def outputPennCnv(gtc_file, manifest_file, outFile):
manifest = BeadPoolManifest(manifest_file)
gtc = GenotypeCalls(gtc_file)
genotypes = gtc.get_genotypes()
LRR = gtc.get_logr_ratios()
BAF = gtc.get_ballele_freqs()
with open(outFile, 'w') as output:
output.write(
'Name\tChr\tPosition\tGtype\tLog R Ratio\tB Allele Freq\n')
for (name, chrom, map_info, genotype, lrr,
baf) in zip(manifest.names, manifest.chroms, manifest.map_infos,
genotypes, LRR, BAF):
if genotype == 1:
geno = 'AA'
elif genotype == 2:
geno = 'AB'
elif genotype == 3:
geno = 'BB'
else:
geno = '--'
output.write('\t'.join([
name, chrom,
str(map_info), geno,
str(lrr), str(baf)
]) + '\n')
def get_manifest(manifest_path, extraction_path, sep=','):
"""
Extract manifest data about SNPs and write it to a ../data/extracted folder
:param manifest_path: str - path to file
:param extraction_path: str - path to directory where extracted files will be stored
:param sep: str - separator which is used for writing, comma by default
:return:
"""
# Add {} to use it later in formatting names
path_to_save = extraction_path + '/{}'
# Get data
manifest = BeadPoolManifest(manifest_path)
# Check whether name of manifest is the same as coded in the file
assert manifest.manifest_name == manifest_path.split('/')[-1], \
"Name of manifest file doesn't match with manifest name from file"
# List of fields which should be extracted from the manifest
manifest_extract = ['names', 'chroms', 'map_infos', 'ref_strands', 'source_strands', 'snps']
content = []
# Iterate over attributes of manifest object
for attr in manifest_extract:
content.append((attr, map(str, manifest.__getattribute__(attr))))
# Initialize variables
name = manifest.manifest_name.split('.')[0] + '_old.csv'
length = len(content[0][1])
rows = []
# Make header
header = sep.join([content[i][0] for i in range(len(content))])
# Create normal df structure
for i in range(length):
row = sep.join([content[j][1][i] for j in range(len(content))])
rows.append(row)
# Write to a file
with open(path_to_save.format(name), 'w') as dest:
dest.write(header + '\n' + '\n'.join(rows))
def __init__(self, bpm_file, logger):
"""
Initialize a BPM reader with a file path
Args:
bpm_file (string): Path to the BPM manifest
logger (Logger) : A logger
Returns:
BeadPoolReader
"""
self.source_file = bpm_file
self._bpm = BeadPoolManifest(bpm_file)
self._logger = logger
def driver(gtc_dir, manifest_filename, output_filename, project_name, delim, logger):
logger.info("Reading manifest file")
bpm = BeadPoolManifest(manifest_filename)
samples = []
logger.info("Initializing genotype data")
gtc_files = []
for gtc_file in os.listdir(gtc_dir):
if gtc_file.endswith(".gtc"):
gtc_files.append(os.path.join(gtc_dir, gtc_file))
logger.info("Generating report")
loci = range(len(bpm.normalization_lookups))
with open(output_filename, "w") as output_handle:
output_handle.write("DNA Report on " + os.path.abspath(output_filename) + "\n")
header = [""]
header.append("# LOCI = {}".format(len(loci)))
header.append("# DNAs = {}".format(len(samples)))
header.append("ProjectName = {}".format(project_name))
header.append("GenCall Version = NaN")
header.append("Low GenCall Score Cutoff = NaN")
output_handle.write(delim.join(header) + "\n")
output_handle.write(delim.join("Row,DNA_ID,#No_Calls,#Calls,Call_Rate,A/A_Freq,A/B_Freq,B/B_Freq,Minor_Freq,50%_GC_Score,10%_GC_Score".split(",")) + "\n")
row = 0
for gtc_file in gtc_files:
row += 1
gtc = GenotypeCalls(gtc_file)
genotypes = gtc.get_genotypes()
scores = gtc.get_genotype_scores()
assert len(genotypes) == len(bpm.names)
row_data = []
row_data.append(row)
row_data.append(gtc.get_sample_name())
row_data += compute_genotypes(genotypes)
row_data.append(ScoreStatistics(scores, 50))
row_data.append(ScoreStatistics(scores, 10))
output_handle.write(delim.join(map(str, row_data)) + "\n")
logger.info("Report generation complete")
delim = "\t"
parser = argparse.ArgumentParser(
"Generate a final report from a directory of GTC files")
parser.add_argument("manifest", help="BPM manifest file")
parser.add_argument("gtc_directory", help="Directory containing GTC files")
parser.add_argument("output_file", help="Location to write report")
args = parser.parse_args()
if os.path.isfile(args.output_file):
sys.stderr.write("Output file already exists, please delete and re-run\n")
sys.exit(-1)
try:
manifest = BeadPoolManifest(args.manifest)
except:
sys.stderr.write("Failed to read data from manifest\n")
sys.exit(-1)
with open(args.output_file, "w") as output_handle:
output_handle.write("[Header]\n")
output_handle.write(
delim.join(
["Processing Date",
datetime.now().strftime("%m/%d/%Y %I:%M %p")]) + "\n")
output_handle.write(
delim.join(["Content", os.path.basename(args.manifest)]) + "\n")
output_handle.write(
delim.join(["Num SNPs", str(len(manifest.names))]) + "\n")
output_handle.write(
from IlluminaBeadArrayFiles import GenotypeCalls, BeadPoolManifest, code2genotype
import sys
import os
from datetime import datetime
delim = "\t"
if len(sys.argv) < 4:
sys.stderr.write("Generate a final report from a directory of GTC files\n")
sys.stderr.write(
"usage: python gtc_final_report.py <BPM manifest file> <GTC directory> <output file>\n"
)
sys.exit(-1)
try:
names = BeadPoolManifest(sys.argv[1]).names
except:
sys.stderr.write("Failed to read loci names from manifest\n")
sys.exit(-1)
output_file = sys.argv[3]
if os.path.isfile(output_file):
sys.stderr.write("Output file already exists, please delete and re-run\n")
sys.exit(-1)
with open(output_file, "w") as output_handle:
output_handle.write("[Header]\n")
output_handle.write(
delim.join(
["Processing Date",
def driver(gtc_dir, manifest_filename, cluster_filename, output_filename,
project_name, delim, logger):
logger.info("Reading cluster file")
with open(cluster_filename, "rb") as cluster_handle:
egt = ClusterFile.read_cluster_file(cluster_handle)
logger.info("Reading manifest file")
bpm = BeadPoolManifest(manifest_filename)
samples = []
logger.info("Initializing genotype data")
gtc_files = []
for gtc_file in os.listdir(gtc_dir):
if gtc_file.endswith(".gtc"):
gtc_files.append(os.path.join(gtc_dir, gtc_file))
samples = map(GenotypeCalls, gtc_files)
ls_genotypes = []
ls_genotype_scores = []
ls_sample_names = []
ls_snps = bpm.names
for sample in samples:
genotypes = sample.get_genotypes()
assert len(genotypes) == len(bpm.names)
ls_genotypes.append(genotypes)
ls_genotype_scores.append(sample.get_genotype_scores())
ls_sample_names.append(sample.get_sample_name())
logger.info("Generating report")
loci = range(len(bpm.normalization_lookups))
row = 0
with open(output_filename, "w") as output_handle:
output_handle.write("Locus Summary on " +
os.path.abspath(output_filename) + "\n")
header = [""]
header.append("# LOCI = {}".format(len(loci)))
header.append("# DNAs = {}".format(len(gtc_files)))
header.append("ProjectName = {}".format(project_name))
header.append("GenCall Version = {}".format(egt.gencall_version))
header.append("Low GenCall Score Cutoff = NaN")
output_handle.write(delim.join(header) + "\n")
output_handle.write(
delim.join(
"Row,Locus_Name,Illumicode_Name,#No_Calls,#Calls,Call_Freq,A/A_Freq,A/B_Freq,B/B_Freq,Minor_Freq,Gentrain_Score,50%_GC_Score,10%_GC_Score,Het_Excess_Freq,ChiTest_P100,Cluster_Sep,AA_T_Mean,AA_T_Std,AB_T_Mean,AB_T_Std,BB_T_Mean,BB_T_Std,AA_R_Mean,AA_R_Std,AB_R_Mean,AB_R_Std,BB_R_Mean,BB_R_Std,Plus/Minus Strand"
.split(",")) + "\n")
for i in range(0, len(ls_snps)):
row += 1
snp_wise_genotypes = [item[i] for item in ls_genotypes]
snp_wise_scores = [item[i] for item in ls_genotype_scores]
locus_summary = summarize_locus(snp_wise_genotypes,
snp_wise_scores)
cluster_record = egt.get_record(ls_snps[i])
row_data = []
row_data.append(row)
row_data.append(ls_snps[i])
row_data.append(cluster_record.address)
row_data.append(locus_summary.genotype_counts.no_calls)
row_data.append(locus_summary.genotype_counts.get_num_calls())
row_data.append(locus_summary.genotype_counts.get_call_frequency())
row_data.append(locus_summary.genotype_counts.get_aa_frequency())
row_data.append(locus_summary.genotype_counts.get_ab_frequency())
row_data.append(locus_summary.genotype_counts.get_bb_frequency())
row_data.append(
locus_summary.genotype_counts.get_minor_frequency())
row_data.append(cluster_record.cluster_score.total_score)
row_data.append(locus_summary.score_stats.gc_50)
row_data.append(locus_summary.score_stats.gc_10)
(hw_equilibrium, het_excess
) = locus_summary.genotype_counts.compute_hardy_weinberg()
row_data.append(het_excess)
row_data.append(hw_equilibrium)
row_data.append(cluster_record.cluster_score.cluster_separation)
for cluster_stats in (cluster_record.aa_cluster_stats,
cluster_record.ab_cluster_stats,
cluster_record.bb_cluster_stats):
row_data.append(cluster_stats.theta_mean)
row_data.append(cluster_stats.theta_dev)
for cluster_stats in (cluster_record.aa_cluster_stats,
cluster_record.ab_cluster_stats,
cluster_record.bb_cluster_stats):
row_data.append(cluster_stats.r_mean)
row_data.append(cluster_stats.r_dev)
if len(bpm.ref_strands) > 0:
row_data.append(RefStrand.to_string(bpm.ref_strands[i]))
