Distance correlation application to gene co-expression network analysis.

ABSTRACT:

Background

To construct gene co-expression networks, it is necessary to evaluate the correlation between different gene expression profiles. However, commonly used correlation metrics, including both linear (such as Pearson's correlation) and monotonic (such as Spearman's correlation) dependence metrics, are not enough to observe the nature of real biological systems. Hence, introducing a more informative correlation metric when constructing gene co-expression networks is still an interesting topic.

Results

In this paper, we test distance correlation, a correlation metric integrating both linear and non-linear dependence, with other three typical metrics (Pearson's correlation, Spearman's correlation, and maximal information coefficient) on four different arrays (macrophage

SUBMITTER: Hou J 

PROVIDER: S-EPMC8862277 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature