Control of dataset bias in combined Affymetrix cohorts of triple negative breast cancer

Heterogenous subtypes of breast cancer need to be analyzed separately. Pooling of datasets can provide reasonable sample sizes but dataset bias is an important concern. We assembled a combined dataset of 579 Affymetrix microarrays from triple negative breast cancer (TNBC) in Gene Expression Omnibus...

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Detalles Bibliográficos
Autores principales: Karn, Thomas, Rody, Achim, Müller, Volkmar, Schmidt, Marcus, Becker, Sven, Holtrich, Uwe, Pusztai, Lajos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2014
Materias:
Data in Brief
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4535974/
https://www.ncbi.nlm.nih.gov/pubmed/26484129
http://dx.doi.org/10.1016/j.gdata.2014.09.014
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author Karn, Thomas
Rody, Achim
Müller, Volkmar
Schmidt, Marcus
Becker, Sven
Holtrich, Uwe
Pusztai, Lajos
author_facet Karn, Thomas
Rody, Achim
Müller, Volkmar
Schmidt, Marcus
Becker, Sven
Holtrich, Uwe
Pusztai, Lajos
author_sort Karn, Thomas
collection PubMed
description Heterogenous subtypes of breast cancer need to be analyzed separately. Pooling of datasets can provide reasonable sample sizes but dataset bias is an important concern. We assembled a combined dataset of 579 Affymetrix microarrays from triple negative breast cancer (TNBC) in Gene Expression Omnibus (GEO) series GSE31519. We developed a method for selecting comparable datasets and to control for the amount of dataset bias of individual probesets.
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spelling pubmed-45359742015-10-19 Control of dataset bias in combined Affymetrix cohorts of triple negative breast cancer Karn, Thomas Rody, Achim Müller, Volkmar Schmidt, Marcus Becker, Sven Holtrich, Uwe Pusztai, Lajos Genom Data Data in Brief Heterogenous subtypes of breast cancer need to be analyzed separately. Pooling of datasets can provide reasonable sample sizes but dataset bias is an important concern. We assembled a combined dataset of 579 Affymetrix microarrays from triple negative breast cancer (TNBC) in Gene Expression Omnibus (GEO) series GSE31519. We developed a method for selecting comparable datasets and to control for the amount of dataset bias of individual probesets. Elsevier 2014-10-23 /pmc/articles/PMC4535974/ /pubmed/26484129 http://dx.doi.org/10.1016/j.gdata.2014.09.014 Text en © 2014 The Authors http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
spellingShingle Data in Brief
Karn, Thomas
Rody, Achim
Müller, Volkmar
Schmidt, Marcus
Becker, Sven
Holtrich, Uwe
Pusztai, Lajos
Control of dataset bias in combined Affymetrix cohorts of triple negative breast cancer
title Control of dataset bias in combined Affymetrix cohorts of triple negative breast cancer
title_full Control of dataset bias in combined Affymetrix cohorts of triple negative breast cancer
title_fullStr Control of dataset bias in combined Affymetrix cohorts of triple negative breast cancer
title_full_unstemmed Control of dataset bias in combined Affymetrix cohorts of triple negative breast cancer
title_short Control of dataset bias in combined Affymetrix cohorts of triple negative breast cancer
title_sort control of dataset bias in combined affymetrix cohorts of triple negative breast cancer
topic Data in Brief
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4535974/
https://www.ncbi.nlm.nih.gov/pubmed/26484129
http://dx.doi.org/10.1016/j.gdata.2014.09.014
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