Meta-analysis approach as a gene selection method in class prediction: does it improve model performance? A case study in acute myeloid leukemia

BMC Bioinformatics

Research Article

Kit C. B. Roes¹  Marinus J. C. Eijkemans¹  Putri W. Novianti²  Victor L. Jong³ 

[1] Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508, Utrecht, GA, The Netherlands;Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508, Utrecht, GA, The Netherlands;Department of Epidemiology and Biostatistics, VU University medical center, Amsterdam, The Netherlands;Department of Pathology, VU University medical center, Amsterdam, The Netherlands;Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3508, Utrecht, GA, The Netherlands;Viroscience Laboratory, Erasmus Medical Center Rotterdam, 3015, Rotterdam, CE, The Netherlands;

关键词: Meta-analysis;    Gene expression;    Predictive modeling;    Acute myeloid leukemia;   

DOI  :  10.1186/s12859-017-1619-7

 received in 2016-10-05, accepted in 2017-03-30,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundAggregating gene expression data across experiments via meta-analysis is expected to increase the precision of the effect estimates and to increase the statistical power to detect a certain fold change. This study evaluates the potential benefit of using a meta-analysis approach as a gene selection method prior to predictive modeling in gene expression data.ResultsSix raw datasets from different gene expression experiments in acute myeloid leukemia (AML) and 11 different classification methods were used to build classification models to classify samples as either AML or healthy control. First, the classification models were trained on gene expression data from single experiments using conventional supervised variable selection and externally validated with the other five gene expression datasets (referred to as the individual-classification approach). Next, gene selection was performed through meta-analysis on four datasets, and predictive models were trained with the selected genes on the fifth dataset and validated on the sixth dataset. For some datasets, gene selection through meta-analysis helped classification models to achieve higher performance as compared to predictive modeling based on a single dataset; but for others, there was no major improvement. Synthetic datasets were generated from nine simulation scenarios. The effect of sample size, fold change and pairwise correlation between differentially expressed (DE) genes on the difference between MA- and individual-classification model was evaluated. The fold change and pairwise correlation significantly contributed to the difference in performance between the two methods. The gene selection via meta-analysis approach was more effective when it was conducted using a set of data with low fold change and high pairwise correlation on the DE genes.ConclusionGene selection through meta-analysis on previously published studies potentially improves the performance of a predictive model on a given gene expression data.

【 授权许可】

CC BY   
© The Author(s). 2017

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