% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/partial_cor.R
\name{partial_cor}
\alias{partial_cor}
\title{Partial correlation analysis}
\usage{
partial_cor(
  data_list = NULL,
  rho_group1 = NULL,
  rho_group2 = NULL,
  p_val = NULL,
  permutation = 1000,
  permutation_thres = 0.05,
  fdr = TRUE
)
}
\arguments{
\item{data_list}{This is a list of pre-processed data outputted by the select_rho_partial function.}

\item{rho_group1}{This is a character string indicating the rule for choosing rho value for group 1, 
"min": minimum rho, "ste": one standard error from minimum, or user can input rho of their choice. The default 
is minimum.}

\item{rho_group2}{This is a character string indicating the rule for choosing rho value for group 2, 
"min": minimum rho, "ste": one standard error from minimum, or user can input rho of their choice, the default 
is minimum.}

\item{p_val}{This is optional. It is a p*1 dataframe that contains the p-value for each biomolecule from DE analysis.}

\item{permutation}{This is a positive integer representing the desired number of permutations. 
The default is 1000.}

\item{permutation_thres}{This is a integer representing the threshold for the permutation test. 
The default is 0.05 to achieve 95 percent confidence.}

\item{fdr}{This is a boolean value indicating whether to apply multiple testing correction (TRUE) 
or not (FALSE). The default is TRUE. However, if users find the output network is too sparse 
even after relaxing the permutation_thres, it's probably a good idea to turn off the multiple testing correction.}
}
\value{
A list containing an activity score dataframe with "ID", "P_value", "Node_Degree" and 
    "Activity_Score" as columns and a differential network dataframe with the binary and the 
    weight connection values.
}
\description{
A method that integrates differential expression (DE) analysis and differential 
    network (DN) analysis to select biomarker candidates for cancer studies. partial_cor is the 
    second step of the partial correlation calculation after getting the result from select_rho_partial function.
}
\examples{
# step 1: select_rho_partial
pre_data <- select_rho_partial(data = Met_GU, class_label = Met_Group_GU, id = Met_name_GU, error_curve = TRUE)
# step 2: partial_cor
result <- partial_cor(data_list = pre_data, rho_group1 = 'min', rho_group2 = "min", p_val = pvalue_M_GU, permutation = 1000,
                      permutation_thres = 0.05, fdr = TRUE)
}