ScBFA: Modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data

Ruoxin Li, Gerald Quon

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Technical variation in feature measurements, such as gene expression and locus accessibility, is a key challenge of large-scale single-cell genomic datasets. We show that this technical variation in both scRNA-seq and scATAC-seq datasets can be mitigated by analyzing feature detection patterns alone and ignoring feature quantification measurements. This result holds when datasets have low detection noise relative to quantification noise. We demonstrate state-of-the-art performance of detection pattern models using our new framework, scBFA, for both cell type identification and trajectory inference. Performance gains can also be realized in one line of R code in existing pipelines.

Original languageEnglish (US)
Article number193
JournalGenome Biology
Volume20
Issue number1
DOIs
StatePublished - Sep 9 2019

Keywords

  • Cell type identification
  • Dimensionality reduction
  • Gene detection
  • Gene quantification
  • scATAC-seq
  • scRNA-seq
  • Technical noise
  • Trajectory inference
  • Variable gene selection

ASJC Scopus subject areas

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

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