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
Descripción
Sumario: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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