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

Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

/pdf/star-ultrafast-universal-rna-seq-aligner-r1vlt61g3c.pdf

STAR: ultrafast universal RNA-seq aligner

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.

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

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

R is not only a program for analyzing and visualizing data, it is an
open and programmable software environment. It can not only easily
access other programs written in a wide variety of languages, but also
be accessed itself from other programs. As such, it can be seen as the
computational Swiss army knife of statistics. Connecting a program to
R can be surprisingly simple, and once the connection is established,
the perhaps largest existing collection of statistical methodology is
available through a unified interface. Embedding R can save a lot of
human time by automating routine tasks, but more importantly, it often
gives a simple way of making our methods accessible to a much wider
audience.

R behind the scenes: Using S the (un)usual way

A cointegration analysis on a triangle of high frequency exchange rates is presented. Market efficiency requires the triangle to be cointegrated and the cointegration term to be a martingale difference sequence. We find empirical evidence against market efficiency for very short time horizons: The cointegration term does not behave like a martingale difference sequence. In an out-of-sample forecasting study the cointegrated vector autoregressive (VAR) model is found to be superior to the naive martingale. Finally, a simple trading strategy shows that the VAR also has a significant forecast value in economic terms even after accounting for transaction costs. (author's abstract)

Cointegration and exchange market efficiency. An analysis of high frequency data.

The field of strategic marketing has long been identified as fruitful ground for gaining competitive advantage. Ever since the market segmentation concept was introduced in the late sixties, research interest steadily increased, covering issues as e.g. which fundamental segmentation strategy is most appropriate, in which ways can segments be identified or constructed, which algorithm provides optimal data-driven segmentation solutions, which number of segments should be constructed etc.. Interestingly, the issue of segment evaluation and choice has not been emphasised very strongly in the past, although this is of primary interest as soon as it comes to practical implementation. This article tries to fill this gap in an experimental manner: the consequences of different corporate segment choice strategies based on different segment evaluation criteria are investigated under different environmental conditions formalised in a complex artificial consumer market. The results indicate that complex decision models for segment choice do not turn out to be superior in general. Both mass marketers and firms concentrating on particular segments based on an a priori logic can be just as successful under "favourable" market conditions, the most influential condition being the available advertising budget. (author's abstract)

Is Thinking Worthwhile? A Comparison of Corporate Segment Choice Strategies

Psychometric Mixture Models

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