A benchmark of batch-effect correction methods for single-cell RNA sequencing data

Volume: 21, Issue: 1, Pages: 1 - 32
Published: Jan 16, 2020
Abstract
Large-scale single-cell transcriptomic datasets generated using different technologies contain batch-specific systematic variations that present a challenge to batch-effect removal and data integration. With continued growth expected in scRNA-seq data, achieving effective batch integration with available computational resources is crucial. Here, we perform an in-depth benchmark study on available batch correction methods to determine the most...
Paper Details
Title
A benchmark of batch-effect correction methods for single-cell RNA sequencing data
Published Date
Jan 16, 2020
Volume
21
Issue
1
Pages
1 - 32
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