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pmid:26484129

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

Genomics data · 2014 doi:10.1016/j.gdata.2014.09.014

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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title
Control of dataset bias in combined Affymetrix cohorts of triple negative breast cancer.
doi
10.1016/j.gdata.2014.09.014
pmid
26484129
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[0]
Thomas Karn
[1]
Achim Rody
[2]
Volkmar Müller
[3]
Marcus Schmidt
[4]
Sven Becker
[5]
Uwe Holtrich
[6]
Lajos Pusztai
journal
Genomics data
year
2014
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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To assess dataset bias, we used Kruskal–Wallis statistic comparing the expression of each probeset with the primary dataset vector across the 394 TNBCs.
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To assess dataset bias, we used Kruskal–Wallis statistic comparing the expression of each probeset with the primary dataset vector across the 394 TNBCs.
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We assembled a combined dataset of 579 Affymetrix microarrays from triple negative breast cancer (TNBC) in Gene Expression Omnibus (GEO) series GSE31519.
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We developed a method for selecting comparable datasets and to control for the amount of dataset bias of individual probesets.
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The paper assembled a combined cohort of 579 TNBC microarrays from GEO series GSE31519 and developed a two-part statistical method that selects comparable datasets and controls dataset-biased probesets.
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title
A Clinically Relevant Gene Signature in Triple-Negative and Basal-Like Breast Cancer
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To assess dataset bias, we used Kruskal–Wallis statistic comparing the expression of each probeset with the primary dataset vector across the 394 TNBCs.
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