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

Thomas Karn; Achim Rody; Volkmar Müller; Marcus Schmidt; Sven Becker; Uwe Holtrich; Lajos Pusztai

doi:10.1016/j.gdata.2014.09.014

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

Thomas Karn, Achim Rody, Volkmar Müller, Marcus Schmidt, Sven Becker, Uwe Holtrich, Lajos Pusztai

Clinic of Gynecology and Obstetrics

9 Citations (Scopus)

Abstract

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.

Original language	English
Journal	Genomics Data
Volume	2
Pages (from-to)	354-6
Number of pages	3
ISSN	2213-5960
DOIs	https://doi.org/10.1016/j.gdata.2014.09.014
Publication status	Published - 2014

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10.1016/j.gdata.2014.09.014

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title = "Control of dataset bias in combined Affymetrix cohorts of triple negative breast cancer",

abstract = "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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T1 - Control of dataset bias in combined Affymetrix cohorts of triple negative breast cancer

AU - Karn, Thomas

AU - Rody, Achim

AU - Müller, Volkmar

AU - Schmidt, Marcus

AU - Becker, Sven

AU - Holtrich, Uwe

AU - Pusztai, Lajos

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

U2 - 10.1016/j.gdata.2014.09.014

DO - 10.1016/j.gdata.2014.09.014

M3 - Journal articles

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SN - 2213-5960

VL - 2

SP - 354

EP - 356

JO - Genomics Data

JF - Genomics Data

ER -

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

Abstract

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