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      Clustering-based approaches to SAGE data mining

      review-article
      Author(s): 1 , , 1 , 2
      Publication date (Electronic):
      Journal: BioData Mining
      Publisher: BioMed Central

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          Abstract

          Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical applications, such as the prediction of new gene functions and the identification of biomarkers in human cancer research. Clustering techniques have become fundamental approaches in these applications. This paper reviews relevant clustering techniques specifically designed for this type of data. It places an emphasis on current limitations and opportunities in this area for supporting biologically-meaningful data mining and visualisation.

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          Cluster analysis and display of genome-wide expression patterns.

          P. T. Spellman, P. O. Brown, D Botstein … (1998)
          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
          Article 0 comments Cited 1865 times – based on 0 reviews     Review now
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            Serial analysis of gene expression.

            V. E. Velculescu, L. Zhang, B Vogelstein … (1995)
            The characteristics of an organism are determined by the genes expressed within it. A method was developed, called serial analysis of gene expression (SAGE), that allows the quantitative and simultaneous analysis of a large number of transcripts. To demonstrate this strategy, short diagnostic sequence tags were isolated from pancreas, concatenated, and cloned. Manual sequencing of 1000 tags revealed a gene expression pattern characteristic of pancreatic function. New pancreatic transcripts corresponding to novel tags were identified. SAGE should provide a broadly applicable means for the quantitative cataloging and comparison of expressed genes in a variety of normal, developmental, and disease states.
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              Computational cluster validation in post-genomic data analysis.

              J. Handl, J. Knowles, D. B. Kell (2005)
              The discovery of novel biological knowledge from the ab initio analysis of post-genomic data relies upon the use of unsupervised processing methods, in particular clustering techniques. Much recent research in bioinformatics has therefore been focused on the transfer of clustering methods introduced in other scientific fields and on the development of novel algorithms specifically designed to tackle the challenges posed by post-genomic data. The partitions returned by a clustering algorithm are commonly validated using visual inspection and concordance with prior biological knowledge--whether the clusters actually correspond to the real structure in the data is somewhat less frequently considered. Suitable computational cluster validation techniques are available in the general data-mining literature, but have been given only a fraction of the same attention in bioinformatics. This review paper aims to familiarize the reader with the battery of techniques available for the validation of clustering results, with a particular focus on their application to post-genomic data analysis. Synthetic and real biological datasets are used to demonstrate the benefits, and also some of the perils, of analytical clustervalidation. The software used in the experiments is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/. Enlarged colour plots are provided in the Supplementary Material, which is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/.
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                Author and article information

                Journal
                Journal ID (nlm-ta): BioData Min
                Title: BioData Mining
                Publisher: BioMed Central
                ISSN (Electronic): 1756-0381
                Publication date Collection: 2008
                Publication date (Electronic): 17 July 2008
                Volume: 1
                Page: 5
                Affiliations
                [1 ]School of Computing and Mathematics, University of Ulster, Newtownabbey, BT37 0QB, Co. Antrim, Northern Ireland, UK
                [2 ]Research Centre for Public Health (CRP-Santé), Laboratory of Cardiovascular Research, 1A, rue Thomas Edison, L-1445, Strassen, Luxembourg
                Article
                Publisher ID: 1756-0381-1-5
                DOI: 10.1186/1756-0381-1-5
                PMC ID: 2553774
                PubMed ID: 18822151
                SO-VID: 4c9d72dc-4806-4c8a-a58e-909b170cdf6e
                Copyright © Copyright © 2008 Wang et al; licensee BioMed Central Ltd.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 25 January 2008
                Date accepted : 17 July 2008
                Categories
                Subject: Review

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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