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Categorizing individual cells into one of many known cell type categories,also known as cell type annotation, is a critical step in the analysis ofsingle-cell genomics data. The current process of annotation is time-intensiveand subjective, which has led to different studies describing cell types withlabels of varying degrees of resolution. While supervised learning approacheshave provided automated solutions to annotation, there remains a significantchallenge in fitting a unified model for multiple datasets with inconsistentlabels. In this article, we propose a new multinomial logistic regressionestimator which can be used to model cell type probabilities by integratingmultiple datasets with labels of varying resolution. To compute our estimator,we solve a nonconvex optimization problem using a blockwise proximal gradientdescent algorithm. We show through simulation studies that our approachestimates cell type probabilities more accurately than competitors in a widevariety of scenarios. We apply our method to ten single-cell RNA-seq datasetsand demonstrate its utility in predicting fine resolution cell type labels onunlabeled data as well as refining cell type labels on data with existingcoarse resolution annotations. An R package implementing the method isavailable at https://github.com/keshav-motwani/IBMR and the collection ofdatasets we analyze is available athttps://github.com/keshav-motwani/AnnotatedPBMC.

Binned multinomial logistic regression for integrative cell type annotation