Outlier detection for improved differential splicing quantification from RNA-Seq experiments with replicates.

ABSTRACT:

Motivation

A key component in many RNA-Seq-based studies is contrasting multiple replicates from different experimental conditions. In this setup, replicates play a key role as they allow to capture underlying biological variability inherent to the compared conditions, as well as experimental variability. However, what constitutes a 'bad' replicate is not necessarily well defined. Consequently, researchers might discard valuable data or downstream analysis may be hampered by failed experiments.

Results

Here we develop a probability model to weigh a given RNA-Seq sample as a representative of an experimental condition when performing alternative splicing analysis. We demonstrate that this model detects outlier samples which are consistently and significantly different compare

SUBMITTER: Norton SS 

PROVIDER: S-EPMC6454425 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature