Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261002T002920Z-deck-extra-metrics-scsemi-prep/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scsemi/tools/check_units.py

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+#!/usr/bin/env python
+"""Dry run of scSemiProfiler queue files (CPU only; never imports torch; writes nothing under work/ or generated/).
+
+For every cv / budget line of the given queue files, in queue order:
+  1. runs the real input builder in --check-only mode (tools/prepare_draw_inputs.py: split, LoRA RUN.json and
+     CAL_BUDGET checks for budget lines, modeled genes, every representative's cells, depth, condition NPZ);
+  2. checks the representatives / targets against the LoRA run of the same fold: cv lines against
+     runs/<ds>/fold<k>/real_lora/RUN.json (train + inner-val rows; test rows), budget lines against lora_n<n>
+     (the builder already refuses a mismatch) and, for reuse_cv lines, that the representatives are a subset of the
+     same fold's cv line, that line comes earlier in the queue, and no target is a cv representative;
+  3. replicates upstream initsetup's bulk PCA (normalize_total 1e4, log1p, sc.tl.pca) and the nearest-representative
+     assignment, then estimates GPU time from the measured few-pair receipts (pretrain 0.0488 s per representative
+     cell; target 8.0 s + 0.0188 s per cell of its representative; README "Time") and disk (sample_sc ~0.78 x
+     (8 n^2 + 12 n G) bytes per representative, geneset_scores 23.4 KB per cell, pretrain checkpoints 2 x 17.7 MB,
+     per-target model 21.3 MB, rep_sc.h5ad 3.3 KB per cell);
+  4. records whether the work dir / output already exist (the queue skips validated outputs and resumes partial ones).
+usage: CUDA_VISIBLE_DEVICES='' check_units.py --out validation/check_units_<queue>.json QUEUE [QUEUE ...]
+       [--runs-extra DIR (<ds>/fold<k>/<run>/RUN.json copied from another host)]
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+import tempfile
+import time
+from datetime import datetime, timezone
+from pathlib import Path
+
+if os.environ.get("CUDA_VISIBLE_DEVICES") != "":
+    raise SystemExit("run with CUDA_VISIBLE_DEVICES=''")
+
+import numpy as np
+
+HERE = Path(__file__).resolve().parents[1]
+TASK = HERE.parent
+sys.path.insert(0, str(HERE / "tools"))
+sys.path.insert(0, str(TASK / "tools"))
+import prepare_draw_inputs as PDI  # noqa: E402
+import prepare_dataset as PD  # noqa: E402
+
+PRETRAIN_S_PER_CELL = 0.0488          # 232 representative pretrains (stage 1 + 2), 25 few-pair draws, both hosts
+TARGET_S0, TARGET_S_PER_CELL = 8.0, 0.0188   # 1,830 targets: s = 8.0 + 0.0188 x cells of the assigned representative
+PREP_S_PER_CELL = 0.0015              # CPU prepare (scprocess + cell graph) incl. lean hvset, few-pair prepared.json
+CKPT_MB, SEMI_MB, REPSC_KB, GS_KB = 17.7, 21.3, 3.3, 23.4
+N_SETS = 2922
+
+
+def sample_sc_bytes(n: int, g: int) -> float:
+    return 0.78 * (8.0 * n * n + 12.0 * n * g)
+
+
+def parse(queue: Path):
+    for raw in queue.read_text().splitlines():
+        line = raw.strip()
+        if not line or line.startswith("#"):
+            continue
+        f = line.split("|")
+        if f[0] == "offload":             # offload|DATASET|cv: file moves only, nothing to dry-run
+            continue
+        mode, ds, j = f[0], f[1], int(f[2])
+        opts = [x for x in (f[3].split(",") if len(f) > 3 else []) if x]
+        n = int(f[4]) if mode == "budget" else None
+        yield {"line": line, "mode": mode, "ds": ds, "fold": j, "opts": opts, "n": n}
+
+
+def assignment(ds: str, reps: list[str], targets: list[str], pidx: list[int], cache: dict) -> dict:
+    """Upstream initsetup PCA + update_membership on this split's bulk rows (replica of the wrapper's prepare)."""
+    import anndata
+    import scanpy as sc
+    if ds not in cache:
+        cache.clear()
+        d = PD.load(TASK / "data" / ds)
+        z = np.load(TASK / "data" / ds / "cells_panel6k.npz", allow_pickle=False)
+        panel = z["panel_gene_ids"].astype(str)
+        gpos = {g: i for i, g in enumerate(d["gene_ids"])}
+        cache[ds] = (d, np.asarray([gpos[g] for g in panel], dtype=np.int64))
+    d, pcanon = cache[ds]
+    pos = {s: i for i, s in enumerate(d["sample_ids"])}
+    rows = np.sort(np.asarray([pos[s] for s in reps + targets], dtype=np.int64))
+    B = d["bulk"][rows][:, pcanon[np.asarray(pidx, dtype=np.int64)]].astype(np.float64)
+    a = anndata.AnnData(B)
+    sc.pp.normalize_total(a, target_sum=1e4)
+    sc.pp.log1p(a)
+    n_comps = 100 if a.X.shape[0] > 100 else a.X.shape[0] - 1
+    sc.tl.pca(a, n_comps=n_comps)
+    pca = np.asarray(a.obsm["X_pca"], dtype=np.float64)
+    sids = d["sample_ids"][rows].tolist()
+    ridx = [sids.index(r) for r in reps]
+    out = {}
+    for t in targets:
+        i = sids.index(t)
+        dist = [float(((pca[j] - pca[i]) ** 2).sum() ** 0.5) for j in ridx]
+        out[t] = reps[int(np.argmin(dist))]
+    return out
+
+
+def main(argv=None) -> int:
+    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("queues", nargs="+", type=Path)
+    ap.add_argument("--out", type=Path, required=True)
+    ap.add_argument("--runs-extra", type=Path, default=None)
+    a = ap.parse_args(argv)
+    units, seen_cv, cache, fails = [], {}, {}, []
+    tmpd = Path(tempfile.mkdtemp(prefix="check_units_"))
+    for q in a.queues:
+        for u in parse(q):
+            t0 = time.monotonic()
+            ds, k, mode, n = u["ds"], u["fold"], u["mode"], u["n"]
+            u["queue"] = str(q)
+            if mode == "cv":
+                work = HERE / "work" / ds / "cv" / f"fold{k}"
+                out = TASK / "generated" / ds / f"fold{k}" / "scsemi__raw.npz"
+                args = ["--dataset", ds, "--mode", "cv", "--fold", str(k)]
+            elif mode == "budget":
+                work = HERE / "work" / ds / "budget" / f"fold{k}" / f"n{n}"
+                out = TASK / "generated" / ds / f"fold{k}" / f"scsemi_n{n}__raw.npz"
+                args = ["--dataset", ds, "--mode", "budget", "--fold", str(k), "--n", str(n)]
+            else:
+                fails.append(f"{u['line']}: mode {mode} not checked here")
+                continue
+            summ_p = tmpd / f"{ds}_{mode}{k}_{n}.json"
+            try:
+                PDI.main(args + ["--work", str(work), "--check-only", "--check-out", str(summ_p)])
+            except SystemExit as e:
+                if e.code not in (0, None):
+                    fails.append(f"{u['line']}: builder refused: {e.code}")
+                    u["builder_ok"] = False
+                    units.append(u)
+                    continue
+            s = json.loads(summ_p.read_text())
+            u.update({"builder_ok": True, "work": str(work), "out": str(out), "n_reps": len(s["reps"]),
+                      "n_targets": len(s["targets"]), "n_rep_cells": s["n_rep_cells"], "modeled_genes": s["modeled_genes"],
+                      "equals_scorer_genes": s["equals_scorer_genes"], "depth": s["depth"],
+                      "work_state": ("prepared" if (work / "prepared.json").exists() else
+                                     "inputs" if (work / "inputs/inputs_manifest.json").exists() else
+                                     "exists" if work.exists() else "absent"),
+                      "output_exists": out.exists(), "output_validated": (out.with_suffix(".validation.json")).exists()})
+            if not s["equals_scorer_genes"]:
+                fails.append(f"{u['line']}: modeled genes differ from the scorer's scoring genes")
+            # LoRA donor lists
+            if mode == "cv":
+                rj = TASK / "runs" / ds / f"fold{k}" / "real_lora" / "RUN.json"
+                if not rj.exists() and a.runs_extra is not None:
+                    rj = a.runs_extra / ds / f"fold{k}" / "real_lora" / "RUN.json"
+                if rj.exists():
+                    run = json.loads(rj.read_text())
+                    fit = sorted(list(run["train_rows"]) + list(run["inner_val_rows"]))
+                    u["lora_run"] = str(rj)
+                    u["reps_equal_lora_all_fit_rows"] = sorted(s["reps"]) == fit
+                    u["targets_equal_lora_test_rows"] = sorted(s["targets"]) == sorted(run["test_rows_never_loaded_for_training"])
+                    u["lora_rows"] = {"train": len(run["train_rows"]), "inner_val": len(run["inner_val_rows"])}
+                    if not (u["reps_equal_lora_all_fit_rows"] and u["targets_equal_lora_test_rows"]):
+                        fails.append(f"{u['line']}: representatives / targets differ from {rj}")
+                else:
+                    u["lora_run"] = None
+                    fails.append(f"{u['line']}: no real_lora RUN.json to check against")
+                seen_cv[(ds, k)] = s
+            else:
+                u["budget"] = {key: s["budget"][key] for key in ("lora_run", "n_train_rows", "n_inner_val_rows")}
+                u["reps_equal_lora_fit_rows"] = True   # the builder refuses otherwise (and checks CAL_BUDGET.json)
+                cvs = seen_cv.get((ds, k))
+                if "reuse_cv" in u["opts"]:
+                    if cvs is None:
+                        fails.append(f"{u['line']}: reuse_cv but the cv line of {ds} fold {k} is not earlier in the queue")
+                    else:
+                        u["reps_subset_of_cv_reps"] = set(s["reps"]) <= set(cvs["reps"])
+                        u["targets_disjoint_from_cv_reps"] = not (set(s["targets"]) & set(cvs["reps"]))
+                        if not (u["reps_subset_of_cv_reps"] and u["targets_disjoint_from_cv_reps"]):
+                            fails.append(f"{u['line']}: representatives not within / targets inside the cv representatives")
+            # assignment, cost, disk
+            asg = assignment(ds, s["reps"], s["targets"], s.get("modeled_panel_index") or [], cache) \
+                if s.get("modeled_panel_index") else None
+            cells = s["rep_cells"]
+            G = s["modeled_genes"]
+            reuse = "reuse_cv" in u["opts"]
+            pre_s = 0.0 if reuse else PRETRAIN_S_PER_CELL * s["n_rep_cells"]
+            if asg is not None:
+                inf_s = sum(TARGET_S0 + TARGET_S_PER_CELL * cells[asg[t]] for t in s["targets"])
+                u["targets_per_rep_max"] = max(list(asg.values()).count(r) for r in set(asg.values()))
+            else:
+                inf_s = len(s["targets"]) * (TARGET_S0 + TARGET_S_PER_CELL * np.mean(list(cells.values())))
+            u["gpu_hours"] = {"pretrain": round(pre_s / 3600, 2), "infer": round(inf_s / 3600, 2),
+                              "total": round((pre_s + inf_s) / 3600, 2)}
+            u["cpu_prepare_hours"] = round(PREP_S_PER_CELL * s["n_rep_cells"] / 3600, 2)
+            u["disk_gb"] = {"transient_sample_sc": round(sum(sample_sc_bytes(c, G) for c in cells.values()) / 1e9, 1),
+                            "transient_geneset_scores": round(GS_KB * 1e3 * s["n_rep_cells"] / 1e9, 1),
+                            "persistent_inputs": round(REPSC_KB * 1e3 * s["n_rep_cells"] / 1e9, 1),
+                            "persistent_pretrain_ckpts": 0.0 if reuse else round(2 * CKPT_MB * len(cells) / 1e3, 1),
+                            "persistent_target_models": round(SEMI_MB * len(s["targets"]) / 1e3, 1)}
+            u["largest_rep_cells"] = max(cells.values())
+            u["check_seconds"] = round(time.monotonic() - t0, 1)
+            units.append(u)
+            print(json.dumps({k_: u.get(k_) for k_ in ("line", "n_reps", "n_targets", "n_rep_cells", "gpu_hours",
+                                                         "reps_equal_lora_all_fit_rows", "reps_subset_of_cv_reps")}),
+                  flush=True)
+    tot = {"units": len(units), "gpu_hours": round(sum(u.get("gpu_hours", {}).get("total", 0) for u in units), 1),
+           "pretrain_hours": round(sum(u.get("gpu_hours", {}).get("pretrain", 0) for u in units), 1),
+           "infer_hours": round(sum(u.get("gpu_hours", {}).get("infer", 0) for u in units), 1),
+           "cpu_prepare_hours": round(sum(u.get("cpu_prepare_hours", 0) for u in units), 1)}
+    res = {"written_utc": datetime.now(timezone.utc).isoformat(), "host": os.uname().nodename,
+           "queues": [str(q) for q in a.queues], "all_ok": not fails, "failures": fails, "totals": tot,
+           "cost_model": {"pretrain_s_per_cell": PRETRAIN_S_PER_CELL, "target_s": [TARGET_S0, TARGET_S_PER_CELL],
+                          "prep_s_per_cell": PREP_S_PER_CELL, "ckpt_mb": CKPT_MB, "semi_mb": SEMI_MB,
+                          "rep_sc_kb_per_cell": REPSC_KB, "geneset_scores_kb_per_cell": GS_KB},
+           "units": units}
+    a.out.parent.mkdir(parents=True, exist_ok=True)
+    a.out.write_text(json.dumps(res, indent=1) + "\n")
+    print(json.dumps({"out": str(a.out), "all_ok": not fails, "failures": fails[:10], **tot}))
+    return 0 if not fails else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

paper-releases/pbisc-diffusion/updates/20261002T002920Z-deck-extra-metrics-scsemi-prep/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/sec07_rank_sweep_slide_text.json

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+{
+ "section": "07-rank",
+ "slides": [
+  {
+   "title": "LoRA rank: r = 4, 16, 64",
+   "layout": "full",
+   "figures": [
+    "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/figures/sec07_rank_sweep.png"
+   ],
+   "keywords": [
+    "ImMicro r64, all pairs: coverage 0.78 → 0.82",
+    "ImMicro r4, all pairs: L1 0.162 → 0.186",
+    "Kept r16; r64 precision 0.92 → 0.90"
+   ],
+   "footnote": "LoRA fine-tune of the final model (f01 6k·1x·E16) with rank r and alpha = r, same recipe as the deck's r = 16 runs (early stop on 7–8 inner-val pairs, 20k step cap); n = training pairs, nested subsets; donor-level 5-fold CV, each pair scored once by its fold model (pooled call). Trainable parameters r4 324,056, r16 1,296,224, r64 5,184,896. ImMicro n = 5: r4: L1 0.162, P/C 0.937 / 0.789 (L1 vs r16 -0.009, Wilcoxon p 0.00088, 107 donors); r16: L1 0.171, P/C 0.946 / 0.839; r64: L1 0.177, P/C 0.946 / 0.847 (L1 vs r16 +0.005, Wilcoxon p 0.0076, 107 donors) | ImMicro n = 20: r4: L1 0.171, P/C 0.925 / 0.715 (L1 vs r16 -0.001, Wilcoxon p 0.32, 107 donors); r16: L1 0.172, P/C 0.920 / 0.755; r64: L1 0.172, P/C 0.905 / 0.794 (L1 vs r16 -0.001, Wilcoxon p 0.66, 107 donors) | ImMicro n = all: r4: L1 0.186, P/C 0.933 / 0.712 (L1 vs r16 +0.024, Wilcoxon p 9.8e-09, 107 donors); r16: L1 0.162, P/C 0.924 / 0.784; r64: L1 0.161, P/C 0.904 / 0.816 (L1 vs r16 -0.001, Wilcoxon p 0.89, 107 donors) | Sepsis n = 5: r4: L1 0.426, P/C 0.907 / 0.742, logFC r 0.27, DEG 2 (L1 vs r16 -0.002, Wilcoxon p 0.97, 82 donors); r16: L1 0.428, P/C 0.911 / 0.767, logFC r 0.29, DEG 5; r64: L1 0.429, P/C 0.906 / 0.766, logFC r 0.31, DEG 1 (L1 vs r16 +0.001, Wilcoxon p 0.66, 82 donors) | Sepsis n = 20: r4: L1 0.378, P/C 0.929 / 0.769, logFC r 0.31, DEG 14 (L1 vs r16 -0.003, Wilcoxon p 0.58, 82 donors); r16: L1 0.380, P/C 0.927 / 0.807, logFC r 0.37, DEG 18; r64: L1 0.376, P/C 0.911 / 0.808, logFC r 0.43, DEG 18 (L1 vs r16 -0.004, Wilcoxon p 0.046, 82 donors) | Sepsis n = all: r4: L1 0.363, P/C 0.923 / 0.789, logFC r 0.48, DEG 18 (L1 vs r16 +0.002, Wilcoxon p 0.77, 82 donors); r16: L1 0.362, P/C 0.926 / 0.829, logFC r 0.53, DEG 23; r64: L1 0.367, P/C 0.907 / 0.824, logFC r 0.55, DEG 26 (L1 vs r16 +0.005, Wilcoxon p 0.66, 82 donors). r64 vs r16 brings small gains in coverage and disease signal but slightly lower precision and no composition change; r4 is worse with many pairs; at n = 5 the ranks are mixed. r = 16 is kept. Scores: scores/<cohort>/lora_rank_20261001/pooled (r4, r64) and scores/<cohort>/pooled{,_deg} (r16); table: scores/<cohort>/rankcal_20261001.md.",
+   "footnote_short": "Sepsis, all pairs: r64 logFC r 0.53 → 0.55, DEG 23 → 26; r4 0.48, DEG 18. Pooled 5-fold scores.",
+   "numbers": [
+    {
+     "value": "ImMicro all pairs coverage r16 0.784 -> r64 0.816; precision 0.924 -> 0.904; L1 r16 0.162, r4 0.186 (Wilcoxon p 9.8e-09)",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/lora_rank_20261001/pooled/summary.json, per_donor.csv"
+    },
+    {
+     "value": "Sepsis all pairs logFC r r16 0.527, r64 0.553, r4 0.476; DEG 23 / 26 / 18",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json, contrast.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/lora_rank_20261001/pooled/summary.json, contrast.csv"
+    },
+    {
+     "value": "trainable parameters: r4 324056, r16 1296224, r64 5184896",
+     "source": "runs/<cohort>/fold<k>/<arm>/RUN.json (trainable.n_trainable)"
+    }
+   ]
+  }
+ ],
+ "scripts": [
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/tools/sec07_rank_sweep.py"
+ ],
+ "generated_from": "tools/sec07_rank_sweep.py (rerun to refresh; do not edit by hand)"
+}

paper-releases/pbisc-diffusion/updates/20261002T002920Z-deck-extra-metrics-scsemi-prep/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/sec09_extra_slide_text.json

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+{
+ "section": "09-extra",
+ "slides": [
+  {
+   "title": "5 donors: realism beyond precision/coverage",
+   "layout": "full",
+   "figures": [
+    "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/figures/sec09_extra_fewpair.png"
+   ],
+   "keywords": [
+    "Classifier AUC: LoRA-5 best in 25/25 draws",
+    "E_N: LoRA-5 below scSemi-5 in 25/25 draws",
+    "Still separable: LoRA-5 AUC 0.89–0.95"
+   ],
+   "footnote": "Same 5-donor draws as the previous slides; every metric on exactly the scored cells (scores_extra/, tools/extra_metrics.py). Real side for AUC and MMD² = held-out real cells of the same samples (R2), projected like the generated cells; real vs real = R2 vs R3 (AUC 0.49–0.50). Classifier AUC: 5-fold CV ROC AUC of a kNN classifier (k = 5) separating real from generated cells, 0.5 = indistinguishable; bars start at 0.5. MMD²: unbiased, Gaussian kernel, bandwidth = median distance among real cells. E_N: scSemiProfiler's normalized error (Wang et al. 2024, eq. 17–18), their code's squared L2 on all cells, per-arm PCA-100; real vs real = within-sample half split (0.07–0.38); every generated arm is above 1. LoRA-5 best of the 4 arms in 25/25 draws on kNN AUC, 25/25 on RF AUC, 25/25 on 1-NN accuracy, 23/25 on MMD², 22/25 on E_N. LoRA-5 better than scSemi-5, zero-shot and zero-shot + calibration on both AUCs, 1-NN accuracy and coverage at k = 3, 5, 10 in every draw. E_N scSemi-5 vs LoRA-5 (cohort means): ImMicro 3.65 vs 1.55; Sepsis 3.83 vs 1.46; Imperial 2.04 vs 1.28; Giroux 3.79 vs 1.99; Ji 4.27 vs 2.60; LoRA-5 lower in 25/25 draws. Draws where LoRA-5 is worse (cohort means in brackets): vs zero-shot + calibration: Sepsis precision k5 1/5 (0.778 vs 0.752), precision k10 2/5 (0.906 vs 0.899); Imperial MMD² (σ = m) 1/5 (0.0156 vs 0.0218), MMD² (σ sum) 1/5 (0.0443 vs 0.0673), E_N (all cells) 3/5 (1.28 vs 1.22), precision k3 2/5 (0.610 vs 0.598), precision k5 2/5 (0.726 vs 0.719), precision k10 2/5 (0.853 vs 0.845); Giroux MMD² (σ = m) 1/5 (0.0178 vs 0.0257), MMD² (σ sum) 1/5 (0.0584 vs 0.0838), precision k5 1/5 (0.995 vs 0.981), precision k10 1/5 (1.000 vs 0.996). LoRA-5 AUC per draw 0.88–0.97 (kNN), RF cohort means 0.90–0.96: a classifier still tells generated from real cells. Scores: scores_extra/<cohort>/fewshot_n5/draw<j>/.",
+   "footnote_short": "E_N: scSemiProfiler's own error metric. Misses only vs calibration, e.g. Imperial E_N 1.28 vs 1.22.",
+   "numbers": [
+    {
+     "value": "LoRA-5 best of 4 arms (draws): kNN AUC 25/25, RF AUC 25/25, 1-NN 25/25, MMD² 23/25, E_N 22/25",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+    },
+    {
+     "value": "E_N LoRA-5 < scSemi-5 in 25/25 draws; cohort means scSemi-5 vs LoRA-5: ImMicro 3.65 vs 1.55; Sepsis 3.83 vs 1.46; Imperial 2.04 vs 1.28; Giroux 3.79 vs 1.99; Ji 4.27 vs 2.60",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/en.csv (cellset all, distance sq)"
+    },
+    {
+     "value": "LoRA-5 kNN AUC cohort means: ImMicro 0.943, Sepsis 0.936, Imperial 0.918, Giroux 0.893, Ji 0.955; per draw 0.878–0.975",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+    },
+    {
+     "value": "real vs real: kNN AUC ImMicro 0.502, Sepsis 0.501, Imperial 0.498, Giroux 0.495, Ji 0.491; E_N ImMicro 0.382, Sepsis 0.100, Imperial 0.260, Giroux 0.145, Ji 0.073",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+    },
+    {
+     "value": "LoRA-5 worse than a comparator (draws): vs zero-shot + calibration: Sepsis precision k5 1/5 (0.778 vs 0.752), precision k10 2/5 (0.906 vs 0.899); Imperial MMD² (σ = m) 1/5 (0.0156 vs 0.0218), MMD² (σ sum) 1/5 (0.0443 vs 0.0673), E_N (all cells) 3/5 (1.28 vs 1.22), precision k3 2/5 (0.610 vs 0.598), precision k5 2/5 (0.726 vs 0.719), precision k10 2/5 (0.853 vs 0.845); Giroux MMD² (σ = m) 1/5 (0.0178 vs 0.0257), MMD² (σ sum) 1/5 (0.0584 vs 0.0838), precision k5 1/5 (0.995 vs 0.981), precision k10 1/5 (1.000 vs 0.996)",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+    },
+    {
+     "value": "cross-check: 720 cohort × arm × metric values equal scores_extra/{fewshot,pooled}_headline.csv",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/fewshot_headline.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/pooled_headline.csv"
+    }
+   ]
+  },
+  {
+   "title": "5-fold: realism beyond precision/coverage",
+   "layout": "full",
+   "figures": [
+    "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/figures/sec09_extra_fivefold.png"
+   ],
+   "keywords": [
+    "Every metric: LoRA first in 5/5 cohorts",
+    "Ji whole blood: E_N 1.9 vs 7.1–10.1",
+    "Still separable: LoRA AUC 0.87–0.94"
+   ],
+   "footnote": "Donor-level 5-fold CV; each pair scored once by the fold model that never trained on its donor (one pooled call per cohort, all cross-fitted pairs). Arms as on the 5-fold slide. Metric definitions as on the 5-donor realism slide. LoRA first among the 4 arms in all 5 cohorts on 12/12 headline metrics (kNN and RF AUC, 1-NN accuracy, MMD² at σ = m and summed, E_N all cells, precision and coverage at k = 3, 5, 10). LoRA kNN AUC ImMicro 0.941, Sepsis 0.886, Imperial 0.866, Giroux 0.881, Ji 0.908; real vs real ImMicro 0.505, Sepsis 0.506, Imperial 0.496, Giroux 0.501, Ji 0.494. E_N LoRA vs other arms: ImMicro 1.56 vs 1.77–4.83; Sepsis 1.20 vs 1.40–5.29; Imperial 1.13 vs 1.19–4.05; Giroux 1.70 vs 2.24–4.48; Ji 1.95 vs 7.08–10.13. scSemiProfiler 5-fold CV not available yet (no extra-metric unit with set scsemi__raw). Scores: scores_extra/<cohort>/pooled/.",
+   "footnote_short": "Donor-level 5-fold CV, one pooled call per cohort. E_N: scSemiProfiler's own error metric.",
+   "numbers": [
+    {
+     "value": "LoRA first among 4 arms in all 5 cohorts on 12/12 metrics",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/pooled/{metrics,en}.csv"
+    },
+    {
+     "value": "Ji E_N: LoRA 1.948; Zero-shot, real bulk 10.125, Zero-shot + calibration 7.076, sc-only fine-tune (+ calibration) 7.215",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/pooled/{metrics,en}.csv"
+    },
+    {
+     "value": "LoRA kNN AUC (pooled): ImMicro 0.941, Sepsis 0.886, Imperial 0.866, Giroux 0.881, Ji 0.908",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/pooled/{metrics,en}.csv"
