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

Klinik für Frauenheilkunde und Geburtshilfe

9 Zitate (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.

Originalsprache	Englisch
Zeitschrift	Genomics Data
Jahrgang	2
Seiten (von - bis)	354-6
Seitenumfang	3
ISSN	2213-5960
DOIs	https://doi.org/10.1016/j.gdata.2014.09.014
Publikationsstatus	Veröffentlicht - 2014

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

SDG 3 – Gesundheit und Wohlergehen

Dokumente

10.1016/j.gdata.2014.09.014

Zitieren

@article{233c75d42a1546019e2c094af75ceff6,

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. ",

author = "Thomas Karn and Achim Rody and Volkmar M{\"u}ller and Marcus Schmidt and Sven Becker and Uwe Holtrich and Lajos Pusztai",

year = "2014",

doi = "10.1016/j.gdata.2014.09.014",

language = "English",

volume = "2",

pages = "354--6",

journal = "Genomics Data",

issn = "2213-5960",

publisher = "Elsevier Inc.",

}

TY - JOUR

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

C2 - 26484129

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

UN SDGs

Dokumente

Fingerprint

Zitieren