264 lines
9.5 KiB
Python
264 lines
9.5 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)
|
|
import collections
|
|
|
|
def goterms_from_gene(genelist, ontology='BP', garbage=['IEA'], ic_cutoff=2.0, verbose=False):
|
|
""" 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("ICsBP_small.rda") # Excludes IEA
|
|
ic = rpy.r.assign("IC",rpy.r.IC, envir=rpy.r.GOSimEnv)
|
|
max_val = 0
|
|
for key, val in ic.items():
|
|
if val != scipy.inf:
|
|
if val>max_val:
|
|
max_val = val
|
|
for key, val in ic.items():
|
|
ic[key] = val/max_val
|
|
else:
|
|
# NB! this IC is just for BP
|
|
ic = rpy.r('get("IC", envir=GOSimEnv)')
|
|
print "loading GO definitions environment"
|
|
|
|
gene2terms = collections.defaultdict(list)
|
|
cc = 0
|
|
dd = 0
|
|
ii = 0
|
|
jj = 0
|
|
kk = 0
|
|
all = rpy.r.mget(genelist, rpy.r.GOENTREZID2GO,ifnotfound="NA")
|
|
n_ic = len(ic)
|
|
print "Number of terms with IC: %d" %n_ic
|
|
stopp = False
|
|
for gene, terms in all.items():
|
|
if verbose:
|
|
print "\n\n ======ITEM========\n"
|
|
print "Gene: " + str(gene)
|
|
print "Number of terms: %d " %len(terms)
|
|
print terms
|
|
print "---\n"
|
|
if stopp:
|
|
1/0
|
|
if terms!="NA":
|
|
for term, desc in terms.items():
|
|
if verbose:
|
|
print "\nChecking term: " + str(term)
|
|
print "With description: " + str(desc)
|
|
if desc['Ontology'].lower() == ontology.lower() and term in ic:
|
|
if ic[term]>ic_cutoff:
|
|
#print ic[term]
|
|
jj+=1
|
|
if verbose:
|
|
print "too high" + str((gene, term))
|
|
stopp = True
|
|
continue
|
|
cc += 1
|
|
if verbose:
|
|
print "accepted" + str((gene, term))
|
|
gene2terms[gene].append(term)
|
|
else:
|
|
if verbose:
|
|
print "Not accepted: " + str((gene, term))
|
|
if term not in ic:
|
|
if verbose:
|
|
print "Not in IC: " + str((gene, term))
|
|
kk+=1
|
|
if desc['Ontology'].lower() != ontology:
|
|
if verbose:
|
|
print "Not in Ontology" + str((gene, term))
|
|
dd+=1
|
|
else:
|
|
ii+=1
|
|
|
|
print "Number of genes total: %d" %len(all)
|
|
print "\nNumber of genes without annotation: (%d (NA))" %ii
|
|
print "\nNumber of terms with annoation but no IC: %d" %kk
|
|
print "\nNumber of terms not in %s : %d " %(ontology, dd)
|
|
print "\nNumber of terms with too high IC : %d " %jj
|
|
print "\n Number of accepted terms: %d" %cc
|
|
|
|
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="entrezUniverse",ontology="BP",chip = "hgu133a",pval_cutoff=0.01,cond=False,test_direction="over"):
|
|
|
|
#assert(scipy.alltrue([True for i in genelist if i in universe]))
|
|
universeGeneIds = universe
|
|
params = rpy.r.new("GOHyperGParams",
|
|
geneIds=genelist,
|
|
annotation="hgu133a",
|
|
ontology=ontology,
|
|
pvalueCutoff=pval_cutoff,
|
|
conditional=cond,
|
|
testDirection=test_direction
|
|
)
|
|
result = rpy.r.summary(rpy.r.hyperGTest(params))
|
|
|
|
return result, params
|
|
|
|
def data_aff2loc_hgu133a(X, aff_ids, verbose=False):
|
|
aff_ids = scipy.asarray(aff_ids)
|
|
if verbose:
|
|
print "\nNumber of probesets in affy list: %s" %len(aff_ids)
|
|
import rpy
|
|
rpy.r.library("hgu133a")
|
|
trans_table = rpy.r.as_list(rpy.r.hgu133aENTREZID)
|
|
if verbose:
|
|
print "Number of entrez ids: %d" %(scipy.asarray(trans_table.values())>0).sum()
|
|
enz2aff = collections.defaultdict(list)
|
|
#aff2enz = collections.defaultdict(list)
|
|
for aff, enz in trans_table.items():
|
|
if int(enz)>0 and (aff in aff_ids):
|
|
enz2aff[enz].append(aff)
|
|
#aff2enz[aff].append(enz)
|
|
if verbose:
|
|
print "\nNumber of translated entrez ids: %d" %len(enz2aff)
|
|
aff2ind = dict(zip(aff_ids, scipy.arange(len(aff_ids))))
|
|
var_x = X.var(0)
|
|
new_data = []
|
|
new_ids = []
|
|
m = 0
|
|
s = 0
|
|
for enz, aff_id_list in enz2aff.items():
|
|
index = [aff2ind[aff_id] for aff_id in aff_id_list]
|
|
if len(index)>1:
|
|
m+=1
|
|
if verbose:
|
|
pass
|
|
#print "\nEntrez id: %s has %d probesets" %(enz, len(index))
|
|
#print index
|
|
xsub = X[:,index]
|
|
choose_this = scipy.argmax(xsub.var(0))
|
|
new_data.append(xsub[:,choose_this].ravel())
|
|
else:
|
|
s+=1
|
|
new_data.append(X[:,index].ravel())
|
|
new_ids.append(enz)
|
|
if verbose:
|
|
print "Ids with multiple probesets: %d" %m
|
|
print "Ids with unique probeset: %d" %s
|
|
X = scipy.asarray(new_data).T
|
|
return X, new_ids
|
|
|
|
def R_PLS(x,y,ncomp=3, validation='"LOO"'):
|
|
rpy.r.library("pls")
|
|
rpy.r.assign("X", x)
|
|
rpy.r.assign("Y", y)
|
|
callstr = "plsr(Y~X, ncomp=" + str(ncomp) + ", validation=" + validation + ")"
|
|
print callstr
|
|
result = rpy.r(callstr)
|
|
return result
|
|
|