Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation.

ABSTRACT:

Background

The integration of single-cell RNA sequencing data from multiple experimental batches and diverse biological conditions holds significant importance in the study of cellular heterogeneity.

Results

To expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning strategy for the integration of dissimilar single-cell RNA data. It constructs a condition-specific domain-adaptive network founded on variational autoencoders. scDisco not only effectively reduces batch effects but also successfully disentangles biological effects and condition-specific effects, and further augmenting condition-specific representations throug

SUBMITTER: Liu R 

PROVIDER: S-EPMC10944609 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature