import os
from typing import List, Optional, Union
import anndata
import numpy as np
import pandas as pd
import scipy
import torch
import torch.nn as nn
from matplotlib import pyplot as plt
from scipy.optimize import linear_sum_assignment
from scipy.stats import entropy, kde
from sklearn.neighbors import KNeighborsRegressor, NearestNeighbors
from scvi.dataset import AnnDatasetFromAnnData
def load_posterior(
dir_path: str,
model: nn.Module,
use_cuda: Optional[Union[bool, str]] = "auto",
**posterior_kwargs
):
"""Function to use in order to retrieve a posterior that was saved using the ``save_posterior`` method
Because of pytorch model loading usage, this function needs a scVI model object initialized with exact same parameters
that during training.
Because saved posteriors correspond to already trained models, data is loaded sequentially using a ``SequentialSampler``.
Parameters
----------
dir_path
directory containing the posterior properties to be retrieved.
model
scVI initialized model.
use_cuda
Specifies if the computations should be perfomed with a GPU.
Default: ``True``
If ``auto``, then cuda availability is inferred, with a preference to load on GPU.
If ``False``, the model will be loaded on the CPU, even if it was trained using a GPU.
**posterior_kwargs
additional parameters to feed to the posterior constructor.
Returns
-------
>>> model = VAE(nb_genes, n_batches, n_hidden=128, n_latent=10)
>>> trainer = UnsupervisedTrainer(vae, dataset, train_size=0.5, use_cuda=use_cuda)
>>> trainer.train(n_epochs=200)
>>> trainer.train_set.save_posterior("./my_run_train_posterior")
>>> model = VAE(nb_genes, n_batches, n_hidden=128, n_latent=10)
>>> post = load_posterior("./my_run_train_posterior", model=model)
"""
# Avoid circular imports
from scvi.inference.total_inference import TotalPosterior
from scvi.inference.jvae_trainer import JPosterior
from scvi.inference.posterior import Posterior
from scvi.inference.annotation import AnnotationPosterior
post_type_path = os.path.join(dir_path, "posterior_type.txt")
dataset_path = os.path.join(dir_path, "anndata_dataset.h5ad")
model_path = os.path.join(dir_path, "model_params.pt")
indices_path = os.path.join(dir_path, "indices.npy")
data_loader_kwargs_path = os.path.join(dir_path, "data_loader_kwargs.h5")
# Infering posterior type
with open(post_type_path, "r") as post_file:
post_class_str = post_file.readline()
str_to_classes = dict(
TotalPosterior=TotalPosterior,
JPosterior=JPosterior,
Posterior=Posterior,
AnnotationPosterior=AnnotationPosterior,
)
if post_class_str not in str_to_classes:
raise ValueError(
"Posterior type {} not eligible for loading".format(post_class_str)
)
post_class = str_to_classes[post_class_str]
# Loading dataset and associated measurements
ad = anndata.read_h5ad(filename=dataset_path)
key = "cell_measurements_col_mappings"
if key in ad.uns:
cell_measurements_col_mappings = ad.uns[key]
else:
cell_measurements_col_mappings = dict()
dataset = AnnDatasetFromAnnData(
ad=ad, cell_measurements_col_mappings=cell_measurements_col_mappings
)
# Loading scVI model
if use_cuda == "auto":
use_cuda = torch.cuda.is_available()
use_cuda = use_cuda and torch.cuda.is_available()
if use_cuda:
model.load_state_dict(torch.load(model_path))
model.cuda()
else:
device = torch.device("cpu")
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
# Loading data loader options and posterior
indices = np.load(file=indices_path)
data_loader_kwargs = pd.read_hdf(
data_loader_kwargs_path, key="data_loader"
).to_dict()
my_post = post_class(
model=model,
gene_dataset=dataset,
shuffle=False,
indices=indices,
use_cuda=use_cuda,
data_loader_kwargs=data_loader_kwargs,
**posterior_kwargs
)
return my_post
def entropy_from_indices(indices):
return entropy(np.array(np.unique(indices, return_counts=True)[1].astype(np.int32)))
def entropy_batch_mixing(
latent_space, batches, n_neighbors=50, n_pools=50, n_samples_per_pool=100
):
def entropy(hist_data):
n_batches = len(np.unique(hist_data))
if n_batches > 2:
raise ValueError("Should be only two clusters for this metric")
frequency = np.mean(hist_data == 1)
if frequency == 0 or frequency == 1:
return 0
return -frequency * np.log(frequency) - (1 - frequency) * np.log(1 - frequency)
n_neighbors = min(n_neighbors, len(latent_space) - 1)
nne = NearestNeighbors(n_neighbors=1 + n_neighbors, n_jobs=8)
nne.fit(latent_space)
kmatrix = nne.kneighbors_graph(latent_space) - scipy.sparse.identity(
latent_space.shape[0]
)
score = 0
for t in range(n_pools):
indices = np.random.choice(
np.arange(latent_space.shape[0]), size=n_samples_per_pool
)
score += np.mean(
[
entropy(
batches[
kmatrix[indices].nonzero()[1][
kmatrix[indices].nonzero()[0] == i
]
]
)
for i in range(n_samples_per_pool)
]
)
return score / float(n_pools)
def plot_imputation(original, imputed, show_plot=True, title="Imputation"):
y = imputed
x = original
ymax = 10
mask = x < ymax
x = x[mask]
y = y[mask]
mask = y < ymax
x = x[mask]
y = y[mask]
l_minimum = np.minimum(x.shape[0], y.shape[0])
x = x[:l_minimum]
y = y[:l_minimum]
data = np.vstack([x, y])
plt.figure(figsize=(5, 5))
axes = plt.gca()
axes.set_xlim([0, ymax])
axes.set_ylim([0, ymax])
nbins = 50
# Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents
k = kde.gaussian_kde(data)
xi, yi = np.mgrid[0 : ymax : nbins * 1j, 0 : ymax : nbins * 1j]
zi = k(np.vstack([xi.flatten(), yi.flatten()]))
plt.title(title, fontsize=12)
plt.ylabel("Imputed counts")
plt.xlabel("Original counts")
plt.pcolormesh(yi, xi, zi.reshape(xi.shape), cmap="Reds")
a, _, _, _ = np.linalg.lstsq(y[:, np.newaxis], x, rcond=-1)
linspace = np.linspace(0, ymax)
plt.plot(linspace, a * linspace, color="black")
plt.plot(linspace, linspace, color="black", linestyle=":")
if show_plot:
plt.show()
plt.savefig(title + ".png")
def nn_overlap(X1, X2, k=100):
"""Compute the overlap between the k-nearest neighbor graph of X1 and X2
Using Spearman correlation of the adjacency matrices.
"""
assert len(X1) == len(X2)
n_samples = len(X1)
k = min(k, n_samples - 1)
nne = NearestNeighbors(n_neighbors=k + 1) # "n_jobs=8
nne.fit(X1)
kmatrix_1 = nne.kneighbors_graph(X1) - scipy.sparse.identity(n_samples)
nne.fit(X2)
kmatrix_2 = nne.kneighbors_graph(X2) - scipy.sparse.identity(n_samples)
# 1 - spearman correlation from knn graphs
spearman_correlation = scipy.stats.spearmanr(
kmatrix_1.A.flatten(), kmatrix_2.A.flatten()
)[0]
# 2 - fold enrichment
set_1 = set(np.where(kmatrix_1.A.flatten() == 1)[0])
set_2 = set(np.where(kmatrix_2.A.flatten() == 1)[0])
fold_enrichment = (
len(set_1.intersection(set_2))
* n_samples ** 2
/ (float(len(set_1)) * len(set_2))
)
return spearman_correlation, fold_enrichment
def unsupervised_clustering_accuracy(
y: Union[np.ndarray, torch.Tensor], y_pred: Union[np.ndarray, torch.Tensor]
) -> tuple:
"""Unsupervised Clustering Accuracy
"""
assert len(y_pred) == len(y)
u = np.unique(np.concatenate((y, y_pred)))
n_clusters = len(u)
mapping = dict(zip(u, range(n_clusters)))
reward_matrix = np.zeros((n_clusters, n_clusters), dtype=np.int64)
for y_pred_, y_ in zip(y_pred, y):
if y_ in mapping:
reward_matrix[mapping[y_pred_], mapping[y_]] += 1
cost_matrix = reward_matrix.max() - reward_matrix
row_assign, col_assign = linear_sum_assignment(cost_matrix)
# Construct optimal assignments matrix
row_assign = row_assign.reshape((-1, 1)) # (n,) to (n, 1) reshape
col_assign = col_assign.reshape((-1, 1)) # (n,) to (n, 1) reshape
assignments = np.concatenate((row_assign, col_assign), axis=1)
optimal_reward = reward_matrix[row_assign, col_assign].sum() * 1.0
return optimal_reward / y_pred.size, assignments
def knn_purity(latent, label, n_neighbors=30):
nbrs = NearestNeighbors(n_neighbors=n_neighbors + 1).fit(latent)
indices = nbrs.kneighbors(latent, return_distance=False)[:, 1:]
neighbors_labels = np.vectorize(lambda i: label[i])(indices)
# pre cell purity scores
scores = ((neighbors_labels - label.reshape(-1, 1)) == 0).mean(axis=1)
res = [
np.mean(scores[label == i]) for i in np.unique(label)
] # per cell-type purity
return np.mean(res)
def proximity_imputation(real_latent1, normed_gene_exp_1, real_latent2, k=4):
knn = KNeighborsRegressor(k, weights="distance")
y = knn.fit(real_latent1, normed_gene_exp_1).predict(real_latent2)
return y
def pairs_sampler(
arr1: Union[List[float], np.ndarray, torch.Tensor],
arr2: Union[List[float], np.ndarray, torch.Tensor],
use_permutation: bool = True,
M_permutation: int = None,
sanity_check_perm: bool = False,
weights1: Union[List[float], np.ndarray, torch.Tensor] = None,
weights2: Union[List[float], np.ndarray, torch.Tensor] = None,
) -> tuple:
"""In a context where we want to estimate a double sum, virtually increases the number
of samples by considering more pairs so as to better estimate the double summation operation
Parameters
----------
arr1
samples from population 1
arr2
samples from population 2
use_permutation
Whether to mix samples from both populations
M_permutation
param sanity_check_perm: If True, resulting mixed arrays arr1 and arr2 are mixed together
In most cases, this parameter should remain False
weights1
probabilities associated to array 1 for random sampling
weights2
probabilities associated to array 2 for random sampling
Returns
-------
type
new_arr1, new_arr2
"""
if use_permutation is True:
# prepare the pairs for sampling
n_arr1 = arr1.shape[0]
n_arr2 = arr2.shape[0]
if not sanity_check_perm:
# case1: no permutation, sample from A and then from B
u, v = (
np.random.choice(n_arr1, size=M_permutation, p=weights1),
np.random.choice(n_arr2, size=M_permutation, p=weights2),
)
first_set = arr1[u]
second_set = arr2[v]
else:
# case2: permutation, sample from A+B twice (sanity check)
u, v = (
np.random.choice(n_arr1 + n_arr2, size=M_permutation),
np.random.choice(n_arr1 + n_arr2, size=M_permutation),
)
concat_arr = np.concatenate((arr1, arr2))
first_set = concat_arr[u]
second_set = concat_arr[v]
else:
first_set = arr1
second_set = arr2
return first_set, second_set
def credible_intervals(
ary: np.ndarray, confidence_level: Union[float, List[float], np.ndarray] = 0.94
) -> np.ndarray:
"""Calculate highest posterior density (HPD) of array for given credible_interval.
Taken from the arviz package
The HPD is the minimum width Bayesian credible interval (BCI). This implementation works only
for unimodal distributions.
Parameters
----------
ary
posterior samples
confidence_level
confidence level
Returns
-------
type
intervals minima, intervals maxima
"""
if ary.ndim > 1:
hpd = np.array(
[
credible_intervals(row, confidence_level=confidence_level)
for row in ary.T
]
)
return hpd
# Make a copy of trace
ary = ary.copy()
n = len(ary)
ary = np.sort(ary)
interval_idx_inc = int(np.floor(confidence_level * n))
n_intervals = n - interval_idx_inc
interval_width = ary[interval_idx_inc:] - ary[:n_intervals]
if len(interval_width) == 0:
raise ValueError(
"Too few elements for interval calculation. "
"Check that credible_interval meets condition 0 =< credible_interval < 1"
)
min_idx = np.argmin(interval_width)
hdi_min = ary[min_idx]
hdi_max = ary[min_idx + interval_idx_inc]
return np.array([hdi_min, hdi_max])
def describe_continuous_distrib(
samples: Union[np.ndarray, torch.Tensor],
credible_intervals_levels: Optional[Union[List[float], np.ndarray]] = None,
) -> dict:
"""Computes properties of distribution based on its samples
Parameters
----------
samples
samples of shape (n_samples, n_features)
credible_intervals_levels
Confidence in (0, 1)
of credible intervals to be computed
Returns
-------
type
properties of distribution
"""
dist_props = dict(
mean=samples.mean(0),
median=np.median(samples, 0),
std=samples.std(0),
min=samples.min(0),
max=samples.max(0),
)
credible_intervals_levels = (
[] if credible_intervals_levels is None else credible_intervals_levels
)
for confidence in credible_intervals_levels:
intervals = credible_intervals(samples, confidence_level=confidence)
interval_min, interval_max = intervals[:, 0], intervals[:, 1]
conf_str = str(confidence)[:5]
dist_props["confidence_interval_{}_min".format(conf_str)] = interval_min
dist_props["confidence_interval_{}_max".format(conf_str)] = interval_max
return dist_props
def save_cluster_xlsx(
filepath: str, de_results: List[pd.DataFrame], cluster_names: List
):
"""Saves multi-clusters DE in an xlsx sheet
Parameters
----------
filepath
xslx save path
de_results
list of pandas Dataframes for each cluster
cluster_names
list of cluster names
"""
writer = pd.ExcelWriter(filepath, engine="xlsxwriter")
for i, x in enumerate(cluster_names):
de_results[i].to_excel(writer, sheet_name=str(x))
writer.close()
