ClusterMatch aligns single-cell RNA-sequencing data at the multi-scale cluster level via stable matching.

ABSTRACT:

Motivation

Unsupervised clustering of single-cell RNA sequencing (scRNA-seq) data holds the promise of characterizing known and novel cell type in various biological and clinical contexts. However, intrinsic multi-scale clustering resolutions poses challenges to deal with multiple sources of variability in the high-dimensional and noisy data.

Results

We present ClusterMatch, a stable match optimization model to align scRNA-seq data at the cluster level. In one hand, ClusterMatch leverages the mutual correspondence by canonical correlation analysis and multi-scale Louvain clustering algorithms to identify cluster with optimized resolutions. In the other hand, it utilizes stable matching framework to align scRNA-seq data in the latent space while maintaining interpretability w

SUBMITTER: Ba T 

PROVIDER: S-EPMC11520419 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature