def testMungePFX6(self):
real_info = {
'sample_id': '6037',
'pfx': '6037',
'assay': 'hotspot-heme',
'mini-pfx': '6037'
}
test_info = munge_pfx('6037_HP06')
def testMungePFX4(self):
real_info = {
'sample_id': '6037',
'pfx': '6037',
'assay': 'MSI-PLUS',
'mini-pfx': '6037'
}
test_info = munge_pfx('6037_MSI-Plus')
def testMungePFX5(self):
real_info = {
'sample_id': '6037',
'pfx': '6037',
'assay': 'hotspot-hereditary',
'mini-pfx': '6037'
}
test_info = munge_pfx('6037_GLT06')
def testMungePFX2(self):
real_info = {
'sample_id': 'LMG-240',
'pfx': 'LMG-240',
'assay': 'Coloseq',
'mini-pfx': 'LMG-240'
}
test_info = munge_pfx('LMG-240')
self.assertDictEqual(real_info, test_info)
def testMungePFX3(self):
real_info = {
'sample_id': '6037',
'pfx': '6037',
'assay': 'Coloseq',
'well': '01',
'mini-pfx': '6037'
}
test_info = munge_pfx('6037_01_BROv8')
def action(args):
specimens = collections.defaultdict(dict)
annotation = {}
prefixes = []
variant_keys = ['Position', 'Ref_Base', 'Var_Base']
files = ifilter(filters.any_analysis, walker(args.path))
files = ifilter(filters.only_analysis, files)
#sort the files so that the output in the workbook is sorted
files=sorted(files)
for pth in files:
pfx = munge_pfx(pth.fname)
reads_pfx=pfx['mini-pfx']+'_Ref|Var'
prefixes.append(reads_pfx)
with open(os.path.join(pth.dir, pth.fname)) as fname:
print pth.fname
reader = csv.DictReader(fname, delimiter='\t')
for row in reader:
variant = tuple(row[k] for k in variant_keys)
specimens[variant][reads_pfx] = row['Ref_Reads']+'|'+row['Var_Reads']
annotation[variant] = row
annotation_headers = [
'Gene',
'Variant_Type',
'Transcripts',
'Clinically_Flagged',
'Cosmic',
'Segdup',
'Polyphen',
'Sift',
'Mutation_Taster',
'Gerp',
'HiSeq_Freq',
'HiSeq_Count',
'MiSeq_Freq',
'MiSeq_Count',
'1000g_ALL',
'EVS_esp6500_ALL',
'1000g_AMR',
'EVS_esp6500_AA',
'1000g_EUR',
'EVS_esp6500_EU',
'1000g_ASN',
'1000g_AFR']
writer = csv.DictWriter(args.outfile, fieldnames = variant_keys + annotation_headers + prefixes, extrasaction = 'ignore', delimiter = '\t')
writer.writeheader()
for variant in sorted(specimens.keys()):
d = {k:v for k,v in zip(variant_keys,variant)}
d.update({pfx:specimens[variant].get(pfx) for pfx in prefixes})
d.update(annotation[variant])
writer.writerow(d)
def testMungePFX1(self):
real_info = {
'control': 'NA12878',
'machine-run': 'HA0201',
'library-version': 'OPXv4',
'well': 'E05',
'run': '60',
'sample_id': '6037',
'pfx': '6037_E05_OPXv4_NA12878_HA0201',
'assay': 'OncoPlex',
'mini-pfx': '6037_NA12878'
}
test_info = munge_pfx('6037_E05_OPXv4_NA12878_HA0201')
self.assertDictEqual(real_info, test_info)
def parse_pindel(variant_keys, files, path):
specimens = collections.defaultdict(dict)
annotation = {}
prefixes = []
for pth in files:
pfx = munge_pfx(pth.fname)
prefixes.append(pfx['mini-pfx'])
with open(os.path.join(pth.dir, pth.fname)) as fname:
print pth.fname
reader = csv.DictReader(fname, delimiter='\t')
for row in reader:
variant = tuple(row[k] for k in variant_keys)
specimens[variant][pfx['mini-pfx']] = row['Reads']
annotation[variant] = row
return specimens, annotation, prefixes
def action(args):
#Grab all analysis files from the path
files = ifilter(filters.any_analysis, walker(args.path))
files = filter(filters.polyhunter_analysis, files)
# interesting_files = glob.glob("*.csv")
df_list = []
df = pd.DataFrame()
sort_order = [
x['barcode_id'] for x in csv.DictReader(args.pipeline_manifest)
]
for sample in sort_order:
#Grab the file for each sample, in specified sort order
pfx_file = [s for s in files if sample in s.fname]
if pfx_file:
pfx_file = pfx_file[0]
pfx = munge_pfx(pfx_file.fname)
#Create a smaller version of this really long string
data = pd.read_csv(os.path.join(pfx_file.dir, pfx_file.fname),
sep='\t')
data.index = [pfx['mini-pfx']] * len(data)
