  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.
