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Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients

Microarray gene expression data sets are jointly analyzed to increase statistical power. They could either be merged together or analyzed by meta-analysis. For a given ensemble of data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works better. In this article, thre...

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Autor principal: Yasrebi, Haleh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5863785/
https://www.ncbi.nlm.nih.gov/pubmed/26504096
http://dx.doi.org/10.1093/bib/bbv092
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author Yasrebi, Haleh
author_facet Yasrebi, Haleh
author_sort Yasrebi, Haleh
collection PubMed
description Microarray gene expression data sets are jointly analyzed to increase statistical power. They could either be merged together or analyzed by meta-analysis. For a given ensemble of data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works better. In this article, three joint analysis methods, Z -score normalization, ComBat and the inverse normal method (meta-analysis) were selected for survival prognosis and risk assessment of breast cancer patients. The methods were applied to eight microarray gene expression data sets, totaling 1324 patients with two clinical endpoints, overall survival and relapse-free survival. The performance derived from the joint analysis methods was evaluated using Cox regression for survival analysis and independent validation used as bias estimation. Overall, Z -score normalization had a better performance than ComBat and meta-analysis. Higher Area Under the Receiver Operating Characteristic curve and hazard ratio were also obtained when independent validation was used as bias estimation. With a lower time and memory complexity, Z -score normalization is a simple method for joint analysis of microarray gene expression data sets. The derived findings suggest further assessment of this method in future survival prediction and cancer classification applications.
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spelling pubmed-58637852018-03-29 Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients Yasrebi, Haleh Brief Bioinform Papers Microarray gene expression data sets are jointly analyzed to increase statistical power. They could either be merged together or analyzed by meta-analysis. For a given ensemble of data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works better. In this article, three joint analysis methods, Z -score normalization, ComBat and the inverse normal method (meta-analysis) were selected for survival prognosis and risk assessment of breast cancer patients. The methods were applied to eight microarray gene expression data sets, totaling 1324 patients with two clinical endpoints, overall survival and relapse-free survival. The performance derived from the joint analysis methods was evaluated using Cox regression for survival analysis and independent validation used as bias estimation. Overall, Z -score normalization had a better performance than ComBat and meta-analysis. Higher Area Under the Receiver Operating Characteristic curve and hazard ratio were also obtained when independent validation was used as bias estimation. With a lower time and memory complexity, Z -score normalization is a simple method for joint analysis of microarray gene expression data sets. The derived findings suggest further assessment of this method in future survival prediction and cancer classification applications. Oxford University Press 2016-09 2015-10-26 /pmc/articles/PMC5863785/ /pubmed/26504096 http://dx.doi.org/10.1093/bib/bbv092 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/ ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Papers
Yasrebi, Haleh
Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients
title Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients
title_full Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients
title_fullStr Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients
title_full_unstemmed Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients
title_short Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients
title_sort comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients
topic Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5863785/
https://www.ncbi.nlm.nih.gov/pubmed/26504096
http://dx.doi.org/10.1093/bib/bbv092
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