Classification using functional data analysis for temporal gene expression data
Classification using functional data analysis for temporal gene expression data
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DOI：
10.1093/bioinformatics/bti742
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发表时间：
2006-01-01
期刊：
影响因子：
5.8
通讯作者：
Müller, HG
中科院分区：
文献类型：
--
作者：
Leng, XY;Müller, HG
动机：时间基因表达谱提供了基因功能的重要特征，因为生物系统主要是发育和动态的。我们提出了一种对时间基因表达曲线集合进行分类的方法，在该方法中，个体表达谱被建模为随机过程的独立实现。该方法使用最近开发的基于函数主成分的函数Logistic回归工具，旨在将基因表达曲线分类为已知基因组。分类器中特征函数的数目可以通过留一交叉验证来选择，目的是最小化分类错误。结果：我们证明该方法对酵母细胞周期基因表达谱和Dictyostelialcell-type特异性基因表达谱都提供了低错误率的分类。它在模拟中也能很好地工作。我们比较了我们的函数主成分方法和B-样条法对酵母细胞周期数据和模拟的函数判别分析的实现。这表明我们的方法使用更少的本征函数/基函数的相对优势。所提出的方法在分析时态基因表达数据和其他数据方面很有前景。
Motivation: Temporal gene expression profiles provide an important characterization of gene function, as biological systems are predominantly developmental and dynamic. We propose a method of classifying collections of temporal gene expression curves in which individual expression profiles are modeled as independent realizations of a stochastic process. The method uses a recently developed functional logistic regression tool based on functional principal components, aimed at classifying gene expression curves into known gene groups. The number of eigenfunctions in the classifier can be chosen by leave-one-out cross-validation with the aim of minimizing the classification error.Results: We demonstrate that this methodology provides low-error-rate classification for both yeast cell-cycle gene expression profiles and Dictyostelium cell-type specific gene expression patterns. It also works well in simulations. We compare our functional principal components approach with a B-spline implementation of functional discriminant analysis for the yeast cell-cycle data and simulations. This indicates comparative advantages of our approach which uses fewer eigenfunctions/base functions. The proposed methodology is promising for the analysis of temporal gene expression data and beyond.