Abstract
In biomedical studies, detecting the changes in a response distribution under different testing conditions is one of the important issues. For example, increase in dose level may lead to increase or decrease in the gene expression level. To address this issue, we propose a nonparametric Bayesian test for testing the difference in location when samples are collected under two different conditions. We apply Dirichlet process priors to estimate the probabilities, which imply constraint on cumulative distribution functions of occurrence evaluated at cut-off value that partitions the expression range of that gene into two intervals. The proposed test can be easily extended for multiple samples comparisons in gene expression analysis.
| Original language | English (US) |
|---|---|
| Title of host publication | Bayesian Inference and Maximum Entropy Methods in Science and Engineering - 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering |
| Pages | 382-388 |
| Number of pages | 7 |
| Volume | 1193 |
| DOIs | |
| State | Published - Dec 1 2009 |
| Event | 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering - Oxford, MS, United States Duration: Jul 5 2009 → Jul 10 2009 |
Other
| Other | 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering |
|---|---|
| Country | United States |
| City | Oxford, MS |
| Period | 7/5/09 → 7/10/09 |
Fingerprint
Keywords
- Dirichlet process
- Gene expression
- Location
- Nonparametric Bayes
ASJC Scopus subject areas
- Physics and Astronomy(all)
Cite this
A nonparametric Bayesian test for detecting the difference in location parameters. / Mathur, Sunil.
Bayesian Inference and Maximum Entropy Methods in Science and Engineering - 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. Vol. 1193 2009. p. 382-388.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A nonparametric Bayesian test for detecting the difference in location parameters
AU - Mathur, Sunil
PY - 2009/12/1
Y1 - 2009/12/1
N2 - In biomedical studies, detecting the changes in a response distribution under different testing conditions is one of the important issues. For example, increase in dose level may lead to increase or decrease in the gene expression level. To address this issue, we propose a nonparametric Bayesian test for testing the difference in location when samples are collected under two different conditions. We apply Dirichlet process priors to estimate the probabilities, which imply constraint on cumulative distribution functions of occurrence evaluated at cut-off value that partitions the expression range of that gene into two intervals. The proposed test can be easily extended for multiple samples comparisons in gene expression analysis.
AB - In biomedical studies, detecting the changes in a response distribution under different testing conditions is one of the important issues. For example, increase in dose level may lead to increase or decrease in the gene expression level. To address this issue, we propose a nonparametric Bayesian test for testing the difference in location when samples are collected under two different conditions. We apply Dirichlet process priors to estimate the probabilities, which imply constraint on cumulative distribution functions of occurrence evaluated at cut-off value that partitions the expression range of that gene into two intervals. The proposed test can be easily extended for multiple samples comparisons in gene expression analysis.
KW - Dirichlet process
KW - Gene expression
KW - Location
KW - Nonparametric Bayes
UR - http://www.scopus.com/inward/record.url?scp=72949090678&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=72949090678&partnerID=8YFLogxK
U2 - 10.1063/1.3275637
DO - 10.1063/1.3275637
M3 - Conference contribution
AN - SCOPUS:72949090678
SN - 9780735407299
VL - 1193
SP - 382
EP - 388
BT - Bayesian Inference and Maximum Entropy Methods in Science and Engineering - 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
ER -