def tsne_emb(model_path: str, proc_dir: str, layer_name: str,
perplexities: List[int], n_iter: int):
model = torch.load(model_path, map_location=torch.device('cpu'))['model']
w = model[layer_name].numpy()
affinities_multiscale_mixture = affinity.Multiscale(
w,
perplexities=perplexities,
metric="cosine",
n_jobs=-1,
random_state=3)
init = initialization.pca(w, random_state=42)
embedding = TSNEEmbedding(init,
affinities_multiscale_mixture,
negative_gradient_method="fft",
n_jobs=-1,
random_state=4,
verbose=True)
embedding = embedding.optimize(n_iter=n_iter,
exaggeration=None,
momentum=0.8,
learning_rate="auto")
df = pd.DataFrame(embedding, columns=['x', 'y'])
df.to_csv(path.join(proc_dir, 'tsne_emb.csv'), index=False)
with open(path.join(proc_dir, 'kl_divergence'), 'w') as f:
f.write(f'{embedding.kl_divergence:.4f}')
def test_1(self):
init = initialization.pca(self.x)
aff = affinity.PerplexityBasedNN(self.x, perplexity=30)
embedding = openTSNE.TSNEEmbedding(init, aff)
embedding.optimize(25, exaggeration=12, momentum=0.5, inplace=True)
embedding.optimize(50, exaggeration=1, momentum=0.8, inplace=True)
self.eval_embedding(embedding, self.y)
new_embedding = embedding.transform(self.x)
self.eval_embedding(new_embedding, self.y, "transform")
def test_2(self):
init = initialization.pca(self.x)
aff = affinity.MultiscaleMixture(self.x, perplexities=[5, 30])
embedding = openTSNE.TSNEEmbedding(init, aff)
embedding.optimize(25, exaggeration=12, momentum=0.5, inplace=True)
embedding.optimize(50, exaggeration=1, momentum=0.8, inplace=True)
self.eval_embedding(embedding, self.y)
new_embedding = embedding.transform(self.x)
self.eval_embedding(new_embedding, self.y, "transform")
def art_of_tsne(X: np.ndarray,
metric: Union[str, Callable] = "euclidean",
exaggeration: float = -1,
perplexity: int = 30,
n_jobs: int = -1) -> TSNEEmbedding:
"""
Implementation of Dmitry Kobak and Philipp Berens
"The art of using t-SNE for single-cell transcriptomics" based on openTSNE.
See https://doi.org/10.1038/s41467-019-13056-x | www.nature.com/naturecommunications
Args:
X The data matrix of shape (n_cells, n_genes) i.e. (n_samples, n_features)
metric Any metric allowed by PyNNDescent (default: 'euclidean')
exaggeration The exaggeration to use for the embedding
perplexity The perplexity to use for the embedding
Returns:
The embedding as an opentsne.TSNEEmbedding object (which can be cast to an np.ndarray)
"""
n = X.shape[0]
if n > 100_000:
if exaggeration == -1:
exaggeration = 1 + n / 333_333
# Subsample, optimize, then add the remaining cells and optimize again
# Also, use exaggeration == 4
logging.info(f"Creating subset of {n // 40} elements")
# Subsample and run a regular art_of_tsne on the subset
indices = np.random.permutation(n)
reverse = np.argsort(indices)
X_sample, X_rest = X[indices[:n // 40]], X[indices[n // 40:]]
logging.info(f"Embedding subset")
Z_sample = art_of_tsne(X_sample)
logging.info(
f"Preparing partial initial embedding of the {n - n // 40} remaining elements"
)
if isinstance(Z_sample.affinities, affinity.Multiscale):
rest_init = Z_sample.prepare_partial(X_rest,
k=1,
perplexities=[1 / 3, 1 / 3])
else:
rest_init = Z_sample.prepare_partial(X_rest, k=1, perplexity=1 / 3)
logging.info(f"Combining the initial embeddings, and standardizing")
init_full = np.vstack((Z_sample, rest_init))[reverse]
init_full = init_full / (np.std(init_full[:, 0]) * 10000)
logging.info(f"Creating multiscale affinities")
affinities = affinity.PerplexityBasedNN(X,
perplexity=perplexity,
metric=metric,
method="approx",
n_jobs=n_jobs)
logging.info(f"Creating TSNE embedding")
Z = TSNEEmbedding(init_full,
affinities,
negative_gradient_method="fft",
n_jobs=n_jobs)
logging.info(f"Optimizing, stage 1")
Z.optimize(n_iter=250,
inplace=True,
exaggeration=12,
momentum=0.5,
learning_rate=n / 12,
n_jobs=n_jobs)
logging.info(f"Optimizing, stage 2")
Z.optimize(n_iter=750,
inplace=True,
exaggeration=exaggeration,
momentum=0.8,
learning_rate=n / 12,
n_jobs=n_jobs)
elif n > 3_000:
if exaggeration == -1:
exaggeration = 1
# Use multiscale perplexity
affinities_multiscale_mixture = affinity.Multiscale(
X,
perplexities=[perplexity, n / 100],
metric=metric,
method="approx",
n_jobs=n_jobs)
init = initialization.pca(X)
Z = TSNEEmbedding(init,
affinities_multiscale_mixture,
negative_gradient_method="fft",
n_jobs=n_jobs)
Z.optimize(n_iter=250,
inplace=True,
exaggeration=12,
momentum=0.5,
learning_rate=n / 12,
n_jobs=n_jobs)
Z.optimize(n_iter=750,
inplace=True,
exaggeration=exaggeration,
momentum=0.8,
learning_rate=n / 12,
n_jobs=n_jobs)
else:
if exaggeration == -1:
exaggeration = 1
# Just a plain TSNE with high learning rate
lr = max(200, n / 12)
aff = affinity.PerplexityBasedNN(X,
perplexity=perplexity,
metric=metric,
method="approx",
n_jobs=n_jobs)
init = initialization.pca(X)
Z = TSNEEmbedding(init,
aff,
learning_rate=lr,
n_jobs=n_jobs,
negative_gradient_method="fft")
Z.optimize(250,
exaggeration=12,
momentum=0.5,
inplace=True,
n_jobs=n_jobs)
Z.optimize(750,
exaggeration=exaggeration,
momentum=0.8,
inplace=True,
n_jobs=n_jobs)
return Z
norm = raw * 256.0 / sum(raw)
profile[0, ] = norm
## 2. Run t-SNE
seed = 211
threads = int(cores)
affinities_multiscale_mixture = affinity.Multiscale(
profile,
perplexities=[30, 300],
metric="cosine",
n_jobs=threads,
random_state=seed,
)
init = initialization.pca(profile, random_state=seed)
embedding = TSNEEmbedding(
init,
affinities_multiscale_mixture,
negative_gradient_method="fft",
n_jobs=threads,
)
embedding1 = embedding.optimize(n_iter=250, exaggeration=6, momentum=0.5)
embedding2 = embedding1.optimize(n_iter=750, exaggeration=1, momentum=0.8)
embedding_multiscale = embedding2.view(np.ndarray)
## 3. Save matrix
df = pd.DataFrame(embedding_multiscale, columns=['Dim1', 'Dim2'], index=ctgs)
df.to_csv(outfile, sep='\t', float_format='%.3f')