Matrix correlations for high-dimensional data: the modified RV-coefficient

Matrix correlations for high-dimensional data: the modified RV-coefficient

MOTIVATION Modern functional genomics generates high-dimensional datasets. It is often convenient to have a single simple number characterizing the relationship between pairs of such high-dimensional datasets in a comprehensive way. Matrix correlations are such numbers and are appealing since they can be interpreted in the same way as Pearson's correlations familiar to biologists. The high-dime...

RV Coefficient and Congruence Coefficient

The congruence coefficient was first introduced by Burt (1948) under the name of unadjusted correlation as a measure of the similarity of two factorial configurations. The name congruence coefficient was later tailored by Tucker (1951, see also Harman, 1976). The congruence coefficient is also sometimes called a monotonicity coefficient (Borg & Groenen, 1997, p. 203). The congruence coefficient...

Methods for regression analysis in high-dimensional data

By evolving science, knowledge and technology, new and precise methods for measuring, collecting and recording information have been innovated, which have resulted in the appearance and development of high-dimensional data. The high-dimensional data set, i.e., a data set in which the number of explanatory variables is much larger than the number of observations, cannot be easily analyzed by ...

Finding High-Order Correlations in High-Dimensional Biological Data

A Modified Apriori Algorithm for Analysing High-Dimensional Gene Data

Modern high-throughput technologies allow the systematic characterisation of an organism but provide excessive amounts of data such as results from microarray gene expression experiments. Combining the information from various experiments will help to expand the knowledge about an organism. However, the analysis of a data set comprising measurements for thousands of genes under many conditions,...