def command(self, gene_list, n_top):
from gprofiler import GProfiler
import numpy as np
gp = GProfiler("")
r0 = gp.gprofile(gene_list,
correction_method=GProfiler.THR_FDR,
ordered=True)
r0 = np.array(r0)
r0 = r0[r0[:, 9] == 'MF']
name_out = r0[0:n_top, -3]
p_out = r0[0:n_top, 2]
return np.array([x for x in zip(name_out, p_out)])
def command(self, gene_list, n_top):
from gprofiler import GProfiler
import numpy as np
gp = GProfiler("")
r0 = gp.gprofile(gene_list,correction_method=GProfiler.THR_FDR,ordered=True)
r0 = np.array(r0)
r0 = r0[r0[:,9]=='MF']
name_out = r0[0:n_top,-3]
p_out = r0[0:n_top,2]
data = np.array([x for x in zip(name_out, p_out)])
dataframe = pd.DataFrame(data=data, columns=["Name", "p-values"])
return dataframe
def profile_genes_with_active_sites(enriched_genes,
background=None) -> DataFrame:
if len(enriched_genes) == 0:
return DataFrame()
gp = GProfiler('ActiveDriverDB', want_header=True)
response = gp.gprofile(enriched_genes, custom_bg=background)
if not response:
return DataFrame()
header, *rows = response
return DataFrame(rows, columns=header)
def gsea_connected_components(G, outdir):
"""
Perform Gene Set Enrichment Analysis on the connected components in G using GProfiler
Returns
-------
rv : list of (set, str)
tuples of gene set that was queried for enrichment and the enrichment output file
"""
rv = []
gp = GProfiler("FluPath/0.1")
if nx.is_directed(G):
G = G.to_undirected()
comps = list(nx.connected_components(G))
comp_no = 0
for comp in comps:
# TODO how are http errors handled?
enrich_out_fp = os.path.join(outdir, "enrich_{}.tsv".format(comp_no))
if not os.path.exists(enrich_out_fp):
enrich = gp.gprofile(comp, src_filter=['GO:BP'])
write_enrich(enrich, enrich_out_fp)
rv.append((comp, enrich_out_fp))
comp_no += 1
return rv
def lookup_enrichment(gene_set):
clean_gene_set = [x for x in gene_set if x is not None]
gp = GProfiler("GTEx/wj")
enrichment_results = gp.gprofile(clean_gene_set)
return enrichment_results
#df.to_csv(join(out_path, node_name + "_sorted.csv"))
print(df_all.shape)
if df_all.shape[0] < args.n_genes:
# raise Exception("cell type '{}' has less than {} significant genes!".format(cell_type, args.n_genes))
print("cell type '{}' has less than {} significant genes!".format(
cell_type, args.n_genes))
continue
df_all = df_all.head(args.n_genes)
gene_list_all = df_all['EntrezID'].astype('str').values
mg = mygene.MyGeneInfo()
result = mg.getgenes(gene_list_all, fields='symbol', species='mouse')
gene_list_all = [d['symbol'] for d in result]
results_all = gp.gprofile(gene_list_all,
organism="mmusculus",
ordered=args.ordered,
correction_method=get_correction_method(
args.correction),
src_filter=[args.go_branch],
custom_bg=background_gene_set)
print("\t # results returned (all genes) = {}".format(
len(results_all)))
# if len(results) == 0:
# continue
filepath = join(args.output_folder,
"{}_all.tex".format(cell_type.replace(':', '_')))
table_tex = write_to_table(cell_type, info_dict, results_all, filepath,
args.rows)
overall_table_all_f.write(table_tex)
overall_table_all_f.write("\n")
filepath = join(args.output_folder,
"{}_all_full.tex".format(cell_type.replace(':', '_')))
class GOEnrichmentTester():
def __init__(self):
self.gp = GProfiler("COSSY++/1.5")
def getGoTerms(self, genelist):
result = []
res = self.gp.gprofile(query=genelist)
for i in range(len(res)):
pvalue = res[i][2]
goid = res[i][8]
gocat = res[i][9]
goterm = res[i][11]
'''
if (gocat =="MF" or gocat == "CC" or gocat == "BP"):
result.append({"pvalue":pvalue, "id":goid, "category":gocat, "term":goterm})
'''
result.append({"pvalue":pvalue, "id":goid, "category":gocat, "term":goterm})
return result
def readTSV(self, fname):
records = []
with open(fname) as reader:
headers = []
for line in reader:
values = [x.replace("\"","") for x in line.split("\t")]
if line.startswith("Gene Symbol"):
headers = values
continue
rec = {headers[i] : values[i] for i in range(len(headers))}
records.append(rec)
return records
def loadCOSMIC(self, fname):
self.result = {"somatic":{}, "germline":{}}
self.diseaseList = []
records = self.readTSV(fname=fname)
for rec in records:
geneSymbol = rec["Gene Symbol"]
somaticTumors = [x.strip() for x in rec["Tumour Types(Somatic)"].strip().split(",")]
germlineTumors = [x.strip() for x in rec["Tumour Types(Germline)"].strip().split(",")]
for tumorType in somaticTumors:
if tumorType == "":
continue;
if tumorType not in self.result["somatic"]:
self.result["somatic"][tumorType] = []
self.result["somatic"][tumorType].append(geneSymbol)
if tumorType not in self.diseaseList:
self.diseaseList.append(tumorType)
for tumorType in germlineTumors:
if tumorType == "":
continue;
if tumorType not in self.result["germline"]:
self.result["germline"][tumorType] = []
self.result["germline"][tumorType].append(geneSymbol)
if tumorType not in self.diseaseList:
self.diseaseList.append(tumorType)
self.makeGOList()
def getGenes(self, disease):
if disease in self.result["somatic"]:
somaticGenes = self.result["somatic"][disease]
else:
somaticGenes = []
if disease in self.result["germline"]:
germlineGenes = self.result["germline"][disease]
else:
germlineGenes = []
return somaticGenes + germlineGenes
def makeGOList(self):
self.GOList = {}
for tumorType in self.diseaseList:
print "."
genes = self.getGenes(tumorType)
goTerms = self.getGoTerms(genes)
goTerms = sorted(goTerms, cmp=self.pvaluecomp)
self.GOList[tumorType] = goTerms
def writeCOSMICGO(self, fname):
with open(fname, "w") as w:
json.dump(self.GOList, w, indent=4)
def corr(self, genes, disease):
inputGO = sorted(self.getGoTerms(genes), cmp=self.pvaluecomp)
inputGO_terms = [x["term"] for x in inputGO]
answerGO = sorted([x for x in self.GOList[disease] if x["term"] in inputGO_terms], cmp=self.pvaluecomp)
answerGO_terms = [x["term"] for x in answerGO]
assert(len(inputGO_terms) != len(answerGO_terms))
inputGO_ranks_pair = [(x,inputGO_terms.index(x)) for x in inputGO_terms]
answerGO_ranks_pair = [(x,answerGO_terms.index(x)) for x in answerGO_terms]
inputGO_ranks = [x[1] for x in sorted(inputGO_ranks_pair, key=itemgetter(0))]
answerGO_ranks = [x[1] for x in sorted(answerGO_ranks_pair, key=itemgetter(0))]
np.correlate(inputGO_ranks, answerGO_ranks, "same")
def pvaluecomp(self, a,b):
x = a['pvalue']
y = b['pvalue']
if x > y:
return 1
elif x < y:
return -1
else:
return 0
