QuantCell: machine learning based cell annotation from qualitative and quantitative imaging profiles

Boohar, W. R.; Wang, B.; Thomas, Z.; Nogalska, A.; Lu, R.

doi:10.64898/2026.02.15.706033

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bioRxiv · 10.64898/2026.02.15.706033

QuantCell: machine learning based cell annotation from qualitative and quantitative imaging profiles

Boohar, W. R.·Wang, B.·Thomas, Z.·Nogalska, A.·Lu, R.

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Abstract

Recent advances in spatial omics enable high-resolution, multiplexed imaging of RNA and protein expression, but cell annotation remains challenging, particularly in complex tissues with numerous markers or rare cell types. Here, we present QuantCell, a machine learning framework that leverages quantitative imaging data to improve annotation derived from qualitative profiles. QuantCell evaluates multiple models and applies a user-defined false discovery rate to ensure high-confidence annotation. Using PhenoCycler imaging of mouse bone marrow, QuantCell increased annotated cells from 33.1% to 90.2% at 5% FDR, achieving 96.5% accuracy. QuantCell supports diverse imaging platforms and robustly detects rare cell populations.

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Boohar, W. R., Wang, B., Thomas, Z., Nogalska, A., Lu, R.. 2026-02-17. QuantCell: machine learning based cell annotation from qualitative and quantitative imaging profiles. https://doi.org/10.64898/2026.02.15.706033

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