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首页> 外文期刊>Journal of biomedical informatics. >Analyzing time-dependent microarray data using independent component analysis derived expression modes from human macrophages infected with F. tularensis holartica.
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Analyzing time-dependent microarray data using independent component analysis derived expression modes from human macrophages infected with F. tularensis holartica.

机译：使用独立的成分分析方法分析时间依赖性微阵列数据，该方法是从感染了霍乱弧菌的人巨噬细胞中获得的表达模式。

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The analysis of large-scale gene expression profiles is still a demanding and extensive task. Modern machine learning and data mining techniques developed in linear algebra, like Independent Component Analysis (ICA), become increasingly popular as appropriate tools for analyzing microarray data. We applied ICA to analyze kinetic gene expression profiles of human monocyte derived macrophages (MDM) from three different donors infected with Francisella tularensis holartica and compared them to more classical methods like hierarchical clustering. Results were compared using a pathway analysis tool, based on the Gene Ontology and the MeSH database. We could show that both methods lead to time-dependent gene regulatory patterns which fit well to known TNFalpha induced immune responses. In comparison, the nonexclusive attribute of ICA results in a more detailed view and a higher resolution in time dependent behavior of the immune response genes. Additionally, we identified NFkappaB as one of the main regulatory genes during response to F. tularensis infection.
机译：大规模基因表达谱的分析仍然是一项艰巨而广泛的任务。在线性代数中开发的现代机器学习和数据挖掘技术，例如独立成分分析（ICA），作为分析微阵列数据的适当工具，变得越来越流行。我们应用ICA分析了来自感染了弗朗西斯菌holartica的三个不同供体的人单核细胞衍生巨噬细胞（MDM）的动力学基因表达谱，并将它们与分层聚类等更经典的方法进行了比较。使用基于基因本体论和MeSH数据库的途径分析工具比较结果。我们可以证明这两种方法都可以导致时间依赖性基因调控模式，这非常适合已知的TNFalpha诱导的免疫反应。相比之下，ICA的非排他性属性可提供更详细的视图，并提高免疫反应基因的时间依赖性行为的分辨率。另外，我们确定了NFkappaB是对图拉菌感染的主要调控基因之一。

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