view cluster_onConnctdComps.py @ 39:e56023008e36 default tip

Changed revision of package_fisher_0_1_4 to be2fc454d121 Changed revision of package_matplotlib_1_2 to a03ee94316b5

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
#       Cluster_GOKEGG.py
#       
#       Copyright 2013 Oscar Reina <[e-mail]>
#       
#       This program is free software; you can redistribute it and/or modify
#       it under the terms of the GNU General Public License as published by
#       the Free Software Foundation; either version 2 of the License, or
#       (at your option) any later version.
#       
#       This program is distributed in the hope that it will be useful,
#       but WITHOUT ANY WARRANTY; without even the implied warranty of
#       MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#       GNU General Public License for more details.
#       
#       You should have received a copy of the GNU General Public License
#       along with this program; if not, write to the Free Software
#       Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
#       MA 02110-1301, USA.

import argparse
import os
from networkx import connected_components,Graph,clustering
from numpy import percentile
from decimal import Decimal,getcontext
from itertools import permutations,combinations
import sys

def rtrnClustrsOnCltrCoff(dNodesWeightMin,threshold,perctile=True):
	"""
	From a file with three columns: nodeA, nodeB and a score, it returns
	the strong and weak connected components produced when the edges 
	below the percentage threshold (or value) are excluded.
	"""
	#~ 
	Gmin = Graph()
	for nodeA,nodeB in dNodesWeightMin:
		wMin=dNodesWeightMin[nodeA,nodeB]
		Gmin.add_edge(nodeA,nodeB,weight=wMin)
	#~ 
	clstrCoffcMin=clustering(Gmin,weight='weight')
	#~ 
	if perctile:
		umbralMin=percentile(clstrCoffcMin.values(),threshold)
	else:
		umbralMin=threshold
	#~ 
	GminNdsRmv=[x for x in clstrCoffcMin if clstrCoffcMin[x]<umbralMin]
	#~ 
	Gmin.remove_nodes_from(GminNdsRmv)
	#~ 
	dTermCmptNumbWkMin=rtrndata(Gmin)
	#~
	salelClustr=[]
	srtdterms=sorted(dTermCmptNumbWkMin.keys())
	for echTerm in srtdterms:
		try:
			MinT=dTermCmptNumbWkMin[echTerm]
		except:
			MinT='-'
		salelClustr.append('\t'.join([echTerm,MinT]))
	#~ 
	return salelClustr
	
def rtrndata(G):
	"""
	returna list of terms and its clustering, as well as clusters from
	a networkx formatted file.
	"""
	#~ 
	cntCompnts=0
	dTermCmptNumbWk={}
	for echCompnt in connected_components(G):
		cntCompnts+=1
		#print '.'.join(echCompnt)
		for echTerm in echCompnt:
			dTermCmptNumbWk[echTerm]=str(cntCompnts)
	#~ 
	return dTermCmptNumbWk

def rtrnCATcENSEMBLc(inCATfile,classClmns,ENSEMBLTcolmn,nonHdr=True):
	"""
	return a dictionary of all the categories in an input file with 
	a set of genes. Takes as input a file with categories an genes.
	"""
	dCAT={}
	dENSEMBLTCAT={}
	for eachl in open(inCATfile,'r'):
		if nonHdr and eachl.strip():
			ENSEMBLT=eachl.splitlines()[0].split('\t')[ENSEMBLTcolmn]
			sCAT=set()
			for CATcolmn in classClmns:
				sCAT.update(set([x for x in eachl.splitlines()[0].split('\t')[CATcolmn].split('.')]))
			sCAT=sCAT.difference(set(['','U','N']))
			if len(sCAT)>0:
				dENSEMBLTCAT[ENSEMBLT]=sCAT
			for CAT in sCAT:
				try:
					dCAT[CAT].add(ENSEMBLT)
				except:
					dCAT[CAT]=set([ENSEMBLT])
		nonHdr=True
	#~ 
	dCAT=dict([(x,len(dCAT[x])) for x in dCAT.keys()])
	#~ 
	return dCAT,dENSEMBLTCAT


def calcDistance(sCAT1,sCAT2):
	"""
	takes as input two set of genesin different categories and returns
	a value 1-percentage of gene shared cat1->cat2, and cat2->cat1.
	"""
	getcontext().prec=5
	lgensS1=Decimal(len(sCAT1))
	lgensS2=Decimal(len(sCAT2))
	shrdGns=sCAT1.intersection(sCAT2)
	lenshrdGns=len(shrdGns)
	#~ 
	dC1C2=1-(lenshrdGns/lgensS1)
	dC2C1=1-(lenshrdGns/lgensS2)
	#~ 
	return dC1C2,dC2C1

def rtnPrwsdtncs(dCAT,dENSEMBLTCAT):
	"""
	return a mcl formated pairwise distances from a list of categories
	"""	
	#~ 
	getcontext().prec=5
	dCATdst={}
	lENSEMBL=dENSEMBLTCAT.keys()
	l=len(lENSEMBL)
	c=0
	for ENSEMBL in lENSEMBL:
		c+=1
		lCAT=dENSEMBLTCAT.pop(ENSEMBL)
		for CAT1,CAT2 in combinations(lCAT, 2):
			try:
				dCATdst[CAT1,CAT2]+=1
			except:
				dCATdst[CAT1,CAT2]=1
			try:
				dCATdst[CAT2,CAT1]+=1
			except:
				dCATdst[CAT2,CAT1]=1
	#~ 
	dNodesWeightMin={}
	l=len(dCATdst)
	for CAT1,CAT2 in dCATdst.keys():
		shrdGns=dCATdst.pop((CAT1,CAT2))
		dC1C2=float(shrdGns)
		nodeA,nodeB=sorted([CAT1,CAT2])
		try:
			cscor=dNodesWeightMin[nodeA,nodeB]
			if cscor>=dC1C2:
				dNodesWeightMin[nodeA,nodeB]=dC1C2
		except:
			dNodesWeightMin[nodeA,nodeB]=dC1C2
	#
	return dNodesWeightMin

def parse_class_columns(val, max_col):
	int_list = []

	for elem in [x.strip() for x in val.split(',')]:
		if elem[0].lower() != 'c':
			print >> sys.stderr, "bad column format:", elem
			sys.exit(1)

        int_val = as_int(elem[1:])

        if int_val is None:
			print >> sys.stderr, "bad column format:", elem
			sys.exit(1)
        elif not 1 <= int_val <= max_col:
			print >> sys.stderr, "column out of range:", elem
			sys.exit(1)

        int_list.append(int_val - 1)

	return int_list

def as_int(val):
    try:
        return int(val)
    except ValueError:
        return None
    else:
        raise

def main():
	"""
	"""
	#~ bpython cluster_onConnctdComps.py --input=../conctFinal_CEU.tsv --outfile=../borrar.txt --threshold=90 --ENSEMBLTcolmn=1 --classClmns='20 22'
	parser = argparse.ArgumentParser(description='Returns the count of genes in ...')
	parser.add_argument('--input',metavar='input TXT file',type=str,help='the input file with the table in txt format.',required=True)
	parser.add_argument('--input_columns',metavar='input INT value',type=int,help='the number of columns in the input file.',required=True)
	parser.add_argument('--outfile',metavar='input TXT file',type=str,help='the output file with the connected components.',required=True)
	parser.add_argument('--threshold',metavar='input FLOAT value',type=float,help='the threshold to disconnect the nodes.',required=True)
	parser.add_argument('--ENSEMBLTcolmn',metavar='input INT file',type=int,help='the column with the ENSEMBLE code in the input.',required=True)
	parser.add_argument('--classClmns',metavar='input STR value',type=str,help='the list of columns with the gene categories separated by space.',required=True)
	args = parser.parse_args()
	infile = args.input
	threshold = args.threshold
	outfile = args.outfile
	ENSEMBLTcolmn = args.ENSEMBLTcolmn
	classClmns = parse_class_columns(args.classClmns, args.input_columns)
	#~ 
	dCAT,dENSEMBLTCAT=rtrnCATcENSEMBLc(infile,classClmns,ENSEMBLTcolmn)
	dNodesWeightMin=rtnPrwsdtncs(dCAT,dENSEMBLTCAT)
	salelClustr=rtrnClustrsOnCltrCoff(dNodesWeightMin,threshold)
	#~ 
	with open(outfile, 'w') as salef:
		print >> salef, '\n'.join(salelClustr)
	#~ 
	#~ 

if __name__ == '__main__':
	main()