+    },
+    {
+     "value": "scSemiProfiler, all training pairs: not available",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/sec09_extra_scsemi_status.csv"
+    }
+   ]
+  },
+  {
+   "title": "5 donors: precision/coverage at k = 3, 5, 10",
+   "layout": "full",
+   "figures": [
+    "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/figures/sec09_extra_ksens.png"
+   ],
+   "keywords": [
+    "Smaller k lowers every set, real included",
+    "Coverage: LoRA-5 first at all k, 25/25",
+    "Precision: LoRA-5 first in 20–23/25 draws"
+   ],
+   "footnote": "Precision and coverage recomputed with k = 3, 5, 10 nearest-neighbour balls in the scorer's PCA-50 (k = 10 reproduces the main-slide values exactly). Means over the 5 draws per cohort; error bars = SD; grey = real vs real (R1 vs R2, same samples). Both metrics fall with smaller k for every set including the real control (real coverage at k = 3: 0.92–0.99), so compare arms with the real row, not with 1. Expected coverage of a perfect generator (Naeem et al. 2020, eq. 9) at k = 3/5/10: 0.88/0.97/1.00 in every cohort. LoRA-5 first on coverage at every k in 25/25 draws; on precision in 23/21/20 of 25 draws (k = 3/5/10); not first: Sepsis 0/1/2, Imperial 2/2/2, Giroux 0/1/1 draws at k = 3/5/10, beaten by zero-shot + calibration. 5-fold pooled design (not drawn): coverage ranking of the 4 arms across k unchanged; LoRA first at every k in 5/5 cohorts. Scores: scores_extra/<cohort>/fewshot_n5/draw<j>/metrics.csv.",
+   "footnote_short": "5 donors, 5 draws per cohort; error bars = SD over draws. k = 10 is the value on the main slides.",
+   "numbers": [
+    {
+     "value": "LoRA-5 first on coverage at k = 3, 5 and 10: 25/25 draws",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+    },
+    {
+     "value": "LoRA-5 first on precision (draws) at k = 3/5/10: 23/21/20 of 25",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+    },
+    {
+     "value": "real vs real coverage at k = 3: ImMicro 0.932, Sepsis 0.925, Imperial 0.919, Giroux 0.993, Ji 0.980",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+    },
+    {
+     "value": "every set falls with smaller k (cohort means): True",
+     "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+    }
+   ]
+  }
+ ],
+ "scripts": [
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/tools/sec09_extra_metrics.py"
+ ],
+ "generated_from": "tools/sec09_extra_metrics.py (rerun to refresh; do not edit by hand)",
+ "scsemi_cv_cohorts": []
+}

paper-releases/pbisc-diffusion/updates/20261002T002920Z-deck-extra-metrics-scsemi-prep/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/slides_spec.json

@@ -0,0 +1,875 @@
+{
+  "deck": {
+    "output_pptx": "PBISC-Diffusion_paper_summary_20260930.pptx",
+    "paths": "figure paths are relative to the deck folder",
+    "note": "footnote = on-slide text (max 2 lines, may be a shortened copy); footnote_full = section author's full text, put in speaker notes when it differs. Generated slide text: section 9 main slides from data/sec09_slide_text.json (sec09_index), other generated slides from the file named in text_source (data/sec09_extra_slide_text.json, data/sec07_rank_sweep_slide_text.json); refresh with tools/sync_sec09_text.py after rerunning the scripts that write them."
+  },
+  "slides": [
+    {
+      "kind": "title",
+      "title": "PBISC-Diffusion: bulk-to-single-cell generation",
+      "subtitle": "Paper summary · 2026-09-30"
+    },
+    {
+      "kind": "agenda",
+      "title": "Outline",
+      "items": [
+        "Model overview: diffusion from bulk to cells",
+        "Backbone: SDD, scDiffusion, bulk conditioning",
+        "Architecture ablation: width, context, gene panel",
+        "SLE benchmark vs scSemiProfiler",
+        "Real bulk ≠ pseudobulk: why zero-shot fails",
+        "Fine-tuning on paired bulk–sc data",
+        "LoRA: compute and ablations (K diversification)",
+        "How many bulk–sc pairs are needed",
+        "Paired-cohort validation vs scSemiProfiler",
+        "Next: cohorts with a paired subset (planned)"
+      ]
+    },
+    {
+      "kind": "content",
+      "section": "1",
+      "section_label": "1 Model overview",
+      "title": "PBISC-Diffusion: bulk in, cells out",
+      "layout": "full",
+      "figures": [
+        "figures/sec01_slide_a.png"
+      ],
+      "keywords": [
+        "Train pair: K-cell pseudobulk → each member cell"
+      ],
+      "footnote": "Fig. 1b redraw. Final model corpus: 33.1M train cells; K = 1–4,000 cells per pseudobulk.",
+      "footnote_full": "Top: deck redraw of manuscript Fig. 1b (larger labels; cells differ by x_T and the Bernoulli support draw, Methods 'Support read-out'). Bottom: corpus of final run f01_6k_1x_seed20260627 (index fingerprint matches). Cells per dataset = base + overlay rows (Model 7 e2_full_source/e2_full_index manifests); 33.1M cells in the donor-level 80/10/10 train split. Registered K grid = 8,192 recipes/epoch. Plus 3,658 top-up recipes over 16 epochs (196-283 per epoch, K 1-4,000), so every train cell is a target once (ALLCELL_DATA_CONTRACT.json coverage_after_topup).",
+      "sources": "tools/sec01_overview.py; data/sec01_model_numbers.csv, data/sec01_corpus_datasets.csv, data/sec01_recipe_k.csv"
+    },
+    {
+      "kind": "content",
+      "section": "1",
+      "section_label": "1 Model overview",
+      "title": "Denoiser: joint value + support diffusion",
+      "layout": "diagram",
+      "figures": [
+        "figures/sec01_fig1a_denoiser.png"
+      ],
+      "keywords": [
+        "6,000 genes · 54.2M params · Bernoulli support draw"
+      ],
+      "footnote": "Fig. 1a redraw. Final model f01 6k 1x, epoch 16, EMA; 50 DDIM steps.",
+      "footnote_full": "Deck redraw of manuscript Fig. 1a (larger labels, legend dropped). Purple dashed = soft self-conditioning feedback; gold dashed = context to all blocks. Final model f01_6k_1x_seed20260627, checkpoint_epoch_016, EMA; 7 blocks 1024-1024-512-256-512-1024-1024 (effective_config.json, RUN_STATUS.json). Sampling: 50 DDIM steps, η = 0, drawn support read-out, keep gene if u < σ(logit) (methods.tex l.58, l.99). 16 epochs = 16 disjoint 8,192-recipe slots = one pass over the 131,072-recipe pool (manuscript: part 16).",
+      "sources": "tools/sec01_overview.py; data/sec01_model_numbers.csv, data/sec01_fig1_label_pt.csv"
+    },
+    {
+      "kind": "content",
+      "section": "2",
+      "section_label": "2 Backbone",
+      "title": "Backbone: SDD → PBISC-Diffusion",
+      "layout": "diagram",
+      "figures": [
+        "figures/sec02_backbone_compare.png"
+      ],
+      "keywords": [
+        "Kept from SDD: value + bit, skip-MLP",
+        "Changed: bulk condition, masked loss, Bernoulli read-out"
+      ],
+      "footnote": "SDD: Ostheimer et al., arXiv 2502.02448. scDiffusion: Luo et al., Bioinformatics 2024.",
+      "sources": "tools/sec02_backbone.py; data/sec02_backbone_compare.csv, data/sec02_slide_text.csv, data/refs/",
+      "footnote_full": "Luo et al., Bioinformatics 2024; Ostheimer et al., arXiv 2502.02448 (code 1d5423c). PBISC: final model f01 6k 1x, part 16. σ = sigmoid; Bernoulli is the final read-out; the logit > 0 cut is also reported."
+    },
+    {
+      "kind": "content",
+      "section": "2",
+      "section_label": "2 Backbone",
+      "title": "Bulk conditioning",
+      "layout": "diagram",
+      "figures": [
+        "figures/sec02_bulk_conditioning.png"
+      ],
+      "keywords": [
+        "Same bulk condition for every generated cell",
+        "Final: no cell types, proportions, sum constraint",
+        "18,006 → 512 context into every block"
+      ],
+      "footnote": "Final model f01 6k 1x. Bar heights schematic.",
+      "sources": "tools/sec02_backbone.py; data/sec02_bulk_conditioning.csv, data/sec02_slide_text.csv",
+      "footnote_full": "Final model f01 6k 1x, part 16. Bar heights schematic."
+    },
+    {
+      "kind": "content",
+      "section": "3",
+      "section_label": "3 Architecture ablation",
+      "title": "Width & context ablation: loss saturation",
+      "layout": "full",
+      "figures": [
+        "figures/sec03_width_loss.png"
+      ],
+      "keywords": [
+        "Trunk x4: val loss −31% (0.197 → 0.136)",
+        "ctx x4, prop hint: loss = 1x",
+        "1x flat E8; trunk x4 still falling"
+      ],
+      "footnote": "Old 6k panel (1% floor); one validation set. 16x: memory-floor stop after part 2.",
+      "say": "All arms share the 6k panel (1% floor), corpus, seed and schedule; validation = 192 recipes x 64 cells. ctx x4, both x4 and 16x were read from server_2 snapshots.",
+      "sources": "tools/sec03_width_loss.py; data/sec03_width_loss.csv, data/sec03_width_saturation.csv, data/sec03_width_loss_headline.csv"
+    },
+    {
+      "kind": "content",
+      "section": "3",
+      "section_label": "3 Architecture ablation",
+      "title": "Width & context ablation: benchmark",
+      "layout": "full",
+      "figures": [
+        "figures/sec03_width_bench.png"
+      ],
+      "keywords": [
+        "trunk x4: loss −31%, no DEG gain",
+        "Prop hint: oracle = predicted, L1 worse",
+        "1x kept: ties ctx x4, 54M vs 102M"
+      ],
+      "footnote": "SLE, 42 donors, 2,980 genes; old 6k panel. ctx x4: better L1 (0.161 vs 0.197).",
+      "say": "Bars = mean of parts 14-16 with the cut p > 0.5; diamonds = Bernoulli read-out (4 width arms only). Prop hint = 9 L1 cell-type proportions; older panel, not the final model.",
+      "sources": "tools/sec03_width_bench.py; data/sec03_width_bench.csv, data/sec03_width_bench_summary.csv, data/sec03_width_bench_headline.csv"
+    },
+    {
+      "kind": "content",
+      "section": "3",
+      "section_label": "3 Architecture ablation",
+      "title": "Gene context: 2k / 3k / 6k / 15k",
+      "layout": "full",
+      "figures": [
+        "figures/sec03_panel.png"
+      ],
+      "keywords": [
+        "6k ≈ 3k: 3k r, DEG inside 6k seed range",
+        "2k best L1; 15k worst r, L1",
+        "6k kept: first panel with CLEC9A (cDC1)"
+      ],
+      "footnote": "SLE, 42 donors, 1,006 genes in every panel. 6k: 5 seeds; base seed best of 5 on r and DEG.",
+      "say": "Runs f01_{2k,3k,6k,p15k}_1x, part 16, Bernoulli, EMA, DDIM 50. Different gene axis from the previous slide, so the two slides cannot be compared. Loss curves are divided by each panel's own part-16 value.",
+      "sources": "tools/sec03_panel.py; data/sec03_panel_bench.csv, data/sec03_panel_seeds.csv, data/sec03_panel_loss.csv, data/sec03_panel_headline.csv"
+    },
+    {
+      "kind": "content",
+      "section": "4",
+      "section_label": "4 SLE benchmark",
+      "title": "SLE benchmark: PBISC vs scSemiProfiler",
+      "layout": "full",
+      "figures": [
+        "figures/sec04_sle_benchmark.png"
+      ],
+      "keywords": [
+        "42 held-out donors, sc-pseudobulk input",
+        "Beats scSemiProfiler on all three metrics",
+        "Copying bulk logFC scores higher (backup)"
+      ],
+      "footnote": "GSE135779, 42 held-out donors (30 SLE / 12 HC). PBISC: 5 seeds, mean ± SD; scSemiProfiler: 1 run.",
+      "sources": "tools/sec04_sle_benchmark.py; data/sec04_sle_benchmark.csv, data/sec04_sle_numbers.csv",
+      "footnote_full": "GSE135779, 42 held-out donors (30 SLE / 12 HC). PBISC f01 6k E16, 5 seeds (mean ± SD); baselines 1 draw. scSemiProfiler also gets real sc of 14 in-cohort donors (1,500 cells each). Dashed: input bulk logFC copied to every type (backup)."
