基于形状相似性的基因表达数据聚类。

Travis J Hestilow, Yufei Huang
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引用次数: 0

摘要

本文介绍了一种利用形状信息从表达谱对基因进行聚类的方法。K-means 等传统聚类方法假定具有相似功能的基因具有相似的表达水平,因此将具有相似表达水平的基因分配到同一聚类中。然而,具有相似功能的基因即使表达量相差甚远,其信号形状也往往具有相似性。因此,本研究根据信号形状的相似性进行聚类。这种形状信息以归一化和时间缩放的正向初差的形式捕获,然后进行变异贝叶斯聚类和非贝叶斯(Silhouette)聚类统计。该统计量在确定正确的聚类数量和分配聚类成分方面显示出更强的能力。根据生成的测试数据和大肠杆菌微阵列表达数据的初步结果,以及对大肠杆菌结果的初步验证,可以看出该方法有望根据形状相似性更好地对时间序列微阵列数据进行聚类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Clustering of gene expression data based on shape similarity.

Clustering of gene expression data based on shape similarity.

Clustering of gene expression data based on shape similarity.

Clustering of gene expression data based on shape similarity.

A method for gene clustering from expression profiles using shape information is presented. The conventional clustering approaches such as K-means assume that genes with similar functions have similar expression levels and hence allocate genes with similar expression levels into the same cluster. However, genes with similar function often exhibit similarity in signal shape even though the expression magnitude can be far apart. Therefore, this investigation studies clustering according to signal shape similarity. This shape information is captured in the form of normalized and time-scaled forward first differences, which then are subject to a variational Bayes clustering plus a non-Bayesian (Silhouette) cluster statistic. The statistic shows an improved ability to identify the correct number of clusters and assign the components of cluster. Based on initial results for both generated test data and Escherichia coli microarray expression data and initial validation of the Escherichia coli results, it is shown that the method has promise in being able to better cluster time-series microarray data according to shape similarity.

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