ScVIC: deep generative modeling of heterogeneity for scRNA-seq data.

ABSTRACT:

Motivation

Single-cell RNA sequencing (scRNA-seq) has become a valuable tool for studying cellular heterogeneity. However, the analysis of scRNA-seq data is challenging because of inherent noise and technical variability. Existing methods often struggle to simultaneously explore heterogeneity across cells, handle dropout events, and account for batch effects. These drawbacks call for a robust and comprehensive method that can address these challenges and provide accurate insights into heterogeneity at the single-cell level.

Results

In this study, we introduce scVIC, an algorithm designed to account for variational inference, while simultaneously handling biological heterogeneity and batch effects at the single-cell level. scVIC explicitly models both biological heterogeneity

SUBMITTER: Xiong J 

PROVIDER: S-EPMC11256938 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature