This package implements INDEED algorithm from Zuo et. al.’s Methods paper: INDEED: Integrated differential expression and differential network analysis of omic data for biomarker discovery (PMID: 27592383).
This R package will generate a csv file containing information such as p-values, node degree and activity score for each biomolecule. A higher activity score indicates that the corresponding biomolecule has more neighbors connceted in the differential network and their p-values are more statistically significant. It will also generate a csv file for the differential network created by INDEED.
You can install INDEED from github with:
# The development version from GitHub: # install.packages("devtools") devtools::install_github("ressomlab/INDEED")
library(INDEED) # Example 1: # Using partial correlation to obtain sparse differential network pre_data <- select_rho_partial(data=Met_GU,class_label = Met_Group_GU,id=Met_name_GU,error_curve = "YES") partial_cor(data_list=pre_data,rho_group1='min',rho_group2="min",permutation = 1000,p_val=pvalue_M_GU,permutation_thres = 0.05) # Example 2: # Using Spearman correlation to obtain differential network non_partial_cor(data=Met_GU,class_label = Met_Group_GU,id=Met_name_GU,method="spearman",permutation_thres = 0.05)
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