Interpretable Multi-task Analysis of Single-Cell RNA-seq Data Through Topological Structure Preservation and Data Denoising.

The advent of single-cell transcriptome sequencing (scRNA-seq) has revolutionized our ability to analyze gene expression at the individual cell level, overcoming the limitations of bulk RNA sequencing. However, the explosive growth of scRNA-seq data and the prevalence of dropout events pose significant challenges for downstream analysis. Existing methodologies often focus on isolated tasks, such as identifying cell communities, processing dropout events, and mitigating batch effects, neglecting collaborative multi-task analysis, and introducing new noise during dropout event handling. In response to these challenges, we propose scIMTA (interpretable multi-task analysis of single-cell), an advanced framework designed to enhance interpretability and effectively address the issues of topological structure preservation and dropout events. The key innovations of scIMTA are that scIMTA enables collaborative multi-task analysis of sparse, high-noise gene expression data, enhances interpretability through biological grounding, robustly handles dropout events by preserving data integrity, and demonstrates efficacy and generalizability through rigorous validation on breast cancer scRNA-seq datasets. scIMTA establishes a new framework for collaborative multi-task analysis, interpretability, and robust dropout handling in single-cell transcriptome studies. This work significantly advances the field and allows a more nuanced exploration of cellular heterogeneity and gene expression dynamics. The source code of scIMTA is available for download at https://github.com/ShengPengYu/scIMTA .