def main():
usage = 'usage: %prog [options] <params_file> <model_file> <genes_hdf5_file>'
parser = OptionParser(usage)
parser.add_option('-b',
dest='batch_size',
default=None,
type='int',
help='Batch size [Default: %default]')
parser.add_option('-i',
dest='ignore_bed',
help='Ignore genes overlapping regions in this BED file')
parser.add_option('-l',
dest='load_preds',
help='Load transcript_preds from file')
parser.add_option('--heat',
dest='plot_heat',
default=False,
action='store_true',
help='Plot big gene-target heatmaps [Default: %default]')
parser.add_option(
'-o',
dest='out_dir',
default='genes_out',
help='Output directory for tables and plots [Default: %default]')
parser.add_option(
'--rc',
dest='rc',
default=False,
action='store_true',
help=
'Average the forward and reverse complement predictions when testing [Default: %default]'
)
parser.add_option(
'-s',
dest='plot_scatter',
default=False,
action='store_true',
help='Make time-consuming accuracy scatter plots [Default: %default]')
parser.add_option(
'--rep',
dest='replicate_labels_file',
help=
'Compare replicate experiments, aided by the given file with long labels'
)
parser.add_option(
'-t',
dest='target_indexes',
default=None,
help=
'File or Comma-separated list of target indexes to scatter plot true versus predicted values'
)
parser.add_option(
'--table',
dest='print_tables',
default=False,
action='store_true',
help='Print big gene/transcript tables [Default: %default]')
parser.add_option(
'-v',
dest='gene_variance',
default=False,
action='store_true',
help=
'Study accuracy with respect to gene variance across targets [Default: %default]'
)
(options, args) = parser.parse_args()
if len(args) != 3:
parser.error(
'Must provide parameters and model files, and genes HDF5 file')
else:
params_file = args[0]
model_file = args[1]
genes_hdf5_file = args[2]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
#################################################################
# read in genes and targets
gene_data = basenji.genes.GeneData(genes_hdf5_file)
# all targets
if options.target_indexes is None:
options.target_indexes = range(transcript_targets.shape[1])
# file targets
elif os.path.isfile(options.target_indexes):
target_indexes_file = options.target_indexes
options.target_indexes = []
for line in open(target_indexes_file):
options.target_indexes.append(int(line.split()[0]))
# comma-separated targets
else:
options.target_indexes = [
int(ti) for ti in options.target_indexes.split(',')
]
options.target_indexes = np.array(options.target_indexes)
# not doing it this way anymore
# if options.ignore_bed:
# seqs_1hot, transcript_map, transcript_targets = ignore_trained_regions(options.ignore_bed, seq_coords, seqs_1hot, transcript_map, transcript_targets)
#################################################################
# transcript predictions
if options.load_preds is not None:
# load from file
transcript_preds = np.load(options.load_preds)
else:
#######################################################
# setup model
t0 = time.time()
print('Constructing model.', end='', flush=True)
job = basenji.dna_io.read_job_params(params_file)
job['batch_length'] = gene_data.seq_length
job['seq_depth'] = gene_data.seq_depth
job['target_pool'] = gene_data.pool_width
job['num_targets'] = gene_data.num_targets
# build model
dr = basenji.seqnn.SeqNN()
dr.build(job)
if options.batch_size is not None:
dr.batch_size = options.batch_size
print(' Done in %ds' % (time.time() - t0), flush=True)
#######################################################
# predict transcripts
print('Computing gene predictions.', end='', flush=True)
# initialize batcher
batcher = basenji.batcher.Batcher(gene_data.seqs_1hot,
batch_size=dr.batch_size)
# initialie saver
saver = tf.train.Saver()
with tf.Session() as sess:
# load variables into session
saver.restore(sess, model_file)
# predict
transcript_preds = dr.predict_genes(sess,
batcher,
gene_data.transcript_map,
rc_avg=options.rc)
# save to file
np.save('%s/preds' % options.out_dir, transcript_preds)
print(' Done in %ds.' % (time.time() - t0), flush=True)
#################################################################
# convert to genes
gene_targets = map_transcripts_genes(gene_data.transcript_targets,
gene_data.transcript_map,
gene_data.transcript_gene_indexes)
gene_preds = map_transcripts_genes(transcript_preds,
gene_data.transcript_map,
gene_data.transcript_gene_indexes)
#################################################################
# correlation statistics
t0 = time.time()
print('Computing correlations.', end='', flush=True)
cor_table(gene_data.transcript_targets, transcript_preds,
gene_data.target_labels, options.target_indexes,
'%s/transcript_cors.txt' % options.out_dir)
cor_table(gene_targets,
gene_preds,
gene_data.target_labels,
options.target_indexes,
'%s/gene_cors.txt' % options.out_dir,
plots=True)
print(' Done in %ds.' % (time.time() - t0), flush=True)
#################################################################
# gene statistics
if options.print_tables:
t0 = time.time()
print('Printing predictions.', end='', flush=True)
gene_table(gene_data.transcript_targets, transcript_preds,
gene_data.transcript_map.keys(), gene_data.target_labels,
options.target_indexes, '%s/transcript' % options.out_dir,
options.plot_scatter)
gene_table(gene_targets, gene_preds, set(gene_data.genes),
gene_data.target_labels, options.target_indexes,
'%s/gene' % options.out_dir, options.plot_scatter)
print(' Done in %ds.' % (time.time() - t0), flush=True)
#################################################################
# gene x target heatmaps
if options.plot_heat or options.gene_variance:
#########################################
# normalize predictions across targets
t0 = time.time()
print('Normalizing values across targets.', end='', flush=True)
gene_targets_qn = normalize_targets(
gene_targets[:, options.target_indexes])
gene_preds_qn = normalize_targets(gene_preds[:,
options.target_indexes])
print(' Done in %ds.' % (time.time() - t0), flush=True)
if options.plot_heat:
#########################################
# plot genes by targets clustermap
t0 = time.time()
print('Plotting heat maps.', end='', flush=True)
sns.set(font_scale=1.3, style='ticks')
plot_genes = 2000
plot_targets = 1000
# choose a set of variable genes
gene_vars = gene_preds_qn.var(axis=1)
indexes_var = np.argsort(gene_vars)[::-1][:plot_genes]
# choose a set of random genes
indexes_rand = np.random.choice(np.arange(gene_preds_qn.shape[0]),
plot_genes,
replace=False)
# choose a set of random targets
if plot_targets < 0.8 * gene_preds_qn.shape[1]:
indexes_targets = np.random.choice(np.arange(
gene_preds_qn.shape[1]),
plot_targets,
replace=False)
else:
indexes_targets = np.arange(gene_preds_qn.shape[1])
# variable gene predictions
clustermap(gene_preds_qn[indexes_var, :][:, indexes_targets],
'%s/gene_heat_var.pdf' % options.out_dir)
clustermap(gene_preds_qn[indexes_var, :][:, indexes_targets],
'%s/gene_heat_var_color.pdf' % options.out_dir,
color='viridis',
table=True)
# random gene predictions
clustermap(gene_preds_qn[indexes_rand, :][:, indexes_targets],
'%s/gene_heat_rand.pdf' % options.out_dir)
# variable gene targets
clustermap(gene_targets_qn[indexes_var, :][:, indexes_targets],
'%s/gene_theat_var.pdf' % options.out_dir)
clustermap(gene_targets_qn[indexes_var, :][:, indexes_targets],
'%s/gene_theat_var_color.pdf' % options.out_dir,
color='viridis',
table=True)
# random gene targets
clustermap(gene_targets_qn[indexes_rand, :][:, indexes_targets],
'%s/gene_theat_rand.pdf' % options.out_dir)
print(' Done in %ds.' % (time.time() - t0), flush=True)
#################################################################
# analyze replicates
if options.replicate_labels_file is not None:
# read long form labels, from which to infer replicates
target_labels_long = []
for line in open(options.replicate_labels_file):
a = line.split('\t')
a[-1] = a[-1].rstrip()
target_labels_long.append(a[-1])
# determine replicates
replicate_lists = infer_replicates(target_labels_long)
# compute correlations
# replicate_correlations(replicate_lists, gene_data.transcript_targets, transcript_preds, options.target_indexes, '%s/transcript_reps' % options.out_dir)
replicate_correlations(replicate_lists, gene_targets, gene_preds,
options.target_indexes, '%s/gene_reps' %
options.out_dir) # , scatter_plots=True)
#################################################################
# gene variance
if options.gene_variance:
variance_accuracy(gene_targets_qn, gene_preds_qn,
'%s/gene' % options.out_dir)
def main():
usage = 'usage: %prog [options] <params_file> <model_file> <genes_hdf5_file>'
parser = OptionParser(usage)
parser.add_option(
'-b',
dest='batch_size',
default=None,
type='int',
help='Batch size [Default: %default]')
parser.add_option(
'-i',
dest='ignore_bed',
help='Ignore genes overlapping regions in this BED file')
parser.add_option(
'-l', dest='load_preds', help='Load tess_preds from file')
parser.add_option(
'--heat',
dest='plot_heat',
default=False,
action='store_true',
help='Plot big gene-target heatmaps [Default: %default]')
parser.add_option(
'-o',
dest='out_dir',
default='genes_out',
help='Output directory for tables and plots [Default: %default]')
parser.add_option(
'-r',
dest='tss_radius',
default=0,
type='int',
help='Radius of bins considered to quantify TSS transcription [Default: %default]')
parser.add_option(
'--rc',
dest='rc',
default=False,
action='store_true',
help=
'Average the forward and reverse complement predictions when testing [Default: %default]'
)
parser.add_option(
'-s',
dest='plot_scatter',
default=False,
action='store_true',
help='Make time-consuming accuracy scatter plots [Default: %default]')
parser.add_option(
'--shifts',
dest='shifts',
default='0',
help='Ensemble prediction shifts [Default: %default]')
parser.add_option(
'--rep',
dest='replicate_labels_file',
help=
'Compare replicate experiments, aided by the given file with long labels')
parser.add_option(
'-t',
dest='target_indexes',
default=None,
help=
'File or Comma-separated list of target indexes to scatter plot true versus predicted values'
)
parser.add_option(
'--table',
dest='print_tables',
default=False,
action='store_true',
help='Print big gene/TSS tables [Default: %default]')
parser.add_option(
'--tss',
dest='tss_alt',
default=False,
action='store_true',
help='Perform alternative TSS analysis [Default: %default]')
parser.add_option(
'-v',
dest='gene_variance',
default=False,
action='store_true',
help=
'Study accuracy with respect to gene variance across targets [Default: %default]'
)
(options, args) = parser.parse_args()
if len(args) != 3:
parser.error('Must provide parameters and model files, and genes HDF5 file')
else:
params_file = args[0]
model_file = args[1]
genes_hdf5_file = args[2]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
options.shifts = [int(shift) for shift in options.shifts.split(',')]
#################################################################
# read in genes and targets
gene_data = genedata.GeneData(genes_hdf5_file)
#################################################################
# TSS predictions
if options.load_preds is not None:
# load from file
tss_preds = np.load(options.load_preds)
else:
#######################################################
# setup model
job = params.read_job_params(params_file)
job['seq_length'] = gene_data.seq_length
job['seq_depth'] = gene_data.seq_depth
job['target_pool'] = gene_data.pool_width
if not 'num_targets' in job:
job['num_targets'] = gene_data.num_targets
# build model
model = seqnn.SeqNN()
model.build(job)
if options.batch_size is not None:
model.hp.batch_size = options.batch_size
#######################################################
# predict TSSs
t0 = time.time()
print('Computing gene predictions.', end='')
sys.stdout.flush()
# initialize batcher
gene_batcher = batcher.Batcher(
gene_data.seqs_1hot, batch_size=model.hp.batch_size)
# initialie saver
saver = tf.train.Saver()
with tf.Session() as sess:
# load variables into session
saver.restore(sess, model_file)
# predict
tss_preds = model.predict_genes(sess, gene_batcher, gene_data.gene_seqs,
rc=options.rc, shifts=options.shifts, tss_radius=options.tss_radius)
# save to file
np.save('%s/preds' % options.out_dir, tss_preds)
print(' Done in %ds.' % (time.time() - t0))
#################################################################
# convert to genes
gene_targets, _ = gene.map_tss_genes(gene_data.tss_targets, gene_data.tss,
tss_radius=options.tss_radius)
gene_preds, _ = gene.map_tss_genes(tss_preds, gene_data.tss,
tss_radius=options.tss_radius)
#################################################################
# determine targets
# all targets
if options.target_indexes is None:
if gene_data.num_targets is None:
print('No targets to test against')
exit()
else:
options.target_indexes = np.arange(gene_data.num_targets)
# file targets
elif os.path.isfile(options.target_indexes):
target_indexes_file = options.target_indexes
options.target_indexes = []
for line in open(target_indexes_file):
options.target_indexes.append(int(line.split()[0]))
# comma-separated targets
else:
options.target_indexes = [
int(ti) for ti in options.target_indexes.split(',')
]
options.target_indexes = np.array(options.target_indexes)
#################################################################
# correlation statistics
t0 = time.time()
print('Computing correlations.', end='')
sys.stdout.flush()
cor_table(gene_data.tss_targets, tss_preds, gene_data.target_ids,
gene_data.target_labels, options.target_indexes,
'%s/tss_cors.txt' % options.out_dir)
cor_table(gene_targets, gene_preds, gene_data.target_ids,
gene_data.target_labels, options.target_indexes,
'%s/gene_cors.txt' % options.out_dir, plots=True)
print(' Done in %ds.' % (time.time() - t0))
#################################################################
# gene statistics
if options.print_tables:
t0 = time.time()
print('Printing predictions.', end='')
sys.stdout.flush()
gene_table(gene_data.tss_targets, tss_preds, gene_data.tss_ids(),
gene_data.target_labels, options.target_indexes,
'%s/transcript' % options.out_dir, options.plot_scatter)
gene_table(gene_targets, gene_preds,
gene_data.gene_ids(), gene_data.target_labels,
options.target_indexes, '%s/gene' % options.out_dir,
options.plot_scatter)
print(' Done in %ds.' % (time.time() - t0))
#################################################################
# gene x target heatmaps
if options.plot_heat or options.gene_variance:
#########################################
# normalize predictions across targets
t0 = time.time()
print('Normalizing values across targets.', end='')
sys.stdout.flush()
gene_targets_qn = normalize_targets(gene_targets[:, options.target_indexes], log_pseudo=1)
gene_preds_qn = normalize_targets(gene_preds[:, options.target_indexes], log_pseudo=1)
print(' Done in %ds.' % (time.time() - t0))
if options.plot_heat:
#########################################
# plot genes by targets clustermap
t0 = time.time()
print('Plotting heat maps.', end='')
sys.stdout.flush()
sns.set(font_scale=1.3, style='ticks')
plot_genes = 1600
plot_targets = 800
# choose a set of variable genes
gene_vars = gene_preds_qn.var(axis=1)
indexes_var = np.argsort(gene_vars)[::-1][:plot_genes]
# choose a set of random genes
if plot_genes < gene_preds_qn.shape[0]:
indexes_rand = np.random.choice(
np.arange(gene_preds_qn.shape[0]), plot_genes, replace=False)
else:
indexes_rand = np.arange(gene_preds_qn.shape[0])
# choose a set of random targets
if plot_targets < 0.8 * gene_preds_qn.shape[1]:
indexes_targets = np.random.choice(
np.arange(gene_preds_qn.shape[1]), plot_targets, replace=False)
else:
indexes_targets = np.arange(gene_preds_qn.shape[1])
# variable gene predictions
clustermap(gene_preds_qn[indexes_var, :][:, indexes_targets],
'%s/gene_heat_var.pdf' % options.out_dir)
clustermap(
gene_preds_qn[indexes_var, :][:, indexes_targets],
'%s/gene_heat_var_color.pdf' % options.out_dir,
color='viridis',
table=True)
# random gene predictions
clustermap(gene_preds_qn[indexes_rand, :][:, indexes_targets],
'%s/gene_heat_rand.pdf' % options.out_dir)
# variable gene targets
clustermap(gene_targets_qn[indexes_var, :][:, indexes_targets],
'%s/gene_theat_var.pdf' % options.out_dir)
clustermap(
gene_targets_qn[indexes_var, :][:, indexes_targets],
'%s/gene_theat_var_color.pdf' % options.out_dir,
color='viridis',
table=True)
# random gene targets (crashes)
# clustermap(gene_targets_qn[indexes_rand, :][:, indexes_targets],
# '%s/gene_theat_rand.pdf' % options.out_dir)
print(' Done in %ds.' % (time.time() - t0))
#################################################################
# analyze replicates
if options.replicate_labels_file is not None:
# read long form labels, from which to infer replicates
target_labels_long = []
for line in open(options.replicate_labels_file):
a = line.split('\t')
a[-1] = a[-1].rstrip()
target_labels_long.append(a[-1])
# determine replicates
replicate_lists = infer_replicates(target_labels_long)
# compute correlations
# replicate_correlations(replicate_lists, gene_data.tss_targets,
# tss_preds, options.target_indexes, '%s/transcript_reps' % options.out_dir)
replicate_correlations(
replicate_lists, gene_targets, gene_preds, options.target_indexes,
'%s/gene_reps' % options.out_dir) # , scatter_plots=True)
#################################################################
# gene variance
if options.gene_variance:
variance_accuracy(gene_targets_qn, gene_preds_qn,
'%s/gene' % options.out_dir)
#################################################################
# alternative TSS
if options.tss_alt:
alternative_tss(gene_data.tss_targets[:,options.target_indexes],
tss_preds[:,options.target_indexes], gene_data,
options.out_dir, log_pseudo=1)
def main():
usage = "usage: %prog [options] <params_file> <model_file> <genes_hdf5_file>"
parser = OptionParser(usage)
parser.add_option(
"-b",
dest="batch_size",
default=None,
type="int",
help="Batch size [Default: %default]",
)
parser.add_option(
"-i",
dest="ignore_bed",
help="Ignore genes overlapping regions in this BED file",
)
parser.add_option("-l", dest="load_preds", help="Load tess_preds from file")
parser.add_option(
"--heat",
dest="plot_heat",
default=False,
action="store_true",
help="Plot big gene-target heatmaps [Default: %default]",
)
parser.add_option(
"-o",
dest="out_dir",
default="genes_out",
help="Output directory for tables and plots [Default: %default]",
)
parser.add_option(
"-r",
dest="tss_radius",
default=0,
type="int",
help="Radius of bins considered to quantify TSS transcription [Default: %default]",
)
parser.add_option(
"--rc",
dest="rc",
default=False,
action="store_true",
help="Average the forward and reverse complement predictions when testing [Default: %default]",
)
parser.add_option(
"-s",
dest="plot_scatter",
default=False,
action="store_true",
help="Make time-consuming accuracy scatter plots [Default: %default]",
)
parser.add_option(
"--shifts",
dest="shifts",
default="0",
help="Ensemble prediction shifts [Default: %default]",
)
parser.add_option(
"--rep",
dest="replicate_labels_file",
help="Compare replicate experiments, aided by the given file with long labels",
)
parser.add_option(
"-t",
dest="targets_file",
default=None,
type="str",
help="File specifying target indexes and labels in table format",
)
parser.add_option(
"--table",
dest="print_tables",
default=False,
action="store_true",
help="Print big gene/TSS tables [Default: %default]",
)
parser.add_option(
"--tss",
dest="tss_alt",
default=False,
action="store_true",
help="Perform alternative TSS analysis [Default: %default]",
)
parser.add_option(
"-v",
dest="gene_variance",
default=False,
action="store_true",
help="Study accuracy with respect to gene variance across targets [Default: %default]",
)
(options, args) = parser.parse_args()
if len(args) != 3:
parser.error("Must provide parameters and model files, and genes HDF5 file")
else:
params_file = args[0]
model_file = args[1]
genes_hdf5_file = args[2]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
options.shifts = [int(shift) for shift in options.shifts.split(",")]
#################################################################
# read in genes and targets
gene_data = genedata.GeneData(genes_hdf5_file)
#################################################################
# TSS predictions
if options.load_preds is not None:
# load from file
tss_preds = np.load(options.load_preds)
else:
#######################################################
# setup model
job = params.read_job_params(params_file)
job["seq_length"] = gene_data.seq_length
job["seq_depth"] = gene_data.seq_depth
job["target_pool"] = gene_data.pool_width
if not "num_targets" in job:
job["num_targets"] = gene_data.num_targets
# build model
model = seqnn.SeqNN()
model.build_feed_sad(job)
if options.batch_size is not None:
model.hp.batch_size = options.batch_size
#######################################################
# predict TSSs
t0 = time.time()
print("Computing gene predictions.", end="")
sys.stdout.flush()
# initialize batcher
gene_batcher = batcher.Batcher(
gene_data.seqs_1hot, batch_size=model.hp.batch_size
)
# initialie saver
saver = tf.train.Saver()
with tf.Session() as sess:
# load variables into session
saver.restore(sess, model_file)
# predict
tss_preds = model.predict_genes(
sess,
gene_batcher,
gene_data.gene_seqs,
rc=options.rc,
shifts=options.shifts,
tss_radius=options.tss_radius,
)
# save to file
np.save("%s/preds" % options.out_dir, tss_preds)
print(" Done in %ds." % (time.time() - t0))
# up-convert
tss_preds = tss_preds.astype("float32")
#################################################################
# convert to genes
gene_targets, _ = gene.map_tss_genes(
gene_data.tss_targets, gene_data.tss, tss_radius=options.tss_radius
)
gene_preds, _ = gene.map_tss_genes(
tss_preds, gene_data.tss, tss_radius=options.tss_radius
)
#################################################################
# determine targets
# read targets
if options.targets_file is not None:
targets_df = pd.read_table(options.targets_file, index_col=0)
target_indexes = targets_df.index
else:
if gene_data.num_targets is None:
print("No targets to test against.")
exit(1)
else:
target_indexes = np.arange(gene_data.num_targets)
#################################################################
# correlation statistics
t0 = time.time()
print("Computing correlations.", end="")
sys.stdout.flush()
cor_table(
gene_data.tss_targets,
tss_preds,
gene_data.target_ids,
gene_data.target_labels,
target_indexes,
"%s/tss_cors.txt" % options.out_dir,
)
cor_table(
gene_targets,
gene_preds,
gene_data.target_ids,
gene_data.target_labels,
target_indexes,
"%s/gene_cors.txt" % options.out_dir,
draw_plots=True,
)
print(" Done in %ds." % (time.time() - t0))
#################################################################
# gene statistics
if options.print_tables:
t0 = time.time()
print("Printing predictions.", end="")
sys.stdout.flush()
gene_table(
gene_data.tss_targets,
tss_preds,
gene_data.tss_ids(),
gene_data.target_labels,
target_indexes,
"%s/transcript" % options.out_dir,
options.plot_scatter,
)
gene_table(
gene_targets,
gene_preds,
gene_data.gene_ids(),
gene_data.target_labels,
target_indexes,
"%s/gene" % options.out_dir,
options.plot_scatter,
)
print(" Done in %ds." % (time.time() - t0))
#################################################################
# gene x target heatmaps
if options.plot_heat or options.gene_variance:
#########################################
# normalize predictions across targets
t0 = time.time()
print("Normalizing values across targets.", end="")
sys.stdout.flush()
gene_targets_qn = normalize_targets(
gene_targets[:, target_indexes], log_pseudo=1
)
gene_preds_qn = normalize_targets(gene_preds[:, target_indexes], log_pseudo=1)
print(" Done in %ds." % (time.time() - t0))
if options.plot_heat:
#########################################
# plot genes by targets clustermap
t0 = time.time()
print("Plotting heat maps.", end="")
sys.stdout.flush()
sns.set(font_scale=1.3, style="ticks")
plot_genes = 1600
plot_targets = 800
# choose a set of variable genes
gene_vars = gene_preds_qn.var(axis=1)
indexes_var = np.argsort(gene_vars)[::-1][:plot_genes]
# choose a set of random genes
if plot_genes < gene_preds_qn.shape[0]:
indexes_rand = np.random.choice(
np.arange(gene_preds_qn.shape[0]), plot_genes, replace=False
)
else:
indexes_rand = np.arange(gene_preds_qn.shape[0])
# choose a set of random targets
if plot_targets < 0.8 * gene_preds_qn.shape[1]:
indexes_targets = np.random.choice(
np.arange(gene_preds_qn.shape[1]), plot_targets, replace=False
)
else:
indexes_targets = np.arange(gene_preds_qn.shape[1])
# variable gene predictions
clustermap(
gene_preds_qn[indexes_var, :][:, indexes_targets],
"%s/gene_heat_var.pdf" % options.out_dir,
)
clustermap(
gene_preds_qn[indexes_var, :][:, indexes_targets],
"%s/gene_heat_var_color.pdf" % options.out_dir,
color="viridis",
table=True,
)
# random gene predictions
clustermap(
gene_preds_qn[indexes_rand, :][:, indexes_targets],
"%s/gene_heat_rand.pdf" % options.out_dir,
)
# variable gene targets
clustermap(
gene_targets_qn[indexes_var, :][:, indexes_targets],
"%s/gene_theat_var.pdf" % options.out_dir,
)
clustermap(
gene_targets_qn[indexes_var, :][:, indexes_targets],
"%s/gene_theat_var_color.pdf" % options.out_dir,
color="viridis",
table=True,
)
# random gene targets (crashes)
# clustermap(gene_targets_qn[indexes_rand, :][:, indexes_targets],
# '%s/gene_theat_rand.pdf' % options.out_dir)
print(" Done in %ds." % (time.time() - t0))
#################################################################
# analyze replicates
if options.replicate_labels_file is not None:
# read long form labels, from which to infer replicates
target_labels_long = []
for line in open(options.replicate_labels_file):
a = line.split("\t")
a[-1] = a[-1].rstrip()
target_labels_long.append(a[-1])
# determine replicates
replicate_lists = infer_replicates(target_labels_long)
# compute correlations
# replicate_correlations(replicate_lists, gene_data.tss_targets,
# tss_preds, options.target_indexes, '%s/transcript_reps' % options.out_dir)
replicate_correlations(
replicate_lists,
gene_targets,
gene_preds,
target_indexes,
"%s/gene_reps" % options.out_dir,
) # , scatter_plots=True)
#################################################################
# gene variance
if options.gene_variance:
variance_accuracy(gene_targets_qn, gene_preds_qn, "%s/gene" % options.out_dir)
#################################################################
# alternative TSS
if options.tss_alt:
alternative_tss(
gene_data.tss_targets[:, target_indexes],
tss_preds[:, target_indexes],
gene_data,
options.out_dir,
log_pseudo=1,
)