def main(args):
if not len(args) == 3:
sys.exit(
"USAGE: python getExpressionFromXena.py cancerType[KIRC] geneList.txt > outFile"
)
# TCGA hub
hub = "https://tcga.xenahubs.net"
# PanCan normalized dataset
dataset = "TCGA." + str(args[1]) + ".sampleMap/HiSeqV2_PANCAN"
# get sample IDs
samples = xena.dataset_samples(hub, dataset, None)
#read geneList into list
f = open(args[2])
line = f.readline()[:-1]
genes = []
while line != "":
gene = line
genes.append(gene)
line = f.readline()[:-1]
#print header
print 'Gene\t' + '\t'.join(samples)
#print expression
nGenes = len(genes)
k = xena.dataset_gene_probe_avg(hub, dataset, samples, genes)
for i in range(0, nGenes):
geneName = k[i]['gene']
scores = k[i]['scores'][0]
print geneName + '\t' + '\t'.join(str(x) for x in scores)
def main(args):
if not len(args) == 2:
sys.exit("USAGE: python checkGeneList.py geneList.txt > outFile")
# TCGA hub
hub = "https://tcga.xenahubs.net"
# PanCan normalized dataset
dataset = "TCGA.KIRC.sampleMap/HiSeqV2_PANCAN"
# get sample IDs
samples = xena.dataset_samples(hub, dataset, None)
#read geneList into list
f = open(args[1])
line = f.readline()[:-1]
genes = []
while line != "":
gene = line
genes.append(gene)
line = f.readline()[:-1]
#print expression
nGenes = len(genes)
k = xena.dataset_gene_probe_avg(hub, dataset, samples, genes)
for i in range(0, nGenes):
if k[i]['position'] == []:
print k[i]['gene']
def add_gene_expression_by_genes(
target_genes: Sequence,
clinicals: pd.DataFrame,
xena_hub: str,
ds: str,
*,
gene_names: Union[None, dict] = None,
colprefix: str = "gex_",
) -> pd.DataFrame:
"""
Downloads gene expression of a set of genes for all samples in the cohort.
Parameters
----------
target_genes
A list of genes whose expression we are interested in.
clinicals
A dataframe containing sample information. A column for each gene will be added.
xena_hub
Url of the data repository hub.
ds
Name of the dataset on the repository hub.
gene_names
If gene names are not identical to gene symbols (or the genome version is
different), this maps genes to their expression data.
colprefix
Added to the gene name to label the column with expression data.
Returns
-------
Dataframe with gene expression data added to the previous content.
"""
genes = []
if gene_names is None:
gene_names = dict()
for g in target_genes:
if g in gene_names:
g = gene_names[g]
genes.append(g)
expression_matrix = xena.dataset_gene_probe_avg(
xena_hub, ds, clinicals.index.values.tolist(), genes)
for i in range(len(target_genes)):
colname = colprefix + target_genes[i]
if len(expression_matrix[i]["scores"][0]) < 1:
print(
colname,
"not found. Are you sure you provided the gene symbol corresponding to the genome version?",
)
clinicals[colname] = "NaN"
else:
clinicals[colname] = expression_matrix[i]["scores"][0]
return clinicals
def gather_expression_data(hub, dataset, samples, genes):
"""Collect the expression data from Xena Hub."""
expression_data = np.array([])
n_genes = len(genes)
t_0 = time.time()
# We collect the expression data 100 genes by 100 genes
for i in range(int(n_genes / 100) + 1):
if i % 20 == 0:
print('%i genes collected in %s s' %
(100 * i, str(time.time() - t_0)))
lower_bound = min(i * 100, n_genes)
upper_bound = min((i + 1) * 100, n_genes)
genes_batch = genes[lower_bound:upper_bound]
new_expression_batch = np.array(
xena.dataset_gene_probe_avg(hub, dataset, samples, genes_batch))
expression_data = np.append(expression_data, new_expression_batch)
return expression_data.tolist()
def findAliasInXena(hub, dataset, samples, gene):
xenaAlias = ''
f = open("aliases.txt")
line = f.readline()[:-1]
while line != "":
aliases = []
aliases.append(line.split("\t")[1])
aliases += line.split("\t")[4].split(',')
aliases += line.split("\t")[5].split(',')
if gene in aliases:
k = xena.dataset_gene_probe_avg(hub, dataset, samples, aliases)
for i in range(0, len(aliases)):
if k[i]['position'] != [] and k[i]['scores'][0][0] != 'NaN':
xenaAlias = k[i]['gene']
break
line = f.readline()[:-1]
f.close()
return xenaAlias
def main(args):
if not len(args) == 3:
sys.exit(
"USAGE: python replaceGeneListWithXenaAliasNames.py cancerType[KIRC] geneList.txt > newGeneList.txt"
)
# TCGA hub
hub = "https://tcga.xenahubs.net"
# PanCan normalized dataset
dataset = "TCGA." + str(args[1]) + ".sampleMap/HiSeqV2_PANCAN"
# get sample IDs
samples = xena.dataset_samples(hub, dataset, None)
#read geneList into list
f = open(args[2])
line = f.readline()[:-1]
genes = []
while line != "":
gene = line
genes.append(gene)
line = f.readline()[:-1]
f.close()
#if in Xena, print gene. If not in Xena, determine if gene has an alias that is in Xena
nGenes = len(genes)
k = xena.dataset_gene_probe_avg(hub, dataset, samples, genes)
for i in range(0, nGenes):
if k[i]['position'] == [] or k[i]['scores'][0][0] == 'NaN':
# determine if gene has an alias in Xena
alias = findAliasInXena(hub, dataset, samples, k[i]['gene'])
if alias != '':
print alias
else:
print k[i]['gene']