Identifying differentially expressed transcripts from RNA-seq data with biological variation

Abstract

Motivation: High-throughput sequencing enables expression analysis at the level of individual transcripts. The analysis of transcriptome expression levels and differential expression (DE) estimation requires a probabilistic approach to properly account for ambiguity caused by shared exons and finite read sampling as well as the intrinsic biological variance of transcript expression.Results: We present Bayesian inference of transcripts from sequencing data (BitSeq), a Bayesian approach for estimation of transcript expression level from RNA-seq experiments. Inferred relative expression is represented by Markov chain Monte Carlo samples from the posterior probability distribution of a generative model of the read data. We propose a novel method for DE analysis across replicates which propagates uncertainty from the sample-level model while modelling biological variance using an expression-level-dependent prior. We demonstrate the advantages of our method using simulated data as well as an RNA-seq dataset with technical and biological replication for both studied conditions.Availability: The implementation of the transcriptome expression estimation and differential expression analysis, BitSeq, has been written in and . The software is available online from http://code.google.com/p/bitseq/, version 0.4 was used for generating results presented in this article.Contact: glaus@cs.man.ac.uk, antti.honkela@hiit.fi or m.rattray@sheffield.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

“…idx <-which(res$adj_pvalue < 0.05) [1] A typical analysis of differential transcript usage would involve asking first: "which genes contain any evidence of DTU? ", and secondly, "which transcripts in the genes that contain some evidence may be participating in the DTU?"…”

Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

“…Several software packages have been developed for performing such “simple” counting (e.g., featureCounts 1 and HTSeq-count 2 ). More recently, the field has seen a surge in methods aimed at quantifying the abundances of individual transcripts (e.g., Cufflinks 3 , RSEM 4 , BitSeq 5 , kallisto 6 and Salmon 7 ). These methods provide higher resolution than simple counting, and by circumventing the computationally costly read alignment step, some are considerably faster.…”

Improved variational Bayes inference for transcript expression estimation

“…Both the number of mixture components (transcripts) and observations (reads) cover a wide range. The method described in Glaus et al (2012) was implemented in order to compute the likelihood of the n reads to the K transcripts, as well as to obtain an MCMC sample from the posterior distribution. Finally, the VB methods were applied.…”

“…Li et al (2010) applied a maximum likelihood approach, using the expectation-maximization (EM) algorithm (Dempster et al, 1977). The Bayesian approach was followed by Katz et al (2010), Turro et al (2011) and Glaus et al (2012), via Markov chain Monte Carlo (MCMC) sampling. However, the high dimensionality of RNA-seq datasets imposes certain new inferential difficulties, making convergence of MCMC methods a time consuming task.…”

“…In particular, we show that it is better to approximate the actual posterior distribution, rather than the joint posterior of model parameters and latent variables. The proposed methodology builds upon the BitSeq model (Glaus et al, 2012) and exploits the solution of standard VB. An optimization is performed over a class of distributions that share the same mean as the VB solution, but the variance is different.…”