def add_gene_name_gprofiler(data_df: pd.DataFrame, col: str,
organism: str) -> pd.DataFrame:
gp = GProfiler(return_dataframe=True)
protein_list = data_df[col].tolist()
# details of what returns the following function : https://pypi.org/project/gprofiler-official/
# TODO : documentation
res = gp.convert(organism=organism,
query=protein_list,
target_namespace='UNIPROTSWISSPROT')
# now add the relevant results to dataframe
res_f = res[['incoming', 'name', 'namespaces']]
res_f.rename(columns={
"incoming": col,
"name": "gene_name",
"namespaces": "gene_name_bank"
},
inplace=True)
res_f = res_f.replace({
'UNIPROTSWISSPROT,UNIPROT_GN_ACC': 'Swiss-Prot',
'UNIPROTSPTREMBL,UNIPROT_GN_ACC': 'TrEMBL'
})
df = data_df.merge(res_f, how='left', on=col)
# gProfiler returns one line for each alias of the gene (as in alias section in Uniprot): keep only the first one
df = df[~df['Accession'].duplicated(keep='first')]
return df
def get_gene_names(geneList):
gp = GProfiler(return_dataframe=True)
df = gp.convert(organism='athaliana',
query=geneList)[['incoming', 'name', 'description']]
df['description'] = df.apply(
lambda x: x['description'].split('[')[0].split(';')[0], axis=1)
return df
def add_gene_name_gprofiler(data_df, col, organism):
print(type(data_df[col].tolist()))
gp = GProfiler(return_dataframe=True)
# details of what returns the following function : caleydo.org/tools/
res = gp.convert(organism=organism,
query=data_df[col].tolist(),
target_namespace='UNIPROTSWISSPROT')
# now add the relevant results to dataframe
res_f = res[['incoming', 'name', 'namespaces']]
res_f.rename(columns={"incoming":col,
"name": "gene_name",
"namespaces": "gene_name_bank"}, inplace=True)
res_f = res_f.replace({'UNIPROTSWISSPROT,UNIPROT_GN_ACC':'Swiss-Prot',
'UNIPROTSPTREMBL,UNIPROT_GN_ACC':'TrEMBL'})
df = data_df.merge(res_f, how='left', on=col)
# TODO check if concordant with description
#df['OK'] = np.where(df['gene_name_PD'] == df['converted_gprofiler'], True, False)
print(df)
return df
def get_gene_list(self, samples_stat):
for sample in samples_stat:
gene = samples_stat[sample]['gene']
if len(gene) == 0:
continue
else:
gp = GProfiler(user_agent='ExampleTool', return_dataframe=True)
df = gp.profile(organism='hsapiens', query=gene)
go = df[df['native'].str.contains('GO')]
go.to_csv('{module}/{sample}/GO_FuncTerm.csv'.format(
module=self.module, sample=sample),
header=True,
index=False,
sep=',')
self.plot_go(go, sample, 'GO')
kegg = df[df['native'].str.contains('KEGG')]
kegg.to_csv('{module}/{sample}/KEGG_FuncTerm.csv'.format(
module=self.module, sample=sample),
header=True,
index=False,
sep=',')
self.plot_go(kegg, sample, 'KEGG')
df = gp.convert(organism='hsapiens',
query=gene,
target_namespace='ENTREZGENE_ACC')
df.to_csv('{module}/{sample}/Entrez_Gene_converted.csv'.format(
module=self.module, sample=sample),
header=True,
index=False,
sep=',')
with open(
'{module}/{sample}/gene_list.txt'.format(
module=self.module, sample=sample), 'wt') as f:
f.write('\n'.join(gene))
def gene_name_annotation_short(genes):
gp = GProfiler(return_dataframe=True)
gene_annot = gp.convert(organism='mmusculus',
query= genes,
target_namespace='ENTREZGENE_ACC')
gene_annot['short_description'] = gene_annot['description'].map(lambda x: re.sub('\[.+\]', '', x)) # delete extra text between []
gene_annot = gene_annot.drop(['description','name', 'converted','n_incoming','n_converted', 'namespaces', 'query'], axis=1)
return gene_annot
def gene_name_annotation_long(genes):
gp = GProfiler(return_dataframe=True)
gene_annot = gp.convert(organism='mmusculus',
query= genes,
target_namespace='ENTREZGENE_ACC')
gene_annot['short_description'] = gene_annot['description'].map(lambda x: re.sub('\[.+\]', '', x)) # delete extra text between []
gene_annot = gene_annot.drop(['description','name', 'converted','n_incoming','n_converted', 'namespaces', 'query'], axis=1)
with pd.option_context('display.max_rows', None, 'display.max_columns', None): # print all lines
return display(gene_annot)
def make_tcga_gtex_id_mapping_file(tcga_gtex_id_df, tcga_gtex_id_addr):
# print(tcga_gtex_id_df)
ensembl_id = tcga_gtex_id_df['sample'].str.split(".", n=1, expand=True)
tcga_gtex_id_df['ensembl_gene'] = ensembl_id[0]
# print(tcga_gtex_id_df)
gp = GProfiler(return_dataframe=True)
ensembl_2_symbol = gp.convert(
organism='hsapiens',
query=tcga_gtex_id_df['ensembl_gene'].tolist(),
target_namespace='ENSG')
# print(ensembl_2_symbol[['incoming','name']])
tcga_gtex_id_df['gene_symbol'] = tcga_gtex_id_df[['ensembl_gene']].merge(
ensembl_2_symbol,
how='left',
right_on='incoming',
left_on='ensembl_gene').name
# print(tcga_gtex_id_df)
tcga_gtex_id_df.to_csv(tcga_gtex_id_addr, sep='\t', index=False)
res
myres
res.columns
myres.columns
myres.goshv
res.pvalue
res.pval
res
res.columns
res.p_value
myres.p_value
res.significant
myres.significant
gp.profile?
myres.columns
gp.convert(organism='mmusculus', query=genes)
gp.convert(organism='mmusculus', query=genes, target_namespace='name')
gp.convert?
gp.convert(organism='mmusculus', query=genes, target_namespace='name')
gp.convert(organism='mmusculus', query=genes, )
gp.convert(organism='mmusculus', query=genes, ).namespaces
gp.convert(organism='mmusculus', query=genes, target_namespace='ENSG' )
r = requests.post(
url='https://biit.cs.ut.ee/gprofiler/api/convert/convert/',
json={
'organism':'mmusculus',
'target':'UCSC',
'query':genes,
}
)
x = r.json()
