Binned multinomial logistic regression for integrative cell-type annotation.

ABSTRACT: 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

SUBMITTER: Motwani K 

PROVIDER: S-EPMC11981643 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature