title: Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods.
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.
authors.affiliation: Computational Biology and Machine Learning Laboratory, Queen's University Belfast, Belfast, UK. v@bio-complexity.com
authors.forename: Frank
authors.initials: F
authors.lastname: Emmert-Streib
authors.forename: Shailesh
authors.initials: S
authors.lastname: Tripathi
authors.forename: Ricardo
authors.initials: R
authors.lastname: de Matos Simoes
mesh_terms: D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D018401:Sample Size; D012984:Software