Why this evidence?
It is linked to a specific model version, scientific task, dataset, split, metric and source. That makes the claim inspectable rather than a detached marketing score.
scGPT: Single-cell representation learning
Peer-reviewed evaluation across representation and downstream single-cell tasks.
Model versionVersion history not yet curated
TaskSingle-cell representation learning
DatasetSingle-cell downstream tasks
SplitSingle-cell downstream-task benchmarks
MetricCell representation
Replicationmultiple-independent-uses
Reported byModel developers
Review statuscurated
Evidence confidence · strong
Confidence is multidimensional, not a universal model score.
Evidence completenessstrongModel version, task, dataset, split, metric, source and provenance fields.
Independent validationstrongmultiple-independent-uses
Source qualitystrongPeer-reviewed
Reproducibility evidencestrongReflects documented replication status, not a universal reproducibility score.
Version specificitystrongVersion history not yet curated
Context applicabilitystrongDepends on task, split and explicit caveats; users must still validate their own context.
Contradiction reviewclearNo direct contradiction signal is currently queued.
BioAtlas reports evidence dimensions separately so a strong source cannot hide weak applicability, incomplete replication or unresolved contradiction.
Why?
Why should this evidence influence a decision?
Why not a universal score?
Performance can change with dataset, split, preprocessing, metric and context of use. BioAtlas therefore keeps confidence dimensions separate.
What could change the conclusion?
Independent replication, a better matched prospective dataset, a version change, a contradictory result or a more relevant validation protocol can reopen this evidence record.
Evidence boundary
What this claim does not prove.
- Performance varies by preprocessing, batch correction and downstream task.
- Representation quality is not equivalent to causal perturbation prediction.
BioAtlas groups benchmark claims only when task, dataset, split, metric and protocol context align. This record is not a universal model score.
Model context
scGPT is a generative pretrained transformer over 33M+ single cells that transfers to cell-type annotation, batch integration, perturbation prediction and gene-network inference. It helped popularize the 'foundation model' framing for single-cell genomics.
Full evidence passport →Known model limitations
- Performance depends on the evaluation dataset and operating conditions.
- Task-specific benchmark results should not be compared across unlike domains.
- Outputs require task-specific scientific and experimental validation.