PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayesian model of transcript differential expression in RNA-seq data with biological variation
Peter Glaus, Antti Honkela and Magnus Rattray
In: Machine Learning in Computational Biology (MLCB) 2011, 17 Dec 2011, Sierra Nevada, Spain.

Abstract

We present BitSeq (Bayesian Inference of Transcripts from Sequencing data), a probabilistic method for transcript isoform differential expression inference from RNA-seq experiments. The BitSeq method consists of two stages: probabilistic transcript expression estimation and a probabilistic model of differential expression using replicates. The inference in both steps is performed using Markov chain Monte Carlo (MCMC). BitSeq is the first method designed to test differential expression on transcript isoform level taking biological variation into account using replicates. We demonstrate it is superior to the state-of-the-art gene-level differential expression analysis method DESeq in this problem.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8811
Deposited By:Antti Honkela
Deposited On:21 February 2012