Summary: ExpressionView is an R package that provides an interactive graphical environment to explore transcription modules identified in gene expression data. A sophisticated ordering algorithm is used to present the modules with the expression in a visually appealing layout that provides an intuitive summary of the results. From this overview, the user can select individual modules and access biologically relevant metadata associated with them. Availability: http://www.unil.ch/cbg/ExpressionView. Screenshots, tutorials and sample data sets can be found on the ExpressionView web site. Contact: sven.bergmann@unil.c

Bergmann, Sven

Csárdi, Gábor

Kutalik, Zoltán

Lüscher, Andreas

Morton de Lachapelle, Aitana

Peter, Bastian

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[09:32 4/8/2010 Bioinformatics-btq334.tex] Page: 2062 2062–2063BIOINFORMATICS APPLICATIONS NOTE Vol. 26 no. 16 2010, pages 2062–2063doi:10.1093/bioinformatics/btq334Gene expression Advance Access publication July 29, 2010ExpressionView—an interactive viewer for modules identiﬁed ingene expression dataAndreas Lüscher1,†, Gábor Csárdi1,2,†, Aitana Morton de Lachapelle1,2,†,Zoltán Kutalik1,2,†, Bastian Peter1,2 and Sven Bergmann1,2,∗1Swiss Institute of Bioinformatics and 2Department of Medical Genetics, University of Lausanne, Lausanne,SwitzerlandAssociate Editor: Martin BishopABSTRACTSummary: ExpressionView is an R package that provides aninteractive graphical environment to explore transcription modulesidentiﬁed in gene expression data. A sophisticated ordering algorithmis used to present the modules with the expression in a visuallyappealing layout that provides an intuitive summary of the results.From this overview, the user can select individual modules andaccess biologically relevant metadata associated with them.Availability: http://www.unil.ch/cbg/ExpressionView. Screenshots,tutorials and sample data sets can be found on the ExpressionViewweb site.Contact: sven.bergmann@unil.chReceived on February 2, 2010; revised on May 31, 2010; acceptedon June 21, 20101 INTRODUCTIONBiclustering is an unsupervised data analysis method that isfrequently used to explore microarray data. Biclustering algorithmsprocess collections of expression profiles to identify groups ofgenes co-expressed under some conditions (samples). We refer tosuch groups as modules. While there is a multitude of biclusteringsoftware available [for reviews and comparisons see (Ihmels andBergmann, 2004; Madeira and Oliveira, 2004; Prelic´ et al., 2006)],packages with intuitive interfaces that allow for an interactiveexploration of the results are sparse.Existing approaches include Bivisu (Cheng et al., 2007) andBicOverlapper (Santamaría et al., 2008). The former is an interactivebiclustering program that plots modules individually, making itdifficult to identify the relationship between the overlappingmodules. The latter is a novel tool for plotting overlapping modules,yet in an abstract space. Our approach in ExpressionView isdifferent, as we use the usual gene-sample space and visualize allmodules together, on top of the reordered expression matrix. Thereordering ensures that the genes and samples that appear in thesame module are kept together.∗To whom correspondence should be addressed.†The authors wish it to be known that, in their opinion, the first four authorsshould be regarded as joint First authors.2 PACKAGE DESIGN AND WORKFLOWWith the ExpressionView package, bicluster analysis can beseparated into two parts. The first part involves finding the modulesin the dataset with some algorithm, possibly running enrichmentanalysis for the modules, and reordering the rows and columns ofthe expression matrix according to the modules. This part is typicallydone by a bioinformatician. The second part of the analysis involvesthe visualization and interactive exploration of the results. This partis typically done by researchers without extensive programmingknowledge.The first part of ExpressionView is written in GNU R(R Development Core Team, 2009) and contains an implementationof the matrix reordering algorithm. The second part is an interactivevisualization tool in the form of an Adobe Flash applet, for whichthe user only needs a Flash-enabled web browser.This dual implementation has the advantage that all the power ofthe GNU R environment and the BioConductor (Gentleman et al.,2004) packages can be used for the analysis itself, e.g. all organismsthat are (and will be) supported by BioConductor are automaticallysupported by ExpressionView. On the other hand, the exploration ofthe results does not need any GNU R knowledge and in most casesno extra software needs to be installed. See Figure 1 for a typicalExpressionView workflow.2.1 Reordering genes and conditionsExpressionView is designed to work with gene expression data in theform of a Bioconductor ExpressionSet. This class provides a user-friendly way to access the actual gene expression matrix and itsassociated metadata. ExpressionView can treat biclustering resultsobtained by the Iterative Signature Algorithm (Bergmann et al.,2003; Csárdi et al., 2010) and any of the methods available in theBiclust package (Kaiser and Leisch, 2008). Since the structure ofbiclustering results is independent of the algorithm, an extension toother methods is straightforward.To present the collection of possibly overlapping modules ina visually appealing form, it is necessary to reorder the rows(conditions) and columns (genes) of the gene expression matrix insuch a way that biclusters form contiguous rectangles. Since it is ingeneral impossible to find such an arrangement for more than twomutually overlapping modules, we propose here an approximatesolution that optimizes the arrangement within the original data,by maximizing the total area of the largest contiguous modulesubsets. [An alternative would be to repeat rows and columns as2062 © The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org[09:32 4/8/2010 Bioinformatics-btq334.tex] Page: 2063 2062–2063ExpressionViewFig. 1. Workflow of ExpressionView, showing the two parts of the analysis. (A) These steps are performed by a bioinformatician, using GNU R. Startingfrom gene expression data in the form of a Bioconductor ExpressionSet, the first step is finding the modules. In a second step, the rows and columns of thegene expression matrix are rearranged to produce an easily readable overview of the results. The last step consists of combining the gene expression dataand its associated metadata, possibly including results of enrichment analysis, with the results from the biclustering and produce an ExpressionView data file.(B) This file can be distributed and finally explored with the interactive Flash applet by the end user. Please see the web site for details on the data file format.necessary (Grothaus et al., 2006), but for many modules this resultsin a very large expression matrix.]This optimization task is an interesting problem on its own,which to the best of our knowledge has not been studied in theliterature. We briefly outline our strategy here (see our web sitefor details). The reordering of the rows is independent from thatof the columns, so the same optimization method can be appliedseparately to rows and columns. For a given order of the elements(either genes or conditions), we compute for each module i the sizeof the largest contiguous sequence of elements (i.e. the maximalnumber of neighboring elements Nmaxi ). Then, as a measure of thequality (Q) of the order, we sum this quantity over all modules(Q=∑i Nmaxi ). To optimize Q, an initial sequence is calculatedusing hierarchical clustering. Two operations are then applied to this:(i) permutations that exchange two elements within a module and(ii) shifts of a sequence of multiple elements of the same module toa different position. We use a greedy iterative scheme that performsthese operations at all possible positions and keeps the best newsequence if it improves Q. The algorithm stops if after a givennumber of operations no significant improvement of Q is achieved.We have studied a large number of perfectly orderable, butinitially scrambled, test cases. We find that the proposed algorithmfinds an order that recovers >99% of the score of the optimal solutionand in most cases, it recovers the correct alignment. For randomsamples, which are more representative for actual gene expressiondata, the execution time increases polynomially with the numberof clusters m as O(mα), where α∈[1.6,2], almost independently ofthe number of elements n. For a given number of clusters, we findO(nα), with α∈[2.5,2.7].Once the optimal order is determined, the program rearrangesthe gene expression matrix accordingly and exports all the relevantinformation into an XML file, that can be placed on a web server ordistributed by email and then imported by the interactive viewer.2.2 VisualizationA screenshot of the viewer is shown in Figure 1. The interfaceis divided in two parts: on the left-hand side, the user findsthe gene expression data in the common heat map form, on topof which the modules are overlaid. On the right-hand side, themetadata associated with the expression data and the results of theenrichment calculations for GO (Ashburner et al., 2000) categoriesand KEGG (Kanehisa et al., 2004) pathways are shown. Whereverpossible, these elements are linked to the corresponding databases.The interface essentially behaves as an image viewer, allowing theuser to zoom and pan around the expression data, getting instantfeedback on the selected item.Funding: Swiss Institute of Bioinformatics, the Swiss NationalScience Foundation (3100AO-116323/1); European FrameworkProject 6 (through the EuroDia and AnEuploidy projects).Conflict of Interest: none declared.REFERENCESAshburner,M. et al. (2000) Gene ontology: tool for the unification of biology. the geneontology consortium. Nat. Genet., 25, 25–29.Bergmann,S. et al. (2003) Iterative signature algorithm for the analysis of large-scalegene expression data. Phys. Rev. E, 67, 031902.Cheng,K. et al. (2007) BiVisu: software tool for bicluster detection and visualization.Bioinformatics, 23, 2342–2344.Csárdi,G. et al. (2010) Modular analysis of gene expression data with R. Bioinformatics,26, 1376–1377.Gentleman,R.C. et al. (2004) Bioconductor: open software development forcomputational biology and bioinformatics. Genome Biol., 5, R80.Grothaus,G. et al. (2006) Automatic layout and visualization of biclusters. AlgorithmsMol. Biol., 1, 15.Ihmels,J.H. and Bergmann,S. (2004) Challenges and prospects in the analysis oflarge-scale gene expression data. Brief. Bioinform., 5, 313–327.Kaiser,S. and Leisch,F. (2008) A toolbox for bicluster analysis in R. In Brito,P. ed.Compstat 2008-Proceedings in Computational Statistics, University of Munich,Physica, Heidelberg, Germany, pp. 201–208.Kanehisa,M. et al. (2004) The kegg resource for deciphering the genome. Nucleic AcidsRes., 32 (Database issue), 277–280.Madeira,S. and Oliveira,A. (2004) Biclustering algorithms for biological data analysis:a survey. IEEE/ACM Trans. Comput. Biol. Bioinform., 1, 24–45.Prelic´,A. et al. (2006) A systematic comparison and evaluation of biclustering methodsfor gene expression data. Bioinformatics, 22, 1122–1129.R Development Core Team (2009) R: A Language and Environment for StatisticalComputing. R Foundation for Statistical Computing, Vienna, Austria. ISBN3-900051-07-0.Santamaría,R. et al. (2008) BicOverlapper: a tool for bicluster visualization.Bioinformatics, 24, 1212–1213.2063

ExpressionView—an interactive viewer for modules identified in gene expression data

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