Name
Title | ||
|---|---|---|
Reconstructing tumor-wise protein expression in tissue microarray studies using a Bayesian cell mixture model. |
Abstract | ||
|---|---|---|
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.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.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.R package to fit CMM model is available at http://www.mskcc.org/mskcc/html/85130.cfm |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1093/bioinformatics/btn536 | Bioinformatics |
Keywords | Field | DocType |
sample mean expression,tumor-wise protein expression,expression pattern,whole-tumor expression pattern,bayesian cell mixture model,various tumor expression characteristic,tissue-specific protein expression,tissue specimen,tumor expression pattern,cell mixture model,cmm expression model,cmm model,tissue microarray study,protein expression,mixture model,tissue microarray | Biology,Markov chain Monte Carlo,Markov chain,Tissue Array Analysis,Tissue microarray,Monte Carlo integration,Bioinformatics,Protein microarray,Mixture model,Bayes' theorem | Journal |
Volume | Issue | ISSN |
24 | 24 | 1367-4811 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ronglai Shen | 1 | 126 | 6.83 |
| Jeremy M. G. Taylor | 2 | 52 | 3.81 |
| Debashis Ghosh | 3 | 496 | 49.16 |