Biomarker detection and categorization in ribonucleic acid sequencing meta-analysis using Bayesian hierarchical models.

ABSTRACT: Meta-analysis combining multiple transcriptomic studies increases statistical power and accuracy in detecting differentially expressed genes. As the next-generation sequencing experiments become mature and affordable, increasing number of RNA-seq datasets are available in the public domain. The count-data based technology provides better experimental accuracy, reproducibility and ability to detect low-expressed genes. A naive approach to combine multiple RNA-seq studies is to apply differential analysis tools such as edgeR and DESeq to each study and then combine the summary statistics of p-values or effect sizes by conventional meta-analysis methods. Such a two-stage approach loses statistical power, especially for genes with short length or low expression abundance. In this paper, we pro

SUBMITTER: Ma T 

PROVIDER: S-EPMC5543999 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature