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BMC Bioinformatics

, 13:335

Transcriptome analysis

Abstract

BackgroundWith 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.

ResultsWe 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.

ConclusionsBy 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 https:-insilicodb.org-app-.

KeywordsBatch effect removal Data integration Gene expression Microarray repositories InSilico DB Reproducibility Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-13-335 contains supplementary material, which is available to authorized users.

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Autor: Jonatan Taminau - Stijn Meganck - Cosmin Lazar - David Steenhoff - Alain Coletta - Colin Molter - Robin Duque - Virginie d

Fuente: https://link.springer.com/







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