Datasets:

junidude14
/

PBISC-data

paper-releases/pbisc-diffusion/updates/20261002T085943Z-deck-restructure-contrast-metrics-zeroshot-len/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/README.md ADDED Viewed

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+# Within-dataset fine-tuning of the final model on paired bulk + scRNA data
+User decision (2026-09-27): release the pseudobulk-pretrained final model as something users fine-tune on their own paired bulk + scRNA data. Validate it inside each paired dataset: split donors, fine-tune on the training donors' real bulk → their cells, and test on held-out donors. Start with ImMicro and Ye 2026 sepsis, run the same way. Feasibility of other datasets: `FEASIBILITY.md`.
+## Setup
+- **Base model:** `runs/f01_6k_1x_seed20260627/checkpoint_epoch_016.pt` (sha 8c7cd0e8…), EMA weights. E16 is also the best-validation epoch (0.13691).
+- **Data** (`data/<dataset>/`):
+  - `features.npz`: bulk and pseudobulk on the 20,097 axis, with measured masks.
+  - `cells_panel6k.npz`: retained cells on the 6k panel. Each row's cells sum exactly to its pseudobulk.
+  - **ImMicro:** 107 donors, 392,251 baseline cells. Built by `tools/build_immicro_cells.py`.
+  - **Sepsis:** 85 pairs, 82 pass identity checks, 610,713 cells. Built by the pairing agent; details in `data/ye2026_sepsis/QC_REPORT.md`. The GEX is 5′ even though GEO says 3′.
+- **Folds** (`tools/prepare_dataset.py`):
+  - Donor-level 5-fold, stratified by group (ImMicro pool; sepsis clinical group).
+  - About 10 % of each fold's training donors are inner-validation, used only for early stopping.
+  - Identity-failed sepsis rows sit in fold −1 and are never used.
+- **Conditions:**
+  - Fixed depth (K = 1000, one depth anchor), the published `build_realbulk_conditions.py` arithmetic.
+  - No test donor's single-cell depth or cell count enters its condition.
+  - The ImMicro raw and pseudo NPZs equal the published fixed ones bit for bit.
+  - Paired calibration α is fitted on each fold's training donors only.
+- **Training** (`tools/ft_sdd.py`):
+  - Pretraining loss, bf16, AdamW, grad clip 5, EMA 0.999, donor-balanced batches of 256.
+  - Up to 20,000 steps. Inner-validation loss is checked every 250 steps and the best EMA state is kept.
+  - Modes: full (54.2 M trainable parameters), LoRA r = 16 (1.3 M, merged before saving), condition encoder only (9.5 M).
+  - Output checkpoints keep the base schema, so `scripts/sample_sdd_standalone.py` samples them unchanged.
+- **Sampling:** test donors only, 1,500 cells each, seed 20260927, DDIM 50, EMA, Bernoulli read-out. GPU only through `tools/run_queue.sh` → `ml-run`.
+- **Scoring** (`tools/score_ft.py`, `tools/score_fold.sh`):
+  - Frozen f01common scANVI six-type gate. Metrics: composition L1, precision/coverage (PCA-50, k = 10, reference = the scored donors' observed cells), pseudobulk r, donor recovery top-1.
+  - For sepsis, also per-cell-type sepsis-vs-control logFC r against observed and against bulk broadcast.
+  - Validated against the published ImMicro native arms: composition L1 and labels are identical.
+  - Precision/coverage depends on the reference draw and on how many donors are scored, so compare arms within a fold.
+  - **Giroux: sex-linked genes excluded from the disease metrics** (user decision 2026-10-01). Its moderate vs mild contrast is fully confounded with sex (scored subjects 4 F / 0 M vs 0 F / 5 M). Giroux logFC r, partial r, top-50 DEG and the broadcast baseline are reported from `scores_nosex/giroux2022/`, scored with `score_ft.py --exclude-genes data/sex_linked_genes/sex_linked_genes_exclude.csv` (196 genes: chrY non-PAR + X-inactivation escape genes of Tukiainen 2017; PAR genes kept) and the original frozen labels (`--reuse-labels`). Details: `scores_nosex/README.md`, `data/sex_linked_genes/README.md`.
+## ImMicro fold 0 pilot (22 test donors; real bulk input unless noted)
+| arm | comp L1 ↓ | precision | coverage | pseudobulk r | donor top-1 | monocyte % (obs 18.5) |
+|---|---:|---:|---:|---:|---:|---:|
+| zero-shot raw | 0.484 | 0.910 | 0.423 | 0.892 | 0.05 | 37.0 |
+| zero-shot paired cal (fold α) | 0.168 | 0.985 | 0.725 | 0.882 | 0.14 | 14.7 |
+| zero-shot pseudobulk | 0.188 | 0.993 | 0.722 | 0.881 | 0.09 | 13.7 |
+| FT condition encoder only | 0.204 | 0.979 | 0.728 | 0.892 | 0.14 | 13.0 |
+| FT LoRA r = 16 | 0.153 | 0.992 | **0.915** | 0.901 | 0.18 | 14.8 |
+| **FT full** | **0.147** | 0.987 | 0.841 | **0.912** | **0.32** | 14.7 |
+| FT on pseudobulk (sc only), raw input | 0.426 | 0.725 | 0.257 | 0.929 | 0.18 | 23.4 |
+| FT on pseudobulk (sc only), paired-cal input | 0.231 | 0.954 | 0.816 | 0.917 | 0.18 | 14.3 |
+Leave-one-out composition null of the observed cells: 0.163. Chance donor top-1: 0.045.
+Selected inner-validation loss (pretrained 0.10985):
+- full 0.10071, still falling at the 20,000-step cap;
+- LoRA 0.10127;
+- condition encoder 0.10429.
+**Reading (one fold, one seed):**
+- Real-bulk fine-tuning beats every zero-shot arm, including the pseudobulk input.
+- It also beats sc-only adaptation. The sc-only model with paired calibration reaches similar coverage (0.82), so coverage comes largely from adapting to the cohort. Composition and donor specificity, however, need the paired bulk: L1 0.147 vs 0.231, top-1 0.32 vs 0.18.
+- LoRA and full are close: LoRA covers more of the observed cells, full is more donor-specific.
+- The condition encoder alone captures about 60 % of full's inner-validation gain but does not beat zero-shot calibration, so it was dropped.
+## ImMicro, all five folds (107 donors, each scored by the fold model that never trained on it)
+Per-fold scores, fold mean ± SD (`scores/immicro/AGGREGATE.md`):
+| arm | comp L1 ↓ | precision | coverage | pseudobulk r |
+|---|---:|---:|---:|---:|
+| zero-shot raw | 0.454 ± 0.032 | 0.877 | 0.424 | 0.892 |
+| zero-shot paired cal | 0.199 ± 0.029 | 0.976 | 0.725 | 0.880 |
+| zero-shot pseudobulk | 0.204 ± 0.016 | 0.986 | 0.721 | 0.881 |
+| FT LoRA r = 16 | 0.162 ± 0.017 | 0.989 | **0.919** | 0.900 |
+| **FT full** | **0.157 ± 0.020** | 0.981 | 0.851 | **0.914** |
+| sc-only FT, raw input | 0.444 ± 0.021 | 0.678 | 0.237 | 0.927 |
+| sc-only FT, paired-cal input | 0.240 ± 0.015 | 0.955 | 0.829 | 0.917 |
+**Donor-level composition L1, paired Wilcoxon over 107 donors** (FT full vs each arm):
+| comparison | Δ L1 | FT full better in | p |
+|---|---:|---:|---:|
+| vs zero-shot paired cal | −0.042 | 68 % | 1e-7 |
+| vs zero-shot pseudobulk | −0.048 | 74 % | 4e-8 |
+| vs sc-only FT + paired cal | −0.083 | 91 % | 2e-16 |
+| vs LoRA | −0.005 | 58 % | 0.14 |
+| vs leave-one-out cohort-mean null (0.161) | −0.007 | 50 % | 0.64 |
+**Across-donor correlation of generated vs observed cell-type fraction (T / B / Mono / NK):**
+- FT full: 0.79 / 0.80 / 0.83 / 0.81
+- zero-shot paired cal: 0.67 / 0.71 / 0.85 / 0.49
+- zero-shot pseudobulk: 0.71 / 0.77 / 0.90 / 0.60
+**Donor top-1:** 0.15 ± 0.10 for FT full vs 0.13 for zero-shot paired cal. The fold-0 value of 0.32 did not replicate.
+**Pooled scoring** (`scores/immicro/pooled/`: one call over all 107 donors, reference = all 107 donors' observed cells, so absolute precision/coverage are lower than the per-fold values):
+| arm | precision / coverage | depth-matched precision / coverage |
+|---|---|---|
+| FT LoRA | 0.924 / 0.784 | 0.961 / 0.962 |
+| FT full | 0.890 / 0.695 | 0.946 / 0.950 |
+| zero-shot paired cal | 0.905 / 0.527 | 0.944 / 0.769 |
+| zero-shot pseudobulk | 0.936 / 0.524 | 0.965 / 0.741 |
+**Reading:**
+- Fine-tuning on real bulk removes the real-bulk bias.
+- It beats the pseudobulk input, which was the zero-shot ceiling.
+- It improves the across-donor ranking of T, B and NK fractions.
+- It does not beat the observed cohort mean on per-donor composition L1. Healthy donors vary little, so that null is strong.
+- Full is better on composition; LoRA gives wider coverage.
+## Sepsis, all five folds (82 identity-passed pairs: 61 sepsis, 21 controls; cross-fitted)
+Pooled scoring over all 82 donors (`scores/ye2026_sepsis/pooled/`). The contrast is computed over all 61 vs 21 donors. The per-fold contrast (4–5 controls per fold) is too noisy to use.
+| arm | comp L1 ↓ | precision | coverage | sepsis-vs-control logFC r (T / B / Mono / NK; mean) | r of bulk broadcast | partial r beyond broadcast |
+|---|---:|---:|---:|---|---:|---:|
+| zero-shot raw | 0.511 | 0.614 | 0.067 | −0.11 / 0.06 / 0.07 / −0.07; **−0.01** | 0.316 | −0.095 |
+| zero-shot paired cal | 0.401 | 0.903 | 0.431 | 0.04 / 0.16 / 0.25 / 0.04; **0.12** | 0.278 | 0.039 |
+| zero-shot pseudobulk (sc-derived input) | **0.233** | 0.915 | 0.480 | 0.55 / 0.50 / 0.40 / 0.43; **0.47** | 0.730 | 0.210 |
+| FT LoRA | 0.362 | **0.926** | **0.829** | 0.63 / 0.57 / 0.58 / 0.34; **0.53** | 0.316 | **0.469** |
+| **FT full** | 0.372 | 0.866 | 0.795 | 0.62 / 0.60 / 0.63 / 0.29; **0.53** | 0.316 | **0.468** |
+| sc-only FT, raw input | 0.472 | 0.606 | 0.134 | 0.01 / 0.22 / 0.34 / 0.03; 0.15 | 0.316 | 0.038 |
+| sc-only FT, paired-cal input | 0.360 | 0.863 | 0.726 | 0.25 / 0.32 / 0.52 / 0.10; 0.30 | 0.278 | 0.190 |
+Leave-one-out composition null: 0.515. Observed monocyte %: 20.8; the FT arms generate 18.0–18.5 %.
+Per-fold means are in `scores/ye2026_sepsis/AGGREGATE.md`. The fold-level ranking is the same: FT full L1 0.372 ± 0.083 vs zero-shot paired cal 0.404 ± 0.071.
+**Donor-level composition L1** (paired Wilcoxon, 82 donors):
+| comparison | Δ L1 | p |
+|---|---:|---:|
+| FT full vs zero-shot paired cal | −0.029 | 0.24 |
+| FT LoRA vs zero-shot paired cal | −0.039 | 0.19 |
+| FT full vs sc-only FT + cal | +0.011 | 0.80 |
+| FT full vs zero-shot pseudobulk | +0.138 | 2e-7 |
+| FT full vs null | −0.144 (better in 72 %) | 7e-6 |
+**Across-donor fraction correlation (T / B / Mono / NK):**
+- FT full: 0.68 / 0.79 / 0.60 / 0.68
+- zero-shot paired cal: 0.64 / 0.71 / 0.60 / 0.59
+- zero-shot pseudobulk: 0.91 / 0.95 / 0.97 / 0.80
+**Reading:**
+- **Disease signal is the sepsis result.**
+  - Fine-tuned models generate cells whose per-cell-type sepsis-vs-control logFC agrees with the observed cells at r 0.53. Zero-shot calibrated bulk gives 0.12.
+  - They beat the real-bulk broadcast baseline in every cell type (T 0.62 vs 0.26, B 0.60 vs 0.28, Mono 0.63 vs 0.55, NK 0.29 vs 0.19). In SLE the zero-shot model lost to broadcast.
+  - The partial r beyond broadcast is 0.47 (zero-shot 0.04).
+  - They also exceed zero-shot generation from the sc-derived pseudobulk (0.47).
+- **The paired bulk matters.** sc-only fine-tuning with paired-cal input reaches 0.30.
+- **Composition does not improve over zero-shot calibration** (n.s.) and stays far behind the pseudobulk input: the sepsis real bulk carries weak composition information, monocytes especially (r 0.60).
+- **Coverage roughly doubles** (0.43 → 0.80–0.83).
+- **Full and LoRA are equivalent here.** LoRA has the higher precision.
+## Sepsis fold 0 pilot (17 test donors: 12 sepsis, 5 controls; real bulk input unless noted)
+Early stopping selected fewer steps than for ImMicro:
+| arm | selected step | inner-val loss (pretrained 0.12500) |
+|---|---:|---:|
+| full | 3,750 | 0.11344 |
+| LoRA | 6,500 | 0.11357 |
+| condition encoder | 5,500 | 0.11606 |
+| sc-only (pseudobulk) | 20,000 | 0.11275 (pretrained 0.11720, pseudobulk conditions) |
+| arm | comp L1 ↓ | precision | coverage | donor top-1 | monocyte % (obs 24.6) | sepsis-vs-control logFC r, mean of T/B/Mono/NK | partial r beyond bulk broadcast |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| zero-shot raw | 0.451 | 0.879 | 0.181 | 0.41 | 37.3 | 0.064 | −0.035 |
+| zero-shot paired cal | 0.388 | 0.984 | 0.626 | 0.41 | 25.7 | 0.255 | 0.161 |
+| zero-shot pseudobulk | **0.236** | 0.990 | 0.700 | **0.65** | 29.5 | 0.328 | 0.187 |
+| FT condition encoder | 0.380 | 0.983 | 0.741 | 0.35 | 17.5 | 0.261 | 0.181 |
+| FT LoRA r = 16 | 0.373 | 0.986 | 0.858 | 0.47 | 15.3 | 0.487 | **0.410** |
+| FT full | 0.388 | 0.978 | **0.871** | 0.35 | 16.2 | **0.489** | 0.393 |
+| FT sc only, raw input | 0.422 | 0.928 | 0.351 | 0.47 | 29.6 | 0.167 | 0.030 |
+| FT sc only, paired-cal input | 0.396 | 0.979 | 0.850 | 0.35 | 21.7 | 0.465 | 0.357 |
+- The leave-one-out composition null is 0.513: donors differ far more than in ImMicro.
+- Contrast = per-cell-type log2 fold change of donor-averaged mean CP10K, over genes detected in ≥ 1 % of observed cells of the type.
+- **Bulk broadcast baseline** (the input real bulk's sepsis-vs-control logFC copied to every cell type):
+  - mean r 0.482 (T 0.393, B 0.470, Mono 0.702, NK 0.361);
+  - with pseudobulk instead of real bulk: 0.668.
+**Reading (one fold, one seed, only 5 controls):**
+- Fine-tuning roughly doubles the per-cell-type disease-signal agreement (0.26 → 0.49).
+- For the first time the generated cells carry a clear part beyond the bulk broadcast (partial r ≈ 0.4). In SLE the zero-shot model gave about 0.2.
+- By cell type, FT beats broadcast for T and B, but not for monocytes (0.57 vs 0.70).
+- Most of the gain comes from adapting to the cohort: sc-only FT with the paired-cal input reaches 0.465 / 0.357.
+- Composition is not improved by any real-bulk arm (0.37–0.39 vs zero-shot pseudobulk 0.236). FT arms under-produce monocytes (15–17 % vs 24.6 %).
+- So in sepsis the real bulk does not carry the donors' composition to the model, unlike ImMicro. One candidate cause not yet checked: granulocyte or low-density-neutrophil signal in sepsis PBMC bulk.
+## Sepsis top-50 DEG (added 2026-09-28; `scores/ye2026_sepsis/pooled_deg/`)
+**Definition:** the NC benchmark's own `bulk2sc-paper/scripts/evaluate_generated_vs_truth.py`, imported, not copied (`welch_bh_deg`, `top_deg_genes`).
+- Per cell type: donor pseudobulk on the 5,975 scoring genes, log1p CPM, two-sided Welch test, BH correction.
+- List: genes with FDR < 0.05, ranked by |logFC|, top 50.
+- Score: the number of genes shared by the observed and generated lists, summed over T/B/Mono/NK/DC (maximum 250). The SLE "81" is the same kind of sum.
+| arm | T | B | Mono | NK | DC | sum / 250 | logFC r |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| zero-shot raw | 3 | 0 | 4 | 1 | 1 | 9 | −0.01 |
+| zero-shot paired cal | 5 | 1 | 4 | 0 | 1 | 11 | 0.12 |
+| zero-shot pseudobulk | 11 | 5 | 1 | 1 | 4 | 22 | 0.47 |
+| sc-only FT + cal | 1 | 1 | 9 | 0 | 4 | 15 | 0.30 |
+| LoRA 5 pairs | 4 | 0 | 1 | 0 | 0 | 5 | 0.29 |
+| LoRA 10 | 3 | 1 | 4 | 0 | 0 | 8 | 0.42 |
+| LoRA 40 | 0 | 3 | 12 | 0 | 7 | 22 | 0.48 |
+| LoRA all (58) | 1 | 1 | 19 | 0 | 2 | 23 | 0.53 |
+| full all | 1 | 2 | 21 | 0 | 0 | 24 | 0.53 |
+Real-bulk broadcast list: 21.
+**Reading:**
+- With enough pairs, top-50 DEG doubles over zero-shot calibration (11 → 23–24).
+- It only reaches the broadcast level, and almost all of it is monocytes.
+- T and NK overlap is near zero because the generated T cells produce almost no significant genes (12 vs 1,578 observed for full). The disease signal is in the right direction (logFC r 0.6) but too weak for FDR-based DEG calls.
+- Paper note: `methods.tex:87` describes CP10K, but the evaluator uses CPM.
+## New datasets and the few-pair scenario (started 2026-09-28)
+**Datasets** (built by agents with the same QC/identity rules; details in each `data/<ds>/QC_REPORT.md`):
+| dataset | bulk | sc | usable pairs (donors) | contrast |
+|---|---|---|---|---|
+| Imperial ESKD COVID (Gisby 2022 + Stephenson 2025) | PBMC, htseq | 10x 5′ CITE atlas | 157 (57 patients; longitudinal) | covid_positive vs covid_negative |
+| Giroux 2022 (Duke outpatient COVID) | PBMC, NuGEN SoLo total RNA (no RPL/RPS/MT genes) | 10x 5′ v1 | 24 (9 subjects) | severe vs mild |
+| Ji 2025 ChILI | **PAXgene whole blood**, QuantSeq 3′ | 10x 5′ | 33 (20 patients) | ChILI (TOL, FU1) vs ICI controls (BC, AC) |
+Dataset notes:
+- **Imperial:** pairs are matched on the metadata swab day. The day in the bulk column name counts from symptom onset.
+- **Giroux:** the author bulk table lacks the exposed and healthy subjects.
+- **Ji:** the bulk is whole blood, so this is a whole-blood → PBMC test.
+**Rules:**
+- Rows with fewer than 200 cells are excluded (strict-QC protocol minimum). Only Imperial is affected.
+- The scorer now accepts several samples per donor: the scored unit is the sample, and the composition null leaves out every sample of that donor.
+**Few-pair scenario** (`tools/prepare_fewshot.py`, `make_fewshot_queue.sh`, `score_fewshot.sh`):
+- **Training donors:** 5, stratified by case/control where the dataset has one. All their pairs are used.
+- **Training:** LoRA for a fixed 3,000 steps with no inner validation (`ft_sdd.py --train-donors-file --fixed-steps`).
+- **Test:** every other usable donor.
+- **Comparators:**
+  - zero-shot raw;
+  - zero-shot paired cal, with alpha from the **same 5 pairs**;
+  - zero-shot pseudobulk (reference).
+- **Repeats:** 5 draws.
+- **Datasets:** also run on ImMicro and sepsis so all five datasets share the protocol.
+**Also run:** the 5-fold CV of `make_cv_queue.sh` on each new dataset (zero-shot ×3, LoRA on all training donors, sc-only control).
+**Hosts:**
+- Imperial runs entirely on server_2 (GPU and scoring; outputs about 110 GB). `tools/run_queue_s2.sh` is the server_2 env copy of `run_queue.sh`.
+- The rest runs on server_1.
+- Scored ImMicro and sepsis generations were moved to server_2, sha-checked, with `.moved` markers (`tools/move_generated.sh`).
+## Results: few-pair scenario (LoRA on 5 donors, fixed 3,000 steps; test = every other donor; mean ± SD over 5 draws)
+| dataset | arm | comp L1 | precision | coverage | case-control logFC r | partial r beyond broadcast | top-50 DEG (sum) |
+|---|---|---:|---:|---:|---:|---:|---:|
+| ImMicro | zero-shot cal (same 5 pairs) | 0.203 ± 0.011 | 0.904 | 0.535 | — | — | — |
+| ImMicro | **LoRA 5** | **0.176 ± 0.012** | **0.952** | **0.792** | — | — | — |
+| sepsis | zero-shot cal | 0.446 ± 0.046 | 0.899 | 0.428 | 0.10 ± 0.04 | 0.00 | 9.4 ± 2.6 |
+| sepsis | **LoRA 5** | 0.466 ± 0.044 | 0.906 | **0.694** | **0.35 ± 0.16** | **0.31** | 16.4 ± 6.7 |
+| Ji (whole blood) | zero-shot cal | 0.514 ± 0.016 | 0.782 | 0.114 | 0.14 ± 0.09 | 0.12 | 0.0 |
+| Ji (whole blood) | **LoRA 5** | 0.513 ± 0.115 | **0.978** | **0.707** | **0.33 ± 0.08** | **0.30** | 0.2 |
+Composition, LoRA 5 vs zero-shot cal (paired over donor-draws):
+- ImMicro: better in 69 %, p 3e-24.
+- Sepsis: p 0.96.
+- Ji: p 0.60.
+Real-bulk broadcast r:
+- sepsis 0.32, top-50 DEG 20;
+- Ji 0.23.
+Zero-shot from the sc-derived pseudobulk (reference):
+- ImMicro L1 0.211;
+- sepsis L1 0.237, r 0.48, DEG 20.8;
+- Ji L1 0.171, r 0.18.
+## Results: Ji 2025, 5-fold (33 pairs pooled; 18 ChILI vs 15 controls; whole-blood bulk → PBMC cells)
+| arm | comp L1 | precision | coverage | ChILI-vs-control logFC r (T/B/Mono/NK; mean) | partial r | top-50 DEG |
+|---|---:|---:|---:|---|---:|---:|
+| zero-shot raw | 0.738 | 0.717 | 0.055 | 0.18/−0.08/0.22/0.02; 0.08 | 0.03 | 0 |
+| zero-shot cal | 0.501 | 0.740 | 0.106 | 0.23/0.00/0.21/0.13; 0.14 | 0.11 | 0 |
+| zero-shot pseudobulk (reference) | 0.173 | 0.985 | 0.685 | 0.28/−0.06/0.32/0.03; 0.14 | 0.03 | 5 |
+| **LoRA (all training donors)** | 0.504 | **0.971** | **0.857** | **0.48/0.24/0.79/0.41; 0.48** | **0.45** | **15** |
+| sc-only FT, raw input | 0.596 | 0.718 | 0.094 | 0.15 | 0.11 | 0 |
+| sc-only FT, cal input | 0.609 | 0.694 | 0.113 | 0.25 | 0.22 | 0 |
+Baselines:
+- Real-bulk broadcast: r 0.24, DEG 4.
+- Leave-donor-out composition null: 0.452.
+**Reading:**
+- Whole-blood bulk is far from PBMC; the zero-shot arms fall outside the observed cell space.
+- LoRA on the paired whole-blood bulk brings the cells back (coverage 0.86), and the ChILI signal agrees with the observed cells at r 0.48 (monocytes 0.79).
+- The sc-only control does not work here (coverage 0.11), so the paired bulk is what teaches the whole-blood → PBMC map.
+- Composition stays at zero-shot-cal level and worse than the cohort mean: whole-blood bulk does not carry PBMC composition.
+## Results: Imperial and Giroux (2026-09-28)
+**Imperial ESKD COVID** (157 samples / 57 patients; COVID+ 113 vs COVID− 34 samples; runs and scores on server_2, copied to `scores/imperial_eskd/`)
+| setting | arm | comp L1 | precision | coverage | COVID logFC r (T/B/Mono/NK; mean) | partial r | top-50 DEG |
+|---|---|---:|---:|---:|---|---:|---:|
+| 5 pairs | zero-shot cal | 0.506 | 0.845 | 0.634 | 0.68 | 0.41 | 104 |
+| 5 pairs | **LoRA 5** | 0.503 | 0.853 | **0.737** | **0.75** | **0.60** | **114** |
+| 5-fold | zero-shot cal | 0.494 | 0.842 | 0.639 | 0.68/0.62/0.71/0.70 | 0.41 | 111 |
+| 5-fold | **LoRA** | **0.376** (p 7e-11) | 0.847 | **0.850** | **0.90/0.75/0.79/0.83; 0.82** | **0.69** | **143** |
+| 5-fold | sc-only FT + cal | 0.461 | 0.691 | 0.555 | 0.81 | 0.63 | 129 |
+Baselines:
+- Real-bulk broadcast: r 0.66, DEG 84 (75 in the 5-pair setting).
+- Zero-shot from the sc pseudobulk: L1 0.300, r 0.68.
+- Composition null: 0.505.
+**Giroux 2022** (24 pairs / 9 subjects; severe 11 vs mild 13)
+| setting | arm | comp L1 | coverage | severe-vs-mild r | partial r | top-50 DEG |
+|---|---|---:|---:|---:|---:|---:|
+| 5 pairs | zero-shot cal | 0.288 | 0.944 | 0.24 | 0.11 | 1.8 |
+| 5 pairs | **LoRA 5** | 0.256 | 0.961 | 0.33 | 0.19 | 11.6 |
+| 5-fold | zero-shot cal | 0.266 | 0.906 | 0.31 | 0.14 | 12 |
+| 5-fold | **LoRA** | 0.258 | 0.928 | **0.50** | **0.33** | **25** |
+- Here the real-bulk broadcast beats every generated arm: r 0.64, DEG 39 (5-pair: r 0.54, DEG 25).
+- The severe-vs-mild difference is shared across cell types.
