Coupled Co-clustering-based Unsupervised Transfer Learning for the Integrative Analysis of Single-Cell Genomic Data

ZENG, P.; Lin, Z.

doi:10.1101/2020.03.28.013938

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bioRxiv · 10.1101/2020.03.28.013938

Coupled Co-clustering-based Unsupervised Transfer Learning for the Integrative Analysis of Single-Cell Genomic Data

ZENG, P.·Lin, Z.

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Abstract

Unsupervised methods, such as clustering methods, are essential to the analysis of single-cell genomic data. Most current clustering methods are designed for one data type only, such as scRNA-seq, scATAC-seq or sc-methylation data alone, and a few are developed for the integrative analysis of multiple data types. Integrative analysis of multimodal single-cell genomic data sets leverages the power in multiple data sets and can deepen the biological insight. 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. 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 data sets. The software and data sets are available at https://github.com/cuhklinlab/coupleCoC.

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ZENG, P., Lin, Z.. 2020-03-29. Coupled Co-clustering-based Unsupervised Transfer Learning for the Integrative Analysis of Single-Cell Genomic Data. https://doi.org/10.1101/2020.03.28.013938

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