Abstract
In this paper we propose a Bayesian approach for inference about dependence of high throughput gene expression. Our goals are to use prior knowledge about pathways to anchor inference about dependence among genes; to account for this dependence while making inferences about differences in mean expression across phenotypes; and to explore differences in the dependence itself across phenotypes. Useful features of the proposed approach are a model-based parsimonious representation of expression as an ordinal outcome, a novel and flexible representation of prior information on the nature of dependencies, and the use of a coherent probability model over both the structure and strength of the dependencies of interest. We valuate our approach through simulations and in the analysis of data on expression of genes in the Complement and Coagulation Cascade pathway in ovarian cancer.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 542-560 |
| Number of pages | 19 |
| Journal | Annals of Applied Statistics |
| Volume | 6 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 2012 |
| Externally published | Yes |
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Keywords
- Conditional independence
- Microarray data
- Probability of expression
- Probit models
- Reciprocal graphs
- Reversible jumps MCMC
ASJC Scopus subject areas
- Statistics, Probability and Uncertainty
- Modeling and Simulation
- Statistics and Probability
Cite this
Modeling dependent gene expression. / Telesca, Donatello; Müller, Peter; Parmigiani, Giovanni; Freedman, Ralph S.
In: Annals of Applied Statistics, Vol. 6, No. 2, 06.2012, p. 542-560.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Modeling dependent gene expression
AU - Telesca, Donatello
AU - Müller, Peter
AU - Parmigiani, Giovanni
AU - Freedman, Ralph S.
PY - 2012/6
Y1 - 2012/6
N2 - In this paper we propose a Bayesian approach for inference about dependence of high throughput gene expression. Our goals are to use prior knowledge about pathways to anchor inference about dependence among genes; to account for this dependence while making inferences about differences in mean expression across phenotypes; and to explore differences in the dependence itself across phenotypes. Useful features of the proposed approach are a model-based parsimonious representation of expression as an ordinal outcome, a novel and flexible representation of prior information on the nature of dependencies, and the use of a coherent probability model over both the structure and strength of the dependencies of interest. We valuate our approach through simulations and in the analysis of data on expression of genes in the Complement and Coagulation Cascade pathway in ovarian cancer.
AB - In this paper we propose a Bayesian approach for inference about dependence of high throughput gene expression. Our goals are to use prior knowledge about pathways to anchor inference about dependence among genes; to account for this dependence while making inferences about differences in mean expression across phenotypes; and to explore differences in the dependence itself across phenotypes. Useful features of the proposed approach are a model-based parsimonious representation of expression as an ordinal outcome, a novel and flexible representation of prior information on the nature of dependencies, and the use of a coherent probability model over both the structure and strength of the dependencies of interest. We valuate our approach through simulations and in the analysis of data on expression of genes in the Complement and Coagulation Cascade pathway in ovarian cancer.
KW - Conditional independence
KW - Microarray data
KW - Probability of expression
KW - Probit models
KW - Reciprocal graphs
KW - Reversible jumps MCMC
UR - http://www.scopus.com/inward/record.url?scp=84866254664&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866254664&partnerID=8YFLogxK
U2 - 10.1214/11-AOAS525
DO - 10.1214/11-AOAS525
M3 - Article
C2 - 28473730
AN - SCOPUS:84866254664
VL - 6
SP - 542
EP - 560
JO - Annals of Applied Statistics
JF - Annals of Applied Statistics
SN - 1932-6157
IS - 2
ER -