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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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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