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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| Veröffentlicht in: | BMC bioinformatics Jg. 13; H. 1; S. 335 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
London
BioMed Central
24.12.2012
BioMed Central Ltd Springer Nature B.V BMC |
| Schlagworte: | |
| ISSN: | 1471-2105, 1471-2105 |
| Online-Zugang: | Volltext |
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| Abstract | 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/
]. |
|---|---|
| AbstractList | 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 [https://insilicodb.org/app/]. 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 [ Keywords: Batch effect removal, Data integration, Gene expression, Microarray repositories, InSilico DB, Reproducibility 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 [https://insilicodb.org/app/]. 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.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.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.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.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/].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/]. Doc number: 335 Abstract 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/ ]. 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/ ]. Abstract 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/]. 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/ ]. |
| ArticleNumber | 335 |
| Audience | Academic |
| Author | Weiss Solís, David Y Molter, Colin Bersini, Hugues Meganck, Stijn Duque, Robin Lazar, Cosmin Steenhoff, David Schaetzen, Virginie de Taminau, Jonatan Coletta, Alain Nowé, Ann |
| AuthorAffiliation | 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 |
| AuthorAffiliation_xml | – name: 1 AI (CoMo), Vrije Universiteit Brussel, 1050 Brussels, Pleinlaan 2, Belgium – name: 2 IRIDIA, Université Libre de Bruxelles, Avenue F. D. Roosevelt 50, 1050 Brussels, Belgium |
| Author_xml | – sequence: 1 givenname: Jonatan surname: Taminau fullname: Taminau, Jonatan email: jtaminau@vub.ac.be organization: AI (CoMo), Vrije Universiteit Brussel – sequence: 2 givenname: Stijn surname: Meganck fullname: Meganck, Stijn organization: AI (CoMo), Vrije Universiteit Brussel – sequence: 3 givenname: Cosmin surname: Lazar fullname: Lazar, Cosmin organization: AI (CoMo), Vrije Universiteit Brussel – sequence: 4 givenname: David surname: Steenhoff fullname: Steenhoff, David organization: AI (CoMo), Vrije Universiteit Brussel – sequence: 5 givenname: Alain surname: Coletta fullname: Coletta, Alain organization: IRIDIA, Université Libre de Bruxelles – sequence: 6 givenname: Colin surname: Molter fullname: Molter, Colin organization: IRIDIA, Université Libre de Bruxelles – sequence: 7 givenname: Robin surname: Duque fullname: Duque, Robin organization: IRIDIA, Université Libre de Bruxelles – sequence: 8 givenname: Virginie de surname: Schaetzen fullname: Schaetzen, Virginie de organization: AI (CoMo), Vrije Universiteit Brussel – sequence: 9 givenname: David Y surname: Weiss Solís fullname: Weiss Solís, David Y organization: IRIDIA, Université Libre de Bruxelles – sequence: 10 givenname: Hugues surname: Bersini fullname: Bersini, Hugues organization: IRIDIA, Université Libre de Bruxelles – sequence: 11 givenname: Ann surname: Nowé fullname: Nowé, Ann organization: AI (CoMo), Vrije Universiteit Brussel |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23259851$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Taminau et al.; licensee BioMed Central Ltd. 2012 COPYRIGHT 2012 BioMed Central Ltd. 2012 Taminau et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright ©2012 Taminau et al.; licensee BioMed Central Ltd. 2012 Taminau et al.; licensee BioMed Central Ltd. |
| Copyright_xml | – notice: Taminau et al.; licensee BioMed Central Ltd. 2012 – notice: COPYRIGHT 2012 BioMed Central Ltd. – notice: 2012 Taminau et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: Copyright ©2012 Taminau et al.; licensee BioMed Central Ltd. 2012 Taminau et al.; licensee BioMed Central Ltd. |
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| Keywords | Microarray repositories Gene expression Reproducibility Data integration Batch effect removal InSilico DB |
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With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple... With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing... Background With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple... Doc number: 335 Abstract Background: With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities... Background: With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple... Abstract Background With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining... |
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| Title | Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages |
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