title: Statistical expression deconvolution from mixed tissue samples.
abstract: BACKGROUND
  Global expression patterns within cells are used for purposes ranging from the identification of disease biomarkers to basic understanding of cellular processes. Unfortunately, tissue samples used in cancer studies are usually composed of multiple cell types and the non-cancerous portions can significantly affect expression profiles. This severely limits the conclusions that can be made about the specificity of gene expression in the cell-type of interest. However, statistical analysis can be used to identify differentially expressed genes that are related to the biological question being studied.
  
  
  RESULTS
  We propose a statistical approach to expression deconvolution from mixed tissue samples in which the proportion of each component cell type is unknown. Our method estimates the proportion of each component in a mixed tissue sample; this estimate can be used to provide estimates of gene expression from each component. We demonstrate our technique on xenograft samples from breast cancer research and publicly available experimental datasets found in the National Center for Biotechnology Information Gene Expression Omnibus repository.
  
  
  BACKGROUND
  R code (http://www.r-project.org/) for estimating sample proportions is freely available to non-commercial users and available at http://www.med.miami.edu/medicine/x2691.xml.
authors.affiliation: Department of Medicine, University of Miami, 1120 NW 14th St, Suite 611, Miami, FL 33136, USA. jclarke@med.miami.edu
authors.forename: Jennifer
authors.initials: J
authors.lastname: Clarke
authors.forename: Pearl
authors.initials: P
authors.lastname: Seo
authors.forename: Bertrand
authors.initials: B
authors.lastname: Clarke
mesh_terms: D045744:Cell Line, Tumor; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated