Accurate integration of multiple heterogeneous single-cell RNA-seq data sets by learning contrastive biological variation.

ABSTRACT: For most biological and medical applications of single-cell transcriptomics, an integrative study of multiple heterogeneous single-cell RNA sequencing (scRNA-seq) data sets is crucial. However, present approaches are unable to integrate diverse data sets from various biological conditions effectively because of the confounding effects of biological and technical differences. We introduce single-cell integration (scInt), an integration method based on accurate, robust cell-cell similarity construction and unified contrastive biological variation learning from multiple scRNA-seq data sets. scInt provides a flexible and effective approach to transfer knowledge from the already integrated reference to the query. We show that scInt outperforms 10 other cutting-edge approaches using both simulat

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC10317120 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature