Local Context Finder (LCF) reveals multidimensional relationships among mRNA expression profiles of Arabidopsis responding to pathogen infection
Local Context Finder (LCF) reveals multidimensional relationships among mRNA expression profiles of Arabidopsis responding to pathogen infection
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DOI：
10.1073/pnas.1934349100
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发表时间：
2003-09-16
影响因子：
11.1
通讯作者：
Glazebrook, J
中科院分区：
文献类型：
--
作者：
Katagiri, F;Glazebrook, J
mRNA表达谱的计算分析中的一个主要任务是基于它们之间的相似性来定义谱之间的关系。这通常通过在高维空间中表示每个轮廓的数据点的分布中的模式识别来实现。通常使用的模式识别算法的一些缺点源于它们使用全局线性空间和/或有限的自由度。本文介绍了一种模式识别方法--局部上下文匹配法（LCF）。LCF使用非线性降维进行模式识别。然后根据非线性降维的结果建立轮廓网络。利用LCF技术分析了植物宿主拟南芥与细菌病原菌假单胞菌相互作用的mRNA表达谱。在一个案例中，LCF揭示了两个维度，这两个维度对于解释NahG转基因和ndr 1突变对抗性和敏感反应的影响至关重要。在另一种情况下，植物突变体缺陷的病原体感染的反应进行了分类的基础上LCF分析他们的配置文件。LCF的分类结果与突变体的生物学特性一致。因此，LCF是从表达谱数据中提取信息的强大方法。
A major task in computational analysis of mRNA expression profiles is definition of relationships among profiles on the basis of similarities among them. This is generally achieved by pattern recognition in the distribution of data points representing each profile in a high-dimensional space. Some drawbacks of commonly used pattern recognition algorithms stem from their use of a globally linear space and/or limited degrees of freedom. A pattern recognition method called Local Context Finder (LCF) is described here. LCF uses nonlinear dimensionality reduction for pattern recognition. Then it builds a network of profiles based on the nonlinear dimensionality reduction results. LCF was used to analyze mRNA expression profiles of the plant host Arabidopsis interacting with the bacterial pathogen Pseudomonas syringae. In one case, LCF revealed two dimensions essential to explain the effects of the NahG transgene and the ndr1 mutation on resistant and susceptible responses. In another case, plant mutants deficient in responses to pathogen infection were classified on the basis of LCF analysis of their profiles. The classification by LCF was consistent with the results of biological characterization of the mutants. Thus, LCF is a powerful method for extracting information from expression profile data.