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ScBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment.

ABSTRACT:

Motivation

Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (RNA-seq) data.

Results

We present scBatch, a numerical algorithm for batch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions on the mechanism of batch-effect generation. As shown in simulations and real data analyses, scBatch outperforms benchmark batch-effect correction methods.

Availability and implementation

The R package is available at github.com/tengfei-emory/scBatch. The code to generate results and figures in this article is available at github.com/tengfei-emory/scBatch-paper-scripts.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Fei T

PROVIDER: S-EPMC7214039 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment.

Fei Teng T Yu Tianwei T

Bioinformatics (Oxford, England) 20200501 10

<h4>Motivation</h4>Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (RNA-seq) data.<h4>Results</h4>We present scBatch, a numerical algorithm for batch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions ...[more]

PMID: 32053185

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