voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Charity W. Law,Charity W. Law,Yunshun Chen,Yunshun Chen,Wei Shi,Wei Shi,Gordon K. Smyth,Gordon K. Smyth +7 more
TLDR
New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments, and the voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline.Abstract:
New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.read more
Citations
More filters
Journal ArticleDOI
PROS-1/Prospero Is a Major Regulator of the Glia-Specific Secretome Controlling Sensory-Neuron Shape and Function in C. elegans
TL;DR: A post-developmental glial role for the PROS-1/Prospero/PROX1 homeodomain protein in sensory-neuron function in C. elegans is described and it is demonstrated that, unlike previously characterized cell fate roles, PROS1 functions post-embryonically to control sense-organ glia-specific secretome expression.
Journal ArticleDOI
A systems genetics resource and analysis of sleep regulation in the mouse
Shanaz Diessler,Maxime Jan,Maxime Jan,Yann Emmenegger,Nicolas Guex,Benita Middleton,Debra J. Skene,Mark Ibberson,Frédéric Burdet,Lou Götz,Marco Pagni,Martial Sankar,Robin Liechti,Charlotte N. Hor,Ioannis Xenarios,Ioannis Xenarios,Paul Franken +16 more
TL;DR: It is found that a one-time, short disruption of sleep already extensively reshaped the systems genetics landscape by altering 60%–78% of the transcriptomes and the metabolome, with numerous genetic loci affecting the magnitude and direction of change.
Journal ArticleDOI
Assessment of statistical methods from single cell, bulk RNA-seq and metagenomics applied to microbiome data
TL;DR: The multivariate and compositional methods developed specifically for microbiome analysis did not outperform univariate methods developed for differential expression analysis of RNA-seq data, and a framework to help scientists make an informed choice of analysis methods in a dataset-specific manner is presented.
Journal ArticleDOI
Regulation of human glioma cell migration, tumor growth, and stemness gene expression using a Lck targeted inhibitor
John P. Zepecki,Kristin M. Snyder,M. M. Moreno,Eduardo Fajardo,Andras Fiser,J. Ness,Atom Sarkar,Steven A. Toms,Nikos Tapinos +8 more
TL;DR: A dorsal root ganglion axon-oligodendrocyte-hGC co-culture to study in real time the migration and interaction of hGCs with their microenvironment and the involvement of Lck in different levels of glioma malignant progression, such as migration, tumor growth, and regulation of cancer stemness makes Lck a potentially important therapeutic target for human glioblastomas.
Journal ArticleDOI
A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis
Ying Ni,Delasa Aghamirzaie,Haitham Elmarakeby,Eva Collakova,Song Li,Ruth Grene,Lenwood S. Heath +6 more
TL;DR: A machine learning approach is presented to predict GRNs specific to developing Arabidopsis thaliana embryos based on a support vector machine (SVM) model and provides evidence for previously unknown inhibitory effects of three positive regulators of gene expression.
References
More filters
Journal ArticleDOI
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian,Pablo Tamayo,Vamsi K. Mootha,Sayan Mukherjee,Benjamin L. Ebert,Michael A. Gillette,Amanda G. Paulovich,Scott L. Pomeroy,Todd R. Golub,Eric S. Lander,Jill P. Mesirov +10 more
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Journal ArticleDOI
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.
TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
Book
Generalized Linear Models
Peter McCullagh,John A. Nelder +1 more
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Journal ArticleDOI
featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features
TL;DR: FeatureCounts as discussed by the authors is a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments, which implements highly efficient chromosome hashing and feature blocking techniques.
Journal ArticleDOI
Differential expression analysis for sequence count data.
Simon Anders,Wolfgang Huber +1 more
TL;DR: A method based on the negative binomial distribution, with variance and mean linked by local regression, is proposed and an implementation, DESeq, as an R/Bioconductor package is presented.