  Categorizing individual cells into one of many known cell type categories,
also known as cell type annotation, is a critical step in the analysis of
single-cell genomics data. The current process of annotation is time-intensive
and subjective, which has led to different studies describing cell types with
labels of varying degrees of resolution. While supervised learning approaches
have provided automated solutions to annotation, there remains a significant
challenge in fitting a unified model for multiple datasets with inconsistent
labels. In this article, we propose a new multinomial logistic regression
estimator which can be used to model cell type probabilities by integrating
multiple datasets with labels of varying resolution. To compute our estimator,
we solve a nonconvex optimization problem using a blockwise proximal gradient
descent algorithm. We show through simulation studies that our approach
estimates cell type probabilities more accurately than competitors in a wide
variety of scenarios. We apply our method to ten single-cell RNA-seq datasets
and demonstrate its utility in predicting fine resolution cell type labels on
unlabeled data as well as refining cell type labels on data with existing
coarse resolution annotations. An R package implementing the method is
available at https://github.com/keshav-motwani/IBMR and the collection of
datasets we analyze is available at
https://github.com/keshav-motwani/AnnotatedPBMC.
