for this task you can regenerate those ontologies using the generatePAM_MAP.groovy.
You can also specify the following optional arguments:
-m: to select MA ontology as a primary axis of classification for the ontology, if not selected it will be MPATH
-t: to select the transitivity option
to generate MAP :
generatePAM_MAP.groovy -m -o MAP.owl
to generate MAPT :
generatePAM_MAP.groovy -m -t -o MAPT.owl
to generate PAM :
generatePAM_MAP.groovy -o PAM.owl
to generate PAMT :
generatePAM_MAP.groovy -t -o PAMT.owl
To compute TMOnto, WMCOnto and DITOnto we used OQuaRE_measures.groovy
To compute semantic similarity we used the following files:
MpathMAAnnotations.groovy to generate the annotations
piarwiseSimMpathMa.groovy to generate the mouse to mouse similarity matrix
we applied four methods for clustering in this analysis: complete linkage(CL), Unweighted Pair Group Method with Arithmetic Mean(UPGMA), neighbor joining(NJ) and K-medoids using the following files and folders:
clustering and statistical analysis/NJ_CL_UPGMA_clustering.m
clustering and statistical analysis/k_medoids_clustering.R
clustering and statistical analysis/similarities
clustering and statistical analysis/mice.csv
we calculated Kendall's tau for the p-values generated by the enrichment analysis using the following files and folders:
clustering and statistical analysis/kendall.py
clustering and statistical analysis/EAcode
for plotting ROC curves and calculating ROC-AUC we used the following files and folders:
clustering and statistical analysis/ROC_curves_and_ROC_AUC.m
clustering and statistical analysis/similarities
clustering and statistical analysis/mice.csv
for calculating Wilcoxon rank sum test we used the following files and folders:
clustering and statistical analysis/wilcoxonSumRankTest.m
clustering and statistical analysis/similarities
clustering and statistical analysis/mice.csv