A Bayesian Semi-parametric Approach for the Differential Analysis of Sequence Counts Data.

ABSTRACT: Data obtained using modern sequencing technologies are often summarized by recording the frequencies of observed sequences. Examples include the analysis of T cell counts in immunological research and studies of gene expression based on counts of RNA fragments. In both cases the items being counted are sequences, of proteins and base pairs, respectively. The resulting sequence-abundance distribution is usually characterized by overdispersion. We propose a Bayesian semi-parametric approach to implement inference for such data. Besides modeling the overdispersion, the approach takes also into account two related sources of bias that are usually associated with sequence counts data: some sequence types may not be recorded during the experiment and the total count may differ from one experimen

SUBMITTER: Guindani M 

PROVIDER: S-EPMC4017673 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature