PK NȤ scgen/__init__.py"""ScGen - Predicting single cell perturbations"""
from .models import *
from .read_load import load_file
from . import plotting
__author__ = ', '.join([
'Mohammad Lotfollahi',
'Mohsen Naghipourfar'
])
__email__ = ', '.join([
'Mohammad.lotfollahi@helmholtz-muenchen.de',
'mohsen.naghipourfar@gmail.com'
])
from get_version import get_version
__version__ = get_version(__file__)
del get_version
PK NIwQ
scgen/data_generator.pyfrom copy import deepcopy
from scipy.ndimage import imread
import numpy as np
import pandas as pd
import anndata
import os
# data_name = "horse2zebra"
# data_path = "../data/" + data_name
#
# train_images = []
# for image in os.listdir(os.path.join(data_path, "trainA")):
# if image.endswith(".jpg"):
# image = imread(fname=os.path.join(data_path, "trainA", image))
# image = np.reshape(image, newshape=(256 * 256 * 3, ))
# image = image.tolist()
# train_images.append(image)
#
# n_horses = len(train_images)
#
# for image in os.listdir(os.path.join(data_path, "trainB")):
# if image.endswith(".jpg"):
# image = imread(fname=os.path.join(data_path, "trainB", image))
# if len(image.shape) == 3:
# image = np.reshape(image, newshape=(256 * 256 * 3, ))
# image = image.tolist()
# train_images.append(image)
#
# train_images = np.array(train_images)
# print(train_images.shape)
# n_zebras = train_images.shape[0] - n_horses
#
# conditions = ["horse"] * n_horses
# conditions += ["zebra"] * n_zebras
# train_adata = anndata.AnnData(train_images, obs={"condition": conditions})
# train_adata.write_h5ad(filename="../data/h2z.h5ad")
#
#
#
import scanpy as sc
# mnist_data = sc.read("../data/normal_thick.h5ad")
# mnist_data = mnist_data.copy()[mnist_data.obs["condition"] == "normal"]
# mnist_data.obs["condition"] = mnist_data.copy().obs["labels"].values.astype(dtype=np.str)
# # mnist_data.obs["labels"] = np.reshape(mnist_data.obs["labels"].values, (-1, 1))
# mnist_data.write_h5ad("../data/mnist.h5ad")
# mnist_data = sc.read("../data/mnist.h5ad")
# mnist_data.obs["condition"] = mnist_data.obs["condition"].astype(np.str)
# print(mnist_data)
# print(mnist_data.obs)
# print(mnist_data[mnist_data.obs["labels"] == 2])
def load_mnist(path, kind='train'):
import os
import gzip
import numpy as np
"""Load MNIST data from `path`"""
labels_path = os.path.join(path,
'%s-labels-idx1-ubyte.gz'
% kind)
images_path = os.path.join(path,
'%s-images-idx3-ubyte.gz'
% kind)
with gzip.open(labels_path, 'rb') as lbpath:
labels = np.frombuffer(lbpath.read(), dtype=np.uint8,
offset=8)
with gzip.open(images_path, 'rb') as imgpath:
images = np.frombuffer(imgpath.read(), dtype=np.uint8,
offset=16).reshape(len(labels), 784)
return images, labels
x_train, y_train = load_mnist(path="../data/fashion/")
fashion_adata = anndata.AnnData(X=x_train, obs={"condition": y_train, "labels": y_train})
print(fashion_adata)
fashion_adata.write_h5ad("../data/fashion.h5ad")PK nNs s scgen/hyperoptim.pyfrom __future__ import print_function
import numpy as np
import scanpy as sc
from hyperas import optim
from hyperas.distributions import choice
from hyperopt import Trials, STATUS_OK, tpe
import scgen
def data():
x_train = sc.read("./data/train.h5ad")
return x_train
def create_model(x_train):
network = scgen.VAEArith(x_dimension=x_train.X.shape[1],
z_dimension={{choice([10, 20, 50, 75, 100])}},
learning_rate={{choice([0.1, 0.01, 0.001, 0.0001])}},
alpha={{choice([0.1, 0.01, 0.001, 0.0001, 0.00001, 0.000001])}},
dropout_rate={{choice([0.2, 0.25, 0.5, 0.75, 0.8])}},
model_path=f"./")
result = network.train(x_train,
n_epochs={{choice([100, 150, 200, 250])}},
batch_size={{choice([32, 64, 128, 256])}},
verbose=2,
shuffle=True,
save=False)
best_loss = np.amin(result.history['loss'])
print('Best Loss of model:', best_loss)
return {'loss': best_loss, 'status': STATUS_OK, 'model': network.vae_model}
if __name__ == '__main__':
best_run, best_model = optim.minimize(model=create_model,
data=data,
algo=tpe.suggest,
max_evals=100,
trials=Trials())
x_train = data()
# print("Evalutation of best performing model:")
# print(best_model.evaluate([x_train.X]))
print("Best performing model chosen hyper-parameters:")
print(best_run)
"""
best run for VAE Arithmetic:
alpha = .01
batch_size = 256
dropout_rate = 0.75
learning_rate = 0.1
n_epochs = 100
z_dimension = 20
"""PK N6 6 scgen/plotting.pyimport numpy
import scanpy as sc
from matplotlib import pyplot
import pandas as pd
from scipy import stats, sparse
from adjustText import adjust_text
import matplotlib
font = {'family' : 'Arial',
# 'weight' : 'bold',
'size' : 14}
matplotlib.rc('font', **font)
matplotlib.rc('ytick', labelsize=14)
matplotlib.rc('xtick', labelsize=14)
def reg_mean_plot(adata, condition_key, axis_keys, labels, path_to_save="./reg_mean.pdf", gene_list=None, top_100_genes=None,
show=False, verbose=False,
legend=True, title=None,
x_coeff=0.30, y_coeff=0.8, fontsize=14, **kwargs):
"""
Plots mean matching figure for a set of specific genes.
# Parameters
adata: `~anndata.AnnData`
Annotated Data Matrix.
condition_key: basestring
Condition state to be used.
axis_keys: dict
dictionary of axes labels.
path_to_save: basestring
path to save the plot.
gene_list: list
list of gene names to be plotted.
show: bool
if `True`: will show to the plot after saving it.
# Example
```python
import anndata
import scgen
import scanpy as sc
train = sc.read("./tests/data/train.h5ad", backup_url="https://goo.gl/33HtVh")
network = scgen.VAEArith(x_dimension=train.shape[1], model_path="../models/test")
network.train(train_data=train, n_epochs=0)
unperturbed_data = train[((train.obs["cell_type"] == "CD4T") & (train.obs["condition"] == "control"))]
condition = {"ctrl": "control", "stim": "stimulated"}
pred, delta = network.predict(adata=train, adata_to_predict=unperturbed_data, conditions=condition)
pred_adata = anndata.AnnData(pred, obs={"condition": ["pred"] * len(pred)}, var={"var_names": train.var_names})
CD4T = train[train.obs["cell_type"] == "CD4T"]
all_adata = CD4T.concatenate(pred_adata)
scgen.plotting.reg_mean_plot(all_adata, condition_key="condition", axis_keys={"x": "control", "y": "pred", "y1": "stimulated"},
gene_list=["ISG15", "CD3D"], path_to_save="tests/reg_mean.pdf", show=False)
network.sess.close()
```
"""
import seaborn as sns
sns.set()
sns.set(color_codes=True)
if sparse.issparse(adata.X):
adata.X = adata.X.A
diff_genes = top_100_genes
stim = adata[adata.obs[condition_key] == axis_keys["y"]]
ctrl = adata[adata.obs[condition_key] == axis_keys["x"]]
if diff_genes is not None:
if hasattr(diff_genes, "tolist"):
diff_genes = diff_genes.tolist()
adata_diff = adata[:, diff_genes]
stim_diff = adata_diff[adata_diff.obs[condition_key] == axis_keys["y"]]
ctrl_diff = adata_diff[adata_diff.obs[condition_key] == axis_keys["x"]]
x_diff = numpy.average(ctrl_diff.X, axis=0)
y_diff = numpy.average(stim_diff.X, axis=0)
m, b, r_value_diff, p_value_diff, std_err_diff = stats.linregress(x_diff, y_diff)
if verbose:
print('top_100 DEGs mean: ', r_value_diff ** 2)
if "y1" in axis_keys.keys():
real_stim = adata[adata.obs[condition_key] == axis_keys["y1"]]
x = numpy.average(ctrl.X, axis=0)
y = numpy.average(stim.X, axis=0)
m, b, r_value, p_value, std_err = stats.linregress(x, y)
if verbose:
print('All genes mean: ', r_value ** 2)
df = pd.DataFrame({axis_keys["x"]: x, axis_keys["y"]: y})
ax = sns.regplot(x=axis_keys["x"], y=axis_keys["y"], data=df)
ax.tick_params(labelsize=fontsize)
if "range" in kwargs:
start, stop, step = kwargs.get("range")
ax.set_xticks(numpy.arange(start, stop, step))
ax.set_yticks(numpy.arange(start, stop, step))
# _p1 = pyplot.scatter(x, y, marker=".", label=f"{axis_keys['x']}-{axis_keys['y']}")
# pyplot.plot(x, m * x + b, "-", color="green")
ax.set_xlabel(labels["x"], fontsize=fontsize)
ax.set_ylabel(labels["y"], fontsize=fontsize)
# if "y1" in axis_keys.keys():
# y1 = numpy.average(real_stim.X, axis=0)
# _p2 = pyplot.scatter(x, y1, marker="*", c="red", alpha=.5, label=f"{axis_keys['x']}-{axis_keys['y1']}")
if gene_list is not None:
texts = []
for i in gene_list:
j = adata.var_names.tolist().index(i)
x_bar = x[j]
y_bar = y[j]
texts.append(pyplot.text(x_bar, y_bar , i, fontsize=11, color ="black"))
pyplot.plot(x_bar, y_bar, 'o', color="red", markersize=5)
# if "y1" in axis_keys.keys():
# y1_bar = y1[j]
# pyplot.text(x_bar, y1_bar, i, fontsize=11, color="black")
if gene_list is not None:
adjust_text(texts, x=x, y=y, arrowprops=dict(arrowstyle="->", color='grey', lw=0.5), force_points=(0.0, 0.0))
if legend:
pyplot.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if title is None:
pyplot.title(f"", fontsize=fontsize)
else:
pyplot.title(title, fontsize=fontsize)
ax.text(max(x) - max(x) * x_coeff, max(y) - y_coeff * max(y), r'$\mathrm{R^2_{\mathrm{\mathsf{all\ genes}}}}$= ' + f"{r_value ** 2:.2f}", fontsize=kwargs.get("textsize", fontsize))
if diff_genes is not None:
ax.text(max(x) - max(x) * x_coeff, max(y) - (y_coeff+0.15) * max(y), r'$\mathrm{R^2_{\mathrm{\mathsf{top\ 100\ DEGs}}}}$= ' + f"{r_value_diff ** 2:.2f}", fontsize=kwargs.get("textsize", fontsize))
pyplot.savefig(f"{path_to_save}", bbox_inches='tight', dpi=100)
if show:
pyplot.show()
pyplot.close()
return r_value ** 2, r_value_diff ** 2
def reg_var_plot(adata, condition_key, axis_keys, labels, path_to_save="./reg_var.pdf", gene_list=None, top_100_genes=None, show=False,
legend=True, title=None, verbose=False,
x_coeff=0.30, y_coeff=0.8, fontsize=14, **kwargs):
"""
Plots variance matching figure for a set of specific genes.
# Parameters
adata: `~anndata.AnnData`
Annotated Data Matrix.
condition_key: basestring
Condition state to be used.
axis_keys: dict
dictionary of axes labels.
path_to_save: basestring
path to save the plot.
gene_list: list
list of gene names to be plotted.
show: bool
if `True`: will show to the plot after saving it.
# Example
```python
import anndata
import scgen
import scanpy as sc
train = sc.read("./tests/data/train.h5ad", backup_url="https://goo.gl/33HtVh")
network = scgen.VAEArith(x_dimension=train.shape[1], model_path="../models/test")
network.train(train_data=train, n_epochs=0)
unperturbed_data = train[((train.obs["cell_type"] == "CD4T") & (train.obs["condition"] == "control"))]
condition = {"ctrl": "control", "stim": "stimulated"}
pred, delta = network.predict(adata=train, adata_to_predict=unperturbed_data, conditions=condition)
pred_adata = anndata.AnnData(pred, obs={"condition": ["pred"] * len(pred)}, var={"var_names": train.var_names})
CD4T = train[train.obs["cell_type"] == "CD4T"]
all_adata = CD4T.concatenate(pred_adata)
scgen.plotting.reg_var_plot(all_adata, condition_key="condition", axis_keys={"x": "control", "y": "pred", "y1": "stimulated"},
gene_list=["ISG15", "CD3D"], path_to_save="tests/reg_var4.pdf", show=False)
network.sess.close()
```
"""
import seaborn as sns
sns.set()
sns.set(color_codes=True)
if sparse.issparse(adata.X):
adata.X = adata.X.A
sc.tl.rank_genes_groups(adata, groupby=condition_key, n_genes=100, method="wilcoxon")
diff_genes = top_100_genes
stim = adata[adata.obs[condition_key] == axis_keys["y"]]
ctrl = adata[adata.obs[condition_key] == axis_keys["x"]]
if diff_genes is not None:
if hasattr(diff_genes, "tolist"):
diff_genes = diff_genes.tolist()
adata_diff = adata[:, diff_genes]
stim_diff = adata_diff[adata_diff.obs[condition_key] == axis_keys["y"]]
ctrl_diff = adata_diff[adata_diff.obs[condition_key] == axis_keys["x"]]
x_diff = numpy.var(ctrl_diff.X, axis=0)
y_diff = numpy.var(stim_diff.X, axis=0)
m, b, r_value_diff, p_value_diff, std_err_diff = stats.linregress(x_diff, y_diff)
if verbose:
print('Top 100 DEGs var: ', r_value_diff ** 2)
if "y1" in axis_keys.keys():
real_stim = adata[adata.obs[condition_key] == axis_keys["y1"]]
x = numpy.var(ctrl.X, axis=0)
y = numpy.var(stim.X, axis=0)
m, b, r_value, p_value, std_err = stats.linregress(x, y)
if verbose:
print('All genes var: ', r_value ** 2)
df = pd.DataFrame({axis_keys["x"]: x, axis_keys["y"]: y})
ax = sns.regplot(x=axis_keys["x"], y=axis_keys["y"], data=df)
ax.tick_params(labelsize=fontsize)
if "range" in kwargs:
start, stop, step = kwargs.get("range")
ax.set_xticks(numpy.arange(start, stop, step))
ax.set_yticks(numpy.arange(start, stop, step))
# _p1 = pyplot.scatter(x, y, marker=".", label=f"{axis_keys['x']}-{axis_keys['y']}")
# pyplot.plot(x, m * x + b, "-", color="green")
ax.set_xlabel(labels['x'], fontsize=fontsize)
ax.set_ylabel(labels['y'], fontsize=fontsize)
if "y1" in axis_keys.keys():
y1 = numpy.var(real_stim.X, axis=0)
_p2 = pyplot.scatter(x, y1, marker="*", c="grey", alpha=.5, label=f"{axis_keys['x']}-{axis_keys['y1']}")
if gene_list is not None:
for i in gene_list:
j = adata.var_names.tolist().index(i)
x_bar = x[j]
y_bar = y[j]
pyplot.text(x_bar, y_bar, i, fontsize=11, color="black")
pyplot.plot(x_bar, y_bar, 'o', color="red", markersize=5)
if "y1" in axis_keys.keys():
y1_bar = y1[j]
pyplot.text(x_bar, y1_bar, '*', color="black", alpha=.5)
if legend:
pyplot.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if title is None:
pyplot.title(f"", fontsize=12)
else:
pyplot.title(title, fontsize=12)
ax.text(max(x) - max(x) * x_coeff, max(y) - y_coeff * max(y), r'$\mathrm{R^2_{\mathrm{\mathsf{all\ genes}}}}$= ' + f"{r_value ** 2:.2f}", fontsize=kwargs.get("textsize", fontsize))
if diff_genes is not None:
ax.text(max(x) - max(x) * x_coeff, max(y) - (y_coeff + 0.15) * max(y), r'$\mathrm{R^2_{\mathrm{\mathsf{top\ 100\ DEGs}}}}$= ' + f"{r_value_diff ** 2:.2f}", fontsize=kwargs.get("textsize", fontsize))
pyplot.savefig(f"{path_to_save}", bbox_inches='tight', dpi=100)
if show:
pyplot.show()
pyplot.close()
return r_value**2, r_value_diff**2
def binary_classifier(scg_object, adata, delta, condition_key, conditions, path_to_save, fontsize=14):
"""
Builds a linear classifier based on the dot product between
the difference vector and the latent representation of each
cell and plots the dot product results between delta and latent
representation.
# Parameters
scg_object: `~scgen.models.VAEArith`
one of scGen models object.
adata: `~anndata.AnnData`
Annotated Data Matrix.
delta: float
Difference between stimulated and control cells in latent space
condition_key: basestring
Condition state to be used.
conditions: dict
dictionary of conditions.
path_to_save: basestring
path to save the plot.
# Example
```python
import anndata
import scgen
import scanpy as sc
train = sc.read("./tests/data/train.h5ad", backup_url="https://goo.gl/33HtVh")
network = scgen.VAEArith(x_dimension=train.shape[1], model_path="../models/test")
network.train(train_data=train, n_epochs=0)
unperturbed_data = train[((train.obs["cell_type"] == "CD4T") & (train.obs["condition"] == "control"))]
condition = {"ctrl": "control", "stim": "stimulated"}
pred, delta = network.predict(adata=train, adata_to_predict=unperturbed_data, conditions=condition)
scgen.plotting.binary_classifier(network, train, delta, condtion_key="condition",
conditions={"ctrl": "control", "stim": "stimulated"},
path_to_save="tests/binary_classifier.pdf")
network.sess.close()
```
"""
# matplotlib.rcParams.update(matplotlib.rcParamsDefault)
pyplot.close("all")
if sparse.issparse(adata.X):
adata.X = adata.X.A
cd = adata[adata.obs[condition_key] == conditions["ctrl"], :]
stim = adata[adata.obs[condition_key] == conditions["stim"], :]
all_latent_cd = scg_object.to_latent(cd.X)
all_latent_stim = scg_object.to_latent(stim.X)
dot_cd = numpy.zeros((len(all_latent_cd)))
dot_sal = numpy.zeros((len(all_latent_stim)))
for ind, vec in enumerate(all_latent_cd):
dot_cd[ind] = numpy.dot(delta, vec)
for ind, vec in enumerate(all_latent_stim):
dot_sal[ind] = numpy.dot(delta, vec)
pyplot.hist(dot_cd, label=conditions["ctrl"], bins=50, )
pyplot.hist(dot_sal, label=conditions["stim"], bins=50)
# pyplot.legend(loc=1, prop={'size': 7})
pyplot.axvline(0, color='k', linestyle='dashed', linewidth=1)
pyplot.title(" ", fontsize=fontsize)
pyplot.xlabel(" ", fontsize=fontsize)
pyplot.ylabel(" ", fontsize=fontsize)
pyplot.xticks(fontsize=fontsize)
pyplot.yticks(fontsize=fontsize)
ax = pyplot.gca()
ax.grid(False)
pyplot.savefig(f"{path_to_save}", bbox_inches='tight', dpi=100)
pyplot.show()
PK N A A scgen/read_load.pyimport os
from pathlib import Path
from urllib.request import urlretrieve
import numpy as np
import pandas as pd
import anndata
def load_file(filename, backup_url=None, **kwargs):#TODO : what if several fileS provided as csv or h5 e.g. x, label1, label2
"""
Loads file in any of pandas, numpy or AnnData's extension.
# Parameters
filename: basestring
name of the file which is going to be loaded.
backup_url: basestring
backup url for downloading data if the file with the specified `filename`
does not exists.
kwargs: dict
dictionary of additional arguments for loading data with each package.
# Returns
The annotated matrix of loaded data.
# Example
```python
import scgen
train_data_filename = "./data/train.h5ad"
train_data = scgen.load_file(train_data_filename)
```
"""
numpy_ext = {'npy', 'npz'}
pandas_ext = {'csv', 'h5'}
adata_ext = {"h5ad"}
if not os.path.exists(filename) and backup_url is None:
raise FileNotFoundError('Did not find file {}.'.format(filename))
elif not os.path.exists(filename):
d = os.path.dirname(filename)
if not os.path.exists(d): os.makedirs(d)
urlretrieve(backup_url, filename)
ext = Path(filename).suffixes[-1][1:]
if ext in numpy_ext:
return np.load(filename, **kwargs)
elif ext in pandas_ext:
return pd.read_csv(filename, **kwargs)
elif ext in adata_ext:
return anndata.read(filename, **kwargs)
else:
raise ValueError('"{}" does not end on a valid extension.\n'
'Please, provide one of the available extensions.\n{}\n'
.format(filename, numpy_ext | pandas_ext))
PK nNk.5 scgen/models/__init__.pyfrom ._vae_keras import VAEArithKeras
from ._vae import VAEArith
from .util import batch_removal, label_encoder, visualize_trained_network_results, data_remover, balancer
PK fO]6Z Z scgen/models/_vae.pyimport logging
import os
import numpy
import tensorflow as tf
from scipy import sparse
from .util import balancer, extractor, shuffle_data
log = logging.getLogger(__file__)
class VAEArith:
"""
VAE with Arithmetic vector Network class. This class contains the implementation of Variational
Auto-encoder network with Vector Arithmetics.
# Parameters
kwargs:
key: `validation_data` : AnnData
must be fed if `use_validation` is true.
key: `dropout_rate`: float
dropout rate
key: `learning_rate`: float
learning rate of optimization algorithm
key: `model_path`: basestring
path to save the model after training
x_dimension: integer
number of gene expression space dimensions.
z_dimension: integer
number of latent space dimensions.
"""
def __init__(self, x_dimension, z_dimension=100, **kwargs):
self.x_dim = x_dimension
self.z_dim = z_dimension
self.learning_rate = kwargs.get("learning_rate", 0.001)
self.dropout_rate = kwargs.get("dropout_rate", 0.2)
self.model_to_use = kwargs.get("model_path", "./models/scgen")
self.alpha = kwargs.get("alpha", 0.00005)
self.is_training = tf.placeholder(tf.bool, name='training_flag')
self.x = tf.placeholder(tf.float32, shape=[None, self.x_dim], name="data")
self.z = tf.placeholder(tf.float32, shape=[None, self.z_dim], name="latent")
self.init_w = tf.contrib.layers.xavier_initializer()
self._create_network()
self._loss_function()
self.sess = tf.Session()
self.saver = tf.train.Saver(max_to_keep=1)
self.init = tf.global_variables_initializer().run(session=self.sess)
def _encoder(self):
"""
Constructs the encoder sub-network of VAE. This function implements the
encoder part of Variational Auto-encoder. It will transform primary
data in the `n_vars` dimension-space to the `z_dimension` latent space.
# Parameters
No parameters are needed.
# Returns
mean: Tensor
A dense layer consists of means of gaussian distributions of latent space dimensions.
log_var: Tensor
A dense layer consists of log transformed variances of gaussian distributions of latent space dimensions.
"""
with tf.variable_scope("encoder", reuse=tf.AUTO_REUSE):
h = tf.layers.dense(inputs=self.x, units=800, kernel_initializer=self.init_w, use_bias=False)
h = tf.layers.batch_normalization(h, axis=1, training=self.is_training)
h = tf.nn.leaky_relu(h)
h = tf.layers.dropout(h, self.dropout_rate, training=self.is_training)
h = tf.layers.dense(inputs=h, units=800, kernel_initializer=self.init_w, use_bias=False)
h = tf.layers.batch_normalization(h, axis=1, training=self.is_training)
h = tf.nn.leaky_relu(h)
h = tf.layers.dropout(h, self.dropout_rate, training=self.is_training)
mean = tf.layers.dense(inputs=h, units=self.z_dim, kernel_initializer=self.init_w)
log_var = tf.layers.dense(inputs=h, units=self.z_dim, kernel_initializer=self.init_w)
return mean, log_var
def _decoder(self):
"""
Constructs the decoder sub-network of VAE. This function implements the
decoder part of Variational Auto-encoder. It will transform constructed
latent space to the previous space of data with n_dimensions = n_vars.
# Parameters
No parameters are needed.
# Returns
h: Tensor
A Tensor for last dense layer with the shape of [n_vars, ] to reconstruct data.
"""
with tf.variable_scope("decoder", reuse=tf.AUTO_REUSE):
h = tf.layers.dense(inputs=self.z_mean, units=800, kernel_initializer=self.init_w, use_bias=False)
h = tf.layers.batch_normalization(h, axis=1, training=self.is_training)
h = tf.nn.leaky_relu(h)
h = tf.layers.dropout(h, self.dropout_rate, training=self.is_training)
h = tf.layers.dense(inputs=h, units=800, kernel_initializer=self.init_w, use_bias=False)
tf.layers.batch_normalization(h, axis=1, training=self.is_training)
h = tf.nn.leaky_relu(h)
h = tf.layers.dropout(h, self.dropout_rate, training=self.is_training)
h = tf.layers.dense(inputs=h, units=self.x_dim, kernel_initializer=self.init_w, use_bias=True)
h = tf.nn.relu(h)
return h
def _sample_z(self):
"""
Samples from standard Normal distribution with shape [size, z_dim] and
applies re-parametrization trick. It is actually sampling from latent
space distributions with N(mu, var) computed in `_encoder` function.
# Parameters
No parameters are needed.
# Returns
The computed Tensor of samples with shape [size, z_dim].
"""
batch_size = tf.shape(self.mu)[0]
eps = tf.random_normal(shape=[batch_size, self.z_dim])
return self.mu + tf.exp(self.log_var / 2) * eps
def _create_network(self):
"""
Constructs the whole VAE network. It is step-by-step constructing the VAE
network. First, It will construct the encoder part and get mu, log_var of
latent space. Second, It will sample from the latent space to feed the
decoder part in next step. Finally, It will reconstruct the data by
constructing decoder part of VAE.
# Parameters
No parameters are needed.
# Returns
Nothing will be returned.
"""
self.mu, self.log_var = self._encoder()
self.z_mean = self._sample_z()
self.x_hat = self._decoder()
def _loss_function(self):
"""
Defines the loss function of VAE network after constructing the whole
network. This will define the KL Divergence and Reconstruction loss for
VAE and also defines the Optimization algorithm for network. The VAE Loss
will be weighted sum of reconstruction loss and KL Divergence loss.
# Parameters
No parameters are needed.
# Returns
Nothing will be returned.
"""
kl_loss = 0.5 * tf.reduce_sum(
tf.exp(self.log_var) + tf.square(self.mu) - 1. - self.log_var, 1)
recon_loss = 0.5 * tf.reduce_sum(tf.square((self.x - self.x_hat)), 1)
self.vae_loss = tf.reduce_mean(recon_loss + self.alpha * kl_loss)
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
self.solver = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.vae_loss)
def to_latent(self, data):
"""
Map `data` in to the latent space. This function will feed data
in encoder part of VAE and compute the latent space coordinates
for each sample in data.
# Parameters
data: numpy nd-array
Numpy nd-array to be mapped to latent space. `data.X` has to be in shape [n_obs, n_vars].
# Returns
latent: numpy nd-array
Returns array containing latent space encoding of 'data'
"""
latent = self.sess.run(self.z_mean, feed_dict={self.x: data, self.is_training: False})
return latent
def _avg_vector(self, data):
"""
Computes the average of points which computed from mapping `data`
to encoder part of VAE.
# Parameters
data: numpy nd-array
Numpy nd-array matrix to be mapped to latent space. Note that `data.X` has to be in shape [n_obs, n_vars].
# Returns
The average of latent space mapping in numpy nd-array.
"""
latent = self.to_latent(data)
latent_avg = numpy.average(latent, axis=0)
return latent_avg
def reconstruct(self, data, use_data=False):
"""
Map back the latent space encoding via the decoder.
# Parameters
data: `~anndata.AnnData`
Annotated data matrix whether in latent space or gene expression space.
use_data: bool
This flag determines whether the `data` is already in latent space or not.
if `True`: The `data` is in latent space (`data.X` is in shape [n_obs, z_dim]).
if `False`: The `data` is not in latent space (`data.X` is in shape [n_obs, n_vars]).
# Returns
rec_data: 'numpy nd-array'
Returns 'numpy nd-array` containing reconstructed 'data' in shape [n_obs, n_vars].
"""
if use_data:
latent = data
else:
latent = self.to_latent(data)
rec_data = self.sess.run(self.x_hat, feed_dict={self.z_mean: latent, self.is_training: False})
return rec_data
def linear_interpolation(self, source_adata, dest_adata, n_steps):
"""
Maps `source_adata` and `dest_adata` into latent space and linearly interpolate
`n_steps` points between them.
# Parameters
source_adata: `~anndata.AnnData`
Annotated data matrix of source cells in gene expression space (`x.X` must be in shape [n_obs, n_vars])
dest_adata: `~anndata.AnnData`
Annotated data matrix of destinations cells in gene expression space (`y.X` must be in shape [n_obs, n_vars])
n_steps: int
Number of steps to interpolate points between `source_adata`, `dest_adata`.
# Returns
interpolation: numpy nd-array
Returns the `numpy nd-array` of interpolated points in gene expression space.
# Example
```python
import anndata
import scgen
train_data = anndata.read("./data/train.h5ad")
validation_data = anndata.read("./data/validation.h5ad")
network = scgen.VAEArith(x_dimension= train_data.shape[1], model_path="./models/test" )
network.train(train_data=train_data, use_validation=True, valid_data=validation_data, shuffle=True, n_epochs=2)
souece = train_data[((train_data.obs["cell_type"] == "CD8T") & (train_data.obs["condition"] == "control"))]
destination = train_data[((train_data.obs["cell_type"] == "CD8T") & (train_data.obs["condition"] == "stimulated"))]
interpolation = network.linear_interpolation(souece, destination, n_steps=25)
```
"""
if sparse.issparse(source_adata.X):
source_average = source_adata.X.A.mean(axis=0).reshape((1, source_adata.shape[1]))
else:
source_average = source_adata.X.mean(axis=0).reshape((1, source_adata.shape[1]))
if sparse.issparse(dest_adata.X):
dest_average = dest_adata.X.A.mean(axis=0).reshape((1, dest_adata.shape[1]))
else:
dest_average = dest_adata.X.mean(axis=0).reshape((1, dest_adata.shape[1]))
start = self.to_latent(source_average)
end = self.to_latent(dest_average)
vectors = numpy.zeros((n_steps, start.shape[1]))
alpha_values = numpy.linspace(0, 1, n_steps)
for i, alpha in enumerate(alpha_values):
vector = start * (1 - alpha) + end * alpha
vectors[i, :] = vector
vectors = numpy.array(vectors)
interpolation = self.reconstruct(vectors, use_data=True)
return interpolation
def predict(self, adata, conditions, cell_type_key, condition_key, adata_to_predict=None, celltype_to_predict=None,
obs_key="all", biased=False):
"""
Predicts the cell type provided by the user in stimulated condition.
# Parameters
celltype_to_predict: basestring
The cell type you want to be predicted.
obs_key: basestring or dict
Dictionary of celltypes you want to be observed for prediction.
adata_to_predict: `~anndata.AnnData`
Adata for unpertubed cells you want to be predicted.
# Returns
predicted_cells: numpy nd-array
`numpy nd-array` of predicted cells in primary space.
delta: float
Difference between stimulated and control cells in latent space
# Example
```python
import anndata
import scgen
train_data = anndata.read("./data/train.h5ad"
validation_data = anndata.read("./data/validation.h5ad")
network = scgen.VAEArith(x_dimension= train_data.shape[1], model_path="./models/test" )
network.train(train_data=train_data, use_validation=True, valid_data=validation_data, shuffle=True, n_epochs=2)
prediction, delta = network.predict(adata= train_data, celltype_to_predict= "CD4T", conditions={"ctrl": "control", "stim": "stimulated"})
```
"""
if obs_key == "all":
ctrl_x = adata[adata.obs[condition_key] == conditions["ctrl"], :]
stim_x = adata[adata.obs[condition_key] == conditions["stim"], :]
if not biased:
ctrl_x = balancer(ctrl_x, cell_type_key=cell_type_key, condition_key=condition_key)
stim_x = balancer(stim_x, cell_type_key=cell_type_key, condition_key=condition_key)
else:
key = list(obs_key.keys())[0]
values = obs_key[key]
subset = adata[adata.obs[key].isin(values)]
ctrl_x = subset[subset.obs[condition_key] == conditions["ctrl"], :]
stim_x = subset[subset.obs[condition_key] == conditions["stim"], :]
if len(values) > 1 and not biased:
ctrl_x = balancer(ctrl_x, cell_type_key=cell_type_key, condition_key=condition_key)
stim_x = balancer(stim_x, cell_type_key=cell_type_key, condition_key=condition_key)
if celltype_to_predict is not None and adata_to_predict is not None:
raise Exception("Please provide either a cell type or adata not both!")
if celltype_to_predict is None and adata_to_predict is None:
raise Exception("Please provide a cell type name or adata for your unperturbed cells")
if celltype_to_predict is not None:
ctrl_pred = extractor(adata, celltype_to_predict, conditions, cell_type_key, condition_key)[1]
else:
ctrl_pred = adata_to_predict
if not biased:
eq = min(ctrl_x.X.shape[0], stim_x.X.shape[0])
cd_ind = numpy.random.choice(range(ctrl_x.shape[0]), size=eq, replace=False)
stim_ind = numpy.random.choice(range(stim_x.shape[0]), size=eq, replace=False)
else:
cd_ind = numpy.random.choice(range(ctrl_x.shape[0]), size=ctrl_x.shape[0], replace=False)
stim_ind = numpy.random.choice(range(stim_x.shape[0]), size=stim_x.shape[0], replace=False)
if sparse.issparse(ctrl_x.X) and sparse.issparse(stim_x.X):
latent_ctrl = self._avg_vector(ctrl_x.X.A[cd_ind, :])
latent_sim = self._avg_vector(stim_x.X.A[stim_ind, :])
else:
latent_ctrl = self._avg_vector(ctrl_x.X[cd_ind, :])
latent_sim = self._avg_vector(stim_x.X[stim_ind, :])
delta = latent_sim - latent_ctrl
if sparse.issparse(ctrl_pred.X):
latent_cd = self.to_latent(ctrl_pred.X.A)
else:
latent_cd = self.to_latent(ctrl_pred.X)
stim_pred = delta + latent_cd
predicted_cells = self.reconstruct(stim_pred, use_data=True)
return predicted_cells, delta
def predict_cross(self, train, data, conditions):
cd_x = train.copy()[train.obs["condition"] == conditions["ctrl"], :]
cd_x = balancer(cd_x)
stim_x = train.copy()[train.obs["condition"] == conditions["stim"], :]
stim_x = balancer(stim_x)
cd_y = data.copy()
eq = min(cd_x.X.shape[0], stim_x.X.shape[0])
cd_ind = numpy.random.choice(range(cd_x.shape[0]), size=eq, replace=False)
stim_ind = numpy.random.choice(range(stim_x.shape[0]), size=eq, replace=False)
lat_cd = self._avg_vector(cd_x.X[cd_ind, :])
lat_stim = self._avg_vector(stim_x.X[stim_ind, :])
delta = lat_stim - lat_cd
latent_cd = self.to_latent(cd_y.X)
stim_pred = delta + latent_cd
predicted_cells = self.reconstruct(stim_pred, use_data=True)
return predicted_cells, delta
def restore_model(self):
"""
restores model weights from `model_to_use`.
# Parameters
No parameters are needed.
# Returns
Nothing will be returned.
# Example
```python
import anndata
import scgen
train_data = anndata.read("./data/train.h5ad")
validation_data = anndata.read("./data/validation.h5ad")
network = scgen.VAEArith(x_dimension= train_data.shape[1], model_path="./models/test" )
network.restore_model()
```
"""
self.saver.restore(self.sess, self.model_to_use)
def train(self, train_data, use_validation=False, valid_data=None, n_epochs=25, batch_size=32, early_stop_limit=20,
threshold=0.0025, initial_run=True, shuffle=True, save=True, verbose=False):
"""
Trains the network `n_epochs` times with given `train_data`
and validates the model using validation_data if it was given
in the constructor function. This function is using `early stopping`
technique to prevent over-fitting.
# Parameters
train_data: scanpy AnnData
Annotated Data Matrix for training VAE network.
use_validation: bool
if `True`: must feed a valid AnnData object to `valid_data` argument.
valid_data: scanpy AnnData
Annotated Data Matrix for validating VAE network after each epoch.
n_epochs: int
Number of epochs to iterate and optimize network weights
batch_size: integer
size of each batch of training dataset to be fed to network while training.
early_stop_limit: int
Number of consecutive epochs in which network loss is not going lower.
After this limit, the network will stop training.
threshold: float
Threshold for difference between consecutive validation loss values
if the difference is upper than this `threshold`, this epoch will not
considered as an epoch in early stopping.
initial_run: bool
if `True`: The network will initiate training and log some useful initial messages.
if `False`: Network will resume the training using `restore_model` function in order
to restore last model which has been trained with some training dataset.
shuffle: bool
if `True`: shuffles the training dataset
# Returns
Nothing will be returned
# Example
```python
import anndata
import scgen
train_data = anndata.read("./data/train.h5ad"
validation_data = anndata.read("./data/validation.h5ad"
network = scgen.VAEArith(x_dimension= train_data.shape[1], model_path="./models/test")
network.train(train_data=train_data, use_validation=True, valid_data=validation_data, shuffle=True, n_epochs=2)
```
"""
if initial_run:
log.info("----Training----")
if not initial_run:
self.saver.restore(self.sess, self.model_to_use)
if use_validation and valid_data is None:
raise Exception("valid_data is None but use_validation is True.")
if shuffle:
train_data = shuffle_data(train_data)
loss_hist = []
patience = early_stop_limit
min_delta = threshold
patience_cnt = 0
for it in range(n_epochs):
train_loss = 0.0
for lower in range(0, train_data.shape[0], batch_size):
upper = min(lower + batch_size, train_data.shape[0])
if sparse.issparse(train_data.X):
x_mb = train_data[lower:upper, :].X.A
else:
x_mb = train_data[lower:upper, :].X
if upper - lower > 1:
_, current_loss_train = self.sess.run([self.solver, self.vae_loss],
feed_dict={self.x: x_mb, self.is_training: True})
train_loss += current_loss_train
if use_validation:
valid_loss = 0
for lower in range(0, valid_data.shape[0], batch_size):
upper = min(lower + batch_size, valid_data.shape[0])
if sparse.issparse(valid_data.X):
x_mb = valid_data[lower:upper, :].X.A
else:
x_mb = valid_data[lower:upper, :].X
current_loss_valid = self.sess.run(self.vae_loss,
feed_dict={self.x: x_mb, self.is_training: False})
valid_loss += current_loss_valid
loss_hist.append(valid_loss / valid_data.shape[0])
if it > 0 and loss_hist[it - 1] - loss_hist[it] > min_delta:
patience_cnt = 0
else:
patience_cnt += 1
if patience_cnt > patience:
save_path = self.saver.save(self.sess, self.model_to_use)
break
if verbose:
print(f"Epoch {it}: Train Loss: {train_loss / (train_data.shape[0] // batch_size)} Valid Loss: {valid_loss / (valid_data.shape[0] // batch_size)}")
else:
if verbose:
print(f"Epoch {it}: Train Loss: {train_loss / (train_data.shape[0] // batch_size)}")
if save:
os.makedirs(self.model_to_use, exist_ok=True)
save_path = self.saver.save(self.sess, self.model_to_use)
log.info(f"Model saved in file: {save_path}. Training finished")
PK fO`RPX/X /X scgen/models/_vae_keras.pyimport logging
import os
import keras
import numpy
import tensorflow as tf
from keras import backend as K, Model
from keras.callbacks import CSVLogger, LambdaCallback, EarlyStopping
from keras.layers import Input, Dense, BatchNormalization, LeakyReLU, Dropout, Lambda
from keras.models import load_model
from scipy import sparse
import scgen
from .util import balancer, extractor, shuffle_data
log = logging.getLogger(__file__)
class VAEArithKeras:
"""
VAE with Arithmetic vector Network class. This class contains the implementation of Variational
Auto-encoder network with Vector Arithmetics.
Parameters
----------
kwargs:
:key `validation_data` : AnnData
must be fed if `use_validation` is true.
:key dropout_rate: float
dropout rate
:key learning_rate: float
learning rate of optimization algorithm
:key model_path: basestring
path to save the model after training
x_dimension: integer
number of gene expression space dimensions.
z_dimension: integer
number of latent space dimensions.
See also
--------
CVAE from scgen.models._cvae : Conditional VAE implementation.
"""
def __init__(self, x_dimension, z_dimension=100, **kwargs):
self.x_dim = x_dimension
self.z_dim = z_dimension
self.learning_rate = kwargs.get("learning_rate", 0.001)
self.dropout_rate = kwargs.get("dropout_rate", 0.2)
self.model_to_use = kwargs.get("model_path", "./models/")
self.alpha = kwargs.get("alpha", 0.00005)
self.x = Input(shape=(x_dimension,), name="input")
self.z = Input(shape=(z_dimension,), name="latent")
self.init_w = keras.initializers.glorot_normal()
self._create_network()
self._loss_function()
self.vae_model.summary()
def _encoder(self):
"""
Constructs the encoder sub-network of VAE. This function implements the
encoder part of Variational Auto-encoder. It will transform primary
data in the `n_vars` dimension-space to the `z_dimension` latent space.
Parameters
----------
No parameters are needed.
Returns
-------
mean: Tensor
A dense layer consists of means of gaussian distributions of latent space dimensions.
log_var: Tensor
A dense layer consists of log transformed variances of gaussian distributions of latent space dimensions.
"""
h = Dense(800, kernel_initializer=self.init_w, use_bias=False)(self.x)
h = BatchNormalization(axis=1)(h)
h = LeakyReLU()(h)
h = Dropout(self.dropout_rate)(h)
h = Dense(800, kernel_initializer=self.init_w, use_bias=False)(h)
h = BatchNormalization(axis=1)(h)
h = LeakyReLU()(h)
h = Dropout(self.dropout_rate)(h)
# h = Dense(512, kernel_initializer=self.init_w, use_bias=False)(h)
# h = BatchNormalization()(h)
# h = LeakyReLU()(h)
# h = Dropout(self.dropout_rate)(h)
# h = Dense(256, kernel_initializer=self.init_w, use_bias=False)(h)
# h = BatchNormalization()(h)
# h = LeakyReLU()(h)
# h = Dropout(self.dropout_rate)(h)
mean = Dense(self.z_dim, kernel_initializer=self.init_w)(h)
log_var = Dense(self.z_dim, kernel_initializer=self.init_w)(h)
z = Lambda(self._sample_z, output_shape=(self.z_dim,), name="Z")([mean, log_var])
self.encoder_model = Model(inputs=self.x, outputs=z, name="encoder")
return mean, log_var
def _decoder(self):
"""
Constructs the decoder sub-network of VAE. This function implements the
decoder part of Variational Auto-encoder. It will transform constructed
latent space to the previous space of data with n_dimensions = n_vars.
Parameters
----------
No parameters are needed.
Returns
-------
h: Tensor
A Tensor for last dense layer with the shape of [n_vars, ] to reconstruct data.
"""
h = Dense(800, kernel_initializer=self.init_w, use_bias=False)(self.z)
h = BatchNormalization(axis=1)(h)
h = LeakyReLU()(h)
h = Dropout(self.dropout_rate)(h)
h = Dense(800, kernel_initializer=self.init_w, use_bias=False)(h)
h = BatchNormalization(axis=1)(h)
h = LeakyReLU()(h)
h = Dropout(self.dropout_rate)(h)
# h = Dense(768, kernel_initializer=self.init_w, use_bias=False)(h)
# h = BatchNormalization()(h)
# h = LeakyReLU()(h)
# h = Dropout(self.dropout_rate)(h)
# h = Dense(1024, kernel_initializer=self.init_w, use_bias=False)(h)
# h = BatchNormalization()(h)
# h = LeakyReLU()(h)
# h = Dropout(self.dropout_rate)(h)
h = Dense(self.x_dim, kernel_initializer=self.init_w, use_bias=True)(h)
self.decoder_model = Model(inputs=self.z, outputs=h, name="decoder")
return h
@staticmethod
def _sample_z(args):
"""
Samples from standard Normal distribution with shape [size, z_dim] and
applies re-parametrization trick. It is actually sampling from latent
space distributions with N(mu, var) computed in `_encoder` function.
Parameters
----------
No parameters are needed.
Returns
-------
The computed Tensor of samples with shape [size, z_dim].
"""
mu, log_var = args
batch_size = K.shape(mu)[0]
z_dim = K.shape(mu)[1]
eps = K.random_normal(shape=[batch_size, z_dim])
return mu + K.exp(log_var / 2) * eps
def _create_network(self):
"""
Constructs the whole VAE network. It is step-by-step constructing the VAE
network. First, It will construct the encoder part and get mu, log_var of
latent space. Second, It will sample from the latent space to feed the
decoder part in next step. Finally, It will reconstruct the data by
constructing decoder part of VAE.
Parameters
----------
No parameters are needed.
Returns
-------
Nothing will be returned.
"""
self.mu, self.log_var = self._encoder()
self.x_hat = self._decoder()
self.vae_model = Model(inputs=self.x, outputs=self.decoder_model(self.encoder_model(self.x)), name="VAE")
def _loss_function(self):
"""
Defines the loss function of VAE network after constructing the whole
network. This will define the KL Divergence and Reconstruction loss for
VAE and also defines the Optimization algorithm for network. The VAE Loss
will be weighted sum of reconstruction loss and KL Divergence loss.
Parameters
----------
No parameters are needed.
Returns
-------
Nothing will be returned.
"""
def vae_loss(y_true, y_pred):
return K.mean(recon_loss(y_true, y_pred) + self.alpha * kl_loss(y_true, y_pred))
def kl_loss(y_true, y_pred):
return 0.5 * K.sum(K.exp(self.log_var) + K.square(self.mu) - 1. - self.log_var, axis=1)
def recon_loss(y_true, y_pred):
return 0.5 * K.sum(K.square((y_true - y_pred)), axis=1)
self.vae_optimizer = keras.optimizers.Adam(lr=self.learning_rate)
self.vae_model.compile(optimizer=self.vae_optimizer, loss=vae_loss, metrics=[kl_loss, recon_loss])
def to_latent(self, data):
"""
Map `data` in to the latent space. This function will feed data
in encoder part of VAE and compute the latent space coordinates
for each sample in data.
Parameters
----------
data: numpy nd-array
Numpy nd-array to be mapped to latent space. `data.X` has to be in shape [n_obs, n_vars].
Returns
-------
latent: numpy nd-array
Returns array containing latent space encoding of 'data'
"""
latent = self.encoder_model.predict(data)
return latent
def _avg_vector(self, data):
"""
Computes the average of points which computed from mapping `data`
to encoder part of VAE.
Parameters
----------
data: numpy nd-array
Numpy nd-array matrix to be mapped to latent space. Note that `data.X` has to be in shape [n_obs, n_vars].
Returns
-------
The average of latent space mapping in numpy nd-array.
"""
latent = self.to_latent(data)
latent_avg = numpy.average(latent, axis=0)
return latent_avg
def reconstruct(self, data):
"""
Map back the latent space encoding via the decoder.
Parameters
----------
data: `~anndata.AnnData`
Annotated data matrix whether in latent space or gene expression space.
use_data: bool
This flag determines whether the `data` is already in latent space or not.
if `True`: The `data` is in latent space (`data.X` is in shape [n_obs, z_dim]).
if `False`: The `data` is not in latent space (`data.X` is in shape [n_obs, n_vars]).
Returns
-------
rec_data: 'numpy nd-array'
Returns 'numpy nd-array` containing reconstructed 'data' in shape [n_obs, n_vars].
"""
rec_data = self.decoder_model.predict(x=data)
return rec_data
def linear_interpolation(self, source_adata, dest_adata, n_steps):
"""
Maps `source_adata` and `dest_adata` into latent space and linearly interpolate
`n_steps` points between them.
Parameters
----------
source_adata: `~anndata.AnnData`
Annotated data matrix of source cells in gene expression space (`x.X` must be in shape [n_obs, n_vars])
dest_adata: `~anndata.AnnData`
Annotated data matrix of destinations cells in gene expression space (`y.X` must be in shape [n_obs, n_vars])
n_steps: int
Number of steps to interpolate points between `source_adata`, `dest_adata`.
Returns
-------
interpolation: numpy nd-array
Returns the `numpy nd-array` of interpolated points in gene expression space.
Example
--------
>>> import anndata
>>> import scgen
>>> train_data = anndata.read("./data/train.h5ad")
>>> validation_data = anndata.read("./data/validation.h5ad")
>>> network = scgen.VAEArith(x_dimension= train_data.shape[1], model_path="./models/test" )
>>> network.train(train_data=train_data, use_validation=True, validation_data=validation_data, shuffle=True, n_epochs=2)
>>> souece = train_data[((train_data.obs["cell_type"] == "CD8T") & (train_data.obs["condition"] == "control"))]
>>> destination = train_data[((train_data.obs["cell_type"] == "CD8T") & (train_data.obs["condition"] == "stimulated"))]
>>> interpolation = network.linear_interpolation(souece, destination, n_steps=25)
"""
if sparse.issparse(source_adata.X):
source_average = source_adata.X.A.mean(axis=0).reshape((1, source_adata.shape[1]))
else:
source_average = source_adata.X.A.mean(axis=0).reshape((1, source_adata.shape[1]))
if sparse.issparse(dest_adata.X):
dest_average = dest_adata.X.A.mean(axis=0).reshape((1, dest_adata.shape[1]))
else:
dest_average = dest_adata.X.A.mean(axis=0).reshape((1, dest_adata.shape[1]))
start = self.to_latent(source_average)
end = self.to_latent(dest_average)
vectors = numpy.zeros((n_steps, start.shape[1]))
alpha_values = numpy.linspace(0, 1, n_steps)
for i, alpha in enumerate(alpha_values):
vector = start * (1 - alpha) + end * alpha
vectors[i, :] = vector
vectors = numpy.array(vectors)
interpolation = self.reconstruct(vectors)
return interpolation
def predict(self, adata, conditions, cell_type_key, condition_key, adata_to_predict=None, celltype_to_predict=None, obs_key="all"):
"""
Predicts the cell type provided by the user in stimulated condition.
Parameters
----------
celltype_to_predict: basestring
The cell type you want to be predicted.
obs_key: basestring or dict
Dictionary of celltypes you want to be observed for prediction.
adata_to_predict: `~anndata.AnnData`
Adata for unpertubed cells you want to be predicted.
Returns
-------
predicted_cells: numpy nd-array
`numpy nd-array` of predicted cells in primary space.
delta: float
Difference between stimulated and control cells in latent space
Example
--------
>>> import anndata
>>> import scgen
>>> train_data = anndata.read("./data/train.h5ad"
>>> validation_data = anndata.read("./data/validation.h5ad")
>>> network = scgen.VAEArith(x_dimension= train_data.shape[1], model_path="./models/test" )
>>> network.train(train_data=train_data, use_validation=True, validation_data=validation_data, shuffle=True, n_epochs=2)
>>> prediction, delta = network.predict(adata= train_data, celltype_to_predict= "CD4T", conditions={"ctrl": "control", "stim": "stimulated"})
"""
if obs_key == "all":
ctrl_x = adata[adata.obs["condition"] == conditions["ctrl"], :]
stim_x = adata[adata.obs["condition"] == conditions["stim"], :]
ctrl_x = balancer(ctrl_x, cell_type_key=cell_type_key, condition_key=condition_key)
stim_x = balancer(stim_x, cell_type_key=cell_type_key, condition_key=condition_key)
else:
key = list(obs_key.keys())[0]
values = obs_key[key]
subset = adata[adata.obs[key].isin(values)]
ctrl_x = subset[subset.obs["condition"] == conditions["ctrl"], :]
stim_x = subset[subset.obs["condition"] == conditions["stim"], :]
if len(values) > 1:
ctrl_x = balancer(ctrl_x, cell_type_key=cell_type_key, condition_key=condition_key)
stim_x = balancer(stim_x, cell_type_key=cell_type_key, condition_key=condition_key)
if celltype_to_predict is not None and adata_to_predict is not None:
raise Exception("Please provide either a cell type or adata not both!")
if celltype_to_predict is None and adata_to_predict is None:
raise Exception("Please provide a cell type name or adata for your unperturbed cells")
if celltype_to_predict is not None:
ctrl_pred = extractor(adata, celltype_to_predict, conditions, cell_type_key, condition_key)[1]
else:
ctrl_pred = adata_to_predict
eq = min(ctrl_x.X.shape[0], stim_x.X.shape[0])
cd_ind = numpy.random.choice(range(ctrl_x.shape[0]), size=eq, replace=False)
stim_ind = numpy.random.choice(range(stim_x.shape[0]), size=eq, replace=False)
if sparse.issparse(ctrl_x.X) and sparse.issparse(stim_x.X):
latent_ctrl = self._avg_vector(ctrl_x.X.A[cd_ind, :])
latent_sim = self._avg_vector(stim_x.X.A[stim_ind, :])
else:
latent_ctrl = self._avg_vector(ctrl_x.X[cd_ind, :])
latent_sim = self._avg_vector(stim_x.X[stim_ind, :])
delta = latent_sim - latent_ctrl
if sparse.issparse(ctrl_pred.X):
latent_cd = self.to_latent(ctrl_pred.X.A)
else:
latent_cd = self.to_latent(ctrl_pred.X)
stim_pred = delta + latent_cd
predicted_cells = self.reconstruct(stim_pred)
return predicted_cells, delta
def restore_model(self):
"""
restores model weights from `model_to_use`.
Parameters
----------
No parameters are needed.
Returns
-------
Nothing will be returned.
Example
--------
>>> import anndata
>>> import scgen
>>> train_data = anndata.read("./data/train.h5ad")
>>> validation_data = anndata.read("./data/validation.h5ad")
>>> network = scgen.VAEArith(x_dimension= train_data.shape[1], model_path="./models/test" )
>>> network.restore_model()
"""
self.vae_model = load_model(os.path.join(self.model_to_use, 'vae.h5'), compile=False)
self.encoder_model = load_model(os.path.join(self.model_to_use, 'encoder.h5'), compile=False)
self.decoder_model = load_model(os.path.join(self.model_to_use, 'decoder.h5'), compile=False)
self._loss_function()
def train(self, train_data, validation_data=None,
n_epochs=25,
batch_size=32,
early_stop_limit=20,
threshold=0.0025,
initial_run=True,
shuffle=True,
verbose=1,
save=True,
checkpoint=50,
**kwargs):
"""
Trains the network `n_epochs` times with given `train_data`
and validates the model using validation_data if it was given
in the constructor function. This function is using `early stopping`
technique to prevent over-fitting.
Parameters
----------
train_data: scanpy AnnData
Annotated Data Matrix for training VAE network.
validation_data: scanpy AnnData
Annotated Data Matrix for validating VAE network after each epoch.
n_epochs: int
Number of epochs to iterate and optimize network weights
batch_size: integer
size of each batch of training dataset to be fed to network while training.
early_stop_limit: int
Number of consecutive epochs in which network loss is not going lower.
After this limit, the network will stop training.
threshold: float
Threshold for difference between consecutive validation loss values
if the difference is upper than this `threshold`, this epoch will not
considered as an epoch in early stopping.
initial_run: bool
if `True`: The network will initiate training and log some useful initial messages.
if `False`: Network will resume the training using `restore_model` function in order
to restore last model which has been trained with some training dataset.
shuffle: bool
if `True`: shuffles the training dataset
Returns
-------
Nothing will be returned
Example
--------
```python
import anndata
import scgen
train_data = anndata.read("./data/train.h5ad"
validation_data = anndata.read("./data/validation.h5ad"
network = scgen.VAEArith(x_dimension= train_data.shape[1], model_path="./models/test")
network.train(train_data=train_data, use_validation=True, valid_data=validation_data, shuffle=True, n_epochs=2)
```
"""
if initial_run:
log.info("----Training----")
if shuffle:
train_data = shuffle_data(train_data)
if sparse.issparse(train_data.X):
train_data.X = train_data.X.A
# def on_epoch_end(epoch, logs):
# if epoch % checkpoint == 0:
# path_to_save = os.path.join(kwargs.get("path_to_save"), f"epoch_{epoch}") + "/"
# scgen.visualize_trained_network_results(self, vis_data, kwargs.get("cell_type"),
# kwargs.get("conditions"),
# kwargs.get("condition_key"), kwargs.get("cell_type_key"),
# path_to_save,
# plot_umap=False,
# plot_reg=True)
callbacks = [
# LambdaCallback(on_epoch_end=on_epoch_end),
# EarlyStopping(patience=early_stop_limit, monitor='loss', min_delta=threshold),
CSVLogger(filename="./csv_logger.log")
]
if validation_data is not None:
result = self.vae_model.fit(x=train_data.X,
y=train_data.X,
epochs=n_epochs,
batch_size=batch_size,
validation_data=(validation_data.X, validation_data.X),
shuffle=shuffle,
callbacks=callbacks,
verbose=verbose)
else:
result = self.vae_model.fit(x=train_data.X,
y=train_data.X,
epochs=n_epochs,
batch_size=batch_size,
shuffle=shuffle,
callbacks=callbacks,
verbose=verbose)
if save is True:
os.makedirs(self.model_to_use, exist_ok=True)
self.vae_model.save(os.path.join("vae.h5"), overwrite=True)
self.encoder_model.save(os.path.join("encoder.h5"), overwrite=True)
self.decoder_model.save(os.path.join("decoder.h5"), overwrite=True)
log.info(f"Models are saved in file: {self.model_to_use}. Training finished")
return result
PK &tOY䂱,j ,j scgen/models/util.pyimport os
from random import shuffle
import anndata
import numpy as np
import scanpy as sc
from matplotlib import pyplot as plt
from scipy import sparse
from sklearn import preprocessing
import pandas as pd
import scgen
def data_remover(adata, remain_list, remove_list, cell_type_key, condition_key):
"""
Removes specific cell type in stimulated condition form `adata`.
# Parameters
adata: `~anndata.AnnData`
Annotated data matrix
remain_list: list
list of cell types which are going to be remained in `adata`.
remove_list: list
list of cell types which are going to be removed from `adata`.
# Returns
merged_data: list
returns array of specified cell types in stimulated condition
# Example
```python
import scgen
import anndata
train_data = anndata.read("./data/train_kang.h5ad")
remove_list = ["CD14+Mono", "CD8T"]
remain_list = ["CD4T", "Dendritic"]
filtered_data = data_remover(train_data, remain_list, remove_list)
```
"""
source_data = []
for i in remain_list:
source_data.append(extractor(adata, i, conditions={"ctrl": "control", "stim": "stimulated"},
cell_type_key=cell_type_key, condition_key=condition_key)[3])
target_data = []
for i in remove_list:
target_data.append(extractor(adata, i, conditions={"ctrl": "control", "stim": "stimulated"},
cell_type_key=cell_type_key, condition_key=condition_key)[1])
merged_data = training_data_provider(source_data, target_data)
merged_data.var_names = adata.var_names
return merged_data
def extractor(data, cell_type, conditions, cell_type_key="cell_type", condition_key="condition"):
"""
Returns a list of `data` files while filtering for a specific `cell_type`.
# Parameters
data: `~anndata.AnnData`
Annotated data matrix
cell_type: basestring
specific cell type to be extracted from `data`.
conditions: dict
dictionary of stimulated/control of `data`.
# Returns
list of `data` files while filtering for a specific `cell_type`.
# Example
```python
import scgen
import anndata
train_data = anndata.read("./data/train.h5ad")
test_data = anndata.read("./data/test.h5ad")
train_data_extracted_list = extractor(train_data, "CD4T", conditions={"ctrl": "control", "stim": "stimulated"})
```
"""
cell_with_both_condition = data[data.obs[cell_type_key] == cell_type]
condtion_1 = data[(data.obs[cell_type_key] == cell_type) & (data.obs[condition_key] == conditions["ctrl"])]
condtion_2 = data[(data.obs[cell_type_key] == cell_type) & (data.obs[condition_key] == conditions["stim"])]
training = data[~((data.obs[cell_type_key] == cell_type) & (data.obs[condition_key] == conditions["stim"]))]
return [training, condtion_1, condtion_2, cell_with_both_condition]
def training_data_provider(train_s, train_t):
"""
Concatenates two lists containing adata files
# Parameters
train_s: `~anndata.AnnData`
Annotated data matrix.
train_t: `~anndata.AnnData`
Annotated data matrix.
# Returns
Concatenated Annotated data matrix.
# Example
```python
import scgen
import anndata
train_data = anndata.read("./data/train_kang.h5ad")
test_data = anndata.read("./data/test.h5ad")
whole_data = training_data_provider(train_data, test_data)
```
"""
train_s_X = []
train_s_diet = []
train_s_groups = []
for i in train_s:
train_s_X.append(i.X.A)
train_s_diet.append(i.obs["condition"].tolist())
train_s_groups.append(i.obs["cell_type"].tolist())
train_s_X = np.concatenate(train_s_X)
temp = []
for i in train_s_diet:
temp = temp + i
train_s_diet = temp
temp = []
for i in train_s_groups:
temp = temp + i
train_s_groups = temp
train_t_X = []
train_t_diet = []
train_t_groups = []
for i in train_t:
train_t_X.append(i.X.A)
train_t_diet.append(i.obs["condition"].tolist())
train_t_groups.append(i.obs["cell_type"].tolist())
temp = []
for i in train_t_diet:
temp = temp + i
train_t_diet = temp
temp = []
for i in train_t_groups:
temp = temp + i
train_t_groups = temp
train_t_X = np.concatenate(train_t_X)
train_real = np.concatenate([train_s_X, train_t_X]) # concat all
train_real = anndata.AnnData(train_real)
train_real.obs["condition"] = train_s_diet + train_t_diet
train_real.obs["cell_type"] = train_s_groups + train_t_groups
return train_real
def balancer(adata, cell_type_key="cell_type", condition_key="condition"):
"""
Makes cell type population equal.
# Parameters
adata: `~anndata.AnnData`
Annotated data matrix.
# Returns
balanced_data: `~anndata.AnnData`
Equal cell type population Annotated data matrix.
# Example
```python
import scgen
import anndata
train_data = anndata.read("./train_kang.h5ad")
train_ctrl = train_data[train_data.obs["condition"] == "control", :]
train_ctrl = balancer(train_ctrl)
```
"""
class_names = np.unique(adata.obs[cell_type_key])
class_pop = {}
for cls in class_names:
class_pop[cls] = adata.copy()[adata.obs[cell_type_key] == cls].shape[0]
max_number = np.max(list(class_pop.values()))
all_data_x = []
all_data_label = []
all_data_condition = []
for cls in class_names:
temp = adata.copy()[adata.obs[cell_type_key] == cls]
index = np.random.choice(range(len(temp)), max_number)
if sparse.issparse(temp.X):
temp_x = temp.X.A[index]
else:
temp_x = temp.X[index]
all_data_x.append(temp_x)
temp_ct = np.repeat(cls, max_number)
all_data_label.append(temp_ct)
temp_cc = np.repeat(np.unique(temp.obs[condition_key]), max_number)
all_data_condition.append(temp_cc)
balanced_data = anndata.AnnData(np.concatenate(all_data_x))
balanced_data.obs[cell_type_key] = np.concatenate(all_data_label)
balanced_data.obs[condition_key] = np.concatenate(all_data_label)
class_names = np.unique(balanced_data.obs[cell_type_key])
class_pop = {}
for cls in class_names:
class_pop[cls] = len(balanced_data[balanced_data.obs[cell_type_key] == cls])
return balanced_data
def shuffle_data(adata, labels=None):
"""
Shuffles the `adata`.
# Parameters
adata: `~anndata.AnnData`
Annotated data matrix.
labels: numpy nd-array
list of encoded labels
# Returns
adata: `~anndata.AnnData`
Shuffled annotated data matrix.
labels: numpy nd-array
Array of shuffled labels if `labels` is not None.
# Example
```python
import scgen
import anndata
import pandas as pd
train_data = anndata.read("./data/train.h5ad")
train_labels = pd.read_csv("./data/train_labels.csv", header=None)
train_data, train_labels = shuffle_data(train_data, train_labels)
```
"""
ind_list = [i for i in range(adata.shape[0])]
shuffle(ind_list)
if sparse.issparse(adata.X):
x = adata.X.A[ind_list, :]
else:
x = adata.X[ind_list, :]
if labels is not None:
labels = labels[ind_list]
adata = anndata.AnnData(x, obs={"labels": list(labels)})
return adata, labels
else:
return anndata.AnnData(x, obs=adata.obs)
def batch_removal(network, adata, batch_key="batch", cell_label_key="cell_type"):
"""
Removes batch effect of adata
# Parameters
network: `scgen VAE`
Variational Auto-encoder class object after training the network.
adata: `~anndata.AnnData`
Annotated data matrix. adata must have `batch` and `cell_type` column in its obs.
# Returns
corrected: `~anndata.AnnData`
Annotated matrix of corrected data consisting of all cell types whether they have batch effect or not.
# Example
```python
import scgen
import anndata
train = anndata.read("data/pancreas.h5ad")
train.obs["cell_type"] = train.obs["celltype"].tolist()
network = scgen.VAEArith(x_dimension=train.shape[1], model_path="./models/batch")
network.train(train_data=train, n_epochs=20)
corrected_adata = scgen.batch_removal(network, train)
```
"""
if sparse.issparse(adata.X):
latent_all = network.to_latent(adata.X.A)
else:
latent_all = network.to_latent(adata.X)
adata_latent = anndata.AnnData(latent_all)
adata_latent.obs = adata.obs.copy(deep=True)
unique_cell_types = np.unique(adata_latent.obs[cell_label_key])
shared_ct = []
not_shared_ct = []
for cell_type in unique_cell_types:
temp_cell = adata_latent[adata_latent.obs[cell_label_key] == cell_type]
if len(np.unique(temp_cell.obs[batch_key])) < 2:
cell_type_ann = adata_latent[adata_latent.obs[cell_label_key] == cell_type]
not_shared_ct.append(cell_type_ann)
continue
temp_cell = adata_latent[adata_latent.obs[cell_label_key] == cell_type]
batch_list = {}
batch_ind = {}
max_batch = 0
max_batch_ind = ""
batches = np.unique(temp_cell.obs[batch_key])
for i in batches:
temp = temp_cell[temp_cell.obs[batch_key] == i]
temp_ind = temp_cell.obs[batch_key] == i
if max_batch < len(temp):
max_batch = len(temp)
max_batch_ind = i
batch_list[i] = temp
batch_ind[i] = temp_ind
max_batch_ann = batch_list[max_batch_ind]
for study in batch_list:
delta = np.average(max_batch_ann.X, axis=0) - np.average(batch_list[study].X, axis=0)
batch_list[study].X = delta + batch_list[study].X
temp_cell[batch_ind[study]].X = batch_list[study].X
shared_ct.append(temp_cell)
all_shared_ann = anndata.AnnData.concatenate(*shared_ct, batch_key="concat_batch")
if "concat_batch" in all_shared_ann.obs.columns:
del all_shared_ann.obs["concat_batch"]
if len(not_shared_ct) < 1:
corrected = anndata.AnnData(network.reconstruct(all_shared_ann.X, use_data=True))
corrected.obs = all_shared_ann.obs.copy(deep=True)
corrected.var_names = adata.var_names.tolist()
corrected.obs_names = adata.obs_names.tolist()
return corrected
else:
all_not_shared_ann = anndata.AnnData.concatenate(*not_shared_ct, batch_key="concat_batch")
all_corrected_data = anndata.AnnData.concatenate(all_shared_ann, all_not_shared_ann, batch_key="concat_batch")
if "concat_batch" in all_shared_ann.obs.columns:
del all_corrected_data.obs["concat_batch"]
corrected = anndata.AnnData(network.reconstruct(all_corrected_data.X, use_data=True), )
corrected.obs = pd.concat([all_shared_ann.obs, all_not_shared_ann.obs])
corrected.var_names = adata.var_names.tolist()
corrected.obs_names = adata.obs_names.tolist()
return corrected
def label_encoder(adata):
"""
Encode labels of Annotated `adata` matrix using sklearn.preprocessing.LabelEncoder class.
Parameters
----------
adata: `~anndata.AnnData`
Annotated data matrix.
Returns
-------
labels: numpy nd-array
Array of encoded labels
Example
--------
>>> import scgen
>>> import scanpy as sc
>>> train_data = sc.read("./data/train.h5ad")
>>> train_labels, label_encoder = label_encoder(train_data)
"""
le = preprocessing.LabelEncoder()
labels = le.fit_transform(adata.obs["condition"].tolist())
return labels.reshape(-1, 1), le
def visualize_trained_network_results(network, train, cell_type,
conditions={"ctrl": "control", "stim": "stimulated"},
condition_key="condition",
cell_type_key="cell_type",
path_to_save="./figures/",
plot_umap=True,
plot_reg=True):
plt.close("all")
os.makedirs(path_to_save, exist_ok=True)
sc.settings.figdir = os.path.abspath(path_to_save)
if isinstance(network, scgen.VAEArithKeras):
if sparse.issparse(train.X):
latent = network.to_latent(train.X.A)
else:
latent = network.to_latent(train.X)
latent = sc.AnnData(X=latent,
obs={condition_key: train.obs[condition_key].tolist(),
cell_type_key: train.obs[cell_type_key].tolist()})
if plot_umap:
sc.pp.neighbors(latent)
sc.tl.umap(latent)
sc.pl.umap(latent, color=[condition_key, cell_type_key],
save=f"_latent",
show=False)
cell_type_data = train[train.obs[cell_type_key] == cell_type]
pred, delta = network.predict(adata=cell_type_data,
conditions=conditions,
cell_type_key=cell_type_key,
condition_key=condition_key,
celltype_to_predict=cell_type)
pred_adata = anndata.AnnData(pred, obs={condition_key: ["pred"] * len(pred)},
var={"var_names": cell_type_data.var_names})
all_adata = cell_type_data.concatenate(pred_adata)
sc.tl.rank_genes_groups(cell_type_data, groupby=condition_key, n_genes=100)
diff_genes = cell_type_data.uns["rank_genes_groups"]["names"][conditions["stim"]]
if plot_reg:
scgen.plotting.reg_mean_plot(all_adata, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_mean_all_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_var_all_genes.pdf"))
all_adata_top_100_genes = all_adata.copy()[:, diff_genes.tolist()]
scgen.plotting.reg_mean_plot(all_adata_top_100_genes, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_mean_top_100_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata_top_100_genes, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_var_top_100_genes.pdf"))
all_adata_top_50_genes = all_adata.copy()[:, diff_genes.tolist()[:50]]
scgen.plotting.reg_mean_plot(all_adata_top_50_genes, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_mean_top_50_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata_top_50_genes, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_var_top_50_genes.pdf"))
if plot_umap:
sc.pp.neighbors(all_adata)
sc.tl.umap(all_adata)
sc.pl.umap(all_adata, color=condition_key,
save="pred_all_genes",
show=False)
sc.pp.neighbors(all_adata_top_100_genes)
sc.tl.umap(all_adata_top_100_genes)
sc.pl.umap(all_adata_top_100_genes, color=condition_key,
save="pred_top_100_genes",
show=False)
sc.pp.neighbors(all_adata_top_50_genes)
sc.tl.umap(all_adata_top_50_genes)
sc.pl.umap(all_adata_top_50_genes, color=condition_key,
save="pred_top_50_genes",
show=False)
sc.pl.violin(all_adata, keys=diff_genes.tolist()[0], groupby=condition_key,
save=f"_{diff_genes.tolist()[0]}",
show=False)
plt.close("all")
elif isinstance(network, scgen.VAEArith):
if sparse.issparse(train.X):
latent = network.to_latent(train.X.A)
else:
latent = network.to_latent(train.X)
latent = sc.AnnData(X=latent,
obs={condition_key: train.obs[condition_key].tolist(),
cell_type_key: train.obs[cell_type_key].tolist()})
if plot_umap:
sc.pp.neighbors(latent)
sc.tl.umap(latent)
sc.pl.umap(latent, color=[condition_key, cell_type_key],
save=f"_latent",
show=False)
cell_type_data = train[train.obs[cell_type_key] == cell_type]
pred, delta = network.predict(adata=cell_type_data,
conditions=conditions,
cell_type_key=cell_type_key,
condition_key=condition_key,
celltype_to_predict=cell_type)
pred_adata = anndata.AnnData(pred, obs={condition_key: ["pred"] * len(pred)},
var={"var_names": cell_type_data.var_names})
all_adata = cell_type_data.concatenate(pred_adata)
sc.tl.rank_genes_groups(cell_type_data, groupby=condition_key, n_genes=100)
diff_genes = cell_type_data.uns["rank_genes_groups"]["names"][conditions["stim"]]
if plot_reg:
scgen.plotting.reg_mean_plot(all_adata, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_mean_all_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_var_all_genes.pdf"))
all_adata_top_100_genes = all_adata.copy()[:, diff_genes.tolist()]
scgen.plotting.reg_mean_plot(all_adata_top_100_genes, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_mean_top_100_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata_top_100_genes, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_var_top_100_genes.pdf"))
all_adata_top_50_genes = all_adata.copy()[:, diff_genes.tolist()[:50]]
scgen.plotting.reg_mean_plot(all_adata_top_50_genes, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_mean_top_50_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata_top_50_genes, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_var_top_50_genes.pdf"))
if plot_umap:
sc.pp.neighbors(all_adata)
sc.tl.umap(all_adata)
sc.pl.umap(all_adata, color=condition_key,
save="pred_all_genes",
show=False)
sc.pp.neighbors(all_adata_top_100_genes)
sc.tl.umap(all_adata_top_100_genes)
sc.pl.umap(all_adata_top_100_genes, color=condition_key,
save="pred_top_100_genes",
show=False)
sc.pp.neighbors(all_adata_top_50_genes)
sc.tl.umap(all_adata_top_50_genes)
sc.pl.umap(all_adata_top_50_genes, color=condition_key,
save="pred_top_50_genes",
show=False)
sc.pl.violin(all_adata, keys=diff_genes.tolist()[0], groupby=condition_key,
save=f"_{diff_genes.tolist()[0]}",
show=False)
plt.close("all")
elif isinstance(network, scgen.CVAE):
true_labels, _ = scgen.label_encoder(train)
if sparse.issparse(train.X):
latent = network.to_latent(train.X.A, labels=true_labels)
else:
latent = network.to_latent(train.X, labels=true_labels)
latent = sc.AnnData(X=latent,
obs={condition_key: train.obs[condition_key].tolist(),
cell_type_key: train.obs[cell_type_key].tolist()})
if plot_umap:
sc.pp.neighbors(latent)
sc.tl.umap(latent)
sc.pl.umap(latent, color=[condition_key, cell_type_key],
save=f"_latent",
show=False)
cell_type_data = train[train.obs[cell_type_key] == cell_type]
fake_labels = np.ones(shape=(cell_type_data.shape[0], 1))
pred = network.predict(data=cell_type_data, labels=fake_labels)
pred_adata = anndata.AnnData(pred, obs={condition_key: ["pred"] * len(pred)},
var={"var_names": cell_type_data.var_names})
all_adata = cell_type_data.concatenate(pred_adata)
sc.tl.rank_genes_groups(cell_type_data, groupby=condition_key, n_genes=100)
diff_genes = cell_type_data.uns["rank_genes_groups"]["names"][conditions["stim"]]
if plot_reg:
scgen.plotting.reg_mean_plot(all_adata, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_mean_all_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_var_all_genes.pdf"))
all_adata_top_100_genes = all_adata.copy()[:, diff_genes.tolist()]
scgen.plotting.reg_mean_plot(all_adata_top_100_genes, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_mean_top_100_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata_top_100_genes, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_var_top_100_genes.pdf"))
all_adata_top_50_genes = all_adata.copy()[:, diff_genes.tolist()[:50]]
scgen.plotting.reg_mean_plot(all_adata_top_50_genes, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_mean_top_50_genes.pdf"))
scgen.plotting.reg_var_plot(all_adata_top_50_genes, condition_key=condition_key,
axis_keys={"x": "pred", "y": conditions["stim"]},
gene_list=diff_genes[:5],
path_to_save=os.path.join(path_to_save, f"reg_var_top_50_genes.pdf"))
if plot_umap:
sc.pp.neighbors(all_adata)
sc.tl.umap(all_adata)
sc.pl.umap(all_adata, color=condition_key,
save="pred_all_genes",
show=False)
sc.pp.neighbors(all_adata_top_100_genes)
sc.tl.umap(all_adata_top_100_genes)
sc.pl.umap(all_adata_top_100_genes, color=condition_key,
save="pred_top_100_genes",
show=False)
sc.pp.neighbors(all_adata_top_50_genes)
sc.tl.umap(all_adata_top_50_genes)
sc.pl.umap(all_adata_top_50_genes, color=condition_key,
save="pred_top_50_genes",
show=False)
sc.pl.violin(all_adata, keys=diff_genes.tolist()[0], groupby=condition_key,
save=f"_{diff_genes.tolist()[0]}",
show=False)
plt.close("all")
PK N
~ ~ scgen-1.1.3.dist-info/LICENSE GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
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