mAPKL: R/ Bioconductor package for detecting gene exemplars and revealing their characteristics

BMC Bioinformatics

George Spyrou¹  Argiris Sakellariou² 

[1]Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Street, Athens 115 27, Greece

[2]Department of Informatics and Telecommunications, National & Kapodistrian University of Athens, Athens, Greece

关键词: Bioconductor;    R;    Gene expression;    Microarray;    Differential expression;    Feature extraction;   

Others  :  1229461
DOI  :  10.1186/s12859-015-0719-5

 received in 2015-05-22, accepted in 2015-08-24,  发布年份 2015

【 摘 要 】

Background

So far many algorithms have been proposed towards the detection of significant genes in microarray analysis problems. Several of those approaches are freely available as R-packages though their engagement in gene expression analysis by non-bioinformaticians is usually a frustrating task. Besides, only some of those packages offer a complete suite of tools starting from initial data import and ending to analysis report. Here we present an R/Bioconductor package that implements a hybrid gene selection method along with a bunch of functions to facilitate a thorough and convenient gene expression profiling analysis.

Results

mAPKL is an open-source R/Bioconductor package that implements the mAP-KL hybrid gene selection method. The advantage of this method is that selects a small number of gene exemplars while achieving comparable classification results to other well established algorithms on a variety of datasets and dataset sizes. The mAPKL package is accompanied with extra functionalities including (i) solid data import; (ii) data sampling following a user-defined proportion; (iii) preprocessing through several normalization and transformation alternatives; (iv) classification with the aid of SVM and performance evaluation; (v) network analysis of the significant genes (exemplars), including degree of centrality, closeness, betweeness, clustering coefficient as well as the construction of an edge list table; (vi) gene annotation analysis, (vii) pathway analysis and (viii) auto-generated analysis reporting.

Conclusions

Users are able to run a thorough gene expression analysis in a timely manner starting from raw data and concluding to network characteristics of the selected gene exemplars. Detailed instructions and example data are provided in the R package, which is freely available at Bioconductor under the GPL-2 or later license http://www.bioconductor.org/packages/3.1/bioc/html/mAPKL.html.

【 授权许可】

   
2015 Sakellariou and Spyrou.

【 图 表 】

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