def split_one_cluster(
data: MultimodalData,
clust_label: str,
clust_id: str,
n_clust: int,
res_label: str,
rep: str = "pca",
random_state: int = 0,
) -> None:
"""
Use Leiden algorithm to split 'clust_id' in 'clust_label' into 'n_components' clusters and write the new clusting results to 'res_label'. Assume 'clust_label' named clusters as numbers (in str format).
Parameters
----------
data: ``pegasusio.MultimodalData``
Annotated data matrix with rows for cells and columns for genes.
clust_label: `str`
Use existing clustering stored in data.obs['clust_label'].
clust_id: `str`
Cluster ID in data.obs['clust_label'].
n_clust: `int`
Split 'clust_id' into `n_clust' subclusters.
res_label: `str`,
Write new clustering in data.obs['res_label']. The largest subcluster will use 'clust_id' as its cluster ID, while other subclusters will be numbered after existing clusters.
rep: ``str``, optional, default: ``"pca"``
The embedding representation used for Kmeans clustering. Keyword ``'X_' + rep`` must exist in ``data.obsm``. By default, use PCA coordinates.
n_jobs : `int`, optional (default: -1)
Number of threads to use for the KMeans step in 'spectral_louvain' and 'spectral_leiden'. -1 refers to using all physical CPU cores.
random_state: ``int``, optional, default: ``0``
Random seed for reproducing results.
Returns
-------
``None``
Update ``data.obs``:
* ``data.obs[res_label]``: New cluster labels of cells as categorical data.
Examples
--------
>>> pg.split_one_cluster(data, 'leiden_labels', '15', 2, 'leiden_labels_split')
"""
idx = np.where(data.obs[clust_label] == clust_id)[0]
tmpdat = data[idx].copy()
from pegasus.tools import neighbors
neighbors(tmpdat, rep=rep, use_cache=False)
leiden(tmpdat,
rep=rep,
resolution=None,
n_clust=n_clust,
random_state=random_state)
new_clust = data.obs[clust_label].values.astype(int)
new_label = new_clust.max() + 1
for label in tmpdat.obs['leiden_labels'].value_counts().index[1:]:
new_clust[idx[(
tmpdat.obs['leiden_labels'] == label).values]] = new_label
new_label += 1
data.obs[res_label] = pd.Categorical(values=new_clust.astype(str),
categories=np.array(
range(1, new_label)).astype(str))
data.register_attr(res_label, "cluster")
del tmpdat
def get_neighbors(
data: MultimodalData,
K: int = 100,
rep: str = "pca",
n_comps: int = None,
n_jobs: int = -1,
random_state: int = 0,
full_speed: bool = False,
use_cache: bool = True,
dist: str = "l2",
) -> Tuple[List[int], List[float]]:
"""Find K nearest neighbors for each data point and return the indices and distances arrays.
Parameters
----------
data : `pegasusio.MultimodalData`
An AnnData object.
K : `int`, optional (default: 100)
Number of neighbors, including the data point itself.
rep : `str`, optional (default: 'pca')
Representation used to calculate kNN. If `None` use data.X
n_comps: `int`, optional (default: None)
Number of components to be used in the `rep`. If n_comps == None, use all components; otherwise, use the minimum of n_comps and rep's dimensions.
n_jobs : `int`, optional (default: -1)
Number of threads to use. -1 refers to using all physical CPU cores.
random_state: `int`, optional (default: 0)
Random seed for random number generator.
full_speed: `bool`, optional (default: False)
If full_speed, use multiple threads in constructing hnsw index. However, the kNN results are not reproducible. If not full_speed, use only one thread to make sure results are reproducible.
use_cache: `bool`, optional (default: True)
If use_cache and found cached knn results, will not recompute.
dist: `str`, optional (default: 'l2')
Distance metric to use. By default, use squared L2 distance. Available options, inner product 'ip' or cosine similarity 'cosine'.
Returns
-------
kNN indices and distances arrays.
Examples
--------
>>> indices, distances = tools.get_neighbors(data)
"""
rep = update_rep(rep)
indices_key = rep + "_knn_indices"
distances_key = rep + "_knn_distances"
if use_cache and knn_is_cached(data, indices_key, distances_key, K):
indices = data.obsm[indices_key]
distances = data.obsm[distances_key]
logger.info("Found cached kNN results, no calculation is required.")
else:
indices, distances = calculate_nearest_neighbors(
X_from_rep(data, rep, n_comps),
K=K,
n_jobs=eff_n_jobs(n_jobs),
random_state=random_state,
full_speed=full_speed,
dist=dist,
)
data.obsm[indices_key] = indices
data.register_attr(indices_key, "knn")
data.obsm[distances_key] = distances
data.register_attr(distances_key, "knn")
return indices, distances
def leiden(
data: MultimodalData,
rep: str = "pca",
resolution: int = 1.3,
n_clust: int = None,
n_iter: int = -1,
random_state: int = 0,
class_label: str = "leiden_labels",
) -> None:
"""Cluster the data using Leiden algorithm. [Traag19]_
Parameters
----------
data: ``pegasusio.MultimodalData``
Annotated data matrix with rows for cells and columns for genes.
rep: ``str``, optional, default: ``"pca"``
The embedding representation used for clustering. Keyword ``'X_' + rep`` must exist in ``data.obsm`` and nearest neighbors must be calculated so that affinity matrix ``'W_' + rep`` exists in ``data.uns``. By default, use PCA coordinates.
resolution: ``int``, optional, default: ``1.3``
Resolution factor. Higher resolution tends to find more clusters.
n_clust: ``int``, optional, default: ``None``
This option only takes effect if 'resolution = None'. Try to find an appropriate resolution by binary search such that the total number of clusters matches 'n_clust'. The range of resolution to search is (0.01, 2.0].
n_iter: ``int``, optional, default: ``-1``
Number of iterations that Leiden algorithm runs. If ``-1``, run the algorithm until reaching its optimal clustering.
random_state: ``int``, optional, default: ``0``
Random seed for reproducing results.
class_label: ``str``, optional, default: ``"leiden_labels"``
Key name for storing cluster labels in ``data.obs``.
Returns
-------
``None``
Update ``data.obs``:
* ``data.obs[class_label]``: Cluster labels of cells as categorical data.
Examples
--------
>>> pg.leiden(data)
"""
try:
import leidenalg
except ImportError:
import sys
logger.error("Need leidenalg! Try 'pip install leidenalg'.")
sys.exit(-1)
rep_key = "W_" + rep
if rep_key not in data.obsp:
raise ValueError(
"Cannot find affinity matrix. Please run neighbors first!")
W = data.obsp[rep_key]
G = construct_graph(W)
if resolution is not None:
membership = _run_community_detection("leiden", leidenalg, G,
resolution, random_state, n_iter)
else:
assert isinstance(n_clust, int)
resolution, membership = _find_optimal_resolution(
"leiden", leidenalg, n_clust, 2.0, G, random_state, n_iter)
data.uns["leiden_resolution"] = resolution
labels = np.array([str(x + 1) for x in membership])
categories = natsorted(np.unique(labels))
data.obs[class_label] = pd.Categorical(values=labels,
categories=categories)
data.register_attr(class_label, "cluster")
n_clusters = data.obs[class_label].cat.categories.size
logger.info(f"Leiden clustering is done. Get {n_clusters} clusters.")
def spectral_leiden(
data: MultimodalData,
rep: str = "pca",
resolution: float = 1.3,
rep_kmeans: str = "diffmap",
n_clusters: int = 30,
n_clusters2: int = 50,
n_init: int = 10,
n_jobs: int = -1,
random_state: int = 0,
class_label: str = "spectral_leiden_labels",
) -> None:
"""Cluster the data using Spectral Leiden algorithm. [Li20]_
Parameters
----------
data: ``pegasusio.MultimodalData``
Annotated data matrix with rows for cells and columns for genes.
rep: ``str``, optional, default: ``"pca"``
The embedding representation used for clustering. Keyword ``'X_' + rep`` must exist in ``data.obsm``. By default, use PCA coordinates.
resolution: ``int``, optional, default: ``1.3``
Resolution factor. Higher resolution tends to find more clusters.
rep_kmeans: ``str``, optional, default: ``"diffmap"``
The embedding representation on which the KMeans runs. Keyword must exist in ``data.obsm``. By default, use Diffusion Map coordinates. If diffmap is not calculated, use PCA coordinates instead.
n_clusters: ``int``, optional, default: ``30``
The number of first level clusters.
n_clusters2: ``int``, optional, default: ``50``
The number of second level clusters.
n_init: ``int``, optional, default: ``10``
Number of kmeans tries for the first level clustering. Default is set to be the same as scikit-learn Kmeans function.
n_jobs : `int`, optional (default: -1)
Number of threads to use for the KMeans step. -1 refers to using all physical CPU cores.
random_state: ``int``, optional, default: ``0``
Random seed for reproducing results.
class_label: ``str``, optional, default: ``"spectral_leiden_labels"``
Key name for storing cluster labels in ``data.obs``.
Returns
-------
``None``
Update ``data.obs``:
* ``data.obs[class_label]``: Cluster labels for cells as categorical data.
Examples
--------
>>> pg.spectral_leiden(data)
"""
try:
import leidenalg
except ImportError:
import sys
logger.error("Need leidenalg! Try 'pip install leidenalg'.")
sys.exit(-1)
if f"X_{rep_kmeans}" not in data.obsm.keys():
logger.warning(
f"{rep_kmeans} is not calculated, switch to pca instead.")
rep_kmeans = "pca"
if f"X_{rep_kmeans}" not in data.obsm.keys():
raise ValueError(f"Please run {rep_kmeans} first!")
if f"W_{rep}" not in data.obsp:
raise ValueError(
"Cannot find affinity matrix. Please run neighbors first!")
labels = partition_cells_by_kmeans(
data.obsm[f"X_{rep_kmeans}"],
n_clusters,
n_clusters2,
n_init,
n_jobs,
random_state,
)
W = data.obsp[f"W_{rep}"]
G = construct_graph(W)
partition_type = leidenalg.RBConfigurationVertexPartition
partition = partition_type(G,
resolution_parameter=resolution,
weights="weight",
initial_membership=labels)
partition_agg = partition.aggregate_partition()
optimiser = leidenalg.Optimiser()
optimiser.set_rng_seed(random_state)
diff = optimiser.optimise_partition(partition_agg, -1)
partition.from_coarse_partition(partition_agg)
labels = np.array([str(x + 1) for x in partition.membership])
categories = natsorted(np.unique(labels))
data.obs[class_label] = pd.Categorical(values=labels,
categories=categories)
data.register_attr(class_label, "cluster")
n_clusters = data.obs[class_label].cat.categories.size
logger.info(
f"Spectral Leiden clustering is done. Get {n_clusters} clusters.")
def net_fle(
data: MultimodalData,
file_name: str = None,
n_jobs: int = -1,
rep: str = "diffmap",
K: int = 50,
full_speed: bool = False,
target_change_per_node: float = 2.0,
target_steps: int = 5000,
is3d: bool = False,
memory: int = 8,
random_state: int = 0,
select_frac: float = 0.1,
select_K: int = 25,
select_alpha: float = 1.0,
net_alpha: float = 0.1,
polish_target_steps: int = 1500,
out_basis: str = "net_fle",
) -> None:
"""Construct Net-Force-directed (FLE) graph.
Net-FLE is an approximated FLE graph using Deep Learning model to improve the speed.
In specific, the deep model used is MLPRegressor_, the *scikit-learn* implementation of Multi-layer Perceptron regressor.
See [Li20]_ for details.
.. _MLPRegressor: https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html
Parameters
----------
data: ``pegasusio.MultimodalData``
Annotated data matrix with rows for cells and columns for genes.
file_name: ``str``, optional, default: ``None``
Temporary file to store the coordinates as the input to forceatlas2. If ``None``, use ``tempfile.mkstemp`` to generate file name.
n_jobs: ``int``, optional, default: ``-1``
Number of threads to use. If ``-1``, use all physical CPU cores.
rep: ``str``, optional, default: ``"diffmap"``
Representation of data used for the calculation. By default, use Diffusion Map coordinates. If ``None``, use the count matrix ``data.X``.
K: ``int``, optional, default: ``50``
Number of nearest neighbors to be considered during the computation.
full_speed: ``bool``, optional, default: ``False``
* If ``True``, use multiple threads in constructing ``hnsw`` index. However, the kNN results are not reproducible.
* Otherwise, use only one thread to make sure results are reproducible.
target_change_per_node: ``float``, optional, default: ``2.0``
Target change per node to stop ForceAtlas2.
target_steps: ``int``, optional, default: ``5000``
Maximum number of iterations before stopping the ForceAtlas2 algorithm.
is3d: ``bool``, optional, default: ``False``
If ``True``, calculate 3D force-directed layout.
memory: ``int``, optional, default: ``8``
Memory size in GB for the Java FA2 component. By default, use 8GB memory.
random_state: ``int``, optional, default: ``0``
Random seed set for reproducing results.
select_frac: ``float``, optional, default: ``0.1``
Down sampling fraction on the cells.
select_K: ``int``, optional, default: ``25``
Number of neighbors to be used to estimate local density for each data point for down sampling.
select_alpha: ``float``, optional, default: ``1.0``
Weight the down sample to be proportional to ``radius ** select_alpha``.
net_alpha: ``float``, optional, default: ``0.1``
L2 penalty (regularization term) parameter of the deep regressor.
polish_target_steps: ``int``, optional, default: ``1500``
After running the deep regressor to predict new coordinate, Number of ForceAtlas2 iterations.
out_basis: ``str``, optional, default: ``"net_fle"``
Key name for calculated FLE coordinates to store.
Returns
-------
``None``
Update ``data.obsm``:
* ``data.obsm['X_' + out_basis]``: Net FLE coordinates of the data.
Update ``data.obs``:
* ``data.obs['ds_selected']``: Boolean array to indicate which cells are selected during the down sampling phase.
Examples
--------
>>> pg.net_fle(data)
"""
if file_name is None:
if file_name is None:
import tempfile
_, file_name = tempfile.mkstemp()
rep = update_rep(rep)
n_jobs = eff_n_jobs(n_jobs)
if ("W_" + rep) not in data.uns:
neighbors(
data,
K=K,
rep=rep,
n_jobs=n_jobs,
random_state=random_state,
full_speed=full_speed,
)
knn_indices, knn_dists = get_neighbors(data,
K=select_K,
rep=rep,
n_jobs=n_jobs,
random_state=random_state,
full_speed=full_speed)
selected = select_cells(
knn_dists,
select_frac,
K=select_K,
alpha=select_alpha,
random_state=random_state,
)
X_full = X_from_rep(data, rep)
X = X_full[selected, :]
ds_indices_key = "ds_" + rep + "_knn_indices"
ds_distances_key = "ds_" + rep + "_knn_distances"
indices, distances = calculate_nearest_neighbors(X,
K=K,
n_jobs=n_jobs,
random_state=random_state,
full_speed=full_speed)
data.uns[ds_indices_key] = indices
data.uns[ds_distances_key] = distances
W = calculate_affinity_matrix(indices, distances)
X_fle = calc_force_directed_layout(
W,
file_name + ".small",
n_jobs,
target_change_per_node,
target_steps,
is3d,
memory,
random_state,
)
data.uns["X_" + out_basis + "_small"] = X_fle
data.obs["ds_diffmap_selected"] = selected
n_components = 2 if not is3d else 3
Y_init = np.zeros((data.shape[0], n_components), dtype=np.float64)
Y_init[selected, :] = X_fle
Y_init[~selected, :] = net_train_and_predict(X,
X_fle,
X_full[~selected, :],
net_alpha,
n_jobs,
random_state,
verbose=True)
data.obsm["X_" + out_basis + "_pred"] = Y_init
key = f"X_{out_basis}"
data.obsm[key] = calc_force_directed_layout(
W_from_rep(data, rep),
file_name,
n_jobs,
target_change_per_node,
polish_target_steps,
is3d,
memory,
random_state,
init=Y_init,
)
data.register_attr(key, "basis")
def net_umap(
data: MultimodalData,
rep: str = "pca",
n_jobs: int = -1,
n_components: int = 2,
n_neighbors: int = 15,
min_dist: float = 0.5,
spread: float = 1.0,
densmap: bool = False,
dens_lambda: float = 2.0,
dens_frac: float = 0.3,
dens_var_shift: float = 0.1,
random_state: int = 0,
select_frac: float = 0.1,
select_K: int = 25,
select_alpha: float = 1.0,
full_speed: bool = False,
net_alpha: float = 0.1,
polish_learning_rate: float = 10.0,
polish_n_epochs: int = 30,
out_basis: str = "net_umap",
) -> None:
"""Calculate Net-UMAP embedding of cells.
Net-UMAP is an approximated UMAP embedding using Deep Learning model to improve the speed.
In specific, the deep model used is MLPRegressor_, the *scikit-learn* implementation of Multi-layer Perceptron regressor.
See [Li20]_ for details.
.. _MLPRegressor: https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html
Parameters
----------
data: ``pegasusio.MultimodalData``
Annotated data matrix with rows for cells and columns for genes.
rep: ``str``, optional, default: ``"pca"``
Representation of data used for the calculation. By default, use PCA coordinates. If ``None``, use the count matrix ``data.X``.
n_jobs: ``int``, optional, default: ``-1``
Number of threads to use. If ``-1``, use all physical CPU cores.
n_components: ``int``, optional, default: ``2``
Dimension of calculated UMAP coordinates. By default, generate 2-dimensional data for 2D visualization.
n_neighbors: ``int``, optional, default: ``15``
Number of nearest neighbors considered during the computation.
min_dist: ``float``, optional, default: ``0.5``
The effective minimum distance between embedded data points.
spread: ``float``, optional, default: ``1.0``
The effective scale of embedded data points.
densmap: ``bool``, optional, default: ``False``
Whether the density-augmented objective of densMAP should be used for optimization, which will generate an embedding where
local densities are encouraged to be correlated with those in the original space.
dens_lambda: ``float``, optional, default: ``2.0``
Controls the regularization weight of the density correlation term in densMAP. Only works when *densmap* is ``True``.
Larger values prioritize density preservation over the UMAP objective, while values closer to 0 for the opposite direction.
Notice that setting this parameter to ``0`` is equivalent to running the original UMAP algorithm.
dens_frac: ``float``, optional, default: ``0.3``
Controls the fraction of epochs (between 0 and 1) where the density-augmented objective is used in densMAP. Only works when
*densmap* is ``True``.
The first ``(1 - dens_frac)`` fraction of epochs optimize the original UMAP objective before introducing the density
correlation term.
dens_var_shift: ``float``, optional, default, ``0.1``
A small constant added to the variance of local radii in the embedding when calculating the density correlation objective to
prevent numerical instability from dividing by a small number. Only works when *densmap* is ``True``.
random_state: ``int``, optional, default: ``0``
Random seed set for reproducing results.
select_frac: ``float``, optional, default: ``0.1``
Down sampling fraction on the cells.
select_K: ``int``, optional, default: ``25``
Number of neighbors to be used to estimate local density for each data point for down sampling.
select_alpha: ``float``, optional, default: ``1.0``
Weight the down sample to be proportional to ``radius ** select_alpha``.
full_speed: ``bool``, optional, default: ``False``
* If ``True``, use multiple threads in constructing ``hnsw`` index. However, the kNN results are not reproducible.
* Otherwise, use only one thread to make sure results are reproducible.
net_alpha: ``float``, optional, default: ``0.1``
L2 penalty (regularization term) parameter of the deep regressor.
polish_learning_frac: ``float``, optional, default: ``10.0``
After running the deep regressor to predict new coordinates, use ``polish_learning_frac`` * ``n_obs`` as the learning rate to polish the coordinates.
polish_n_iter: ``int``, optional, default: ``30``
Number of iterations for polishing UMAP run.
out_basis: ``str``, optional, default: ``"net_umap"``
Key name for calculated UMAP coordinates to store.
Returns
-------
``None``
Update ``data.obsm``:
* ``data.obsm['X_' + out_basis]``: Net UMAP coordinates of the data.
Update ``data.obs``:
* ``data.obs['ds_selected']``: Boolean array to indicate which cells are selected during the down sampling phase.
Examples
--------
>>> pg.net_umap(data)
"""
rep = update_rep(rep)
n_jobs = eff_n_jobs(n_jobs)
knn_indices, knn_dists = get_neighbors(data,
K=select_K,
rep=rep,
n_jobs=n_jobs,
random_state=random_state,
full_speed=full_speed)
selected = select_cells(
knn_dists,
select_frac,
K=select_K,
alpha=select_alpha,
random_state=random_state,
)
X_full = X_from_rep(data, rep)
X = X_full[selected, :]
if data.shape[0] < n_neighbors:
logger.warning(
f"Warning: Number of samples = {data.shape[0]} < K = {n_neighbors}!\n Set K to {data.shape[0]}."
)
n_neighbors = data.shape[0]
ds_indices_key = "ds_" + rep + "_knn_indices" # ds refers to down-sampling
ds_distances_key = "ds_" + rep + "_knn_distances"
indices, distances = calculate_nearest_neighbors(
X,
K=n_neighbors,
n_jobs=n_jobs,
random_state=random_state,
full_speed=full_speed,
)
data.uns[ds_indices_key] = indices
data.uns[ds_distances_key] = distances
knn_indices = np.insert(data.uns[ds_indices_key][:, 0:n_neighbors - 1],
0,
range(X.shape[0]),
axis=1)
knn_dists = np.insert(data.uns[ds_distances_key][:, 0:n_neighbors - 1],
0,
0.0,
axis=1)
X_umap = calc_umap(
X,
n_components=n_components,
n_neighbors=n_neighbors,
min_dist=min_dist,
spread=spread,
densmap=densmap,
dens_lambda=dens_lambda,
dens_frac=dens_frac,
dens_var_shift=dens_var_shift,
random_state=random_state,
knn_indices=knn_indices,
knn_dists=knn_dists,
)
data.uns["X_" + out_basis + "_small"] = X_umap
data.obs["ds_selected"] = selected
Y_init = np.zeros((data.shape[0], n_components), dtype=np.float64)
Y_init[selected, :] = X_umap
Y_init[~selected, :] = net_train_and_predict(X,
X_umap,
X_full[~selected, :],
net_alpha,
n_jobs,
random_state,
verbose=True)
data.obsm["X_" + out_basis + "_pred"] = Y_init
knn_indices, knn_dists = get_neighbors(data,
K=n_neighbors,
rep=rep,
n_jobs=n_jobs,
random_state=random_state,
full_speed=full_speed)
knn_indices = np.insert(knn_indices[:, 0:n_neighbors - 1],
0,
range(data.shape[0]),
axis=1)
knn_dists = np.insert(knn_dists[:, 0:n_neighbors - 1], 0, 0.0, axis=1)
key = f"X_{out_basis}"
data.obsm[key] = calc_umap(
X_full,
n_components=n_components,
n_neighbors=n_neighbors,
min_dist=min_dist,
spread=spread,
densmap=densmap,
dens_lambda=dens_lambda,
dens_frac=dens_frac,
dens_var_shift=dens_var_shift,
random_state=random_state,
init=Y_init,
n_epochs=polish_n_epochs,
learning_rate=polish_learning_rate,
knn_indices=knn_indices,
knn_dists=knn_dists,
)
data.register_attr(key, "basis")
def fle(
data: MultimodalData,
file_name: str = None,
n_jobs: int = -1,
rep: str = "diffmap",
rep_ncomps: int = None,
K: int = 50,
full_speed: bool = False,
target_change_per_node: float = 2.0,
target_steps: int = 5000,
is3d: bool = False,
memory: int = 8,
random_state: int = 0,
out_basis: str = "fle",
) -> None:
"""Construct the Force-directed (FLE) graph.
This implementation uses forceatlas2-python_ package, which is a Python wrapper of ForceAtlas2_.
See [Jacomy14]_ for details on FLE.
.. _forceatlas2-python: https://github.com/klarman-cell-observatory/forceatlas2-python
.. _ForceAtlas2: https://github.com/klarman-cell-observatory/forceatlas2
Parameters
----------
data: ``pegasusio.MultimodalData``
Annotated data matrix with rows for cells and columns for genes.
file_name: ``str``, optional, default: ``None``
Temporary file to store the coordinates as the input to forceatlas2. If ``None``, use ``tempfile.mkstemp`` to generate file name.
n_jobs: ``int``, optional, default: ``-1``
Number of threads to use. If ``-1``, use all physical CPU cores.
rep: ``str``, optional, default: ``"diffmap"``
Representation of data used for the calculation. By default, use Diffusion Map coordinates. If ``None``, use the count matrix ``data.X``.
rep_ncomps: ``int``, optional (default: None)
Number of components to be used in `rep`. If rep_ncomps == None, use all components; otherwise, use the minimum of rep_ncomps and rep's dimensions.
K: ``int``, optional, default: ``50``
Number of nearest neighbors to be considered during the computation.
full_speed: ``bool``, optional, default: ``False``
* If ``True``, use multiple threads in constructing ``hnsw`` index. However, the kNN results are not reproducible.
* Otherwise, use only one thread to make sure results are reproducible.
target_change_per_node: ``float``, optional, default: ``2.0``
Target change per node to stop ForceAtlas2.
target_steps: ``int``, optional, default: ``5000``
Maximum number of iterations before stopping the ForceAtlas2 algorithm.
is3d: ``bool``, optional, default: ``False``
If ``True``, calculate 3D force-directed layout.
memory: ``int``, optional, default: ``8``
Memory size in GB for the Java FA2 component. By default, use 8GB memory.
random_state: ``int``, optional, default: ``0``
Random seed set for reproducing results.
out_basis: ``str``, optional, default: ``"fle"``
Key name for calculated FLE coordinates to store.
Returns
-------
``None``
Update ``data.obsm``:
* ``data.obsm['X_' + out_basis]``: FLE coordinates of the data.
Examples
--------
>>> pg.fle(data)
"""
if file_name is None:
import tempfile
_, file_name = tempfile.mkstemp()
rep = update_rep(rep)
n_jobs = eff_n_jobs(n_jobs)
if ("W_" + rep) not in data.uns:
neighbors(
data,
K=K,
rep=rep,
n_comps=rep_ncomps,
n_jobs=n_jobs,
random_state=random_state,
full_speed=full_speed,
)
key = f"X_{out_basis}"
data.obsm[key] = calc_force_directed_layout(
W_from_rep(data, rep),
file_name,
n_jobs,
target_change_per_node,
target_steps,
is3d,
memory,
random_state,
)
data.register_attr(key, "basis")
def umap(
data: MultimodalData,
rep: str = "pca",
rep_ncomps: int = None,
n_components: int = 2,
n_neighbors: int = 15,
min_dist: float = 0.5,
spread: float = 1.0,
densmap: bool = False,
dens_lambda: float = 2.0,
dens_frac: float = 0.3,
dens_var_shift: float = 0.1,
n_jobs: int = -1,
full_speed: bool = False,
random_state: int = 0,
out_basis: str = "umap",
) -> None:
"""Calculate UMAP embedding of cells.
This function uses umap-learn_ package. See [McInnes18]_ for details on UMAP.
.. _umap-learn: https://github.com/lmcinnes/umap
Parameters
----------
data: ``pegasusio.MultimodalData``
Annotated data matrix with rows for cells and columns for genes.
rep: ``str``, optional, default: ``"pca"``
Representation of data used for the calculation. By default, use PCA coordinates. If ``None``, use the count matrix ``data.X``.
rep_ncomps: `int`, optional (default: None)
Number of components to be used in `rep`. If rep_ncomps == None, use all components; otherwise, use the minimum of rep_ncomps and rep's dimensions.
n_components: ``int``, optional, default: ``2``
Dimension of calculated UMAP coordinates. By default, generate 2-dimensional data for 2D visualization.
n_neighbors: ``int``, optional, default: ``15``
Number of nearest neighbors considered during the computation.
min_dist: ``float``, optional, default: ``0.5``
The effective minimum distance between embedded data points.
spread: ``float``, optional, default: ``1.0``
The effective scale of embedded data points.
densmap: ``bool``, optional, default: ``False``
Whether the density-augmented objective of densMAP should be used for optimization, which will generate an embedding where
local densities are encouraged to be correlated with those in the original space.
dens_lambda: ``float``, optional, default: ``2.0``
Controls the regularization weight of the density correlation term in densMAP. Only works when *densmap* is ``True``.
Larger values prioritize density preservation over the UMAP objective, while values closer to 0 for the opposite direction.
Notice that setting this parameter to ``0`` is equivalent to running the original UMAP algorithm.
dens_frac: ``float``, optional, default: ``0.3``
Controls the fraction of epochs (between 0 and 1) where the density-augmented objective is used in densMAP. Only works when
*densmap* is ``True``.
The first ``(1 - dens_frac)`` fraction of epochs optimize the original UMAP objective before introducing the density
correlation term.
dens_var_shift: ``float``, optional, default, ``0.1``
A small constant added to the variance of local radii in the embedding when calculating the density correlation objective to
prevent numerical instability from dividing by a small number. Only works when *densmap* is ``True``.
n_jobs: ``int``, optional, default: ``-1``
Number of threads to use for computing kNN graphs. If ``-1``, use all physical CPU cores.
full_speed: ``bool``, optional, default: ``False``
* If ``True``, use multiple threads in constructing ``hnsw`` index. However, the kNN results are not reproducible.
* Otherwise, use only one thread to make sure results are reproducible.
random_state: ``int``, optional, default: ``0``
Random seed set for reproducing results.
out_basis: ``str``, optional, default: ``"umap"``
Key name for calculated UMAP coordinates to store.
Returns
-------
``None``
Update ``data.obsm``:
* ``data.obsm['X_' + out_basis]``: UMAP coordinates of the data.
Examples
--------
>>> pg.umap(data)
"""
rep = update_rep(rep)
X = X_from_rep(data, rep, rep_ncomps)
if data.shape[0] < n_neighbors:
logger.warning(
f"Warning: Number of samples = {data.shape[0]} < K = {n_neighbors}!\n Set K to {data.shape[0]}."
