Benchmarking deep learning methods for biologically conserved single-cell integration.

ABSTRACT:

Background

Advancements in single-cell RNA sequencing have enabled the analysis of millions of cells, but integrating such data across samples and methods while mitigating batch effects remains challenging. Deep learning approaches address this by learning biologically conserved gene expression representations, yet systematic benchmarking of loss functions and integration performance is lacking.

Results

We evaluate 16 integration methods using a unified variational autoencoder framework, incorporating batch and cell-type information. Results reveal limitations in the single-cell integration benchmarking index (scIB) for preserving intra-cell-type information. To address this, we introduce a correlation-based loss function and enhance benchmarking metrics to better capture bi

SUBMITTER: Yi C 

PROVIDER: S-EPMC12636214 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature