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To browse Academia. This study evaluates the benefit of a meta-analysis approach as a gene selection method in class prediction. Six raw datasets from different microarray experiments in acute myeloid leukemia AML were used to build classification models to classify samples to either AML or the healthy group.
We first trained the classification models on gene expression data from single experiments and externally validated with the other five gene expression datasets referred to as the individual-classification approach. We repeated the training, using gene selection based on meta-analysis from the other studies, except from the validation study. Although we applied a proper cross validation technique, we found a significant decrease of the model performances in the external validation sets, with both training approaches.
For some datasets, a meta-analysis approach helped classification models to achieve higher performance as compared to predictive modeling based on a single dataset, while for others there was no significant improvement. The benefit of meta-analysis became significantly larger when the informative probesets resulting from the meta-analysis gene selection procedure had a larger average overall effect sizes than the informative probesets resulting from the individual-classification approach 0.
To conclude, meta-analysis approach could improve prediction models, given previous published studies and a new dataset in hand. Most of the discoveries from gene expression data are driven by a single study, claiming an optimal subset of genes that play a key role in a specific disease.
