Analysis of metabolite profile data using batch-learning self-organizing maps
Analysis of metabolite profile data using batch-learning self-organizing maps
复制标题
DOI：
10.1007/bf03030693
复制
发表时间：
2007-08
影响因子：
2.9
通讯作者：
Jae Kwang Kim;M. Cho;H. Baek;Tae Hun Ryu;Chang-Yeon Yu;Myong-Jo Kim;E. Fukusaki;A. Kobayashi
中科院分区：
文献类型：
--
作者：
Jae Kwang Kim;M. Cho;H. Baek;Tae Hun Ryu;Chang-Yeon Yu;Myong-Jo Kim;E. Fukusaki;A. Kobayashi
需要新的工具来有效地分析和可视化与代谢组学相关的大量数据集。在这里，我们描述了一个批学习自组织映射（BL-SOM）的代谢组信息学，使学习过程和所得的地图独立的数据输入的顺序。该方法已成功应用于盐胁迫下拟南芥细胞代谢组数据的分析和组织。我们的6 × 4矩阵显示了不同时间段的代谢物水平模式。在这种胁迫下，与糖酵解代谢相关的氨基酸和代谢产物的水平呈负相关。因此，BL-SOM可以是一个很好的工具，聚类和可视化高维，复杂的代谢组数据在一个单一的地图。
Novel tools are needed for efficient analysis and visualization of the massive data sets associated with metabolomics. Here, we describe a batch-learning self-organizing map (BL-SOM) for metabolome informatics that makes the learning process and resulting map independent of the order of data input. This approach was successfully used in analyzing and organizing the metabolome data forArabidopsis thalianacells cultured under salt stress. Our 6 × 4 matrix presented patterns of metabolite levels at different time periods. A negative correlation was found between the levels of amino acids and metabolites related to glycolysis metabolism in response to this stress. Therefore, BL-SOM could be an excellent tool for clustering and visualizing high dimensional, complex metabolome data in a single map.