111 lines
3.8 KiB
Python
111 lines
3.8 KiB
Python
""" Module for Gene ontology related functions called in R"""
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import scipy
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import rpy
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silent_eval = rpy.with_mode(rpy.NO_CONVERSION, rpy.r)
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def get_term_sim(termlist, method = "JiangConrath", verbose=False):
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"""Returns the similariy matrix between go-terms.
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Arguments:
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termlist: character vector of GO terms
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method: one of
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("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin")
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verbose: print out various information or not
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"""
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_methods = ("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin")
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assert(method in _methods)
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assert(termlist[0][:2]=='GO')
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rpy.r.library("GOSim")
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return rpy.r.getTermSim(termlist, method = method, verbose = verbose)
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def get_gene_sim(genelist, similarity='OA',
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distance="Resnick"):
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rpy.r.library("GOSim")
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rpy.r.assign("ids", genelist)
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silent_eval('a<-getGeneSim(ids)', verbose=FALSE)
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def goterms_from_gene(genelist, ontology=['BP'], garbage = ['IEA', 'ISS', 'ND']):
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""" Returns the go-terms from a specified genelist (Entrez id).
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"""
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rpy.r.library("GO")
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_CODES = {"IMP" : "inferred from mutant phenotype",
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"IGI" : "inferred from genetic interaction",
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"IPI" :"inferred from physical interaction",
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"ISS" : "inferred from sequence similarity",
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"IDA" : "inferred from direct assay",
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"IEP" : "inferred from expression pattern",
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"IEA" : "inferred from electronic annotation",
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"TAS" : "traceable author statement",
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"NAS" : "non-traceable author statement",
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"ND" : "no biological data available",
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"IC" : "inferred by curator"
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}
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_ONTOLOGIES = ['BP', 'CC', 'MF']
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assert(scipy.all([(code in _CODES) for code in garbage]))
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assert(scipy.all([(ont in _ONTOLOGIES) for ont in ontology]))
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goterms = {}
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for gene in genelist:
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goterms[gene] = []
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info = rpy.r('GOENTREZID2GO[["' + str(gene) + '"]]')
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#print info
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if info:
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for term, desc in info.items():
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if desc['Ontology'] in ontology and desc['Evidence'] not in garbage:
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goterms[gene].append(term)
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return goterms
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def genego_matrix(goterms, tmat, gene_ids, term_ids, func=min):
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ngenes = len(gene_ids)
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nterms = len(term_ids)
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gene2indx = {}
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for i,id in enumerate(gene_ids):
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gene2indx[id]=i
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term2indx = {}
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for i,id in enumerate(term_ids):
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term2indx[id]=i
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#G = scipy.empty((nterms, ngenes),'d')
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G = []
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newindex = []
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for gene, terms in goterms.items():
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g_ind = gene2indx[gene]
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if len(terms)>0:
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t_ind = []
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newindex.append(g_ind)
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for term in terms:
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if term2indx.has_key(term): t_ind.append(term2indx[term])
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print t_ind
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subsim = tmat[t_ind, :]
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gene_vec = scipy.apply_along_axis(func, 0, subsim)
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G.append(gene_vec)
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return scipy.asarray(G), newindex
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def goterm2desc(gotermlist):
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"""Returns the go-terms description keyed by go-term
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"""
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rpy.r.library("GO")
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term2desc = {}
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for term in gotermlist:
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try:
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desc = rpy.r('Term(GOTERM[["' +str(term)+ '"]])')
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term2desc[str(term)] = desc
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except:
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raise Warning("Description not found for %s\n Mapping incomplete" %term)
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return term2desc
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def parents_dag(go_terms, ontology=['BP']):
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""" Returns a list of lists representation of a GO DAG parents of goterms."""
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try:
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rpy.r.library("GOstats")
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except:
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raise ImportError, "Gostats"
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assert(go_terms[0][:3]=='GO:')
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# go valid namespace
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go_env = {'BP':rpy.r.BPPARENTS, 'MF':rpy.r.MFPARENTS, 'CC': rpy.r.CCPARENTS}
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