Noise-robust soft clustering of gene expression time-course data
Noise-robust soft clustering of gene expression time-course data
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DOI：
10.1142/s0219720005001375
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发表时间：
2005-08-01
影响因子：
1
通讯作者：
Carlisle, Bronwyn
中科院分区：
文献类型：
--
作者：
Futschik, Matthias E.;Carlisle, Bronwyn
聚类分析是微阵列数据分析的重要工具。这种无监督学习技术通常用于揭示隐藏在大型基因表达数据集中的结构。到目前为止，绝大多数应用的聚类算法产生数据的硬分区，即每个基因被精确地分配到一个聚类。如果聚类被很好地分离，则硬聚类是有利的。然而，这通常不是微阵列时程数据的情况下，基因簇经常重叠。此外，硬聚类算法往往对噪声高度敏感，为了克服硬聚类的局限性，我们采用软聚类，这为研究人员提供了几个优点。首先，它生成可访问的内部簇结构，即它指示相应的簇代表基因的程度。这可以用于更有针对性地搜索调节元件。其次，可以定义集群之间的总体关系，从而定义全局集群结构。此外，软聚类具有更强的噪声鲁棒性，并且可以避免基因的先验预过滤。这防止了从数据分析中排除生物学相关基因。这里使用模糊c均值算法实现软聚类。程序，以找到最佳的聚类参数。基于开源统计语言R开发了一个软聚类软件包。名为Mfuzz的软件包是免费提供的。
Clustering is an important tool in microarray data analysis. This unsupervised learning technique is commonly used to reveal structures hidden in large gene expression data sets. The vast majority of clustering algorithms applied so far produce hard partitions of the data, i.e. each gene is assigned exactly to one cluster. Hard clustering is favourable if clusters are well separated. However, this is generally not the case for microarray time-course data, where gene clusters frequently overlap. Additionally, hard clustering algorithms are often highly sensitive to noise.To overcome the limitations of hard clustering, we applied soft clustering which offers several advantages for researchers. First, it generates accessible internal cluster structures, i.e. it indicates how well corresponding clusters represent genes. This can be used for the more targeted search for regulatory elements. Second, the overall relation between clusters, and thus a global clustering structure, can be defined. Additionally, soft clustering is more noise robust and a priori pre-filtering of genes can be avoided. This prevents the exclusion of biologically relevant genes from the data analysis. Soft clustering was implemented here using the fuzzy c-means algorithm. Procedures to find optimal clustering parameters were developed. A software package for soft clustering has been developed based on the open-source statistical language R. The package called Mfuzz is freely available.