CYCLONE: recycle contrastive learning for integrating single-cell gene expression data.

ABSTRACT:

Background

Combining single-cell transcriptome sequencing results from several batches reduces batch effect, which improves our understanding of cellular identity and function.

Results

This paper introduces CYCLONE, a new method for integrating single-cell gene expression data using a recycle contrastive learning network. The contrastive learning network and the VAE model work together to jointly train the low-dimensional representations. Additionally, they update the indices of inter-batch MNN pairs to generate positive pairs from a reduced-noise low-dimensional space. Meanwhile, CYCLONE cyclically updates the MNN pairs by iteratively training the low-dimensional space to gradually improve the confidence of the positive sample pairs, and augments the MNN pairs with KNN pair

SUBMITTER: Ji H 

PROVIDER: S-EPMC12312599 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature