期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 40, 期 9, 页码 3841-3845出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2012.12.074
关键词
Clustering; Bioinformatics; Dimensional reduction; Topology preservation
类别
资金
- National Scientific and Technical Research Council [PIP 1122008, PIP 2072008, PIP 1142009]
- National Agency for the Promotion of Science and Technology [PICT 2008 00100, PICT 2008 01940, PAE 37122, PAE-PICT 00052]
- National Institute for Agricultural Technology of Argentina [PE 243542]
Dimensional reduction is a widely used technique for exploratory analysis of large volume of data. In biological datasets, each object is described by a large number of variables (or dimensions) and it is crucial to perform their analyses in a smaller space, to extract useful information. Kohonen self-organizing maps (SOMs) have been recently proposed in systems biology as a useful tool for exploratory analysis, data integration and discovery of new relationships in *omics datasets. SOMs have been traditionally used for clustering in several data mining problems, mainly due to their ability to preserve input data topology and reduce a high dimensional input space into a 2-D map. In spite of this, the above-mentioned dimensional reduction can lead to counterintuitive results. Sometimes, maps having almost the same size, trained on the same dataset, and with identical learning algorithms and parameters, may find different clusters. However, one would expect that small changes in map sizes or another training condition would not result in an abrupt different location of any of the grouped patterns. The aim of this work is to analyze and explain this issue through a real case study involving transcriptomic and metabolomic data, since it might have an important impact when interpreting clustering results over a biological dataset. (C) 2012 Elsevier Ltd. All rights reserved.
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