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

doi:10.14348/molcells.2023.0009

Paper

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

Published Feb 24, 2023 · Yeonjae Ryu, Geun-Hee Han, Eunsoo Jung

Molecules and Cells

41

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1

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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 in particular disease-related conditions, are consistently observed in different datasets generated under similar or distinct conditions. In this review, we summarize the concepts and approaches of the integration methods and their pros and cons as has been reported in previous literature.

Literature Review

Study Snapshot

Integrating single-cell RNA-seq datasets can reveal consistent cellular features and condition-dependent variations, but batch effects may limit the accuracy of the analysis.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

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

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