oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on bioconductor
oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on bioconductor
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DOI：
10.1093/bioinformatics/btv342
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发表时间：
2015-10-01
期刊：
影响因子：
5.8
通讯作者：
Binder, Hans
中科院分区：
文献类型：
--
作者：
Loeffler-Wirth, Henry;Kalcher, Martin;Binder, Hans
全基因组分子数据的综合分析对生物信息学方法提出了挑战，包括单样本分辨率的直观可视化、生物标志物选择、功能信息挖掘和样本类别的高度粒度分层。oposSOM结合了这些功能，利用基于自组织映射（SOM）机器学习的综合分析和可视化策略，我们称之为“高维数据描绘”。该方法已成功应用于一系列研究中，主要使用转录组数据，但也使用其他OMIC领域的数据。
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.