CanCellCap: robust cancer cell capture across tissue types on single-cell RNA-seq data by multi-domain learning.

ABSTRACT:

Background

The advent of single-cell RNA sequencing (scRNA-seq) has provided unprecedented insights into cancer cellular diversity, enabling a comprehensive understanding of cancer at the single-cell level. However, identifying cancer cells remains challenging due to gene expression variability caused by tumor or tissue heterogeneity, which negatively impacts generalization and robustness.

Results

We propose CanCellCap, a multi-domain learning framework, to identify cancer cells in scRNA-seq data suitable for all tissues, cancers, and sequencing platforms. Integrating domain adversarial learning and Mixture of Experts, CanCellCap is able to simultaneously extract common and specific patterns in gene expression profiles across different tissues for cancer or normal cells. Mor

SUBMITTER: Bai J 

PROVIDER: S-EPMC12312500 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature