def get_variants_mc3(syn):
"""Reads ICGC mutation data from the MC3 synapse file.
Args:
syn (Synapse): A logged-in synapseclient instance.
Returns:
muts (pandas DataFrame), shape = [n_mutations, mut_levels + 1]
An array of mutation data, with a row for each mutation
appearing in an individual sample.
Examples:
>>> import synapseclient
>>> syn = synapseclient.Synapse()
>>> syn.login()
>>> muts = get_variants_mc3(syn)
"""
mc3 = syn.get('syn7824274')
# defines which mutation annotation MAF columns to use
use_cols = [0, 8, 15, 36, 37, 38, 39, 40, 41, 71, 72]
use_names = ['Gene', 'Form', 'Sample', 'Protein', 'Transcript', 'Exon',
'depth', 'ref_count', 'alt_count', 'SIFT', 'PolyPhen']
# imports mutation data into a DataFrame, parses TCGA sample barcodes
# and PolyPhen scores
i = 0
while i < 10:
try:
muts = pd.read_csv(mc3.path, engine='python',
usecols=use_cols, sep='\t', header=None,
names=use_names, comment='#', skiprows=1)
break
except OSError:
i = i + 1
for annt, null_val in zip(['PolyPhen', 'SIFT'], [0, 1]):
muts[annt] = muts[annt].apply(
lambda val: (np.float(gsub('\)$', '', gsub('^.*\(', '', val)))
if val != '.' else null_val)
)
muts.Sample = muts.Sample.apply(lambda smp: '-'.join(smp.split('-')[:4]))
muts.SIFT = 1 - muts.SIFT
return muts
def get_mtree_newick(mtree):
"""Get the Newick tree format representation of this MuTree."""
newick_str = ''
for nm, mut in sorted(mtree, key=lambda x: x[0]):
if isinstance(mut, MuTree):
newick_str += '(' + gsub(',$', '', get_mtree_newick(mut)) + ')'
if nm == ".":
newick_str += '{*none*},'
else:
newick_str += '{' + nm + '},'
if mtree.depth == 0:
newick_str = gsub(',$', '', newick_str) + ';'
return newick_str
def get_variants_mc3(syn):
"""Reads ICGC mutation data from the MC3 synapse file.
Args:
syn (Synapse): A logged-in synapseclient instance.
Returns:
muts (pandas DataFrame), shape = [n_mutations, mut_levels + 1]
An array of mutation data, with a row for each mutation
appearing in an individual sample.
Examples:
>>> import synapseclient
>>> syn = synapseclient.Synapse()
>>> syn.login()
>>> muts = get_variants_mc3(syn)
"""
mc3 = syn.get('syn7824274')
# defines which mutation annotation MAF columns to use
use_cols = [0, 8, 15, 36, 37, 38, 72]
use_names = [
'Gene', 'Form', 'Sample', 'Protein', 'Transcript', 'Exon', 'PolyPhen'
]
# imports mutation data into a DataFrame, parses TCGA sample barcodes
# and PolyPhen scores
muts = pd.read_csv(mc3.path,
usecols=use_cols,
sep='\t',
header=None,
names=use_names,
comment='#',
skiprows=1)
muts['Sample'] = [
reduce(lambda x, y: x + '-' + y,
s.split('-', 4)[:4]) for s in muts['Sample']
]
muts['PolyPhen'] = [
gsub('\)$', '', gsub('^.*\(', '', x)) if x != '.' else 0
for x in muts['PolyPhen']
]
return muts
def eval_node(self, variables, fallback):
# self.variableValues =variables #woot?
whot = self.full_value()
try:
whot = re.gsub("\\", "", whot) # where from?? token?
res = eval(whot) # except fallback ## v0.0
return res
except SyntaxError as se:
return fallback
def get_newick(self):
"""Get the Newick tree format representation of this MuTree."""
newick_str = ''
for nm, mut in self.sort_iter():
if isinstance(mut, MuTree):
newick_str += '(' + gsub(',$', '', mut.get_newick()) + ')'
if nm == "0":
newick_str += '{*none*},'
else:
newick_str += '{' + nm + '},'
if self.depth == 0:
newick_str = gsub(',$', '', newick_str) + ';'
return newick_str
def sort_iter(self):
"""Iterates through the branches of the tree, ordering mutation
attributes where possible."""
if self.mut_level in ['Exon', 'Location']:
return iter(
sorted([("0", branch) if lbl == '.' else (lbl, branch)
for lbl, branch in self._child.items()],
key=lambda x: int(
gsub('[^0-9]', '0', x[0].split('/')[0]))))
else:
return self.__iter__()
def get_gencode():
"""Gets annotation data for protein-coding genes on non-sex
chromosomes from a Gencode file.
