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 (GEO) series GSE31519. We developed a method for selecting comparable datasets and to control for the amount of dataset bias of individual probesets.
Department of Gynecology, Goethe-University Frankfurt, Frankfurt am Main, Germany.
Thomas
T
Karn
Department of Obstetrics and Gynecology, University Hospital Lübeck, Germany.
Achim
A
Rody
Department of Gynecology, University Hospital Hamburg-Eppendorf, Hamburg, Germany.
Volkmar
V
Müller
Department of Obstetrics and Gynecology, Johannes Gutenberg-University Mainz, Mainz, Germany.
Marcus
M
Schmidt
Department of Gynecology, Goethe-University Frankfurt, Frankfurt am Main, Germany.
Sven
S
Becker
Department of Gynecology, Goethe-University Frankfurt, Frankfurt am Main, Germany.
Uwe
U
Holtrich
Yale Cancer Center, New Haven, CT, USA.
Lajos
L
Pusztai
Breast cancer; Dataset bias; Gene expression; Microarray; Pooling