Meta-analysis of gene expression microarrays with missing replicates

Background: Many different microarray experiments are publicly available today. It is natural to ask whether different experiments for the same phenotypic conditions can be combined using meta-analysis, in order to increase the overall sample size. However, some genes are not measured in all experiments, hence they cannot be included or their statistical significance cannot be appropriately estimated in traditional meta-analysis. Nonetheless, these genes, which we refer to as incomplete genes, may also be informative and useful. Results: We propose a meta-analysis framework, called “Incomplete Gene Meta-analysis”, which can include incomplete genes by imputing the significance of missing replicates, and computing a meta-score for every gene across all datasets. We demonstrate that the incomplete genes are worthy of being included and our method is able to appropriately estimate their significance in two groups of experiments. We first apply the Incomplete Gene Meta-analysis and se...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Fan Shi, Gad Abraham, Christopher Leckie, Izhak Haviv, Adam Kowalczyk
• Keywords: Artificial Intelligence, BMCBI 2011, Cancer Datasets, Incomplete Gene Meta-analysis, Incomplete Genes