+    },
+    {
+      "kind": "content",
+      "section": "4",
+      "section_label": "4 SLE benchmark",
+      "title": "SLE benchmark: per cell type",
+      "layout": "full",
+      "figures": [
+        "figures/sec04_sle_per_celltype.png"
+      ],
+      "keywords": [
+        "Beats scSemiProfiler in T, B, Mono, NK",
+        "Loses in Other; DC r ties, fewer DEGs",
+        "DC: ~15 generated cells per donor"
+      ],
+      "footnote": "Same 42 donors, 1,006 genes. PBISC: 5 seeds, mean ± SD; scSemiProfiler: 1 run. Other: top-50 of 12.",
+      "say": "PBISC's DC top list in the base seed has only 13 genes, because few DC cells are generated.",
+      "sources": "tools/sec04_sle_benchmark.py; data/sec04_sle_per_celltype.csv, data/sec04_sle_numbers.csv",
+      "footnote_full": "Same 42 donors, 1,006 genes. PBISC 5 seeds (mean ± SD), baselines 1 draw. Tick %: share of 251,179 real cells. Other top-50 is out of 12 truth genes. Bulk copy: r 0.68, top-50 113 (backup)."
+    },
+    {
+      "kind": "content",
+      "section": "5",
+      "section_label": "5 Real bulk vs pseudobulk",
+      "title": "Real bulk ≠ pseudobulk (model input)",
+      "layout": "figure_keywords",
+      "figures": [
+        "figures/sec05_condition_space.png"
+      ],
+      "keywords": [
+        "Real bulk: 2.46 D from own pseudobulk",
+        "95% of squared offset is one shift",
+        "Shift removed (diagnostic): own donor 7.5% → 75.7%"
+      ],
+      "footnote": "ImMicro, 107 healthy donors. Model input space (log1p CPM). D = median donor-to-donor distance.",
+      "say": "The two pseudobulk clusters on PC1 are sc pools DB1-3 vs DB4-7, so the pairing lines cross mainly because of sc batch. Shift removal subtracts the mean offset of the other 106 donors; a bulk-only deployment would not have them.",
+      "sources": "tools/sec05_condition_space.py; data/sec05_condition_space_numbers.csv, data/sec05_condition_space_coords.csv",
+      "footnote_full": "ImMicro, 107 healthy donors. Model inputs (log1p CPM, 5,508 genes), not learned ctx. D = median donor-to-donor distance; PCA-30 values are lower bounds (full space 3.19 D, 0.91 shared). Shift removal is a diagnostic that uses paired pseudobulks. Pseudobulk PC1 is sc pool batch (pool R² 0.96 on PC1)."
+    },
+    {
+      "kind": "content",
+      "section": "5",
+      "section_label": "5 Real bulk vs pseudobulk",
+      "title": "Consequence: cells off-manifold",
+      "layout": "two_figures",
+      "figures": [
+        "figures/sec05_cells_umap.png",
+        "figures/sec05_cells_metrics.png"
+      ],
+      "keywords": [
+        "Monocytes: raw 30.6% vs real 16.7%",
+        "Same noise: 19% of T cells become monocytes",
+        "Precision: raw 0.36 vs pseudobulk 0.92"
+      ],
+      "footnote": "ImMicro, 107 donors; same starting noise for every input. Coverage: raw 0.08, sc pseudobulk 0.51.",
+      "footnote_full": "ImMicro, 107 donors. Final model f01 6k 1× E16 (DDIM 50, EMA); each cell starts from the same noise under every input. Precision/coverage from one scoring call. Composition L1: raw 0.455, pseudobulk 0.166, cohort-mean null 0.161. The UMAP is fitted on real cells and overstates overlap.",
+      "sources": "tools/sec05_cells_offmanifold.py; data/sec05_cells_metrics.csv, data/sec05_cells_umap_coords.csv"
+    },
+    {
+      "kind": "content",
+      "section": "5",
+      "section_label": "5 Real bulk vs pseudobulk",
+      "title": "Consequence: disease direction flips",
+      "layout": "figure_keywords",
+      "figures": [
+        "figures/sec05_behcet_direction.png"
+      ],
+      "keywords": [
+        "CD16 gap: raw −3.97 vs observed +4.94",
+        "C1Q flip vanishes at common depth",
+        "sc pseudobulk: right sign in 5/5 seeds"
+      ],
+      "footnote": "Behçet. Bulk GSE198533 (9 BD / 10 HC); sc GSE198616 (4 / 4, different people) gives observed and pseudobulk.",
+      "say": "Raw bulk gives the wrong sign for CD16 and C1Q in 5/5 seeds. At one common monocyte depth the C1Q gap is −0.08 ± 0.09 pp (1/5 seeds positive), so the C1Q flip is a depth effect; the CD16 flip stays at −3.59. sc pseudobulk of the 8 sc donors gives the observed sign in 5/5 seeds (CD16 +5.48, C1Q +1.82). Observed p is descriptive (4 vs 4). No paired data in this cohort, so no fine-tune here.",
+      "sources": "tools/sec05_behcet_direction.py; data/sec05_behcet_direction.csv",
+      "footnote_full": "Behçet, final model. Bulk GSE198533 (BD 9, HC 10), 5,000 cells per sample; observed = sc GSE198616 (BD 4, HC 4, different people; descriptive p). At common depth, raw CD16 is −3.59 (seed 0) and raw C1Q is −0.08 ± 0.09 pp."
+    },
+    {
+      "kind": "content",
+      "section": "6",
+      "section_label": "6 Paired fine-tuning",
+      "title": "Fine-tuning with paired bulk + sc",
+      "layout": "diagram",
+      "figures": [
+        "figures/sec06_ft_method.png"
+      ],
+      "keywords": [
+        "LoRA 2–14 min vs pretraining 15.9 h"
+      ],
+      "footnote": "Base: final model f01 6k 1x E16. Times from 40 LoRA runs on this host.",
+      "sources": "tools/sec06_finetune_method.py; data/sec06_ft_method.csv",
+      "footnote_full": "Receipts: immicro/fold0/real_lora and ye2026_sepsis/fewshot_n5/draw0/lora RUN.json. Base: f01_6k_1x E16. Times: 40 local LoRA runs."
+    },
+    {
+      "kind": "content",
+      "section": "7",
+      "section_label": "7 LoRA ablations",
+      "title": "Full vs LoRA vs cond.-encoder-only",
+      "layout": "full",
+      "figures": [
+        "figures/sec07_ft_modes.png"
+      ],
+      "keywords": [
+        "LoRA ≈ full FT, 42× fewer params",
+        "Cond.-encoder-only ≈ ZS + cal. on ImMicro",
+        "LoRA beats both on coverage"
+      ],
+      "footnote": "Fold 0, cond.-enc. vs ZS + cal.: coverage 0.73 vs 0.73 / 0.74 vs 0.63, L1 p 0.06 / 0.93 (ImMicro / sepsis).",
+      "sources": "tools/sec07_ft_modes.py; data/sec07_ft_modes_accuracy.csv, data/sec07_ft_modes_compute.csv, data/sec07_ft_modes_wilcoxon.csv",
+      "footnote_full": "Base f01_6k_1x E16. ImMicro / sepsis; paired Wilcoxon on donor values, uncorrected. Full vs LoRA L1 p 0.25 / 0.71 (pooled); 42× fewer trainable params (1.3 M). Zero-shot + calibration: α_g = mean CPM(pseudobulk_g) / mean CPM(bulk_g) over the fold's 85–86 / 65–66 paired training donors (train + inner-val); bulk CPM × α, renormalised. The sc-only FT never saw real bulk, so it is fed calibrated bulk. Raw → calibrated zero-shot (pooled): L1 0.45 → 0.20 / 0.51 → 0.40, coverage 0.19 → 0.53 / 0.07 → 0.43, sepsis logFC r −0.01 → 0.12. LoRA vs ZS + cal. (pooled): coverage 0.78 vs 0.53 / 0.83 vs 0.43 (p 7e-19 / 4e-15); L1 0.16 vs 0.20 (p 2e-7) / 0.36 vs 0.40 (p 0.19); logFC r 0.53 vs 0.12. Fold 0, cond.-enc. vs ZS + cal.: L1 0.20 vs 0.17 (p 0.06) / 0.38 vs 0.39 (p 0.93), coverage 0.73 vs 0.73 (p 0.68) / 0.74 vs 0.63 (p 7e-4). Fold 0, LoRA: coverage 0.91 / 0.86; vs cond.-enc. p 4e-5 / 1e-3, vs ZS + cal. p 6e-5 / 3e-4; L1 vs cond.-enc. p 3e-5 / 0.52, vs ZS + cal. p 0.29 / 0.96. sc-only FT (+ cal.), pooled: coverage 0.62 / 0.73, logFC r 0.30. Open = fold-0 pilot; compare same marker only. Per-step time: runs early-stop differently."
+    },
+    {
+      "kind": "content",
+      "section": "7",
+      "section_label": "7 LoRA ablations",
+      "title": "K diversification: no gain",
+      "layout": "figure_keywords",
+      "figures": [
+        "figures/sec07_k_aug.png"
+      ],
+      "keywords": [
+        "No gain; keep single K 1000",
+        "Multi-K slightly worse on ImMicro (p 0.04)",
+        "Inner-val loss Δ ≤ 1.2e-4"
+      ],
+      "footnote": "LoRA r16, 5-fold pooled. p = paired Wilcoxon on donor L1, uncorrected.",
+      "sources": "tools/sec07_k_aug.py; data/sec07_k_aug.csv, data/sec07_k_aug_wilcoxon.csv, data/sec07_k_aug_inner_val_delta.csv",
+      "footnote_full": "LoRA r16 on f01_6k_1x E16, 5-fold pooled. p: paired Wilcoxon, donor L1 vs plain LoRA, uncorrected. Recipes arm: K 1000 real bulk + ½-batch pretraining pseudobulk, no multi-K."
