A Self-Directed Method for Cell-Type Identification and Separation of Gene Expression Microarrays

Neta S. Zuckerman, Yair Noam, Andrea J. Goldsmith, Peter P. Lee

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Gene expression analysis is generally performed on heterogeneous tissue samples consisting of multiple cell types. Current methods developed to separate heterogeneous gene expression rely on prior knowledge of the cell-type composition and/or signatures - these are not available in most public datasets. We present a novel method to identify the cell-type composition, signatures and proportions per sample without need for a-priori information. The method was successfully tested on controlled and semi-controlled datasets and performed as accurately as current methods that do require additional information. As such, this method enables the analysis of cell-type specific gene expression using existing large pools of publically available microarray datasets.

Original languageEnglish (US)
Article numbere1003189
JournalPLoS computational biology
Volume9
Issue number8
DOIs
StatePublished - Aug 2013
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

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