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Robust semiparametric mixing for detecting differentially expressed genes in microarray experiments
- Publication Year :
- 2007
- Publisher :
- ELSEVIER SCIENCE BV, 2007.
-
Abstract
- An important goal of microarray studies is the detection of genes that show significant changes in observed expressions when two or more classes of biological samples such as treatment and control are compared. Using the c-fold rule, a gene is declared to be differentially expressed if its average expression level varies by more than a constant factor c between treatment and control (typically c=2). While often used, however, this simple rule is not completely convincing. By modeling this filter, a binary variable is defined at the genexexperiment level, allowing for a more powerful treatment of the corresponding information. A gene-specific random term is introduced to control for both dependence among genes and variability with respect to the c-fold threshold. Inference is carried out via a two-level finite mixture model under a likelihood approach. Then, parameter estimates are also derived using the counting distribution under a Bayesian nonparametric approach which allows to keep under control some error rate of erroneous discoveries. The effectiveness of both proposed approaches is illustrated through a large-scale simulation study and a well known benchmark data set.
- Subjects :
- Statistics and Probability
False discovery rate
business.industry
Applied Mathematics
Inference
Word error rate
counting distribution
Pattern recognition
Filter (signal processing)
Mixture model
microarray data
Expression (mathematics)
Set (abstract data type)
Computational Mathematics
Computational Theory and Mathematics
false discovery rate
mixture models
up-regulated genes
Artificial intelligence
business
Likelihood function
Mathematics
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....7e317349c2f7323e556271783a59cdbe