EMA - A R package for Easy Microarray data analysis

BackgroundThe increasing number of methodologies and tools currently available to analyse gene expression microarray data can be confusing for non specialist users.FindingsBased on the experience of biostatisticians of Institut Curie, we propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results.ConclusionsStrategy and tools proposed in the EMA R package could provide a useful starting point for many microarrays users. EMA is part of Comprehensive R Archive Network and is freely available at http://bioinfo.curie.fr/projects/ema/.

[1]  Anne-Laure Boulesteix,et al.  CMA – a comprehensive Bioconductor package for supervised classification with high dimensional data , 2008, BMC Bioinformatics.

[2]  Emmanuel Barillot,et al.  Frequent PTEN genomic alterations and activated phosphatidylinositol 3-kinase pathway in basal-like breast cancer cells , 2008, Breast Cancer Research.

[3]  Giorgio Valentini,et al.  Model order selection for bio-molecular data clustering , 2007, BMC Bioinformatics.

[4]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[5]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[6]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[7]  Rainer Breitling,et al.  RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis , 2006, Bioinform..

[8]  Terry Speed,et al.  Normalization of cDNA microarray data. , 2003, Methods.

[9]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[10]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[11]  Audrey Kauffmann,et al.  Bioinformatics Applications Note Arrayqualitymetrics—a Bioconductor Package for Quality Assessment of Microarray Data , 2022 .

[12]  Rafael A. Irizarry,et al.  A Model-Based Background Adjustment for Oligonucleotide Expression Arrays , 2004 .

[13]  Sébastien Lê,et al.  FactoMineR: An R Package for Multivariate Analysis , 2008 .

[14]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Kimberly Van Auken,et al.  WormBase: a multi-species resource for nematode biology and genomics , 2004, Nucleic Acids Res..

[16]  Stat Pairs,et al.  Statistical Algorithms Description Document , 2022 .

[17]  Robert Gentleman,et al.  Using GOstats to test gene lists for GO term association , 2007, Bioinform..

[18]  Gene Ontology Consortium The Gene Ontology (GO) database and informatics resource , 2003 .

[19]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[20]  Pan Du,et al.  lumi: a pipeline for processing Illumina microarray , 2008, Bioinform..

[21]  Martin Vingron,et al.  Variance stabilization applied to microarray data calibration and to the quantification of differential expression , 2002, ISMB.