+    },
+    {
+      "kind": "content",
+      "section": "8",
+      "section_label": "8 Pairs needed",
+      "title": "Pairs needed: composition, coverage, training steps",
+      "layout": "full",
+      "figures": [
+        "figures/sec08_pairs_realism.png"
+      ],
+      "keywords": [
+        "Calibration lifts coverage to 0.53 / 0.43",
+        "More pairs do not help calibration",
+        "LoRA, 5+7 pairs: coverage 0.84 / 0.77"
+      ],
+      "footnote": "ImMicro / sepsis, 5-fold CV (107 / 82 donors). N+7 = N training + 7 early-stop pairs; squares: same pairs.",
+      "footnote_full": "0 = no LoRA training: zero-shot on real bulk (pale) and zero-shot + calibration (dark blue). Calibration α_g = mean CPM(pseudobulk_g) / mean CPM(bulk_g) over all paired training donors of the fold (train + inner-val: 85–86 / 65–66, the same donors LoRA 'all' trains and early-stops on); bulk CPM × α, renormalised. ImMicro L1 axis is broken (zero-shot 0.455). Raw → calibrated: coverage 0.19 → 0.53 / 0.07 → 0.43, L1 0.455 → 0.199 / 0.511 → 0.401. L1 at 5+7 pairs vs raw zero-shot: 0.171 vs 0.455 (ImMicro, p 7e-18), 0.428 vs 0.511 (sepsis, p 0.04). LoRA vs ZS + cal.: coverage higher at every n in both cohorts (p ≤ 4e-15); L1 lower on ImMicro (all pairs 0.162 vs 0.199, p 2e-7) but not on sepsis (0.362 vs 0.401, p 0.19; 5+7 pairs 0.428). With α from the same 5 pairs (few-pair call, its own donors; LoRA fixed 3k steps, no inner-val): coverage 0.54 vs LoRA 0.79 (ImMicro), 0.43 vs 0.69 (sepsis). 20k step cap reached by ImMicro folds 1/5 (10), 4/5 (20), 5/5 (40, all); last 2k steps <0.4% of total loss drop. Squares: zero-shot + calibration with alpha fitted on exactly the pairs each LoRA run saw (N training + its inner-val pairs). Coverage stays 0.52-0.53 (ImMicro) and 0.43-0.44 (sepsis) from 5+7 pairs to all; composition L1 0.19-0.20 / 0.40-0.41. LoRA vs calibration at equal pairs, donor L1: ImMicro better at every n (p <= 0.03); sepsis 5+7 not different (0.428 vs 0.412, p 0.68), lower from 10+7 but not significant (p 0.08-0.32).",
+      "say": "Keywords say +7; 2 of 5 sepsis folds use 8 inner-val pairs.",
+      "sources": "tools/sec08_pairs_needed.py; data/sec08_pairs_realism.csv, data/sec08_selected_steps.csv, data/sec08_slide_numbers.csv"
+    },
+    {
+      "kind": "content",
+      "section": "8",
+      "section_label": "8 Pairs needed",
+      "title": "Pairs needed: donor and disease signal",
+      "layout": "figure_keywords",
+      "figures": [
+        "figures/sec08_pairs_signal.png"
+      ],
+      "keywords": [
+        "Calibration alone: logFC r ≤ 0.12",
+        "LoRA r above broadcast from 10+7 pairs",
+        "LoRA DEG passes calibration at 20+7"
+      ],
+      "footnote": "Sepsis: 61 case vs 21 control. logFC r = mean over T / B / Mono / NK. Raw zero-shot: logFC r −0.01, DEG 9.",
+      "footnote_full": "Zero-shot on real bulk: logFC r −0.01, DEG 9, NK r 0.49; zero-shot + calibration (α from all 85–86 / 65–66 fold pairs): 0.12, 11, 0.50; LoRA with all pairs: 0.53, 23, 0.70. LoRA passes ZS + cal. from 5+7 pairs on logFC r (0.29), from 20+7 on DEG (18) and from 20+7 on NK r (0.56). ≤10 pairs: NK r (0.20/0.40) and DEG (5/8) below both zero-shot points. ImMicro fraction r: T at or above raw zero-shot from 20 pairs, at or above ZS + cal. (0.67) from 40; Mono only with all. DEG gain is Mono/DC; T overlap 3 (zero-shot), 5 (ZS + cal.) → 0–1 from 20 pairs; NK 0 at every LoRA n (broadcast T 3, NK 2). Squares: calibration with alpha from the same pairs as each LoRA run. Sepsis logFC r 0.08 / 0.10 / 0.10 / 0.12 (5 / 10 / 20 / 40 + 7 pairs) vs LoRA 0.29 / 0.42 / 0.37 / 0.48; partial r about 0 vs 0.25-0.40; top-50 DEG 12 / 10 / 7 / 7 vs 5 / 8 / 18 / 22. ImMicro NK r 0.50-0.53 at every n vs LoRA 0.20 / 0.40 / 0.56 / 0.70.",
+      "sources": "tools/sec08_pairs_needed.py; data/sec08_pairs_signal.csv, data/sec08_slide_numbers.csv"
+    },
+    {
+      "kind": "content",
+      "section": "9",
+      "section_label": "9 Paired validation",
+      "sec09_index": 0,
+      "title": "Five paired cohorts",
+      "layout": "full",
+      "figures": [
+        "figures/sec09_cohorts.png"
+      ],
+      "keywords": [
+        "403 paired samples, 275 donors",
+        "Only identity-pass pairs; donor-level splits",
+        "Ji bulk: whole blood, not PBMC"
+      ],
+      "footnote": "Usable pair = passed identity check and ≥ 200 cells: 403 of 430 pairs; donor-level splits.",
+      "footnote_full": "Usable pair = passed the pairing identity check and >= 200 cells (fold >= 0 in data/<cohort>/folds.csv); dropped 15 identity-fail, 11 borderline and 1 low-cell pairs of 430. ImMicro: no identity flag, all used. Imperial, Giroux, Ji: several pairs per donor. Case vs control = samples in scores/<cohort>/pooled/summary.json. Giroux 'moderate' = GEO 'severe' (paper term); its moderate vs mild subjects are 4F/0M vs 0F/5M (scored), so its disease metrics exclude sex-linked genes. Labels: data/<cohort>/QC_REPORT.md; ImMicro: GEO GSE314922/GSE314416.",
+      "sources": "tools/sec09_paired_validation.py; data/sec09_cohorts.csv, data/sec09_quoted_numbers.csv, data/sec09_slide_text.json",
+      "numbers": [
+        {
+          "value": "403 paired samples (sum of usable rows)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/<cohort>/fewshot_n5/DRAWS.json (n_usable_rows)"
+        },
+        {
+          "value": "275 donors (sum of usable donors)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/<cohort>/fewshot_n5/DRAWS.json (n_usable_donors)"
+        },
+        {
+          "value": "dropped 15 identity-fail, 11 borderline, 1 < 200 cells, of 430",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/<cohort>/features.npz (identity_pass, identity_check); samples.csv (n_cells); /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/prepare_dataset.py (MIN_CELLS)"
+        },
+        {
+          "value": "ImMicro 107 pairs / 107 donors",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro/fewshot_n5/DRAWS.json"
+        },
+        {
+          "value": "Sepsis 82 pairs / 82 donors; sepsis 61 vs control 21",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ye2026_sepsis/fewshot_n5/DRAWS.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json"
+        },
+        {
+          "value": "Imperial 157 pairs / 57 donors; COVID+ 113 vs COVID− 34 (+10 recovery, neither)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/imperial_eskd/fewshot_n5/DRAWS.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/imperial_eskd/pooled/summary.json"
+        },
+        {
+          "value": "Giroux 24 pairs / 9 donors; moderate 11 vs mild 13",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/fewshot_n5/DRAWS.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/giroux2022/pooled/summary.json"
+        },
+        {
+          "value": "Ji 33 pairs / 20 donors; ChILI 18 vs ICI control 15",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ji2025_chili/fewshot_n5/DRAWS.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/pooled/summary.json"
+        }
+      ]
+    },
+    {
+      "kind": "content",
+      "section": "9",
+      "section_label": "9 Paired validation",
+      "sec09_index": 1,
+      "title": "5 donors: LoRA vs scSemiProfiler — cells",
+      "layout": "full",
+      "figures": [
+        "figures/sec09_fewpair_cells.png"
+      ],
+      "keywords": [
+        "LoRA-5 > scSemi-5: coverage, precision 5/5",
+        "Beats zero-shot + calibration: coverage 5/5",
+        "Lower L1 than calibration: ImMicro only"
+      ],
+      "footnote": "5 draws per cohort; test = all other usable pairs. Calibration α from the same 5 donors. scANVI labels.",
+      "footnote_full": "Base f01 6k·1x·E16 (EMA, DDIM 50) + LoRA r=16, 3,000 steps. LoRA-5 / scSemi-5 = LoRA / scSemiProfiler trained on the same 5 donors (5–15 pairs); test = all other usable pairs. Zero-shot = base model without fine-tuning, fed each test pair's real bulk as measured; zero-shot + calibration = the same model fed that bulk after gene-wise calibration, alpha fitted on the same 5 training donors' pairs (both in the same call as LoRA-5). scSemi-5 scored in a separate call, same test set and reference (sha-checked); 25/25 draws. Verdict = better in >= 80% of shared draws. LoRA-5 lower L1 than scSemi-5 in 4/5 (Giroux split 3/5). LoRA-5 > zero-shot + calibration: coverage 5/5, precision 3/5, depth-matched 5/5 and 3/5 cohorts; > zero-shot (real bulk): coverage 5/5, precision 5/5, depth-matched 5/5 and 4/5. Depth-matched P/C vs scSemi-5: same ranking, coverage LoRA-5 0.83–0.97 vs scSemi-5 0.15–0.28. Composition L1, LoRA-5 minus zero-shot + calibration, per-donor Wilcoxon (gain = p < 0.05): ImMicro -0.03 (p 2e-10), Sepsis +0.02 (p 0.79), Imperial +0.02 (p 0.38), Giroux -0.06 (p 0.38), Ji -0.01 (p 0.70); minus zero-shot (real bulk): ImMicro -0.27 (p 2e-18), Sepsis -0.04 (p 0.15), Imperial -0.35 (p 2e-08), Giroux -0.11 (p 0.04), Ji -0.23 (p 0.02). scANVI labels; P/C = PCA-50, k=10 balls. Scores: scores/<cohort>/fewshot_n5/.",
+      "sources": "tools/sec09_paired_validation.py; data/sec09_fewpair_cells.csv, data/sec09_scsemi_status.csv, data/sec09_slide_text.json",
+      "numbers": [
+        {
+          "value": "LoRA-5 vs scSemi-5 coverage (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.792 vs 0.036 (5/5, lora); Sepsis 0.694 vs 0.080 (5/5, lora); Imperial 0.737 vs 0.059 (5/5, lora); Giroux 0.961 vs 0.607 (5/5, lora); Ji 0.707 vs 0.145 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json and draw<j>_scsemi5/summary.json"
+        },
+        {
+          "value": "LoRA-5 vs scSemi-5 precision (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.952 vs 0.518 (5/5, lora); Sepsis 0.906 vs 0.728 (5/5, lora); Imperial 0.853 vs 0.330 (5/5, lora); Giroux 1.000 vs 0.992 (5/5, lora); Ji 0.978 vs 0.840 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json and draw<j>_scsemi5/summary.json"
+        },
+        {
+          "value": "LoRA-5 vs scSemi-5 comp_l1 (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.176 vs 0.233 (5/5, lora); Sepsis 0.466 vs 0.612 (5/5, lora); Imperial 0.503 vs 0.602 (5/5, lora); Giroux 0.256 vs 0.270 (3/5, split); Ji 0.513 vs 0.592 (4/5, lora) -> LoRA verdicts 4/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json and draw<j>_scsemi5/summary.json"
+        },
+        {
+          "value": "LoRA-5 vs scSemi-5 coverage_dm (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.966 vs 0.284 (5/5, lora); Sepsis 0.834 vs 0.158 (5/5, lora); Imperial 0.863 vs 0.152 (5/5, lora); Giroux 0.974 vs 0.238 (5/5, lora); Ji 0.908 vs 0.150 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json and draw<j>_scsemi5/summary.json"
+        },
+        {
