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46 Wang and Ochsresults posttreatments, and a small example for microarray data set is alsoincluded in subdirectory of EXAMPLES.2. The file formats and all further directions relate to the graphical version of LS-NMF.For the command line version, follow the directions included with these files.3. The file format is slightly less flexible than that used by the MEV tool, as only asingle column for IDs is allowed.4. The same application allows the user to run NMF as well as LS-NMF. Hereinfocus is only on LS-NMF.5. By design, NMF algorithms find a local minimum in the misfit between the dataD and the reconstructed data M. This makes the algorithms prone to false minima,and the general approach is to try many simulations and use the one with the bestfit to the data for further analysis.6. After signing a material transfer agreement, advanced academic users mightinstead download the PattRun software, which includes three versions of theBayesian decomposition algorithm as well as LS-NMF. For those users, thesame directions apply but LS-NMF must be chosen from the drop down list ofalgorithms.7. For the command line versions, files must be converted for use with ClutrFree.Two Perl scripts are provided within the LS-NMF package. One script namedForClutrFree.pl is used to prepare the simulation results for ClutrFree, and theother one, ForWebGestalt.pl, is for WebGestalt.8. The power of both LS-NMF and Bayesian decomposition is that they can assigngenes to multiple patterns, which matches biological reality as genes are usuallymultiregulated. In Fig. 4, this can be seen in the multiple assignment of genes suchas SPAC821.09 and SPAPB1E7.10 among many others.9. Analysis with pattern recognition or clustering methods across conditions willtend to link genes that are related in biological processes. This makes it a goodcomplement to sequence-based analysis that links genes with similar molecularfunction, owing to sequence conservation of protein motifs.References1. Lee, D. D. and Seung, H. S. (1999) Nature 401, 788–791.2. Chagoyen, M., Carmona-Saez, P., Shatkay, H., Carazo, J. M., and Pascual-Montano, A. (2006) BMC Bioinformatics 7, 41.3. Sajda, P., Du, S., Brown, T. R., et al. (2004) IEEE Trans Med. Imaging 23,1453–1465.4. Heger, A. and Holm, L. (2003) Bioinformatics 19(Suppl 1), I130–I137.5. Tresch, M. C., Cheung, V. C., and d’Avella, A. (2006) J. Neurophysiol 95,2199–2212.6. Brunet, J. P., Tamayo, P., Golub, T. R., and Mesirov, J. P. (2004) Proc. Natl.Acad. Sci. USA 101, 4164–4169.7. Kim, P. M. and Tidor, B. (2003) Genome Res. 13, 1706–1718.8. Gao, Y. and Church, G. (2005) Bioinformatics 21, 3970–3975.9. Wang, G., Kossenkov, A. V., and Ochs, M. F. (2006) BMC Bioinformatics 7, 175.