+- **Superseded for the disease metrics (2026-10-01):** severity is confounded with sex (scored 4 F / 0 M vs 0 F / 5 M) and the
+  observed and broadcast DEG lists were led by chrY genes. Without the 196 sex-linked genes (`scores_nosex/giroux2022/`):
+| setting | arm | severe-vs-mild r | partial r | top-50 DEG |
+|---|---|---:|---:|---:|
+| 5 pairs | zero-shot cal | 0.23 | 0.11 | 0.0 |
+| 5 pairs | **LoRA 5** | 0.30 | 0.19 | 0.2 |
+| 5-fold | zero-shot cal | 0.27 | 0.13 | 0 |
+| 5-fold | **LoRA** | **0.46** | **0.32** | **5** |
+  - The real-bulk broadcast still beats every arm on r (5-fold 0.58, 5-pair 0.49) but not on DEG: the observed lists
+    shrink (5-fold max 123 → 101) and every overlap, broadcast included, falls to 0–5 (5-fold broadcast 5 = LoRA 5).
+  - chrY-only exclusion (`scores_nochrY/`) gives nearly the same numbers; the same exclusion moves the sex-balanced
+    cohorts' pooled r by ≤ 0.007 and DEG by ≤ 1 (`scores_nosex/README.md`).
+## Five-dataset summary (LoRA vs zero-shot paired calibration)
+| dataset (bulk) | cell realism (coverage) | composition | disease signal vs zero-shot | vs real-bulk broadcast |
+|---|---|---|---|---|
+| ImMicro (PBMC, healthy) | ↑ both settings | ↑ both settings (≈ cohort-mean null) | n/a | n/a |
+| sepsis (PBMC) | ↑ | = | ↑ (0.12 → 0.35 with 5 pairs, 0.53 with all) | above (r); ≈ (DEG) |
+| Ji (whole blood) | ↑↑ (0.11 → 0.71–0.86) | = | ↑ (0.14 → 0.33 / 0.48) | above (r and DEG) |
+| Imperial (PBMC) | ↑ | = with 5 pairs, ↑ with all | ↑ (0.68 → 0.75 / 0.82; DEG 104 → 114 / 143) | above (r and DEG) |
+| Giroux (PBMC, 9 subjects; sex-linked genes excluded) | ≈ (already high) | = | ↑ (0.23 → 0.30 / 0.27 → 0.46) | below (r); DEG ≈ 0 for all |
+## Recommended protocol (user decision 2026-09-28: recommend LoRA)
+1. **Backbone:** the public pseudobulk-pretrained final model (6k, 1×, E16), frozen.
+2. **Paired data:** the user's paired bulk + scRNA from the same sample (raw counts, canonical 20,097 axis, measured masks); check identity (sex genes, expression match).
+   - Hold out about 10 % of the pairs, and at least 5–7 donors, for early stopping.
+3. **Condition:** real bulk at the fixed-depth policy (K = 1000, one depth anchor). No single-cell depth or cell count is needed at inference.
+4. **LoRA training** (`tools/ft_sdd.py --mode lora`):
+   - Rank 16, alpha 16, on all 22 denoiser Linear layers (1.30 M trainable parameters).
+   - AdamW with lr 5e-4, warmup 200 steps, weight decay 0. Grad clip 5, bf16, EMA 0.999.
+   - Donor-balanced batches of 256 cells: draw a donor uniformly, then one of its cells; the condition is that donor's real bulk.
+   - Inner-validation loss every 250 steps; stop after 2,000 steps without improvement; cap at 20,000 steps; keep the best EMA state.
+   - Merge the LoRA into the weights; the standard sampler then works unchanged.
+5. **Apply** only to bulk made with the same bulk protocol as the training pairs.
+**What was run** (LoRA, per fold):
+| dataset | train donors | inner-val donors | train cells | selected step | cell-epochs at selection | wall time |
+|---|---:|---:|---:|---|---|---:|
+| ImMicro | 78–79 | 7 | ~290 k | 19,500–20,000 (cap reached, loss still falling) | ~17–18 | ~13 min |
+| sepsis | 58–59 | 7–8 | ~410–426 k | 3,750–13,000 (early stop) | 2.4–8.0, mostly ~4 | 4–11 min |
+**Open questions being tested:**
+- The minimum number of pairs is not known.
+- Whether varied K helps is not known.
+## LoRA learning curve (2026-09-28)
+One nested subset per fold and n; the inner-validation donors (7–8) sit outside the training subset.
+**Sepsis** (pooled over all 82 donors; contrast 61 vs 21):
+| training pairs | comp L1 | precision | coverage | sepsis-vs-control logFC r (T/B/Mono/NK; mean) | partial r beyond broadcast | selected steps |
+|---:|---:|---:|---:|---|---:|---|
+| 0 (zero-shot paired cal) | 0.401 | 0.903 | 0.431 | 0.04/0.16/0.25/0.04; 0.12 | 0.04 | — |
+| 5 | 0.428 | 0.911 | 0.767 | 0.36/0.37/0.25/0.19; 0.29 | 0.25 | 2,000–3,000 |
+| 10 | 0.390 | 0.916 | 0.786 | 0.54/0.49/0.33/0.32; 0.42 | 0.37 | 2,000–3,250 |
+| 20 | 0.380 | 0.927 | 0.807 | 0.43/0.33/0.47/0.24; 0.37 | 0.30 | 2,500–4,000 |
+| 40 | 0.365 | 0.922 | 0.818 | 0.57/0.52/0.52/0.29; 0.48 | 0.40 | 3,000–7,000 |
+| 58 (all) | 0.362 | 0.926 | 0.829 | 0.63/0.57/0.58/0.34; 0.53 | 0.47 | 3,750–13,000 |
+Reference rows:
+- Real-bulk broadcast: mean r 0.32.
+- sc-only FT with all 58 donors + paired cal: 0.30.
+- Zero-shot from the sc-derived pseudobulk: 0.47, with composition 0.233.
+**ImMicro** (folds 0–3, 86 donors, per-fold scoring; the selected-step column includes fold 4):
+| training pairs | comp L1 (better than zero-shot cal in, p) | coverage | across-donor fraction r (T/B/Mono/NK) | selected steps |
+|---:|---|---:|---|---|
+| 0 (zero-shot paired cal) | 0.206 | 0.724 | 0.65/0.68/0.85/0.47 | — |
+| 5 | 0.176 (70 %, 4e-5) | 0.920 | 0.62/0.71/0.82/0.19 | 2,750–5,000 |
+| 10 | 0.193 (60 %, 0.05) | 0.890 | 0.56/0.72/0.75/0.45 | 3,250–19,500 |
+| 20 | 0.178 (70 %, 1e-4) | 0.898 | 0.63/0.73/0.78/0.61 | 5,250–20,000 (4 of 5 folds at cap) |
+| 40 | 0.171 (73 %, 3e-5) | 0.910 | 0.69/0.75/0.82/0.70 | cap |
+| 78–79 (all) | 0.168 (65 %, 6e-6) | 0.917 | 0.74/0.75/0.88/0.72 | cap |
+**Reading:**
+- **Five pairs already fix most of the systematic real-bulk shift.**
+  - Coverage jumps from 0.43/0.72 to 0.77/0.92.
+  - ImMicro composition drops from 0.206 to 0.176.
+- **Donor-level ranking and the disease signal keep improving with more pairs.**
+  - ImMicro NK r goes 0.19 → 0.45 → 0.61 → 0.70 → 0.72.
