0.33313865479653937,0.3004989267312548,0.2,0.19324923766266502,0.03175063444356422,2.0,0.15848363999937304,0.0048928823034919515,0.0
+Imperial,0.5029703261097016,0.8445385293550187,0.6455487576081367,0.3763856087574566,0.8870870830413495,0.5918271288164071,0.5203155086290455,0.7491552706205521,0.6028412048970822,113.6,0.5976127405567618,0.1866076529400855,72.4,0.2834999227020752,0.06094785618071251,30.8
+Giroux,0.25643971843189417,1.4554372974895542,0.6693819873634712,0.2580442754044374,0.8778647964495544,0.5914100321768722,0.2430769027717485,0.2965942782817652,0.18857337655691103,0.2,0.3714147659649227,0.11665014209738915,0.2,0.09549187007462323,0.0286423958767148,0.0