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scTrans.py
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scTrans.py
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#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import numpy as np
import tensorflow as tf
def xavier_init(fan_in, fan_out, constant=1):
""" Xavier initialization of network weights"""
# https://stackoverflow.com/questions/33640581/how-to-do-xavier-initialization-on-tensorflow
low = -constant*np.sqrt(6.0/(fan_in + fan_out))
high = constant*np.sqrt(6.0/(fan_in + fan_out + 1))
return tf.random.uniform((fan_in, fan_out),
minval=low, maxval=high,
dtype=tf.float32)
class scTranslate(object):
def __init__(self, network_architecture, transfer_fct=tf.nn.relu,
learning_rate=0.001, learning_rate_decay=0.999,
batch_size=100,
lambda_nonzero=0, lambda_reconst_rna=0, lambda_reconst_atac=0,
lambda_trans=0, lambda_latent=0,
lambda_atac_cross=0, lambda_rna_cross=0,
bound_atac_output=False, verbose=False,
rna_importances=None, atac_cross_entropy=False):
self.network_architecture = network_architecture
self.transfer_fct = transfer_fct
self.learning_rate = tf.Variable(learning_rate)
self.learning_rate_decay = learning_rate_decay
self.batch_size = batch_size
self.lambda_nonzero = lambda_nonzero
self.lambda_reconst_rna = lambda_reconst_rna
self.lambda_reconst_atac = lambda_reconst_atac
self.lambda_trans = lambda_trans
self.lambda_latent = lambda_latent
self.lambda_atac_cross = lambda_atac_cross
self.lambda_rna_cross = lambda_rna_cross
self.verbose = verbose
self.bound_atac_output = bound_atac_output
self.atac_cross_entropy = atac_cross_entropy
if rna_importances is not None:
self.rna_importances = rna_importances
else:
self.rna_importances = np.ones((network_architecture["n_input_rna"]))
# tf Graph input
self.x_atac = tf.compat.v1.placeholder(tf.float32,
[None, network_architecture["n_input_atac"]])
self.x_rna = tf.compat.v1.placeholder(tf.float32,
[None, network_architecture["n_input_rna"]])
# Create autoencoder network
self._create_network()
# Define loss function based variational upper-bound and corresponding optimizers.
self._create_loss_optimizers()
# Initializing the tensor flow variables
init = tf.compat.v1.global_variables_initializer()
# Launch the session
self.sess = tf.compat.v1.InteractiveSession()
self.sess.run(init)
all_variables = tf.compat.v1.get_collection_ref(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES)
self.saver = tf.compat.v1.train.Saver(var_list=all_variables)
def _create_network(self):
# Inialize autoencode network weights and biases.
network_weights = self._initialize_weights(self.network_architecture)
# ATAC Network
self.atac_z_mean, self.atac_z_sigma_sq = self._atac_recognition_network(network_weights["atac_weights_recog"],
network_weights["atac_biases_recog"])
# Draw one sample z from Gaussian distribution.
# TODO: Should this be random?
self.rna_z_mean, self.rna_z_sigma_sq = self._rna_recognition_network(network_weights["rna_weights_recog"],
network_weights["rna_biases_recog"])
self.atac_reconst = self._atac_generator_network(network_weights["atac_weights_gener"],
network_weights["atac_biases_gener"],
self.atac_z_mean)
# RNA Network
# Draw one sample z from Gaussian distribution.
# TODO: Should this be random?
self.rna_reconst = self._rna_generator_network(network_weights["rna_weights_gener"],
network_weights["rna_biases_gener"],
self.rna_z_mean)
#self.rna_recontr_mean = tf.compat.v1.Print(self.rna_recontr_mean, [self.rna_recontr_mean], "rna_recontr_mean: ")
# Build translator network.
#self.rna_z_hat = self._atac_trans_network(network_weights["atac_weights_trans"],
# network_weights["atac_biases_trans"])
#self.atac_z_hat = self._rna_trans_network(network_weights["rna_weights_trans"],
# network_weights["rna_biases_trans"])
self.rna_z_hat = self.atac_z_mean
self.rna_reconst_from_atac = self._rna_generator_network(
network_weights["rna_weights_gener"],
network_weights["rna_biases_gener"],
self.rna_z_hat)
self.atac_z_hat = self.rna_z_mean
self.atac_reconst_from_rna = self._atac_generator_network(
network_weights["atac_weights_gener"],
network_weights["atac_biases_gener"],
self.atac_z_hat)
def _initialize_weights(self, network_architecture):
n_hidden_recog_1 = network_architecture["n_hidden_recog_1"]
n_hidden_recog_2 = network_architecture["n_hidden_recog_2"]
n_hidden_recog_3 = network_architecture["n_hidden_recog_3"]
n_hidden_gener_1 = network_architecture["n_hidden_gener_1"]
n_hidden_gener_2 = network_architecture["n_hidden_gener_2"]
n_hidden_gener_3 = network_architecture["n_hidden_gener_3"]
n_input_atac = network_architecture["n_input_atac"]
n_input_rna = network_architecture["n_input_rna"]
atac_n_z = network_architecture["atac_n_z"]
rna_n_z = network_architecture["rna_n_z"]
all_weights = dict()
with tf.compat.v1.variable_scope('atac'):
# ATAC Weights
all_weights['atac_weights_recog'] = {
'h1': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(n_input_atac, n_hidden_recog_1)), 'h1_recog not finite'),
'h2': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(n_hidden_recog_1, n_hidden_recog_2), dtype=tf.float32),
'h2_recog not finite'),
'h3': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(n_hidden_recog_2, n_hidden_recog_3), dtype=tf.float32),
'h3_recog not finite'),
'out_mean': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(n_hidden_recog_3, atac_n_z), dtype=tf.float32),
'out_mean_recog not finite'),
}
all_weights['atac_biases_recog'] = {
'b1': tf.Variable(tf.zeros([n_hidden_recog_1], dtype=tf.float32)),
'b2': tf.Variable(tf.zeros([n_hidden_recog_2], dtype=tf.float32)),
'b3': tf.Variable(tf.zeros([n_hidden_recog_3], dtype=tf.float32)),
'out_mean': tf.Variable(tf.zeros([atac_n_z], dtype=tf.float32)),
#'out_log_sigma': tf.Variable(tf.zeros([n_z], dtype=tf.float32))}
}
all_weights['atac_weights_gener'] = {
'h1': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(atac_n_z, n_hidden_gener_1)),
"h1_gener not finite"),
'h2': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(n_hidden_gener_1, n_hidden_gener_2)),
"h2_gener not finite"),
'h3': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(n_hidden_gener_2, n_hidden_gener_3)),
"h3_gener not finite"),
'out_mean': tf.Variable(xavier_init(n_hidden_gener_3, n_input_atac))
}
all_weights['atac_biases_gener'] = {
'b1': tf.Variable(tf.zeros([n_hidden_gener_1], dtype=tf.float32)),
'b2': tf.Variable(tf.zeros([n_hidden_gener_2], dtype=tf.float32)),
'b3': tf.Variable(tf.zeros([n_hidden_gener_3], dtype=tf.float32)),
'out_mean': tf.Variable(tf.zeros([n_input_atac], dtype=tf.float32)),
}
with tf.compat.v1.variable_scope("rna"):
# RNA Weights
all_weights['rna_weights_recog'] = {
'h1': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(n_input_rna, n_hidden_recog_1)), 'h1_recog not finite'),
'h2': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(n_hidden_recog_1, n_hidden_recog_2), dtype=tf.float32),
'h2_recog not finite'),
'h3': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(n_hidden_recog_2, n_hidden_recog_3), dtype=tf.float32),
'h3_recog not finite'),
'out_mean': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(n_hidden_recog_3, rna_n_z), dtype=tf.float32),
'out_mean_recog not finite'),
}
all_weights['rna_biases_recog'] = {
'b1': tf.Variable(tf.zeros([n_hidden_recog_1], dtype=tf.float32)),
'b2': tf.Variable(tf.zeros([n_hidden_recog_2], dtype=tf.float32)),
'b3': tf.Variable(tf.zeros([n_hidden_recog_3], dtype=tf.float32)),
'out_mean': tf.Variable(tf.zeros([rna_n_z], dtype=tf.float32)),
#'out_log_sigma': tf.Variable(tf.zeros([n_z], dtype=tf.float32))}
}
all_weights['rna_weights_gener'] = {
'h1': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(rna_n_z, n_hidden_gener_1)),
"h1_gener not finite"),
'h2': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(n_hidden_gener_1, n_hidden_gener_2)),
"h2_gener not finite"),
'h3': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(n_hidden_gener_2, n_hidden_gener_3)),
"h3_gener not finite"),
'out_mean': tf.Variable(xavier_init(n_hidden_gener_3, n_input_rna))
}
all_weights['rna_biases_gener'] = {
'b1': tf.Variable(tf.zeros([n_hidden_gener_1], dtype=tf.float32)),
'b2': tf.Variable(tf.zeros([n_hidden_gener_2], dtype=tf.float32)),
'b3': tf.Variable(tf.zeros([n_hidden_gener_3], dtype=tf.float32)),
'out_mean': tf.Variable(tf.zeros([n_input_rna], dtype=tf.float32)),
}
with tf.compat.v1.variable_scope('trans'):
all_weights['atac_weights_trans'] = {
'out_mean': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(atac_n_z, rna_n_z)), "h1_atac_trans not finite")
}
all_weights['atac_biases_trans'] = {
'out_mean': tf.Variable(tf.zeros([rna_n_z], dtype=tf.float32))
}
all_weights['rna_weights_trans'] = {
'out_mean': tf.debugging.assert_all_finite(
tf.Variable(xavier_init(rna_n_z, atac_n_z)), "h1_rna_trans not finite")
}
all_weights['rna_biases_trans'] = {
'out_mean': tf.Variable(tf.zeros([atac_n_z], dtype=tf.float32))
}
return all_weights
def _recognition_network(self, weights, biases, x):
# Generate probabilistic encoder (recognition network), which
# maps inputs onto a normal distribution in latent space.
layer_1 = self.transfer_fct(tf.add(tf.matmul(x, weights['h1']),
biases['b1']))
layer_1_safe = tf.debugging.assert_all_finite(layer_1, "recog layer 1 not finite")
layer_2 = self.transfer_fct(tf.add(tf.matmul(layer_1_safe, weights['h2']),
biases['b2']))
layer_2_safe = tf.debugging.assert_all_finite(layer_2, "recog layer 2 not finite")
layer_3 = self.transfer_fct(tf.add(tf.matmul(layer_2_safe, weights['h3']),
biases['b3']))
layer_3_safe = tf.debugging.assert_all_finite(layer_3, "gen layer 3 not finite")
z_mean = tf.add(tf.matmul(layer_3_safe, weights['out_mean']),
biases['out_mean'])
return z_mean, tf.square(z_mean - tf.reduce_mean(z_mean, axis=0))
def _atac_recognition_network(self, weights, biases):
return self._recognition_network(weights, biases, self.x_atac)
def _rna_recognition_network(self, weights, biases):
return self._recognition_network(weights, biases, self.x_rna)
def _generator_network(self, weights, biases, z):
# Generate probabilistic decoder (decoder network), which
# maps points in latent space onto data space.
layer_1 = self.transfer_fct(tf.add(tf.matmul(z, weights['h1']),
biases['b1']))
layer_1_safe = tf.debugging.assert_all_finite(layer_1, "gen layer 1 not finite")
layer_2 = self.transfer_fct(tf.add(tf.matmul(layer_1_safe, weights['h2']),
biases['b2']))
layer_2_safe = tf.debugging.assert_all_finite(layer_2, "gen layer 2 not finite")
layer_3 = self.transfer_fct(tf.add(tf.matmul(layer_2_safe, weights['h3']),
biases['b3']))
layer_3_safe = tf.debugging.assert_all_finite(layer_3, "gen layer 3 not finite")
x_reconstr_mean = tf.add(tf.matmul(layer_3_safe, weights['out_mean']),
biases['out_mean'])
return x_reconstr_mean
def _atac_generator_network(self, weights, biases, z):
if self.bound_atac_output:
return tf.math.sigmoid(self._generator_network(weights, biases, z))
else:
return self._generator_network(weights, biases, z)
def _rna_generator_network(self, weights, biases, z):
return self._generator_network(weights, biases, z)
def _trans_network(self, weights, biases, z1):
return tf.add(tf.matmul(z1, weights['out_mean']),
biases['out_mean'])
def _atac_trans_network(self, weights, biases):
return self._trans_network(weights, biases, self.atac_z_mean)
def _rna_trans_network(self, weights, biases):
return self._trans_network(weights, biases, self.rna_z_mean)
def _create_reconst_loss(self, x, x_hat, cross_entropy=False, feat_importances=None):
# TODO: upweight nonzeros?
# TODO: This cross-entropy is wrong.
if cross_entropy:
#loss = tf.multiply(x, tf.math.exp(tf.clip_by_value(1-x_hat, 1e-5, 1.0))) \
# + tf.multiply((1-x), tf.math.exp(tf.clip_by_value(x_hat, 1e-5, 1.0)))
loss = tf.multiply(x, tf.math.log(x_hat)) + tf.multiply((1-x), tf.math.log(1-x_hat))
else:
loss = tf.square(x - x_hat)
#nonzero = zero*tf.compat.v1.to_float(tf.abs(x) > 1e-1)
#zero_loss = (1.-self.lambda_nonzero)*tf.reduce_mean(zero, axis=1)
#nonzero_loss = self.lambda_nonzero*tf.reduce_mean(nonzero, axis=1)
if feat_importances is not None:
# TODO:
return tf.reduce_mean(tf.reduce_sum(loss, axis=1), axis=0)
else:
return tf.reduce_mean(tf.reduce_sum(loss, axis=1), axis=0)
def _create_latent_loss(self, mean, sigma_sq):
# a = tf.square(1. - tf.reduce_sum(sigma_sq, 1))
a = tf.square(1. - tf.reduce_mean(sigma_sq, 1))
b = tf.reduce_sum(tf.square(mean), 1)
#c = tf.square(tf.reduce_sum(sigma_sq, 1))
#a_print = tf.compat.v1.Print(a, [a], "A: ")
return tf.reduce_mean(0.5*(a+b), axis=0)
def _create_trans_loss(self):
#rna_error = tf.square(self.rna_z - self.rna_z_hat)
#atac_error = tf.square(self.atac_z - self.atac_z_hat)
#return tf.reduce_mean(tf.reduce_mean(rna_error, axis=1)
# + tf.reduce_mean(atac_error, axis=1), axis=0)
#return tf.reduce_mean(
# tf.square(self.rna_z - self.atac_z))
return tf.norm(self.rna_z_hat - self.rna_z_mean, ord='euclidean') + tf.norm(self.atac_z_hat - self.atac_z_mean, ord='euclidean')
def _create_loss_optimizers(self):
# Loss is composed of:
# 1) Reconstruction loss.
# 2) Latent loss.
# 4) Translation loss.
self.atac_reconstr_loss = self.lambda_reconst_atac*(
self._create_reconst_loss(self.x_atac, self.atac_reconst, self.atac_cross_entropy))
self.atac_cross_loss = self.lambda_atac_cross*(
self._create_reconst_loss(self.x_atac, self.atac_reconst_from_rna, self.atac_cross_entropy))
self.rna_reconstr_loss = self.lambda_reconst_rna*(
self._create_reconst_loss(self.x_rna, self.rna_reconst, feat_importances=self.rna_importances))
self.rna_cross_loss = self.lambda_rna_cross*(
self._create_reconst_loss(self.x_rna, self.rna_reconst_from_atac, feat_importances=self.rna_importances))
self.atac_latent_loss = self.lambda_latent*self._create_latent_loss(self.atac_z_mean, self.atac_z_sigma_sq)
self.rna_latent_loss = self.lambda_latent*self._create_latent_loss(self.rna_z_mean, self.rna_z_sigma_sq)
# self.atac_loss = self.atac_reconstr_loss + self.atac_cross_loss #self.atac_latent_loss
# self.rna_loss = self.rna_reconstr_loss + self.rna_cross_loss #self.rna_latent_loss
################################
###reoncstruction loss + KL loss
self.atac_loss = self.atac_reconstr_loss + self.atac_latent_loss
self.rna_loss = self.rna_reconstr_loss + self.rna_latent_loss
################################
self.trans_loss = self.lambda_trans*self._create_trans_loss()
if self.verbose:
self.atac_loss = tf.compat.v1.Print(self.atac_loss, [self.atac_loss], "ATAC Loss: ")
self.rna_loss = tf.compat.v1.Print(self.rna_loss, [self.rna_loss], "RNA Loss: ")
self.trans_loss = tf.compat.v1.Print(self.trans_loss, [self.trans_loss], "Trans Loss: ")
self.matched_loss = self.atac_loss + self.rna_loss + self.trans_loss
if self.verbose:
self.matched_loss = tf.compat.v1.Print(self.matched_loss, [self.matched_loss], "Loss: ")
atac_vars = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES,
"atac")
rna_vars = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES,
"rna")
trans_vars = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES,
"trans")
self.atac_optimizer = tf.compat.v1.train.GradientDescentOptimizer(
learning_rate=self.learning_rate).minimize(self.atac_loss,
var_list=atac_vars)
self.rna_optimizer = tf.compat.v1.train.GradientDescentOptimizer(
learning_rate=self.learning_rate).minimize(self.rna_loss,
var_list=rna_vars)
self.matched_optimizer = tf.compat.v1.train.GradientDescentOptimizer(
learning_rate=self.learning_rate).minimize(self.matched_loss)
def train_atac(self, data):
empty_rna = np.zeros((data.shape[0], self.network_architecture["n_input_rna"]))
opt, cost = self.sess.run((self.atac_optimizer, self.atac_loss),
feed_dict={self.x_atac: data, self.x_rna: empty_rna})
return cost
def train_rna(self, data):
empty_atac = np.zeros((data.shape[0], self.network_architecture["n_input_atac"]))
opt, cost = self.sess.run((self.rna_optimizer, self.rna_loss),
feed_dict={self.x_rna: data,
self.x_atac: empty_atac})
return cost
def train_matched(self, atac_data, rna_data, silence=False):
opt, cost, atac_reconstr_loss, atac_latent_loss, rna_reconstr_loss, rna_latent_loss, atac_cross_loss, rna_cross_loss, trans_loss = self.sess.run(
(self.matched_optimizer, self.matched_loss,
self.atac_reconstr_loss, self.atac_latent_loss,
self.rna_reconstr_loss, self.rna_latent_loss,
self.atac_cross_loss, self.rna_cross_loss,
self.trans_loss),
feed_dict={self.x_atac: atac_data, self.x_rna: rna_data})
#print(cost)
if self.verbose and not silence:
print("atac_reconstr_loss: ", atac_reconstr_loss)
print("atac_latent_loss: ", atac_latent_loss)
print("rna_reconstr_loss: ", rna_reconstr_loss)
print("rna_latent_loss: ", rna_latent_loss)
print("atac_cross_loss: ", atac_cross_loss)
print("rna_cross_loss: ", rna_cross_loss)
print("trans_loss", trans_loss)
return cost, atac_reconstr_loss, atac_latent_loss, rna_reconstr_loss, rna_latent_loss, atac_cross_loss, rna_cross_loss, trans_loss
def transform_atac(self, X):
return self.sess.run(self.atac_z_mean, feed_dict={self.x_atac: X})
def transform_rna(self, X):
"""Transform data by mapping it into the latent space."""
# Note: This maps to mean of distribution, we could alternatively
# sample from Gaussian distribution
return self.sess.run(self.rna_z_mean, feed_dict={self.x_rna: X})
def generate_atac(self, z_mu=None):
""" Generate data by sampling from latent space.
If z_mu is not None, data for this point in latent space is
generated. Otherwise, z_mu is drawn from prior in latent
space.
"""
if z_mu is None:
z_mu = np.random.normal(size=self.network_architecture["atac_n_z"])
# Note: This maps to mean of distribution, we could alternatively
# sample from Gaussian distribution
return self.sess.run(self.atac_reconst,
feed_dict={self.atac_z_mean: z_mu})
def generate_rna(self, z_mu=None):
if z_mu is None:
z_mu = np.random.normal(size=self.network_architecture["rna_n_z"])
# Note: This maps to mean of distribution, we could alternatively
# sample from Gaussian distribution
return self.sess.run(self.rna_reconst,
feed_dict={self.rna_z_mean: z_mu})
def reconstruct_atac(self, X):
""" Use VAE to reconstruct given data. """
return self.sess.run(self.atac_reconst,
feed_dict={self.x_atac: X})
def reconstruct_rna(self, X):
""" Use VAE to reconstruct given data. """
return self.sess.run(self.rna_reconst,
feed_dict={self.x_rna: X})
def translate_atac(self, X):
""" Translate ATAC-seq data into RNA-seq data. """
return self.sess.run(self.rna_reconst_from_atac,
feed_dict={self.x_atac: X})
def translate_rna(self, X):
""" Translate RNA-seq data into ATAC-seq data. """
return self.sess.run(self.atac_reconst_from_rna,
feed_dict={self.x_rna: X})
def translate_atac_to_z(self, X):
""" Translate ATAC-seq data into RNA-seq data. """
return self.sess.run(self.rna_z_hat,
feed_dict={self.x_atac: X})
def translate_rna_to_z(self, X):
""" Translate RNA-seq data into ATAC-seq data. """
return self.sess.run(self.atac_z_hat,
feed_dict={self.x_rna: X})
def save_weights(self, fname):
self.saver.save(self.sess, fname)
def load_weights(self, fname):
self.saver.restore(self.sess, fname)
def get_lr(self):
lr = self.sess.run(self.learning_rate)
return lr