@article{Wirth2011,
abstract = {BACKGROUND:Parallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.RESULTS:The method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues). SOM mapping reduces the dimension of expression data from ten of thousands of genes to a few thousand metagenes, each representing a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of genes related to specific molecular processes in the respective tissue. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering are better represented and provide better signal-to-noise ratios if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues broadly into three clusters containing nervous, immune system and the remaining tissues.CONCLUSIONS:The SOM technique provides a more intuitive and informative global view of the behavior of a few well-defined modules of correlated and differentially expressed genes than the separate discovery of the expression levels of hundreds or thousands of individual genes. The program is available as R-package 'oposSOM'.},
author = {Wirth, Henry and Loffler, Markus and von Bergen, Martin and Binder, Hans},
doi = {10.1186/1471-2105-12-306},
file = {:H$\backslash$:/Eigene Dateien/Library/Wirth et al. - 2011 - Expression cartography of human tissues using self organizing maps.pdf:pdf;:H$\backslash$:/Eigene Dateien/Library/Wirth et al. - 2011 - Expression cartography of human tissues using self organizing maps(2).pdf:pdf},
issn = {1471-2105},
journal = {BMC Bioinformatics},
keywords = {Clustering,Correlation,Henry Wirth,SOM visualization,Tissue Data},
mendeley-tags = {Clustering,Correlation,Henry Wirth,SOM visualization,Tissue Data},
number = {1},
pages = {306},
title = {{Expression cartography of human tissues using self organizing maps}},
url = {http://www.biomedcentral.com/1471-2105/12/306},
volume = {12},
year = {2011}
}
@article{Wirth2012,
abstract = {ABSTRACT: BACKGROUND: Self organizing maps (SOM) enable the straightforward portraying of high-dimensional data of large sample collections in terms of sample-specific images. The analysis of their texture provides so-called spot-clusters of co-expressed genes which require subsequent significance filtering and functional interpretation. We address feature selection in terms of the gene ranking problem and the interpretation of the obtained spot-related lists using concepts of molecular function. RESULTS: Different expression scores based either on simple fold change-measures or on regularized Student's t-statistics are applied to spot-related gene lists and compared with special emphasis on the error characteristics of microarray expression data. The spot-clusters are analyzed using different methods of gene set enrichment analysis with the focus on overexpression and/or overrepresentation of predefined sets of genes. Metagene-related overrepresentation of selected gene sets was mapped into the SOM images to assign gene function to different regions. Alternatively we estimated set-related overexpression profiles over all samples studied using a gene set enrichment score. It was also applied to the spot-clusters to generate lists of enriched gene sets. We used the tissue body index data set, a collection of expression data of human tissues as an illustrative example. We found that tissue related spots typically contain enriched populations of gene sets well corresponding to molecular processes in the respective tissues. In addition, we display special sets of housekeeping and of consistently weak and high expressed genes using SOM data filtering. CONCLUSIONS: The presented methods allow the comprehensive downstream analysis of SOM-transformed expression data in terms of cluster-related gene lists and enriched gene sets for functional interpretation. SOM clustering implies the ability to define either new gene sets using selected SOM spots or to verify and/or to amend existing ones.},
author = {Wirth, Henry and von Bergen, Martin and Binder, Hans},
doi = {10.1186/1756-0381-5-18},
file = {:H$\backslash$:/Eigene Dateien/Library/Wirth, von Bergen, Binder - 2012 - Mining SOM expression portraits feature selection and integrating concepts of molecular function.pdf:pdf},
issn = {1756-0381},
journal = {BioData Mining},
keywords = {Gene Set Analysis,Henry Wirth,SOM fundamental,Significance Analysis Methods,Tissue Data},
mendeley-tags = {Gene Set Analysis,Henry Wirth,SOM fundamental,Significance Analysis Methods,Tissue Data},
month = oct,
number = {1},
pages = {18},
pmid = {23043905},
title = {{Mining SOM expression portraits: feature selection and integrating concepts of molecular function.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23043905},
volume = {5},
year = {2012}
}
@article{Wirth2012m,
abstract = {MALDI-typing has become a frequently used approach for the identification of microorganisms and recently also of invertebrates. Similarity-comparisons are usually based on single-spectral data. We apply self-organizing maps (SOM) to portray the MS-spectral data with individual resolution and to improve the typing of Prototheca algae by using meta-spectra representing prototypes of groups of similar-behaving single spectra.},
author = {Wirth, Henry and von Bergen, Martin and Murugaiyan, Jayaseelan and R\"{o}sler, Uwe and Stokowy, Tomasz and Binder, Hans},
doi = {10.1016/j.mimet.2011.10.013},
file = {:H$\backslash$:/Eigene Dateien/Library/Wirth et al. - 2012 - MALDI-typing of infectious algae of the genus Prototheca using SOM portraits.pdf:pdf},
issn = {1872-8359},
journal = {Journal of microbiological methods},
keywords = {Henry Wirth,SOM visualization,Spectra},
mendeley-tags = {Henry Wirth,SOM visualization,Spectra},
month = jan,
number = {1},
pages = {83--97},
pmid = {22062088},
title = {{MALDI-typing of infectious algae of the genus Prototheca using SOM portraits.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22062088},
volume = {88},
year = {2012}
}
@phdthesis{Wirth2012a,
author = {Wirth, Henry},
file = {:H$\backslash$:/Eigene Dateien/Library/Wirth - 2012 - Analysis of large-scale molecular biological data using self-organizing maps.pdf:pdf},
keywords = {Cancer,Gene Set Analysis,Glioma,Henry Wirth,Lymphoma,SNP - Population Atlas,SOM clustering,SOM fundamental,SOM visualization,Tissue Data,Toxicology,Yeast},
mendeley-tags = {Cancer,Gene Set Analysis,Glioma,Henry Wirth,Lymphoma,SNP - Population Atlas,SOM clustering,SOM fundamental,SOM visualization,Tissue Data,Toxicology,Yeast},
pages = {1--147},
school = {Leipzig University},
title = {{Analysis of large-scale molecular biological data using self-organizing maps}},
type = {Dissertation},
url = {http://www.qucosa.de/fileadmin/data/qucosa/documents/10129/Dissertation Henry Wirth.pdf},
year = {2012}
}
@article{Steiner2012,
author = {Steiner, Lydia and Hopp, Lydia and Wirth, Henry and Galle, J\"{o}rg and Binder, Hans and Prohaska, Sonja and Rohlf, Thimo},
file = {:H$\backslash$:/Eigene Dateien/Library/Steiner et al. - 2012 - A global genome segmentation method for exploration of epigenetic patterns.pdf:pdf},
journal = {PloS one},
keywords = {Epigenetic,Henry Wirth,SOM visualization},
mendeley-tags = {Epigenetic,Henry Wirth,SOM visualization},
number = {10},
title = {{A global genome segmentation method for exploration of epigenetic patterns}},
volume = {7},
year = {2012}
}
@article{Reifenberger2014,
abstract = {The prognosis of glioblastoma, the most malignant type of glioma, is still poor, with only a minority of patients showing long-term survival of more than three years after diagnosis. To elucidate the molecular aberrations in glioblastomas of long-term survivors, we performed genome- and/or transcriptome-wide molecular profiling of glioblastoma samples from 94 patients, including 28 long-term survivors with >36 months overall survival (OS), 20 short-term survivors with <12 months OS, and 46 patients with intermediate OS. Integrative bioinformatic analyses were used to characterize molecular aberrations in the distinct survival groups considering established molecular markers such as isocitrate dehydrogenase 1 or 2 (IDH1/2) mutations, and O(6) -methylguanine DNA methyltransferase (MGMT) promoter methylation. Patients with long-term survival were younger and more often had IDH1/2-mutant and MGMT-methylated tumors. Gene expression profiling revealed over-representation of a distinct (proneural-like) expression signature in long-term survivors that was linked to IDH1/2 mutation. However, IDH1/2-wildtype glioblastomas from long-term survivors did not show distinct gene expression profiles and included proneural, classical and mesenchymal glioblastoma subtypes. Genomic imbalances also differed between IDH1/2-mutant and IDH1/2-wildtype tumors, but not between survival groups of IDH1/2-wildtype patients. Thus, our data support an important role for MGMT promoter methylation and IDH1/2 mutation in glioblastoma long-term survival and corroborate the association of IDH1/2 mutation with distinct genomic and transcriptional profiles. Importantly, however, IDH1/2-wildtype glioblastomas in our cohort of long-term survivors lacked distinctive DNA copy number changes and gene expression signatures, indicating that other factors might have been responsible for long survival in this particular subgroup of patients. © 2014 Wiley Periodicals, Inc.},
