RevigoPythonExample.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# An example of using a REVIGO core library for your own projects.
# To Run this example you need RevigoCore library and a 
# set of database files available at: http://revigo.irb.hr/RevigoDatabases.zip
# 
# Authors:
#		Rajko Horvat (https://github.com/rajko-horvat)
#	
# License:
# 	MIT License
#		Copyright(c) 2011-2023 Ruðer Boškoviæ Institute
#		
#		Permission is hereby granted, free of charge, to any person obtaining a copy
#		of this software and associated documentation files (the "Software"), to deal
#		in the Software without restriction, including without limitation the rights
#		to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#		copies of the Software, and to permit persons to whom the Software is
#		furnished to do so, subject to the following conditions:
#
#		The above copyright notice and this permission notice shall be included in all
#		copies or substantial portions of the Software.
#
#		THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#		IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#		FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#		AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
#		LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
#		OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
#		SOFTWARE.

import time
import math

from clr_loader import get_coreclr
from pythonnet import set_runtime

set_runtime(get_coreclr(runtime_config="PythonRuntimeConfig.json"))

import clr

clr.AddReference("RevigoCore")
from IRB.Revigo.Core.Worker import RevigoWorker, ValueTypeEnum, RequestSourceEnum
from IRB.Revigo.Core import SemanticSimilarityTypeEnum, RevigoTerm, RevigoTermCollection, Utilities
from IRB.Revigo.Core.Databases import GeneOntology, SpeciesAnnotationList

clr.AddReference("mscorlib")
from System import TimeSpan
from System.IO import StreamWriter

def main():
	dCutoff = 0.7
	eValueType = ValueTypeEnum.PValue
	iSpeciesTaxon = 0
	eMeasure = SemanticSimilarityTypeEnum.SIMREL
	bRemoveObsolete = True
	print("Loading Ontology")
	oOntology = GeneOntology.Deserialize("C:\\Revigo\\Databases\\Current\\GeneOntology.xml.gz")
	print("Loading Species Annotations")
	oAnnotations = SpeciesAnnotationList.Deserialize("C:\\Revigo\\Databases\\Current\\SpeciesAnnotations.xml.gz")
	sExample1 = None
	sExample2 = None
	sExample3 = None

	with open('Example1.csv', 'r') as file:
		sExample1 = file.read()

	with open('Example2.csv', 'r') as file:
		sExample2 = file.read()

	with open('Example3.csv', 'r') as file:
		sExample3 = file.read()

	# Create worker 1
	oWorker1 = RevigoWorker(
		# JobID
		1, 
		# Ontology
		oOntology, 
		# Annotations for a given dataset
		oAnnotations.GetByID(iSpeciesTaxon), 
		# Timeout
		TimeSpan(0, 15, 0), 
		# Job source
		RequestSourceEnum.JobSubmitting,
		# Dataset
		sExample1, 
		# Job parameters
		dCutoff, eValueType, eMeasure, bRemoveObsolete)

	# Create worker 2
	oWorker2 = RevigoWorker(2, oOntology, oAnnotations.GetByID(9606), TimeSpan(0, 15, 0), RequestSourceEnum.JobSubmitting,
		sExample2, 0.9, eValueType, SemanticSimilarityTypeEnum.LIN, bRemoveObsolete);

	# Create worker 3
	oWorker3 = RevigoWorker(3, oOntology, oAnnotations.GetByID(iSpeciesTaxon), TimeSpan(0, 15, 0), RequestSourceEnum.JobSubmitting,
		sExample3, 0.4, eValueType, eMeasure, bRemoveObsolete);

	# Workers will notify when the are finished processing the data
	oWorker1.OnFinish += OWorker_OnFinish
	oWorker2.OnFinish += OWorker_OnFinish
	oWorker3.OnFinish += OWorker_OnFinish

	# Start Workers and wait for their completion
	# They will automatically be assigned to different CPU core, if available
	print("Starting Workers...")
	oWorker1.Start()
	oWorker2.Start()
	oWorker3.Start()

	while oWorker1.IsFinished != True or oWorker2.IsFinished != True or oWorker3.IsFinished != True:
		time.sleep(0.1)

	print("All Workers have finished processing.")

	# export our results
	print("Exporting data.")
	ExportTable(oOntology, oWorker1, oWorker1.BPVisualizer, "..\\Example1_BPTable.tsv")
	ExportScatterplot(oOntology, oWorker2, oWorker2.CCVisualizer, "..\\Example2_CCScatterplot.tsv")
	ExportTreeMap(oOntology, oWorker3, oWorker3.MFVisualizer, "..\\Example3_MFTreeMap.tsv")
	ExportCytoscapeXGMML(oWorker1.BPVisualizer, "..\\Example1_BPCytoscape.xgmml")
	ExportSimMat(oWorker1.BPVisualizer, "..\\Example1_BPSimilarityMatrix.tsv")
	ExportWordClouds(oWorker1, "..\\Example1_WordClouds.json")

	# We are finished

def ExportTable(ontology, worker, visualizer, fileName):
	if visualizer.IsEmpty != True:
		with open(fileName, 'w') as oWriter:
			oTerms = visualizer.Terms.FindClustersAndSortByThem(ontology, worker.CutOff)

			oWriter.write("TermID\tName\tValue\t")
			c = 1
			while c < worker.MinNumColsPerGoTerm:
				oWriter.write("UserValue_{0}\t", c - 1)
				c += 1
			
			oWriter.write("LogSize\tFrequency\tUniqueness\tDispensability\tRepresentative\n")

			# print the data
			i = 0
			while i < oTerms.Count:
				term = oTerms[i]

				oWriter.write("\"{}\"\t".format(term.GOTerm.FormattedID))
				oWriter.write("\"{}\"\t".format(term.GOTerm.Name))
				oWriter.write("{}\t".format(term.Value))

				c = 1
				while c < worker.MinNumColsPerGoTerm:
					oWriter.write("{}\t".format(term.UserValues[c - 1]))
					c += 1

				oWriter.write("{}\t".format(term.LogAnnotationSize))
				oWriter.write("{}\t".format(term.AnnotationFrequency * 100.0))
				oWriter.write("{}\t".format(term.Uniqueness))
				oWriter.write("{}\t".format(term.Dispensability))

				if term.RepresentativeID > 0:
					oWriter.write("{}".format(term.RepresentativeID));
				else:
					oWriter.write("null");

				oWriter.write("\n")
				i += 1

def ExportScatterplot(ontology, worker, visualizer, fileName):
	if visualizer.IsEmpty != True:
		with open(fileName, 'w') as oWriter:
			oTerms = visualizer.Terms.FindClustersAndSortByThem(ontology, worker.CutOff)

			oWriter.write("TermID\tName\tValue\tLogSize\tFrequency\tUniqueness\tDispensability\tPC_0\tPC_1\tRepresentative\n")

			# print the data
			i = 0
			while i < oTerms.Count:
				term = oTerms[i]

				oWriter.write("\"{}\"\t".format(term.GOTerm.FormattedID))
				oWriter.write("\"{}\"\t".format(term.GOTerm.Name))
				oWriter.write("{}\t".format(term.Value))

				oWriter.write("{}\t".format(term.LogAnnotationSize))
				oWriter.write("{}\t".format(term.AnnotationFrequency * 100.0))
				oWriter.write("{}\t".format(term.Uniqueness))
				oWriter.write("{}\t".format(term.Dispensability))

				# 2D
				oWriter.write("{}\t".format(term.PC[0] if (term.PC.Count > 0) else "null"))
				oWriter.write("{}\t".format(term.PC[1] if (term.PC.Count > 1) else "null"))

				oWriter.write("{}".format(term.RepresentativeID if (term.RepresentativeID > 0) else "null"))

				oWriter.write("\n")
				i += 1

def ExportTreeMap(ontology, worker, visualizer, fileName):
	if visualizer.IsEmpty != True:
		with open(fileName, 'w') as oWriter:
			terms = visualizer.Terms.FindClustersAndSortByThem(ontology, 0.1)

			oWriter.write("# WARNING - This exported Revigo data is only useful for the specific purpose of constructing a TreeMap visualization.\n")
			oWriter.write("# Do not use this table as a general list of non-redundant GO categories, as it sets an extremely permissive \n")
			oWriter.write("# threshold to detect redundancies (c=0.10) and fill the 'representative' column, while normally c>=0.4 is recommended.\n")
			oWriter.write("# To export a reduced-redundancy set of GO terms, go to the Scatterplot or Table tab, and export from there.\n")

			oWriter.write("TermID\tName\tFrequency\tValue\t")
			c = 1
			while c < worker.MinNumColsPerGoTerm:
				oWriter.write("UserValue_{}\t".format(c - 1))
				c += 1
			oWriter.write("Uniqueness\tDispensability\tRepresentative\n")

