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Analysis of gene expression data using functional principal components.

机译:使用功能主要成分分析基因表达数据。

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摘要

The large amount of data involved in DNA microarrays implies the development of efficient computer algorithms to analyze the gene expressions, and thus to study the transcriptome. Numerous techniques already exist and we propose a new method based on the key idea that gene profiles may be considered as continuous curves. The analysis of the set of curves stemming from the DNA microarray may be then performed using a functional analysis which can exhibit the main modes of variations in this set, gather genes with similar variations and extract characteristic parameters of gene profiles. We aim here at introducing this method, called the Functional Principal Component Analysis. A prospective study has been performed on two available datasets, concerning on the one hand the sporulation data of the Saccharomyces cerevisiae, and on the other hand data of tumor cell lines. Results are very promising: the method is able to extract characteristic parameters from the datasets, to extract significant modes of variations in the set of gene profiles, and to link these variations to biological processes already studied in literature.
机译:DNA微阵列中涉及的大量数据意味着开发了有效的计算机算法来分析基因表达,从而研究转录组。已经存在多种技术,我们基于基因图谱可被视为连续曲线的关键思想提出了一种新方法。然后可以使用功能分析对源自DNA微阵列的一组曲线进行分析,该功能分析可以显示该组中主要的变异模式,收集具有相似变异的基因并提取基因概况的特征参数。我们在这里旨在介绍这种方法,称为功能主成分分析。在两个可用的数据集上进行了前瞻性研究,一方面涉及啤酒酵母的孢子形成数据,另一方面涉及肿瘤细胞系的数据。结果是非常有希望的:该方法能够从数据集中提取特征参数,提取出一系列基因图谱的重要变异模式,并将这些变异与文献中已研究的生物学过程联系起来。

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