Reconstructing tumor-wise protein expression in tissue microarray studies using a Bayesian cell mixture model.

ABSTRACT:

Motivation

Tissue microarrays (TMAs) quantify tissue-specific protein expression of cancer biomarkers via high-density immuno-histochemical staining assays. Standard analysis approach estimates a sample mean expression in the tumor, ignoring the complex tissue-specific staining patterns observed on tissue arrays.

Methods

In this article, a cell mixture model (CMM) is proposed to reconstruct tumor expression patterns in TMA experiments. The concept is to assemble the whole-tumor expression pattern by aggregating over the subpopulation of tissue specimens sampled by needle biopsies. The expression pattern in each individual tissue element is assumed to be a zero-augmented Gamma distribution to assimilate the non-staining areas and the staining areas. A hierarchical Bayes model

SUBMITTER: Shen R 

PROVIDER: S-EPMC4505790 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature