Module-related subsequent analysis tool for WGCNA
This tool is totally developed by Python script which means it could run directly by Python in command line mode.
Prerequisite packages: python (>=3.7)
numpy (>=1.17.4)
pandas (>=0.23.4)
seaborn (>=0.9.0)
matplotlib (>=2.2.3)
sklearn (>=0.19.2)
scipy (>=1.1.0)
The function of modulate-phenotype-link could be used like this:
#modulate-phenotype-link.py
#1 python modulate-phenotype-link.py expression.csv module.txt phenotype.csv output_prefix
Input file:
expression.csv (seperated by tab, the first column is "ID")
ID SAMPLE1 SAMPLE2...
gene1 12 20
gene2 30 40
module.txt (seperated by space)
gene1 0.9 yellow
gene2 0.8 blue
phenotype.txt (seperated by comma, the index of the column is "Sample")
Sample,tissue1,tissue2...
sample1,pheno1,pheno2
Output file:
output_prefix.csv (Pearson correlationship matrix)
output_prefix_pvalue.csv (P value of pearson correlation)
output_prefix.png
#interaction-construct.py
#2 python interaction-construct.py gene gene_list_file expression.csv
Input file:
gene: gene name
gene_list_file:
gene1 gene2
expression.csv (The same format as the expression.csv in modulate-phenotype-link.py function)