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Merging R into the web: DNMAD & Tnasas, web-based resources for microarray data analysis. Juan M. Vaquerizas, Joaquín Dopazo and Ramón Díaz-Uriarte Bioinformatics Unit, CNIO (Spanish National Cancer Centre) Melchor Fernández Almagro 3, 28029 Madrid, Spain. April, 2004 Abstract DNMAD and Tnasas are two web-based resources for microarray data analysis (normalization and class prediction), that implement R code into a web server configuration. These tools are included in GEPAS, a suite for gene expression data analysis [4]. The first tool we present is called DNMAD that stands for “Diagnosis and Normalization for MicroArray Data”. DNMAD is essentially a web-based interface for the Bioconductor package limma [7]. DNMAD has been designed to provide a simple and at the same time robust and fast way for normalization of microarray data. We provide global and print-tip loess normalization [9]. We also offer options for the use of quality flags, background adjustment, input of compressed files with many of arrays, and slide-scale normalization. Finally, to enable a fully featured pipeline of analysis, direct links to the central hub of GEPAS, the Preprocessor [5] are provided. The tool, help files, and tutorials are available at http://dnmad.bioinfo.cnio.es. The second tool we present is called Tnasas for “This is not a substitute for a statistician”. Tnasas performs class prediction (using either SVM, KNN, DLDA, or Random Forest) together with gene selection, including cross-validation of the gene selection process to account for “selection bias” [1], and cross-validation of the process of selecting the number of genes that yields the smallest error. This tool has mainly an exploratory and pedagogical purpose: to make users aware of the widespread problem of selection bias and to provide a benchmark against some (overly) optimistic claims that occasionally are attached to new methods and algorithms; Tnasas can also be used as a “quick and dirty” way of building a predictor. Tnasas uses packages e1071 [2], randomForest [6], and class (part of the VR bundle [8]). The tool, help files, and tutorials are available at http://tnasas.bioinfo.cnio.es. Both tools use a Perl-CGI script that checks all the data input by the user and, once checked, sends it to an R script that performs the actions requested by the user. To merge the Perl-CGI script with the script in R we use the R package CGIwithR [3]. Source code for both applications will be released under the GNU GPL license. References [1] C. Ambroise and G.J. McLachlan. Selection bias in gene extraction on the basis of microarray geneexpression data. Proc Natl Acad Sci USA, 99: 6562–6566, 2002. [2] E. Dimitriadou, K. Hornik, F. Leisch, D. Meyer, and A. Weingess. e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. http://cran.r-project.org/src/contrib/PACKAGES.html#e1071 [3] D. Firth. CGIwithR: Facilities for the use of R to write CGI scripts. http://cran.r-project.org/src/contrib/PACKAGES.html#CGIwithR [4] J. Herrero, F. Al-Shahrour, R. Díaz-Uriarte, Á. Mateos, J. M. Vaquerizas, J. Santoyo, and J. Dopazo. GEPAS: A web-based resource for microarray gene expression data analysis. Nucleic Acids Res, 31:3461– 3467, 2003. 1
[5] J. Herrero, R. Díaz-Uriarte, and J. Dopazo. Gene expression data preprocessing. Bioinformatics, 19:655–656, 2003. [6] A. Liaw, M. Wiener, with original Fortran code from L. Breiman and A. Cutler. randomForest: Breiman’s random forest for classification and regression. http://cran.r-project.org/src/contrib/PACKAGES.html#randomForest [7] G. K. Smyth, M. Ritchie, J. Wettenhall, and N. Thorne. limma: Data analysis, linear models and differential expression for microarray data. http://www.bioconductor.org/repository/release1.3/package/html/limma.html [8] B. D. Ripley and W. Venables. VR: Functions and datasets to support Venables and Ripley, ’Modern Applied Statistics with S’ (4th edition). http://cran.r-project.org/src/contrib/PACKAGES.html#VR [9] Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng, J. Ngai, and T. P. Speed. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res, 30:e15, 2002. 2
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