Fast and accurate approximate inference of transcript expression from RNA-seq data

Hensman, James; Papastamoulis, Panagiotis; Glaus, Peter; Honkela, Antti; Rattray, Magnus

doi:10.1093/bioinformatics/btv483

Cited by 38 publications

(70 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This dataset contains gene expression profiles of 82 donors, 21 of which were identified as diabetic and/or having a high level of hba1c and 47 of which were healthy controls. We downloaded the raw reads data from GEO dataset GSE50244 and TPM matrices were generated using Kallisto (Hensman et al, 2015). Reads were then aligned using ENSEMBL transcripts.…”

Section: Methods Detailsmentioning

confidence: 99%

A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure

et al. 2016

View full text Add to dashboard Cite

Summary Although the function of the mammalian pancreas hinges on complex interactions of distinct cell types, gene expression profiles have primarily been described with bulk mixtures. Here we implemented a droplet-based, single-cell RNA-seq method to determine the transcriptomes of over 12,000 individual pancreatic cells from four human donors and two mouse strains. Cells could be divided into 15 clusters that matched previously characterized cell types: all endocrine cell types, including rare epsilon-cells; exocrine cell types; vascular cells; Schwann cells; quiescent and activated stellate cells; and four types of immune cells. We detected subpopulations of ductal cells with distinct expression profiles and validated their existence with immuno-histochemistry stains. Moreover, among human beta- cells, we detected heterogeneity in the regulation of genes relating to functional maturation and levels of ER stress. Finally, we deconvolved bulk gene expression samples using the single-cell data to detect disease-associated differential expression. Our dataset provides a resource for the discovery of novel cell type-specific transcription factors, signaling receptors, and medically relevant genes.

show abstract

Section: Methods Detailsmentioning

confidence: 99%

A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure

et al. 2016

View full text Add to dashboard Cite

show abstract

“…However, Tigar2 is by far the slowest algorithm, taking more than 9 hours to process 100 million reads even with multi-thread processing [20]. BitSeq also uses Bayesian inference and a user can choose either markov chain monte carlo (MCMC) or variational Bayesian (VB) methods for quantification [41,42]. MCMC is generally slower than VB method, but provides better accuracy.…”

Section: Isoform Quantification Of Known Transcriptsmentioning

confidence: 99%

Bioinformatics Tools for RNA-seq Gene and Isoform Quantification

Zhang¹,

Zhang²,

Vincent³

et al. 2016

Next Generat Sequenc & Applic

View full text Add to dashboard Cite

In recent years, RNA-seq has emerged as a powerful technology in estimation of gene or transcript expression. 'Union-exon' and transcript based approaches are widely used in gene quantification. The 'Union-exon' based approach is simple, but it does not distinguish between isoforms when multiple alternatively spliced transcripts are expressed from the same gene. Because a gene is expressed in one or more transcript isoforms, the transcript based approach is more biologically meaningful than the 'union exon'-based approach. However, estimating the expression of individual isoform is intrinsically challenging because different isoforms of a gene usually have a high proportion of genomic overlap. Recently, a number of tools have been developed for RNA-seq isoform quantification. We review those methods and their main features to serve as guidance for users to choose suitable tools for their specific projects.

show abstract

“…However, the high dimensionality of RNA-seq datasets imposes certain new inferential difficulties, making convergence of MCMC methods a time consuming task. On the other hand, approximate inference using variational Bayesian methods (Jordan et al, 1999) offers an attractive alternative which has also been applied to transcript abundance inference (Hensman et al, 2012(Hensman et al, , 2013Nariai et al, 2013). The posterior distribution is approximated by another one which is available in analytical form.…”

Section: Introductionmentioning

confidence: 99%

“…Their method is an order of magnitude faster than traditional VB implementation, which is itself often faster than MCMC. This algorithm provides a significant speed-up in comparison to VBEM for transcript quantification (Hensman et al, 2012(Hensman et al, , 2013.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved variational Bayes inference for transcript expression estimation

Papastamoulis

Hensman²,

Glaus³

et al. 2014

Statistical Applications in Genetics and Molecular Biology

Self Cite

View full text Add to dashboard Cite

RNA-seq studies allow for the quantification of transcript expression by aligning millions of short reads to a reference genome. However, transcripts share much of their sequence, so that many reads map to more than one place and their origin remains uncertain. This problem can be dealt using mixtures of distributions and transcript expression reduces to estimating the weights of the mixture. In this paper, variational Bayesian (VB) techniques are used in order to approximate the posterior distribution of transcript expression. VB has previously been shown to be more computationally efficient for this problem than Markov chain Monte Carlo. VB methodology can precisely estimate the posterior means, but leads to variance underestimation. For this reason, a novel approach is introduced which integrates the latent allocation variables out of the VB approximation. It is shown that this modification leads to a better marginal likelihood bound and improved estimate of the posterior variance. A set of simulation studies and application to real RNA-seq datasets highlight the improved performance of the proposed method.

show abstract

Fast and accurate approximate inference of transcript expression from RNA-seq data

Cited by 38 publications

References 28 publications

A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure

A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure

Bioinformatics Tools for RNA-seq Gene and Isoform Quantification

Improved variational Bayes inference for transcript expression estimation

Contact Info

Product

Resources

About