Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates

Lund, Steven P.; Nettleton, Dan; McCarthy, Davis J.; Smyth, Gordon K.

doi:10.1515/1544-6115.1826

Cited by 289 publications

(297 citation statements)

References 19 publications

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“…growth condition main effects), and interactions between genotypes and growth conditions using the R package QuasiSeq (http://cran.r-project.org/ web/packages/QuasiSeq). The negative binomial QLShrink method implemented in the QuasiSeq package as described previously (Lund et al, 2012) was used to compute a P value for each gene and each test described above. The log of each count mean was modeled as the sum of an intercept term, a genotype effect, a growth condition effect, an interaction between genotype and growth condition, and an offset normalization factor, determined for each sample by the log of the TMM normalization factor (Robinson and Oshlack, 2010).…”

Section: Statistical Analysesmentioning

confidence: 99%

Discovery and Characterization of the 3-Hydroxyacyl-ACP Dehydratase Component of the Plant Mitochondrial Fatty Acid Synthase System

et al. 2017

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We report the characterization of the Arabidopsis (Arabidopsis thaliana) 3-hydroxyacyl-acyl carrier protein dehydratase (mtHD) component of the mitochondrial fatty acid synthase (mtFAS) system, encoded by AT5G60335. The mitochondrial localization and catalytic capability of mtHD were demonstrated with a green fluorescent protein transgenesis experiment and by in vivo complementation and in vitro enzymatic assays. RNA interference (RNAi) knockdown lines with reduced mtHD expression exhibit traits typically associated with mtFAS mutants, namely a miniaturized morphological appearance, reduced lipoylation of lipoylated proteins, and altered metabolomes consistent with the reduced catalytic activity of lipoylated enzymes. These alterations are reversed when mthd-rnai mutant plants are grown in a 1% CO 2 atmosphere, indicating the link between mtFAS and photorespiratory deficiency due to the reduced lipoylation of glycine decarboxylase. In vivo biochemical feeding experiments illustrate that sucrose and glycolate are the metabolic modulators that mediate the alterations in morphology and lipid accumulation. In addition, both mthd-rnai and mtkas mutants exhibit reduced accumulation of 3-hydroxytetradecanoic acid (i.e. a hallmark of lipid A-like molecules) and abnormal chloroplastic starch granules; these changes are not reversible by the 1% CO 2 atmosphere, demonstrating two novel mtFAS functions that are independent of photorespiration. Finally, RNA sequencing analysis revealed that mthd-rnai and mtkas mutants are nearly equivalent to each other in altering the transcriptome, and these analyses further identified genes whose expression is affected by a functional mtFAS system but independent of photorespiratory deficiency. These data demonstrate the nonredundant nature of the mtFAS system, which contributes unique lipid components needed to support plant cell structure and metabolism.

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Section: Statistical Analysesmentioning

confidence: 99%

Discovery and Characterization of the 3-Hydroxyacyl-ACP Dehydratase Component of the Plant Mitochondrial Fatty Acid Synthase System

et al. 2017

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“…Following [11], we assume that the prior distribution for σ 2 g is a scaled inverse χ 2 -distribution with degrees of freedom d 0 and scaling factor s 2 0 d 0 , i.e.,…”

Section: Estimating Prior Weightmentioning

confidence: 99%

“…In particular, loess-style weighting is used to improved the weighted likelihood approach, and an analogy with quasi-likelihood [11] is used to estimate the optimal weight to be given to the empirical Bayes prior. The article includes a fully worked case study.…”

Section: Introductionmentioning

confidence: 99%

Differential Expression Analysis of Complex RNA-seq Experiments Using edgeR

Chen

Lun

2014

Statistical Analysis of Next Generation Sequencing Data

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This article reviews the statistical theory underlying the edgeR software package for differential expression of RNA-seq data. Negative binomial models are used to capture the quadratic mean-variance relationship that can be observed in RNA-seq data. Conditional likelihood methods are used to avoid bias when estimating the level of variation. Empirical Bayes methods are used to allow gene-specific variation estimates even when the number of replicate samples is very small. Generalized linear models are used to accommodate arbitrarily complex designs. A key feature of the edgeR package is the use of weighted likelihood methods to implement a flexible empirical Bayes approach in the absence of easily tractable sampling distributions. The methodology is implemented in flexible software that is easy to use even for users who are not professional statisticians or bioinformaticians. The software is part of the Bioconductor project.This article describes some recently implemented features. Loess-style weighting is used to improve the weighted likelihood approach, and an analogy with quasilikelihood is used to estimate the optimal weight to be given to the empirical Bayes prior. The article includes a fully worked case study with complete code.

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“…The other approach (see discussion after Section 2) is to choose a method of single imputation of missing values and then to use available approaches to identify differentially expressed genes from RNA-seq data. Some available packages in R such as DEGseq (Wang et al, 2010), easyRNAseq (Delhomme et al, 2012) and QuasiSeq (Lund et al, 2012) can be used for this purpose. One approach is to use a regression method for identifying differentially expressed genes as follows:…”

Section: Identifying Differentially Expressed Genes From Rna-seq Datamentioning

confidence: 99%

Missing Value Imputation for RNA-Sequencing Data Using Statistical Models: A Comparative Study

Baghfalaki¹,

Ganjali²,

Berridge³

2016

JSTA

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RNA-seq technology has been widely used as an alternative approach to traditional microarrays in transcript analysis. Sometimes gene expression by sequencing, which generates RNA-seq data set, may have missing read counts. These missing values can adversely affect downstream analyses. Most of the methods for analysing the RNA-seq data sets require a complete matrix of RNA-seq data. In the past few years, researchers have been putting a great deal of effort into presenting evaluations of the different imputation algorithms in microarray gene expression data sets, However, these are limited works for RNA-seq data sets and a comparative study for investigating the performance of the missing value imputation for RNA-seq data is essential. In this paper, we propose the use of some parametric models such as Regression imputation, Bayesian generalized linear model, Poisson mixture model, EM approach , Bayesian Poisson regression, Bayesian quasi-Poisson regression and the Bootstrap version of two latter for single imputation of missing values in RNA-seq count data sets. The approaches are also applied for identifying differentially expressed genes in the presence of missing values. Multiple imputation, proposed by Rubin (1978), is also used for multiple imputation of missing RNA-seq counts. This approach allows appropriate assessment of imputation uncertainty for missing values. The performance of the single and multiple imputations are investigated using some simulation studies. Also, some real data sets are analyzed using the proposed approaches.

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Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates

Cited by 289 publications

References 19 publications

Discovery and Characterization of the 3-Hydroxyacyl-ACP Dehydratase Component of the Plant Mitochondrial Fatty Acid Synthase System

Discovery and Characterization of the 3-Hydroxyacyl-ACP Dehydratase Component of the Plant Mitochondrial Fatty Acid Synthase System

Differential Expression Analysis of Complex RNA-seq Experiments Using edgeR

Missing Value Imputation for RNA-Sequencing Data Using Statistical Models: A Comparative Study

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