dist.corr: Distance Correlattion

In GSDA: Gene Set Distance Analysis (GSDA)

Description

Calculate the distance correlation for a gene set's omic data matrix with another variable.

Usage

dist.corr(X, Y, x.dist = "me", y.dist = "me")

Arguments

X	The omic numeric data matrix with subjects as rows and variables as columns. Note this is the TRANSPOSE of how some omic data analysis packages represent omic data and how the omic data is represented in the gsda function of this package.
Y	Numeric data matrix, vector, or data.frame. The rows of X and rows of Y must represent the same set of subjects in the same order.
x.dist	The distance metric for omic data (X), may be "oe" (overall Euclidean), "me" (marginal Euclidean), "om" (overall Manhattan), or "mm" (marginal Manhattan).
y.dist	The distance metric for clinical data, may be "oe" (overall Euclidean), "me" (marginal Euclidean), "om" (overall Manhattan), or "mm" (marginal Manhattan), same options as for X

Details

The function calculates distance matrix for X and Y using one of the four methods "oe" (overall Euclidean), "me" (marginal Euclidean), "om" (overall Manhattan), or "mm" (marginal Manhattan). Then, the distance matrices are centered by U-centering and distance correlation is calculated as the inner product of the two U-centered distance matrices over the squared of inner product of each of the two U-centered distance matrices. The distance correlation t-statistics follows a t-distribution with n*(n-3)/2 degree of freedom according to Zhu et al.(2020).

Value

A list with the following components:

odCor	overall distance correlation statistic
t.odCor	t-stat for overall distance correlation statistic
p.odCor	p-value for overall distance correlation statistic
dCor	distance-based correlation matrix for each pair of variables.
t.dCor	t-stat for distance-based correlation matrix
p.dCor	p-value for distance-based correlation matrix
X	echo input data matrice X
Y	echo input data matrice Y
x.dist	echo input distance metric for X
y.dist	echo input distance metric for Y

Author(s)

Xueyuan Cao [e-mail] and Stanley Pounds [e-mail]

References

Cao X and Pounds S (2021) Gene-Set Distance Associations (GSDA): A Powerful Tool for Gene-Set Association Analysis.

Zhu C, Yao S, Zhang X and Shao X (2020) Distance-based and RKHS-based Dependence Metrics in High Dimension. arXiv:1902.03291

See Also

best.dist.corr

Examples

data(target.aml.clin)
data(target.aml.expr)
target.aml.expr=sqrt(target.aml.expr)
target.aml.expr=t(target.aml.expr)

dc.chl=dist.corr(target.aml.expr,
                 target.aml.clin$Chloroma,
                 "oe","ct")

GSDA documentation built on Jan. 18, 2021, 5:05 p.m.