def grad_times_input_to_df(x, grad, alphabet='ACGT'):
"""generate pandas dataframe for saliency plot
based on grad x inputs """
x_index = np.argmax(np.squeeze(x), axis=1)
grad = np.squeeze(grad)
L, A = grad.shape
seq = ''
saliency = np.zeros((L))
for i in range(L):
seq += alphabet[x_index[i]]
saliency[i] = grad[i, x_index[i]]
# create saliency matrix
saliency_df = logomaker.saliency_to_matrix(seq=seq, values=saliency)
return saliency_df
def l2_norm_to_df(x, scores, alphabet='ACGT'):
"""generate pandas dataframe for saliency plot
based on l2-norm of scores (i.e. mutagenesis) """
x = np.squeeze(x)
x_index = np.argmax(x, axis=1)
scores = np.squeeze(scores)
L, A = scores.shape
# calculate l2-norm
scores = np.sqrt(np.sum(scores**2, axis=1) + 1e-10)
# create dataframe
seq = ''
saliency = np.zeros((L))
for i in range(L):
seq += alphabet[x_index[i]]
saliency[i] = scores[i]
# create saliency matrix
saliency_df = logomaker.saliency_to_matrix(seq=seq, values=saliency)
return saliency_df
def plotPromoters():
########################
#command line arguments#
########################
parser = argparse.ArgumentParser()
#PARAMETERS
parser.add_argument(
"--sequences",
help="Full path to a fasta-file containing the promoter sequences.",
type=str)
parser.add_argument("--outdir", help="Full path to the output directory.")
parser.add_argument(
"--N",
help=
"How many references are used for averaging single signal sequence contributions.",
type=int,
default=10)
parser.add_argument("--model",
help="Full path to the trained keras model.",
type=str,
default=None)
parser.add_argument(
"--background",
help="Full path to a fasta-file containing the background sequences.",
type=str)
parser.add_argument("--target_layer",
help="Target layer index for deeplift (default=-3).",
type=int,
default=-3)
parser.add_argument("--ylim",
help="Limits for y-axis.",
type=float,
nargs=2,
default=None)
parser.add_argument(
"--labels",
help=
"Full path to a file containing labels used as figure titles. If not given, use fasta IDs.",
type=str,
default=None)
parser.add_argument("--logoType",
help="Logo image file extension (default=pdf).",
type=str,
default='pdf',
choices=['png', 'pdf'])
args = parser.parse_args()
#reading in the promoter sequences
ids = []
signal = []
signal_seq = []
for seq in pyfastx.Fasta(args.sequences):
ids.append(seq.name)
signal_seq.append(str(seq.seq).upper())
#and one-hot encoding
for i in range(0, len(signal_seq)):
signal.append(vectorizeSequence(signal_seq[i]))
signal = np.array(signal)
#reading in the background sequences
bg = []
for seq in pyfastx.Fasta(args.background):
bg.append(str(seq.seq).upper())
#and one-hot encoding
for i in range(0, len(bg)):
bg[i] = vectorizeSequence(bg[i])
bg = np.array(bg)
#reading in labels if given
if args.labels != None:
labels = []
f = open(args.labels, 'rt')
for row in f:
labels.append(row)
f.close()
else:
labels = ids
#initialize the deeplift model
deeplift_model = kc.convert_model_from_saved_files(
args.model,
nonlinear_mxts_mode=deeplift.layers.NonlinearMxtsMode.
DeepLIFT_GenomicsDefault)
find_scores_layer_idx = 0 #computes importance scores for inpur layer input
deeplift_contribs_func = deeplift_model.get_target_contribs_func(
find_scores_layer_idx=find_scores_layer_idx,
target_layer_idx=args.target_layer)
#and then score each sequence against args.N different background sequences
scores = np.zeros(shape=(args.N, signal.shape[0], signal.shape[1]))
for i in range(0, args.N):
scores[i, :, :] = np.sum(deeplift_contribs_func(
task_idx=1,
input_data_list=[signal],
input_references_list=[bg[:signal.shape[0], :, :]],
batch_size=10,
progress_update=None),
axis=2)
bg = np.roll(bg, 1, axis=0)
scores = np.mean(scores, axis=0)
#now the contributions have been calculated, next plotting the sequence logos weighted by the contributions
for ind in range(0, len(signal_seq)):
#first plotting the sequence
seq = signal_seq[ind]
fig, ax = plt.subplots()
matrix_df = lm.saliency_to_matrix(
seq, scores[ind, :]) #pd.DataFrame(scores[i,:])
logo = lm.Logo(df=matrix_df, color_scheme='classic')
logo.ax.set_xlabel('position')
logo.ax.set_ylabel('contribution')
title = labels[ind]
logo.ax.set_title(title)
if args.ylim != None: logo.ax.set_ylim(args.ylim)
plt.tight_layout()
plt.savefig(args.outdir + ids[ind] + '.' + args.logoType,
dpi=150,
bbox_inches='tight',
pad_inches=0)
plt.close(fig)
plt.clf()
plt.cla()
#and then saving the importance scores to a file
np.savetxt(args.outdir + ids[ind] + '.txt', scores[ind, :])