Can cell type biomarkers provide the essential biological information to optimally identify statistically deconvoluted expressions?
Can statistical gene expression deconvolution methods be used to overcome the problem of tissue heterogeneity, a major confounding factor in microarray-based gene expression analysis to optimally identify disease related genes?
Within a sample of mixed cell type populations, different cell types exhibit distinct programs of transcription. When transcription levels are measured in a typical microarray experiment, the measured levels actually represent the weighted average of many transcriptional programs. Therefore, gene expression profiles of heterogeneous tissue samples can be difficult to interpret biologically. By utilizing a computational approach, such as deconvolution, gene expression profiles of mixed tissue samples can be decomposed into the cell type specific sub-profiles. Cell type biomarkers provide the essential biological information to correlate the post-deconvoluted pure cell expression profiles to their corresponding cell types. Statistical test methods can then be performed on deconvoluted pure cell expressions to identify disease related genes that may otherwise be undetectable.
Overview of gene expression deconvolution: By decomposing global gene expression profiles of heterogeneous tissue samples into component expression profiles, cell-type specific differential analysis can be performed to identify disease related genes [Shen-Orr, 2010].