title: Gene set analysis for self-contained tests: complex null and specific alternative hypotheses.
abstract: BACKGROUND
  The analysis of differentially expressed gene sets became a routine in the analyses of gene expression data. There is a multitude of tests available, ranging from aggregation tests that summarize gene-level statistics for a gene set to true multivariate tests, accounting for intergene correlations. Most of them detect complex departures from the null hypothesis but when the null hypothesis is rejected, the specific alternative leading to the rejection is not easily identifiable.
  
  
  RESULTS
  In this article we compare the power and Type I error rates of minimum-spanning tree (MST)-based non-parametric multivariate tests with several multivariate and aggregation tests, which are frequently used for pathway analyses. In our simulation study, we demonstrate that MST-based tests have power that is for many settings comparable with the power of conventional approaches, but outperform them in specific regions of the parameter space corresponding to biologically relevant configurations. Further, we find for simulated and for gene expression data that MST-based tests discriminate well against shift and scale alternatives. As a general result, we suggest a two-step practical analysis strategy that may increase the interpretability of experimental data: first, apply the most powerful multivariate test to find the subset of pathways for which the null hypothesis is rejected and second, apply MST-based tests to these pathways to select those that support specific alternative hypotheses.
  
  
  BACKGROUND
  gvglazko@uams.edu or yrahmatallah@uams.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA. yrahmatallah@uams.edu
authors.forename: Y
authors.initials: Y
authors.lastname: Rahmatallah
authors.forename: F
authors.initials: F
authors.lastname: Emmert-Streib
authors.forename: G
authors.initials: G
authors.lastname: Glazko
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D020869:Gene Expression Profiling; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D009369:Neoplasms / Q000235:genetics