scICML: Information-theoretic Co-clustering-based Multi-view Learning for the Integrative Analysis of Single-cell Multi-omics data

TL;DR

scICML is a novel information-theoretic co-clustering method that integrates multi-omics single-cell data, effectively handling noise and sparsity to improve clustering and biological interpretation.

Contribution

It introduces a new multi-view learning approach that automatically matches feature clusters across data types for better single-cell data integration.

Findings

Improves clustering accuracy on real-world datasets.

Uncovers biologically meaningful patterns in single-cell multi-omics data.

Enhances understanding of cellular heterogeneity.

Abstract

Modern high-throughput sequencing technologies have enabled us to profile multiple molecular modalities from the same single cell, providing unprecedented opportunities to assay celluar heterogeneity from multiple biological layers. However, the datasets generated from these technologies tend to have high level of noise and are highly sparse, bringing challenges to data analysis. In this paper, we develop a novel information-theoretic co-clustering-based multi-view learning (scICML) method for multi-omics single-cell data integration. scICML utilizes co-clusterings to aggregate similar features for each view of data and uncover the common clustering pattern for cells. In addition, scICML automatically matches the clusters of the linked features across different data types for considering the biological dependency structure across different types of genomic features. Our experiments on four real-world datasets demonstrate that scICML improves the overall clustering performance and provides biological insights into the data analysis of peripheral blood mononuclear cells.