limma powers differential expression analyses for RNA-sequencing and microarray studies
Matthew E. Ritchie,Belinda Phipson,Di Wu,Yifang Hu,Charity W. Law,Wei Shi,Gordon K. Smyth,Gordon K. Smyth +7 more
Reads0
Chats0
TLDR
The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.Abstract:
limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.read more
Citations
More filters
Journal ArticleDOI
Integrating single-cell transcriptomic data across different conditions, technologies, and species.
TL;DR: An analytical strategy for integrating scRNA-seq data sets based on common sources of variation is introduced, enabling the identification of shared populations across data sets and downstream comparative analysis.
Journal ArticleDOI
Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown
TL;DR: This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts.
Journal ArticleDOI
Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: A single-arm, multicentre, phase 2 trial
Jonathan E. Rosenberg,Jean H. Hoffman-Censits,Thomas Powles,Michiel S. van der Heijden,Arjun Vasant Balar,Andrea Necchi,Nancy A. Dawson,Peter H. O'Donnell,Ani Balmanoukian,Yohann Loriot,Sandy Srinivas,Margitta Retz,Petros Grivas,Richard W. Joseph,Matthew D. Galsky,Mark D. Fleming,Daniel P. Petrylak,Jose Luis Perez-Gracia,Howard A. Burris,Daniel Castellano,Christina Canil,Joaquim Bellmunt,Dean F. Bajorin,Dorothee Nickles,Richard Bourgon,Garrett M. Frampton,Na Cui,Sanjeev Mariathasan,Oyewale O. Abidoye,Gregg Fine,Robert Dreicer +30 more
TL;DR: Treatment with atezolizumab resulted in a significantly improved RECIST v1.1 response rate, compared with a historical control overall response rate of 10%, and Exploratory analyses showed The Cancer Genome Atlas (TCGA) subtypes and mutation load to be independently predictive for response to atezolediazepine.
Journal ArticleDOI
Fast, sensitive and accurate integration of single-cell data with Harmony.
Ilya Korsunsky,Nghia Millard,Jean Fan,Kamil Slowikowski,Fan Zhang,Kevin Wei,Yuriy Baglaenko,Michael B. Brenner,Po-Ru Loh,Po-Ru Loh,Po-Ru Loh,Soumya Raychaudhuri +11 more
TL;DR: Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.
Journal ArticleDOI
Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences
TL;DR: It is illustrated that while the presence of differential isoform usage can lead to inflated false discovery rates in differential expression analyses on simple count matrices and transcript-level abundance estimates improve the performance in simulated data, the difference is relatively minor in several real data sets.
References
More filters
Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Journal ArticleDOI
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini,Yosef Hochberg +1 more
TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Journal ArticleDOI
Statistical methods for assessing agreement between two methods of clinical measurement.
TL;DR: An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.
Journal ArticleDOI
Gene Ontology: tool for the unification of biology
M Ashburner,Catherine A. Ball,Judith A. Blake,David Botstein,Heather Butler,J. M. Cherry,Allan Peter Davis,Kara Dolinski,Selina S. Dwight,J.T. Eppig,Midori A. Harris,David P. Hill,Laurie Issel-Tarver,Andrew Kasarskis,Suzanna E. Lewis,John C. Matese,Joel E. Richardson,M. Ringwald,Gerald M. Rubin,Gavin Sherlock +19 more
TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
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.