Next Generation Analysis Tools for Single-Cell Functional Genomic Data

• Activity Number: 408 - Methods for Single-Cell Genomic Analysis
• Type: Contributed
• Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Weiqiang Zhou* and Zhicheng Ji and Hongkai Ji
• Companies: Johns Hopkins University Bloomberg School of Public Health and Johns Hopkins University Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health
• Keywords: single-cell ATAC-seq ; single-cell DNase-seq ; single-cell ChIP-seq ; high-throughput sequencing

Abstract

Emerging single-cell technologies (e.g., single-cell ATAC-seq, DNase-seq or ChIP-seq) have made it possible to assay regulome of individual cells. Unlike data from the traditional bulk technologies which are relatively continuous, single-cell regulome data are highly sparse and discrete. Therefore, conventional tools developed for analyzing bulk data are not suitable for single-cell data. New software tools designed for single-cell data are urgently needed. Here, we present SCRAT, a Single-Cell Regulome Analysis Toolbox with a graphical user interface, for studying cell heterogeneity using single-cell regulome data. SCRAT can be used to conveniently summarize regulatory activities according to different features (e.g., gene sets, transcription factor binding motif sites, etc.). Using these features, users can identify cell subpopulations in a heterogeneous biological sample, infer cell identities of each subpopulation, and discover distinguishing features such as gene sets and transcription factors that show different activities among subpopulations.