MFP

This article mainly introduces the relevant knowledge of "MFP". The editor shows you the operation process through actual cases. The operation method is simple, fast and practical. I hope this "MFP" article can help you solve the problem.

Molecular functional portrait

# Python3.7import pandas as pdfrom portraits.clustering import clustering_profile_metrics, clustering_profile_metrics_plotfrom portraits.utils import read_gene_sets, ssgsea_formula, median_scale# Example script# Read signaturesgmt = read_gene_sets ('signatures.gmt') # GMT format like in MSIGdb# Read expressionsexp = pd.read_csv (' expression.tsv', sep='\ tasking, index_col=0) # log2+1 transformed Genes in columnsexp=exp.T# Calc signature scoressignature_scores = ssgsea_formula (exp, gmt) # Scale signaturessignature_scores_scaled = median_scale (signature_scores) signature_scores_scaled.to_csv ('signature_scores.tsv', sep='\ tasking, index=True) # Check the clustering within a range of 30 to 65% similarity.# > 65%-usually graph is not connected