def main():
usage = 'usage: %prog [options] <model> <vcf_file>'
parser = OptionParser(usage)
parser.add_option('-c',
dest='slice_center',
default=None,
type='int',
help='Slice center positions [Default: %default]')
parser.add_option('-f',
dest='genome_fasta',
default='%s/data/hg38.fa' % os.environ['BASENJIDIR'],
help='Genome FASTA for sequences [Default: %default]')
parser.add_option('-n',
dest='norm_file',
default=None,
help='Normalize SAD scores')
parser.add_option(
'-o',
dest='out_dir',
default='sad',
help='Output directory for tables and plots [Default: %default]')
parser.add_option('-p',
dest='processes',
default=None,
type='int',
help='Number of processes, passed by multi script')
parser.add_option('--pseudo',
dest='log_pseudo',
default=1,
type='float',
help='Log2 pseudocount [Default: %default]')
parser.add_option(
'--rc',
dest='rc',
default=False,
action='store_true',
help=
'Average forward and reverse complement predictions [Default: %default]'
)
parser.add_option('--shifts',
dest='shifts',
default='0',
type='str',
help='Ensemble prediction shifts [Default: %default]')
parser.add_option('--species', dest='species', default='human')
parser.add_option(
'--stats',
dest='sad_stats',
default='SAD',
help='Comma-separated list of stats to save. [Default: %default]')
parser.add_option(
'-t',
dest='targets_file',
default=None,
type='str',
help='File specifying target indexes and labels in table format')
(options, args) = parser.parse_args()
if len(args) == 2:
# single worker
model_file = args[0]
vcf_file = args[1]
elif len(args) == 3:
# multi separate
options_pkl_file = args[0]
model_file = args[1]
vcf_file = args[2]
# save out dir
out_dir = options.out_dir
# load options
options_pkl = open(options_pkl_file, 'rb')
options = pickle.load(options_pkl)
options_pkl.close()
# update output directory
options.out_dir = out_dir
elif len(args) == 4:
# multi worker
options_pkl_file = args[0]
model_file = args[1]
vcf_file = args[2]
worker_index = int(args[3])
# load options
options_pkl = open(options_pkl_file, 'rb')
options = pickle.load(options_pkl)
options_pkl.close()
# update output directory
options.out_dir = '%s/job%d' % (options.out_dir, worker_index)
else:
parser.error('Must provide model and VCF file')
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
options.shifts = [int(shift) for shift in options.shifts.split(',')]
options.sad_stats = options.sad_stats.split(',')
#################################################################
# read parameters and targets
if options.targets_file is None:
target_slice = None
else:
targets_df = pd.read_csv(options.targets_file, sep='\t', index_col=0)
target_ids = targets_df.identifier
target_labels = targets_df.description
target_slice = targets_df.index
#################################################################
# setup model
seqnn_model = tf.saved_model.load(model_file).model
# query num model targets
seq_length = seqnn_model.predict_on_batch.input_signature[0].shape[1]
null_1hot = np.zeros((1, seq_length, 4))
null_preds = seqnn_model.predict_on_batch(null_1hot)
null_preds = null_preds[options.species].numpy()
_, targets_length, num_targets = null_preds.shape
if options.targets_file is None:
target_ids = ['t%d' % ti for ti in range(num_targets)]
target_labels = [''] * len(target_ids)
#################################################################
# load SNPs
# filter for worker SNPs
if options.processes is not None:
# determine boundaries
num_snps = bvcf.vcf_count(vcf_file)
worker_bounds = np.linspace(0,
num_snps,
options.processes + 1,
dtype='int')
# read SNPs form VCF
snps = bvcf.vcf_snps(vcf_file,
start_i=worker_bounds[worker_index],
end_i=worker_bounds[worker_index + 1])
else:
# read SNPs form VCF
snps = bvcf.vcf_snps(vcf_file)
num_snps = len(snps)
# open genome FASTA
genome_open = pysam.Fastafile(options.genome_fasta)
# create SNP sequence generator
def snp_gen():
for snp in snps:
# get SNP sequences
snp_1hot_list = bvcf.snp_seq1(snp, seq_length, genome_open)
for snp_1hot in snp_1hot_list:
yield snp_1hot
#################################################################
# setup output
sad_out = initialize_output_h5(options.out_dir, options.sad_stats, snps,
target_ids, target_labels, targets_length)
#################################################################
# predict SNP scores, write output
# initialize predictions stream
preds_stream = PredStreamGen(seqnn_model,
snp_gen(),
rc=options.rc,
shifts=options.shifts,
slice_center=options.slice_center,
species=options.species)
# predictions index
pi = 0
for si in range(num_snps):
# get predictions
ref_preds = preds_stream[pi]
pi += 1
alt_preds = preds_stream[pi]
pi += 1
# process SNP
write_snp(ref_preds, alt_preds, sad_out, si, options.sad_stats,
options.log_pseudo)
# close genome
genome_open.close()
###################################################
# compute SAD distributions across variants
write_pct(sad_out, options.sad_stats)
sad_out.close()
def main():
usage = 'usage: %prog [options] <params_file> <model_file> <vcf_file>'
parser = OptionParser(usage)
parser.add_option(
'-c',
dest='center_pct',
default=0.25,
type='float',
help='Require clustered SNPs lie in center region [Default: %default]')
parser.add_option('-f',
dest='genome_fasta',
default='%s/data/hg19.fa' % os.environ['BASENJIDIR'],
help='Genome FASTA for sequences [Default: %default]')
parser.add_option(
'--flip',
dest='flip_ref',
default=False,
action='store_true',
help='Flip reference/alternate alleles when simple [Default: %default]'
)
parser.add_option('--local',
dest='local',
default=1024,
type='int',
help='Local SAD score [Default: %default]')
parser.add_option('-n',
dest='norm_file',
default=None,
help='Normalize SAD scores')
parser.add_option(
'-o',
dest='out_dir',
default='sad',
help='Output directory for tables and plots [Default: %default]')
parser.add_option('-p',
dest='processes',
default=None,
type='int',
help='Number of processes, passed by multi script')
parser.add_option('--pseudo',
dest='log_pseudo',
default=1,
type='float',
help='Log2 pseudocount [Default: %default]')
parser.add_option(
'--rc',
dest='rc',
default=False,
action='store_true',
help=
'Average forward and reverse complement predictions [Default: %default]'
)
parser.add_option('--shifts',
dest='shifts',
default='0',
type='str',
help='Ensemble prediction shifts [Default: %default]')
parser.add_option(
'--stats',
dest='sad_stats',
default='SAD',
help='Comma-separated list of stats to save. [Default: %default]')
parser.add_option(
'-t',
dest='targets_file',
default=None,
type='str',
help='File specifying target indexes and labels in table format')
parser.add_option(
'--ti',
dest='track_indexes',
default=None,
type='str',
help='Comma-separated list of target indexes to output BigWig tracks')
parser.add_option(
'--threads',
dest='threads',
default=False,
action='store_true',
help='Run CPU math and output in a separate thread [Default: %default]'
)
parser.add_option(
'-u',
dest='penultimate',
default=False,
action='store_true',
help='Compute SED in the penultimate layer [Default: %default]')
(options, args) = parser.parse_args()
if len(args) == 3:
# single worker
params_file = args[0]
model_file = args[1]
vcf_file = args[2]
elif len(args) == 5:
# multi worker
options_pkl_file = args[0]
params_file = args[1]
model_file = args[2]
vcf_file = args[3]
worker_index = int(args[4])
# load options
options_pkl = open(options_pkl_file, 'rb')
options = pickle.load(options_pkl)
options_pkl.close()
# update output directory
options.out_dir = '%s/job%d' % (options.out_dir, worker_index)
else:
parser.error(
'Must provide parameters and model files and QTL VCF file')
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
if options.track_indexes is None:
options.track_indexes = []
else:
options.track_indexes = [
int(ti) for ti in options.track_indexes.split(',')
]
if not os.path.isdir('%s/tracks' % options.out_dir):
os.mkdir('%s/tracks' % options.out_dir)
options.shifts = [int(shift) for shift in options.shifts.split(',')]
options.sad_stats = options.sad_stats.split(',')
#################################################################
# read parameters and targets
# read model parameters
with open(params_file) as params_open:
params = json.load(params_open)
params_model = params['model']
params_train = params['train']
if options.targets_file is None:
target_slice = None
else:
targets_df = pd.read_csv(options.targets_file, sep='\t', index_col=0)
target_ids = targets_df.identifier
target_labels = targets_df.description
target_slice = targets_df.index
if options.penultimate:
parser.error('Not implemented for TF2')
#################################################################
# setup model
seqnn_model = seqnn.SeqNN(params_model)
seqnn_model.restore(model_file)
seqnn_model.build_slice(target_slice)
seqnn_model.build_ensemble(options.rc, options.shifts)
num_targets = seqnn_model.num_targets()
if options.targets_file is None:
target_ids = ['t%d' % ti for ti in range(num_targets)]
target_labels = [''] * len(target_ids)
#################################################################
# load SNPs
# filter for worker SNPs
if options.processes is not None:
# determine boundaries
num_snps = bvcf.vcf_count(vcf_file)
worker_bounds = np.linspace(0,
num_snps,
options.processes + 1,
dtype='int')
# read sorted SNPs from VCF
snps = bvcf.vcf_snps(vcf_file,
require_sorted=True,
flip_ref=options.flip_ref,
validate_ref_fasta=options.genome_fasta,
start_i=worker_bounds[worker_index],
end_i=worker_bounds[worker_index + 1])
else:
# read sorted SNPs from VCF
snps = bvcf.vcf_snps(vcf_file,
require_sorted=True,
flip_ref=options.flip_ref,
validate_ref_fasta=options.genome_fasta)
# cluster SNPs by position
snp_clusters = cluster_snps(snps, params_model['seq_length'],
options.center_pct)
# delimit sequence boundaries
[sc.delimit(params_model['seq_length']) for sc in snp_clusters]
# open genome FASTA
genome_open = pysam.Fastafile(options.genome_fasta)
# make SNP sequence generator
def snp_gen():
for sc in snp_clusters:
snp_1hot_list = sc.get_1hots(genome_open)
for snp_1hot in snp_1hot_list:
yield snp_1hot
#################################################################
# setup output
snp_flips = np.array([snp.flipped for snp in snps], dtype='bool')
sad_out = initialize_output_h5(options.out_dir, options.sad_stats, snps,
target_ids, target_labels)
if options.threads:
snp_threads = []
snp_queue = Queue()
for i in range(1):
sw = SNPWorker(snp_queue, sad_out, options.sad_stats,
options.log_pseudo)
sw.start()
snp_threads.append(sw)
#################################################################
# predict SNP scores, write output
# initialize predictions stream
preds_stream = stream.PredStreamGen(seqnn_model, snp_gen(),
params['train']['batch_size'])
# predictions index
pi = 0
# SNP index
si = 0
for snp_cluster in snp_clusters:
ref_preds = preds_stream[pi]
pi += 1
for snp in snp_cluster.snps:
# print(snp, flush=True)
alt_preds = preds_stream[pi]
pi += 1
if snp_flips[si]:
ref_preds, alt_preds = alt_preds, ref_preds
if options.threads:
# queue SNP
snp_queue.put((ref_preds, alt_preds, si))
else:
# process SNP
write_snp(ref_preds, alt_preds, sad_out, si, options.sad_stats,
options.log_pseudo)
# update SNP index
si += 1
# finish queue
if options.threads:
print('Waiting for threads to finish.', flush=True)
snp_queue.join()
# close genome
genome_open.close()
###################################################
# compute SAD distributions across variants
write_pct(sad_out, options.sad_stats)
sad_out.close()
def main():
usage = 'usage: %prog [options] <params_file> <model_file> <vcf_file>'
parser = OptionParser(usage)
parser.add_option('-c', dest='center_pct',
default=0.25, type='float',
help='Require clustered SNPs lie in center region [Default: %default]')
parser.add_option('-f', dest='genome_fasta',
default='%s/data/hg19.fa' % os.environ['BASENJIDIR'],
help='Genome FASTA for sequences [Default: %default]')
parser.add_option('--flip', dest='flip_ref',
default=False, action='store_true',
help='Flip reference/alternate alleles when simple [Default: %default]')
parser.add_option('--local', dest='local',
default=1024, type='int',
help='Local SAD score [Default: %default]')
parser.add_option('-n', dest='norm_file',
default=None,
help='Normalize SAD scores')
parser.add_option('-o',dest='out_dir',
default='sad',
help='Output directory for tables and plots [Default: %default]')
parser.add_option('-p', dest='processes',
default=None, type='int',
help='Number of processes, passed by multi script')
parser.add_option('--pseudo', dest='log_pseudo',
default=1, type='float',
help='Log2 pseudocount [Default: %default]')
parser.add_option('--rc', dest='rc',
default=False, action='store_true',
help='Average forward and reverse complement predictions [Default: %default]')
parser.add_option('--shifts', dest='shifts',
default='0', type='str',
help='Ensemble prediction shifts [Default: %default]')
parser.add_option('--stats', dest='sad_stats',
default='SAD',
help='Comma-separated list of stats to save. [Default: %default]')
parser.add_option('-t', dest='targets_file',
default=None, type='str',
help='File specifying target indexes and labels in table format')
parser.add_option('--ti', dest='track_indexes',
default=None, type='str',
help='Comma-separated list of target indexes to output BigWig tracks')
parser.add_option('-u', dest='penultimate',
default=False, action='store_true',
help='Compute SED in the penultimate layer [Default: %default]')
(options, args) = parser.parse_args()
if len(args) == 3:
# single worker
params_file = args[0]
model_file = args[1]
vcf_file = args[2]
elif len(args) == 5:
# multi worker
options_pkl_file = args[0]
params_file = args[1]
model_file = args[2]
vcf_file = args[3]
worker_index = int(args[4])
# load options
options_pkl = open(options_pkl_file, 'rb')
options = pickle.load(options_pkl)
options_pkl.close()
# update output directory
options.out_dir = '%s/job%d' % (options.out_dir, worker_index)
else:
parser.error('Must provide parameters and model files and QTL VCF file')
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
if options.track_indexes is None:
options.track_indexes = []
else:
options.track_indexes = [int(ti) for ti in options.track_indexes.split(',')]
if not os.path.isdir('%s/tracks' % options.out_dir):
os.mkdir('%s/tracks' % options.out_dir)
options.shifts = [int(shift) for shift in options.shifts.split(',')]
options.sad_stats = options.sad_stats.split(',')
#################################################################
# read parameters and collet target information
job = params.read_job_params(params_file, require=['seq_length','num_targets'])
if options.targets_file is None:
target_ids = ['t%d' % ti for ti in range(job['num_targets'])]
target_labels = ['']*len(target_ids)
target_subset = None
else:
targets_df = pd.read_table(options.targets_file, index_col=0)
target_ids = targets_df.identifier
target_labels = targets_df.description
target_subset = targets_df.index
if len(target_subset) == job['num_targets']:
target_subset = None
#################################################################
# load SNPs
# read sorted SNPs from VCF
snps = bvcf.vcf_snps(vcf_file, require_sorted=True, flip_ref=options.flip_ref,
validate_ref_fasta=options.genome_fasta)
# filter for worker SNPs
if options.processes is not None:
worker_bounds = np.linspace(0, len(snps), options.processes+1, dtype='int')
snps = snps[worker_bounds[worker_index]:worker_bounds[worker_index+1]]
num_snps = len(snps)
# cluster SNPs by position
snp_clusters = cluster_snps(snps, job['seq_length'], options.center_pct)
# delimit sequence boundaries
[sc.delimit(job['seq_length']) for sc in snp_clusters]
# open genome FASTA
genome_open = pysam.Fastafile(options.genome_fasta)
# make SNP sequence generator
def snp_gen():
for sc in snp_clusters:
snp_1hot_list = sc.get_1hots(genome_open)
for snp_1hot in snp_1hot_list:
yield {'sequence':snp_1hot}
snp_types = {'sequence': tf.float32}
snp_shapes = {'sequence': tf.TensorShape([tf.Dimension(job['seq_length']),
tf.Dimension(4)])}
dataset = tf.data.Dataset.from_generator(snp_gen,
output_types=snp_types,
output_shapes=snp_shapes)
dataset = dataset.batch(job['batch_size'])
dataset = dataset.prefetch(2*job['batch_size'])
# dataset = dataset.apply(tf.contrib.data.prefetch_to_device('/device:GPU:0'))
iterator = dataset.make_one_shot_iterator()
data_ops = iterator.get_next()
#################################################################
# setup model
# build model
t0 = time.time()
model = seqnn.SeqNN()
model.build_sad(job, data_ops,
ensemble_rc=options.rc, ensemble_shifts=options.shifts,
embed_penultimate=options.penultimate, target_subset=target_subset)
print('Model building time %f' % (time.time() - t0), flush=True)
if options.penultimate:
# labels become inappropriate
target_ids = ['']*model.hp.cnn_filters[-1]
target_labels = target_ids
# read target normalization factors
target_norms = np.ones(len(target_labels))
if options.norm_file is not None:
ti = 0
for line in open(options.norm_file):
target_norms[ti] = float(line.strip())
ti += 1
num_targets = len(target_ids)
#################################################################
# setup output
snp_flips = np.array([snp.flipped for snp in snps], dtype='bool')
sad_out = initialize_output_h5(options.out_dir, options.sad_stats,
snps, target_ids, target_labels)
snp_threads = []
snp_queue = Queue()
for i in range(1):
sw = SNPWorker(snp_queue, sad_out, options.sad_stats, options.log_pseudo)
sw.start()
snp_threads.append(sw)
#################################################################
# predict SNP scores, write output
# initialize saver
saver = tf.train.Saver()
with tf.Session() as sess:
# load variables into session
saver.restore(sess, model_file)
# initialize predictions stream
preds_stream = PredStream(sess, model, 32)
# predictions index
pi = 0
# SNP index
si = 0
for snp_cluster in snp_clusters:
ref_preds = preds_stream[pi]
pi += 1
for snp in snp_cluster.snps:
# print(snp, flush=True)
alt_preds = preds_stream[pi]
pi += 1
# queue SNP
if snp_flips[si]:
snp_queue.put((alt_preds, ref_preds, si))
else:
snp_queue.put((ref_preds, alt_preds, si))
# update SNP index
si += 1
# finish queue
print('Waiting for threads to finish.', flush=True)
snp_queue.join()
# close genome
genome_open.close()
###################################################
# compute SAD distributions across variants
# define percentiles
d_fine = 0.001
d_coarse = 0.01
percentiles_neg = np.arange(d_fine, 0.1, d_fine)
percentiles_base = np.arange(0.1, 0.9, d_coarse)
percentiles_pos = np.arange(0.9, 1, d_fine)
percentiles = np.concatenate([percentiles_neg, percentiles_base, percentiles_pos])
sad_out.create_dataset('percentiles', data=percentiles)
pct_len = len(percentiles)
for sad_stat in options.sad_stats:
sad_stat_pct = '%s_pct' % sad_stat
# compute
sad_pct = np.percentile(sad_out[sad_stat], 100*percentiles, axis=0).T
sad_pct = sad_pct.astype('float16')
# save
sad_out.create_dataset(sad_stat_pct, data=sad_pct, dtype='float16')
sad_out.close()
def main():
usage = "usage: %prog [options] <params_file> <model_file> <vcf_file>"
parser = OptionParser(usage)
parser.add_option(
"-c",
dest="center_pct",
default=0.25,
type="float",
help="Require clustered SNPs lie in center region [Default: %default]",
)
parser.add_option(
"-f",
dest="genome_fasta",
default="%s/data/hg19.fa" % os.environ["BASENJIDIR"],
help="Genome FASTA for sequences [Default: %default]",
)
parser.add_option(
"-g",
dest="genome_file",
default="%s/data/human.hg19.genome" % os.environ["BASENJIDIR"],
help="Chromosome lengths file [Default: %default]",
)
parser.add_option(
"--local",
dest="local",
default=1024,
type="int",
help="Local SAD score [Default: %default]",
)
parser.add_option("-n", dest="norm_file", default=None, help="Normalize SAD scores")
parser.add_option(
"-o",
dest="out_dir",
default="sad",
help="Output directory for tables and plots [Default: %default]",
)
parser.add_option(
"-p",
dest="processes",
default=None,
type="int",
help="Number of processes, passed by multi script",
)
parser.add_option(
"--pseudo",
dest="log_pseudo",
default=1,
type="float",
help="Log2 pseudocount [Default: %default]",
)
parser.add_option(
"--rc",
dest="rc",
default=False,
action="store_true",
help="Average forward and reverse complement predictions [Default: %default]",
)
parser.add_option(
"--shifts",
dest="shifts",
default="0",
type="str",
help="Ensemble prediction shifts [Default: %default]",
)
parser.add_option(
"--stats",
dest="sad_stats",
default="SAD",
help="Comma-separated list of stats to save. [Default: %default]",
)
parser.add_option(
"-t",
dest="targets_file",
default=None,
type="str",
help="File specifying target indexes and labels in table format",
)
parser.add_option(
"--ti",
dest="track_indexes",
default=None,
type="str",
help="Comma-separated list of target indexes to output BigWig tracks",
)
parser.add_option(
"-u",
dest="penultimate",
default=False,
action="store_true",
help="Compute SED in the penultimate layer [Default: %default]",
)
(options, args) = parser.parse_args()
if len(args) == 3:
# single worker
params_file = args[0]
model_file = args[1]
vcf_file = args[2]
elif len(args) == 5:
# multi worker
options_pkl_file = args[0]
params_file = args[1]
model_file = args[2]
vcf_file = args[3]
worker_index = int(args[4])
# load options
options_pkl = open(options_pkl_file, "rb")
options = pickle.load(options_pkl)
options_pkl.close()
# update output directory
options.out_dir = "%s/job%d" % (options.out_dir, worker_index)
else:
parser.error("Must provide parameters and model files and QTL VCF file")
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
if options.track_indexes is None:
options.track_indexes = []
else:
options.track_indexes = [int(ti) for ti in options.track_indexes.split(",")]
if not os.path.isdir("%s/tracks" % options.out_dir):
os.mkdir("%s/tracks" % options.out_dir)
options.shifts = [int(shift) for shift in options.shifts.split(",")]
options.sad_stats = options.sad_stats.split(",")
#################################################################
# read parameters and collet target information
job = params.read_job_params(params_file, require=["seq_length", "num_targets"])
if options.targets_file is None:
target_ids = ["t%d" % ti for ti in range(job["num_targets"])]
target_labels = [""] * len(target_ids)
target_subset = None
else:
targets_df = pd.read_table(options.targets_file, index_col=0)
target_ids = targets_df.identifier
target_labels = targets_df.description
target_subset = targets_df.index
if len(target_subset) == job["num_targets"]:
target_subset = None
#################################################################
