A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics.

ABSTRACT: Recent developments of single-cell RNA-seq (scRNA-seq) technologies have led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effects, which are inevitable in studies involving human tissues. Most existing methods remove batch effects in a low-dimensional embedding space. Although useful for clustering, batch effects are still present in the gene expression space, leaving downstream gene-level analysis susceptible to batch effects. Recent studies have shown that batch effect correction in the gene expression space is much harder than in the embedding space. Methods such as Seurat 3.0 rely on the mutual nearest neighbor (MNN) approach to remove batch effects in gene expression, but MNN can only analyze two batches at a t

SUBMITTER: Lakkis J 

PROVIDER: S-EPMC8494213 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature