A discriminative learning approach to differential expression analysis for single-cell RNA-seq

Nat Methods. 2019 Feb;16(2):163-166. doi: 10.1038/s41592-018-0303-9. Epub 2019 Jan 21.

Abstract

Single-cell RNA-seq makes it possible to characterize the transcriptomes of cell types across different conditions and to identify their transcriptional signatures via differential analysis. Our method detects changes in transcript dynamics and in overall gene abundance in large numbers of cells to determine differential expression. When applied to transcript compatibility counts obtained via pseudoalignment, our approach provides a quantification-free analysis of 3' single-cell RNA-seq that can identify previously undetectable marker genes.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Computer Simulation
  • Databases, Genetic
  • Gene Expression Profiling
  • Gene Expression Regulation
  • Genetic Markers
  • Humans
  • Leukocytes, Mononuclear / cytology
  • Protein Isoforms
  • RNA / genetics
  • Regression Analysis
  • Sequence Analysis, RNA*
  • Single-Cell Analysis / instrumentation*
  • Single-Cell Analysis / methods*
  • Software
  • T-Lymphocytes, Cytotoxic / cytology
  • Transcriptome

Substances

  • Genetic Markers
  • Protein Isoforms
  • RNA