However, this procedure might still infer the false pathway-GO term associations due to the quality of GO annotations of genes [ 47] and the intrinsic limitations of statistical analysis [ 3].
Metabolic pathways were extracted from four databases as each database may contain false positive pathway predictions.
False positive pathway interactions were identified on occasion.
This analysis indicates that the permutation tests filter out a substantial part of false positive pathway candidates.
We systematically investigated the impact of three different permutation test approaches for over-representation analysis to detect false positive pathway candidates and evaluate them on genome-wide association data of Dilated Cardiomyopathy (DCM) and Ulcerative Colitis (UC).
In simple shortest path analysis, pool metabolites often cause false positive pathways to be identified, as the shortest paths often traverse via them [ 24].
The original motivation for permutation tests is to cross-check the p-values obtained by classical tests such as the Hypergeometric distribution to discover putative false positive pathways.
However, while scaling well with network size, graph-theoretic methods generally tend to return more false positive pathways than constraint-based methods.
To decrease the number of the false positives pathways, we first identified target genes shared by at least three PCa DE-miRNAs datasets, which were then mapped to GO, KEGG pathway database by DAVID, and GeneGO pathway database by MetaCore (Gene, Inc).
The objective was to develop a method that would increase our understanding of the pathways involved in various traits and to detect the effects that would have been missed by traditional single-marker analysis without generating an excess of false-positive pathway associations.
To discover potential false positive functional pathway candidates and to support the results from standard statistical tests such as the Hypergeometric test, permutation tests of case control data should be carried out.
