Unknown

Dataset Information

0

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 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.

SUBMITTER: Ryu Y 

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

REPOSITORIES: biostudies-literature

altmetric image

Publications

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

Ryu Yeonjae Y   Han Geun Hee GH   Jung Eunsoo E   Hwang Daehee D  

Molecules and cells 20230224 2


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 div  ...[more]

Similar Datasets

| S-EPMC7276436 | biostudies-literature
| S-EPMC8980964 | biostudies-literature
| S-EPMC9278041 | biostudies-literature
| S-EPMC11415828 | biostudies-literature
| S-EPMC7549635 | biostudies-literature
| S-EPMC10336778 | biostudies-literature
| S-EPMC10094055 | biostudies-literature
| S-EPMC11261982 | biostudies-literature
| S-EPMC5845381 | biostudies-literature
| S-EPMC10635119 | biostudies-literature