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

Background 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 b...

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Published in:BMC bioinformatics Vol. 13; no. 1; p. 335
Main Authors: Taminau, Jonatan, Meganck, Stijn, Lazar, Cosmin, Steenhoff, David, Coletta, Alain, Molter, Colin, Duque, Robin, Schaetzen, Virginie de, Weiss Solís, David Y, Bersini, Hugues, Nowé, Ann
Format: Journal Article
Language:English
Published: London BioMed Central 24.12.2012
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Access to Information
Algorithms
Analysis
Anopheles
Batch effect removal
Bioinformatics
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Data integration
Gene expression
Gene Expression Profiling - statistics & numerical data
Genes
Hostages
Humans
InSilico DB
Life Sciences
Microarray repositories
Microarrays
Oligonucleotide Array Sequence Analysis - statistics & numerical data
Reproducibility
Software
Transcriptome analysis
Microarray repositories
Gene expression
Reproducibility
Data integration
Batch effect removal
InSilico DB
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary:Background 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. Results 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. Conclusions 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 [ https://insilicodb.org/app/ ].
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ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-13-335