partial_cor: Partial correlaton analysis

In INDEED: Interactive Visualization of Integrated Differential Expression and Differential Network Analysis for Biomarker Candidate Selection Package

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 partial correlation calculation after getting the result from select_rho_partial function.

Usage

partial_cor(data_list = NULL, rho_group1 = NULL, rho_group2 = NULL,
  permutation = 1000, p_val = NULL, permutation_thres = 0.05)

Arguments

data_list	This is a list of pre-processed data outputed by the select_rho_partial function.
rho_group1	This is the rule for choosing rho for group 1, "min": minimum rho, "ste": one standard error from minimum, or user can input rho of their choice, the default is minimum.
rho_group2	This is the rule for choosing rho for group 2, "min": minimum rho, "ste": one standard error from minimum, or user can input rho of their choice, the default is minimum.
permutation	This is a positive integer of the desired number of permutations. The default is 1000 permutations.
p_val	This is optional. It is a data frame that contains p-values for each biomolecule.
permutation_thres	This is the threshold for permutation. The defalut is 0.05 to make 95 percent confidence.

Value

A list containing a score table with "ID", "P_value", "Node_Degree", "Activity_Score" and a differential network table with "Node1", "Node2", the binary link value and the weight link value.

Examples

# step 1: select_rho_partial
preprocess<- select_rho_partial(data = Met_GU, class_label = Met_Group_GU, id = Met_name_GU,
                                error_curve = "YES")
# step 2: partial_cor
partial_cor(data_list = preprocess, rho_group1 = 'min', rho_group2 = "min", permutation = 1000,
            p_val = pvalue_M_GU, permutation_thres = 0.05)

INDEED documentation built on Nov. 8, 2020, 11:12 p.m.