Abstract
Probabilistic association discovery aims at identifying the association between random vectors, regardless of number of variables involved or linear/nonlinear functional forms. Recently, applications in high-dimensional data have generated rising interest in probabilistic association discovery. We developed a framework based on functions on the observation graph, named MeDiA (Mean Distance Association). We generalize its property to a group of functions on the observation graph. The group of functions encapsulates major existing methods in association discovery, e.g. mutual information and Brownian Covariance, and can be expanded to more complicated forms. We conducted numerical comparison of the statistical power of related methods under multiple scenarios. We further demonstrated the application of MeDiA as a method of gene set analysis that captures a broader range of responses than traditional gene set analysis methods.
Highlights
In the analysis of high-throughput biological data, such as gene expression data, proteomics data, and metabolomics data, it is often of interest to examine the behavior of groups of variables
A number of methods were developed in the area of gene set analysis to test for shifts of overall expression levels of genes involved in a gene set under different treatment conditions [3,4,5]
Besides analyzing the behavior of each gene set in response to certain biological conditions, another class of methods examine the relations between gene sets, both under a single treatment condition [6] and between different treatment conditions [7,8,9]
Summary
In the analysis of high-throughput biological data, such as gene expression data, proteomics data, and metabolomics data, it is often of interest to examine the behavior of groups of variables (genes, proteins or metabolites). A number of methods were developed in the area of gene set analysis to test for shifts of overall expression levels of genes involved in a gene set under different treatment conditions [3,4,5]. This approach is commonly referred to as gene set analysis. Besides analyzing the behavior of each gene set in response to certain biological conditions, another class of methods examine the relations between gene sets, both under a single treatment condition [6] and between different treatment conditions [7,8,9].
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