def calculate_potential(self, diff_op, t):
"""Calculates the diffusion potential
Parameters
----------
diff_op : array-like, shape=[n_samples, n_samples] or [n_landmarks, n_landmarks]
The diffusion operator fit on the input data
t : int
power to which the diffusion operator is powered
sets the level of diffusion
Returns
-------
diff_potential : array-like, shape=[n_samples, n_samples]
The diffusion potential fit on the input data
"""
tasklogger.log_start("diffusion potential")
# diffused diffusion operator
diff_op_t = np.linalg.matrix_power(diff_op, t)
if self.gamma == 1:
# handling small values
diff_op_t = diff_op_t + 1e-7
diff_potential = -1 * np.log(diff_op_t)
elif self.gamma == -1:
diff_potential = diff_op_t
else:
c = (1 - self.gamma) / 2
diff_potential = ((diff_op_t)**c) / c
tasklogger.log_complete("diffusion potential")
return diff_potential
def fit(self, X):
if not len(X.shape) == 3:
raise ValueError("Expected X to be a tensor with three dimensions."
" Got shape {}".format(X.shape))
if self.normalize:
X = utils.normalize(X)
tasklogger.log_start("multislice kernel")
K = kernel.multislice_kernel(X,
intraslice_knn=self.intraslice_knn,
interslice_knn=self.interslice_knn,
decay=self.decay,
n_pca=self.n_pca,
distance=self.knn_dist,
n_jobs=self.n_jobs)
tasklogger.log_complete("multislice kernel")
tasklogger.log_start("graph and diffusion operator")
n_landmark = self.n_landmark if self.n_landmark < K.shape[0] else None
self.graph = graphtools.Graph(K,
precomputed="affinity",
n_landmark=n_landmark,
n_svd=self.n_svd,
n_jobs=self.n_jobs,
verbose=self.verbose,
random_state=self.random_state,
**(self.kwargs))
self.diff_op
tasklogger.log_complete("graph and diffusion operator")
result = super().fit(self.graph)
return result
def fit_transform(self, X, graph=None, **kwargs):
"""Computes the diffusion operator and the position of the cells in the
embedding space
Parameters
----------
X : array, shape=[n_samples, n_features]
input data with `n_samples` samples and `n_features`
dimensions. Accepted data types: `numpy.ndarray`,
`scipy.sparse.spmatrix`, `pd.DataFrame`, `anndata.AnnData`.
graph : `graphtools.Graph`, optional (default: None)
If given, provides a precomputed kernel matrix with which to
perform diffusion.
kwargs : further arguments for `PHATE.transform()`
Keyword arguments as specified in :func:`~phate.PHATE.transform`
Returns
-------
X_magic : array, shape=[n_samples, n_genes]
The gene expression values after diffusion
"""
tasklogger.log_start('MAGIC')
self.fit(X, graph=graph)
X_magic = self.transform(**kwargs)
tasklogger.log_complete('MAGIC')
return X_magic
def fit_transform(self, X, **kwargs):
"""Computes the diffusion operator and the position of the cells in the
embedding space
Parameters
----------
X : array, shape=[n_samples, n_features]
input data with `n_samples` samples and `n_dimensions`
dimensions. Accepted data types: `numpy.ndarray`,
`scipy.sparse.spmatrix`, `pd.DataFrame`, `anndata.AnnData` If
`knn_dist` is 'precomputed', `data` should be a n_samples x
n_samples distance or affinity matrix
kwargs : further arguments for `PHATE.transform()`
Keyword arguments as specified in :func:`~phate.PHATE.transform`
Returns
-------
embedding : array, shape=[n_samples, n_dimensions]
The cells embedded in a lower dimensional space using PHATE
"""
tasklogger.log_start('PHATE')
self.fit(X)
embedding = self.transform(**kwargs)
tasklogger.log_complete('PHATE')
return embedding
def _reduce_data(self):
"""Private method to reduce data dimension.
If data is dense, uses randomized PCA. If data is sparse, uses
randomized SVD.
TODO: should we subtract and store the mean?
Returns
-------
Reduced data matrix
"""
if self.n_pca is not None and self.n_pca < self.data.shape[1]:
tasklogger.log_start("PCA")
if sparse.issparse(self.data):
if isinstance(self.data, sparse.coo_matrix) or \
isinstance(self.data, sparse.lil_matrix) or \
isinstance(self.data, sparse.dok_matrix):
self.data = self.data.tocsr()
self.data_pca = TruncatedSVD(self.n_pca,
random_state=self.random_state)
else:
self.data_pca = PCA(self.n_pca,
svd_solver='randomized',
random_state=self.random_state)
self.data_pca.fit(self.data)
data_nu = self.data_pca.transform(self.data)
tasklogger.log_complete("PCA")
return data_nu
else:
data_nu = self.data
if sparse.issparse(data_nu) and not isinstance(
data_nu,
(sparse.csr_matrix, sparse.csc_matrix, sparse.bsr_matrix)):
data_nu = data_nu.tocsr()
return data_nu
def fit(self, X):
"""Computes the diffusion operator
Parameters
----------
X : array, shape=[n_samples, n_features]
input data with `n_samples` samples and `n_dimensions`
dimensions. Accepted data types: `numpy.ndarray`,
`scipy.sparse.spmatrix`, `pd.DataFrame`, `anndata.AnnData`. If
`knn_dist` is 'precomputed', `data` should be a n_samples x
n_samples distance or affinity matrix
Returns
-------
phate_operator : PHATE
The estimator object
"""
X, n_pca, precomputed, update_graph = self._parse_input(X)
if precomputed is None:
tasklogger.log_info(
"Running PHATE on {} cells and {} genes.".format(
X.shape[0], X.shape[1]))
else:
tasklogger.log_info(
"Running PHATE on precomputed {} matrix with {} cells.".format(
precomputed, X.shape[0]))
if self.n_landmark is None or X.shape[0] <= self.n_landmark:
n_landmark = None
else:
n_landmark = self.n_landmark
if self.graph is not None and update_graph:
self._update_graph(X, precomputed, n_pca, n_landmark)
self.X = X
if self.graph is None:
tasklogger.log_start("graph and diffusion operator")
self.graph = graphtools.Graph(
X,
n_pca=n_pca,
n_landmark=n_landmark,
distance=self.knn_dist,
precomputed=precomputed,
knn=self.knn,
decay=self.decay,
thresh=1e-4,
n_jobs=self.n_jobs,
verbose=self.verbose,
random_state=self.random_state,
**(self.kwargs))
tasklogger.log_complete("graph and diffusion operator")
# landmark op doesn't build unless forced
self.diff_op
return self
def PCA(X, *args, is_graph=False, seed=None, n_components=2, **kwargs):
X = scprep.utils.toarray(X)
tasklogger.log_start("PCA")
Y = sklearn.decomposition.PCA(*args,
n_components=n_components,
random_state=seed,
**kwargs).fit_transform(X)
tasklogger.log_complete("PCA")
return Y
def ISOMAP(X, *args, is_graph=False, seed=None, **kwargs):
np.random.seed(seed)
if is_graph:
X = utils.geodesic_distance(X)
tasklogger.log_start("ISOMAP")
Y = Isomap_(*args, precomputed=is_graph, random_state=seed,
**kwargs).fit_transform(X)
tasklogger.log_complete("ISOMAP")
return Y
def _calculate_potential(self,
t=None,
t_max=100,
plot_optimal_t=False,
ax=None):
"""Calculates the diffusion potential
Parameters
----------
t : int
power to which the diffusion operator is powered
sets the level of diffusion
t_max : int, default: 100
Maximum value of `t` to test
plot_optimal_t : boolean, default: False
If true, plots the Von Neumann Entropy and knee point
ax : matplotlib.Axes, default: None
If plot=True and ax is not None, plots the VNE on the given axis
Otherwise, creates a new axis and displays the plot
Returns
-------
diff_potential : array-like, shape=[n_samples, n_samples]
The diffusion potential fit on the input data
"""
if t is None:
t = self.t
if self._diff_potential is None:
if t == 'auto':
t = self._find_optimal_t(t_max=t_max,
plot=plot_optimal_t,
ax=ax)
else:
t = self.t
tasklogger.log_start("diffusion potential")
# diffused diffusion operator
diff_op_t = np.linalg.matrix_power(self.diff_op, t)
if self.gamma == 1:
# handling small values
diff_op_t = diff_op_t + 1e-7
self._diff_potential = -1 * np.log(diff_op_t)
elif self.gamma == -1:
self._diff_potential = diff_op_t
else:
c = (1 - self.gamma) / 2
self._diff_potential = ((diff_op_t)**c) / c
tasklogger.log_complete("diffusion potential")
elif plot_optimal_t:
self._find_optimal_t(t_max=t_max, plot=plot_optimal_t, ax=ax)
return self._diff_potential
def build_kernel(self):
"""Build the MNN kernel.
Build a mutual nearest neighbors kernel.
Returns
-------
K : kernel matrix, shape=[n_samples, n_samples]
symmetric matrix with ones down the diagonal
with no non-negative entries.
"""
tasklogger.log_start("subgraphs")
self.subgraphs = []
from .api import Graph
# iterate through sample ids
for i, idx in enumerate(self.samples):
tasklogger.log_debug("subgraph {}: sample {}, "
"n = {}, knn = {}".format(
i, idx, np.sum(self.sample_idx == idx),
self.weighted_knn[i]))
# select data for sample
data = self.data_nu[self.sample_idx == idx]
# build a kNN graph for cells within sample
graph = Graph(data,
n_pca=None,
knn=self.weighted_knn[i],
decay=self.decay,
distance=self.distance,
thresh=self.thresh,
verbose=self.verbose,
random_state=self.random_state,
n_jobs=self.n_jobs,
initialize=False)
self.subgraphs.append(graph) # append to list of subgraphs
tasklogger.log_complete("subgraphs")
if self.thresh > 0 or self.decay is None:
K = sparse.lil_matrix(
(self.data_nu.shape[0], self.data_nu.shape[0]))
else:
K = np.zeros([self.data_nu.shape[0], self.data_nu.shape[0]])
for i, X in enumerate(self.subgraphs):
for j, Y in enumerate(self.subgraphs):
tasklogger.log_start("kernel from sample {} to {}".format(
self.samples[i], self.samples[j]))
Kij = Y.build_kernel_to_data(X.data_nu,
knn=self.weighted_knn[i])
if i == j:
# downweight within-batch affinities by beta
Kij = Kij * self.beta
K = set_submatrix(K, self.sample_idx == self.samples[i],
self.sample_idx == self.samples[j], Kij)
tasklogger.log_complete("kernel from sample {} to {}".format(
self.samples[i], self.samples[j]))
return K
def TSNE(X, *args, is_graph=False, metric='euclidean', seed=None, **kwargs):
if is_graph:
X = utils.geodesic_distance(X)
metric = 'precomputed'
tasklogger.log_start("TSNE")
Y = sklearn.manifold.TSNE(*args,
metric=metric,
random_state=seed,
**kwargs).fit_transform(X)
tasklogger.log_complete("TSNE")
return Y
def Spring(X, *args, is_graph=False, seed=None, **kwargs):
np.random.seed(seed)
if not is_graph:
G = graphtools.Graph(X, knn=3, decay=None, use_pygsp=True)
else:
G = pygsp.graphs.Graph(X)
G = networkx.from_numpy_matrix(G.W.toarray())
tasklogger.log_start("Spring")
X = networkx.spring_layout(G, *args, **kwargs)
tasklogger.log_complete("Spring")
X = np.vstack(list(X.values()))
return X
def measure_method(data_noised, method, labels, data_name, subsample_idx=None):
if subsample_idx is not None:
data_noised = data_noised[subsample_idx]
tasklogger.log_start(method.__name__, logger="demap")
embedding = method(data_noised)
tasklogger.log_complete(method.__name__, logger="demap")
ari_score = demap.ari.ARI(labels, embedding, subsample_idx=subsample_idx)
df = pd.DataFrame(
{
"dataset": data_name,
"method": method.__name__,
"ARI": ari_score
},
index=[""])
return df
def build_kernel_to_data(self, Y, knn=None):
"""Build transition matrix from new data to the graph
Creates a transition matrix such that `Y` can be approximated by
a linear combination of landmarks. Any
transformation of the landmarks can be trivially applied to `Y` by
performing
`transform_Y = transitions.dot(transform)`
Parameters
----------
Y: array-like, [n_samples_y, n_features]
new data for which an affinity matrix is calculated
to the existing data. `n_features` must match
either the ambient or PCA dimensions
Returns
-------
transitions : array-like, [n_samples_y, self.data.shape[0]]
Transition matrix from `Y` to `self.data`
Raises
------
ValueError: if `precomputed` is not `None`, then the graph cannot
be extended.
"""
if knn is None:
knn = self.knn
if self.precomputed is not None:
raise ValueError("Cannot extend kernel on precomputed graph")
else:
tasklogger.log_start("affinities")
Y = self._check_extension_shape(Y)
pdx = cdist(Y, self.data_nu, metric=self.distance)
knn_dist = np.partition(pdx, knn, axis=1)[:, :knn]
epsilon = np.max(knn_dist, axis=1)
pdx = (pdx.T / epsilon).T
K = np.exp(-1 * pdx**self.decay)
# handle nan
K = np.where(np.isnan(K), 1, K)
K[K < self.thresh] = 0
tasklogger.log_complete("affinities")
return K
def _find_optimal_t(self, t_max=100, plot=False, ax=None):
"""Find the optimal value of t
Selects the optimal value of t based on the knee point of the
Von Neumann Entropy of the diffusion operator.
