title: Gene expression deconvolution in clinical samples.
abstract: Cell type heterogeneity may have a substantial effect on gene expression profiling of human tissue. Several in silico methods for deconvoluting a gene expression profile into cell-type-specific subprofiles have been published but not widely used. Here, we consider recent methods and the experimental validations available for them. Shen-Orr et al. recently developed an approach called cell-type-specific significance analysis of microarray for deconvoluting gene expression. This method requires the measurement of the proportion of each cell type in each sample and the expression profiles of the heterogeneous samples. It determines how gene expression varies among pre-defined phenotypes for each cell type. Gene expression can vary substantially among cell types and sample heterogeneity can mask the identification of biologically important phenotypic correlations. Consequently, the deconvolution approach can be useful in the analysis of mixtures of cell populations in clinical samples.
authors.affiliation: Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. zhaoy@nih.gov.
authors.forename: Yingdong
authors.initials: Y
authors.lastname: Zhao
authors.forename: Richard
authors.initials: R
authors.lastname: Simon