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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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Distinct structural classes of activating FOXA1 alterations in advanced prostate cancer

TL;DR: This study reaffirms the central role of FOXA1 in mediating oncogenesis driven by the androgen receptor, and provides mechanistic insights into how the classes of FoxA1 alteration promote the initiation and/or metastatic progression of prostate cancer.
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Immune cytolytic activity stratifies molecular subsets of human pancreatic cancer

TL;DR: Examination of the immune landscape of PDA as it relates to aspects of tumor biology suggests that intrinsic oncogenic processes drive immune inactivity in human PDA, highlighting the potential importance of immune checkpoints other than PD-L1/PD-1 as therapeutic targets in this lethal disease.
Journal ArticleDOI

Evaluation of variability in human kidney organoids.

TL;DR: A transcriptional analysis of kidney organoids reveals batch effects as the key drivers of variation, mainly through differences in maturity, and provides a list of highly variable genes and a method for estimating differentiation stage for improved disease modeling.
Journal ArticleDOI

A field guide for the compositional analysis of any-omics data.

TL;DR: This work synthesizes the extant literature to provide a concise guide on how to apply compositional data analysis to NGS count data and proposes the log-ratio transformation as a general solution to answer the question, “Relative to some important activity of the cell, what is changing?”
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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