Ranking metrics in gene set enrichment analysis: do they matter?

ABSTRACT:

Background

There exist many methods for describing the complex relation between changes of gene expression in molecular pathways or gene ontologies under different experimental conditions. Among them, Gene Set Enrichment Analysis seems to be one of the most commonly used (over 10,000 citations). An important parameter, which could affect the final result, is the choice of a metric for the ranking of genes. Applying a default ranking metric may lead to poor results.

Methods and results

In this work 28 benchmark data sets were used to evaluate the sensitivity and false positive rate of gene set analysis for 16 different ranking metrics including new proposals. Furthermore, the robustness of the chosen methods to sample size was tested. Using k-means clustering algorithm a grou

SUBMITTER: Zyla J 

PROVIDER: S-EPMC5427619 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature