Developments in the study of genomes over the last decade using high-throughput sequencing of ribonucleic acid (RNA) have opened a new era of transcriptome analysis. Different statistical methods and software tools have been developed to analyze RNA-sequenced (RNA-seq) data in order to detect genes (features) that are differentially expressed (DE) in an experiment. We developed a new method (gibbSeq) based on log-normal distribution and full Bayesian inference using Gibbs sampling for detecting DE genes. We compare the performance of our method with some existing methods for analyzing RNA-seq data. Using simulated and real biological data, we find that our method performs as well or even better than some of the existing methods, for a wide range of simulated conditions in controlling type I error rate and false discoveries.
|Commitee:||Borchers, Brian, Hossain, Anwar|
|School:||New Mexico Institute of Mining and Technology|
|School Location:||United States -- New Mexico|
|Source:||MAI 56/04M(E), Masters Abstracts International|
|Keywords:||Differential expression, Gibbs sampling, High throughput sequencing|
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