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

Abstract

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. RationaleWhile microarray technology has given biologists unprecedented access to gene expression data, reliable and effective data analysis remains a difficult problem. There are many freely or commercially available software packages, but biologists are often faced with trading off power and flexibility for usability and accessibility. In addition to the potentially prohibitive costs, researchers using commercial software tools may find themselves waiting for state-of-the-art algorithms to be implemented with the packages. The Bioconductor project [1,2] is an open source software project that provides a wide range of statistical tools primarily based on the R programming environment and language [3,4]. Taking advantage of R's powerful statistical and graphical capabilities, developers have created and contributed numerous Bioconductor packages to solve a variety of data analysis needs. The use of these packages, however, requires a basic understanding of the R programming/command language and an understanding of the documentation accompanying each package. The primary users of R and the Bioconductor packages have been computational scientists, statisticians and the more computationally oriented biologists. However, in our experience, many biologists find themselves uncomfortable issuing command lines in a terminal. Hence, there is a need for a graphical user interface (GUI) for Bioconductor packages that will allow biologists easy access to data analytical tools without learning the command line syntax. The tcltk package in R adds GUI elements to R by allowing programmers to write GUI-driven modules by embedding Tk commands into the R language [5]. There are also GUIs developed for basic statistical analysis in R, such as the R Commander [6] and windows-based SciViews [7]. However, these GUIs are not designed for microarray analysis. There are Bioconductor packages, such as limmaGUI [8], affylmGUI [9] and OLINgui [10] that are built on the R tcltk package to provide GUIs. LimmaGUI and affylmGUI provide GUIs for the analysis of designed experiments and the assessment of differential expression for twocolor spotted microarrays and single-color Affymetrix data, respectively. OLINgui provides a GUI for the visualization, normalization and quality testing of two-channel microarray

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“…org/mev/). 23,24 Accession codes The microarray data complies with MIAME guidelines, and the data set was deposited at Gene Expression Omnibus (National Center for Biotechnology Information), accession number GSE43513.…”

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“…To visualize the expression profiles of developmental stages, expression levels were normalized to obtain relative expression levels across each developmental stage (sum to an expression of one across all developmental stages) and heatmaps were generated using MeV (MultiExperiment Viewer) (Chu et al, 2008). Target genes of CTSMs and non-targets of CTSMs were clustered separately using kmeans (10 clusters) with a distance matrix constructed from the Pearson correlations.…”