+  - Sepsis contrast goes 0.29 → 0.42 → 0.37 → 0.48 → 0.53. The dip at 20 is one subset per fold, so it is noise-level.
+- About 10 pairs already beat the real-bulk broadcast in sepsis.
+- With few pairs, early stopping lands at about 2,000–3,000 steps. That is a usable fixed schedule when no pairs can be spared for inner validation.
+**ImMicro, all five folds pooled (107 donors; reference = all 107 donors):**
+| training pairs | comp L1 (vs zero-shot cal, p) | across-donor NK r |
+|---:|---|---:|
+| 5 | 0.171 (2e-5) | 0.20 |
+| 10 | 0.188 (0.06) | 0.40 |
+| 20 | 0.172 (4e-5) | 0.56 |
+| 40 | 0.171 (1e-4) | 0.70 |
+| all | 0.162 (2e-7) | 0.70 |
+Zero-shot paired cal gives L1 0.199.
+## K augmentation (2026-09-28)
+**Sepsis, pooled over 82 donors; each arm vs plain LoRA:**
+| arm | comp L1 | coverage | sepsis-vs-control r (mean) | partial r beyond broadcast |
+|---|---:|---:|---:|---:|
+| plain LoRA (K = 1000) | 0.362 | 0.829 | 0.527 | 0.469 |
+| multi-K real bulk (K 250–4000) | 0.366 (p 0.70) | 0.819 | 0.509 | 0.447 |
+| + half-batch pretraining recipes | 0.365 (p 0.73) | 0.817 | 0.496 | 0.431 |
+- Inner-validation losses were within 0.0001 of plain LoRA in every fold.
+- Neither augmentation helps: every metric is equal or slightly worse.
+**ImMicro, pooled over 107 donors:**
+| arm | comp L1 (vs plain LoRA, p) | precision | coverage | across-donor r (T/B/Mono/NK) |
+|---|---|---:|---:|---|
+| plain LoRA | 0.162 | 0.924 | 0.784 | 0.74/0.75/0.86/0.70 |
+| multi-K | 0.168 (p 0.04, worse) | 0.928 | 0.775 | 0.74/0.76/0.85/0.66 |
+| recipes | 0.161 (p 0.75) | 0.933 | 0.766 | 0.78/0.78/0.86/0.65 |
+Inner-validation losses were within 0.0001 of plain LoRA in every fold.
+**Conclusion:**
+- Varying K brings no gain in either dataset. On the real-bulk side K is only a scale label.
+- Recipes repeat the pretraining task, which does not teach the real-bulk mapping.
+- The protocol keeps a single K = 1000 for the real-bulk condition.
+## Running (2026-09-28)
+- **LoRA learning curve:**
+  - n training donors ∈ {5, 10, 20, 40}, nested and stratified (sepsis keeps its case:control ratio), subset seed 1000 + fold.
+  - Inner-validation and test rows are unchanged. `--train-n` in `ft_sdd.py`.
+  - Queues `*_lora_learning_curve.txt`: sepsis first, then ImMicro.
+- **K augmentation** (`tools/ft_sdd_aug.py`, queues `*_lora_aug.txt`, after the learning curve):
+  - `lora_multik`: each training cell sees its donor's real bulk at K ∈ {250, 500, 1000, 2000, 4000}, with the per-cell depth kept.
+  - `lora_recipe`: half of each batch comes from one pretraining-style recipe. A recipe is K cells of one training donor (K from the pretraining grid, capped at that donor's cell count); its condition is their full-axis pseudobulk at recipe size K.
+  - Recipes need the full-axis cells in `data/<dataset>/cells_full.npz`, built by `tools/build_full_cells.py`. Their panel columns equal `cells_panel6k.npz` and their per-row sums equal `pseudo_raw_sum`.
+- **Automation:** fold scoring, aggregation and pooled scoring follow automatically (`ft-lc-finalize-*`, `ft-aug-finalize-*` units).
+## Queue
+- Sepsis fold 0 pilot (same eight arms) → ImMicro folds 1–4 → sepsis folds 1–4.
+- Arms for folds 1–4: zero-shot raw/cal/pseudo, FT full, FT LoRA, sc-only FT control.
+- Chained as systemd user units (`ft-*`), logs in `logs/`.
+## Additions of 2026-10-02 (CPU analyses + one GPU sampling step)
+**No-pair x = 0 of the learning curve: exon-length-corrected zero-shot.** The learning curve's x = 0 used zero-shot +
+calibration with alpha from every training pair of the fold, which is not "0 pairs" (user, 2026-10-02). It is replaced
+by two arms that use no paired data:
+- the real bulk as measured (pooled `zs__raw`);
+- the same bulk after exon-length correction (`zs__len`). Counts are divided by the Ensembl union-exon kb, using the
+  `conditions_more` rule imported unchanged. The rest is the same fixed-depth arithmetic.
+How the `zs__len` arm was built and scored:
+- **Conditions:** `tools/build_tpm_zeroshot.py` writes `data/<ds>/zeroshot_tpm_20261002/all_usable_tpm.npz`. The raw arm
+  rebuilt through the same path equals `fewshot_n5/all_usable_raw.npz` bit for bit. Sepsis loses 112 measured genes
+  that have no valid length.
+- **Sampling:** `tools/sample_lockwait.sh` runs the same sampler flags as `sample_queue.sh` and queues on the GPU lock
+  through `ml-run`. Seed 20260927 gives the same per-cell noise as the raw all-usable arm. Outputs:
+  `generated/<ds>/zeroshot_tpm_20261002/zs__tpm_all.npz`, server_1, with receipts.
+- **Scoring:** `tools/score_tpm_zeroshot.sh` writes `scores/<ds>/zeroshot_tpm_20261002/pooled/`. It uses the same
+  donors and reference cells as `scores/<ds>/pooled`, and its `zs__raw` arm reproduces the pooled call. The pooled raw
+  NPZ and conditions were read from sha-checked copies of the server_2 originals; the copies were deleted after
+  scoring.
+| pooled, 5-fold donors | composition L1 | precision | coverage | sepsis logFC r | sepsis top-50 DEG |
+|---|---|---|---|---|---|
+| ImMicro, as measured / length-corrected / calibration (all pairs) | 0.455 / 0.420 / 0.199 | 0.680 / 0.889 / 0.905 | 0.192 / 0.505 / 0.527 | | |
+| Sepsis, as measured / length-corrected / calibration (all pairs) | 0.511 / 0.557 / 0.401 | 0.614 / 0.816 / 0.903 | 0.067 / 0.399 / 0.431 | −0.01 / 0.11 / 0.12 | 9 / 11 / 11 |
+Length correction alone recovers most of the coverage that calibration gives, without any pair. It does not fix
+composition (ImMicro L1 0.42; sepsis gets worse) or the disease signal (r 0.11). Deck section 8 and paper Fig. 3 now
+show both no-pair points at x = 0. The rightmost point is labelled with its training-pair count (78–79 / 58–59), not
+"all".
+**Rank and threshold-free disease metrics: `scores_contrast/`** (tools/contrast_extra.py, `contrast_extra_limma.R`,
+`contrast_extra_tables.py`; README and TABLES.md there).
