作者
Ralf Steuer, Jürgen Kurths, Carsten O Daub, Janko Weise, Joachim Selbig
发表日期
2002/10
期刊
Bioinformatics
卷号
18
期号
suppl_2
页码范围
S231-S240
出版商
Oxford University Press
简介
Motivation: Clustering co-expressed genes usually requires the definition of ‘distance’or ‘similarity’between measured datasets, the most common choices being Pearson correlation or Euclidean distance. With the size of available datasets steadily increasing, it has become feasible to consider other, more general, definitions as well. One alternative, based on information theory, is the mutual information, providing a general measure of dependencies between variables. While the use of mutual information in cluster analysis and visualization of large-scale gene expression data has been suggested previously, the earlier studies did not focus on comparing different algorithms to estimate the mutual information from finite data.
Results: Here we describe and review several approaches to estimate the mutual information from finite datasets. Our findings show that the algorithms used so far may be quite substantially improved upon. In particular when dealing with small datasets, finite sample effects and other sources of potentially misleading results have to be taken into account.
Contact: steuer@ agnld. uni-potsdam. de
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