author = {Reifenberger, Guido and Weber, Ruthild G. and Riehmer, Vera and Kaulich, Kerstin and Willscher, Edith and Wirth, Henry and Gietzelt, Jens and Hentschel, Bettina and Westphal, Manfred and Simon, Matthias and Schackert, Gabriele and Schramm, Johannes and Matschke, Jakob and Sabel, Michael C. and Gramatzki, Dorothee and Felsberg, J\"{o}rg and Hartmann, Christian and Steinbach, Joachim P. and Schlegel, Uwe and Wick, Wolfgang and Radlwimmer, Bernhard and Pietsch, Torsten and Tonn, J\"{o}rg C. and von Deimling, Andreas and Binder, Hans and Weller, Michael and Loeffler, Markus},
doi = {10.1002/ijc.28836},
file = {:H$\backslash$:/Eigene Dateien/Library/Reifenberger et al. - 2014 - Molecular characterization of long-term survivors of glioblastoma using genome- and transcriptome-wide profiling.pdf:pdf},
issn = {00207136},
journal = {International Journal of Cancer},
keywords = {Glioma,Henry Wirth},
mendeley-tags = {Glioma,Henry Wirth},
month = mar,
pages = {n/a--n/a},
pmid = {24615357},
title = {{Molecular characterization of long-term survivors of glioblastoma using genome- and transcriptome-wide profiling}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24615357},
year = {2014}
}
@article{Hopp2013,
author = {Hopp, Lydia and Wirth, Henry and Fasold, Mario and Binder, Hans},
file = {:H$\backslash$:/Eigene Dateien/Library/Hopp et al. - 2013 - Portraying the expression landscapes of cancer subtypes A glioblastoma multiforme and prostate cancer case study.pdf:pdf},
journal = {Systems Biomedicine},
keywords = {Cancer,Glioma,Henry Wirth,SOM visualization},
mendeley-tags = {Cancer,Glioma,Henry Wirth,SOM visualization},
number = {2},
pages = {1--23},
title = {{Portraying the expression landscapes of cancer subtypes: A glioblastoma multiforme and prostate cancer case study}},
url = {http://www.landesbioscience.com/journals/systemsbiomedicine/toc/volume/1/issue/2/},
volume = {1},
year = {2013}
}
@article{Hopp2013a,
author = {Hopp, Lydia and Lembcke, Kathrin and Binder, Hans and Wirth, Henry},
doi = {10.3390/biology2041411},
file = {:H$\backslash$:/Eigene Dateien/Library/Hopp et al. - 2013 - Portraying the Expression Landscapes of B-Cell Lymphoma - Intuitive Detection of Outlier Samples and of Molecular Subtypes.pdf:pdf},
journal = {Biology},
keywords = {Classification,Henry Wirth,Lymphoma,MYC,SOM class/pred,SOM clustering,SOM visualization},
mendeley-tags = {Classification,Henry Wirth,Lymphoma,MYC,SOM class/pred,SOM clustering,SOM visualization},
number = {4},
pages = {1411--1437},
title = {{Portraying the Expression Landscapes of B-Cell Lymphoma - Intuitive Detection of Outlier Samples and of Molecular Subtypes.}},
volume = {2},
year = {2013}
}
@incollection{Binder2012,
author = {Binder, Hans and Hopp, Lydia and Cakir, Volkan and Fasold, Mario and von Bergen, Martin and Wirth, Henry},
booktitle = {Health Informatics and Bioinformatics (HIBIT), 2011 6th International Symposium},
doi = {10.1109/HIBIT.2011.6450817},
editor = {Allmer, Jens},
file = {:H$\backslash$:/Eigene Dateien/Library/Binder et al. - 2012 - Molecular phenotypic portraits - Exploring the ‘OMES’ with individual resolution.pdf:pdf},
isbn = {978-2-4673-4394-4},
keywords = {Cancer,Henry Wirth,Lymphoma,Proteomics,SNP - Population Atlas,SOM visualization,Stem Cells,Tissue Data},
mendeley-tags = {Cancer,Henry Wirth,Lymphoma,Proteomics,SNP - Population Atlas,SOM visualization,Stem Cells,Tissue Data},
pages = {99--107},
publisher = {IEEE Xplore},
title = {{Molecular phenotypic portraits - Exploring the ‘OMES’ with individual resolution}},
year = {2012}
}
@article{Loffler-Wirth2015,
abstract = {MOTIVATION: Comprehensive analysis of genome-wide molecular data challenges bioinformatics methodology in terms of intuitive visualization with single-sample resolution, biomarker selection, functional information mining and highly granular stratification of sample classes. oposSOM combines those functionalities making use of a comprehensive analysis and visualization strategy based on self-organizing maps (SOM) machine learning which we call 'high-dimensional data portraying'. The method was successfully applied in a series of studies using mostly transcriptome data but also data of other OMICs realms. oposSOM is now publicly available as Bioconductor R package.},
author = {L\"{o}ffler-Wirth, Henry and Kalcher, Martin and Binder, Hans},
doi = {10.1093/bioinformatics/btv342},
file = {:H$\backslash$:/Eigene Dateien/Library/L\"{o}ffler-Wirth, Kalcher, Binder - 2015 - oposSOM R-package for high-dimensional portraying of genome-wide expression landscapes on Biocon.pdf:pdf},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
keywords = {R pack,oposSOM},
mendeley-tags = {oposSOM,R pack},
month = jun,
pmid = {26063839},
title = {{oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on Bioconductor.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26063839},
year = {2015}
}
