HDMC: a novel deep learning-based framework for removing batch effects in single-cell RNA-seq data.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Feb 7, 2022
Abstract
MOTIVATION: With the development of single-cell RNA sequencing (scRNA-seq) techniques, increasingly more large-scale gene expression datasets become available. However, to analyze datasets produced by different experiments, batch effects among different datasets must be considered. Although several methods have been recently published to remove batch effects in scRNA-seq data, two problems remain to be challenging and not completely solved: (i) how to reduce the distribution differences of different batches more accurately; and (ii) how to align samples from different batches to recover the cell type clusters.