rmRNAseq: differential expression analysis for repeated-measures RNA-seq data

Nguyen, Yet; Nettleton, Dan

doi:10.1093/bioinformatics/btaa525

Cited by 11 publications

(13 citation statements)

References 33 publications

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“…In general, our results are consistent with another recently published methodology for analyzing RNA-Seq data with repeated measures [52]. Their strategy largely mirrors our use of the VST paired with fitting normal LMMs, with the major differences being the transformation used (VOOM versus the VST) and the specification of the correlation structure (auto-regressive versus compound symmetric as implied by the use of a random intercept in our implementation).…”

Section: Discussionsupporting

confidence: 87%

MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments

et al. 2020

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Background As the barriers to incorporating RNA sequencing (RNA-Seq) into biomedical studies continue to decrease, the complexity and size of RNA-Seq experiments are rapidly growing. Paired, longitudinal, and other correlated designs are becoming commonplace, and these studies offer immense potential for understanding how transcriptional changes within an individual over time differ depending on treatment or environmental conditions. While several methods have been proposed for dealing with repeated measures within RNA-Seq analyses, they are either restricted to handling only paired measurements, can only test for differences between two groups, and/or have issues with maintaining nominal false positive and false discovery rates. In this work, we propose a Bayesian hierarchical negative binomial generalized linear mixed model framework that can flexibly model RNA-Seq counts from studies with arbitrarily many repeated observations, can include covariates, and also maintains nominal false positive and false discovery rates in its posterior inference. Results In simulation studies, we showed that our proposed method (MCMSeq) best combines high statistical power (i.e. sensitivity or recall) with maintenance of nominal false positive and false discovery rates compared the other available strategies, especially at the smaller sample sizes investigated. This behavior was then replicated in an application to real RNA-Seq data where MCMSeq was able to find previously reported genes associated with tuberculosis infection in a cohort with longitudinal measurements. Conclusions Failing to account for repeated measurements when analyzing RNA-Seq experiments can result in significantly inflated false positive and false discovery rates. Of the methods we investigated, whether they model RNA-Seq counts directly or worked on transformed values, the Bayesian hierarchical model implemented in the mcmseq R package (available at https://github.com/stop-pre16/mcmseq ) best combined sensitivity and nominal error rate control.

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Section: Discussionsupporting

confidence: 87%

MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments

et al. 2020

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“…Q. rmRNAseq [82]: As another voom-incorporated R package, it has been also proposed for the longitudinally measured multi-series of time course data to account for correlated samples within-individual. It is based on generalized linear model with the continuous autoregressive correlation structure, parametric bootstrap method for estimation of temporally differential expression, residual maximum likelihood for estimation of parameters.…”

Section: Single Gene-by-gene Testing For Non-periodical Time Course Datamentioning

confidence: 99%

Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

2021

Genes

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Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.

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“…Some researchers have proposed employing standard statistical models typically used for longitudinal and correlated data outside of the context of RNA-seq data, as these well-developed modeling frameworks allow for flexible modeling and hypothesis testing [ 18 – 20 ]. In applying these methods to RNA-seq data, considerations still must be made to account for the non-normality of the data, for example, by choosing a repeated measures model with an underlying distribution for overdispersed counts.…”

Section: Introductionmentioning

confidence: 99%

A comparison of methods for multiple degree of freedom testing in repeated measures RNA-sequencing experiments

Wynn

Vestal

Fingerlin

et al. 2022

BMC Med Res Methodol

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Background As the cost of RNA-sequencing decreases, complex study designs, including paired, longitudinal, and other correlated designs, become increasingly feasible. These studies often include multiple hypotheses and thus multiple degree of freedom tests, or tests that evaluate multiple hypotheses jointly, are often useful for filtering the gene list to a set of interesting features for further exploration while controlling the false discovery rate. Though there are several methods which have been proposed for analyzing correlated RNA-sequencing data, there has been little research evaluating and comparing the performance of multiple degree of freedom tests across methods. Methods We evaluated 11 different methods for modelling correlated RNA-sequencing data by performing a simulation study to compare the false discovery rate, power, and model convergence rate across several hypothesis tests and sample size scenarios. We also applied each method to a real longitudinal RNA-sequencing dataset. Results Linear mixed modelling using transformed data had the best false discovery rate control while maintaining relatively high power. However, this method had high model non-convergence, particularly at small sample sizes. No method had high power at the lowest sample size. We found a mix of conservative and anti-conservative behavior across the other methods, which was influenced by the sample size and the hypothesis being evaluated. The patterns observed in the simulation study were largely replicated in the analysis of a longitudinal study including data from intensive care unit patients experiencing cardiogenic or septic shock. Conclusions Multiple degree of freedom testing is a valuable tool in longitudinal and other correlated RNA-sequencing experiments. Of the methods that we investigated, linear mixed modelling had the best overall combination of power and false discovery rate control. Other methods may also be appropriate in some scenarios.

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rmRNAseq: differential expression analysis for repeated-measures RNA-seq data

Cited by 11 publications

References 33 publications

MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments

MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments

Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

A comparison of methods for multiple degree of freedom testing in repeated measures RNA-sequencing experiments

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