non_partial_cor: Non-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. non_partial_cor is a one step function for user to perform the analysis based on typical correlation analysis, no pre-processing step required.

Usage

non_partial_cor(data = NULL, class_label = NULL, id = NULL,
  method = "pearson", p_val = NULL, permutation = 1000,
  permutation_thres = 0.05)

Arguments

data	This is a matrix of expression from all biomolecules and all samples.
class_label	this is a binary array with 0 for group 1 and 1 for group 2.
id	This is an array of biomolecule IDs.
method	This is a character string indicating which correlation coefficient is to be computed. The options are either "pearson" as the default or "spearman".
p_val	This is optional, it is a dataframe containing p-value for each biomolecule.
permutation	This is a positive integer representing the desired number of permutations, default is 1000.
permutation_thres	This is a 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

non_partial_cor(data = Met_GU, class_label = Met_Group_GU, id = Met_name_GU,
                        method = "pearson", permutation = 1000, permutation_thres = 0.05)

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