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
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
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Opportunities and challenges in long-read sequencing data analysis.
Shanika L. Amarasinghe,Shanika L. Amarasinghe,Shian Su,Shian Su,Xueyi Dong,Xueyi Dong,Luke Zappia,Matthew E. Ritchie,Matthew E. Ritchie,Quentin Gouil,Quentin Gouil +10 more
TL;DR: The current landscape of available tools is reviewed, the principles of error correction, base modification detection, and long-read transcriptomics analysis are focused on, and the challenges that remain are highlighted.
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A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor
TL;DR: This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project, which covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment.
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SC3: consensus clustering of single-cell RNA-seq data
Vladimir Yu. Kiselev,Kristina Kirschner,Michael T. Schaub,Michael T. Schaub,Tallulah S. Andrews,Andrew Yiu,Tamir Chandra,Tamir Chandra,Kedar Nath Natarajan,Kedar Nath Natarajan,Wolf Reik,Wolf Reik,Wolf Reik,Mauricio Barahona,Anthony R. Green,Martin Hemberg +15 more
TL;DR: It is demonstrated that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients and achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach.
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SCENIC: Single-Cell Regulatory Network Inference And Clustering
Sara Aibar,Carmen Bravo González-Blas,Thomas Moerman,Jasper Wouters,Vân Anh Huynh-Thu,Hana Imrichova,Zeynep Kalender Atak,Gert Hulselmans,Michael Dewaele,Florian Rambow,Pierre Geurts,Jan Aerts,Jean-Christophe Marine,Joost van den Oord,Stein Aerts +14 more
TL;DR: SCENIC (Single Cell rEgulatory Network Inference and Clustering) is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs.
Journal ArticleDOI
Adaptive resistance to therapeutic PD-1 blockade is associated with upregulation of alternative immune checkpoints.
Shohei Koyama,Esra A. Akbay,Yvonne Y. Li,Grit S. Herter-Sprie,Kevin A. Buczkowski,William G. Richards,Leena Gandhi,Amanda J. Redig,Scott J. Rodig,Hajime Asahina,Robert E. Jones,Meghana M. Kulkarni,Mari Kuraguchi,Sangeetha Palakurthi,Peter E. Fecci,Bruce E. Johnson,Pasi A. Jänne,Jeffrey A. Engelman,Sidharta P. Gangadharan,Daniel B. Costa,Gordon J. Freeman,Raphael Bueno,F. Stephen Hodi,Glenn Dranoff,Kwok-Kin Wong,Peter S. Hammerman,Peter S. Hammerman +26 more
TL;DR: Analysis of the tumour immune microenvironment in the context of anti-PD-1 therapy in two fully immunocompetent mouse models of lung adenocarcinoma suggests that upregulation of TIM-3 and other immune checkpoints may be targetable biomarkers associated with adaptive resistance to PD-1 blockade.
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