Importing MAGE-ML format microarray data into BioConductor

The microarray gene expression markup language (MAGE-ML) is a widely used XML (eXtensible Markup Language) standard for describing and exchanging information about microarray experiments. It can describe microarray designs, microarray experiment designs, gene expression data and data analysis results. We describe RMAGEML, a new Bioconductor package that provides a link between cDNA microarray data stored in MAGE-ML format and the Bioconductor framework for preprocessing, visualization and analysis of microarray experiments. AVAILABILITY: http://www.bioconductor.org. Open Source.     

Codelink: an R package for analysis of GE healthcare gene expression bioarrays

MOTIVATION: Microarray-based expression profiles have become a standard methodology in any high-throughput analysis. Several commercial platforms are available, each with its strengths and weaknesses. The R platform for statistical analysis and graphics is a powerful environment for the analysis of microarray data, because it has many integrated statistical methods available as well as the specialized microarray analysis project Bioconductor. Many packages have been added in the last few years increasing the range of possible analysis. Here, we report the availability of a package for reading and analyzing data from GE Healthcare Gene Expression Bioarrays within the R environment. AVAILABILITY: The software is implemented in the R language, is open source and available for download free of charge through the Bioconductor (http://www.bioconductor.org) project.     

RefPlus: an R package extending the RMA Algorithm

RMA has become a widely used methodology to pre-process Affymetrix gene expression microarrays. A limitation of RMA is that the calculated probeset intensities change when a set of microarrays is re-pre-processed after the inclusion of additional microarrays into the analysis set. Here we report the availability of the RefPlus package containing functions to perform the Extrapolation Strategy and Extrapolation Averaging algorithms which address these issues. AVAILABILITY: The software is implemented in the R language and can be downloaded from the Bioconductor project website (http://www.bioconductor.org). SUPPLEMENTARY INFORMATION: Further details of the workings and evaluation of these functions are given in the documentation available on the Bioconductor website.     

CALIB: a Bioconductor package for estimating absolute expression levels from two-color microarray data

In this article we describe a new Bioconductor package 'CALIB' for normalization of two-color microarray data. This approach is based on the measurements of external controls and estimates an absolute target level for each gene and condition pair, as opposed to working with log-ratios as a relative measure of expression. Moreover, this method makes no assumptions regarding the distribution of gene expression divergence. AVAILABILITY: http://bioconductor.org/packages/2.0/bioc Open Source.     

RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis

While meta-analysis provides a powerful tool for analyzing microarray experiments by combining data from multiple studies, it presents unique computational challenges. The Bioconductor package RankProd provides a new and intuitive tool for this purpose in detecting differentially expressed genes under two experimental conditions. The package modifies and extends the rank product method proposed by Breitling et al., [(2004) FEBS Lett., 573, 83-92] to integrate multiple microarray studies from different laboratories and/or platforms. It offers several advantages over t-test based methods and accepts pre-processed expression datasets produced from a wide variety of platforms. The significance of the detection is assessed by a non-parametric permutation test, and the associated P-value and false discovery rate (FDR) are included in the output alongside the genes that are detected by user-defined criteria. A visualization plot is provided to view actual expression levels for each gene with estimated significance measurements. AVAILABILITY: RankProd is available at Bioconductor http://www.bioconductor.org. A web-based interface will soon be available at http://cactus.salk.edu/RankProd     

MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation

MSnbase is an R/Bioconductor package for the analysis of quantitative proteomics experiments that use isobaric tagging. It provides an exploratory data analysis framework for reproducible research, allowing raw data import, quality control, visualization, data processing and quantitation. MSnbase allows direct integration of quantitative proteomics data with additional facilities for statistical analysis provided by the Bioconductor project. AVAILABILITY: MSnbase is implemented in R (version ≥ 2.13.0) and available at the Bioconductor web site (http://www.bioconductor.org/). Vignettes outlining typical workflows, input/output capabilities and detailing underlying infrastructure are included in the package.     

SScore: an R package for detecting differential gene expression without gene expression summaries

SUMMARY: SScore is an R package that facilitates the comparison of gene expression between Affymetrix GeneChips using the S-score algorithm. The S-score algorithm uses probe level data directly to assess differences in gene expression, without requiring a preliminary separate step of probe set expression summary estimation. Therefore, the algorithm avoids introduction of error associated with the expression summary estimation process and has been demonstrated to improve the accuracy of identifying differentially expressed genes. The S-score produces accurate results even when few or no replicates are available. AVAILABILITY: The R package SScore is available from Bioconductor at http://www.bioconductor.org     

A stochastic downhill search algorithm for estimating the local false discovery rate

Screening for differential gene expression in microarray studies leads to difficult large-scale multiple testing problems. The local false discovery rate is a statistical concept for quantifying uncertainty in multiple testing. We introduce a novel estimator for the local false discovery rate that is based on an algorithm which splits all genes into two groups, representing induced and noninduced genes, respectively. Starting from the full set of genes, we successively exclude genes until the gene-wise p-values of the remaining genes look like a typical sample from a uniform distribution. In comparison to other methods, our algorithm performs compatibly in detecting the shape of the local false discovery rate and has a smaller bias with respect to estimating the overall percentage of noninduced genes. Our algorithm is implemented in the Bioconductor compatible R package TWILIGHT version 1.0.1, which is available from http://compdiag.molgen.mpg.de/software or from the Bioconductor project at http://www.bioconductor.org.     

