文章基本信息

标题：HAMSTER: visualizing microarray experiments as a set of minimum spanning trees
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作者：Raymond Wan ; Larisa Kiseleva ; Hajime Harada 等
期刊名称：Source Code for Biology and Medicine
印刷版ISSN：1751-0473
电子版ISSN：1751-0473
出版年度：2009
卷号：4
期号：1
页码：8
DOI：10.1186/1751-0473-4-8
语种：English
出版社：BioMed Central
摘要：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. 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. 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. 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/ .
关键词：Hierarchical Cluster ; West Nile Virus ; Gene Expression Omnibus ; Minimum Span Tree ; Edge Weight