% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helper_function.R
\name{permutation_cor}
\alias{permutation_cor}
\title{Permutations to build a differential network based on correlation analysis}
\usage{
permutation_cor(
  m,
  p,
  n_group_1,
  n_group_2,
  data_group_1,
  data_group_2,
  type_of_cor
)
}
\arguments{
\item{m}{This is the number of permutations.}

\item{p}{This is the number of biomarker candidates.}

\item{n_group_1}{This is the number of subjects in group 1.}

\item{n_group_2}{This is the number of subjects in group 2.}

\item{data_group_1}{This is a \eqn{n*p} matrix containing group 1 data.}

\item{data_group_2}{THis is a \eqn{n*p} matrix containing group 2 data.}

\item{type_of_cor}{If this is NULL, pearson correlation coefficient will be calculated as 
default. Otherwise, a character string "spearman" will calculate the spearman correlation 
coefficient.}
}
\value{
A multi-dimensional matrix that contains the permutation result.
}
\description{
A permutation test that randomly permutes the sample labels in distinct
    biological groups for each biomolecule. The difference in each paired biomolecule
    is considered statistically significant if it falls into the 2.5% tails on either end of the 
    empirical distribution curve.
}