Dataset Information

Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes.

ABSTRACT: Existing disease cluster detection methods cannot detect clusters of all shapes and sizes or identify highly irregular sets that overestimate the true extent of the cluster. We introduce a graph-theoretical method for detecting arbitrarily shaped clusters based on the Euclidean minimum spanning tree of cartogram-transformed case locations, which overcomes these shortcomings. The method is illustrated by using several clusters, including historical data sets from West Nile virus and inhalational anthrax outbreaks. Sensitivity and accuracy comparisons with the prevailing cluster detection method show that the method performs similarly on approximately circular historical clusters and greatly improves detection for noncircular clusters.

SUBMITTER: Wieland SC

PROVIDER: S-EPMC1890507 | biostudies-literature | 2007 May

REPOSITORIES: biostudies-literature

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Publications

Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes.

Wieland Shannon C SC Brownstein John S JS Berger Bonnie B Mandl Kenneth D KD

Proceedings of the National Academy of Sciences of the United States of America 20070522 22

Existing disease cluster detection methods cannot detect clusters of all shapes and sizes or identify highly irregular sets that overestimate the true extent of the cluster. We introduce a graph-theoretical method for detecting arbitrarily shaped clusters based on the Euclidean minimum spanning tree of cartogram-transformed case locations, which overcomes these shortcomings. The method is illustrated by using several clusters, including historical data sets from West Nile virus and inhalational ...[more]

PMID: 17519338

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Project description: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/.

Dataset Information

Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes.

Publications

Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes.

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