Abstract
SciCraft is a general open source data analysis tool which can be used in the analysis of microarrays. The main advantage of SciCraft is its ability to integrate different types of software through an intuitive and user friendly graphical interface. The user is able to control the flow of analysis and visualisation through a visual programming environment (VPE) where programs are drawn as diagrams. These diagrams consist of nodes and links where the nodes are methods or operators and the links are lines showing the flow of data between the nodes. The diagrammatic approach used in SciCraft is particularly suited to represent the various data analysis pipelines being used in the analysis of microarrays.
Efficient integration of methods from different computer languages and programs is accomplished through various plug-ins that handle all the necessary communication and data format handling. Currently available plug-ins are Octave (an open source Matlab clone), Python and R.
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References
Bioconductor: Open source software for bioinformatics, http://www.bioconductor.org/
Gentleman, R., Ihaka, R.: The R project for statistical computing, http://www.r-project.org/
Dalgaard, P.: Introductory Statistics with R. Springer, ISBN 0-387-95475-9 (2002)
Ihaka, R., Gentleman, R.: R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics 5, 299–314 (1996)
Eaton, J.W.: GNU Octave Manual. Network Theory Ltd. (2002)
Challet, D., Du, Y.L.: Closed source versus open source in a model of software bug dynamics. arXiv.org e-Print archive: Condensed Matter (2003), http://arxiv.org/abs/cond-mat/0306511
Stallman, R.: GNU general Public License (2003), http://www.gnu.org/copyleft/gpl.html
Raymond, E.S.: The Cathedral and the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary. Revised edition edn. O’Reilly and Associates (2001)
Maubach, J., Drenth, W.: Data-flow oriented visual programming libraries for scientific computing. In: Sloot, P.M.A., Tan, C.J.K., Dongarra, J., Hoekstra, A.G. (eds.) ICCS-ComputSci 2002. LNCS, vol. 2329, pp. 429–438. Springer, Heidelberg (2002)
Takatsuka, M., Gahegan, M.: Geovista studio: a codeless visual programming environment for geoscientific data analysis and visualization. Comput. Geosci. 28(10), 1131–1144 (2002)
Spinellis, D.: Unix tools as visual programming components in a gui-builder environment. Softw.-Pract. Exp. 32(1), 57–71 (2002)
Acacio, M., Canovas, O., Garcia, J., Lopez-de Teruel, P.: Mpi-delphi: an MPI implementation for visual programming environments and heterogeneous computing. Futur. Gener. Comp. Syst. 18(3), 317–333 (2002)
The Python Project (2003), http://www.python.org
Beazley, D.M., Rossum, G.V.: Python Essential Reference, 2nd edn., Que (2001)
Rempt, B.: GUI Programming With Python: Using the Qt Toolkit. Book and CD-rom edn., Opendocs Llc (2002)
Dalheimer, M.K.: Programming with Qt, 2nd edn. O’Reilly and Associate, Sebastopol (2002)
Trolltech, A.S. (2003), http://www.trolltech.com
Nash, D.: The KDE Bible. Book and CD-rom edn., Hungry Minds, Inc. (2000)
Schroeder, W., Martin, K., Lorensen, B.: The Visualization Toolkit: An Object Oriented Approach to 3D Graphics, 3rd edn. Kitware, Inc.
Kitware Inc. (2003), http://www.kitware.com
Rathmann, U., Vermeulen, G., Bieber, M., Dennington, R., Wilgen, J.: Qwt - Qt Widgets for technical applications, http://qwt.sourceforge.net/
Vermeulen, G., Colclough, M.: PyQwt plots data with numerical python and PyQt, http://pyqwt.sourceforge.net/
Wang, J., Nygaard, V., Smith-Sorensen, B., Hovig, E., Myklebost, O.: Marray: analysing single, replicated or reversed microarray experiments. Bioinformatics 18, 1139–1140 (2002)
Wang, J., Myklebost, O., Hovig, E.: MGraph: Graphical models for microarray data analysis, http://folk.uio.no/junbaiw/mgraph/mgraph.html
Churchill, G.: MA-ANOVA 2.0, http://www.jax.org/staff/churchill/labsite/software
Venet, D.: MatArray: a matlab toolbox for microarray data. Bioinformatics 19, 659–660 (2003)
Venet, D.: MatArray toolbox, http://www.ulb.ac.be/medecine/iribhm/microarray/toolbox/
Nabney, I.: Netlab: Algorithms for pattern recognition. Springer, Heidelberg (2004)
Stork, D., Yom-Tov, E.: Computer Manual in MATLAB to Accompany Pattern Classification, 2nd edn. Wiley Interscience, Hoboken (2004)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley Interscience, Hoboken (2002)
Vesanto, J.: SOM toolbox, http://www.cis.hut.fi/projects/somtoolbox/
Cawley, G.C.: MATLAB support vector machine toolbox. University of East Anglia, School of Information Systems, Norwich, Norfolk, U.K. NR4 7TJ (2000), http://theoval.sys.uea.ac.uk/gcc/svm/toolbox
The Comprehensive R Archive Network, http://lib.stat.cmu.edu/R/CRAN
Oliphant, T., Peterson, P., Jones, E.: SciPy - scientific tools for Python, http://www.scipy.org/
The BioPython Project, http://www.biopython.org
de Hoon, M., Imoto, S., Nolan, J., Miyano, S.: Open source clustering software. Bioinformatics 20, 1453–1454 (2004)
PyCluster, http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/software.htm
Alon, U., Barkai, N., Notterman, D., Gish, K., Ybarra, S., Mack, D., Levine, A.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 96, pp. 6745–6750 (1999)
Alon, U., Barkai, N., Notterman, D., Gish, K., Ybarra, S., Mack, D., Levine, A.: http://microarray.princeton.edu/oncology/
Massart, D., Vandeginste, B.G.M., Buydens, L., Jong, S., Lewi, P., Verbeke-Smeyers, J.: Handbook of Chemometrics and Qualimetrics: Part A and B. Elsevier Science, Amsterdam (1997)
Martens, H., Naes, T.: Multivariate Calibration. John Wiley & Sons, New York (1989)
Datta, S.: Exploring relationships in gene expressions: A partial least squares approach. Gene expression 9(6), 249–255 (2001)
Barra, V.: Analysis of gene expression data using functional principal components. Computer methods and programs in biomedicine 75(1), 1–9 (2004)
Ghosh, D.: Penalized discriminant methods for the classification of tumors from gene expression data. Biometrics 59(4), 992–1000 (2003)
Wouters, L., Gohlmann, H., Bijnens, L., Kass, S., Molenberghs, G., Lewi, P.: Graphical exploration of gene expression data: A comparative study of three multivariate methods. Biometrics 59(4), 1131–1139 (2003)
Conde, L., Mateos, A., Herrero, J., Dopazo, J.: Improved class prediction in DNA microarray gene expression data by unsupervised reduction of the dimensionality followed by supervised learning with a perceptron. Journal of VLSI signal processing systems for signal image and videotechnology 35(3), 245–253 (2003)
Wang, Z., Wang, Y., Lu, J., Kung, S., Zhang, J., Lee, R., Xuan, J., Khan, J.: Discriminatory mining of gene expression microarray data. Journal of VLSI signal processing systems for signal image and videotechnology 35(3), 255–272 (2003)
Bicciato, S., Luchini, A., Di Bello, C.: PCA disjoint models for multiclass cancer analysis using gene expression data. Bioinformatics 19(5), 571–578 (2003)
Bicciato, S., Luchini, A., Di Bello, C.: Disjoint PCA models for marker identification and classification of cancer types using gene expression data. Minerva biotecnologica 14(3-4), 281–290 (2002)
Nguyen, D., Rocke, D.: Multi-class cancer classification via partial least squares with gene expression profiles. Bioinformatics 18(9), 1216–1226 (2002)
Mendez, M., Hodar, C., Vulpe, C., Gonzalez, M., Cambiazo, V.: Discriminant analysis to evaluate clustering of gene expression data. FEBS letters 522(1-3), 24–28 (2002)
Nguyen, D., Rocke, D.: Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18(1), 39–50 (2002)
Chapman, S., Schenk, P., Kazan, K., Manners, J.: Using biplots to interpret gene expression patterns in plants. Bioinformatics 18(1), 202–204 (2002)
Perez-Enciso, M., Tenenhaus, M.: Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach. Human genetics 112(5-6), 581–592 (2003)
Alsberg, B.K., Kell, D.B., Goodacre, R.: Variable selection in discriminant partial least squares analysis. Analytical Chemistry 70, 4126–4133 (1998)
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Alsberg, B.K., Kirkhus, L., Tangstad, T., Anderssen, E. (2004). Data Analysis of Microarrays Using SciCraft. In: López, J.A., Benfenati, E., Dubitzky, W. (eds) Knowledge Exploration in Life Science Informatics. KELSI 2004. Lecture Notes in Computer Science(), vol 3303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30478-4_6
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DOI: https://doi.org/10.1007/978-3-540-30478-4_6
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