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      Pathway-GPS and SIGORA: identifying relevant pathways based on the over-representation of their gene-pair signatures

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          Abstract

          Motivation. Predominant pathway analysis approaches treat pathways as collections of individual genes and consider all pathway members as equally informative. As a result, at times spurious and misleading pathways are inappropriately identified as statistically significant, solely due to components that they share with the more relevant pathways.

          Results. We introduce the concept of Pathway Gene-Pair Signatures (Pathway-GPS) as pairs of genes that, as a combination, are specific to a single pathway. We devised and implemented a novel approach to pathway analysis, Signature Over-representation Analysis (SIGORA), which focuses on the statistically significant enrichment of Pathway-GPS in a user-specified gene list of interest. In a comparative evaluation of several published datasets, SIGORA outperformed traditional methods by delivering biologically more plausible and relevant results.

          Availability. An efficient implementation of SIGORA, as an R package with precompiled GPS data for several human and mouse pathway repositories is available for download from http://sigora.googlecode.com/svn/.

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          Most cited references 28

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          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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            Ontological analysis of gene expression data: current tools, limitations, and open problems.

            Independent of the platform and the analysis methods used, the result of a microarray experiment is, in most cases, a list of differentially expressed genes. An automatic ontological analysis approach has been recently proposed to help with the biological interpretation of such results. Currently, this approach is the de facto standard for the secondary analysis of high throughput experiments and a large number of tools have been developed for this purpose. We present a detailed comparison of 14 such tools using the following criteria: scope of the analysis, visualization capabilities, statistical model(s) used, correction for multiple comparisons, reference microarrays available, installation issues and sources of annotation data. This detailed analysis of the capabilities of these tools will help researchers choose the most appropriate tool for a given type of analysis. More importantly, in spite of the fact that this type of analysis has been generally adopted, this approach has several important intrinsic drawbacks. These drawbacks are associated with all tools discussed and represent conceptual limitations of the current state-of-the-art in ontological analysis. We propose these as challenges for the next generation of secondary data analysis tools.
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              The structure of scientific collaboration networks

               M. Newman (2000)
              We investigate the structure of scientific collaboration networks. We consider two scientists to be connected if they have authored a paper together, and construct explicit networks of such connections using data drawn from a number of databases, including MEDLINE (biomedical research), the Los Alamos e-Print Archive (physics), and NCSTRL (computer science). We show that these collaboration networks form "small worlds" in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances. We further give results for mean and distribution of numbers of collaborators of authors, demonstrate the presence of clustering in the networks, and highlight a number of apparent differences in the patterns of collaboration between the fields studied.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                19 December 2013
                2013
                : 1
                Affiliations
                [1 ]Animal & Bioscience Research Department, AGRIC, Teagasc , Grange, Dunsany, Co. Meath, Ireland
                [2 ]Department of Molecular Biology and Biochemistry, Simon Fraser University , Burnaby, British Columbia, Canada
                Article
                229
                10.7717/peerj.229
                3883547
                24432194
                © 2013 Foroushani et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Product
                Funding
                Funded by: Teagasc
                Award ID: RMIS6018
                Funded by: Teagasc Walsh Fellowship
                Funded by: AllerGen 12B&B2
                Funded by: Genome Canada
                Funded by: Michael Smith Foundation for Health Research
                The project was primarily supported by Teagasc RMIS6018 and the Teagasc Walsh Fellowship scheme, with some support by AllerGen 12B&B2 and Genome Canada. FSLB was a Michael Smith Foundation for Health Research Senior Scholar. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Bioinformatics
                Computational Biology
                Molecular Biology
                Immunology
                Infectious Diseases

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