Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

Frank Emmert-Streib*, Shailesh Tripathi, Ricardo de Matos Simoes

*Corresponding author for this work

Research output: Contribution to journalLiterature review

18 Citations (Scopus)
227 Downloads (Pure)

Abstract

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

Original languageEnglish
Article number44
Number of pages25
JournalBiology Direct
Volume7
DOIs
Publication statusPublished - 10 Dec 2012

Keywords

  • Gene expression data
  • Cancer data
  • Statistical analysis methods
  • Pathway methods
  • Correlation structure
  • Cancer genomics
  • SET ENRICHMENT ANALYSIS
  • TRANSCRIPTIONAL REGULATORY NETWORKS
  • CHRONIC-FATIGUE-SYNDROME
  • FALSE DISCOVERY RATES
  • B-CELL LYMPHOMA
  • MICROARRAY DATA
  • SYSTEMS BIOLOGY
  • COVARIANCE-MATRIX
  • DIFFERENTIAL COEXPRESSION
  • GRAPHICAL LASSO

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