Multi-batch single-cell comparative atlas construction by deep learning disentanglement.

ABSTRACT: Cell state atlases constructed through single-cell RNA-seq and ATAC-seq analysis are powerful tools for analyzing the effects of genetic and drug treatment-induced perturbations on complex cell systems. Comparative analysis of such atlases can yield new insights into cell state and trajectory alterations. Perturbation experiments often require that single-cell assays be carried out in multiple batches, which can introduce technical distortions that confound the comparison of biological quantities between different batches. Here we propose CODAL, a variational autoencoder-based statistical model which uses a mutual information regularization technique to explicitly disentangle factors related to technical and biological effects. We demonstrate CODAL's capacity for batch-confounded cell type

SUBMITTER: Lynch AW 

PROVIDER: S-EPMC10336139 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature