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Open AccessJournal ArticleDOI

voom: precision weights unlock linear model analysis tools for RNA-seq read counts

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.

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Journal ArticleDOI

Plasticity of distal nephron epithelia from human kidney organoids enables the induction of ureteric tip and stalk.

TL;DR: In this article, the authors re-analyzed the transcriptional distinction between distal nephron and ureteric epithelium in human fetal kidney, and showed that the distal kidney segment alone displays significant in vitro plasticity and can adopt a uteric tip identity when isolated and cultured in defined conditions.
Posted ContentDOI

diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering

TL;DR: diffcyt, a new computational framework for differential discovery analyses in these datasets, based on (i) high-resolution clustering and (ii) empirical Bayes moderated tests adapted from transcriptomics is presented.
Journal ArticleDOI

Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size.

TL;DR: It is concluded that the replicate number has a larger impact than the library size on the power of the DE analysis, except for low-expressed genes, for which both parameters seem to have the same impact.
References
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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

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, +1 more
- 27 Oct 2010 - 
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.
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