ScCorrector: a robust method for integrating multi-study single-cell data.

ABSTRACT: The advent of single-cell sequencing technologies has revolutionized cell biology studies. However, integrative analyses of diverse single-cell data face serious challenges, including technological noise, sample heterogeneity, and different modalities and species. To address these problems, we propose scCorrector, a variational autoencoder-based model that can integrate single-cell data from different studies and map them into a common space. Specifically, we designed a Study Specific Adaptive Normalization for each study in decoder to implement these features. scCorrector substantially achieves competitive and robust performance compared with state-of-the-art methods and brings novel insights under various circumstances (e.g. various batches, multi-omics, cross-species, and development st

SUBMITTER: Guo ZH 

PROVIDER: S-EPMC10810333 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature