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

Frank Emmert-Streib, Shailesh Tripathi, Ricardo D. Matos Simoes

Research output: Contribution to journalReview articlepeer-review

19 Citations (Scopus)


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
Pages (from-to)44
JournalBiology Direct
Publication statusPublished - 10 Dec 2012
Externally publishedYes


  • Cancer data
  • Cancer genomics
  • Correlation structure
  • Gene expression data
  • Pathway methods
  • Statistical analysis methods
  • Gene Expression Profiling/methods
  • Data Interpretation, Statistical
  • Humans
  • Gene Expression Regulation, Neoplastic
  • Neoplasms/genetics
  • Oligonucleotide Array Sequence Analysis/methods
  • Sample Size
  • Software


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