Hierarchical Probabilistic Interaction Modeling for Multiple Gene Expression Replicates
| Autor: | Kristopher L. Patton, Daniel R. Lewis, Gloria K. Muday, James L. Norris, David J. John |
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| Rok vydání: | 2014 |
| Předmět: |
Posterior probability
Arabidopsis Gene regulatory network Computational biology Biology Bayesian inference Machine learning computer.software_genre Bayes' theorem Genetics Bayesian hierarchical modeling Gene Regulatory Networks Oligonucleotide Array Sequence Analysis Models Statistical Models Genetic business.industry Gene Expression Profiling Applied Mathematics Computational Biology Bayes Theorem Replicate Gene expression profiling Gene chip analysis Artificial intelligence business computer Algorithms Biotechnology |
| Zdroj: | IEEE/ACM Transactions on Computational Biology and Bioinformatics. 11:336-346 |
| ISSN: | 1545-5963 |
| DOI: | 10.1109/tcbb.2014.2299804 |
| Popis: | Microarray technology allows for the collection of multiple replicates of gene expression time course data for hundreds of genes at a handful of time points. Developing hypotheses about a gene transcriptional network, based on time course gene expression data is an important and very challenging problem. In many situations there are similarities which suggest a hierarchical structure between the replicates. This paper develops posterior probabilities for network features based on multiple hierarchical replications. Through Bayesian inference, in conjunction with the Metropolis-Hastings algorithm and model averaging, a hierarchical multiple replicate algorithm is applied to seven sets of simulated data and to a set of Arabidopsis thaliana gene expression data. The models of the simulated data suggest high posterior probabilities for pairs of genes which have at least moderate signal partial correlation. For the Arabidopsis model, many of the highest posterior probability edges agree with the literature. |
| Databáze: | OpenAIRE |
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