In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html
 .

/pdf/moderated-estimation-of-fold-change-and-dispersion-for-rna-3ywnbkcan0.pdf

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).

http://dmrocke.ucdavis.edu/Class/BST226.2014.Winter/edgeR%20bioinformatics%202010.pdf

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

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.

/pdf/limma-powers-differential-expression-analyses-for-rna-4yl7ae3arh.pdf

limma powers differential expression analyses for RNA-sequencing and microarray studies

Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

/pdf/the-genome-analysis-toolkit-a-mapreduce-framework-for-4aygfa9b66.pdf

The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-Seq data, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data. DESeq2 uses shrinkage estimation for dispersions and fold changes to improve stability and interpretability of the estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression and facilitates downstream tasks such as gene ranking and visualization. DESeq2 is available as an R/Bioconductor package.

/pdf/moderated-estimation-of-fold-change-and-dispersion-for-rna-59glcgpwlk.pdf

Summary: In this paper, we review the central concepts and implementations of tools for working with network structures in Bioconductor. Interfaces to open source resources for visualization (AT&T Graphviz) and network algorithms (Boost) have been developed to support analysis of graphical structures in genomics and computational biology.

Availability: Packages graph, Rgraphviz, RBGL of Bioconductor (www.bioconductor.org).

Contact: stvjc@channing.harvard.edu

/pdf/network-structures-and-algorithms-in-bioconductor-zoqces2frq.pdf

Network structures and algorithms in Bioconductor

Summary: Automated analysis of flow cytometry (FCM) data is essential for it to become successful as a high throughput technology. We believe that the principles of Trellis graphics can be adapted to provide useful visualizations that can aid such automation. In this article, we describe the R/Bioconductor package flowViz that implements such visualizations.

Availability: flowViz is available as an R package from the Bioconductor project: http://bioconductor.org

Contact: dsarkar@fhcrc.org

Using flowViz to visualize flow cytometry data

RNA-editing is a tightly regulated, and essential cellular process for a properly functioning brain. Dysfunction of A-to-I RNA editing can have catastrophic effects, particularly in the central nervous system. Thus, understanding how the process of RNA-editing is regulated has important implications for human health. However, at present, very little is known about the regulation of editing across tissues, and individuals. Here we present an analysis of RNA-editing patterns from 9 different tissues harvested from a single mouse. For comparison, we also analyzed data for 5 of these tissues harvested from 15 additional animals. We find that tissue specificity of editing largely reflects differential expression of substrate transcripts across tissues. We identified a surprising enrichment of editing in intronic regions of brain transcripts, that could account for previously reported higher levels of editing in brain. There exists a small but remarkable amount of editing which is tissue-specific, despite comparable expression levels of the edit site across multiple tissues. Expression levels of editing enzymes and their isoforms can explain some, but not all of this variation. Together, these data suggest a complex regulation of the RNA-editing process beyond transcript expression levels.

/pdf/complex-regulation-of-adar-mediated-rna-editing-across-1fzx7v0dwj.pdf

Complex regulation of ADAR-mediated RNA-editing across tissues.

Programming environments such as S and Lisp-Stat have languages for performing computations, data storage mechanisms, and a graphical interface. These languages provide an interactive interface to data analysis that is invaluable. To take full advantage of these programming environments, statisticians must understand the differences between them. Ihaka and Gentleman introduced R, a version of S which uses a different scoping regimen. In some ways this makes R behave more like Lisp-Stat. This article discusses the concept of scoping rules and shows how lexical scope can enhance the functionality of a language.

Lexical Scope and Statistical Computing

Motivation: Gene-set enrichment analysis (GSEA) can be greatly enhanced by linear model (regression) diagnostic techniques. Diagnostics can be used to identify outlying or influential samples, and also to evaluate model fit and explore model expansion.

Results: We demonstrate this methodology on an adult acute lymphoblastic leukemia (ALL) dataset, using GSEA based on chromosome-band mapping of genes. Individual residuals, grouped or aggregated by chromosomal loci, indicate problematic samples and potential data-entry errors, and help identify hyperdiploidy as a factor playing a key role in expression for this dataset. Subsequent analysis pinpoints suspected DNA copy number abnormalities of specific samples and chromosomes (most prevalent are chromosomes X, 21 and 14), and also reveals significant expression differences between the hyperdiploid and diploid groups on other chromosomes (most prominently 19, 22, 3 and 13)—differences which are apparently not associated with copy number.

Availability: Software for the statistical tools demonstrated in this article is available as Bioconductor package GSEAlm.

Contact: assaf.oron@gmail.com

Supplementary information: Supplementary data are available at Bioinformatics online.

/pdf/gene-set-enrichment-analysis-using-linear-models-and-2fykcww6vr.pdf

Robert Gentleman

Papers

Network structures and algorithms in Bioconductor

Using flowViz to visualize flow cytometry data

Complex regulation of ADAR-mediated RNA-editing across tissues.

Lexical Scope and Statistical Computing

Gene set enrichment analysis using linear models and diagnostics