An Iterative Leave-One-Out Approach to Outlier Detection in RNA-Seq Data.

ABSTRACT: The discrete data structure and large sequencing depth of RNA sequencing (RNA-seq) experiments can often generate outlier read counts in one or more RNA samples within a homogeneous group. Thus, how to identify and manage outlier observations in RNA-seq data is an emerging topic of interest. One of the main objectives in these research efforts is to develop statistical methodology that effectively balances the impact of outlier observations and achieves maximal power for statistical testing. To reach that goal, strengthening the accuracy of outlier detection is an important precursor. Current outlier detection algorithms for RNA-seq data are executed within a testing framework and may be sensitive to sparse data and heavy-tailed distributions. Therefore, we propose a univariate algorithm t

SUBMITTER: George NI 

PROVIDER: S-EPMC4454687 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature