scContrast: A Contrastive Learning Based Approach for Encoding Single-Cell Gene Expression Data

Abstract

Abstract Single-cell RNA sequencing (scRNA-seq) captures gene expression at a individual cell resolution, which reveals critical insights into cellular diversity, disease processes, and developmental biology. However, a key challenge in scRNA-seq analysis is clustering similar cells across multiple batches, particularly when distinct sequencing protocols are used. In this work, we present scContrast, a semi-supervised contrastive learning method tailored for embedding scRNA-seq data from both plate- and droplet-based protocols into a universal representation space. By leveraging five simple augmentations, scContrast extracts biologically relevant signals from gene expression data while filtering out batch effects and technical artifacts. We trained scContrast on a subset of Tabula Muris tissues and evaluated its zero-shot performance on unseen tissues. Our results demonstrate that scContrast generalizes effectively to new tissues and outperforms the leading UCE approach in integrating scRNA-seq data from droplet- and plate-based sequencing protocols.