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Journal Article

Microarray Based Diagnosis Profits from Better Documentation of Gene Expression Signatures

MPS-Authors

Kostka,  Dennis
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50564

Spang,  Rainer
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Fulltext (public)

journal.pcbi.0040022.pdf
(Any fulltext), 108KB

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Citation

Kostka, D., & Spang, R. (2008). Microarray Based Diagnosis Profits from Better Documentation of Gene Expression Signatures. PLoS Computational Biology, 4(2), e22-e22. doi:10.1371/journal.pcbi.0040022.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-805E-C
Abstract
Microarray gene expression signatures hold great promise to improve diagnosis and prognosis of disease. However, current documentation standards of such signatures do not allow for an unambiguous application to study-external patients. This hinders independent evaluation, effectively delaying the use of signatures in clinical practice. Data from eight publicly available clinical microarray studies were analyzed and the consistency of study-internal with study-external diagnoses was evaluated. Study-external classifications were based on documented information only. Documenting a signature is conceptually different from reporting a list of genes. We show that even the exact quantitative specification of a classification rule alone does not define a signature unambiguously. We found that discrepancy between study-internal and study-external diagnoses can be as frequent as 30% (worst case) and 18% (median). By using the proposed documentation by value strategy, which documents quantitative preprocessing information, the median discrepancy was reduced to 1%. The process of evaluating microarray gene expression diagnostic signatures and bringing them to clinical practice can be substantially improved and made more reliable by better documentation of the signatures.