Literature DB >> 19225024

gcExplorer: interactive exploration of gene clusters.

Theresa Scharl1, Friedrich Leisch.   

Abstract

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.

Mesh:

Year:  2009        PMID: 19225024     DOI: 10.1093/bioinformatics/btp099

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  5 in total

1.  Clustering of High Throughput Gene Expression Data.

Authors:  Harun Pirim; Burak Ekşioğlu; Andy Perkins; Cetin Yüceer
Journal:  Comput Oper Res       Date:  2012-12       Impact factor: 4.008

2.  Comparative transcription profiling and in-depth characterization of plasmid-based and plasmid-free Escherichia coli expression systems under production conditions.

Authors:  Juergen Mairhofer; Theresa Scharl; Karoline Marisch; Monika Cserjan-Puschmann; Gerald Striedner
Journal:  Appl Environ Microbiol       Date:  2013-04-12       Impact factor: 4.792

3.  Modelling time course gene expression data with finite mixtures of linear additive models.

Authors:  Bettina Grün; Theresa Scharl; Friedrich Leisch
Journal:  Bioinformatics       Date:  2011-11-26       Impact factor: 6.937

4.  Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli.

Authors:  Theresa Scharl; Gerald Striedner; Florentina Pötschacher; Friedrich Leisch; Karl Bayer
Journal:  Microb Cell Fact       Date:  2009-07-15       Impact factor: 5.328

5.  Exploratory and inferential analysis of gene cluster neighborhood graphs.

Authors:  Theresa Scharl; Ingo Voglhuber; Friedrich Leisch
Journal:  BMC Bioinformatics       Date:  2009-09-14       Impact factor: 3.169

  5 in total

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