227 lines
7.9 KiB
Python
227 lines
7.9 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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import collections
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def goterms_from_gene(genelist, ontology='BP', garbage=None, ic_cutoff=2.0):
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""" Returns the go-terms from a specified genelist (Entrez id).
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Recalculates the information content if needed based on selected evidence codes.
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"""
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rpy.r.library("GOSim")
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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]) or garbage==None)
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assert(ontology in _ONTOLOGIES)
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dummy = rpy.r.setOntology(ontology)
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ddef = False
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if ontology=='BP' and garbage!=None:
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# This is for ont=BP and garbage =['IEA', 'ISS', 'ND']
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rpy.r.load("ICsBPIMP_IGI_IPI_ISS_IDA_IEP_TAS_NAS_IC.rda")
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ic = rpy.r.assign("IC",rpy.r.IC, envir=rpy.r.GOSimEnv)
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print len(ic)
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else:
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ic = rpy.r('get("IC", envir=GOSimEnv)')
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print "loading GO definitions environment"
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gene2terms = collections.defaultdict(list)
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cc = 0
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dd = 0
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ii = 0
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jj = 0
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all = rpy.r.mget(gene_ids, rpy.r.GOENTREZID2GO,ifnotfound="NA")
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for gene, terms in all.items():
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if terms!="NA":
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for term,desc in terms.items():
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if desc['Ontology'].lower() == ontology and term in ic:
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if ic[term]>.88:
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jj+=1
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continue
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cc+=1
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gene2terms[gene].append(term)
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else:
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dd+=1
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else:
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ii+=1
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print "\nNumber of genes without annotation: %d" %ii
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print "\nNumber of genes not in %s : %d " %(ontology, dd)
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print "\nNumber of genes with too high IC : %d " %jj
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return gene2terms
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def genego_matrix(goterms, tmat, gene_ids, term_ids, func=max):
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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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new_gene_index = []
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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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new_gene_index.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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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), new_gene_index
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def genego_sim(gene2go, gene_ids, all_go_terms, STerm, go_term_sim="OA", term_sim="Lin", verbose=False):
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"""Returns go-terms x genes similarity matrix.
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:input:
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- gene2go: dict: keys: gene_id, values: go_terms
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- gene_ids: list of gene ids (entrez ids)
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- STerm: (go_terms x go_terms) similarity matrix
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- go_terms_sim: similarity measure between a gene and multiple go terms (max, mean, OA)
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- term_sim: similarity measure between two go-terms
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- verbose
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"""
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rpy.r.library("GOSim")
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#gene_ids = gene2go.keys()
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GG = scipy.empty((len(all_go_terms), len(gene_ids)), 'd')
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for j,gene in enumerate(gene_ids):
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for i,go_term in enumerate(all_go_terms):
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if verbose:
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print "\nAssigning similarity from %s to terms(gene): %s" %(go_term,gene)
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GG_ij = rpy.r.getGSim(go_term, gene2go[gene], similarity=go_term_sim,
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similarityTerm=term_sim, STerm=STerm, verbose=verbose)
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GG[i,j] = GG_ij
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return GG
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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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make the networkx graph by:
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G = networkx.Digraph()
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G = networkx.from_dict_of_lists(edge_dict, G)
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"""
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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.GOBPPARENTS, 'MF':rpy.r.GOMFPARENTS, 'CC': rpy.r.GOCCPARENTS}
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graph = rpy.r.GOGraph(go_terms, go_env[ontology[0]])
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edges = rpy.r.edges(graph)
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edges.pop('all')
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edge_dict = {}
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for head, neighbours in edges.items():
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for nn in neighbours.values():
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if edge_dict.has_key(nn):
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edge_dict[nn].append(head)
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else:
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edge_dict[nn] = [head]
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return edge_dict
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def gene_GO_hypergeo_test(genelist,universe="entrezUniverse",ontology="BP",chip = "hgu133a",pval_cutoff=0.01,cond=False,test_direction="over"):
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#assert(scipy.alltrue([True for i in genelist if i in universe]))
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universeGeneIds=universe
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params = rpy.r.new("GOHyperGParams",
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geneIds=genelist,
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annotation="hgu133a",
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ontology=ontology,
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pvalueCutoff=pval_cutoff,
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conditional=cond,
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testDirection=test_direction
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)
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result = rpy.r.summary(rpy.r.hyperGTest(params))
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return result, params
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def data_aff2loc_hgu133a(X, aff_ids, verbose=False):
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aff_ids = scipy.asarray(aff_ids)
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if verbose:
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print "\nNumber of probesets in affy list: %s" %len(aff_ids)
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import rpy
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rpy.r.library("hgu133a")
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trans_table = rpy.r.as_list(rpy.r.hgu133aENTREZID)
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if verbose:
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print "Number of entrez ids: %d" %(scipy.asarray(trans_table.values())>0).sum()
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enz2aff = collections.defaultdict(list)
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#aff2enz = collections.defaultdict(list)
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for aff, enz in trans_table.items():
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if int(enz)>0 and (aff in aff_ids):
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enz2aff[enz].append(aff)
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#aff2enz[aff].append(enz)
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if verbose:
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print "\nNumber of translated entrez ids: %d" %len(enz2aff)
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aff2ind = dict(zip(aff_ids, scipy.arange(len(aff_ids))))
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var_x = X.var(0)
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new_data = []
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new_ids = []
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m = 0
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s = 0
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for enz, aff_id_list in enz2aff.items():
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index = [aff2ind[aff_id] for aff_id in aff_id_list]
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if len(index)>1:
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m+=1
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if verbose:
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pass
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#print "\nEntrez id: %s has %d probesets" %(enz, len(index))
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#print index
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xsub = X[:,index]
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choose_this = scipy.argmax(xsub.var(0))
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new_data.append(xsub[:,choose_this].ravel())
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else:
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s+=1
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new_data.append(X[:,index].ravel())
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new_ids.append(enz)
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if verbose:
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print "Ids with multiple probesets: %d" %m
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print "Ids with unique probeset: %d" %s
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X = scipy.asarray(new_data).T
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return X, new_ids
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def R_PLS(x,y,ncomp=3, validation='"LOO"'):
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rpy.r.library("pls")
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rpy.r.assign("X", x)
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rpy.r.assign("Y", y)
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callstr = "plsr(Y~X, ncomp=" + str(ncomp) + ", validation=" + validation + ")"
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print callstr
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result = rpy.r(callstr)
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return result
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def gogene()
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