Integration of Single-Cell RNA-Seq Datasets: A Review of Computational Methods.

ABSTRACT: With the increased number of single-cell RNA sequencing (scRNA-seq) datasets in public repositories, integrative analysis of multiple scRNA-seq datasets has become commonplace. Batch effects among different datasets are inevitable because of differences in cell isolation and handling protocols, library preparation technology, and sequencing platforms. To remove these batch effects for effective integration of multiple scRNA-seq datasets, a number of methodologies have been developed based on diverse concepts and approaches. These methods have proven useful for examining whether cellular features, such as cell subpopulations and marker genes, identified from a certain dataset, are consistently present, or whether their condition-dependent variations, such as increases in cell subpopulations

SUBMITTER: Ryu Y 

PROVIDER: S-EPMC9982060 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature