def main():
usage = 'usage: %prog [options] <model_file> <vcf_file>'
parser = OptionParser(usage)
parser.add_option('-d', dest='model_hdf5_file', default=None, help='Pre-computed model output as HDF5 [Default: %default]')
parser.add_option('-f', dest='genome_fasta', default='%s/data/genomes/hg19.fa'%os.environ['BASSETDIR'], help='Genome FASTA from which sequences will be drawn [Default: %default]')
parser.add_option('--f1', dest='genome1_fasta', default=None, help='Genome FASTA which which major allele sequences will be drawn')
parser.add_option('--f2', dest='genome2_fasta', default=None, help='Genome FASTA which which minor allele sequences will be drawn')
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('-l', dest='seq_len', type='int', default=600, help='Sequence length provided to the model [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='Nt around the SNP to mutate and plot in the heatmap [Default: %default]')
parser.add_option('-o', dest='out_dir', default='heat', help='Output directory [Default: %default]')
parser.add_option('-t', dest='targets', default='-1', 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 SNPs in VCF format')
else:
model_file = args[0]
vcf_file = args[1]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
#################################################################
# prep SNP sequences
#################################################################
# load SNPs
snps = vcf.vcf_snps(vcf_file, pos2=(options.genome2_fasta is not None))
# get one hot coded input sequences
if not options.genome1_fasta or not options.genome2_fasta:
seqs_1hot, seq_headers, snps, seqs = vcf.snps_seq1(snps, options.seq_len, options.genome_fasta, return_seqs=True)
else:
seqs_1hot, seq_headers, snps, seqs = vcf.snps2_seq1(snps, options.seq_len, options.genome1_fasta, options.genome2_fasta, return_seqs=True)
# reshape sequences for torch
seqs_1hot = seqs_1hot.reshape((seqs_1hot.shape[0],4,1,seqs_1hot.shape[1]/4))
# write to HDF5
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()
#################################################################
# 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)
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
delta_start = 0
delta_len = seq_mod_preds.shape[2]
if delta_len < options.seq_len:
delta_start = (options.seq_len - delta_len)/2
for si in range(len(seqs)):
seqs[si] = seqs[si][delta_start:delta_start+delta_len]
# decide which cells to plot
if options.targets == '-1':
plot_cells = xrange(seq_mod_preds.shape[3])
else:
plot_cells = [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]):
header = seq_headers[si]
seq = seqs[si]
# plot some descriptive heatmaps for each individual cell type
for ci in plot_cells:
seq_mod_preds_cell = seq_mod_preds[si,:,:,ci]
real_pred_cell = basset_sat.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})
spp = basset_sat.subplot_params(seq_mod_preds_cell.shape[1])
fig = plt.figure(figsize=(20,3))
ax_logo = plt.subplot2grid((3,spp['heat_cols']), (0,spp['logo_start']), colspan=(spp['logo_end']-spp['logo_start']))
ax_sad = plt.subplot2grid((3,spp['heat_cols']), (1,spp['sad_start']), colspan=(spp['sad_end']-spp['sad_start']))
ax_heat = plt.subplot2grid((3,spp['heat_cols']), (2,0), colspan=spp['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.replace(':','_'), ci)
seq_logo(seq, seq_heights, logo_eps, color_mode='meme')
# 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 = basset_sat.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> <vcf_file>'
parser = OptionParser(usage)
parser.add_option('-d', dest='model_hdf5_file', default=None, help='Pre-computed model output as HDF5 [Default: %default]')
parser.add_option('-f', dest='genome_fasta', default='%s/data/genomes/hg19.fa'%os.environ['BASSETDIR'], help='Genome FASTA from which sequences will be drawn [Default: %default]')
parser.add_option('--f1', dest='genome1_fasta', default=None, help='Genome FASTA which which major allele sequences will be drawn')
parser.add_option('--f2', dest='genome2_fasta', default=None, help='Genome FASTA which which minor allele sequences will be drawn')
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('-l', dest='seq_len', type='int', default=600, help='Sequence length provided to the model [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='Nt around the SNP to mutate and plot in the heatmap [Default: %default]')
parser.add_option('-o', dest='out_dir', default='heat', help='Output directory [Default: %default]')
parser.add_option('-t', dest='targets', default='-1', 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 SNPs in VCF format')
else:
model_file = args[0]
vcf_file = args[1]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
#################################################################
# prep SNP sequences
#################################################################
# load SNPs
snps = vcf.vcf_snps(vcf_file, pos2=(options.genome2_fasta is not None))
# get one hot coded input sequences
if not options.genome1_fasta or not options.genome2_fasta:
seqs_1hot, seq_headers, snps, seqs = vcf.snps_seq1(snps, options.seq_len, options.genome_fasta, return_seqs=True)
else:
seqs_1hot, seq_headers, snps, seqs = vcf.snps2_seq1(snps, options.seq_len, options.genome1_fasta, options.genome2_fasta, return_seqs=True)
# reshape sequences for torch
seqs_1hot = seqs_1hot.reshape((seqs_1hot.shape[0],4,1,seqs_1hot.shape[1]/4))
# write to HDF5
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()
#################################################################
# 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)
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
delta_start = 0
delta_len = seq_mod_preds.shape[2]
if delta_len < options.seq_len:
delta_start = (options.seq_len - delta_len)/2
for si in range(len(seqs)):
seqs[si] = seqs[si][delta_start:delta_start+delta_len]
# decide which cells to plot
if options.targets == '-1':
plot_cells = xrange(seq_mod_preds.shape[3])
else:
plot_cells = [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]):
header = seq_headers[si]
header_fs = fs_clean(header)
seq = seqs[si]
# plot some descriptive heatmaps for each individual cell type
for ci in plot_cells:
seq_mod_preds_cell = seq_mod_preds[si,:,:,ci]
real_pred_cell = basset_sat.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})
spp = basset_sat.subplot_params(seq_mod_preds_cell.shape[1])
fig = plt.figure(figsize=(20,3))
ax_logo = plt.subplot2grid((3,spp['heat_cols']), (0,spp['logo_start']), colspan=(spp['logo_end']-spp['logo_start']))
ax_sad = plt.subplot2grid((3,spp['heat_cols']), (1,spp['sad_start']), colspan=(spp['sad_end']-spp['sad_start']))
ax_heat = plt.subplot2grid((3,spp['heat_cols']), (2,0), colspan=spp['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_fs, ci)
seq_logo(seq, seq_heights, logo_eps, color_mode='meme')
# 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_fs, 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 = basset_sat.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()