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 available threads.
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()
n_jobs = effective_n_jobs(n_jobs)
rep = update_rep(rep)
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,
)
indices_key = rep + "_knn_indices"
distances_key = rep + "_knn_distances"
if not knn_is_cached(data, indices_key, distances_key, select_K):
raise ValueError("Please run neighbors first!")
selected = select_cells(
data.uns[distances_key],
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,
random_state,
verbose=True)
data.obsm["X_" + out_basis + "_pred"] = Y_init
data.obsm["X_" + out_basis] = 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,
)
def net_tsne(
data: MultimodalData,
rep: str = "pca",
n_jobs: int = -1,
n_components: int = 2,
perplexity: float = 30,
early_exaggeration: int = 12,
learning_rate: float = 1000,
random_state: int = 0,
select_frac: float = 0.1,
select_K: int = 25,
select_alpha: float = 1.0,
net_alpha: float = 0.1,
polish_learning_frac: float = 0.33,
polish_n_iter: int = 150,
out_basis: str = "net_tsne",
) -> None:
"""Calculate Net-tSNE embedding of cells.
Net-tSNE is an approximated tSNE embedding using Deep Learning model to improve the calculation 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 (``n_obs``) and columns for genes (``n_feature``).
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 available threads.
n_components: ``int``, optional, default: ``2``
Dimension of calculated 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: ``1000``
The learning rate can be a critical parameter, which should be between 100 and 1000.
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_learning_frac: ``float``, optional, default: ``0.33``
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: ``150``
Number of iterations for polishing tSNE run.
out_basis: ``str``, optional, default: ``"net_tsne"``
Key name for the approximated tSNE coordinates calculated.
Returns
-------
``None``
Update ``data.obsm``:
* ``data.obsm['X_' + out_basis]``: Net tSNE 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_tsne(data)
"""
rep = update_rep(rep)
indices_key = rep + "_knn_indices"
distances_key = rep + "_knn_distances"
if not knn_is_cached(data, indices_key, distances_key, select_K):
raise ValueError("Please run neighbors first!")
n_jobs = effective_n_jobs(n_jobs)
selected = select_cells(
data.uns[distances_key],
select_frac,
K=select_K,
alpha=select_alpha,
random_state=random_state,
)
X_full = X_from_rep(data, rep)
X = X_full[selected, :]
X_tsne = calc_tsne(
X,
n_jobs,
n_components,
perplexity,
early_exaggeration,
learning_rate,
random_state,
)
data.uns["X_" + out_basis + "_small"] = X_tsne
data.obs["ds_selected"] = selected
Y_init = np.zeros((data.shape[0], n_components), dtype=np.float64)
Y_init[selected, :] = X_tsne
Y_init[~selected, :] = net_train_and_predict(X,
X_tsne,
X_full[~selected, :],
net_alpha,
random_state,
verbose=True)
data.obsm["X_" + out_basis + "_pred"] = Y_init
polish_learning_rate = polish_learning_frac * data.shape[0]
data.obsm["X_" + out_basis] = calc_tsne(
X_full,
n_jobs,
n_components,
perplexity,
early_exaggeration,
polish_learning_rate,
random_state,
init=Y_init,
n_iter=polish_n_iter,
n_iter_early_exag=0,
)
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,
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_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.
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)
indices_key = rep + "_knn_indices"
distances_key = rep + "_knn_distances"
if not knn_is_cached(data, indices_key, distances_key, select_K):
raise ValueError("Please run neighbors first!")
n_jobs = effective_n_jobs(n_jobs)
selected = select_cells(
data.uns[distances_key],
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 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_neighbors,
min_dist,
spread,
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,
random_state,
verbose=True)
data.obsm["X_" + out_basis + "_pred"] = Y_init
knn_indices = np.insert(data.uns[indices_key][:, 0:n_neighbors - 1],
0,
range(data.shape[0]),
axis=1)
knn_dists = np.insert(data.uns[distances_key][:, 0:n_neighbors - 1],
0,
0.0,
axis=1)
data.obsm["X_" + out_basis] = calc_umap(
X_full,
n_components,
n_neighbors,
min_dist,
spread,
random_state,
init=Y_init,
n_epochs=polish_n_epochs,
learning_rate=polish_learning_rate,
knn_indices=knn_indices,
knn_dists=knn_dists,
)
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")