OposSOM-Browser: an interactive tool to explore omics data landscapes in health science

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

doi:10.1186/s12859-020-03806-w

Henry Loeffler-Wirth, Siras Hakobyan + Show 3 more

Open Access

https://doi.org/10.1186/s12859-020-03806-w

Copy DOI

Abstract

BackgroundoposSOM is a comprehensive, machine learning based open-source data analysis software combining functionalities such as diversity analyses, biomarker selection, function mining, and visualization.ResultsThese 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.ConclusionThe 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.

Highlights

OposSOM is a comprehensive, machine learning based open-source data analysis software combining functionalities such as diversity analyses, biomarker selection, function mining, and visualization
The method is based on self-organizing map (SOM) machine learning for dimension reduction, visualization, and comprehensive downstream analysis
Datasets Five datasets are currently available in the oposSOM-Browser: (1) 917 specimen of germinal B cell lymphomas and selected healthy control samples [3], see below; (2) 80 melanoma and nevi samples [5]; (3) 137 low-grade gliomas [4]; (4) 180 blood samples of community acquired pneumonia patients [7]; and (5) 3388 blood samples collected from healthy participants of a population-based health study [8]

Summary

Introduction

OposSOM is a comprehensive, machine learning based open-source data analysis software combining functionalities such as diversity analyses, biomarker selection, function mining, and visualization. The oposSOM-Browser complements and extends the functionalities of the oposSOM software package by interactive functionalities in the context of gene-expression and gene-function profiling, associations with phenotypes, and pathway activities in selected transcriptome data sets on different cancer entities and blood transcriptomes.

Results

Conclusion