			# print the data
			i = 0
			while i < terms.Count:
				term = terms[i]
				
				isTermEliminated = term.Dispensability > worker.CutOff
				if (isTermEliminated):
					i += 1
					continue # will not output terms below the dispensability threshold at all

				oWriter.write("\"{}\"\t".format(term.GOTerm.FormattedID))
				oWriter.write("\"{}\"\t".format(term.GOTerm.Name))
				oWriter.write("{}\t".format(term.AnnotationFrequency * 100.0))

				oWriter.write("{}\t".format(term.Value))

				c = 1
				while c < worker.MinNumColsPerGoTerm:
					oWriter.write("{}\t".format(term.UserValues[c - 1]))
					c += 1

				oWriter.write("{}\t".format(term.Uniqueness))
				oWriter.write("{}\t".format(term.Dispensability))
				if term.RepresentativeID > 0:
					oWriter.write("\"{}\"".format(ontology.Terms.GetValueByKey(term.RepresentativeID).Name))
				else:
					oWriter.write("null")

				oWriter.write("\n")
				i += 1

def ExportCytoscapeXGMML(visualizer, fileName):
	if visualizer.IsEmpty != True:
		oWriter = StreamWriter(fileName)
		visualizer.SimpleOntologram.GraphToXGMML(oWriter)
		oWriter.Close()

def ExportSimMat(visualizer, fileName):
	if visualizer.IsEmpty != True:
		with open(fileName, 'w') as oWriter:
			i = 0
			while i < visualizer.Terms.Count:
				oWriter.write("\t{}".format(visualizer.Terms[i].GOTerm.FormattedID))
				i += 1

			oWriter.write("\n")
			i = 0
			while i < visualizer.Terms.Count:
				oWriter.write(visualizer.Terms[i].GOTerm.FormattedID)
				j = 0
				while j < visualizer.Terms.Count:
					oWriter.write("\t{}".format(visualizer.Matrix.GetSimilarity(i, j)))
					j += 1

				oWriter.write("\n")
				i += 1

def ExportWordClouds(worker, fileName):
	with open(fileName, 'w') as oWriter:
		oWriter.write("{")
		if worker.Enrichments.Count > 0:
			oWriter.write("\"Enrichments\":[")

			MIN_UNIT_SIZE = 1.0
			MAX_UNIT_SIZE = 9.0
			RANGE_UNIT_SIZE = MAX_UNIT_SIZE - MIN_UNIT_SIZE
			minFreq = 999999.0
			maxFreq = 0.0

			i = 0
			while i < worker.Enrichments.Count:
				dFrequency = math.sqrt(worker.Enrichments[i].Value)
				if dFrequency > 0.0:
					minFreq = min(minFreq, dFrequency)
					maxFreq = max(maxFreq, dFrequency)
				i += 1
			

			if minFreq > maxFreq:
				dTemp = minFreq
				minFreq = maxFreq
				maxFreq = dTemp
			
			if minFreq == maxFreq:
				maxFreq += 1
			
			range = maxFreq - minFreq
			bFirst = True

			i = 0
			while i < worker.Enrichments.Count:
				sWord = worker.Enrichments[i].Key.replace("'", "")
				dFrequency = math.sqrt(worker.Enrichments[i].Value)

				if dFrequency > 0.0:
					if bFirst == False:
						oWriter.write(",")

					size = math.ceil(MIN_UNIT_SIZE + round(((dFrequency - minFreq) * RANGE_UNIT_SIZE) / range))
					oWriter.write("{{\"Word\":\"{0}\",\"Size\":{1}}}".format(Utilities.StringToJSON(sWord), size))
					bFirst = False
				i += 1
			
			oWriter.write("]")

		if worker.Correlations.Count > 0:
			if worker.Enrichments.Count > 0:
				oWriter.write(",")

			oWriter.write("\"Correlations\":[")

			MIN_UNIT_SIZE = 1.0
			MAX_UNIT_SIZE = 9.0
			RANGE_UNIT_SIZE = MAX_UNIT_SIZE - MIN_UNIT_SIZE
			minFreq = 999999.0
			maxFreq = 0.0
			i = 0
			while i < worker.Correlations.Count:
				dFrequency = worker.Correlations[i].Value;
				if dFrequency > 0.0:
					minFreq = min(minFreq, dFrequency)
					maxFreq = max(maxFreq, dFrequency)
				i += 1

			if minFreq > maxFreq:
				dTemp = minFreq
				minFreq = maxFreq
				maxFreq = dTemp
			
			if minFreq == maxFreq:
				maxFreq += 1
			
			range = maxFreq - minFreq
			bFirst = True

			i = 0
			while i < worker.Correlations.Count:
				sWord = worker.Correlations[i].Key.replace("'", "")
				dFrequency = worker.Correlations[i].Value

				if dFrequency > 0.0:
					if bFirst == False:
						oWriter.write(",")

					size = math.ceil(MIN_UNIT_SIZE + round(((dFrequency - minFreq) * RANGE_UNIT_SIZE) / range))
					oWriter.write("{{\"Word\":\"{0}\",\"Size\":{1}}}".format(Utilities.StringToJSON(sWord), size))
					bFirst = False
				i += 1
			
			oWriter.write("]");
		
		oWriter.write("}");

def OWorker_OnFinish(sender, e):
	if sender != None:
		print("Worker {} has finished processing the data in {} seconds.".format(sender.JobID, sender.ExecutingTime.TotalSeconds))

if __name__ == '__main__':
	main()