Generalized Measure of Dependency for Analysis of Omics Data

Abstract

J Data Mining Genomics Proteomics ISSN: 2153-0602 JDMGP, an open access journal Volume 7 • Issue 1 • 1000183 As a popular measure of association, the Pearson’s correlation coefficient has been frequently used in omics data analysis e.g. in feature selection process during prediction model building using high dimensional gene expression data [1] and proteomics data [2]. However, Pearson’s correlation coefficient captures only linear relationships which greatly limit its use in situations of nonlinear association. Statistical modeling for dealing with nonlinear patterns can be complicated [3] and requires intensive computation in case of high dimensional data such as microarray data or genome sequence data. In the analysis of omics data, high dimension means that there can be diverse patterns of dependence not limited to linearity. In this situation, the generalized measures of association more adequate than the Pearson’s correlation and capable of capturing both linear and nonlinear correlations are needed. Recently, generalized correlation coefficients have been frequently discussed [4] and their application to large scale genomic data illustrated through microarray gene expression time-course analysis [5].