oposSOM-Browser: an interactive tool to explore omics data landscapes in health science
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oposSOM‑Browser: an interactive tool to explore omics data landscapes in health science Henry Loeffler‑Wirth1* , Jasmin Reikowski1, Siras Hakobyan2, Jonas Wagner3 and Hans Binder1 *Correspondence: [email protected]‑leipzig.de 1 Interdisciplinary Centre for Bioinformatics, Leipzig University, Härtelstraße 16‑18, 04107 Leipzig, Germany Full list of author information is available at the end of the article
Abstract 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 applica‑ tion 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/oposs om-browser. Keywords: Interactive data analysis, Transcriptomics, Results browser
Background Many bioinformatics tools are currently in transition from software libraries to interactive solutions designed for a broader user community including data scientists, outputoriented medical researchers and experimenters with needs in intuitive visualization and exploration for complex, multidimensional data. We here present an interactive webtool which extends functionalities of our Bioconductor R-package ‘oposSOM’ designed to analyse transcriptome data in cancer and health research [1]. The method is based on self-organizing map (SOM) machine learning for dimension reduction, visualization, and comprehensive downstream analysis. This so-called ‘high-dimensional data portraying’ visualizes individual data landscapes, and performs function mining, modular feature selection, sample stratification, diversity analysis, and phenotype mapping [2]. It was applied to a series of data types (transcriptome, methylome, proteome, genome), diseases (cancers such as melanoma, lymphoma; autoimmune diseases) on the level of patient-cohort and cell system specimen (see, e.g., [3–5]). The method was so far used
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