else:
row_data.append("U")
output_handle.write(delim.join(map(str, row_data)) + "\n")
logger.info("Report generation complete")
def extract(gtc_path,
extraction_path,
manifest_path="/home/ailin/repo_new/data/BovineSNP50_v3_A1.bpm"):
"""
Extract genotyping data -
ballele_freqs, base_calls, genotypes, genotype_scores, logr_ratios, raw_x_intensities, raw_y_intensities,
normalized_intensities, names, chroms, map_infos, ref_strands, source_strands and snps -
and write it to a file
Also extract general sample information -
call_rate, cluster_file, gender, imaging_date, autocall_date, scanner_data, snp_manifest, is_write_complete,
sample_name, sample_plate, sample_well -
and write it to .sinfo file
:param gtc_path: str - path to gtc with genotyping data
:param extraction_path: str - path to directory where extracted files will be stored
:param manifest_path: str - path to manifest file used for creation of this gtc
:return:
"""
# Add {} to use it later in formatting names
path_to_save = extraction_path + '/{}'
# Get gtc and manifest objects
gtc = GenotypeCalls(gtc_path)
manifest = BeadPoolManifest(manifest_path)
# Structure for ordered names and methods of gtc fields
field = namedtuple('field', ['name', 'method'])
# List of fields which should be extracted from gtc
gtc_extract = [
field('ballele_freqs', GenotypeCalls.get_ballele_freqs),
field('genotypes', GenotypeCalls.get_genotypes),
field('genotype_scores', GenotypeCalls.get_genotype_scores),
field('logr_ratios', GenotypeCalls.get_logr_ratios),
field('raw_x_intensities', GenotypeCalls.get_raw_x_intensities),
field('raw_y_intensities', GenotypeCalls.get_raw_y_intensities),
field(
'normalized_intensities',
lambda x: GenotypeCalls.get_normalized_intensities(
x, manifest.normalization_lookups))
]
# I don't see place in db where we use this data
# List of fields which correspond to a whole sample and extracted from gtc
sample_info = [
field('call_rate', GenotypeCalls.get_call_rate),
field('cluster_file', GenotypeCalls.get_cluster_file),
field('gender', GenotypeCalls.get_gender),
field('imaging_date', GenotypeCalls.get_imaging_date),
field('autocall_date', GenotypeCalls.get_autocall_date),
field('scanner_data', GenotypeCalls.get_scanner_data),
field('snp_manifest', GenotypeCalls.get_snp_manifest),
field('is_write_complete', GenotypeCalls.is_write_complete),
field('sample_name', GenotypeCalls.get_sample_name),
field('sample_plate', GenotypeCalls.get_sample_plate),
field('sample_well', GenotypeCalls.get_sample_well)
]
# Containers for data
content = []
general_info = []
# Get content from gtc
# Iterate over fields which should be extracted in gtc, transform them to str
for name, method in gtc_extract:
res = method(gtc)
# For normalized_intensities divide the list of (x, y) intensities into lists of x and y
if name != 'normalized_intensities':
if not isinstance(res, str):
try:
res = map(str, res)
except TypeError:
res = str(res)
content.append((name, res))
else:
# Compute r and theta values
polar = map(NormalizationTransform.rect_to_polar, res)
content.append(
('normalized_x_intensities', [str(x) for x, y in res]))
content.append(
('normalized_y_intensities', [str(y) for x, y in res]))
content.append(('r', [str(r) for r, theta in polar]))
content.append(('theta', [str(theta) for r, theta in polar]))
# Get base calls and their forward encoding
base_calls = GenotypeCalls.get_base_calls(gtc)
genotype_forward = GenotypeCalls.get_base_calls_forward_strand(
gtc, base_calls,
[SourceStrand.Forward for i in range(len(base_calls))])
# Write them to collection
content.append(('base_calls', base_calls))
content.append(('genotype_forward', genotype_forward))
# Iterate over sample information attributes of gtc object
for name, method in sample_info:
res = str(method(gtc))
general_info.append((name, res))
# Initialize variables
length = len(content[0][1])
sep = ','
rows = []
# Make header
header = sep.join([content[i][0] for i in range(len(content))])
try:
# Create normal df structure
for i in range(length):
row = sep.join([content[j][1][i] for j in range(len(content))])
rows.append(row)
except:
print(gtc_path)
return
# File names
name = gtc_path.split('/')[-1].split('.')[0]
sinfo_name = name + '_old.sinfo'
data_name = name + '_old.csv'
# Write data to a file
with open(path_to_save.format(data_name), 'w') as dest:
dest.write(header + '\n' + '\n'.join(rows))
# Write sample information to a file
with open(path_to_save.format(sinfo_name), 'w') as dest:
dest.write('\n'.join([sep.join(record) for record in general_info]))