def main():
usage = 'usage: %prog [options] <model_file> <input_file>'
parser = OptionParser(usage)
parser.add_option(
'-a',
dest='input_activity_file',
help='Optional activity table corresponding to an input FASTA file')
parser.add_option(
'-d',
dest='model_hdf5_file',
default=None,
help='Pre-computed model output as HDF5 [Default: %default]')
parser.add_option(
'-g',
dest='gain_height',
default=False,
action='store_true',
help=
'Nucleotide heights determined by the max of loss and gain [Default: %default]'
)
parser.add_option('-m',
dest='min_limit',
default=0.1,
type='float',
help='Minimum heatmap limit [Default: %default]')
parser.add_option(
'-n',
dest='center_nt',
default=200,
type='int',
help='Center nt to mutate and plot in the heat map [Default: %default]'
)
parser.add_option('-o',
dest='out_dir',
default='heat',
help='Output directory [Default: %default]')
parser.add_option(
'-s',
dest='sample',
default=None,
type='int',
help='Sample sequences from the test set [Default:%default]')
parser.add_option(
'-t',
dest='targets',
default='0',
help=
'Comma-separated list of target indexes to plot (or -1 for all) [Default: %default]'
)
(options, args) = parser.parse_args()
if len(args) != 2:
parser.error(
'Must provide Basset model file and input sequences (as a FASTA file or test data in an HDF file'
)
else:
model_file = args[0]
input_file = args[1]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
#################################################################
# parse input file
#################################################################
try:
# input_file is FASTA
# load sequences and headers
seqs = []
seq_headers = []
for line in open(input_file):
if line[0] == '>':
seq_headers.append(line[1:].rstrip())
seqs.append('')
else:
seqs[-1] += line.rstrip()
model_input_hdf5 = '%s/model_in.h5' % options.out_dir
if options.input_activity_file:
# one hot code
seqs_1hot, targets = dna_io.load_data_1hot(
input_file,
options.input_activity_file,
mean_norm=False,
whiten=False,
permute=False,
sort=False)
# read in target names
target_labels = open(
options.input_activity_file).readline().strip().split('\t')
else:
# load sequences
seqs_1hot = dna_io.load_sequences(input_file, permute=False)
targets = None
target_labels = None
# sample
if options.sample:
sample_i = np.array(
random.sample(xrange(seqs_1hot.shape[0]), options.sample))
seqs_1hot = seqs_1hot[sample_i]
seq_headers = seq_headers[sample_i]
if targets is not None:
targets = targets[sample_i]
# reshape sequences for torch
seqs_1hot = seqs_1hot.reshape(
(seqs_1hot.shape[0], 4, 1, seqs_1hot.shape[1] / 4))
# write as test data to a HDF5 file
h5f = h5py.File(model_input_hdf5, 'w')
h5f.create_dataset('test_in', data=seqs_1hot)
h5f.close()
except (IOError, IndexError):
# input_file is HDF5
try:
model_input_hdf5 = input_file
# load (sampled) test data from HDF5
hdf5_in = h5py.File(input_file, 'r')
seqs_1hot = np.array(hdf5_in['test_in'])
targets = np.array(hdf5_in['test_out'])
try: # TEMP
seq_headers = np.array(hdf5_in['test_headers'])
target_labels = np.array(hdf5_in['target_labels'])
except:
seq_headers = None
target_labels = None
hdf5_in.close()
# sample
if options.sample:
sample_i = np.array(
random.sample(xrange(seqs_1hot.shape[0]), options.sample))
seqs_1hot = seqs_1hot[sample_i]
seq_headers = seq_headers[sample_i]
targets = targets[sample_i]
# write sampled data to a new HDF5 file
model_input_hdf5 = '%s/model_in.h5' % options.out_dir
h5f = h5py.File(model_input_hdf5, 'w')
h5f.create_dataset('test_in', data=seqs_1hot)
h5f.close()
# convert to ACGT sequences
seqs = dna_io.vecs2dna(seqs_1hot)
except IOError:
parser.error('Could not parse input file as FASTA or HDF5.')
#################################################################
# Torch predict modifications
#################################################################
if options.model_hdf5_file is None:
options.model_hdf5_file = '%s/model_out.h5' % options.out_dir
torch_cmd = 'basset_sat_predict.lua -center_nt %d %s %s %s' % (
options.center_nt, model_file, model_input_hdf5,
options.model_hdf5_file)
print torch_cmd
subprocess.call(torch_cmd, shell=True)
#################################################################
# load modification predictions
#################################################################
hdf5_in = h5py.File(options.model_hdf5_file, 'r')
seq_mod_preds = np.array(hdf5_in['seq_mod_preds'])
hdf5_in.close()
# trim seqs to match seq_mod_preds length
seq_len = len(seqs[0])
delta_start = 0
delta_len = seq_mod_preds.shape[2]
if delta_len < seq_len:
delta_start = (seq_len - delta_len) / 2
for i in range(len(seqs)):
seqs[i] = seqs[i][delta_start:delta_start + delta_len]
# decide which cells to plot
if options.targets == '-1':
plot_targets = xrange(seq_mod_preds.shape[3])
else:
plot_targets = [int(ci) for ci in options.targets.split(',')]
#################################################################
# plot
#################################################################
table_out = open('%s/table.txt' % options.out_dir, 'w')
rdbu = sns.color_palette("RdBu_r", 10)
nts = 'ACGT'
for si in range(seq_mod_preds.shape[0]):
try:
header = seq_headers[si]
except TypeError:
header = 'seq%d' % si
seq = seqs[si]
# plot some descriptive heatmaps for each individual cell type
for ci in plot_targets:
seq_mod_preds_cell = seq_mod_preds[si, :, :, ci]
real_pred_cell = get_real_pred(seq_mod_preds_cell, seq)
# compute matrices
norm_matrix = seq_mod_preds_cell - real_pred_cell
min_scores = seq_mod_preds_cell.min(axis=0)
max_scores = seq_mod_preds_cell.max(axis=0)
minmax_matrix = np.vstack(
[min_scores - real_pred_cell, max_scores - real_pred_cell])
# prepare figure
sns.set(style='white', font_scale=0.5)
sns.axes_style({'axes.linewidth': 1})
heat_cols = 400
sad_start = 1
sad_end = 323
logo_start = 0
logo_end = 324
fig = plt.figure(figsize=(20, 3))
ax_logo = plt.subplot2grid((3, heat_cols), (0, logo_start),
colspan=(logo_end - logo_start))
ax_sad = plt.subplot2grid((3, heat_cols), (1, sad_start),
colspan=(sad_end - sad_start))
ax_heat = plt.subplot2grid((3, heat_cols), (2, 0),
colspan=heat_cols)
# print a WebLogo of the sequence
vlim = max(options.min_limit, abs(minmax_matrix).max())
if options.gain_height:
seq_heights = 0.25 + 1.75 / vlim * (abs(minmax_matrix).max(
axis=0))
else:
seq_heights = 0.25 + 1.75 / vlim * (-minmax_matrix[0])
logo_eps = '%s/%s_c%d_seq.eps' % (options.out_dir,
header_filename(header), ci)
seq_logo(seq, seq_heights, logo_eps)
# add to figure
logo_png = '%s.png' % logo_eps[:-4]
subprocess.call('convert -density 300 %s %s' %
(logo_eps, logo_png),
shell=True)
logo = Image.open(logo_png)
ax_logo.imshow(logo)
ax_logo.set_axis_off()
# plot loss and gain SAD scores
ax_sad.plot(-minmax_matrix[0],
c=rdbu[0],
label='loss',
linewidth=1)
ax_sad.plot(minmax_matrix[1],
c=rdbu[-1],
label='gain',
linewidth=1)
ax_sad.set_xlim(0, minmax_matrix.shape[1])
ax_sad.legend()
# ax_sad.grid(True, linestyle=':')
for axis in ['top', 'bottom', 'left', 'right']:
ax_sad.spines[axis].set_linewidth(0.5)
# plot real-normalized scores
vlim = max(options.min_limit, abs(norm_matrix).max())
sns.heatmap(norm_matrix,
linewidths=0,
cmap='RdBu_r',
vmin=-vlim,
vmax=vlim,
xticklabels=False,
ax=ax_heat)
ax_heat.yaxis.set_ticklabels('TGCA',
rotation='horizontal') # , size=10)
# save final figure
plt.tight_layout()
plt.savefig('%s/%s_c%d_heat.pdf' %
(options.out_dir, header.replace(':', '_'), ci),
dpi=300)
plt.close()
#################################################################
# print table of nt variability for each cell
#################################################################
for ci in range(seq_mod_preds.shape[3]):
seq_mod_preds_cell = seq_mod_preds[si, :, :, ci]
real_pred_cell = get_real_pred(seq_mod_preds_cell, seq)
min_scores = seq_mod_preds_cell.min(axis=0)
max_scores = seq_mod_preds_cell.max(axis=0)
loss_matrix = real_pred_cell - seq_mod_preds_cell.min(axis=0)
gain_matrix = seq_mod_preds_cell.max(axis=0) - real_pred_cell
for pos in range(seq_mod_preds_cell.shape[1]):
cols = [
header, delta_start + pos, ci, loss_matrix[pos],
gain_matrix[pos]
]
print >> table_out, '\t'.join([str(c) for c in cols])
table_out.close()
def main():
usage = 'usage: %prog [options] <fasta_file> <targets_file> <out_file>'
parser = OptionParser(usage)
parser.add_option('-a',
dest='add_features_file',
default=None,
help='Table of additional features')
parser.add_option('-b',
dest='batch_size',
default=None,
type='int',
help='Align sizes with batch size')
parser.add_option(
'-c',
dest='counts',
default=False,
action='store_true',
help=
'Validation and training proportions are given as raw counts [Default: %default]'
)
parser.add_option(
'-e',
dest='extend_length',
type='int',
default=None,
help='Extend all sequences to this length [Default: %default]')
parser.add_option('-r',
dest='permute',
default=False,
action='store_true',
help='Permute sequences [Default: %default]')
parser.add_option('-s',
dest='random_seed',
default=1,
type='int',
help='numpy.random seed [Default: %default]')
parser.add_option('-t',
dest='test_pct',
default=0,
type='float',
help='Test % [Default: %default]')
parser.add_option('-v',
dest='valid_pct',
default=0,
type='float',
help='Validation % [Default: %default]')
parser.add_option('--vt',
dest='valid_test',
default=False,
action='store_true',
help='Use validation as test, too [Default: %default]')
(options, args) = parser.parse_args()
if len(args) != 3:
parser.error(
'Must provide fasta file, targets file, and an output prefix')
else:
fasta_file = args[0]
targets_file = args[1]
out_file = args[2]
# seed rng before shuffle
npr.seed(options.random_seed)
#################################################################
# load data
#################################################################
seqs, targets = dna_io.load_data_1hot(fasta_file,
targets_file,
extend_len=options.extend_length,
mean_norm=False,
whiten=False,
permute=False,
sort=False)
# reshape sequences for torch
seqs = seqs.reshape((seqs.shape[0], 4, 1, seqs.shape[1] / 4))
# read headers
headers = []
for line in open(fasta_file):
if line[0] == '>':
headers.append(line[1:].rstrip())
headers = np.array(headers)
# read labels
target_labels = open(targets_file).readline().strip().split('\t')
# read additional features
if options.add_features_file:
df_add = pd.read_table(options.add_features_file, index_col=0)
df_add = df_add.astype(np.float32, copy=False)
# permute
if options.permute:
order = npr.permutation(seqs.shape[0])
seqs = seqs[order]
targets = targets[order]
headers = headers[order]
if options.add_features_file:
df_add = df_add.iloc[order]
# check proper sum
if options.counts:
assert (options.test_pct + options.valid_pct <= seqs.shape[0])
else:
assert (options.test_pct + options.valid_pct <= 1.0)
#################################################################
# divide data
#################################################################
if options.counts:
test_count = int(options.test_pct)
valid_count = int(options.valid_pct)
else:
test_count = int(0.5 + options.test_pct * seqs.shape[0])
valid_count = int(0.5 + options.valid_pct * seqs.shape[0])
train_count = seqs.shape[0] - test_count - valid_count
train_count = batch_round(train_count, options.batch_size)
print >> sys.stderr, '%d training sequences ' % train_count
test_count = batch_round(test_count, options.batch_size)
print >> sys.stderr, '%d test sequences ' % test_count
valid_count = batch_round(valid_count, options.batch_size)
print >> sys.stderr, '%d validation sequences ' % valid_count
i = 0
train_seqs, train_targets = seqs[i:i +
train_count, :], targets[i:i +
train_count, :]
i += train_count
valid_seqs, valid_targets, valid_headers = seqs[
i:i + valid_count, :], targets[i:i +
valid_count, :], headers[i:i +
valid_count]
i += valid_count
test_seqs, test_targets, test_headers = seqs[i:i + test_count, :], targets[
i:i + test_count, :], headers[i:i + test_count]
if options.add_features_file:
i = 0
train_add = df_add.iloc[i:i + train_count]
i += train_count
valid_add = df_add.iloc[i:i + valid_count]
i += valid_count
test_add = df_add.iloc[i:i + test_count]
#################################################################
# construct hdf5 representation
#################################################################
h5f = h5py.File(out_file, 'w')
h5f.create_dataset('target_labels', data=target_labels)
if train_count > 0:
h5f.create_dataset('train_in', data=train_seqs)
h5f.create_dataset('train_out', data=train_targets)
if valid_count > 0:
h5f.create_dataset('valid_in', data=valid_seqs)
h5f.create_dataset('valid_out', data=valid_targets)
if test_count > 0:
h5f.create_dataset('test_in', data=test_seqs)
h5f.create_dataset('test_out', data=test_targets)
h5f.create_dataset('test_headers', data=test_headers)
elif options.valid_test:
h5f.create_dataset('test_in', data=valid_seqs)
h5f.create_dataset('test_out', data=valid_targets)
h5f.create_dataset('test_headers', data=valid_headers)
if options.add_features_file:
h5f.create_dataset('add_labels', data=list(df_add.columns))
if train_count > 0:
h5f.create_dataset('train_add', data=train_add.as_matrix())
if valid_count > 0:
h5f.create_dataset('valid_add', data=valid_add.as_matrix())
if test_count > 0:
h5f.create_dataset('test_add', data=test_add.as_matrix())
elif options.valid_test:
h5f.create_dataset('test_add', data=valid_add.as_matrix())
h5f.close()
quit()
# filenames
fasta_file = args[0]
targets_file = args[1]
target_labels_file = args[2]
train_x_file = args[3]
train_y_file = args[4]
val_x_file = args[5]
val_y_file = args[6]
test_x_file = args[7]
test_y_file = args[8]
# get data
print "getting data"
seqs, targets = dna_io.load_data_1hot(fasta_file, targets_file, extend_len=None, mean_norm=False, \
whiten=False, permute=False, sort=False)
assert (seqs.shape[0] == targets.shape[0])
seqs = seqs.reshape(
(seqs.shape[0], 4, seqs.shape[1] / 4)) # shape = (dataset_size, 4, 600)
seqs = np.transpose(seqs, (0, 2, 1)) # make shape = (dataset_size, 600, 4)
# get an array of the cell types
print "getting target labels"
target_labels = []
with open(targets_file, "r") as target_file:
target_labels = target_file.readline().strip().split("\t")
# permute data if need be
if options.permute:
def main():
usage = 'usage: %prog [options] <model_file> <input_file>'
parser = OptionParser(usage)
parser.add_option('-a', dest='input_activity_file', help='Optional activity table corresponding to an input FASTA file')
parser.add_option('-d', dest='model_hdf5_file', default=None, help='Pre-computed model output as HDF5 [Default: %default]')
parser.add_option('-g', dest='gain_height', default=False, action='store_true', help='Nucleotide heights determined by the max of loss and gain [Default: %default]')
parser.add_option('-m', dest='min_limit', default=0.1, type='float', help='Minimum heatmap limit [Default: %default]')
parser.add_option('-n', dest='center_nt', default=0, type='int', help='Center nt to mutate and plot in the heat map [Default: %default]')
parser.add_option('-o', dest='out_dir', default='heat', help='Output directory [Default: %default]')
parser.add_option('-p', dest='print_table_all', default=False, action='store_true', help='Print all targets to the table [Default: %default]')
parser.add_option('-r', dest='rng_seed', default=1, type='float', help='Random number generator seed [Default: %default]')
parser.add_option('-s', dest='sample', default=None, type='int', help='Sample sequences from the test set [Default:%default]')
parser.add_option('-t', dest='targets', default='0', help='Comma-separated list of target indexes to plot (or -1 for all) [Default: %default]')
(options,args) = parser.parse_args()
if len(args) != 2:
parser.error('Must provide Basset model file and input sequences (as a FASTA file or test data in an HDF file')
else:
model_file = args[0]
input_file = args[1]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
random.seed(options.rng_seed)
#################################################################
# parse input file
#################################################################
try:
# input_file is FASTA
# load sequences and headers
seqs = []
seq_headers = []
for line in open(input_file):
if line[0] == '>':
seq_headers.append(line[1:].rstrip())
seqs.append('')
else:
seqs[-1] += line.rstrip()
model_input_hdf5 = '%s/model_in.h5'%options.out_dir
if options.input_activity_file:
# one hot code
seqs_1hot, targets = dna_io.load_data_1hot(input_file, options.input_activity_file, mean_norm=False, whiten=False, permute=False, sort=False)
# read in target names
target_labels = open(options.input_activity_file).readline().strip().split('\t')
else:
# load sequences
seqs_1hot = dna_io.load_sequences(input_file, permute=False)
targets = None
target_labels = None
# sample
if options.sample:
sample_i = np.array(random.sample(xrange(seqs_1hot.shape[0]), options.sample))
seqs_1hot = seqs_1hot[sample_i]
seq_headers = seq_headers[sample_i]
if targets is not None:
targets = targets[sample_i]
# reshape sequences for torch
seqs_1hot = seqs_1hot.reshape((seqs_1hot.shape[0],4,1,seqs_1hot.shape[1]/4))
# write as test data to a HDF5 file
h5f = h5py.File(model_input_hdf5, 'w')
h5f.create_dataset('test_in', data=seqs_1hot)
h5f.close()
except (IOError, IndexError):
# input_file is HDF5
try:
model_input_hdf5 = input_file
# load (sampled) test data from HDF5
hdf5_in = h5py.File(input_file, 'r')
seqs_1hot = np.array(hdf5_in['test_in'])
targets = np.array(hdf5_in['test_out'])
try: # TEMP
seq_headers = np.array(hdf5_in['test_headers'])
target_labels = np.array(hdf5_in['target_labels'])
except:
seq_headers = None
target_labels = None
hdf5_in.close()
# sample
if options.sample:
sample_i = np.array(random.sample(xrange(seqs_1hot.shape[0]), options.sample))
seqs_1hot = seqs_1hot[sample_i]
seq_headers = seq_headers[sample_i]
targets = targets[sample_i]
# write sampled data to a new HDF5 file
model_input_hdf5 = '%s/model_in.h5'%options.out_dir
h5f = h5py.File(model_input_hdf5, 'w')
h5f.create_dataset('test_in', data=seqs_1hot)
h5f.close()
# convert to ACGT sequences
seqs = dna_io.vecs2dna(seqs_1hot)
except IOError:
parser.error('Could not parse input file as FASTA or HDF5.')
#################################################################
# Torch predict modifications
#################################################################
if options.model_hdf5_file is None:
options.model_hdf5_file = '%s/model_out.h5' % options.out_dir
torch_cmd = 'basset_sat_predict.lua -center_nt %d %s %s %s' % (options.center_nt, model_file, model_input_hdf5, options.model_hdf5_file)
print torch_cmd
subprocess.call(torch_cmd, shell=True)
#################################################################
# load modification predictions
#################################################################
hdf5_in = h5py.File(options.model_hdf5_file, 'r')
seq_mod_preds = np.array(hdf5_in['seq_mod_preds'])
hdf5_in.close()
# trim seqs to match seq_mod_preds length
seq_len = len(seqs[0])
delta_start = 0
delta_len = seq_mod_preds.shape[2]
if delta_len < seq_len:
delta_start = (seq_len - delta_len)/2
for i in range(len(seqs)):
seqs[i] = seqs[i][delta_start:delta_start+delta_len]
# decide which cells to plot
if options.targets == '-1':
plot_targets = xrange(seq_mod_preds.shape[3])
else:
plot_targets = [int(ci) for ci in options.targets.split(',')]
#################################################################
# plot
#################################################################
table_out = open('%s/table.txt' % options.out_dir, 'w')
rdbu = sns.color_palette("RdBu_r", 10)
nts = 'ACGT'
for si in range(seq_mod_preds.shape[0]):
try:
header = seq_headers[si]
except TypeError:
header = 'seq%d' % si
seq = seqs[si]
# plot some descriptive heatmaps for each individual cell type
for ci in plot_targets:
seq_mod_preds_cell = seq_mod_preds[si,:,:,ci]
real_pred_cell = get_real_pred(seq_mod_preds_cell, seq)
# compute matrices
norm_matrix = seq_mod_preds_cell - real_pred_cell
min_scores = seq_mod_preds_cell.min(axis=0)
max_scores = seq_mod_preds_cell.max(axis=0)
minmax_matrix = np.vstack([min_scores - real_pred_cell, max_scores - real_pred_cell])
# prepare figure
sns.set(style='white', font_scale=0.5)
sns.axes_style({'axes.linewidth':1})
heat_cols = 400
sad_start = 1
sad_end = 323
logo_start = 0
logo_end = 324
fig = plt.figure(figsize=(20,3))
ax_logo = plt.subplot2grid((3,heat_cols), (0,logo_start), colspan=(logo_end-logo_start))
ax_sad = plt.subplot2grid((3,heat_cols), (1,sad_start), colspan=(sad_end-sad_start))
ax_heat = plt.subplot2grid((3,heat_cols), (2,0), colspan=heat_cols)
# print a WebLogo of the sequence
vlim = max(options.min_limit, abs(minmax_matrix).max())
if options.gain_height:
seq_heights = 0.25 + 1.75/vlim*(abs(minmax_matrix).max(axis=0))
else:
seq_heights = 0.25 + 1.75/vlim*(-minmax_matrix[0])
logo_eps = '%s/%s_c%d_seq.eps' % (options.out_dir, header_filename(header), ci)
seq_logo(seq, seq_heights, logo_eps)
# add to figure
logo_png = '%s.png' % logo_eps[:-4]
subprocess.call('convert -density 300 %s %s' % (logo_eps, logo_png), shell=True)
logo = Image.open(logo_png)
ax_logo.imshow(logo)
ax_logo.set_axis_off()
# plot loss and gain SAD scores
ax_sad.plot(-minmax_matrix[0], c=rdbu[0], label='loss', linewidth=1)
ax_sad.plot(minmax_matrix[1], c=rdbu[-1], label='gain', linewidth=1)
ax_sad.set_xlim(0,minmax_matrix.shape[1])
ax_sad.legend()
# ax_sad.grid(True, linestyle=':')
for axis in ['top','bottom','left','right']:
ax_sad.spines[axis].set_linewidth(0.5)
# plot real-normalized scores
vlim = max(options.min_limit, abs(norm_matrix).max())
sns.heatmap(norm_matrix, linewidths=0, cmap='RdBu_r', vmin=-vlim, vmax=vlim, xticklabels=False, ax=ax_heat)
ax_heat.yaxis.set_ticklabels('TGCA', rotation='horizontal') # , size=10)
# save final figure
plt.tight_layout()
plt.savefig('%s/%s_c%d_heat.pdf' % (options.out_dir,header.replace(':','_'), ci), dpi=300)
plt.close()
#################################################################
# print table of nt variability for each cell
#################################################################
print_targets = plot_targets
if options.print_table_all:
print_targets = range(seq_mod_preds.shape[3])
for ci in print_targets:
seq_mod_preds_cell = seq_mod_preds[si,:,:,ci]
real_pred_cell = get_real_pred(seq_mod_preds_cell, seq)
min_scores = seq_mod_preds_cell.min(axis=0)
max_scores = seq_mod_preds_cell.max(axis=0)
loss_matrix = real_pred_cell - seq_mod_preds_cell.min(axis=0)
gain_matrix = seq_mod_preds_cell.max(axis=0) - real_pred_cell
for pos in range(seq_mod_preds_cell.shape[1]):
cols = [header, delta_start+pos, ci, loss_matrix[pos], gain_matrix[pos]]
print >> table_out, '\t'.join([str(c) for c in cols])
table_out.close()
def main():
usage = 'usage: %prog [options] <model_file> <profile_file> <input_file>'
parser = OptionParser(usage)
parser.add_option(
'-a',
dest='input_activity_file',
help='Optional activity table corresponding to an input FASTA file')
parser.add_option(
'--all',
dest='all_data',
default=False,
action='store_true',
help=
'Search all training/valid/test sequences. By default we search only the test set. [Default: %default]'
)
parser.add_option('--cuda',
dest='cuda',
default=False,
action='store_true',
help='Run on GPGPU [Default: %default]')
parser.add_option('--cudnn',
dest='cudnn',
default=False,
action='store_true',
help='Run on GPGPU w/cuDNN [Default: %default]')
parser.add_option(
'-d',
dest='model_out_file',
default=None,
help='Pre-computed model predictions output table [Default: %default]')
parser.add_option(
'-e',
dest='norm_even',
default=False,
action='store_true',
help=
'Normalize the weights for the positive and negative datasets to be even [Default: %default]'
)
parser.add_option('-f',
dest='font_heat',
default=6,
type='int',
help='Heat map axis font size [Default: %default]')
parser.add_option('-n',
dest='num_dissect',
default=10,
type='int',
help='Dissect the top n hits [Default: %default]')
parser.add_option('-o',
dest='out_dir',
default='profile',
help='Output directory [Default: %default]')
parser.add_option(
'-r',
dest='norm_preds',
default=False,
action='store_true',
help='Normalize predictions to have equal frequency [Default: %default]'
)
parser.add_option(
'-z',
dest='weight_zero',
default=1.0,
type='float',
help=
'Adjust the weights for the zero samples by this value [Default: %default]'
)
(options, args) = parser.parse_args()
if len(args) != 3:
parser.error(
'Must provide Basset model file, activity profile file, and input sequences (as a FASTA file or test data in an HDF file)'
)
else:
model_file = args[0]
profile_file = args[1]
input_file = args[2]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
#################################################################
# parse input file
#################################################################
try:
# input_file is FASTA
# load sequences and headers
seqs = []
seq_headers = []
for line in open(input_file):
if line[0] == '>':
seq_headers.append(line[1:].rstrip())
seqs.append('')
else:
seqs[-1] += line.rstrip()
# convert to arrays
seqs = np.array(seqs)
seq_headers = np.array(seq_headers)
model_input_hdf5 = '%s/model_in.h5' % options.out_dir
if options.input_activity_file:
# one hot code
seqs_1hot, targets = dna_io.load_data_1hot(
input_file,
options.input_activity_file,
mean_norm=False,
whiten=False,
permute=False,
sort=False)
else:
# load sequences
seqs_1hot = dna_io.load_sequences(input_file, permute=False)
targets = None
# reshape sequences for torch
seqs_1hot = seqs_1hot.reshape(
(seqs_1hot.shape[0], 4, 1, seqs_1hot.shape[1] / 4))
# write as test data to a HDF5 file
h5f = h5py.File(model_input_hdf5, 'w')
h5f.create_dataset('test_in', data=seqs_1hot)
h5f.close()
except (IOError, IndexError, UnicodeDecodeError):
# input_file is HDF5
try:
model_input_hdf5 = input_file
# load (sampled) test data from HDF5
hdf5_in = h5py.File(input_file, 'r')
seqs_1hot = np.array(hdf5_in['test_in'])
targets = np.array(hdf5_in['test_out'])
seq_headers = np.array(
[h.decode('UTF-8') for h in hdf5_in['test_headers']])
hdf5_in.close()
# convert to ACGT sequences
seqs = dna_io.vecs2dna(seqs_1hot)
except IOError:
parser.error('Could not parse input file as FASTA or HDF5.')
#################################################################
# Torch predict modifications
#################################################################
# GPU options (needed below, too)
gpgpu_str = ''
if options.cudnn:
gpgpu_str = '-cudnn'
elif options.cuda:
gpgpu_str = '-cuda'
if options.model_out_file is None:
options.model_out_file = '%s/preds.txt' % options.out_dir
torch_cmd = 'basset_predict.lua -mc_n 10 -rc %s %s %s %s' % (
gpgpu_str, model_file, model_input_hdf5, options.model_out_file)
print(torch_cmd)
subprocess.call(torch_cmd, shell=True)
# read in predictions
seqs_preds = np.loadtxt(options.model_out_file)
num_targets = seqs_preds.shape[1]
#################################################################
# parse profile file
#################################################################
activity_profile, profile_weights, profile_mask, target_labels = load_profile(
profile_file, num_targets, options.norm_even, options.weight_zero)
# normalize predictions
if options.norm_preds:
pred_means = seqs_preds.mean(axis=0)
# save to file for basset_refine.py
np.save('%s/pred_means' % options.out_dir, pred_means)
# aim for profile weighted average
aim_mean = np.average(pred_means[profile_mask],
weights=profile_weights[profile_mask])
# normalize
for ti in range(seqs_preds.shape[1]):
ratio_ti = pred_means[ti] / aim_mean
if profile_mask[ti] and (ratio_ti < 1 / 4 or ratio_ti > 4):
print(
'WARNING: target %d with mean %.4f differs 4-fold from the median %.3f'
% (ti, pred_means[ti], aim_mean),
file=sys.stderr)
seqs_preds[:, ti] = znorm(seqs_preds[:, ti], pred_means[ti],
aim_mean)
#################################################################
# plot clustered heat map limited to relevant targets
#################################################################
seqs_preds_prof = seqs_preds[:, profile_mask]
seqs_preds_var = seqs_preds_prof.var(axis=1)
seqs_sort_var = np.argsort(seqs_preds_var)[::-1]
# heat map
plt.figure()
g = sns.clustermap(np.transpose(seqs_preds_prof[seqs_sort_var[:1500]]),
metric='cosine',
linewidths=0,
yticklabels=target_labels[profile_mask],
xticklabels=False)
plt.setp(g.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
for label in g.ax_heatmap.yaxis.get_majorticklabels():
label.set_fontsize(options.font_heat)
plt.savefig('%s/heat_clust.pdf' % options.out_dir)
plt.close()
# dimension reduction
# model_pca = PCA(n_components=50)
# spp_pca = model.fit_transform(np.transpose(seqs_preds_prof))
# model = TSNE(n_components=2, perplexity=5, metric='euclidean')
# spp_dr = model.fit_transform(spp_pca)
model = PCA(n_components=2)
spp_dr = model.fit_transform(np.transpose(seqs_preds_prof))
plt.figure()
plt.scatter(spp_dr[:, 0], spp_dr[:, 1], c='black', s=5)
target_labels_prof_concise = [
tl.split(':')[-1] for tl in target_labels[profile_mask]
]
for label, x, y, activity in zip(target_labels_prof_concise, spp_dr[:, 0],
spp_dr[:, 1],
activity_profile[profile_mask]):
plt.annotate(label,
xy=(x, y),
size=10,
color=sns.color_palette('deep')[int(activity)])
plt.savefig('%s/dim_red.pdf' % options.out_dir)
plt.close()
#################################################################
# compute profile distances
#################################################################
# compute prediction distances
seqs_pdists = []
for si in range(seqs_preds.shape[0]):
# sd = np.power(seqs_preds[si,profile_mask]-activity_profile[profile_mask], 2).sum()
sd = log_loss(activity_profile[profile_mask],
seqs_preds[si, profile_mask],
sample_weight=profile_weights[profile_mask])
seqs_pdists.append(sd)
seqs_pdists = np.array(seqs_pdists)
# obtain sorted indexes
seqs_sort_dist = np.argsort(seqs_pdists)
# compute target distances
seqs_tdists = []
for si in range(seqs_preds.shape[0]):
tdists = np.absolute(targets[si, profile_mask] -
activity_profile[profile_mask])
tdists_weight = np.multiply(tdists, profile_weights[profile_mask])
td = tdists_weight.sum()
seqs_tdists.append(td)
seqs_tdists = np.array(seqs_tdists)
# print as table
table_out = open('%s/table.txt' % options.out_dir, 'w')
for si in seqs_sort_dist:
cols = [si, seqs_pdists[si], seqs_tdists[si]] + list(
seqs_preds[si, profile_mask])
print('\t'.join([str(c) for c in cols]), file=table_out)
table_out.close()
#################################################################
# plot sorted heat map
#################################################################
plt.figure()
g = sns.clustermap(np.transpose(seqs_preds_prof[seqs_sort_dist[:1000]]),
col_cluster=False,
metric='cosine',
linewidths=0,
yticklabels=target_labels[profile_mask],
xticklabels=False)
plt.setp(g.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
for label in g.ax_heatmap.yaxis.get_majorticklabels():
label.set_fontsize(options.font_heat)
plt.savefig('%s/heat_rank.pdf' % options.out_dir)
plt.close()
#################################################################
# dissect the top hits
#################################################################
satmut_targets = ','.join(
[str(ti) for ti in range(len(activity_profile)) if profile_mask[ti]])
if gpgpu_str != '':
gpgpu_str = '-%s' % gpgpu_str
for ni in range(options.num_dissect):
si = seqs_sort_dist[ni]
# print FASTA
fasta_file = '%s/seq%d.fa' % (options.out_dir, ni)
fasta_out = open(fasta_file, 'w')
print('>%s\n%s' % (seq_headers[si], seqs[si]), file=fasta_out)
fasta_out.close()
# saturated mutagenesis
cmd = 'basset_sat.py %s --mc_n 10 -n 500 -o %s/satmut%d -t %s %s %s' % (
gpgpu_str, options.out_dir, ni, satmut_targets, model_file,
fasta_file)
subprocess.call(cmd, shell=True)
# predictions and targets heat
profile_sort = np.argsort(activity_profile[profile_mask])
heat_mat = np.array([
activity_profile[profile_mask], targets[si, profile_mask],
seqs_preds_prof[si]
])
heat_mat = heat_mat[:, profile_sort]
plt.figure()
ax = sns.heatmap(np.transpose(heat_mat),
yticklabels=target_labels[profile_mask][profile_sort],
xticklabels=['Desired', 'Experiment', 'Prediction'])
plt.setp(ax.xaxis.get_majorticklabels(), rotation=-45)
plt.setp(ax.yaxis.get_majorticklabels(), rotation=-0)
for label in ax.yaxis.get_majorticklabels():
label.set_fontsize(options.font_heat)
plt.savefig('%s/heat%d.pdf' % (options.out_dir, ni))
plt.close()
def main():
usage = "usage: %prog [options] <model_file> <input_file>"
parser = OptionParser(usage)
parser.add_option(
"-a", dest="input_activity_file", help="Optional activity table corresponding to an input FASTA file"
)
parser.add_option(
"-d", dest="model_hdf5_file", default=None, help="Pre-computed model output as HDF5 [Default: %default]"
)
parser.add_option(
"-g",
dest="gain_height",
default=False,
action="store_true",
help="Nucleotide heights determined by the max of loss and gain [Default: %default]",
)
parser.add_option(
"-m", dest="min_limit", default=0.1, type="float", help="Minimum heatmap limit [Default: %default]"
)
parser.add_option(
"-n",
dest="center_nt",
default=200,
type="int",
help="Center nt to mutate and plot in the heat map [Default: %default]",
)
parser.add_option("-o", dest="out_dir", default="heat", help="Output directory [Default: %default]")
parser.add_option(
"-s", dest="sample", default=None, type="int", help="Sample sequences from the test set [Default:%default]"
)
parser.add_option(
"-t",
dest="targets",
default="0",
help="Comma-separated list of target indexes to plot (or -1 for all) [Default: %default]",
)
(options, args) = parser.parse_args()
if len(args) != 2:
parser.error("Must provide Basset model file and input sequences (as a FASTA file or test data in an HDF file")
else:
model_file = args[0]
input_file = args[1]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
#################################################################
# parse input file
#################################################################
try:
# input_file is FASTA
# load sequences and headers
seqs = []
seq_headers = []
for line in open(input_file):
if line[0] == ">":
seq_headers.append(line[1:].rstrip())
seqs.append("")
else:
seqs[-1] += line.rstrip()
model_input_hdf5 = "%s/model_in.h5" % options.out_dir
if options.input_activity_file:
# one hot code
seqs_1hot, targets = dna_io.load_data_1hot(
input_file, options.input_activity_file, mean_norm=False, whiten=False, permute=False, sort=False
)
# read in target names
target_labels = open(options.input_activity_file).readline().strip().split("\t")
else:
# load sequences
seqs_1hot = dna_io.load_sequences(input_file, permute=False)
targets = None
target_labels = None
# sample
if options.sample:
sample_i = np.array(random.sample(xrange(seqs_1hot.shape[0]), options.sample))
seqs_1hot = seqs_1hot[sample_i]
seq_headers = seq_headers[sample_i]
if targets is not None:
targets = targets[sample_i]
# reshape sequences for torch
seqs_1hot = seqs_1hot.reshape((seqs_1hot.shape[0], 4, 1, seqs_1hot.shape[1] / 4))
# write as test data to a HDF5 file
h5f = h5py.File(model_input_hdf5, "w")
h5f.create_dataset("test_in", data=seqs_1hot)
h5f.close()
except (IOError, IndexError):
# input_file is HDF5
try:
model_input_hdf5 = input_file
# load (sampled) test data from HDF5
hdf5_in = h5py.File(input_file, "r")
seqs_1hot = np.array(hdf5_in["test_in"])
targets = np.array(hdf5_in["test_out"])
try: # TEMP
seq_headers = np.array(hdf5_in["test_headers"])
target_labels = np.array(hdf5_in["target_labels"])
except:
seq_headers = None
target_labels = None
hdf5_in.close()
# sample
if options.sample:
sample_i = np.array(random.sample(xrange(seqs_1hot.shape[0]), options.sample))
seqs_1hot = seqs_1hot[sample_i]
seq_headers = seq_headers[sample_i]
targets = targets[sample_i]
# write sampled data to a new HDF5 file
model_input_hdf5 = "%s/model_in.h5" % options.out_dir
h5f = h5py.File(model_input_hdf5, "w")
h5f.create_dataset("test_in", data=seqs_1hot)
h5f.close()
# convert to ACGT sequences
seqs = dna_io.vecs2dna(seqs_1hot)
except IOError:
parser.error("Could not parse input file as FASTA or HDF5.")
#################################################################
# Torch predict modifications
#################################################################
if options.model_hdf5_file is None:
options.model_hdf5_file = "%s/model_out.h5" % options.out_dir
torch_cmd = "basset_sat_predict.lua -center_nt %d %s %s %s" % (
options.center_nt,
model_file,
model_input_hdf5,
options.model_hdf5_file,
)
if subprocess.call(torch_cmd, shell=True):
message("Error running basset_sat_predict.lua", "error")
#################################################################
# load modification predictions
#################################################################
hdf5_in = h5py.File(options.model_hdf5_file, "r")
seq_mod_preds = np.array(hdf5_in["seq_mod_preds"])
hdf5_in.close()
# trim seqs to match seq_mod_preds length
seq_len = len(seqs[0])
delta_start = 0
delta_len = seq_mod_preds.shape[2]
if delta_len < seq_len:
delta_start = (seq_len - delta_len) / 2
for i in range(len(seqs)):
seqs[i] = seqs[i][delta_start : delta_start + delta_len]
# decide which cells to plot
if options.targets == "-1":
plot_targets = xrange(seq_mod_preds.shape[3])
else:
plot_targets = [int(ci) for ci in options.targets.split(",")]
#################################################################
# plot
#################################################################
table_out = open("%s/table.txt" % options.out_dir, "w")
rdbu = sns.color_palette("RdBu_r", 10)
nts = "ACGT"
for si in range(seq_mod_preds.shape[0]):
try:
header = seq_headers[si]
except TypeError:
header = "seq%d" % si
seq = seqs[si]
# plot some descriptive heatmaps for each individual cell type
for ci in plot_targets:
seq_mod_preds_cell = seq_mod_preds[si, :, :, ci]
real_pred_cell = get_real_pred(seq_mod_preds_cell, seq)
# compute matrices
norm_matrix = seq_mod_preds_cell - real_pred_cell
min_scores = seq_mod_preds_cell.min(axis=0)
max_scores = seq_mod_preds_cell.max(axis=0)
minmax_matrix = np.vstack([min_scores - real_pred_cell, max_scores - real_pred_cell])
# prepare figure
sns.set(style="white", font_scale=0.5)
sns.axes_style({"axes.linewidth": 1})
heat_cols = 400
sad_start = 1
sad_end = 323
logo_start = 0
logo_end = 324
fig = plt.figure(figsize=(20, 3))
ax_logo = plt.subplot2grid((3, heat_cols), (0, logo_start), colspan=(logo_end - logo_start))
ax_sad = plt.subplot2grid((3, heat_cols), (1, sad_start), colspan=(sad_end - sad_start))
ax_heat = plt.subplot2grid((3, heat_cols), (2, 0), colspan=heat_cols)
# print a WebLogo of the sequence
vlim = max(options.min_limit, abs(minmax_matrix).max())
if options.gain_height:
seq_heights = 0.25 + 1.75 / vlim * (abs(minmax_matrix).max(axis=0))
else:
seq_heights = 0.25 + 1.75 / vlim * (-minmax_matrix[0])
logo_eps = "%s/%s_c%d_seq.eps" % (options.out_dir, header_filename(header), ci)
seq_logo(seq, seq_heights, logo_eps)
# add to figure
logo_png = "%s.png" % logo_eps[:-4]
logo_cmd = "convert -density 300 %s %s" % (logo_eps, logo_png)
if subprocess.call(logo_cmd, shell=True):
message("Error running convert", "error")
logo = Image.open(logo_png)
ax_logo.imshow(logo)
ax_logo.set_axis_off()
# plot loss and gain SAD scores
ax_sad.plot(-minmax_matrix[0], c=rdbu[0], label="loss", linewidth=1)
ax_sad.plot(minmax_matrix[1], c=rdbu[-1], label="gain", linewidth=1)
ax_sad.set_xlim(0, minmax_matrix.shape[1])
ax_sad.legend()
# ax_sad.grid(True, linestyle=':')
for axis in ["top", "bottom", "left", "right"]:
ax_sad.spines[axis].set_linewidth(0.5)
# plot real-normalized scores
vlim = max(options.min_limit, abs(norm_matrix).max())
sns.heatmap(norm_matrix, linewidths=0, cmap="RdBu_r", vmin=-vlim, vmax=vlim, xticklabels=False, ax=ax_heat)
ax_heat.yaxis.set_ticklabels("TGCA", rotation="horizontal") # , size=10)
# save final figure
plt.tight_layout()
plt.savefig("%s/%s_c%d_heat.pdf" % (options.out_dir, header.replace(":", "_"), ci), dpi=300)
plt.close()
#################################################################
# print table of nt variability for each cell
#################################################################
for ci in range(seq_mod_preds.shape[3]):
seq_mod_preds_cell = seq_mod_preds[si, :, :, ci]
real_pred_cell = get_real_pred(seq_mod_preds_cell, seq)
min_scores = seq_mod_preds_cell.min(axis=0)
max_scores = seq_mod_preds_cell.max(axis=0)
loss_matrix = real_pred_cell - seq_mod_preds_cell.min(axis=0)
gain_matrix = seq_mod_preds_cell.max(axis=0) - real_pred_cell
for pos in range(seq_mod_preds_cell.shape[1]):
cols = [header, delta_start + pos, ci, loss_matrix[pos], gain_matrix[pos]]
print >> table_out, "\t".join([str(c) for c in cols])
table_out.close()
def main():
usage = 'usage: %prog [options] <fasta_file> <targets_file> <out_file>'
parser = OptionParser(usage)
parser.add_option('-a', dest='add_features_file', default=None, help='Table of additional features')
parser.add_option('-b', dest='batch_size', default=None, type='int', help='Align sizes with batch size')
parser.add_option('-c', dest='counts', default=False, action='store_true', help='Validation and training proportions are given as raw counts [Default: %default]')
parser.add_option('-e', dest='extend_length', type='int', default=None, help='Extend all sequences to this length [Default: %default]')
parser.add_option('-r', dest='permute', default=False, action='store_true', help='Permute sequences [Default: %default]')
parser.add_option('-s', dest='random_seed', default=1, type='int', help='numpy.random seed [Default: %default]')
parser.add_option('-t', dest='test_pct', default=0, type='float', help='Test % [Default: %default]')
parser.add_option('-v', dest='valid_pct', default=0, type='float', help='Validation % [Default: %default]')
parser.add_option('--vt', dest='valid_test', default=False, action='store_true', help='Use validation ')
(options,args) = parser.parse_args()
if len(args) != 3:
parser.error('Must provide fasta file, targets file, and an output prefix')
else:
fasta_file = args[0]
targets_file = args[1]
out_file = args[2]
# seed rng before shuffle
npr.seed(options.random_seed)
#################################################################
# load data
#################################################################
seqs, targets = dna_io.load_data_1hot(fasta_file, targets_file, extend_len=options.extend_length, mean_norm=False, whiten=False, permute=False, sort=False)
# reshape sequences for torch
seqs = seqs.reshape((seqs.shape[0],4,1,seqs.shape[1]/4))
# read headers
headers = []
for line in open(fasta_file):
if line[0] == '>':
headers.append(line[1:].rstrip())
headers = np.array(headers)
# read labels
target_labels = open(targets_file).readline().strip().split('\t')
# read additional features
if options.add_features_file:
df_add = pd.read_table(options.add_features_file, index_col=0)
df_add = df_add.astype(np.float32, copy=False)
# permute
if options.permute:
order = npr.permutation(seqs.shape[0])
seqs = seqs[order]
targets = targets[order]
headers = headers[order]
if options.add_features_file:
df_add = df_add.iloc[order]
# check proper sum
if options.counts:
assert(options.test_pct + options.valid_pct <= seqs.shape[0])
else:
assert(options.test_pct + options.valid_pct <= 1.0)
#################################################################
# divide data
#################################################################
if options.counts:
test_count = options.test_pct
valid_count = options.valid_pct
else:
test_count = int(0.5 + options.test_pct * seqs.shape[0])
valid_count = int(0.5 + options.valid_pct * seqs.shape[0])
train_count = seqs.shape[0] - test_count - valid_count
train_count = batch_round(train_count, options.batch_size)
print >> sys.stderr, '%d training sequences ' % train_count
test_count = batch_round(test_count, options.batch_size)
print >> sys.stderr, '%d test sequences ' % test_count
valid_count = batch_round(valid_count, options.batch_size)
print >> sys.stderr, '%d validation sequences ' % valid_count
i = 0
train_seqs, train_targets = seqs[i:i+train_count,:], targets[i:i+train_count,:]
i += train_count
valid_seqs, valid_targets, valid_headers = seqs[i:i+valid_count,:], targets[i:i+valid_count,:], headers[i:i+valid_count]
i += valid_count
test_seqs, test_targets, test_headers = seqs[i:i+test_count,:], targets[i:i+test_count,:], headers[i:i+test_count]
if options.add_features_file:
i = 0
train_add = df_add.iloc[i:i+train_count]
i += train_count
valid_add = df_add.iloc[i:i+valid_count]
i += valid_count
test_add = df_add.iloc[i:i+test_count]
#################################################################
# construct hdf5 representation
#################################################################
h5f = h5py.File(out_file, 'w')
h5f.create_dataset('target_labels', data=target_labels)
if train_count > 0:
h5f.create_dataset('train_in', data=train_seqs)
h5f.create_dataset('train_out', data=train_targets)
if valid_count > 0:
h5f.create_dataset('valid_in', data=valid_seqs)
h5f.create_dataset('valid_out', data=valid_targets)
if test_count > 0:
h5f.create_dataset('test_in', data=test_seqs)
h5f.create_dataset('test_out', data=test_targets)
h5f.create_dataset('test_headers', data=test_headers)
elif options.valid_test:
h5f.create_dataset('test_in', data=valid_seqs)
h5f.create_dataset('test_out', data=valid_targets)
h5f.create_dataset('test_headers', data=valid_headers)
if options.add_features_file:
h5f.create_dataset('add_labels', data=list(df_add.columns))
if train_count > 0:
h5f.create_dataset('train_add', data=train_add.as_matrix())
if valid_count > 0:
h5f.create_dataset('valid_add', data=valid_add.as_matrix())
if test_count > 0:
h5f.create_dataset('test_add', data=test_add.as_matrix())
elif options.valid_test:
h5f.create_dataset('test_add', data=valid_add.as_matrix())
h5f.close()
def main():
usage = 'usage: %prog [options] <fasta_file> <targets_file> <out_file>'
parser = OptionParser(usage)
(options, args) = parser.parse_args()
if len(args) != 3:
parser.error(
'Must provide fasta file, targets file, and an output prefix')
else:
fasta_file = args[0]
targets_file = args[1]
out_file = args[2]
seqs, targets = dna_io.load_data_1hot(fasta_file, targets_file)
seqs = seqs.reshape((seqs.shape[0], 4, 1, seqs.shape[1] / 4))
target_labels = open(targets_file).readline().strip().split('\t')
order = npr.permutation(seqs.shape[0])
seqs = seqs[order]
targets = targets[order]
order = npr.permutation(seqs.shape[0])
seqs = seqs[order]
targets = targets[order]
seqsnum1 = int(seqs.shape[0] / 20)
x = 0
kseqs = []
ktargets = []
for i in range(19):
kseqs.append(seqs[x:x + seqsnum1, :].tolist())
ktargets.append(targets[x:x + seqsnum1, :].tolist())
x += seqsnum1
kseqs.append(seqs[x:seqs.shape[0], :].tolist())
ktargets.append(targets[x:seqs.shape[0], :].tolist())
length = len(kseqs[0])
for i in range(20):
name = out_file + str(i + 1) + '.h5'
valid_seqs = kseqs[i]
valid_targets = ktargets[i]
if i != 0:
train_seqs = kseqs[0][:][:][:][:]
train_targets = ktargets[0][:][:][:][:]
else:
train_seqs = kseqs[1][:][:][:][:]
train_targets = ktargets[1][:][:][:][:]
for j in range(1, 20):
if (j != i) and (i != 0):
train_seqs += kseqs[j][:][:][:][:]
train_targets += ktargets[j][:][:][:][:]
elif (j != 1) and (i == 0):
train_seqs += kseqs[j][:][:][:][:]
train_targets += ktargets[j][:][:][:][:]
h5f = h5py.File(name, 'w')
h5f.create_dataset('target_labels', data=target_labels)
h5f.create_dataset('train_in', data=train_seqs)
h5f.create_dataset('train_out', data=train_targets)
h5f.create_dataset('valid_in', data=valid_seqs)
h5f.create_dataset('valid_out', data=valid_targets)
h5f.close()
def main():
usage = 'usage: %prog [options] <model_file> <input_file>'
parser = OptionParser(usage)
parser.add_option('-a', dest='input_activity_file', help='Optional activity table corresponding to an input FASTA file')
parser.add_option('-d', dest='model_hdf5_file', default=None, help='Pre-computed model output as HDF5 [Default: %default]')
parser.add_option('-o', dest='out_dir', default='heat', help='Output directory [Default: %default]')
parser.add_option('-r', dest='rng_seed', default=1, type='float', help='Random number generator seed [Default: %default]')
parser.add_option('-s', dest='sample', default=None, type='int', help='Sample sequences from the test set [Default:%default]')
parser.add_option('-t', dest='targets', default='0', help='Comma-separated list of target indexes to plot (or -1 for all) [Default: %default]')
(options,args) = parser.parse_args()
if len(args) != 2:
parser.error('Must provide Basset model file and input sequences (as a FASTA file or test data in an HDF file')
else:
model_file = args[0]
input_file = args[1]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
random.seed(options.rng_seed)
#################################################################
# parse input file
#################################################################
try:
# input_file is FASTA
# load sequences and headers
seqs = []
seq_headers = []
for line in open(input_file):
if line[0] == '>':
seq_headers.append(line[1:].rstrip())
seqs.append('')
else:
seqs[-1] += line.rstrip()
model_input_hdf5 = '%s/model_in.h5'%options.out_dir
if options.input_activity_file:
# one hot code
seqs_1hot, targets = dna_io.load_data_1hot(input_file, options.input_activity_file, mean_norm=False, whiten=False, permute=False, sort=False)
# read in target names
target_labels = open(options.input_activity_file).readline().strip().split('\t')
else:
# load sequences
seqs_1hot = dna_io.load_sequences(input_file, permute=False)
targets = None
target_labels = None
# sample
if options.sample:
sample_i = np.array(random.sample(xrange(seqs_1hot.shape[0]), options.sample))
seqs_1hot = seqs_1hot[sample_i]
seq_headers = seq_headers[sample_i]
if targets is not None:
targets = targets[sample_i]
# reshape sequences for torch
seqs_1hot = seqs_1hot.reshape((seqs_1hot.shape[0],4,1,seqs_1hot.shape[1]/4))
# write as test data to a HDF5 file
h5f = h5py.File(model_input_hdf5, 'w')
h5f.create_dataset('test_in', data=seqs_1hot)
h5f.close()
except (IOError, IndexError):
# input_file is HDF5
try:
model_input_hdf5 = input_file
# load (sampled) test data from HDF5
hdf5_in = h5py.File(input_file, 'r')
seqs_1hot = np.array(hdf5_in['test_in'])
targets = np.array(hdf5_in['test_out'])
try: # TEMP
seq_headers = np.array(hdf5_in['test_headers'])
target_labels = np.array(hdf5_in['target_labels'])
except:
seq_headers = None
target_labels = None
hdf5_in.close()
# sample
if options.sample:
sample_i = np.array(random.sample(xrange(seqs_1hot.shape[0]), options.sample))
seqs_1hot = seqs_1hot[sample_i]
seq_headers = seq_headers[sample_i]
targets = targets[sample_i]
# write sampled data to a new HDF5 file
model_input_hdf5 = '%s/model_in.h5'%options.out_dir
h5f = h5py.File(model_input_hdf5, 'w')
h5f.create_dataset('test_in', data=seqs_1hot)
h5f.close()
# convert to ACGT sequences
seqs = dna_io.vecs2dna(seqs_1hot)
except IOError:
parser.error('Could not parse input file as FASTA or HDF5.')
#################################################################
# Torch predict modifications
#################################################################
if options.model_hdf5_file is None:
options.model_hdf5_file = '%s/model_out.h5' % options.out_dir
torch_cmd = 'basset_net_predict.lua %s %s %s' % (model_file, model_input_hdf5, options.model_hdf5_file)
print torch_cmd
subprocess.call(torch_cmd, shell=True)
#################################################################
# load modification predictions
#################################################################
hdf5_in = h5py.File(options.model_hdf5_file, 'r')
reprs = []
l = 1
while 'reprs%d'%l in hdf5_in.keys():
reprs.append(np.array(hdf5_in['reprs%d'%l]))
l += 1
hdf5_in.close()
#################################################################
# plot
#################################################################
print len(reprs)
for l in range(len(reprs)):
for si in range(len(seq_headers)):
plt.figure()
# just write the sequence out above it
# or maybe I'll ultimately want to write an
# influence version. yea probably.
print reprs[l][si].shape
sns.heatmap(reprs[l][si], linewidths=0, xticklabels=False)
plt.savefig('%s/%s_l%d.pdf' % (options.out_dir, header_filename(seq_headers[si]), l))
plt.close()
def main():
usage = "usage: %prog [options] <model_file> <profile_file> <input_file>"
parser = OptionParser(usage)
parser.add_option(
"-a", dest="input_activity_file", help="Optional activity table corresponding to an input FASTA file"
)
parser.add_option(
"--all",
dest="all_data",
default=False,
action="store_true",
help="Search all training/valid/test sequences. By default we search only the test set. [Default: %default]",
)
parser.add_option(
"--cuda", dest="cuda", default=False, action="store_true", help="Run on GPGPU [Default: %default]"
)
parser.add_option(
"--cudnn", dest="cudnn", default=False, action="store_true", help="Run on GPGPU w/cuDNN [Default: %default]"
)
parser.add_option(
"-d",
dest="model_out_file",
default=None,
help="Pre-computed model predictions output table [Default: %default]",
)
parser.add_option(
"-e",
dest="norm_even",
default=False,
action="store_true",
help="Normalize the weights for the positive and negative datasets to be even [Default: %default]",
)
parser.add_option("-f", dest="font_heat", default=6, type="int", help="Heat map axis font size [Default: %default]")
parser.add_option(
"-n", dest="num_dissect", default=10, type="int", help="Dissect the top n hits [Default: %default]"
)
parser.add_option("-o", dest="out_dir", default="profile", help="Output directory [Default: %default]")
parser.add_option(
"-r",
dest="norm_preds",
default=False,
action="store_true",
help="Normalize predictions to have equal frequency [Default: %default]",
)
parser.add_option(
"-z",
dest="weight_zero",
default=1.0,
type="float",
help="Adjust the weights for the zero samples by this value [Default: %default]",
)
(options, args) = parser.parse_args()
if len(args) != 3:
parser.error(
"Must provide Basset model file, activity profile file, and input sequences (as a FASTA file or test data in an HDF file)"
)
else:
model_file = args[0]
profile_file = args[1]
input_file = args[2]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
#################################################################
# parse input file
#################################################################
try:
# input_file is FASTA
# load sequences and headers
seqs = []
seq_headers = []
for line in open(input_file):
if line[0] == ">":
seq_headers.append(line[1:].rstrip())
seqs.append("")
else:
seqs[-1] += line.rstrip()
# convert to arrays
seqs = np.array(seqs)
seq_headers = np.array(seq_headers)
model_input_hdf5 = "%s/model_in.h5" % options.out_dir
if options.input_activity_file:
# one hot code
seqs_1hot, targets = dna_io.load_data_1hot(
input_file, options.input_activity_file, mean_norm=False, whiten=False, permute=False, sort=False
)
else:
# load sequences
seqs_1hot = dna_io.load_sequences(input_file, permute=False)
targets = None
# reshape sequences for torch
seqs_1hot = seqs_1hot.reshape((seqs_1hot.shape[0], 4, 1, seqs_1hot.shape[1] / 4))
# write as test data to a HDF5 file
h5f = h5py.File(model_input_hdf5, "w")
h5f.create_dataset("test_in", data=seqs_1hot)
h5f.close()
except (IOError, IndexError, UnicodeDecodeError):
# input_file is HDF5
try:
model_input_hdf5 = input_file
# load (sampled) test data from HDF5
hdf5_in = h5py.File(input_file, "r")
seqs_1hot = np.array(hdf5_in["test_in"])
targets = np.array(hdf5_in["test_out"])
seq_headers = np.array([h.decode("UTF-8") for h in hdf5_in["test_headers"]])
hdf5_in.close()
# convert to ACGT sequences
seqs = dna_io.vecs2dna(seqs_1hot)
except IOError:
parser.error("Could not parse input file as FASTA or HDF5.")
#################################################################
# Torch predict modifications
#################################################################
# GPU options (needed below, too)
gpgpu_str = ""
if options.cudnn:
gpgpu_str = "-cudnn"
elif options.cuda:
gpgpu_str = "-cuda"
if options.model_out_file is None:
options.model_out_file = "%s/preds.txt" % options.out_dir
torch_cmd = "basset_predict.lua -mc_n 10 -rc %s %s %s %s" % (
gpgpu_str,
model_file,
model_input_hdf5,
options.model_out_file,
)
print(torch_cmd)
subprocess.call(torch_cmd, shell=True)
# read in predictions
seqs_preds = np.loadtxt(options.model_out_file)
num_targets = seqs_preds.shape[1]
#################################################################
# parse profile file
#################################################################
activity_profile, profile_weights, profile_mask, target_labels = load_profile(
profile_file, num_targets, options.norm_even, options.weight_zero
)
# normalize predictions
if options.norm_preds:
pred_means = seqs_preds.mean(axis=0)
# save to file for basset_refine.py
np.save("%s/pred_means" % options.out_dir, pred_means)
# aim for profile weighted average
aim_mean = np.average(pred_means[profile_mask], weights=profile_weights[profile_mask])
# normalize
for ti in range(seqs_preds.shape[1]):
ratio_ti = pred_means[ti] / aim_mean
if profile_mask[ti] and (ratio_ti < 1 / 4 or ratio_ti > 4):
print(
"WARNING: target %d with mean %.4f differs 4-fold from the median %.3f"
% (ti, pred_means[ti], aim_mean),
file=sys.stderr,
)
seqs_preds[:, ti] = znorm(seqs_preds[:, ti], pred_means[ti], aim_mean)
#################################################################
# plot clustered heat map limited to relevant targets
#################################################################
seqs_preds_prof = seqs_preds[:, profile_mask]
seqs_preds_var = seqs_preds_prof.var(axis=1)
seqs_sort_var = np.argsort(seqs_preds_var)[::-1]
# heat map
plt.figure()
g = sns.clustermap(
np.transpose(seqs_preds_prof[seqs_sort_var[:1500]]),
metric="cosine",
linewidths=0,
yticklabels=target_labels[profile_mask],
xticklabels=False,
)
plt.setp(g.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
for label in g.ax_heatmap.yaxis.get_majorticklabels():
label.set_fontsize(options.font_heat)
plt.savefig("%s/heat_clust.pdf" % options.out_dir)
plt.close()
# dimension reduction
# model_pca = PCA(n_components=50)
# spp_pca = model.fit_transform(np.transpose(seqs_preds_prof))
# model = TSNE(n_components=2, perplexity=5, metric='euclidean')
# spp_dr = model.fit_transform(spp_pca)
model = PCA(n_components=2)
spp_dr = model.fit_transform(np.transpose(seqs_preds_prof))
plt.figure()
plt.scatter(spp_dr[:, 0], spp_dr[:, 1], c="black", s=5)
target_labels_prof_concise = [tl.split(":")[-1] for tl in target_labels[profile_mask]]
for label, x, y, activity in zip(
target_labels_prof_concise, spp_dr[:, 0], spp_dr[:, 1], activity_profile[profile_mask]
):
plt.annotate(label, xy=(x, y), size=10, color=sns.color_palette("deep")[int(activity)])
plt.savefig("%s/dim_red.pdf" % options.out_dir)
plt.close()
#################################################################
# compute profile distances
#################################################################
# compute prediction distances
seqs_pdists = []
for si in range(seqs_preds.shape[0]):
# sd = np.power(seqs_preds[si,profile_mask]-activity_profile[profile_mask], 2).sum()
sd = log_loss(
activity_profile[profile_mask], seqs_preds[si, profile_mask], sample_weight=profile_weights[profile_mask]
)
seqs_pdists.append(sd)
seqs_pdists = np.array(seqs_pdists)
# obtain sorted indexes
seqs_sort_dist = np.argsort(seqs_pdists)
# compute target distances
seqs_tdists = []
for si in range(seqs_preds.shape[0]):
tdists = np.absolute(targets[si, profile_mask] - activity_profile[profile_mask])
tdists_weight = np.multiply(tdists, profile_weights[profile_mask])
td = tdists_weight.sum()
seqs_tdists.append(td)
seqs_tdists = np.array(seqs_tdists)
# print as table
table_out = open("%s/table.txt" % options.out_dir, "w")
for si in seqs_sort_dist:
cols = [si, seqs_pdists[si], seqs_tdists[si]] + list(seqs_preds[si, profile_mask])
print("\t".join([str(c) for c in cols]), file=table_out)
table_out.close()
#################################################################
# plot sorted heat map
#################################################################
plt.figure()
g = sns.clustermap(
np.transpose(seqs_preds_prof[seqs_sort_dist[:1000]]),
col_cluster=False,
metric="cosine",
linewidths=0,
yticklabels=target_labels[profile_mask],
xticklabels=False,
)
plt.setp(g.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
for label in g.ax_heatmap.yaxis.get_majorticklabels():
label.set_fontsize(options.font_heat)
plt.savefig("%s/heat_rank.pdf" % options.out_dir)
plt.close()
#################################################################
# dissect the top hits
#################################################################
satmut_targets = ",".join([str(ti) for ti in range(len(activity_profile)) if profile_mask[ti]])
if gpgpu_str != "":
gpgpu_str = "-%s" % gpgpu_str
for ni in range(options.num_dissect):
si = seqs_sort_dist[ni]
# print FASTA
fasta_file = "%s/seq%d.fa" % (options.out_dir, ni)
fasta_out = open(fasta_file, "w")
print(">%s\n%s" % (seq_headers[si], seqs[si]), file=fasta_out)
fasta_out.close()
# saturated mutagenesis
cmd = "basset_sat.py %s --mc_n 10 -n 500 -o %s/satmut%d -t %s %s %s" % (
gpgpu_str,
options.out_dir,
ni,
satmut_targets,
model_file,
fasta_file,
)
subprocess.call(cmd, shell=True)
# predictions and targets heat
profile_sort = np.argsort(activity_profile[profile_mask])
heat_mat = np.array([activity_profile[profile_mask], targets[si, profile_mask], seqs_preds_prof[si]])
heat_mat = heat_mat[:, profile_sort]
plt.figure()
ax = sns.heatmap(
np.transpose(heat_mat),
yticklabels=target_labels[profile_mask][profile_sort],
xticklabels=["Desired", "Experiment", "Prediction"],
)
plt.setp(ax.xaxis.get_majorticklabels(), rotation=-45)
plt.setp(ax.yaxis.get_majorticklabels(), rotation=-0)
for label in ax.yaxis.get_majorticklabels():
label.set_fontsize(options.font_heat)
plt.savefig("%s/heat%d.pdf" % (options.out_dir, ni))
plt.close()
def main():
usage = 'usage: %prog [options] <model_file> <input_file>'
parser = OptionParser(usage)
parser.add_option(
'-a',
dest='input_activity_file',
help='Optional activity table corresponding to an input FASTA file')
parser.add_option(
'-d',
dest='model_hdf5_file',
default=None,
help='Pre-computed model output as HDF5 [Default: %default]')
parser.add_option('-o',
dest='out_dir',
default='heat',
help='Output directory [Default: %default]')
parser.add_option('-r',
dest='rng_seed',
default=1,
type='float',
help='Random number generator seed [Default: %default]')
parser.add_option(
'-s',
dest='sample',
default=None,
type='int',
help='Sample sequences from the test set [Default:%default]')
parser.add_option(
'-t',
dest='targets',
default='0',
help=
'Comma-separated list of target indexes to plot (or -1 for all) [Default: %default]'
)
(options, args) = parser.parse_args()
if len(args) != 2:
parser.error(
'Must provide Basset model file and input sequences (as a FASTA file or test data in an HDF file'
)
else:
model_file = args[0]
input_file = args[1]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
random.seed(options.rng_seed)
#################################################################
# parse input file
#################################################################
try:
# input_file is FASTA
# load sequences and headers
seqs = []
seq_headers = []
for line in open(input_file):
if line[0] == '>':
seq_headers.append(line[1:].rstrip())
seqs.append('')
else:
seqs[-1] += line.rstrip()
model_input_hdf5 = '%s/model_in.h5' % options.out_dir
if options.input_activity_file:
# one hot code
seqs_1hot, targets = dna_io.load_data_1hot(
input_file,
options.input_activity_file,
mean_norm=False,
whiten=False,
permute=False,
sort=False)
# read in target names
target_labels = open(
options.input_activity_file).readline().strip().split('\t')
else:
# load sequences
seqs_1hot = dna_io.load_sequences(input_file, permute=False)
targets = None
target_labels = None
# sample
if options.sample:
sample_i = np.array(
random.sample(xrange(seqs_1hot.shape[0]), options.sample))
seqs_1hot = seqs_1hot[sample_i]
seq_headers = seq_headers[sample_i]
if targets is not None:
targets = targets[sample_i]
# reshape sequences for torch
seqs_1hot = seqs_1hot.reshape(
(seqs_1hot.shape[0], 4, 1, seqs_1hot.shape[1] / 4))
# write as test data to a HDF5 file
h5f = h5py.File(model_input_hdf5, 'w')
h5f.create_dataset('test_in', data=seqs_1hot)
h5f.close()
except (IOError, IndexError):
# input_file is HDF5
try:
model_input_hdf5 = input_file
# load (sampled) test data from HDF5
hdf5_in = h5py.File(input_file, 'r')
seqs_1hot = np.array(hdf5_in['test_in'])
targets = np.array(hdf5_in['test_out'])
try: # TEMP
seq_headers = np.array(hdf5_in['test_headers'])
target_labels = np.array(hdf5_in['target_labels'])
except:
seq_headers = None
target_labels = None
hdf5_in.close()
# sample
if options.sample:
sample_i = np.array(
random.sample(xrange(seqs_1hot.shape[0]), options.sample))
seqs_1hot = seqs_1hot[sample_i]
seq_headers = seq_headers[sample_i]
targets = targets[sample_i]
# write sampled data to a new HDF5 file
model_input_hdf5 = '%s/model_in.h5' % options.out_dir
h5f = h5py.File(model_input_hdf5, 'w')
h5f.create_dataset('test_in', data=seqs_1hot)
h5f.close()
# convert to ACGT sequences
seqs = dna_io.vecs2dna(seqs_1hot)
except IOError:
parser.error('Could not parse input file as FASTA or HDF5.')
#################################################################
# Torch predict modifications
#################################################################
if options.model_hdf5_file is None:
options.model_hdf5_file = '%s/model_out.h5' % options.out_dir
torch_cmd = 'basset_net_predict.lua %s %s %s' % (
model_file, model_input_hdf5, options.model_hdf5_file)
print torch_cmd
subprocess.call(torch_cmd, shell=True)
#################################################################
# load modification predictions
#################################################################
hdf5_in = h5py.File(options.model_hdf5_file, 'r')
reprs = []
l = 1
while 'reprs%d' % l in hdf5_in.keys():
reprs.append(np.array(hdf5_in['reprs%d' % l]))
l += 1
hdf5_in.close()
#################################################################
# plot
#################################################################
print len(reprs)
for l in range(len(reprs)):
for si in range(len(seq_headers)):
plt.figure()
# just write the sequence out above it
# or maybe I'll ultimately want to write an
# influence version. yea probably.
print reprs[l][si].shape
sns.heatmap(reprs[l][si], linewidths=0, xticklabels=False)
plt.savefig('%s/%s_l%d.pdf' %
(options.out_dir, header_filename(seq_headers[si]), l))
plt.close()