+          "value": "LoRA-5 vs scSemi-5 precision_dm (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.975 vs 0.829 (5/5, lora); Sepsis 0.914 vs 0.504 (5/5, lora); Imperial 0.881 vs 0.429 (5/5, lora); Giroux 0.995 vs 0.426 (5/5, lora); Ji 0.974 vs 0.564 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json and draw<j>_scsemi5/summary.json"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot + calibration coverage (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.792 vs 0.535 (5/5, lora); Sepsis 0.694 vs 0.428 (5/5, lora); Imperial 0.737 vs 0.634 (5/5, lora); Giroux 0.961 vs 0.944 (5/5, lora); Ji 0.707 vs 0.114 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_cal)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot + calibration precision (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.952 vs 0.904 (5/5, lora); Sepsis 0.906 vs 0.899 (3/5, split); Imperial 0.853 vs 0.845 (3/5, split); Giroux 1.000 vs 0.996 (4/5, lora); Ji 0.978 vs 0.782 (5/5, lora) -> LoRA verdicts 3/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_cal)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot + calibration comp_l1 (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.176 vs 0.203 (5/5, lora); Sepsis 0.466 vs 0.446 (2/5, split); Imperial 0.503 vs 0.506 (2/5, split); Giroux 0.256 vs 0.288 (2/5, split); Ji 0.513 vs 0.514 (3/5, split) -> LoRA verdicts 1/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_cal)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot + calibration coverage_dm (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.966 vs 0.780 (5/5, lora); Sepsis 0.834 vs 0.658 (5/5, lora); Imperial 0.863 vs 0.681 (5/5, lora); Giroux 0.974 vs 0.937 (5/5, lora); Ji 0.908 vs 0.357 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_cal)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot + calibration precision_dm (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.975 vs 0.940 (5/5, lora); Sepsis 0.914 vs 0.909 (3/5, split); Imperial 0.881 vs 0.868 (3/5, split); Giroux 0.995 vs 0.989 (5/5, lora); Ji 0.974 vs 0.658 (5/5, lora) -> LoRA verdicts 3/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_cal)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot (real bulk) coverage (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.792 vs 0.190 (5/5, lora); Sepsis 0.694 vs 0.076 (5/5, lora); Imperial 0.737 vs 0.114 (5/5, lora); Giroux 0.961 vs 0.472 (5/5, lora); Ji 0.707 vs 0.066 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_raw)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot (real bulk) precision (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.952 vs 0.677 (5/5, lora); Sepsis 0.906 vs 0.625 (5/5, lora); Imperial 0.853 vs 0.500 (5/5, lora); Giroux 1.000 vs 0.980 (5/5, lora); Ji 0.978 vs 0.799 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_raw)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot (real bulk) comp_l1 (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.176 vs 0.445 (5/5, lora); Sepsis 0.466 vs 0.510 (5/5, lora); Imperial 0.503 vs 0.806 (5/5, lora); Giroux 0.256 vs 0.351 (5/5, lora); Ji 0.513 vs 0.721 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_raw)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot (real bulk) coverage_dm (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.966 vs 0.213 (5/5, lora); Sepsis 0.834 vs 0.073 (5/5, lora); Imperial 0.863 vs 0.151 (5/5, lora); Giroux 0.974 vs 0.549 (5/5, lora); Ji 0.908 vs 0.199 (5/5, lora) -> LoRA verdicts 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_raw)"
+        },
+        {
+          "value": "LoRA-5 vs zero-shot (real bulk) precision_dm (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.975 vs 0.671 (5/5, lora); Sepsis 0.914 vs 0.595 (5/5, lora); Imperial 0.881 vs 0.561 (5/5, lora); Giroux 0.995 vs 0.996 (2/5, split); Ji 0.974 vs 0.480 (5/5, lora) -> LoRA verdicts 4/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (arms lora5, zs_raw)"
+        },
+        {
+          "value": "coverage cohort means over all draws: LoRA-5 ImMicro 0.792, Sepsis 0.694, Imperial 0.737, Giroux 0.961, Ji 0.707; scSemi-5 ImMicro 0.036, Sepsis 0.080, Imperial 0.059, Giroux 0.607, Ji 0.145; zero-shot + calibration ImMicro 0.535, Sepsis 0.428, Imperial 0.634, Giroux 0.944, Ji 0.114; zero-shot ImMicro 0.190, Sepsis 0.076, Imperial 0.114, Giroux 0.472, Ji 0.066",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json and draw<j>_scsemi5/summary.json"
+        },
+        {
+          "value": "LoRA-5 minus zero-shot + calibration composition L1, per-donor Wilcoxon (draw-averaged): ImMicro -0.027, n=107, p=2.2e-10; Sepsis +0.018, n=82, p=0.79; Imperial +0.016, n=57, p=0.38; Giroux -0.065, n=8, p=0.38; Ji -0.012, n=20, p=0.7 -> gain in 1/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/per_donor.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/<cohort>/folds.csv"
+        },
+        {
+          "value": "LoRA-5 minus zero-shot (real bulk) composition L1, per-donor Wilcoxon (draw-averaged): ImMicro -0.270, n=107, p=2.2e-18; Sepsis -0.044, n=82, p=0.15; Imperial -0.348, n=57, p=1.6e-08; Giroux -0.107, n=8, p=0.039; Ji -0.232, n=20, p=0.017 -> gain in 4/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/per_donor.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/<cohort>/folds.csv"
+        },
+        {
+          "value": "scSemiProfiler draws used / LoRA draws: ImMicro 5/5, Sepsis 5/5, Imperial 5/5, Giroux 5/5, Ji 5/5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/sec09_scsemi_status.csv"
+        },
+        {
+          "value": "training pairs per draw: ImMicro 5, Sepsis 5, Imperial 12–15, Giroux 12–14, Ji 7–9; test pairs: ImMicro 102, Sepsis 77, Imperial 142–145, Giroux 10–12, Ji 24–26",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/<cohort>/fewshot_n5/DRAWS.json (n_train_rows, n_test_rows)"
+        },
+        {
+          "value": "LoRA r = 16; 3000 fixed steps; base runs/f01_6k_1x_seed20260627/checkpoint_epoch_016.pt; sampling: test donors only, 1,500 cells each, seed 20260927, DDIM 50, EMA, Bernoulli read-out",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fewshot_n5/draw0/lora/RUN.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/README.md"
+        },
+        {
+          "value": "P/C definition: precision/coverage/density (PCA-50 + k = 10 balls fitted on the scored donors' observed R1 cells); PCA components in every summary.json = 50",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py"
+        }
+      ]
+    },
+    {
+      "kind": "content",
+      "section": "9",
+      "section_label": "9 Paired validation",
+      "sec09_index": 2,
+      "title": "5 donors: LoRA vs scSemiProfiler — disease signal",
+      "layout": "figure_keywords",
+      "figures": [
+        "figures/sec09_fewpair_disease.png"
+      ],
+      "keywords": [
+        "LoRA-5 > scSemi-5: DEG 2/4, logFC r 2/4",
+        "logFC r: LoRA-5 > calibrated zero-shot 4/4",
+        "Giroux: broadcast r beats all arms"
+      ],
+      "footnote": "Giroux: sex-linked genes excluded (severity confounded with sex: 4F/0M vs 0F/5M). Top-50 DEG over 5 types.",
+      "footnote_full": "Same draws and verdict rule as the previous slide; DEG tie = half the draws equal or mean gap < 1 gene. logFC r = mean over T/B/Mono/NK of case-vs-control log2FC r (generated vs observed); top-50 DEG = Welch+BH overlap summed over 5 types (max 250; Giroux 1–63, Ji 75–250 per draw). Giroux test = 2 vs 2 subjects per draw. LoRA-5-better draws (verdict): vs scSemi-5 logFC r Sepsis split 3/5, Imperial lora 5/5, Giroux scSemi-5 0/5, Ji lora 5/5; DEG Sepsis lora 5/5, Imperial lora 5/5, Giroux tie 1/5, Ji tie 1/5. Vs zero-shot + calibration: logFC r Sepsis lora 4/5, Imperial lora 5/5, Giroux lora 4/5, Ji lora 5/5; DEG Sepsis lora 4/5, Imperial lora 5/5, Giroux tie 1/5, Ji tie 1/5. Vs zero-shot (real bulk): logFC r Sepsis lora 5/5, Imperial lora 5/5, Giroux split 3/5, Ji lora 5/5; DEG Sepsis lora 4/5, Imperial lora 5/5, Giroux tie 1/5, Ji tie 1/5. Broadcast = the input real bulk's own logFC. Giroux: moderate vs mild subjects are 4F/0M vs 0F/5M (scored; all paired subjects 4F/1M vs 0F/5M; sex from bulk chrY expression = sc = GEO), so its disease metrics (logFC r, partial r, top-50 DEG, broadcast) exclude 196 sex-linked genes (43 chrY non-PAR + 153 X-inactivation escape / variable-escape genes of Tukiainen et al. 2017; PAR genes kept), 65 of them scoring genes; same labels and cells as before (scores_nosex/giroux2022/). Scores: scores/<cohort>/fewshot_n5/draw<j>{,_scsemi5}/.",
+      "sources": "tools/sec09_paired_validation.py; data/sec09_fewpair_disease.csv, data/sec09_slide_text.json",
+      "numbers": [
+        {
+          "value": "Giroux sex by contrast arm (subjects, F/M): scored moderate 4F/0M vs mild 0F/5M; all paired 4F/1M vs 0F/5M",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/features.npz (sex_bulk_expr = sex_sc_expr = sex_geo); /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/folds.csv"
+        },
+        {
+          "value": "Giroux sex-linked genes excluded from the disease metrics: 196 listed (43 chrY non-PAR + 153 X escape), 65 of 5563 scoring genes",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/sex_linked_genes/sex_linked_genes_exclude.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/sex_linked_genes/BUILD.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json (gene_exclusion)"
+        },
+        {
+          "value": "Top-50 DEG LoRA-5 vs scSemi-5 (shared-draw means; LoRA-better / tied draws; verdict): Sepsis 16.4 vs 5.2 (5/5, 0 tied, lora); Imperial 113.6 vs 46.8 (5/5, 0 tied, lora); Giroux 0.2 vs 0.0 (1/5, 4 tied, tie); Ji 0.2 vs 0.0 (1/5, 4 tied, tie)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)"
+        },
+        {
+          "value": "Top-50 DEG LoRA-5 vs zero-shot + calibration (shared-draw means; LoRA-better / tied draws; verdict): Sepsis 16.4 vs 9.4 (4/5, 0 tied, lora); Imperial 113.6 vs 103.8 (5/5, 0 tied, lora); Giroux 0.2 vs 0.0 (1/5, 4 tied, tie); Ji 0.2 vs 0.0 (1/5, 4 tied, tie)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)"
+        },
+        {
+          "value": "Top-50 DEG LoRA-5 vs zero-shot (real bulk) (shared-draw means; LoRA-better / tied draws; verdict): Sepsis 16.4 vs 10.0 (4/5, 0 tied, lora); Imperial 113.6 vs 105.6 (5/5, 0 tied, lora); Giroux 0.2 vs 0.0 (1/5, 4 tied, tie); Ji 0.2 vs 0.0 (1/5, 4 tied, tie)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)"
+        },
+        {
+          "value": "logFC r LoRA-5 vs scSemi-5 (shared-draw means; LoRA-better draws; verdict): Sepsis 0.350 vs 0.402 (3/5, split); Imperial 0.749 vs 0.562 (5/5, lora); Giroux 0.297 vs 0.397 (0/5, other); Ji 0.333 vs 0.153 (5/5, lora)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)"
+        },
+        {
+          "value": "logFC r LoRA-5 vs zero-shot + calibration (shared-draw means; LoRA-better draws; verdict): Sepsis 0.350 vs 0.096 (4/5, lora); Imperial 0.749 vs 0.676 (5/5, lora); Giroux 0.297 vs 0.228 (4/5, lora); Ji 0.333 vs 0.140 (5/5, lora)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)"
+        },
+        {
+          "value": "logFC r LoRA-5 vs zero-shot (real bulk) (shared-draw means; LoRA-better draws; verdict): Sepsis 0.350 vs -0.024 (5/5, lora); Imperial 0.749 vs 0.638 (5/5, lora); Giroux 0.297 vs 0.255 (3/5, split); Ji 0.333 vs 0.087 (5/5, lora)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)"
+        },
+        {
+          "value": "Sepsis broadcast logFC r 0.320 vs best arm 0.402; broadcast DEG 20.0 vs best arm 16.4 (means over all draws; arms: zero-shot, zero-shot + calibration, scSemi-5, LoRA-5)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/fewshot_n5/draw<j>{,_scsemi5}/summary.json"
+        },
+        {
+          "value": "Imperial broadcast logFC r 0.656 vs best arm 0.749; broadcast DEG 74.8 vs best arm 113.6 (means over all draws; arms: zero-shot, zero-shot + calibration, scSemi-5, LoRA-5)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/imperial_eskd/fewshot_n5/draw<j>{,_scsemi5}/summary.json"
+        },
+        {
+          "value": "Giroux broadcast logFC r 0.492 vs best arm 0.397; broadcast DEG 0.0 vs best arm 0.2 (means over all draws; arms: zero-shot, zero-shot + calibration, scSemi-5, LoRA-5)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/fewshot_n5/draw<j>{,_scsemi5}/summary.json"
+        },
+        {
+          "value": "Ji broadcast logFC r 0.234 vs best arm 0.333; broadcast DEG 1.0 vs best arm 0.2 (means over all draws; arms: zero-shot, zero-shot + calibration, scSemi-5, LoRA-5)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw<j>{,_scsemi5}/summary.json"
