JojoSCL: Shrinkage Contrastive Learning for single-cell RNA sequence Clustering

TL;DR

JojoSCL introduces a self-supervised contrastive learning framework with a shrinkage estimator for improved clustering of high-dimensional, sparse single-cell RNA sequencing data, outperforming existing methods across multiple datasets.

Contribution

The paper presents a novel contrastive learning method incorporating hierarchical Bayesian shrinkage and SURE optimization for better scRNA-seq clustering.

Findings

JojoSCL outperforms existing clustering methods on ten scRNA-seq datasets.

The shrinkage estimator reduces intra-cluster dispersion effectively.

Robustness and ablation studies validate the method's practicality.

Abstract

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular processes by enabling gene expression analysis at the individual cell level. Clustering allows for the identification of cell types and the further discovery of intrinsic patterns in single-cell data. However, the high dimensionality and sparsity of scRNA-seq data continue to challenge existing clustering models. In this paper, we introduce JojoSCL, a novel self-supervised contrastive learning framework for scRNA-seq clustering. By incorporating a shrinkage estimator based on hierarchical Bayesian estimation, which adjusts gene expression estimates towards more reliable cluster centroids to reduce intra-cluster dispersion, and optimized using Stein's Unbiased Risk Estimate (SURE), JojoSCL refines both instance-level and cluster-level contrastive learning. Experiments on ten scRNA-seq datasets substantiate that JojoSCL consistently outperforms prevalent clustering methods, with further validation of its practicality through robustness analysis and ablation studies. JojoSCL's code is available at: https://github.com/ziwenwang28/JojoSCL.