A Regularized Multi-Task Learning Approach for Cell Type Detection in Single-Cell RNA Sequencing Data.

ABSTRACT: Cell type prediction is one of the most challenging goals in single-cell RNA sequencing (scRNA-seq) data. Existing methods use unsupervised learning to identify signature genes in each cluster, followed by a literature survey to look up those genes for assigning cell types. However, finding potential marker genes in each cluster is cumbersome, which impedes the systematic analysis of single-cell RNA sequencing data. To address this challenge, we proposed a framework based on regularized multi-task learning (RMTL) that enables us to simultaneously learn the subpopulation associated with a particular cell type. Learning the structure of subpopulations is treated as a separate task in the multi-task learner. Regularization is used to modulate the multi-task model (e.g., W ₁,

SUBMITTER: Upadhyay P 

PROVIDER: S-EPMC9043858 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature