Summary: Cluster analysis plays an important role in the analysis of gene expression data since the early beginning of microarray studies and is routinely used to find groups of genes with common expression pattern. In order to make cluster analysis helpful for users, visualization of cluster solutions is of utmost importance. Here, we present the new R package gcExplorer for the interactive exploration of gene clusters. gcExplorer facilitates the interpretation of cluster results and allows to investigate extensive information about clusters. Availability: The latest release of gcExplorer is always available at the Comprehensive R Archive Network CRAN
Microarray technology has now becoming a systematical way to study the expression level of thousands...
Clustering is commonly used in the analysis of geneexpression data to nd groups of co{expressed gene...
Motivation: Over the last decade, a large variety of clustering algorithms have been developed to de...
Summary: Cluster analysis plays an important role in the analysis of gene expression data since the ...
Background: Many different cluster methods are frequently used in gene expression data analysis to f...
Many different cluster methods are frequently used in gene expression data analysis to find groups o...
The visualization of cluster solutions in gene expression data analysis gives practitioners an under...
Abstract Background Gene expression technologies have opened up new ways to diagnose and treat cance...
Background: Interpretation of comprehensive DNA microarray data sets is a challenging task for biolo...
Discovering co-expressed genes and coherent ex-pression patterns in gene expression data is an impor...
Motivation: Microarrays have become a central tool in bio-logical research. Their applications range...
Abstract Background A widely applied approach to extract knowledge from high-throughput genomic data...
AbstractClustering algorithms have been shown to be useful to explore large-scale gene expression pr...
Motivation: Cluster analysis (of gene-expression data) is a useful tool for identifying biologically...
Massive amounts of gene expression data are generated using microarrays for functional studies of ge...
Microarray technology has now becoming a systematical way to study the expression level of thousands...
Clustering is commonly used in the analysis of geneexpression data to nd groups of co{expressed gene...
Motivation: Over the last decade, a large variety of clustering algorithms have been developed to de...
Summary: Cluster analysis plays an important role in the analysis of gene expression data since the ...
Background: Many different cluster methods are frequently used in gene expression data analysis to f...
Many different cluster methods are frequently used in gene expression data analysis to find groups o...
The visualization of cluster solutions in gene expression data analysis gives practitioners an under...
Abstract Background Gene expression technologies have opened up new ways to diagnose and treat cance...
Background: Interpretation of comprehensive DNA microarray data sets is a challenging task for biolo...
Discovering co-expressed genes and coherent ex-pression patterns in gene expression data is an impor...
Motivation: Microarrays have become a central tool in bio-logical research. Their applications range...
Abstract Background A widely applied approach to extract knowledge from high-throughput genomic data...
AbstractClustering algorithms have been shown to be useful to explore large-scale gene expression pr...
Motivation: Cluster analysis (of gene-expression data) is a useful tool for identifying biologically...
Massive amounts of gene expression data are generated using microarrays for functional studies of ge...
Microarray technology has now becoming a systematical way to study the expression level of thousands...
Clustering is commonly used in the analysis of geneexpression data to nd groups of co{expressed gene...
Motivation: Over the last decade, a large variety of clustering algorithms have been developed to de...