oposSOM Browser
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oposSOM is a comprehensive, machine learning based open-source data analysis software combining functionalities such as diversity analyses, biomarker selection, function mining, and visualization. These functionalities are now available as interactive web-browser application for a broader user audience interested in extracting detailed information from high-throughput omics data sets pre-processed by oposSOM. It enables interactive browsing of single-gene and gene set profiles, of molecular 'portrait landscapes', of associated phenotype diversity, and signalling pathway activation patterns.

LHA ID: 82R3E60M4E-2

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Model type: Self-Organizing Map

Model format: R package

Execution or visualisation environment: Shiny


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Views: 2081   Downloads: 72

Created: 22nd Jun 2020 at 09:12

Last updated: 28th Jan 2022 at 15:21

Last used: 16th Apr 2024 at 19:46

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Version 3 (latest) Created 23rd Nov 2020 at 14:13 by René Hänsel

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LHA - Leipzig Health Atlas

The Leipzig Health Atlas (LHA) is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research fields in which our team has scientific and clinical expertise.

Programme: i:DSem - Integrative Datensemantik in der Systemmedizin

Public web page: https://www.health-atlas.de

Start date: 1st Apr 2016

End date: 28th Feb 2021

The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health.
LIFE Adult, oBIG - omics Bioinformatics for Health

Abstract (Expand)

Background: The blood transcriptome is expected to provide a detailed picture of an organism's physiological state with potential outcomes for applications in medical diagnostics and molecular and … epidemiological research. We here present the analysis of blood specimens of 3,388 adult individuals, together with phenotype characteristics such as disease history, medication status, lifestyle factors, and body mass index (BMI). The size and heterogeneity of this data challenges analytics in terms of dimension reduction, knowledge mining, feature extraction, and data integration. Methods: Self-organizing maps (SOM)-machine learning was applied to study transcriptional states on a population-wide scale. This method permits a detailed description and visualization of the molecular heterogeneity of transcriptomes and of their association with different phenotypic features. Results: The diversity of transcriptomes is described by personalized SOM-portraits, which specify the samples in terms of modules of co-expressed genes of different functional context. We identified two major blood transcriptome types where type 1 was found more in men, the elderly, and overweight people and it upregulated genes associated with inflammation and increased heme metabolism, while type 2 was predominantly found in women, younger, and normal weight participants and it was associated with activated immune responses, transcriptional, ribosomal, mitochondrial, and telomere-maintenance cell-functions. We find a striking overlap of signatures shared by multiple diseases, aging, and obesity driven by an underlying common pattern, which was associated with the immune response and the increase of inflammatory processes. Conclusions: Machine learning applications for large and heterogeneous omics data provide a holistic view on the diversity of the human blood transcriptome. It provides a tool for comparative analyses of transcriptional signatures and of associated phenotypes in population studies and medical applications.

Authors: M. Schmidt, L. Hopp, A. Arakelyan, H. Kirsten, C. Engel, K. Wirkner, K. Krohn, R. Burkhardt, J. Thiery, M. Loeffler, H. Loeffler-Wirth, H. Binder

Date Published: 11th Mar 2021

Publication Type: Journal article

oposSOM-Browser: an interactive tool to explore omics data landscapes in health science
LHA - Leipzig Health Atlas, oBIG - omics Bioinformatics for Health

Abstract (Expand)

Background: oposSOM is a comprehensive, machine learning based open-source data analysis software combining functionalities such as diversity analyses, biomarker selection, function mining, and … visualization. Results: These functionalities are now available as interactive web-browser application for a broader user audience interested in extracting detailed information from high-throughput omics data sets pre-processed by oposSOM. It enables interactive browsing of single-gene and gene set profiles, of molecular 'portrait landscapes', of associated phenotype diversity, and signalling pathway activation patterns. Conclusion: The oposSOM-Browser makes available interactive data browsing for five transcriptome data sets of cancer (melanomas, B-cell lymphomas, gliomas) and of peripheral blood (sepsis and healthy individuals) at www.izbi.uni-leipzig.de/opossom-browser .

Authors: Henry Loeffler-Wirth, Jasmin Reikowski, Siras Hakobyan, Jonas Wagner, Hans Binder

Date Published: 1st Dec 2020

Publication Type: Journal article

René Hänsel adminproject_administratorasset_housekeeperasset_gatekeeperprogramme_administrator

Projects: LHA - Leipzig Health Atlas, LIFE Adult, LIFE - Leipzig Research Center for Civilization Diseases, LIFE HNC - Head and Neck Cancer Group, LIFE Heart, MMML - Molecular mechanisms in malignant lymphoma, GLA - German Lymphoma Alliance, MMML Demonstrators - Molecular Mechanisms in Malignant Lymphomas - Demonstrators of Personalized Medicine, HaematoOpt - Individualized model-based managing of the next-cycle thrombopenia of CHOEP/CHOP treated patients based on platelets dynamics during the previous cycles, e:Med, GC-HBOC - German Consortium for Hereditary Breast and Ovarian Cancer, GC-HNPCC - German Consortium for Hereditary Non-Polyposis Colorectal Cancer, MMML-MYC-SYS, NLP4CR - Natural Language Processing for Clinical Research, Genetical Statistics and Systems Biology, SepNet - German Competence Network Sepsis, LIFE Child, HNPCC-Sys - Genomic and transcriptomic heterogeneity of colorectal tumours arising in Lynch syndrome, GGN - German Glioma Network, CAPSys - Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome, CapSys - Systems Medicine of Community Acquired Pneumonia, ProstataCA, HaematoSys - Systems biology of haematopoiesis and haematopoietic neoplasia, SMITH - Smart Medical Information Technology for Healthcare, Task Force COVID-19 Leipzig, NFDI4Health, POLAR - Polypharmacy, Drug Interactions, Risks, Management of health information systems, LivSys Transfer - Transfer of the LivSys in vitro system for hepatotoxicity into application, Project Test Demonstrator, Fundus photography as tool for analysis of eyes of subjects with diabetes, Clinical Trials Leipzig, NFDI4Health - TA3 Services, STOP-NUC, SCALE-TORT

Institutions: Institute for Medical Informatics, Statistics and Epidemiology (IMISE)

https://orcid.org/0000-0001-8344-0658
Henry Löffler-Wirth project_administratorprogramme_administrator

Projects: Leipzig Melanoma Studies, GLA - German Lymphoma Alliance, MMML-MYC-SYS, GGN - German Glioma Network, LIFE Adult, LIFE - Leipzig Research Center for Civilization Diseases, Task Force COVID-19 Leipzig, NFDI4Health, LHA - Leipzig Health Atlas, Think different, imSAVAR - Immune safety avatar: nonclinical mimicking of the immune system effects of immunomodulatory therapies, oBIG - omics Bioinformatics for Health

Institutions: University of Leipzig, Interdisciplinary Center for Bioinformatics (IZBI)

https://orcid.org/0000-0001-8239-440X
i:DSem - Integrative Datensemantik in der Systemmedizin

Within the framework of the funding measure "i:DSem - Integrative Data Semantics in Systems Medicine", the Federal Ministry of Education and Research supports research projects that take up this approach. Interdisciplinary research projects are to create the basis so that in the future, for example, the attending physician will have application-oriented computer programmes at his or her disposal that will enable him or her to access all clinically relevant data. This should significantly support ...

Projects: LHA - Leipzig Health Atlas

Web page: https://www.gesundheitsforschung-bmbf.de/de/i-dsem-integrative-datensemantik-in-der-systemmedizin-3367.php

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Health Atlas - Local Data Hub/Leipzig

The Health Atlas - Local Data Hub/Leipzig is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research fields.

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