)
n_neighbors = data.shape[0]
knn_indices, knn_dists = get_neighbors(data,
K=n_neighbors,
rep=rep,
n_jobs=n_jobs,
random_state=random_state,
full_speed=full_speed)
knn_indices = np.insert(knn_indices[:, 0:n_neighbors - 1],
0,
range(data.shape[0]),
axis=1)
knn_dists = np.insert(knn_dists[:, 0:n_neighbors - 1], 0, 0.0, axis=1)
key = f"X_{out_basis}"
data.obsm[key] = calc_umap(
X,
n_components=n_components,
n_neighbors=n_neighbors,
min_dist=min_dist,
spread=spread,
densmap=densmap,
dens_lambda=dens_lambda,
dens_frac=dens_frac,
dens_var_shift=dens_var_shift,
random_state=random_state,
knn_indices=knn_indices,
knn_dists=knn_dists,
)
data.register_attr(key, "basis")
def tsne(
data: MultimodalData,
rep: str = "pca",
rep_ncomps: int = None,
n_jobs: int = -1,
n_components: int = 2,
perplexity: float = 30,
early_exaggeration: int = 12,
learning_rate: float = "auto",
initialization: str = "pca",
random_state: int = 0,
out_basis: str = "tsne",
) -> None:
"""Calculate t-SNE embedding of cells using the FIt-SNE package.
This function uses fitsne_ package. See [Linderman19]_ for details on FIt-SNE algorithm.
.. _fitsne: https://github.com/KlugerLab/FIt-SNE
Parameters
----------
data: ``pegasusio.MultimodalData``
Annotated data matrix with rows for cells and columns for genes.
rep: ``str``, optional, default: ``"pca"``
Representation of data used for the calculation. By default, use PCA coordinates. If ``None``, use the count matrix ``data.X``.
rep_ncomps: `int`, optional (default: None)
Number of components to be used in `rep`. If rep_ncomps == None, use all components; otherwise, use the minimum of rep_ncomps and rep's dimensions.
n_jobs: ``int``, optional, default: ``-1``
Number of threads to use. If ``-1``, use all physical CPU cores.
n_components: ``int``, optional, default: ``2``
Dimension of calculated FI-tSNE coordinates. By default, generate 2-dimensional data for 2D visualization.
perplexity: ``float``, optional, default: ``30``
The perplexity is related to the number of nearest neighbors used in other manifold learning algorithms. Larger datasets usually require a larger perplexity.
early_exaggeration: ``int``, optional, default: ``12``
Controls how tight natural clusters in the original space are in the embedded space, and how much space will be between them.
learning_rate: ``float``, optional, default: ``auto``
By default, the learning rate is determined automatically as max(data.shape[0] / early_exaggeration, 200). See [Belkina19]_ and [Kobak19]_ for details.
initialization: ``str``, optional, default: ``pca``
Initialization can be either ``pca`` or ``random`` or np.ndarray. By default, we use ``pca`` initialization according to [Kobak19]_.
random_state: ``int``, optional, default: ``0``
Random seed set for reproducing results.
out_basis: ``str``, optional, default: ``"fitsne"``
Key name for calculated FI-tSNE coordinates to store.
Returns
-------
``None``
Update ``data.obsm``:
* ``data.obsm['X_' + out_basis]``: FI-tSNE coordinates of the data.
Examples
--------
>>> pg.tsne(data)
"""
rep = update_rep(rep)
n_jobs = eff_n_jobs(n_jobs)
X = X_from_rep(data, rep, rep_ncomps).astype(np.float64)
if learning_rate == "auto":
learning_rate = max(X.shape[0] / early_exaggeration, 200.0)
if initialization == "random":
initialization = None
elif initialization == "pca":
if rep == "pca":
initialization = X[:, 0:n_components].copy()
else:
from sklearn.decomposition import PCA
pca = PCA(n_components=n_components, random_state=random_state)
with threadpool_limits(limits=n_jobs):
initialization = np.ascontiguousarray(pca.fit_transform(X))
initialization = initialization / np.std(initialization[:, 0]) * 0.0001
else:
assert isinstance(
initialization,
np.ndarray) and initialization.ndim == 2 and initialization.shape[
0] == X.shape[0] and initialization.shape[1] == n_components
if initialization.dtype != np.float64:
initialization = initialization.astype(np.float64)
key = f"X_{out_basis}"
data.obsm[key] = calc_tsne(
X,
n_jobs,
n_components,
perplexity,
early_exaggeration,
learning_rate,
random_state,
initialization,
)
data.register_attr(key, "basis")