+- **Metrics:** for every few-pair draw and every pooled call, it adds Spearman logFC r, the split-half ceiling
+  (observed vs observed, Spearman–Brown), limma-voom top-50 DEG, the chance overlap of both DE tests, CAT curves
+  (same sign, chance k / 2G) and RBO.
+- **Checks:** 135 / 135 reproduction checks pass against the scorer's Pearson r, partial r and Welch top-50.
+- **Findings:**
+  - The ceiling (0.88–0.96 split-half, 0.96–0.99 noise-free) is not limiting.
+  - Spearman is lower than Pearson for generated arms.
+  - In sepsis the broadcast is ahead of LoRA on Spearman and on limma top-50 (pooled 36 vs 18), while CAT and RBO
+    favour LoRA.
+  - Imperial and Ji favour LoRA on every metric; Giroux favours the broadcast on every metric.
+  - Repeated samples of one patient (Imperial, Giroux, Ji) count as separate samples in both DE tests, as in the scorer.

	@@ -0,0 +1,56 @@

+# Figures: paired bulk → single-cell fine-tuning benchmark
+Generated by `figures/make_figures.py` on 2026-10-02 15:41. Do not edit by hand: the numbers below are filled in from the score files. Re-run the script after new scores arrive.
+Every figure is PNG (300 dpi) + PDF, sized for one column (89 mm wide). Each panel's plotted numbers, with the score file they came from, are in `source_data/<figure>.csv` (column `panel`) and `source_data/<figure>/panel_<id>.csv`. Colours are the same in every figure (Okabe–Ito palette): zero-shot raw light blue, zero-shot paired calibration blue, scSemiProfiler orange, LoRA vermillion, sc-only fine-tune purple. Reference lines: dashed = zero-shot generation from the sc-derived pseudobulk; dotted = real-bulk broadcast; dash-dot = cohort-mean null.
+**Metric definitions (all from `tools/score_ft.py`).**
+- **Composition L1**: sum over six scANVI labels of |generated − observed| cell-type fraction, averaged over scored samples. Lower is better. The null is each sample's observed composition vs the mean of the other donors' samples (`comp_l1_obs_loo_null`).
+- **Precision / coverage**: PCA-50, k = 10 nearest-neighbour balls on up to 150 observed reference cells per scored sample. They depend on which samples are scored, so arms are compared only inside one scoring call (one draw, or one pooled call).
+- **Disease logFC r**: per cell type (T, B, monocyte, NK), Pearson r between the generated and observed case-vs-control log2 fold change of donor-averaged mean CP10K; the plotted value is the mean of the four r. **Real-bulk broadcast** = the same r for the input real bulk's own logFC, copied to every cell type.
+- **Top-50 DEG**: per cell type, Welch test + BH on donor pseudobulk (log1p CPM); overlap of the observed and generated top-50 lists (FDR < 0.05, ranked by |logFC|), summed over T/B/Mono/NK/DC (maximum 250; Giroux 123, because some observed top lists hold fewer than 50 significant genes).
+- **Across-donor fraction r**: Pearson r across scored samples of generated vs observed fraction of one cell type (from `per_donor.csv`).
+## Fig. 1 — Few-pair scenario (`fig1_fewpair`)
+**Plotted.** Rows a–e: composition L1, coverage, precision, disease logFC r, top-50 DEG. Columns: the five datasets. Each draw fine-tunes on 5 training donors (all their pairs) and scores every other usable sample (n under each dataset name = scored samples per draw). Arms: zero-shot on raw real bulk; zero-shot with paired calibration fitted on the same 5 pairs; scSemiProfiler with the same 5 pairs as representatives; LoRA on the same 5 pairs (fixed 3,000 steps, no inner validation; training rows per draw: ImMicro 5; Sepsis 5; Ji 7–9; Giroux 12–14; Imperial runs are on server_2 and not checked here). Bars = mean over draws; whiskers = SD; dots = single draws (5 draws). Reference segments are means over draws. In row e, Imperial has its own y-axis on the right because its DEG counts are about 5× larger. ImMicro has no case–control groups, so rows d–e are blank there. scSemiProfiler draws available: ImMicro 5/5, Sepsis 5/5, Ji 5/5, Giroux 5/5, Imperial 5/5. When fewer than 5 draws exist, the count is printed above its bar ("n=0" = none yet).
+**Takeaway.** Five pairs are enough to fix most of the real-bulk shift in cell realism: LoRA-5 raises coverage over zero-shot calibration in 5/5 datasets. It raises the disease signal in every disease dataset (Sepsis 0.10 → 0.35, Ji 0.14 → 0.33, Giroux 0.24 → 0.33, Imperial 0.68 → 0.75). That is above the real-bulk broadcast (Sepsis 0.32, Ji 0.23, Giroux 0.54, Imperial 0.66) in Sepsis, Ji and Imperial, but not in Giroux. Composition improves significantly only in ImMicro (paired Wilcoxon over donor-draws, p 2.9e-24; Sepsis, Ji, Giroux and Imperial: p ≥ 0.11). In ImMicro, L1 goes 0.203 → 0.176. On the draws where both exist, LoRA-5 is better than scSemiProfiler-5 in this many datasets (same draws only): Composition 5/5; Coverage 5/5; Precision 5/5; disease r 2/4; Top-50 4/4. This sentence is computed by the script; rewrite it by hand once all 25 draws are in.
+## Fig. 2 — Five-fold CV, pooled (`fig2_fivefold`)
+**Plotted.** Same rows as Fig. 1. Each sample is scored once, by the fold model that never trained on it, in one pooled scoring call per dataset (n under each name = scored samples). Arms: zero-shot with paired calibration (α fitted on the fold's training donors); sc-only fine-tune (full model on the training donors' sc pseudobulk, no real bulk) with paired-calibrated input; LoRA fine-tuned on all training donors' real bulk → cells (early stopping on inner-validation donors). One value per arm, so no error bars. Sepsis DEG comes from `scores/ye2026_sepsis/pooled_deg/`. The Ji column is shaded: its bulk is whole blood, not PBMC.
+**Takeaway.** Trained on every training donor of the other four folds (never the test fold), LoRA has the highest coverage of the three arms in 5/5 datasets and the highest disease signal in 4/4 disease datasets. On Ji whole-blood bulk the sc-only model fails (coverage 0.11, disease r 0.25) while LoRA reaches coverage 0.86 and r 0.48: the paired bulk is what teaches the whole-blood → PBMC map. Composition improves significantly over zero-shot calibration only in ImMicro and Imperial (ImMicro L1 0.199 → 0.162, p 1.9e-07; Imperial L1 0.494 → 0.376, p 7.0e-11; paired Wilcoxon over scored samples). Zero-shot generation from the sc pseudobulk, which real-bulk users do not have, is still better on composition in Sepsis, Ji and Imperial; LoRA matches or beats it in ImMicro and Giroux.
