| --- |
| tags: |
| - pytorch |
| - diffusion |
| - bulk-rna-seq |
| - single-cell-rna-seq |
| datasets: |
| - junidude14/PBISC-data |
| --- |
| # PBISC-Diffusion |
|
|
| The first diffusion reference is **paper-2k-e3-ema**, the manuscript's fixed |
| 2,000-output-gene epoch-3 EMA checkpoint (143,293 optimizer steps). It consumes |
| the ordered 20,097-gene input axis. This is a manuscript reference, not a claim |
| of superiority over later checkpoints or larger gene panels. |
|
|
| Code and installation: https://github.com/junidude/PBISC |
| Training data: https://huggingface.co/datasets/junidude14/PBISC-data |
|
|
| `variants/paper-2k-e3-ema/` contains safe inference weights, full input/output |
| gene axes, train-fitted conditioner/scaler, masks, sampling configuration and |
| provenance. The complete bound dataset manifest is included for identity checks; |
| the corpus itself is unnecessary for inference. Generated count-scale values |
| are continuous predictions and must not be presented as measured raw counts. |
|
|
| Install the PBISC package and load this repository with an immutable revision: |
|
|
| ```python |
| from pbisc import PBISCPipeline |
| pipe = PBISCPipeline.from_pretrained("junidude14/PBISC", revision="MODEL_COMMIT_SHA") |
| result = pipe.sample("conditions.npz", steps=50, seed=0) |
| pipe.save_sample("generated.npz", result) |
| ``` |
|
|
| Use the model SHA from the GitHub release lock. Inputs must supply all canonical |
| gene IDs in exact order, bulk values, bulk measurement fractions, output assay |
| availability and recipe K. Sampling uses CPU-drawn initial noise and DDIM eta=0. |
| A repeated seed does not guarantee bitwise identity across hardware/software. |
|
|
| `training/paper-2k-e3/resume.pt` preserves the trusted original checkpoint with |
| raw/EMA/optimizer/RNG state. Follow the training documentation and verify its |
| SHA before loading. The lightweight inference bundle cannot resume training. |
|
|
| Prior CVAE/refiner checkpoints remain available at HF revision |
| `b1b84d60ea084bcd12fb14747d3dc485fb61ec53`. They are distinct model families. |
| No new blanket license is asserted for model weights or source datasets. |
| Release verification covers artifact identity and selected CPU parity tests, |
| not clinical utility or complete GPU training reproducibility. |
|
|
| ## 0.1%-floor panel campaign (2026-09-23/24) |
|
|
| `training/floor01_20260923/{2k,6k,p15k}/` holds the part-16 checkpoints of three panels selected at |
| a 0.1% detection floor instead of the published 1% one, which was relative and therefore excluded |
| every gene specific to a cell type rarer than 1% of PBMC (pDC, cDC1, HSPC, platelet, erythroid). |
| The panels differ only in how many genes the model emits: same 1x architecture, data, seed, |
| schedule, sampler and drawn read-out. |
|
|
| On the held-out childhood SLE cohort, scored on the 1,006 genes every panel contains, widening the |
| panel does not help: 2k 0.348 logFC r / 53 top-50 DEG / 0.131 composition L1, 6k 0.399 / 81 / |
| 0.181, p15k 0.344 / 62 / 0.297. `MANIFEST.json` carries the per-file checksums and the full result; |
| `CHECKPOINTS.sha256` the digests alone. Code and the campaign record are in |
| [reproduce/floor01-panel-campaign.md](https://github.com/junidude/PBISC/blob/main/reproduce/floor01-panel-campaign.md). |
|
|
| These are training checkpoints, not inference bundles: they resume training and require the |
| repository's training code, not `PBISCPipeline`. |
|
|
