Bayesian Correlation Analysis for Sequence Count Data.

ABSTRACT: Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities' measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In co

SUBMITTER: Sanchez-Taltavull D 

PROVIDER: S-EPMC5049778 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature