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タイトル: HAMSTER: visualizing microarray experiments as a set of minimum spanning trees.
著者: Wan, Raymond
Kiseleva, Larisa
Harada, Hajime
Mamitsuka, Hiroshi  kyouindb  KAKEN_id
Horton, Paul
発行日: 2009
出版者: BioMed Central Ltd.
誌名: Source code for biology and medicine
巻: 4
論文番号: 8
抄録: [Background]: Visualization tools allow researchers to obtain a global view of the interrelationships between the probes or experiments of a gene expression (e.g. microarray) data set. Some existing methods include hierarchical clustering and k-means. In recent years, others have proposed applying minimum spanning trees (MST) for microarray clustering. Although MST-based clustering is formally equivalent to the dendrograms produced by hierarchical clustering under certain conditions; visually they can be quite different. [Methods]: HAMSTER (Helpful Abstraction using Minimum Spanning Trees for Expression Relations) is an open source system for generating a set of MSTs from the experiments of a microarray data set. While previous works have generated a single MST from a data set for data clustering, we recursively merge experiments and repeat this process to obtain a set of MSTs for data visualization. Depending on the parameters chosen, each tree is analogous to a snapshot of one step of the hierarchical clustering process. We scored and ranked these trees using one of three proposed schemes. HAMSTER is implemented in C++ and makes use of Graphviz for laying out each MST. [Results]: We report on the running time of HAMSTER and demonstrate using data sets from the NCBI Gene Expression Omnibus (GEO) that the images created by HAMSTER offer insights that differ from the dendrograms of hierarchical clustering. In addition to the C++ program which is available as open source, we also provided a web-based version (HAMSTER+) which allows users to apply our system through a web browser without any computer programming knowledge. [Conclusion]: Researchers may find it helpful to include HAMSTER in their microarray analysis workflow as it can offer insights that differ from hierarchical clustering. We believe that HAMSTER would be useful for certain types of gradient data sets (e.g time-series data) and data that indicate relationships between cells/tissues. Both the source and the web server variant of HAMSTER are available from http://hamster.cbrc.jp/
著作権等: © 2009 Wan et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
URI: http://hdl.handle.net/2433/161051
DOI(出版社版): 10.1186/1751-0473-4-8
PubMed ID: 19925686
出現コレクション:学術雑誌掲載論文等

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