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laydi/scripts/lpls/rpy_go.py
2007-07-28 16:05:11 +00:00

159 lines
5.8 KiB
Python

""" Module for Gene ontology related functions called in R"""
import scipy
import rpy
silent_eval = rpy.with_mode(rpy.NO_CONVERSION, rpy.r)
def goterms_from_gene(genelist, ontology='BP', garbage=None):
""" Returns the go-terms from a specified genelist (Entrez id).
Recalculates the information content if needed based on selected evidence codes.
"""
rpy.r.library("GOSim")
_CODES = {"IMP" : "inferred from mutant phenotype",
"IGI" : "inferred from genetic interaction",
"IPI" :"inferred from physical interaction",
"ISS" : "inferred from sequence similarity",
"IDA" : "inferred from direct assay",
"IEP" : "inferred from expression pattern",
"IEA" : "inferred from electronic annotation",
"TAS" : "traceable author statement",
"NAS" : "non-traceable author statement",
"ND" : "no biological data available",
"IC" : "inferred by curator"
}
_ONTOLOGIES = ['BP', 'CC', 'MF']
#assert(scipy.all([(code in _CODES) for code in garbage]) or garbage==None)
assert(ontology in _ONTOLOGIES)
dummy = rpy.r.setOntology(ontology)
ddef = False
if ontology=='BP' and garbage!=None:
# This is for ont=BP and garbage =['IEA', 'ISS', 'ND']
rpy.r.load("ICsBPIMP_IGI_IPI_ISS_IDA_IEP_TAS_NAS_IC.rda")
ic = rpy.r.assign("IC",rpy.r.IC, envir=rpy.r.GOSimEnv)
print len(ic)
else:
ic = rpy.r('get("IC", envir=GOSimEnv)')
print "loading GO definitions environment"
gene2terms = {}
for gene in genelist:
info = rpy.r('GOENTREZID2GO[["' + str(gene) + '"]]')
#print info
if info:
skip=False
for term, desc in info.items():
if ic.get(term)==scipy.isinf:
print "\nIC is Inf on this GO term %s for this gene: %s" %(term,gene)
skip=True
if ic.get(term)==None:
#print "\nHave no IC on this GO term %s for this gene: %s" %(term,gene)
skip=True
if desc['Ontology']!=ontology:
#print "\nThis GO term %s belongs to: %s:" %(term,desc['Ontology'])
skip = True
if not skip:
if gene2terms.has_key(gene):
gene2terms[gene].append(term)
else:
gene2terms[gene] = [term]
else:
print "\nHave no Annotation on this gene: %s" %gene
return gene2terms
def genego_matrix(goterms, tmat, gene_ids, term_ids, func=max):
ngenes = len(gene_ids)
nterms = len(term_ids)
gene2indx = {}
for i,id in enumerate(gene_ids):
gene2indx[id]=i
term2indx = {}
for i,id in enumerate(term_ids):
term2indx[id]=i
#G = scipy.empty((nterms, ngenes),'d')
G = []
new_gene_index = []
for gene, terms in goterms.items():
g_ind = gene2indx[gene]
if len(terms)>0:
t_ind = []
new_gene_index.append(g_ind)
for term in terms:
if term2indx.has_key(term): t_ind.append(term2indx[term])
subsim = tmat[t_ind, :]
gene_vec = scipy.apply_along_axis(func, 0, subsim)
G.append(gene_vec)
return scipy.asarray(G), new_gene_index
def genego_sim(gene2go, gene_ids, all_go_terms, STerm, go_term_sim="OA", term_sim="Lin", verbose=False):
"""Returns go-terms x genes similarity matrix.
:input:
- gene2go: dict: keys: gene_id, values: go_terms
- gene_ids: list of gene ids (entrez ids)
- STerm: (go_terms x go_terms) similarity matrix
- go_terms_sim: similarity measure between a gene and multiple go terms (max, mean, OA)
- term_sim: similarity measure between two go-terms
- verbose
"""
rpy.r.library("GOSim")
#gene_ids = gene2go.keys()
GG = scipy.empty((len(all_go_terms), len(gene_ids)), 'd')
for j,gene in enumerate(gene_ids):
for i,go_term in enumerate(all_go_terms):
if verbose:
print "\nAssigning similarity from %s to terms(gene): %s" %(go_term,gene)
GG_ij = rpy.r.getGSim(go_term, gene2go[gene], similarity=go_term_sim,
similarityTerm=term_sim, STerm=STerm, verbose=verbose)
GG[i,j] = GG_ij
return GG
def goterm2desc(gotermlist):
"""Returns the go-terms description keyed by go-term.
"""
rpy.r.library("GO")
term2desc = {}
for term in gotermlist:
try:
desc = rpy.r('Term(GOTERM[["' +str(term)+ '"]])')
term2desc[str(term)] = desc
except:
raise Warning("Description not found for %s\n Mapping incomplete" %term)
return term2desc
def parents_dag(go_terms, ontology=['BP']):
""" Returns a list of lists representation of a GO DAG parents of goterms.
make the networkx graph by:
G = networkx.Digraph()
G = networkx.from_dict_of_lists(edge_dict, G)
"""
try:
rpy.r.library("GOstats")
except:
raise ImportError, "Gostats"
assert(go_terms[0][:3]=='GO:')
# go valid namespace
go_env = {'BP':rpy.r.GOBPPARENTS, 'MF':rpy.r.GOMFPARENTS, 'CC': rpy.r.GOCCPARENTS}
graph = rpy.r.GOGraph(go_terms, go_env[ontology[0]])
edges = rpy.r.edges(graph)
edges.pop('all')
edge_dict = {}
for head, neighbours in edges.items():
for nn in neighbours.values():
if edge_dict.has_key(nn):
edge_dict[nn].append(head)
else:
edge_dict[nn] = [head]
return edge_dict
def gene_GO_hypergeo_test(genelist, universe, ontology = ['BP']):
pvals = geneGoHyperGeoTest(entrezGeneIds, lib=None, ontology=ontology[0], universe=universe)
return pvals