A correlated meta-analysis strategy for data mining "OMIC" scans.

ABSTRACT: Meta-analysis is becoming an increasingly popular and powerful tool to integrate findings across studies and OMIC dimensions. But there is the danger that hidden dependencies between putatively "independent" studies can cause inflation of type I error, due to reinforcement of the evidence from false-positive findings. We present here a simple method for conducting meta-analyses that automatically estimates the degree of any such non-independence between OMIC scans and corrects the inference for it, retaining the proper type I error structure. The method does not require the original data from the source studies, but operates only on summary analysis results from these in OMIC scans. The method is applicable in a wide variety of situations including combining GWAS and or sequencing scan res

SUBMITTER: Province MA 

PROVIDER: S-EPMC3773990 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature