scAMACE: Model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation.

Abstract

MOTIVATION\nThe advancement in technologies and the growth of available single-cell datasets motivate integrative analysis of multiple single-cell genomic datasets. Integrative analysis of multimodal single-cell datasets combines complementary information offered by single-omic datasets and can offer deeper insights on complex biological process. Clustering methods that identify the unknown cell types are among the first few steps in the analysis of single-cell datasets, and they are important for downstream analysis built upon the identified cell types.\n\n\nRESULTS\nWe propose scAMACE for the integrative analysis and clustering of single-cell data on chromatin accessibility, gene expression and methylation. We demonstrate that cell types are better identified and characterized through analyzing the three data types jointly. We develop an efficient expectationmaximization (EM) algorithm to perform statistical inference, and evaluate our methods on both simulation study and real data applications. We also provide the GPU implementation of scAMACE, making it scalable to large datasets.\n\n\nAVAILABILITY\nThe software and datasets are available at https://github.com/cuhklinlab/scAMACE_py (python implementation) and https://github.com/cuhklinlab/scAMACE (R implementation).\n\n\nSUPPLEMENTARY INFORMATION\nSupplementary data are available at Bioinformatics online.