+        },
+        {
+          "value": "DEG maximum per draw: Sepsis 250, Imperial 250, Giroux 1–63, Ji 75–250",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/fewshot_n5/draw<j>/summary.json (deg_top50_max; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex)"
+        },
+        {
+          "value": "Giroux test subjects per draw: 2 vs 2 (moderate vs mild); test pairs 10–12",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/fewshot_n5/DRAWS.json (train_donors); /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/folds.csv (group)"
+        }
+      ]
+    },
+    {
+      "kind": "content",
+      "section": "9",
+      "section_label": "9 Paired validation",
+      "text_source": {
+        "file": "data/sec09_extra_slide_text.json",
+        "index": 0
+      },
+      "title": "5 donors: realism beyond precision/coverage",
+      "layout": "full",
+      "figures": [
+        "figures/sec09_extra_fewpair.png"
+      ],
+      "keywords": [
+        "Classifier AUC: LoRA-5 best in 25/25 draws",
+        "E_N: LoRA-5 below scSemi-5 in 25/25 draws",
+        "Still separable: LoRA-5 AUC 0.89–0.95"
+      ],
+      "footnote": "E_N: scSemiProfiler's own error metric. Misses only vs calibration, e.g. Imperial E_N 1.28 vs 1.22.",
+      "footnote_full": "Same 5-donor draws as the previous slides; every metric on exactly the scored cells (scores_extra/, tools/extra_metrics.py). Real side for AUC and MMD² = held-out real cells of the same samples (R2), projected like the generated cells; real vs real = R2 vs R3 (AUC 0.49–0.50). Classifier AUC: 5-fold CV ROC AUC of a kNN classifier (k = 5) separating real from generated cells, 0.5 = indistinguishable; bars start at 0.5. MMD²: unbiased, Gaussian kernel, bandwidth = median distance among real cells. E_N: scSemiProfiler's normalized error (Wang et al. 2024, eq. 17–18), their code's squared L2 on all cells, per-arm PCA-100; real vs real = within-sample half split (0.07–0.38); every generated arm is above 1. LoRA-5 best of the 4 arms in 25/25 draws on kNN AUC, 25/25 on RF AUC, 25/25 on 1-NN accuracy, 23/25 on MMD², 22/25 on E_N. LoRA-5 better than scSemi-5, zero-shot and zero-shot + calibration on both AUCs, 1-NN accuracy and coverage at k = 3, 5, 10 in every draw. E_N scSemi-5 vs LoRA-5 (cohort means): ImMicro 3.65 vs 1.55; Sepsis 3.83 vs 1.46; Imperial 2.04 vs 1.28; Giroux 3.79 vs 1.99; Ji 4.27 vs 2.60; LoRA-5 lower in 25/25 draws. Draws where LoRA-5 is worse (cohort means in brackets): vs zero-shot + calibration: Sepsis precision k5 1/5 (0.778 vs 0.752), precision k10 2/5 (0.906 vs 0.899); Imperial MMD² (σ = m) 1/5 (0.0156 vs 0.0218), MMD² (σ sum) 1/5 (0.0443 vs 0.0673), E_N (all cells) 3/5 (1.28 vs 1.22), precision k3 2/5 (0.610 vs 0.598), precision k5 2/5 (0.726 vs 0.719), precision k10 2/5 (0.853 vs 0.845); Giroux MMD² (σ = m) 1/5 (0.0178 vs 0.0257), MMD² (σ sum) 1/5 (0.0584 vs 0.0838), precision k5 1/5 (0.995 vs 0.981), precision k10 1/5 (1.000 vs 0.996). LoRA-5 AUC per draw 0.88–0.97 (kNN), RF cohort means 0.90–0.96: a classifier still tells generated from real cells. Scores: scores_extra/<cohort>/fewshot_n5/draw<j>/.",
+      "sources": "tools/sec09_extra_metrics.py; data/sec09_extra_fewpair.csv, data/sec09_extra_quoted_numbers.csv; plans/paired_finetune_intradataset_20260927/scores_extra/ (README.md, TABLES.md)",
+      "numbers": [
+        {
+          "value": "LoRA-5 best of 4 arms (draws): kNN AUC 25/25, RF AUC 25/25, 1-NN 25/25, MMD² 23/25, E_N 22/25",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+        },
+        {
+          "value": "E_N LoRA-5 < scSemi-5 in 25/25 draws; cohort means scSemi-5 vs LoRA-5: ImMicro 3.65 vs 1.55; Sepsis 3.83 vs 1.46; Imperial 2.04 vs 1.28; Giroux 3.79 vs 1.99; Ji 4.27 vs 2.60",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/en.csv (cellset all, distance sq)"
+        },
+        {
+          "value": "LoRA-5 kNN AUC cohort means: ImMicro 0.943, Sepsis 0.936, Imperial 0.918, Giroux 0.893, Ji 0.955; per draw 0.878–0.975",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+        },
+        {
+          "value": "real vs real: kNN AUC ImMicro 0.502, Sepsis 0.501, Imperial 0.498, Giroux 0.495, Ji 0.491; E_N ImMicro 0.382, Sepsis 0.100, Imperial 0.260, Giroux 0.145, Ji 0.073",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+        },
+        {
+          "value": "LoRA-5 worse than a comparator (draws): vs zero-shot + calibration: Sepsis precision k5 1/5 (0.778 vs 0.752), precision k10 2/5 (0.906 vs 0.899); Imperial MMD² (σ = m) 1/5 (0.0156 vs 0.0218), MMD² (σ sum) 1/5 (0.0443 vs 0.0673), E_N (all cells) 3/5 (1.28 vs 1.22), precision k3 2/5 (0.610 vs 0.598), precision k5 2/5 (0.726 vs 0.719), precision k10 2/5 (0.853 vs 0.845); Giroux MMD² (σ = m) 1/5 (0.0178 vs 0.0257), MMD² (σ sum) 1/5 (0.0584 vs 0.0838), precision k5 1/5 (0.995 vs 0.981), precision k10 1/5 (1.000 vs 0.996)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+        },
+        {
+          "value": "cross-check: 720 cohort × arm × metric values equal scores_extra/{fewshot,pooled}_headline.csv",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/fewshot_headline.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/pooled_headline.csv"
+        }
+      ]
+    },
+    {
+      "kind": "content",
+      "section": "9",
+      "section_label": "9 Paired validation",
+      "sec09_index": 3,
+      "title": "All pairs, 5-fold",
+      "layout": "full",
+      "figures": [
+        "figures/sec09_fivefold.png"
+      ],
+      "keywords": [
+        "LoRA tops coverage 5/5, logFC r 4/4",
+        "Ji whole blood: LoRA 0.86, others 0.06–0.11",
+        "Giroux: broadcast r 0.58 > LoRA 0.46"
+      ],
+      "footnote": "Donor-level 5-fold CV. Giroux: sex-linked genes excluded (severity confounded with sex: 4F/0M vs 0F/5M).",
+      "footnote_full": "Donor-level 5-fold CV; each pair scored once, by the fold model that never trained on its donor (one pooled call per cohort). LoRA r=16 on all training donors' real bulk, early stop on inner-val donors. Zero-shot = base model without fine-tuning, fed the test pair's real bulk as measured; zero-shot + calibration = the same model fed gene-wise calibrated bulk (alpha fitted on the fold's training donors); sc-only fine-tune (+ calibration) = full fine-tune on training donors' sc pseudobulk, fed calibrated bulk. Base f01 6k·1x·E16. LoRA best of 4 arms: coverage 5/5, logFC r 4/4, top-50 DEG 4/4. Broadcast = raw real bulk. Sepsis DEG from pooled_deg. Giroux: moderate vs mild subjects are 4F/0M vs 0F/5M (scored; all paired subjects 4F/1M vs 0F/5M; sex from bulk chrY expression = sc = GEO), so its disease metrics (logFC r, partial r, top-50 DEG, broadcast) exclude 196 sex-linked genes (43 chrY non-PAR + 153 X-inactivation escape / variable-escape genes of Tukiainen et al. 2017; PAR genes kept), 65 of them scoring genes; same labels and cells as before (scores_nosex/giroux2022/). Scores: scores/<cohort>/pooled/summary.json.",
+      "sources": "tools/sec09_paired_validation.py; data/sec09_fivefold.csv, data/sec09_slide_text.json",
+      "numbers": [
+        {
+          "value": "LoRA best of 4 arms: coverage 5/5, logFC r 4/4, top-50 DEG 4/4",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)"
+        },
+        {
+          "value": "Ji coverage: zero-shot 0.055, zero-shot + calibration 0.106, sc-only (+ calibration) 0.113, LoRA 0.857",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/pooled/summary.json"
+        },
+        {
+          "value": "Giroux logFC r: broadcast 0.575 vs LoRA 0.461; DEG broadcast 5 vs LoRA 5",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json"
+        },
+        {
+          "value": "LoRA logFC r vs broadcast: Sepsis 0.527 vs 0.316, Imperial 0.819 vs 0.661, Giroux 0.461 vs 0.575, Ji 0.479 vs 0.240",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)"
+        },
+        {
+          "value": "coverage LoRA / zero-shot + calibration / zero-shot / sc-only (+ calibration): ImMicro 0.784 / 0.527 / 0.192 / 0.623, Sepsis 0.829 / 0.431 / 0.067 / 0.726, Imperial 0.850 / 0.639 / 0.112 / 0.555, Giroux 0.928 / 0.906 / 0.281 / 0.908, Ji 0.857 / 0.106 / 0.055 / 0.113",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)"
+        },
+        {
+          "value": "logFC r LoRA / zero-shot + calibration / zero-shot / sc-only (+ calibration): Sepsis 0.527 / 0.123 / -0.012 / 0.296, Imperial 0.819 / 0.679 / 0.644 / 0.808, Giroux 0.461 / 0.269 / 0.334 / 0.344, Ji 0.479 / 0.142 / 0.084 / 0.245",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)"
+        },
+        {
+          "value": "top-50 DEG LoRA / zero-shot + calibration / zero-shot / sc-only (+ calibration): Sepsis 23 / 11 / 9 / 15, Imperial 143 / 111 / 106 / 129, Giroux 5 / 0 / 1 / 0, Ji 15 / 0 / 0 / 0",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)"
+        },
+        {
+          "value": "Pooled n (pairs / donors): ImMicro 107/107, Sepsis 82/82, Imperial 157/57, Giroux 24/9, Ji 33/20",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/<cohort>/pooled/summary.json (n_donors); /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/<cohort>/fewshot_n5/DRAWS.json"
+        },
+        {
+          "value": "early stopping: About 10 % of each fold's training donors are inner-validation, used only for early stopping",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/README.md"
+        }
+      ]
+    },
+    {
+      "kind": "content",
+      "section": "10",
+      "section_label": "10 Paired-subset cohorts",
+      "title": "Next: cohorts with a paired subset (planned)",
+      "layout": "figure_keywords",
+      "figures": [
+        "figures/sec10_paired_subset_cohorts.png"
+      ],
+      "keywords": [
+        "Train LoRA on pairs, generate bulk-only",
+        "Sepsis controls: 21 paired + 54 bulk-only",
+        "ChILI pre-ICI: 11 bulk, 4 unpaired sc"
+      ],
+      "footnote": "Counts from pairing manifests. Model: f01 6k 1x E16 + LoRA r16.",
+      "say": "Planned: no LoRA cells exist yet for any bulk-only sample. Ji bulk is PAXgene whole blood. Only 7 of the Arunachalam pairs are strict same-visit pairs. Asan is omitted because it has no donor map.",
+      "sources": "tools/sec10_paired_subset_cohorts.py; data/sec10_cohort_counts.csv, data/sec10_bulk_only_groups.csv, data/sec10_quoted_numbers.csv",
+      "footnote_full": "Counts from pairing manifests. QC = identity check (Imperial also >=200 cells); sex-flag = bulk sex differs from metadata or is ambiguous. Not shown: Asan (no donor map), Giroux (24 pairs, 17 bulk-only; broadcast beats LoRA). Model: f01 6k 1x E16 + LoRA r16."
+    },
+    {
+      "kind": "divider",
+      "title": "Backup"
+    },
+    {
+      "kind": "content",
+      "section": "4",
+      "section_label": "Backup · 4 SLE benchmark",
+      "backup": true,
+      "title": "Bulk broadcast baseline",
+      "layout": "full",
+      "figures": [
+        "figures/sec04_sle_broadcast.png"
+      ],
+      "keywords": [
+        "Bulk logFC copied: r 0.68 vs 0.37",
+        "Type-specific part: partial r 0.21",
+        "scSemiProfiler: r 0.32, partial r 0.17"
+      ],
+      "footnote": "SLE, 42 donors, 1,006 genes. Partial r: both logFCs residualised on bulk logFC.",
+      "sources": "tools/sec04_sle_benchmark.py; data/sec04_sle_broadcast.csv, data/sec04_sle_numbers.csv",
+      "footnote_full": "SLE held-out, 42 donors, 1,006 genes; PBISC 5 seeds. Partial r: truth and method logFC residualised on bulk logFC. Broadcast has no residual, so it is not scored. Split-half and BayesPrism checks: sle_broadcast/RESULTS.md."