Returns
-------
annot : dict
Dictionary with keys corresponding to Ensembl gene IDs and values
consisting of dicts with annotation fields.
"""
annot = pd.read_csv(DATA_PATH + "gencode.v22.annotation.gtf.gz",
usecols=[0, 2, 3, 4, 8],
names=['Chr', 'Type', 'Start', 'End', 'Info'],
sep='\t',
header=None,
comment='#')
# filter out annotation records that aren't
# protein-coding genes on non-sex chromosomes
chroms_use = ['chr' + str(i + 1) for i in range(22)]
annot = annot.loc[annot['Type'] == 'gene', ]
chr_indx = np.array([chrom in chroms_use for chrom in annot['Chr']])
annot = annot.loc[chr_indx, ]
# parse the info field to get each gene's annotation data
gn_annot = {
gsub('\.[0-9]+', '', z['gene_id']).replace('"', ''): z
for z in [
dict([['chr', an[0]]] + [['Start', an[2]]] + [['End', an[3]]] + [
y for y in [x.split(' ')
for x in an[4].split('; ')] if len(y) == 2
]) for an in annot.values
] if z['gene_type'] == '"protein_coding"'
}
for g in gn_annot:
gn_annot[g]['gene_name'] = gn_annot[g]['gene_name'].replace('"', '')
return gn_annot
def __str__(self):
"""Printing a MuTree shows each of the branches of the tree and
the samples at the end of each branch."""
new_str = self.mut_level
for nm, mut in self:
new_str += ' IS {}'.format(nm)
if isinstance(mut, MuTree):
new_str += (' AND ' + '\n' + '\t' * (self.depth + 1) +
str(mut))
# if we have reached a root node, print the samples
elif len(mut) > 8:
new_str += ': ({} samples)'.format(len(mut))
else:
new_str += ': {}'.format(
reduce(lambda x, y: '{},{}'.format(x, y), mut))
new_str += ('\n' + '\t' * self.depth)
new_str = gsub('\n$', '', new_str)
return new_str
def no_attrib_error(module, text):
output = """<div class="%s">%s</div>"""
text = re.gsub(r"\r\n|\n", "", text[0:20])
print "* Error: '%s' with text: %s...\n" % (module, text[0:20])
return output % ("general-error", "Error! No attribue is found for '%s' with text => %s ..." % (module,text[0:20]))
def plot_performance(clf_set='base', mtype_set='default'):
"""Plots barplots of classifier performance for a set of mutations."""
out_data = load_output('baseline', clf_set, mtype_set)
alg_order = [clf.__name__ for clf in clf_list[clf_set]]
# gets AUC data, sets up plot and subplots
auc_data = [x['AUC'] for x in out_data]
auc_min = min([min(x.values()) for x in auc_data]) * 0.9
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(7, 11))
for i, gene in enumerate(mtype_list[mtype_set]):
# cast performance data into matrix format
perf_data = pd.DataFrame(
[{k[0].split('_')[0]: v
for k, v in x.items() if k[1] == gene} for x in auc_data])
alg_indx = [list(perf_data.columns).index(x) for x in alg_order]
perf_data = perf_data.ix[:, alg_indx]
# create and plot the subplot titles describing mutation types
gene_lbl = '{}-{}'.format(gene[0], gsub('(-|, )', '\n', str(gene[1])))
axes[i // 3, i % 3].set_title(gene_lbl, fontsize=13)
# plot the boxes showing performances
axes[i // 3, i % 3].boxplot(x=np.array(perf_data),
boxprops={'linewidth': 1.5},
medianprops={
'linewidth': 3,
'color': '#960c20'
},
flierprops={'markersize': 2})
# label x-axis ticks with algorithm names if we are on bottom row
if (i // 3) == 1:
axes[i // 3, i % 3].set_xticklabels(perf_data.columns,
fontsize=12,
rotation=45,
ha='right')
else:
axes[i // 3, i % 3].set_xticklabels(np.repeat('', len(alg_indx)))
# add y-axis title if we are on left-most column
if (i % 3) == 0:
axes[i // 3, i % 3].set_ylabel('AUC', fontsize=19)
else:
axes[i // 3, i % 3].set_yticklabels([])
# add dotted line at AUC=0.5, set AUC axis limits
axes[i // 3, i % 3].plot(list(range(len(alg_indx) + 2)),
np.repeat(0.5,
len(alg_indx) + 2),
c="black",
lw=0.8,
ls='--',
alpha=0.8)
axes[i // 3, i % 3].set_ylim(auc_min, 1.0)
# tweak subplot spacing and save plot to file
plt.tight_layout(w_pad=-1.2, h_pad=1.5)
plt.savefig(base_dir + '/plots/' + get_set_plotlbl(clf_set) + '_' +
get_set_plotlbl(mtype_set) + '__performance.png',
dpi=700)
def get_variant_data(cohort, var_source, **var_args):
if var_source == 'mc3':
mc3 = var_args['syn'].get('syn7824274')
field_dict = (('Gene', 0), ('Chr', 4), ('Start', 5), ('End', 6),
('Strand', 7), ('Form', 8), ('RefAllele', 10),
('TumorAllele', 12), ('Sample', 15), ('HGVS', 34),
('Protein', 36), ('Transcript', 37), ('Exon', 38),
('depth', 39), ('ref_count', 40), ('alt_count', 41),
('SIFT', 71), ('PolyPhen', 72), ('Filter', 108))
if 'mut_fields' not in var_args or var_args['mut_fields'] is None:
use_fields, use_cols = tuple(zip(*field_dict))
else:
use_fields, use_cols = tuple(
zip(*[(name, col) for name, col in field_dict
if name in {'Sample', 'Filter'}
| set(var_args['mut_fields'])]))
# imports mutation data into a DataFrame, parses TCGA sample barcodes
# and PolyPhen scores
i = 0
while i < 10:
#TODO: handle I/O errors on the cohort/experiment level?
try:
var_data = pd.read_csv(mc3.path,
engine='c',
dtype='object',
sep='\t',
header=None,
usecols=use_cols,
names=use_fields,
comment='#',
skiprows=1)
break
except OSError:
i = i + 1
#TODO: more fine-grained Filtering control?
var_data = var_data.loc[~var_data.Filter.str.
contains('nonpreferredpair')]
for annt, null_val in zip(['PolyPhen', 'SIFT'], [0, 1]):
if annt in var_data:
var_data[annt] = var_data[annt].apply(lambda val: (
np.float(gsub('\)$', '', gsub('^.*\(', '', val)))
if val != '.' else null_val))
if annt == 'SIFT':
var_data[annt] = 1 - var_data[annt]
var_data.Sample = var_data.Sample.apply(
lambda smp: '-'.join(smp.split('-')[:4]))
elif var_source == 'Firehose':
mut_tar = tarfile.open(
glob.glob(
os.path.join(
data_dir, "stddata__2016_01_28", cohort, "20160128",
"*Mutation_Packager_Oncotated_Calls.Level_3*tar.gz"))[0])
mut_list = []
for mut_fl in mut_tar.getmembers():
try:
mut_tbl = pd.read_csv(
BytesIO(mut_tar.extractfile(mut_fl).read()),
sep='\t',
skiprows=4,
usecols=[0, 8, 15, 37, 41],
names=['Gene', 'Form', 'Sample', 'Exon', 'Protein'])
mut_list += [mut_tbl]
except:
print("Skipping mutations for {}".format(mut_fl))
muts = pd.concat(mut_list)
muts.Sample = muts.Sample.apply(
lambda smp: "-".join(smp.split("-")[:4]))
mut_tar.close()
elif var_source == 'BMEG':
oph = Ophion("http://bmeg.io")
mut_list = {samp: {} for samp in sample_list}
gene_lbls = ["gene:" + gn for gn in gene_list]
print(oph.query().has(
"gid", "biosample:" +
sample_list[0]).incoming("variantInBiosample").outEdge(
"variantInGene").mark("variant").inVertex().has(
"gid", oph.within(gene_lbls)).count().execute())
# .mark("gene").select(["gene", "variant"]).count().execute())
for samp in sample_list:
for i in oph.query().has("gid", "biosample:" + samp)\
.incoming("variantInBiosample")\
.outEdge("variantInGene").mark("variant")\
.inVertex().has("gid", oph.within(gene_lbls))\
.mark("gene").select(["gene", "variant"]).execute():
dt = json.loads(i)
gene_name = dt["gene"]["properties"]["symbol"]
mut_list[samp][gene_name] = {
k: v
for k, v in dt["variant"]["properties"].items()
if k in mut_fields
}
mut_table = pd.DataFrame(mut_list)
else:
raise ValueError("Unrecognized source of variant data!")
return var_data
def subn_filter(s, find, replace, count=0):
"""A non-optimal implementation of a regex filter"""
return re.gsub(find, replace, count, s)