GEOmetadb: powerful alternative search engine for the Gene Expression Omnibus

The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data in GEO can be challenging. We have developed GEOmetadb in an attempt to make querying the GEO metadata both easier and more powerful. All GEO metadata records as well as the relationships between them are parsed and stored in a local MySQL database. A powerful, flexible web search interface with several convenient utilities provides query capabilities not available via NCBI tools. In addition, a Bioconductor package, GEOmetadb that utilizes a SQLite export of the entire GEOmetadb database is also available, rendering the entire GEO database accessible with full power of SQL-based queries from within R. AVAILABILITY: The web interface and SQLite databases available at http://gbnci.abcc.ncifcrf.gov/geo/. The Bioconductor package is available via the Bioconductor project. The corresponding MATLAB implementation is also available at the same website.     

Network structures and algorithms in Bioconductor

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).     

beadarray: R classes and methods for Illumina bead-based data

The R/Bioconductor package beadarray allows raw data from Illumina experiments to be read and stored in convenient R classes. Users are free to choose between various methods of image processing, background correction and normalization in their analysis rather than using the defaults in Illumina's; proprietary software. The package also allows quality assessment to be carried out on the raw data. The data can then be summarized and stored in a format which can be used by other R/Bioconductor packages to perform downstream analyses. Summarized data processed by Illumina's; BeadStudio software can also be read and analysed in the same manner. Availability: The beadarray package is available from the Bioconductor web page at www.bioconductor.org. A user's guide and example data sets are provided with the package.     

MeV+R: using MeV as a graphical user interface for Bioconductor applications in microarray analysis

We present MeV+R, an integration of the JAVA MultiExperiment Viewer program with Bioconductor packages. This integration of MultiExperiment Viewer and R is easily extensible to other R packages and provides users with point and click access to traditionally command line driven tools written in R. We demonstrate the ability to use MultiExperiment Viewer as a graphical user interface for Bioconductor applications in microarray data analysis by incorporating three Bioconductor packages, RAMA, BRIDGE and iterativeBMA.     

inSilicoDb: an R/Bioconductor package for accessing human Affymetrix expert-curated datasets from GEO

Microarray technology has become an integral part of biomedical research and increasing amounts of datasets become available through public repositories. However, re-use of these datasets is severely hindered by unstructured, missing or incorrect biological samples information; as well as the wide variety of preprocessing methods in use. The inSilicoDb R/Bioconductor package is a command-line front-end to the InSilico DB, a web-based database currently containing 86 104 expert-curated human Affymetrix expression profiles compiled from 1937 GEO repository series. The use of this package builds on the Bioconductor project's focus on reproducibility by enabling a clear workflow in which not only analysis, but also the retrieval of verified data is supported.     

OrderedList--a bioconductor package for detecting similarity in ordered gene lists

SUMMARY: OrderedList is a Bioconductor compliant package for meta-analysis based on ordered gene lists like those resulting from differential gene expression analysis. Our package quantifies the similarity between gene lists. The significance of the similarity score is estimated from random scores computed on perturbed data. OrderedList illustrates list similarity in intuitive plots and determines the score-driving genes for further analysis. AVAILABILITY: http://www.bioconductor.org CONTACT: claudio.lottaz@molgen.mpg.de SUPPLEMENTARY INFORMATION: Please visit our webpage on http://compdiag.molgen.mpg.de/software.     

Bioconductor: open software development for computational biology and bioinformatics

The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.     

Bioinformatics and Computational Biology Solutions Using R and Bioconductor. * Edited by Robert Gentleman, Wolfgang Huber, Vincent J. Carey, Rafael A. Irizarry and Sandrine Dudoit

This book guides through practical bioinformatics data analysisusing the Bioconductor toolkit, which is based on the statisticallanguage R. R itself is an open-source recreation of the languageS-Plus. The Bioconductor is a collection of R-packages for theanalysis of genomic and molecular biological data generatedin high-throughput experiments. High-throughput experimentsare characterized by large amounts of data generated in shortperiods of time on a sizable number of samples. This poses newchallenges to the analysis such as assessing and adjusting fornoise, exploration using cluster-analysis, visualization, andlinking to (or ‘annotating with’) biomedical knowledgebases. The book focuses on gene expression microarrays, the high-throughputtechnology for which statistical methods are best developedtoday. In addition, a     

Using GOstats to test gene lists for GO term association

MOTIVATION: Functional analyses based on the association of Gene Ontology (GO) terms to genes in a selected gene list are useful bioinformatic tools and the GOstats package has been widely used to perform such computations. In this paper we report significant improvements and extensions such as support for conditional testing. RESULTS: We discuss the capabilities of GOstats, a Bioconductor package written in R, that allows users to test GO terms for over or under-representation using either a classical hypergeometric test or a conditional hypergeometric that uses the relationships among GO terms to decorrelate the results. AVAILABILITY: GOstats is available as an R package from the Bioconductor project: http://bioconductor.org