scgpt-single-cell

name	scgpt-single-cell
description	Run pinned scGPT checkpoints for single-cell embeddings, cell-type annotation, reference mapping, perturbation modeling, or fine-tuning. Use when a pretrained single-cell transformer is requested and donor/batch/feature provenance can be preserved.
license	MIT

scGPT Single-Cell Analysis

Gate

Ask once before installing a pinned scGPT revision, downloading checkpoints or reference assets, and using GPU compute. Never upload private human data without explicit authorization and governance review. Follow $cx-compute-environment.

Workflow

1. Define the independent biological unit, task, donors, tissues, batches, perturbations, count layer, gene identifiers/release, labels, and deployment population. Split by donor/sample/experiment before cell-level processing.
2. Pin code/checkpoint/token dictionary, gene mapping, normalization/binning, max sequence length, feature selection, pooling, fine-tuning configuration, seeds, and hardware. Fit learned preprocessing on training data only.
3. Smoke-test a small public example. Run into artifacts/<run-id>/scgpt/; retain AnnData schema, split IDs, preprocessing, embeddings/predictions, checkpoint/config, learning curves, environment, logs, and excluded cells.
4. Compare PCA/logistic, nearest-reference, and batch-aware baselines. Report donor-level uncertainty, batch/tissue slices, calibration, OOD detection, and nuisance predictability from embeddings.
5. Record with $science-provenance; review with $science-review.

Boundaries

• Cells from one sample are not independent replicates.
• Integration can erase biology; cell-type labels and perturbation predictions remain model outputs, not experimental or clinical truth.