DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

Abstract

There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same primary transcript. There are situations where differences (e.g. between normal and disease state) in the relative ratio of expressed isoforms may have significant phenotypic consequences or lead to prognostic capabilities. Similarly, knowledge of single nucleotide polymorphisms (SNPs) that affect splicing, so-called splicing quantitative trait loci (sQTL) will help to characterize the effects of genetic variation on gene expression. RNA sequencing (RNA-seq) has provided an attractive toolbox to carefully unravel alternative splicing outcomes and recently, fast and accurate methods for transcript quantification have become available. We propose a statistical framework based on the Dirichlet-multinomial distribution that can discover changes in isoform usage between conditions and SNPs that affect relative expression of transcripts using these quantifications. The Dirichlet-multinomial model naturally accounts for the differential gene expression without losing information about overall gene abundance and by joint modeling of isoform expression, it has the capability to account for their correlated nature. The main challenge in this approach is to get robust estimates of model parameters with limited numbers of replicates. We approach this by sharing information and show that our method improves on existing approaches in terms of standard statistical performance metrics. The framework is applicable to other multivariate scenarios, such as Poly-A-seq or where beta-binomial models have been applied (e.g., differential DNA methylation). Our method is available as a Bioconductor R package called DRIMSeq.

Dirichlet‐multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data

“…For instance, Fordyce, Gompert, Forister, and Nice () rely on Dirichlet‐multinomial modelling (DMM) to analyze ecological count data, such as counts of behavioural and dietary choices of animals (also see Coblentz, Rosenblatt, & Novak, ). Similar models have been applied to large counts of DNA sequences—for instance, Fernandes et al (; aldex2 ), Nowicka and Robinson (; drim‐seq ), and Rosa et al (; hmp ) use DMM to estimate and compare feature‐specific relative abundances in transcriptomes and microbiomes. Additionally, DMM has been used to model mixtures of compositions, a situation that could arise in a laboratory‐derived microbial assemblage occurring as a contaminant within samples, or in mixtures of different communities in nature ( microbedmm , Holmes, Harris, & Quince, ; sourcetracker , Knights et al, ; biomico , Shafiei et al, ; feast , Shenhav et al, ; ecostructure , White, Dey, Mohan, Stephens, & Price, ).…”

“…For instance, Fordyce, Gompert, Forister, and Nice (2011) rely on Dirichlet-multinomial modelling (DMM) to analyze ecological count data, such as counts of behavioural and dietary choices of animals (also see Coblentz, Rosenblatt, & Novak, 2017). Similar models have been applied to large counts of DNA sequences-for instance, Fernandes et al (2014;aldex2), Nowicka and Robinson (2016;drim-seq), and Rosa et al (2012;hmp) Consequently, we conducted a simulation experiment to learn the limits and benefits of DMM through the analysis of data that encompass much of the variety in attributes encountered across scientific domains (e.g. replication, number of observations, and so on; Figure 2).…”

A powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists

“…Analysis of RNA-seq data for most biologists is a bottleneck because of reliance on the skills of often over-stretched bioinformaticians who are needed to process large datasets and apply complex analytical programs to experimental data. Many RNA-seq differential analysis programs do not have the flexibility to handle complex experimental designs (such as time-course or developmental series data) and are error prone (Love et al, 2014;Hardcastle and Kelly, 2010;Anders et al, 2012;Nowicka and Robinson, 2016). Results can be inconsistent due to the use of multiple different combinations of tools or pipelines by different bioinformaticians.…”

ACTOR: a latent Dirichlet model to compare expressed isoform proportions to a reference panel

“…DEXSeq, on the other hand, utilizes a Negative Binomial distribution to model the counts per exon. It was originally targeted for identifying differential exon usage, but has been also evaluated in the context of transcript usage (Soneson and others, 2016;Nowicka and Robinson, 2016;Love and others, 2018). SUPPA2 uses biological replicates to estimate differences in isoform proportions across conditions and between biological replicates.…”