ArticleBMC bioinformatics2008
Performing statistical analyses on quantitative data in Taverna workflows: an example using R and maxdBrowse to identify differentially-expressed genes from microarray data.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- ANIMA: Association network integration for multiscale analysis.Wellcome open research · 2018Article
- BIFI: a Taverna plugin for a simplified and user-friendly workflow platform.BMC research notes · 2014Article
- Architecture for interoperable software in biology.Briefings in bioinformatics · 2014Article
- Scientific workflow management in proteomics.Molecular & cellular proteomics : MCP · 2012Article
- Workflows for microarray data processing in the Kepler environment.BMC bioinformatics · 2012Article
- Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets.Bioinformatics (Oxford, England) · 2011Article
- The iPlant Collaborative: Cyberinfrastructure for Plant Biology.Frontiers in plant science · 2011Article
- CaGrid Workflow Toolkit: a Taverna based workflow tool for cancer grid.BMC bioinformatics · 2010Article
- myExperiment: a repository and social network for the sharing of bioinformatics workflows.Nucleic acids research · 2010Article
- mu-CS: an extension of the TM4 platform to manage Affymetrix binary data.BMC bioinformatics · 2010Article
- Using R in Taverna: RShell v1.2.BMC research notes · 2009Article
- Iron behaving badly: inappropriate iron chelation as a major contributor to the aetiology of vascular and other progressive inflammatory and degenerative diseases.BMC medical genomics · 2009Article
- A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs.Metabolomics : Official journal of the Metabolomic SocietyArticle
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12 authors.
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Abstract
backgroundThere has been a dramatic increase in the amount of quantitative data derived from the measurement of changes at different levels of biological complexity during the post-genomic era. However, there are a number of issues associated with the use of computational tools employed for the analysis of such data. For example, computational tools such as R and MATLAB require prior knowledge of their programming languages in order to implement statistical analyses on data. Combining two or more tools in an analysis may also be problematic since data may have to be manually copied and pasted between separate user interfaces for each tool. Furthermore, this transfer of data may require a reconciliation step in order for there to be interoperability between computational tools.
resultsDevelopments in the Taverna workflow system have enabled pipelines to be constructed and enacted for generic and ad hoc analyses of quantitative data. Here, we present an example of such a workflow involving the statistical identification of differentially-expressed genes from microarray data followed by the annotation of their relationships to cellular processes. This workflow makes use of customised maxdBrowse web services, a system that allows Taverna to query and retrieve gene expression data from the maxdLoad2 microarray database. These data are then analysed by R to identify differentially-expressed genes using the Taverna RShell processor which has been developed for invoking this tool when it has been deployed as a service using the RServe library. In addition, the workflow uses Beanshell scripts to reconcile mismatches of data between services as well as to implement a form of user interaction for selecting subsets of microarray data for analysis as part of the workflow execution. A new plugin system in the Taverna software architecture is demonstrated by the use of renderers for displaying PDF files and CSV formatted data within the Taverna workbench.
conclusionTaverna can be used by data analysis experts as a generic tool for composing ad hoc analyses of quantitative data by combining the use of scripts written in the R programming language with tools exposed as services in workflows. When these workflows are shared with colleagues and the wider scientific community, they provide an approach for other scientists wanting to use tools such as R without having to learn the corresponding programming language to analyse their own data.
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