A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions.

ABSTRACT:

Background

We address the problem of integratively analyzing multiple gene expression, microarray datasets in order to reconstruct gene-gene interaction networks. Integrating multiple datasets is generally believed to provide increased statistical power and to lead to a better characterization of the system under study. However, the presence of systematic variation across different studies makes network reverse-engineering tasks particularly challenging. We contrast two approaches that have been frequently used in the literature for addressing systematic biases: meta-analysis methods, which first calculate opportune statistics on single datasets and successively summarize them, and data-merging methods, which directly analyze the pooled data after removing eventual biases. This com

SUBMITTER: Lagani V 

PROVIDER: S-EPMC4905611 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature