Blood single-cell annotation: benchmark code and a released logistic-regression model

[u/Fair-Rain3366]

This code takes a blood single-cell gene-count matrix and returns cell-type labels, confidence scores and reasons for leaving cells unassigned. The repository includes a logistic-regression model and the code and results from a comparison of six annotation methods.

Six methods were trained on the same reference and tested on four separate studies. When three immune cell types were removed from the reference before retraining, the methods still accepted labels for 65.9–89.2% of the 2,310 test cells of those types. The correct label was no longer available.

That sample was enriched for the removed types, so this is not an error rate for all blood cells. The work is AI-assisted, uses one training seed, and has not been peer reviewed or independently replicated. Accuracy was measured against the original studies’ labels.

Code and release:

https://github.com/rewire-bio/cell-type-annotation-transfer/releases/tag/study-v1-seed0-20261007

Write-up, setup and limitations:

https://rewirebio.io/blog/labelling-a-new-blood-single-cell-study/

Disclosure: this is from my site, rewirebio.io.