Dataset Information

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 is imposed to borrow strength across tissue specimens and across tumors. A joint model is presented to link the CMM expression model with a survival model for censored failure time observations. The implementation involves imputation steps within each Markov chain Monte Carlo iteration and Monte Carlo integration technique.

Results

The model-based approach provides estimates for various tumor expression characteristics including the percentage of staining, mean intensity of staining and a composite meanstaining to associate with patient survival outcome.

Availability

R package to fit CMM model is available at http://www.mskcc.org/mskcc/html/85130.cfm

SUBMITTER: Shen R

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

REPOSITORIES: biostudies-literature

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Publications

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

Shen Ronglai R Taylor Jeremy M G JM Ghosh Debashis D

Bioinformatics (Oxford, England) 20081014 24

<h4>Motivation</h4>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.<h4>Methods</h4>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 ex ...[more]

PMID: 18922808

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Project description:BackgroundInference of gene networks typically relies on measurements across a wide range of conditions or treatments. Although one network structure is predicted, the relationship between genes could vary across conditions. A comprehensive approach to infer general and condition-dependent gene networks was evaluated. This approach integrated Bayesian network and Gaussian mixture models to describe continuous microarray gene expression measurements, and three gene networks were predicted.ResultsThe first reconstructions of a circadian rhythm pathway in honey bees and an adherens junction pathway in mouse embryos were obtained. In addition, general and condition-specific gene relationships, some unexpected, were detected in these two pathways and in a yeast cell-cycle pathway. The mixture Bayesian network approach identified all (honey bee circadian rhythm and mouse adherens junction pathways) or the vast majority (yeast cell-cycle pathway) of the gene relationships reported in empirical studies. Findings across the three pathways and data sets indicate that the mixture Bayesian network approach is well-suited to infer gene pathways based on microarray data. Furthermore, the interpretation of model estimates provided a broader understanding of the relationships between genes. The mixture models offered a comprehensive description of the relationships among genes in complex biological processes or across a wide range of conditions. The mixture parameter estimates and corresponding odds that the gene network inferred for a sample pertained to each mixture component allowed the uncovering of both general and condition-dependent gene relationships and patterns of expression.ConclusionThis study demonstrated the two main benefits of learning gene pathways using mixture Bayesian networks. First, the identification of the optimal number of mixture components supported by the data offered a robust approach to infer gene relationships and estimate gene expression profiles. Second, the classification of conditions and observations into groups that support particular mixture components helped to uncover both gene relationships that are unique or common across conditions. Results from the application of mixture Bayesian networks substantially augmented the understanding of gene networks and demonstrated the added-value of this methodology to infer gene networks.

Dataset Information

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

Motivation

Methods

Results

Availability

Publications

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

Similar Datasets

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