df = df.append(data)
cols = natsorted(df.columns)
df.to_csv(args.outfile, sep='\t', na_rep='0', columns=cols)
def action(args):
specimens = collections.defaultdict(dict)
annotation = {}
prefixes = []
# apply a series of filters to files
files = ifilter(filters.any_analysis, walker(args.path))
if args.type == "Exon":
files = ifilter(filters.cnv_exon_analysis, files)
elif args.type == "Gene":
files = ifilter(filters.cnv_gene_analysis, files)
variant_keys = ["Position", "Gene"]
# sort the files so that the output in the workbook is sorted
for pth in files:
pfx = munge_pfx(pth.fname)
log_pfx = pfx["mini-pfx"] + "_Log"
prefixes.append(log_pfx)
with open(os.path.join(pth.dir, pth.fname)) as fname:
print pth.fname
reader = csv.DictReader(fname, delimiter="\t")
for row in reader:
variant = tuple(row[k] for k in variant_keys)
specimens[variant][log_pfx] = row["Ave_Adjusted_Log_Ratio"]
annotation[variant] = row
annotation_headers = ["Transcripts"]
writer = csv.DictWriter(
args.outfile, fieldnames=variant_keys + annotation_headers + prefixes, extrasaction="ignore", delimiter="\t"
)
writer.writeheader()
for variant in sorted(specimens.keys()):
d = {k: v for k, v in zip(variant_keys, variant)}
d.update({pfx: specimens[variant].get(pfx) for pfx in prefixes})
d.update(annotation[variant])
writer.writerow(d)
def action(args):
#read in the data, adding the name into the df and skipping the 'version'
filelist = ifilter(filters.hs_file_finder, walker(args.path))
df_list = []
pd.set_option('display.width', 100)
for pfx_file in filelist:
pfx = munge_pfx(pfx_file.fname)
log_pfx = pfx['mini-pfx']
data = pd.read_csv(os.path.join(pfx_file.dir, pfx_file.fname),
sep='\t',
comment='#',
error_bad_lines=False).assign(SAMPLE=log_pfx)
df_list.append(data[0:1])
#concatenate them together
big_df = pd.concat(df_list, ignore_index=True)
# #now, lets grab just the data we want
qc_df = big_df[[
'SAMPLE',
'MEAN_TARGET_COVERAGE',
# 'MEDIAN_TARGET_COVERAGE','MAX_TARGET_COVERAGE',
'PCT_USABLE_BASES_ON_TARGET',
'PF_UNIQUE_READS',
]]
#Setup the values we wish to plot
qc_df['On Target Reads'] = qc_df['PF_UNIQUE_READS'] * qc_df[
'PCT_USABLE_BASES_ON_TARGET']
qc_df['Off Target Reads'] = qc_df['PF_UNIQUE_READS'] - qc_df[
'On Target Reads']
qc_df['On Target Reads'] = qc_df['On Target Reads'].astype(int)
qc_df['Off Target Reads'] = qc_df['Off Target Reads'].astype(int)
qc_df = qc_df.sort_values(by=['SAMPLE'])
#Setup the plot
data1 = go.Bar(
x=qc_df['SAMPLE'], # assign x as the dataframe column 'x'
y=qc_df['Off Target Reads'],
name='Off Target Reads',
xaxis='x1',
yaxis='y1')
data2 = go.Bar(x=qc_df['SAMPLE'],
y=qc_df['On Target Reads'],
name='On Target Reads',
xaxis='x1',
yaxis='y1')
layout = {
'title': 'QC Metrics',
'xaxis': {
'type':
'category', #required so mini sample names are strings instead of numbers
'domain': [0, 1]
},
'yaxis': {
'hoverformat': ',f', #print real numbers, not 149.786k
'domain': [.7, 1]
}, #only take bottom portion of screen},
'barmode': 'stack'
}
#setup table
table = go.Table(
header=dict(values=[
'Sample ID', 'Mean Target Coverage', 'Total Read Pairs',
'On Target Reads', 'Off Target Reads'
],
line=dict(color='#7D7F80'),
fill=dict(color='#a1c3d1'),
align=['left'] * 5),
cells=dict(
values=[
qc_df['SAMPLE'], qc_df['MEAN_TARGET_COVERAGE'],
qc_df['PF_UNIQUE_READS'], qc_df['On Target Reads'],
qc_df['Off Target Reads']
],
line=dict(color='#7D7F80'),
fill=dict(color=[
'rgb(245,245,245)', #color for the first column, red if Target Coverage below 500
[
'rgba(0,250,0, 0.8)'
if val >= 500 else 'rgba(250,0,0, 0.8)'
for val in qc_df['MEAN_TARGET_COVERAGE']
]
]),
align=['left'] * 5),
domain=dict(
x=[0, 1], #above/belowe
y=[0, .5]))
#Make the plot
fig = go.Figure(data=[data1, data2, table], layout=layout)
plotly.offline.plot(fig, filename=args.outfile, auto_open=false)