+    },
+    {
+      "kind": "content",
+      "section": "5",
+      "section_label": "Backup · 5 Real bulk vs pseudobulk",
+      "backup": true,
+      "title": "Same shift in three paired cohorts",
+      "layout": "full",
+      "figures": [
+        "figures/sec05_shift_cohorts.png"
+      ],
+      "keywords": [
+        "Shared shift in all 3 cohorts"
+      ],
+      "footnote": "Model inputs only, no generation. PCA fitted per cohort on sc pseudobulks.",
+      "sources": "tools/sec05_shift_cohorts.py; data/sec05_shift_cohorts.csv",
+      "footnote_full": "Model inputs only, no generation. PCA is fitted on each cohort's sc pseudobulks (Arunachalam: 11 PCs, n = 12). D uses same-condition pairs only for Randolph and Arunachalam. Shift removal is a leave-one-out diagnostic."
+    },
+    {
+      "kind": "content",
+      "section": "6",
+      "section_label": "Backup · 6 Paired fine-tuning",
+      "backup": true,
+      "title": "Evaluation design",
+      "layout": "full",
+      "figures": [
+        "figures/sec06_eval_design.png"
+      ],
+      "keywords": [
+        "Donor-level splits; test donors never trained",
+        "5-fold: other folds · few: 5 donors",
+        "Giroux few-pair trains 12–14 of 24 pairs"
+      ],
+      "footnote": "Bars: Ye 2026 sepsis. QC-failed pairs excluded.",
+      "sources": "tools/sec06_finetune_method.py; data/sec06_eval_design.csv, data/sec06_eval_design_sepsis_roles.csv",
+      "footnote_full": "Counts: data/<cohort>/folds.csv and fewshot_n5/DRAWS.json; QC-failed pairs (fold −1) excluded. Bars: Ye 2026 sepsis."
+    },
+    {
+      "kind": "content",
+      "section": "7",
+      "section_label": "Backup · 7 LoRA ablations",
+      "backup": true,
+      "text_source": {
+        "file": "data/sec07_rank_sweep_slide_text.json",
+        "index": 0
+      },
+      "title": "LoRA rank: r = 4, 16, 64",
+      "layout": "full",
+      "figures": [
+        "figures/sec07_rank_sweep.png"
+      ],
+      "keywords": [
+        "ImMicro r64, all pairs: coverage 0.78 → 0.82",
+        "ImMicro r4, all pairs: L1 0.162 → 0.186",
+        "Kept r16; r64 precision 0.92 → 0.90"
+      ],
+      "footnote": "Sepsis, all pairs: r64 logFC r 0.53 → 0.55, DEG 23 → 26; r4 0.48, DEG 18. Pooled 5-fold scores.",
+      "footnote_full": "LoRA fine-tune of the final model (f01 6k·1x·E16) with rank r and alpha = r, same recipe as the deck's r = 16 runs (early stop on 7–8 inner-val pairs, 20k step cap); n = training pairs, nested subsets; donor-level 5-fold CV, each pair scored once by its fold model (pooled call). Trainable parameters r4 324,056, r16 1,296,224, r64 5,184,896. ImMicro n = 5: r4: L1 0.162, P/C 0.937 / 0.789 (L1 vs r16 -0.009, Wilcoxon p 0.00088, 107 donors); r16: L1 0.171, P/C 0.946 / 0.839; r64: L1 0.177, P/C 0.946 / 0.847 (L1 vs r16 +0.005, Wilcoxon p 0.0076, 107 donors) | ImMicro n = 20: r4: L1 0.171, P/C 0.925 / 0.715 (L1 vs r16 -0.001, Wilcoxon p 0.32, 107 donors); r16: L1 0.172, P/C 0.920 / 0.755; r64: L1 0.172, P/C 0.905 / 0.794 (L1 vs r16 -0.001, Wilcoxon p 0.66, 107 donors) | ImMicro n = all: r4: L1 0.186, P/C 0.933 / 0.712 (L1 vs r16 +0.024, Wilcoxon p 9.8e-09, 107 donors); r16: L1 0.162, P/C 0.924 / 0.784; r64: L1 0.161, P/C 0.904 / 0.816 (L1 vs r16 -0.001, Wilcoxon p 0.89, 107 donors) | Sepsis n = 5: r4: L1 0.426, P/C 0.907 / 0.742, logFC r 0.27, DEG 2 (L1 vs r16 -0.002, Wilcoxon p 0.97, 82 donors); r16: L1 0.428, P/C 0.911 / 0.767, logFC r 0.29, DEG 5; r64: L1 0.429, P/C 0.906 / 0.766, logFC r 0.31, DEG 1 (L1 vs r16 +0.001, Wilcoxon p 0.66, 82 donors) | Sepsis n = 20: r4: L1 0.378, P/C 0.929 / 0.769, logFC r 0.31, DEG 14 (L1 vs r16 -0.003, Wilcoxon p 0.58, 82 donors); r16: L1 0.380, P/C 0.927 / 0.807, logFC r 0.37, DEG 18; r64: L1 0.376, P/C 0.911 / 0.808, logFC r 0.43, DEG 18 (L1 vs r16 -0.004, Wilcoxon p 0.046, 82 donors) | Sepsis n = all: r4: L1 0.363, P/C 0.923 / 0.789, logFC r 0.48, DEG 18 (L1 vs r16 +0.002, Wilcoxon p 0.77, 82 donors); r16: L1 0.362, P/C 0.926 / 0.829, logFC r 0.53, DEG 23; r64: L1 0.367, P/C 0.907 / 0.824, logFC r 0.55, DEG 26 (L1 vs r16 +0.005, Wilcoxon p 0.66, 82 donors). r64 vs r16 brings small gains in coverage and disease signal but slightly lower precision and no composition change; r4 is worse with many pairs; at n = 5 the ranks are mixed. r = 16 is kept. Scores: scores/<cohort>/lora_rank_20261001/pooled (r4, r64) and scores/<cohort>/pooled{,_deg} (r16); table: scores/<cohort>/rankcal_20261001.md.",
+      "sources": "tools/sec07_rank_sweep.py; data/sec07_rank_sweep.csv, data/sec07_rank_sweep_runs.csv; plans/paired_finetune_intradataset_20260927/scores/<cohort>/rankcal_20261001.md",
+      "numbers": [
+        {
+          "value": "ImMicro all pairs coverage r16 0.784 -> r64 0.816; precision 0.924 -> 0.904; L1 r16 0.162, r4 0.186 (Wilcoxon p 9.8e-09)",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/lora_rank_20261001/pooled/summary.json, per_donor.csv"
+        },
+        {
+          "value": "Sepsis all pairs logFC r r16 0.527, r64 0.553, r4 0.476; DEG 23 / 26 / 18",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json, contrast.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/lora_rank_20261001/pooled/summary.json, contrast.csv"
+        },
+        {
+          "value": "trainable parameters: r4 324056, r16 1296224, r64 5184896",
+          "source": "runs/<cohort>/fold<k>/<arm>/RUN.json (trainable.n_trainable)"
+        }
+      ]
+    },
+    {
+      "kind": "content",
+      "section": "9",
+      "section_label": "Backup · 9 Paired validation",
+      "backup": true,
+      "text_source": {
+        "file": "data/sec09_extra_slide_text.json",
+        "index": 1
+      },
+      "title": "5-fold: realism beyond precision/coverage",
+      "layout": "full",
+      "figures": [
+        "figures/sec09_extra_fivefold.png"
+      ],
+      "keywords": [
+        "Every metric: LoRA first in 5/5 cohorts",
+        "Ji whole blood: E_N 1.9 vs 7.1–10.1",
+        "Still separable: LoRA AUC 0.87–0.94"
+      ],
+      "footnote": "Donor-level 5-fold CV, one pooled call per cohort. E_N: scSemiProfiler's own error metric.",
+      "footnote_full": "Donor-level 5-fold CV; each pair scored once by the fold model that never trained on its donor (one pooled call per cohort, all cross-fitted pairs). Arms as on the 5-fold slide. Metric definitions as on the 5-donor realism slide. LoRA first among the 4 arms in all 5 cohorts on 12/12 headline metrics (kNN and RF AUC, 1-NN accuracy, MMD² at σ = m and summed, E_N all cells, precision and coverage at k = 3, 5, 10). LoRA kNN AUC ImMicro 0.941, Sepsis 0.886, Imperial 0.866, Giroux 0.881, Ji 0.908; real vs real ImMicro 0.505, Sepsis 0.506, Imperial 0.496, Giroux 0.501, Ji 0.494. E_N LoRA vs other arms: ImMicro 1.56 vs 1.77–4.83; Sepsis 1.20 vs 1.40–5.29; Imperial 1.13 vs 1.19–4.05; Giroux 1.70 vs 2.24–4.48; Ji 1.95 vs 7.08–10.13. scSemiProfiler 5-fold CV not available yet (no extra-metric unit with set scsemi__raw). Scores: scores_extra/<cohort>/pooled/.",
+      "sources": "tools/sec09_extra_metrics.py; data/sec09_extra_fivefold.csv, data/sec09_extra_scsemi_status.csv; plans/paired_finetune_intradataset_20260927/scores_extra/",
+      "numbers": [
+        {
+          "value": "LoRA first among 4 arms in all 5 cohorts on 12/12 metrics",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/pooled/{metrics,en}.csv"
+        },
+        {
+          "value": "Ji E_N: LoRA 1.948; Zero-shot, real bulk 10.125, Zero-shot + calibration 7.076, sc-only fine-tune (+ calibration) 7.215",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/pooled/{metrics,en}.csv"
+        },
+        {
+          "value": "LoRA kNN AUC (pooled): ImMicro 0.941, Sepsis 0.886, Imperial 0.866, Giroux 0.881, Ji 0.908",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/pooled/{metrics,en}.csv"
+        },
+        {
+          "value": "scSemiProfiler, all training pairs: not available",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/sec09_extra_scsemi_status.csv"
+        }
+      ]
+    },
+    {
+      "kind": "content",
+      "section": "9",
+      "section_label": "Backup · 9 Paired validation",
+      "backup": true,
+      "text_source": {
+        "file": "data/sec09_extra_slide_text.json",
+        "index": 2
+      },
+      "title": "5 donors: precision/coverage at k = 3, 5, 10",
+      "layout": "full",
+      "figures": [
+        "figures/sec09_extra_ksens.png"
+      ],
+      "keywords": [
+        "Smaller k lowers every set, real included",
+        "Coverage: LoRA-5 first at all k, 25/25",
+        "Precision: LoRA-5 first in 20–23/25 draws"
+      ],
+      "footnote": "5 donors, 5 draws per cohort; error bars = SD over draws. k = 10 is the value on the main slides.",
+      "footnote_full": "Precision and coverage recomputed with k = 3, 5, 10 nearest-neighbour balls in the scorer's PCA-50 (k = 10 reproduces the main-slide values exactly). Means over the 5 draws per cohort; error bars = SD; grey = real vs real (R1 vs R2, same samples). Both metrics fall with smaller k for every set including the real control (real coverage at k = 3: 0.92–0.99), so compare arms with the real row, not with 1. Expected coverage of a perfect generator (Naeem et al. 2020, eq. 9) at k = 3/5/10: 0.88/0.97/1.00 in every cohort. LoRA-5 first on coverage at every k in 25/25 draws; on precision in 23/21/20 of 25 draws (k = 3/5/10); not first: Sepsis 0/1/2, Imperial 2/2/2, Giroux 0/1/1 draws at k = 3/5/10, beaten by zero-shot + calibration. 5-fold pooled design (not drawn): coverage ranking of the 4 arms across k unchanged; LoRA first at every k in 5/5 cohorts. Scores: scores_extra/<cohort>/fewshot_n5/draw<j>/metrics.csv.",
+      "sources": "tools/sec09_extra_metrics.py; data/sec09_extra_ksens.csv; plans/paired_finetune_intradataset_20260927/scores_extra/",
+      "numbers": [
+        {
+          "value": "LoRA-5 first on coverage at k = 3, 5 and 10: 25/25 draws",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+        },
+        {
+          "value": "LoRA-5 first on precision (draws) at k = 3/5/10: 23/21/20 of 25",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+        },
+        {
+          "value": "real vs real coverage at k = 3: ImMicro 0.932, Sepsis 0.925, Imperial 0.919, Giroux 0.993, Ji 0.980",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+        },
+        {
+          "value": "every set falls with smaller k (cohort means): True",
+          "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/<cohort>/fewshot_n5/draw<j>/{metrics,en}.csv"
+        }
+      ]
+    }
+  ]
+}