title: PathVar: analysis of gene and protein expression variance in cellular pathways using microarray data.
abstract: CONCLUSIONS
  Finding significant differences between the expression levels of genes or proteins across diverse biological conditions is one of the primary goals in the analysis of functional genomics data. However, existing methods for identifying differentially expressed genes or sets of genes by comparing measures of the average expression across predefined sample groups do not detect differential variance in the expression levels across genes in cellular pathways. Since corresponding pathway deregulations occur frequently in microarray gene or protein expression data, we present a new dedicated web application, PathVar, to analyze these data sources. The software ranks pathway-representing gene/protein sets in terms of the differences of the variance in the within-pathway expression levels across different biological conditions. Apart from identifying new pathway deregulation patterns, the tool exploits these patterns by combining different machine learning methods to find clusters of similar samples and build sample classification models.
  
  
  BACKGROUND
  freely available at http://pathvar.embl.de
  
  
  BACKGROUND
  enrico.glaab@uni.lu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Structural and Computational Biology Unit, EMBL, Meyerhofstrasse 1, 69117, Heidelberg, Germany. enrico.glaab@uni.lu
authors: Enrico
authors: E
authors: Glaab
authors: Reinhard
authors: R
authors: Schneider
mesh_terms: D020869:Gene Expression Profiling; D006801:Humans; D020407:Internet; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis; D011471:Prostatic Neoplasms / Q000235:genetics / Q000378:metabolism; D011506:Proteins / Q000235:genetics / Q000378:metabolism; D012984:Software
chemical_list: D011506:Proteins