# load SNPs
# read sorted SNPs from VCF
snps = bvcf.vcf_snps(
vcf_file,
require_sorted=True,
flip_ref=False,
validate_ref_fasta=options.genome_fasta,
)
# filter for worker SNPs
if options.processes is not None:
worker_bounds = np.linspace(0, len(snps), options.processes + 1, dtype="int")
snps = snps[worker_bounds[worker_index] : worker_bounds[worker_index + 1]]
num_snps = len(snps)
# cluster SNPs by position
snp_clusters = cluster_snps(snps, job["seq_length"], options.center_pct)
# delimit sequence boundaries
[sc.delimit(job["seq_length"]) for sc in snp_clusters]
# open genome FASTA
genome_open = pysam.Fastafile(options.genome_fasta)
# make SNP sequence generator
def snp_gen():
for sc in snp_clusters:
snp_1hot_list = sc.get_1hots(genome_open)
for snp_1hot in snp_1hot_list:
yield {"sequence": snp_1hot}
snp_types = {"sequence": tf.float32}
snp_shapes = {
"sequence": tf.TensorShape([tf.Dimension(job["seq_length"]), tf.Dimension(4)])
}
dataset = tf.data.Dataset.from_generator(
snp_gen, output_types=snp_types, output_shapes=snp_shapes
)
dataset = dataset.batch(job["batch_size"])
dataset = dataset.prefetch(2 * job["batch_size"])
# dataset = dataset.apply(tf.contrib.data.prefetch_to_device('/device:GPU:0'))
iterator = dataset.make_one_shot_iterator()
data_ops = iterator.get_next()
#################################################################
# setup model
# build model
t0 = time.time()
model = seqnn.SeqNN()
model.build_sad(
job,
data_ops,
ensemble_rc=options.rc,
ensemble_shifts=options.shifts,
embed_penultimate=options.penultimate,
target_subset=target_subset,
)
print("Model building time %f" % (time.time() - t0), flush=True)
if options.penultimate:
# labels become inappropriate
target_ids = [""] * model.hp.cnn_filters[-1]
target_labels = target_ids
# read target normalization factors
target_norms = np.ones(len(target_labels))
if options.norm_file is not None:
ti = 0
for line in open(options.norm_file):
target_norms[ti] = float(line.strip())
ti += 1
num_targets = len(target_ids)
#################################################################
# setup output
sad_out = initialize_output_h5(
options.out_dir, options.sad_stats, snps, target_ids, target_labels
)
snp_threads = []
snp_queue = Queue()
for i in range(1):
sw = SNPWorker(snp_queue, sad_out, options.sad_stats, options.log_pseudo)
sw.start()
snp_threads.append(sw)
#################################################################
# predict SNP scores, write output
# initialize saver
saver = tf.train.Saver()
with tf.Session() as sess:
# load variables into session
saver.restore(sess, model_file)
# initialize predictions stream
preds_stream = PredStream(sess, model, 32)
# predictions index
pi = 0
# SNP index
si = 0
for snp_cluster in snp_clusters:
ref_preds = preds_stream[pi]
pi += 1
for snp in snp_cluster.snps:
# print(snp, flush=True)
alt_preds = preds_stream[pi]
pi += 1
# queue SNP
snp_queue.put((ref_preds, alt_preds, si))
# update SNP index
si += 1
# finish queue
print("Waiting for threads to finish.", flush=True)
snp_queue.join()
# close genome
genome_open.close()
###################################################
# compute SAD distributions across variants
# define percentiles
d_fine = 0.001
d_coarse = 0.01
percentiles_neg = np.arange(d_fine, 0.1, d_fine)
percentiles_base = np.arange(0.1, 0.9, d_coarse)
percentiles_pos = np.arange(0.9, 1, d_fine)
percentiles = np.concatenate([percentiles_neg, percentiles_base, percentiles_pos])
sad_out.create_dataset("percentiles", data=percentiles)
pct_len = len(percentiles)
for sad_stat in options.sad_stats:
sad_stat_pct = "%s_pct" % sad_stat
# compute
sad_pct = np.percentile(sad_out[sad_stat], 100 * percentiles, axis=0).T
sad_pct = sad_pct.astype("float16")
# save
sad_out.create_dataset(sad_stat_pct, data=sad_pct, dtype="float16")
sad_out.close()
