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Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages

Jonatan Taminau1*, Stijn Meganck1, Cosmin Lazar1, David Steenhoff1, Alain Coletta2, Colin Molter2, Robin Duque2, Virginie de Schaetzen1, David Y Weiss Solís2, Hugues Bersini2 and Ann Nowé1

Author Affiliations

1 AI (CoMo), Vrije Universiteit Brussel, 1050 Brussels, Pleinlaan 2, Belgium

2 IRIDIA, Université Libre de Bruxelles, Avenue F. D. Roosevelt 50, 1050 Brussels, Belgium

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BMC Bioinformatics 2012, 13:335  doi:10.1186/1471-2105-13-335

Published: 24 December 2012



With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing data sets. In this new context, analysis itself is no longer the problem, but retrieving and consistently integrating all this data before delivering it to the wide variety of existing analysis tools becomes the new bottleneck.


We present the newly released inSilicoMerging R/Bioconductor package which, together with the earlier released inSilicoDb R/Bioconductor package, allows consistent retrieval, integration and analysis of publicly available microarray gene expression data sets. Inside the inSilicoMerging package a set of five visual and six quantitative validation measures are available as well.


By providing (i) access to uniformly curated and preprocessed data, (ii) a collection of techniques to remove the batch effects between data sets from different sources, and (iii) several validation tools enabling the inspection of the integration process, these packages enable researchers to fully explore the potential of combining gene expression data for downstream analysis. The power of using both packages is demonstrated by programmatically retrieving and integrating gene expression studies from the InSilico DB repository [ webcite].

Batch effect removal; Data integration; Gene expression; Microarray repositories; InSilico DB; Reproducibility