Coupled co-clustering-based unsupervised transfer learning for the integrative analysis of single-cell genomic data

Abstract

Unsupervised methods, such as clustering methods, are essential to the analysis of single-cell genomic data. The most current clustering methods are designed for one data type only, such as single-cell RNA sequencing (scRNA-seq), single-cell ATAC sequencing (scATAC-seq) or sc-methylation data alone, and a few are developed for the integrative analysis of multiple data types. The integrative analysis of multimodal single-cell genomic data sets leverages the power in multiple data sets and can deepen the biological insight. In this paper, we propose a coupled co-clustering-based unsupervised transfer learning algorithm (coupleCoC) for the integrative analysis of multimodal single-cell data. Our proposed coupleCoC builds upon the information theoretic co-clustering framework. In co-clustering, both the cells and the genomic features are simultaneously clustered. Clustering similar genomic features reduces the noise in single-cell data and facilitates transfer of knowledge across single-cell datasets. We applied coupleCoC for the integrative analysis of scATAC-seq and scRNA-seq data, sc-methylation and scRNA-seq data and scRNA-seq data from mouse and human. We demonstrate that coupleCoC improves the overall clustering performance and matches the cell subpopulations across multimodal single-cell genomic datasets. Our method coupleCoC is also computationally efficient and can scale up to large datasets. Availability: The software and datasets are available at https://github.com/cuhklinlab/coupleCoC.

coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data

“…As mentioned before, the two terms ℓ T (C Y , C Z ) and ℓ S (C X , C X ) in formula ( 5) share the same feature cluster C Z , which can be viewed as a bridge to transfer knowledge between the source data and the target data [42,47]. The dimension of the feature space shared by the source data S and the data T is reduced by clustering and aggregating similar features.…”

“…Lastly, we note that the major differences between coupleCoC [42] and coupleCoC+ lie in two aspects: (a). coupleCoC does not include the unlinked features in the target data.…”

“…The intuition for choosing N sub is that the KL divergence D KL ðp T ðỸ h T;N sub ;Z T Þkp S ðX h S;N sub ;Z S ÞÞ will be larger if the clusters being matched are less similar when they are forced to be matched. N sub is chosen similarly as in [42]. More details are given in Text C in S1 Text.…”

“…In recent years, some methods are developed for this purpose. They include Seurat [33,34], MOFA [35], coupleNMF [36], scVDMC [37], LIGER [38], scACE [39], MOFA+ [40], scAI [41], coupleCoC [42] and scMC [43]. A more comprehensive discussion on integration of single-cell genomic data is presented in [44].…”

“…coupleCoC+ also performs matching of a subset of cell clusters across the source data and the target data, which may represent shared cell types across the two datasets. We refer our model as coupleCoC+, because it is based on the framework of our previously proposed coupleCoC [42]. coupleCoC+ addresses the limitations of coupleCoC by including the unlinked features in target data and it also integrates co-clustering and cell type matching in one step for better use of information from the source data.…”