QuantCell: machine learning based cell annotation integrating qualitative and quantitative imaging profiles
Year: 2026
Type: preprint
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.
Authors: Wade Boohar, Bowen Wang, Zachary Thomas, Anna Nogalska, Rong Lu
Institution: University of Southern California
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funder: National Institutes of Health
OpenAlex ID: W7130355899