+## Fig. 3 — LoRA learning curve (`fig3_learning_curve`)
+**Plotted.** ImMicro (left) and sepsis (right), pooled 5-fold scoring (n = 107 and 82 samples). x = training pairs per fold: 0 = no pairs, zero-shot on the real bulk as measured (pale) and after exon-length correction (sand); 5, 10, 20, 40 = nested stratified subsets (one subset per fold); the last point = every training donor of the fold (78–79 ImMicro, 58–59 sepsis; the other four folds minus inner-val, never the test fold). Inner-validation donors (7–8 per fold, from RUN.json) are outside every subset. Rows: composition L1; coverage; ImMicro across-donor NK fraction r / sepsis disease logFC r; sepsis top-50 DEG (from `pooled_deg`); selected early-stopping step per fold (dots; bar = median; dashed line = 20,000-step cap), from `runs/<ds>/fold<k>/<arm>/RUN.json`. One pooled value per point, so no error bars.
+**Takeaway.** Five pairs already remove most of the systematic shift: coverage goes 0.19 (zero-shot; 0.50 after length correction) → 0.84 (ImMicro) and 0.07 (0.40) → 0.77 (sepsis). Donor-level and disease signals keep improving with more pairs: ImMicro NK r 0.20 → 0.40 → 0.56 → 0.70 → 0.70; sepsis logFC r 0.29 → 0.42 → 0.37 → 0.48 → 0.53, above the broadcast (0.32) from 10 pairs on. Sepsis DEG reaches the broadcast level (21) only from 40 pairs on. With 5 pairs early stopping lands at 2,000–3,000 steps (sepsis) and 2,750–5,000 (ImMicro); ImMicro runs with 20+ pairs mostly hit the cap (4/5 folds at 20 pairs, 5/5 at 40).
+## Supp. Fig. S1 — Per-cell-type disease signal (`figS1_celltype_contrast`)
+**Plotted.** For each disease dataset (rows) and setting (left: 5 pairs, mean ± SD over 5 draws; right: 5-fold CV, each fold model trained on the other four folds, pooled): the per-cell-type logFC r (T, B, Mono, NK) of zero-shot with paired calibration, LoRA, and the real-bulk broadcast. Statistic `mean_cp10k` from `contrast.csv`. Group sizes (pooled, case vs control): sepsis 61 vs 21, Ji 18 vs 15, Giroux 11 vs 13, Imperial 113 vs 34 samples.
+**Takeaway.** With all pairs, LoRA beats the broadcast in every cell type in Sepsis, Ji and Imperial. The exception: in Giroux LoRA wins only T. There the case-control difference is shared across cell types, so the bulk logFC alone already fits each type well. The largest gain over the broadcast is in Sepsis T (+0.37), Ji Mono (+0.39), Giroux T (+0.08), Imperial T (+0.27). With 5 pairs the per-type r varies between draws; the mean SD over cell types is largest in Sepsis (0.17; Ji 0.11, Giroux 0.08, Imperial 0.03).
+## Supp. Fig. S2 — K augmentation gives no gain (`figS2_k_augmentation`)
+**Plotted.** ImMicro and sepsis, pooled 5-fold scoring. Arms: plain LoRA (real bulk at K = 1000); LoRA where each training cell also sees its donor's real bulk at K ∈ {250, 500, 1000, 2000, 4000}; LoRA where half of each batch is a pretraining-style recipe. Panels a–b: composition L1, precision, coverage (p = paired Wilcoxon of per-sample L1 vs plain LoRA). Panels c–d: per-type across-donor fraction r (ImMicro) or disease logFC r (sepsis). Panel f: sepsis top-50 DEG with the real-bulk broadcast (dotted); panel e is blank (ImMicro has no contrast). One pooled value per bar.
+**Takeaway.** Neither augmentation helps. Multi-K is slightly worse on ImMicro composition (ΔL1 +0.006, p 0.04); every other difference is within noise (p ≥ 0.70). The protocol keeps a single K = 1000.
+## Status
+- scSemiProfiler-5 draws used: ImMicro 5/5, Sepsis 5/5, Ji 5/5, Giroux 5/5, Imperial 5/5.
+- Rejected scSemiProfiler outputs (not plotted): none.
+- Consistency checks vs `scores/<ds>/fewshot_n5/aggregate.json`: all match.
+## Re-run
+```bash
+cd /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927
+python3 figures/make_figures.py
+```
+CPU only; about a minute. It re-reads every score file, so new `scores/<ds>/fewshot_n5/draw<j>_scsemi5/` directories are picked up automatically. A scSemiProfiler output is used only if its donor set and reference equal those of the same draw's other arms and its receipt is verified; otherwise it is listed as rejected above.

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	@@ -0,0 +1,26 @@

+#!/usr/bin/env bash
+# Score the zero-pair, exon-length-corrected arm (zs__len) of the learning curve (2026-10-02; CPU).
+# One score_ft.py call over the same donors as scores/<ds>/pooled (default donors, same reference cells), with the
+# pooled raw zero-shot arm (zs__raw, a sha-checked copy of the NPZ moved to server_2) so the call can be checked
+# against the pooled call. usage: score_tpm_zeroshot.sh DATASET   -> scores/<ds>/zeroshot_tpm_20261002/pooled/
+set -uo pipefail
+T=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927
+PY=/home/sj_server_1/miniforge3/envs/cxg_corpus/bin/python
+ds=$1
+G=$T/generated/$ds/zeroshot_tpm_20261002
+[[ -s $G/zs__tpm_all.npz && -s $G/zs__tpm_all.log ]] || { echo "missing $G/zs__tpm_all.npz"; exit 1; }
+[[ -s $G/_fetch_pooled_raw/zs__raw.npz ]] || { echo "missing pooled raw copy"; exit 1; }
+read -r -a cargs <<< "$(bash "$T/tools/contrast_args.sh" "$ds")"
+# condition NPZs for the broadcast baseline (contrast cohorts only): the pooled raw conditions were moved to server_2,
+# so a sha-checked copy (= its .moved marker) sits next to the raw NPZ copy
+cond=()
+if [[ -n "${cargs[*]}" ]]; then
+  cr=$G/_fetch_pooled_raw/conditions_pooled_raw.npz
+  want=$(grep -o 'sha [0-9a-f]*' "$T/generated/$ds/pooled/conditions_pooled_raw.moved" | cut -d' ' -f2)
+  [[ -s $cr && "$(sha256sum "$cr" | cut -c1-${#want})" == "$want" ]] || { echo "pooled raw conditions copy missing or sha differs"; exit 1; }
+  cond=(--condition "zs__raw=$cr" --condition "zs__len=$T/data/$ds/zeroshot_tpm_20261002/all_usable_tpm.npz")
+fi
+out=$T/scores/$ds/zeroshot_tpm_20261002/pooled
+mkdir -p "$out"
+bash "$T/tools/run_unit.sh" "score-$ds-zstpm" 8 "$PY" "$T/tools/score_ft.py" --dataset "$ds" --out "$out" --threads 8 \
+  --arm "zs__raw=$G/_fetch_pooled_raw/zs__raw.npz" --arm "zs__len=$G/zs__tpm_all.npz" "${cond[@]}" "${cargs[@]}"