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

Differential gene expression in normal prostate epithelium of men with and without prostate cancer: evidence for a prostate cancer field effect

Personal Crunching: Statistical Computing Environments

BACKGROUND: With the emergence of web-based data collection methods, large digital health cohorts offer the opportunity to conduct behavioral and epidemiologic research at an unprecedented scale. The size and breadth of such data sets enable discovery of novel associations across the phenotypic spectrum. 
METHODS: We deployed the digital symbol substitution test (DSST) online to consented 23andMe research participants 50-85 years of age. We tested cross-sectional associations between DSST performance and 824 phenotypes using linear regression models adjusted for age, sex, age*sex interaction, device, time of cohort entry, and ancestry, separately among discovery (n=144,786) and replication (n=93,428) samples; additional analyses further adjusted for education. We post-stratified association estimates on age, sex, and education to adjust for discrepancies across subsamples. Leveraging the rich genetic and phenotypic data available at 23andMe, we also estimated genetic and environmental correlations between DSST and its top correlates using linkage disequilibrium score regression. 
RESULTS: 97 phenotypes were significantly (false discovery rate |0.5|) associated with DSST performance in the discovery phase. Of those, 60 (38 with additional adjustment for education) demonstrated both statistical significance and consistent direction of association in the replication sample. The significantly associated phenotypes largely clustered into the following categories: psychiatric traits (e.g. anxiety, β per 1 SD = -0.74, P-value=3.9x10-169), education (e.g. highest math class completed, β per 1 SD = 2.11, P-value <1 x 10-300), leisure activities (e.g. solitary activities like puzzles, β per 1 SD = 1.85, P-value <1.0x10-300), social determinants (e.g. household income, β per 1 SD = 1.20, P-value= 8.9x10-245, and lifestyle (e.g. years smoked, β per 1 SD = -0.98, P-value= 2.2x10-78). We identified several reproducible genetic correlations between DSST and its top associated exposures (e.g. 0.48 for leisure activities like puzzles, 0.28 for years of education, and -0.24 for anxiety; all P ≤ 7.9x10-26). For almost all exposures, genetic correlations with DSST were considerably stronger than environmental correlations. 
CONCLUSIONS: We have conducted the largest study of cognitive performance to date, building evidence supporting its correlations with many social, lifestyle, and clinical exposures. We established that the observed associations are in part underpinned by shared genetic architecture. Our study illustrates the potential of large-scale digital cohorts to contribute to epidemiologic discovery.

/pdf/an-environment-wide-study-of-adult-cognitive-performance-in-450xndiyvv.pdf

An Environment-Wide Study of Adult Cognitive Performance in the 23andMe Cohort

We propose computational methods and statistical paradigms to explore the relationships between phenotypic data and cellular organizational units, such as multi-protein complexes or pathways. Indeed, while proteins are often the primary unit used by cells to carry out the many different functions that the cell requires for life, they seldom accomplish important tasks alone, but rather assemble into organizational units. Recent studies suggest that some control of phenotype can be usefully attributed to multi-protein complexes rather than genes (Deutschbauer et al., 2005; Spirin et al., 2006) and hence may help provide elucidation of the underlying roles or mechanisms that directly control changes in phenotype.

PCpheno: assessing the role cellular organizational units in determining phenotype

This chapter discusses the design and use of a flexible randomization system for the study of the effect of different treatment allocations in clinical trials. Motivations for diverse allocation schemes are reviewed in brief, and a system that is capable of handling randomization for complex and dynamic experiments is sketched. Several inference schemes and their relationship to randomized designs are reviewed. The evaluation of practical randomization and inference schemes can be conducted using simulation. This is illustrated to assess achievement of balanced allocation, and pursuit of valid but powerful inferences based on rerandomization.

Robert Gentleman

Papers

Differential gene expression in normal prostate epithelium of men with and without prostate cancer: evidence for a prostate cancer field effect

Personal Crunching: Statistical Computing Environments

An Environment-Wide Study of Adult Cognitive Performance in the 23andMe Cohort

PCpheno: assessing the role cellular organizational units in determining phenotype

Randomized and Balancing Allocation Schemes for Clinical Trials: Computational Perspectives on Design and Deployment