SignatureClust: a tool for landmark gene-guided clustering |
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Authors: | Pankaj?Chopra Hanjun?Shin Email author" target="_blank">Jaewoo?KangEmail author Sunwon?Lee |
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Affiliation: | (1) Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA;(2) Department of Computer Science, College of Information and Communication, Korea University, Seoul, South Korea; |
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Abstract: | Over the last several years, many clustering algorithms have been applied to gene expression data. However, most clustering
algorithms force the user into having one set of clusters, resulting in a restrictive biological interpretation of gene function.
It would be difficult to interpret the complex biological regulatory mechanisms and genetic interactions from this restrictive
interpretation of microarray expression data. The software package SignatureClust allows users to select a group of functionally
related genes (called ‘Landmark Genes’), and to project the gene expression data onto these genes. Compared to existing algorithms
and software in this domain, our software package offers two unique benefits. First, by selecting different sets of landmark
genes, it enables the user to cluster the microarray data from multiple biological perspectives. This encourages data exploration
and discovery of new gene associations. Second, most packages associated with clustering provide internal validation measures,
whereas our package validates the biological significance of the new clusters by retrieving significant ontology and pathway
terms associated with the new clusters. SignatureClust is a free software tool that enables biologists to get multiple views
of the microarray data. It highlights new gene associations that were not found using a traditional clustering algorithm.
The software package ‘SignatureClust’ and the user manual can be downloaded from . |
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