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import tensorflow as tf
import numpy as np
import matplotlib
def one_hot_encode_along_row_axis(sequence):
to_return = np.zeros((1, len(sequence), 4), dtype=np.int8)
seq_to_one_hot_fill_in_array(zeros_array=to_return[0], sequence=sequence, one_hot_axis=1)
return to_return
def seq_to_one_hot_fill_in_array(zeros_array, sequence, one_hot_axis):
assert one_hot_axis == 0 or one_hot_axis == 1
if one_hot_axis == 0:
assert zeros_array.shape[1] == len(sequence)
elif one_hot_axis == 1:
assert zeros_array.shape[0] == len(sequence)
for (i, char) in enumerate(sequence):
if char == "A" or char == "a":
char_idx = 0
elif char == "C" or char == "c":
char_idx = 1
elif char == "G" or char == "g":
char_idx = 2
elif char == "T" or char == "t":
char_idx = 3
elif char == "N" or char == "n":
continue
else:
raise RuntimeError("Unsupported character: " + str(char))
if one_hot_axis == 0:
zeros_array[char_idx, i] = 1
elif one_hot_axis == 1:
zeros_array[i, char_idx] = 1
def readfile(filename):
ids = []
ids_d = {}
seqs = {}
f = open(filename, 'r')
lines = f.readlines()
f.close()
seq = []
for line in lines:
if line[0] == '>':
ids.append(line[1:].rstrip('\n'))
id_line = line[1:].rstrip('\n').split('_')[0]
if id_line not in seqs:
seqs[id_line] = []
if id_line not in ids_d:
ids_d[id_line] = id_line
if seq:
seqs[ids[-2].split('_')[0]] = ("".join(seq))
seq = []
else:
seq.append(line.rstrip('\n').upper())
if seq:
seqs[ids[-1].split('_')[0]] = ("".join(seq))
return ids, ids_d, seqs
def prepare_data(filename):
ids, ids_d, seqs, = readfile(filename)
X = np.array([one_hot_encode_along_row_axis(seqs[id_]) for id_ in ids_d]).squeeze(axis=1)
data = X
return data, ids
def plot_prediction_givenax(model, fig, ntrack, track_no, seq_onehot):
NUM_CLASSES = model.output_shape[1]
real_score = model.predict(seq_onehot)[0]
ax = fig.add_subplot(ntrack, 2, track_no*2-1)
ax.margins(x=0)
ax.set_ylabel('Prediction', color='red')
ax.plot(real_score, '--', color='gray', linewidth=3)
ax.scatter(range(NUM_CLASSES), real_score, marker='o', color='red', linewidth=11)
ax.tick_params(axis='y', labelcolor='red')
ax.set_xticks(range(NUM_CLASSES),)
ax.set_xticklabels(range(1, NUM_CLASSES+1))
ax.grid(True)
return ax
def create_saturation_mutagenesis_x(onehot):
mutagenesis_X = {"X":[],"ids":[]}
onehot = onehot.squeeze()
for mutloc,nt in enumerate(onehot):
new_X = np.copy(onehot)
if list(nt) == [1, 0, 0, 0]:
new_X[mutloc,:] = np.array([0, 1, 0, 0], dtype='int8')
mutagenesis_X["X"].append(np.copy(new_X))
new_X[mutloc,:] = np.array([0, 0, 1, 0], dtype='int8')
mutagenesis_X["X"].append(np.copy(new_X))
new_X[mutloc,:] = np.array([0, 0, 0, 1], dtype='int8')
mutagenesis_X["X"].append(np.copy(new_X))
mutagenesis_X["ids"].append(str(mutloc)+"_C")
mutagenesis_X["ids"].append(str(mutloc)+"_G")
mutagenesis_X["ids"].append(str(mutloc)+"_T")
if list(nt) == [0, 1, 0, 0]:
new_X[mutloc,:] = np.array([1, 0, 0, 0], dtype='int8')
mutagenesis_X["X"].append(np.copy(new_X))
new_X[mutloc,:] = np.array([0, 0, 1, 0], dtype='int8')
mutagenesis_X["X"].append(np.copy(new_X))
new_X[mutloc,:] = np.array([0, 0, 0, 1], dtype='int8')
mutagenesis_X["X"].append(np.copy(new_X))
mutagenesis_X["ids"].append(str(mutloc)+"_A")
mutagenesis_X["ids"].append(str(mutloc)+"_G")
mutagenesis_X["ids"].append(str(mutloc)+"_T")
if list(nt) == [0, 0, 1, 0]:
new_X[mutloc,:] = np.array([1, 0, 0, 0], dtype='int8')
mutagenesis_X["X"].append(np.copy(new_X))
new_X[mutloc,:] = np.array([0, 1, 0, 0], dtype='int8')
mutagenesis_X["X"].append(np.copy(new_X))
new_X[mutloc,:] = np.array([0, 0, 0, 1], dtype='int8')
mutagenesis_X["X"].append(np.copy(new_X))
mutagenesis_X["ids"].append(str(mutloc)+"_A")
mutagenesis_X["ids"].append(str(mutloc)+"_C")
mutagenesis_X["ids"].append(str(mutloc)+"_T")
if list(nt) == [0, 0, 0, 1]:
new_X[mutloc,:] = np.array([1, 0, 0, 0], dtype='int8')
mutagenesis_X["X"].append(np.copy(new_X))
new_X[mutloc,:] = np.array([0, 1, 0, 0], dtype='int8')
mutagenesis_X["X"].append(np.copy(new_X))
new_X[mutloc,:] = np.array([0, 0, 1, 0], dtype='int8')
mutagenesis_X["X"].append(np.copy(new_X))
mutagenesis_X["ids"].append(str(mutloc)+"_A")
mutagenesis_X["ids"].append(str(mutloc)+"_C")
mutagenesis_X["ids"].append(str(mutloc)+"_G")
mutagenesis_X["X"] = np.array(mutagenesis_X["X"])
return mutagenesis_X
def plot_mutagenesis_givenax(model, fig, ntrack, track_no, seq_onehot, class_no):
mutagenesis_X = create_saturation_mutagenesis_x(seq_onehot)
prediction_mutagenesis_X = model.predict(mutagenesis_X["X"])
original_prediction = model.predict(seq_onehot)
class_no = class_no-1
seq_shape = (seq_onehot.shape[1],seq_onehot.shape[2])
arr_a = np.zeros(seq_shape[0])
arr_c = np.zeros(seq_shape[0])
arr_g = np.zeros(seq_shape[0])
arr_t = np.zeros(seq_shape[0])
delta_pred = original_prediction[:,class_no] - prediction_mutagenesis_X[:,class_no]
for i,mut in enumerate(mutagenesis_X["ids"]):
if mut.endswith("A"):
arr_a[int(mut.split("_")[0])]=delta_pred[i]
if mut.endswith("C"):
arr_c[int(mut.split("_")[0])]=delta_pred[i]
if mut.endswith("G"):
arr_g[int(mut.split("_")[0])]=delta_pred[i]
if mut.endswith("T"):
arr_t[int(mut.split("_")[0])]=delta_pred[i]
arr_a[arr_a == 0] = None
arr_c[arr_c == 0] = None
arr_g[arr_g == 0] = None
arr_t[arr_t == 0] = None
ax = fig.add_subplot(ntrack, 1, track_no)
ax.set_ylabel('In silico\nMutagenesis')
ax.scatter(range(seq_shape[0]), -1*arr_a, label='A', color='green')
ax.scatter(range(seq_shape[0]), -1*arr_c, label='C', color='blue')
ax.scatter(range(seq_shape[0]), -1*arr_g, label='G', color='orange')
ax.scatter(range(seq_shape[0]), -1*arr_t, label='T', color='red')
ax.legend()
ax.axhline(y=0, linestyle='--', color='gray')
ax.set_xlim((0, seq_shape[0]))
_ = ax.set_xticks(np.arange(0, seq_shape[0]+1, 10))
return ax
def insilico_evolution(regions, model, class_no, n_mutation):
#from scipy.stats import zscore
nuc_to_onehot = {"A":[1, 0, 0, 0],"C":[0, 1, 0, 0],"G":[0, 0, 1, 0],"T":[0, 0, 0, 1]}
mutation_pred = []
mutation_loc = []
print("Sequence index:",end=" ")
for id_ in range(len(regions)):
start_x = np.copy(regions[id_:id_+1])
pred = []
mut = []
for i in range(n_mutation):
mutagenesis_X = create_saturation_mutagenesis_x(start_x)
prediction_mutagenesis_X = model.predict(mutagenesis_X["X"])
original_prediction = model.predict(start_x)
## To use max z-score
# next_one = mutagenesis_X["ids"][np.argmax(zscore(prediction_mutagenesis_X-original_prediction,axis=1)[:,class_no-1])]
## To use max score
next_one = mutagenesis_X["ids"][np.argmax(prediction_mutagenesis_X[:,class_no-1]-original_prediction[:,class_no-1])]
pred.append(original_prediction)
mut.append(next_one)
start_x[0][int(next_one.split("_")[0]),:] = np.array(nuc_to_onehot[next_one.split("_")[1]], dtype='int8')
original_prediction = model.predict(start_x)
pred.append(original_prediction)
mutation_pred.append(pred)
mutation_loc.append(mut)
print(id_,end=",")
mutation_pred = np.array(mutation_pred).squeeze()
mutation_loc = np.array(mutation_loc)
return mutation_pred, mutation_loc
def random_sequence_by_shuffling(seq_to_shuffle, number_of_random_regions):
seq_to_shuffle_onehot = one_hot_encode_along_row_axis(seq_to_shuffle)
shuffled_regions = []
for i in range(number_of_random_regions):
np.random.shuffle(seq_to_shuffle_onehot[0])
shuffled_regions.append(np.copy(seq_to_shuffle_onehot[0]))
shuffled_regions = np.array(shuffled_regions)
return shuffled_regions
def random_sequence(seq_len, number_of_random_regions):
random_regions = []
for k in range(number_of_random_regions):
seq = []
for i in range(seq_len):
seq.append(np.random.choice(["A","C","G","T"]))
random_regions.append(one_hot_encode_along_row_axis("".join(seq)).squeeze())
random_regions = np.array(random_regions)
return random_regions
def random_sequence_gc_adjusted(seq_len, number_of_random_regions, path_to_use_GC_content):
regions_to_use_GC = prepare_data(path_to_use_GC_content)
ACGT_dist = np.sum(regions_to_use_GC[0],axis=0)/len(regions_to_use_GC[0])
random_regions = []
for k in range(number_of_random_regions):
seq = []
for i in range(seq_len):
seq.append(np.random.choice(["A","C","G","T"],p=list(ACGT_dist[i])))
random_regions.append(one_hot_encode_along_row_axis("".join(seq)).squeeze())
random_regions = np.array(random_regions)
return random_regions
def plot_deepexplainer_givenax(explainer, fig, ntrack, track_no, seq_onehot):
shap_values_ = explainer.shap_values(seq_onehot,ranked_outputs=1,check_additivity=False)
_, ax1 = plot_weights(shap_values_[0]*seq_onehot,
fig, ntrack, 1, track_no,
title="", subticks_frequency=10, ylab="")
return ax1
def load_model(path_json, path_hdf5):
model_json_file = open(path_json)
model_json = model_json_file.read()
model = tf.keras.models.model_from_json(model_json)
model.load_weights(path_hdf5)
return model
def add_pattern_to_best_location(pattern, regions, model, class_no):
pattern_added_regions = np.zeros(regions.shape,dtype="int")
pattern_locations = np.zeros(regions.shape[0],dtype="int")
print("Sequence index:",end=" ")
for r, region in enumerate(regions):
tmp_array = np.zeros((regions.shape[1]-pattern.shape[1]+1,regions.shape[1],regions.shape[2]))
for nt in range(tmp_array.shape[0]):
tmp_array[nt] = np.copy(region)
tmp_array[nt,nt:nt+pattern.shape[1],:] = pattern[0]
prediction = model.predict(tmp_array)[:,class_no-1]
pattern_locations[r] = np.argmax(prediction)
pattern_added_regions[r] = tmp_array[pattern_locations[r]]
print(r,end=",")
print("")
return {"regions":pattern_added_regions, "locations":pattern_locations}
def plot_a(ax, base, left_edge, height, color):
a_polygon_coords = [
np.array([
[0.0, 0.0],
[0.5, 1.0],
[0.5, 0.8],
[0.2, 0.0],
]),
np.array([
[1.0, 0.0],
[0.5, 1.0],
[0.5, 0.8],
[0.8, 0.0],
]),
np.array([
[0.225, 0.45],
[0.775, 0.45],
[0.85, 0.3],
[0.15, 0.3],
])
]
for polygon_coords in a_polygon_coords:
ax.add_patch(matplotlib.patches.Polygon((np.array([1, height])[None, :] * polygon_coords
+ np.array([left_edge, base])[None, :]),
facecolor=color, edgecolor=color))
def plot_c(ax, base, left_edge, height, color):
ax.add_patch(matplotlib.patches.Ellipse(xy=[left_edge + 0.65, base + 0.5 * height], width=1.3, height=height,
facecolor=color, edgecolor=color))
ax.add_patch(
matplotlib.patches.Ellipse(xy=[left_edge + 0.65, base + 0.5 * height], width=0.7 * 1.3, height=0.7 * height,
facecolor='white', edgecolor='white'))
ax.add_patch(matplotlib.patches.Rectangle(xy=[left_edge + 1, base], width=1.0, height=height,
facecolor='white', edgecolor='white', fill=True))
def plot_g(ax, base, left_edge, height, color):
ax.add_patch(matplotlib.patches.Ellipse(xy=[left_edge + 0.65, base + 0.5 * height], width=1.3, height=height,
facecolor=color, edgecolor=color))
ax.add_patch(
matplotlib.patches.Ellipse(xy=[left_edge + 0.65, base + 0.5 * height], width=0.7 * 1.3, height=0.7 * height,
facecolor='white', edgecolor='white'))
ax.add_patch(matplotlib.patches.Rectangle(xy=[left_edge + 1, base], width=1.0, height=height,
facecolor='white', edgecolor='white', fill=True))
ax.add_patch(
matplotlib.patches.Rectangle(xy=[left_edge + 0.825, base + 0.085 * height], width=0.174, height=0.415 * height,
facecolor=color, edgecolor=color, fill=True))
ax.add_patch(
matplotlib.patches.Rectangle(xy=[left_edge + 0.625, base + 0.35 * height], width=0.374, height=0.15 * height,
facecolor=color, edgecolor=color, fill=True))
def plot_t(ax, base, left_edge, height, color):
ax.add_patch(matplotlib.patches.Rectangle(xy=[left_edge + 0.4, base],
width=0.2, height=height, facecolor=color, edgecolor=color, fill=True))
ax.add_patch(matplotlib.patches.Rectangle(xy=[left_edge, base + 0.8 * height],
width=1.0, height=0.2 * height, facecolor=color, edgecolor=color,
fill=True))
default_colors = {0: 'green', 1: 'blue', 2: 'orange', 3: 'red'}
default_plot_funcs = {0: plot_a, 1: plot_c, 2: plot_g, 3: plot_t}
def plot_weights_given_ax(ax, array,
height_padding_factor,
length_padding,
subticks_frequency,
highlight,
colors=default_colors,
plot_funcs=default_plot_funcs):
if len(array.shape) == 3:
array = np.squeeze(array)
assert len(array.shape) == 2, array.shape
if array.shape[0] == 4 and array.shape[1] != 4:
array = array.transpose(1, 0)
assert array.shape[1] == 4
max_pos_height = 0.0
min_neg_height = 0.0
heights_at_positions = []
depths_at_positions = []
for i in range(array.shape[0]):
acgt_vals = sorted(enumerate(array[i, :]), key=lambda x: abs(x[1]))
positive_height_so_far = 0.0
negative_height_so_far = 0.0
for letter in acgt_vals:
plot_func = plot_funcs[letter[0]]
color = colors[letter[0]]
if letter[1] > 0:
height_so_far = positive_height_so_far
positive_height_so_far += letter[1]
else:
height_so_far = negative_height_so_far
negative_height_so_far += letter[1]
plot_func(ax=ax, base=height_so_far, left_edge=i, height=letter[1], color=color)
max_pos_height = max(max_pos_height, positive_height_so_far)
min_neg_height = min(min_neg_height, negative_height_so_far)
heights_at_positions.append(positive_height_so_far)
depths_at_positions.append(negative_height_so_far)
for color in highlight:
for start_pos, end_pos in highlight[color]:
assert start_pos >= 0.0 and end_pos <= array.shape[0]
min_depth = np.min(depths_at_positions[start_pos:end_pos])
max_height = np.max(heights_at_positions[start_pos:end_pos])
ax.add_patch(
matplotlib.patches.Rectangle(xy=[start_pos, min_depth],
width=end_pos - start_pos,
height=max_height - min_depth,
edgecolor=color, fill=False))
ax.set_xlim(-length_padding, array.shape[0] + length_padding)
ax.xaxis.set_ticks(np.arange(0.0, array.shape[0] + 1, subticks_frequency))
height_padding = max(abs(min_neg_height) * (height_padding_factor),
abs(max_pos_height) * (height_padding_factor))
ax.set_ylim(min_neg_height - height_padding, max_pos_height + height_padding)
return ax
def plot_weights(array, fig, n, n1, n2, title='', ylab='',
height_padding_factor=0.2,
length_padding=1.0,
subticks_frequency=20,
colors=default_colors,
plot_funcs=default_plot_funcs,
highlight={}):
ax = fig.add_subplot(n, n1, n2)
ax.set_title(title)
ax.set_ylabel(ylab)
y = plot_weights_given_ax(ax=ax, array=array,
height_padding_factor=height_padding_factor,
length_padding=length_padding,
subticks_frequency=subticks_frequency,
colors=colors,
plot_funcs=plot_funcs,
highlight=highlight)
return fig, ax
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