BERMAD: batch effect removal for single-cell RNA-seq data using a multi-layer adaptation autoencoder with dual-channel framework.

ABSTRACT:

Motivation

Removal of batch effect between multiple datasets from different experimental platforms has become an urgent problem, since single-cell RNA sequencing (scRNA-seq) techniques developed rapidly. Although there have been some methods for this problem, most of them still face the challenge of under-correction or over-correction. Specifically, handling batch effect in highly nonlinear scRNA-seq data requires a more powerful model to address under-correction. In the meantime, some previous methods focus too much on removing difference between batches, which may disturb the biological signal heterogeneity of datasets generated from different experiments, thereby leading to over-correction.

Results

In this article, we propose a novel multi-layer adaptation autoencoder wit

SUBMITTER: Zhan X 

PROVIDER: S-EPMC10942801 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature