def generate_cells(self,
cells_no,
checkpoint=None,
sess=None,
save_path=None):
"""
Method that generate cells from the current model.
Parameters
----------
cells_no : int
Number of cells to be generated.
checkpoint : str / None
Path to the checkpoint from which to load the model.
If None, uses the current model loaded in the session.
Default is None.
sess : Session
The TF Session in use.
If None, a Session is created.
Default is None.
save_path : str
Path in which to write the generated cells.
If None, the cells are only returned and not written.
Default is None.
Returns
-------
fake_cells : Numpy array
2-D Array with the gene expression matrix of the generated cells.
"""
if sess is None:
sess = tf.Session()
if checkpoint is not None:
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(checkpoint))
batches_no = int(np.ceil(cells_no / self.batch_size))
fake_cells_tensor = self.generator.fake_outputs
is_training = self.generator.is_training
eval_feed_dict = {is_training: False}
fake_cells = []
for i_batch in range(batches_no):
fc = sess.run([fake_cells_tensor], feed_dict=eval_feed_dict)
fake_cells.append(fc)
fake_cells = np.array(fake_cells, dtype=np.float32)
fake_cells = fake_cells.reshape((-1, fake_cells_tensor.shape[1]))
fake_cells = fake_cells[0:cells_no]
rescale(fake_cells, scaling=self.scaling, scale_value=self.scale_value)
if save_path is not None:
save_generated_cells(fake_cells, save_path)
return fake_cells
def read_valid_cells(self, sess, cells_no):
"""
Method that reads a given number of cells from the validation set.
Parameters
----------
sess : Session
The TF Session in use.
cells_no : int
Number of validation cells to read.
Returns
-------
real_cells : numpy array
Matrix with the required amount of validation cells.
"""
batches_no = int(np.ceil(cells_no // self.batch_size))
real_cells = []
for i_batch in range(batches_no):
test_inputs = sess.run([self.test_cells])
real_cells.append(test_inputs)
real_cells = np.array(real_cells, dtype=np.float32)
real_cells = real_cells.reshape((-1, self.test_cells.shape[1]))
real_cells = rescale(real_cells,
scaling=self.scaling,
scale_value=self.scale_value)
return real_cells
def generate_cells(self,
cells_no,
clusters_ratios=None,
sess=None,
save_path=None,
checkpoint=None):
"""
Method that generate cells from the current model.
Parameters
----------
cells_no : int or list
Numbers of cells per cluster to be generated.
If the clusters_ratios are provided, should be an int (total number of cells).
If cluster_ratios is None, should be a list of number of cells per cluster.
clusters_ratios : numpy array
List containing the different cluster ratios to use for
the conditional generation.
Default is None.
sess : Session
The TF Session in use.
If None, a Session is created.
Default is None.
save_path : str
Path in which to write the generated cells.
If None, the cells are only returned and not written.
Default is None.
checkpoint : str /None
Path to the checkpoint from which to load the model.
If None, uses the current model loaded in the session.
Default is None.
Returns
-------
fake_cells : Numpy array
2-D Array with the gene expression matrix of the generated cells.
fake_labels : Numpy array
Array containing the cluster index of the generated cells.
"""
if sess is None:
sess = tf.Session()
if checkpoint is not None:
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(checkpoint))
fake_cells = np.empty((0, self.genes_no), dtype=np.float32)
fake_labels = np.empty([0, 1], dtype=np.int32)
if clusters_ratios is None and len(cells_no) > 1:
for cluster, cells_per_cluster in enumerate(cells_no):
if int(cells_per_cluster) == 0:
continue
clusters_ratios = np.zeros((1, self.clusters_no),
dtype=np.float)
clusters_ratios[0][cluster] = 1
fc, fl = self.generate_cells(sess=sess,
checkpoint=checkpoint,
cells_no=int(cells_per_cluster),
clusters_ratios=clusters_ratios)
fake_cells = np.append(fake_cells, fc, axis=0)
fake_labels = np.append(fake_labels, fl)
else:
batches_no = int(np.ceil(cells_no / self.batch_size))
clusters_ratios_ph = self.generator.clusters_ratios
fake_labels_tensor = self.generator.input_clusters
is_training = self.generator.is_training
fake_cells_tensor = self.generator.fake_outputs
eval_feed_dict = {
is_training: False,
clusters_ratios_ph: clusters_ratios
}
for i_batch in range(batches_no):
fc, fl = sess.run([fake_cells_tensor, fake_labels_tensor],
feed_dict=eval_feed_dict)
fake_cells = np.append(fake_cells, fc, axis=0)
fake_labels = np.append(fake_labels, fl)
fake_labels = fake_labels[0:cells_no]
fake_cells = fake_cells[0:cells_no]
rescale(fake_cells, scaling=self.scaling, scale_value=self.scale_value)
if save_path is not None:
save_generated_cells(fake_cells, save_path, fake_labels)
return fake_cells, fake_labels