Single-cell ATAC-seq signal extraction and enhancement with SCATE

Zhicheng Ji, Weiqiang Zhou, Wenpin Hou, Hongkai Ji

Research output: Contribution to journalArticle

Abstract

Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscapes in single cells. Single-cell ATAC-seq data are sparse and noisy, and analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. We present a new statistical framework, SCATE, that adaptively integrates information from co-activated CREs, similar cells, and publicly available regulome data to substantially increase the accuracy for estimating activities of individual CREs. We demonstrate that SCATE can be used to better reconstruct the regulatory landscape of a heterogeneous sample.

Original languageEnglish (US)
Article number1713098
JournalGenome biology
Volume21
Issue number1
DOIs
StatePublished - Jul 3 2020

Keywords

  • Bioinformatics
  • Chromatin
  • DNase-seq
  • Gene regulation
  • Genomics
  • Machine learning
  • Single cell
  • Software
  • Statistical modeling
  • scATAC-seq

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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