import time
import numpy as np
import scipy
import umap as umap_module
import uuid
from threadpoolctl import threadpool_limits
from pegasusio import MultimodalData
from pynndescent import NNDescent
from pegasus.tools import (
eff_n_jobs,
update_rep,
X_from_rep,
W_from_rep,
get_neighbors,
neighbors,
net_train_and_predict,
calculate_nearest_neighbors,
calculate_affinity_matrix,
construct_graph,
)
import logging
logger = logging.getLogger(__name__)
from pegasusio import timer
def calc_tsne(
X,
nthreads,
no_dims,
perplexity,
early_exag_coeff,
learning_rate,
rand_seed,
initialization=None,
max_iter=750,
stop_early_exag_iter=250,
mom_switch_iter=250,
):
"""
TODO: Calculate t-SNE embeddings using the FIt-SNE package
"""
# FItSNE will change X content
# Check if fftw3 is installed.
import ctypes.util
fftw3_loc = ctypes.util.find_library("fftw3")
if fftw3_loc is None:
raise Exception("Please install 'fftw3' first to use the FIt-SNE feature!")
try:
from fitsne import FItSNE
except ModuleNotFoundError:
import sys
logger.error("Need FItSNE! Try 'pip install fitsne' or 'conda install -c conda-forge pyfit-sne'.")
sys.exit(-1)
return FItSNE(
X,
nthreads=nthreads,
no_dims=no_dims,
perplexity=perplexity,
early_exag_coeff=early_exag_coeff,
learning_rate=learning_rate,
rand_seed=rand_seed,
initialization=initialization,
max_iter=max_iter,
stop_early_exag_iter=stop_early_exag_iter,
mom_switch_iter=mom_switch_iter,
)
class DummyNNDescent(NNDescent):
def __init__(self):
None
# Running umap using our own kNN indices
def calc_umap(
X,
n_components,
n_neighbors,
min_dist,
spread,
random_state,
densmap,
dens_lambda,
dens_frac,
dens_var_shift,
init="spectral",
n_epochs=None,
learning_rate=1.0,
knn_indices=None,
knn_dists=None,
):
"""
TODO: Typing
"""
umap_obj = umap_module.UMAP(
n_components=n_components,
n_neighbors=n_neighbors,
min_dist=min_dist,
spread=spread,
random_state=random_state,
init=init,
n_epochs=n_epochs,
learning_rate=learning_rate,
densmap=densmap,
dens_lambda=dens_lambda,
dens_frac=dens_frac,
dens_var_shift=dens_var_shift,
verbose=True,
)
if X.shape[0] < 4096 or knn_indices is None:
logger.info(f"Using umap kNN graph because number of cells {X.shape[0]} is smaller than 4096 or knn_indices is not provided.")
else:
assert knn_dists is not None, "No kNN graph is found! Please calculate it by 'pegasus.neighbors' function first!"
dummy_nnd = DummyNNDescent()
umap_obj.precomputed_knn = (knn_indices, knn_dists, dummy_nnd)
return umap_obj.fit_transform(X)
def calc_force_directed_layout(
W,
file_name,
n_jobs,
target_change_per_node,
target_steps,
is3d,
memory,
random_state,
init=None,
):
"""
TODO: Typing
"""
G = construct_graph(W)
try:
import forceatlas2 as fa2
except ModuleNotFoundError:
import sys
logger.error("Need forceatlas2-python! Try 'pip install forceatlas2-python'.")
sys.exit(-1)
return fa2.forceatlas2(
file_name,
graph=G,
n_jobs=n_jobs,
target_change_per_node=target_change_per_node,
target_steps=target_steps,
is3d=is3d,
memory=memory,
random_state=random_state,
init=init,
)
[docs]@timer(logger=logger)
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")
[docs]@timer(logger=logger)
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,
use_cache: bool = True,
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.
use_cache: ``bool``, optional, default: ``True``
If use_cache and found cached knn results, will not recompute.
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)
knn_indices, knn_dists, n_neighbors = get_neighbors(data, K = n_neighbors, rep = rep, n_jobs = n_jobs, random_state = random_state, full_speed = full_speed, use_cache = use_cache)
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")
[docs]@timer(logger=logger)
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")
@timer(logger=logger)
def select_cells(distances, frac, K=25, alpha=1.0, random_state=0):
"""
TODO: documentation (not user API)
"""
nsample = distances.shape[0]
assert K >= 2
if K > distances.shape[1] + 1:
logger.info(f"Warning: in select_cells, K = {K} > the number of calculated nearest neighbors {distances.shape[1] + 1}!\nSet K to {distances.shape[1] + 1}")
K = distances.shape[1] + 1
probs = np.zeros(nsample)
if alpha == 0.0:
probs[:] = 1.0 # uniform
elif alpha == 1.0:
probs[:] = distances[:, K - 2]
else:
probs[:] = distances[:, K - 2] ** alpha
probs /= probs.sum()
np.random.seed(random_state)
selected = np.zeros(nsample, dtype=bool)
selected[
np.random.choice(nsample, size=int(nsample * frac), replace=False, p=probs)
] = True
return selected
[docs]@timer(logger=logger)
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,
use_cache: bool = True,
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.
use_cache: ``bool``, optional, default: ``True``
If use_cache and found cached knn results, will not recompute.
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, select_K = get_neighbors(data, K = select_K, rep = rep, n_jobs = n_jobs, random_state = random_state, full_speed = full_speed, use_cache = use_cache)
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, n_neighbors = 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, n_neighbors = get_neighbors(data, K = n_neighbors, rep = rep, n_jobs = n_jobs, random_state = random_state, full_speed = full_speed, use_cache = use_cache)
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")
[docs]@timer(logger=logger)
def net_fle(
data: MultimodalData,
file_name: str = None,
n_jobs: int = -1,
rep: str = "diffmap",
K: int = 50,
full_speed: bool = False,
use_cache: bool = True,
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.
use_cache: ``bool``, optional, default: ``True``
If use_cache and found cached knn results, will not recompute.
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, select_K = get_neighbors(data, K = select_K, rep = rep, n_jobs = n_jobs, random_state = random_state, full_speed = full_speed, use_cache = use_cache)
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, K = 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")