Parameters
----------
t_max : int, default: 100
Maximum value of t to test
plot : boolean, default: False
If true, plots the Von Neumann Entropy and knee point
ax : matplotlib.Axes, default: None
If plot=True and ax is not None, plots the VNE on the given axis
Otherwise, creates a new axis and displays the plot
Returns
-------
t_opt : int
The optimal value of t
"""
tasklogger.log_start("optimal t")
t, h = self._von_neumann_entropy(t_max=t_max)
t_opt = vne.find_knee_point(y=h, x=t)
tasklogger.log_info("Automatically selected t = {}".format(t_opt))
tasklogger.log_complete("optimal t")
if plot:
if ax is None:
fig, ax = plt.subplots()
show = True
else:
show = False
ax.plot(t, h)
ax.scatter(t_opt, h[t == t_opt], marker='*', c='k', s=50)
ax.set_xlabel("t")
ax.set_ylabel("Von Neumann Entropy")
ax.set_title("Optimal t = {}".format(t_opt))
if show:
plt.show()
self.optimal_t = t_opt
return t_opt
def graphDiffusionCoordinates(G, n_eigenvectors=None):
# diffusion maps with normalized Laplacian
tasklogger.log_start("eigendecomposition")
if n_eigenvectors is None:
G.compute_fourier_basis()
else:
# temporary workaround until pygsp updates to pypi
from scipy import sparse
G._e, G._U = sparse.linalg.eigsh(G.L, n_eigenvectors, which='SM')
tasklogger.log_complete("eigendecomposition")
phi, lmbda = G.U, G.e
# smallest to largest
lmbda_idx = np.argsort(lmbda)
phi, lmbda = phi[:, lmbda_idx], lmbda[lmbda_idx]
# trim trivial information
phi, lmbda = phi[:, 1:], lmbda[1:]
return phi, lmbda
def MDS(X,
*args,
is_graph=False,
dissimilarity='euclidean',
seed=None,
n_jobs=15,
**kwargs):
if is_graph:
X = utils.geodesic_distance(X)
dissimilarity = 'precomputed'
tasklogger.log_start("MDS")
Y = sklearn.manifold.MDS(*args,
dissimilarity=dissimilarity,
random_state=None,
n_jobs=n_jobs,
**kwargs).fit_transform(X)
tasklogger.log_complete("MDS")
return Y
def fit_transform(self, X, graph=None, **kwargs):
"""Computes the diffusion operator and the position of the cells in the
embedding space
Parameters
----------
X : array, shape=[n_samples, n_features]
input data with `n_samples` samples and `n_features`
dimensions. Accepted data types: `numpy.ndarray`,
`scipy.sparse.spmatrix`, `pd.DataFrame`, `anndata.AnnData`.
graph : `graphtools.Graph`, optional (default: None)
If given, provides a precomputed kernel matrix with which to
perform diffusion.
genes : list or {"all_genes", "pca_only"}, optional (default: None)
List of genes, either as integer indices or column names
if input data is a pandas DataFrame. If "all_genes", the entire
smoothed matrix is returned. If "pca_only", PCA on the smoothed
data is returned. If None, the entire matrix is also
returned, but a warning may be raised if the resultant matrix
is very large.
t_max : int, optional, default: 20
maximum t to test if `t` is set to 'auto'
plot_optimal_t : boolean, optional, default: False
If true and `t` is set to 'auto', plot the disparity used to
select t
ax : matplotlib.axes.Axes, optional
If given and `plot_optimal_t` is true, plot will be drawn
on the given axis.
Returns
-------
X_magic : array, shape=[n_samples, n_genes]
The gene expression values after diffusion
"""
tasklogger.log_start('MAGIC')
self.fit(X, graph=graph)
X_magic = self.transform(**kwargs)
tasklogger.log_complete('MAGIC')
return X_magic
def measure_method(data, data_noised, method, data_name, subsample_idx=None):
if subsample_idx is not None:
data_noised = data_noised[subsample_idx]
tasklogger.log_start(method.__name__, logger="demap")
embedding = method(data_noised)
tasklogger.log_complete(method.__name__, logger="demap")
demap_score = demap.DEMaP(data,
embedding,
knn=5,
subsample_idx=subsample_idx)
df = pd.DataFrame(
{
"dataset": data_name,
"method": method.__name__,
"demap": demap_score
},
index=[""],
)
return df
def call(self, bam_dir, out_dir):
"""call CNV for each chromosome
Parameters
----------
bam_dir : directory path which contains all BAM files
out_dir : the output directory
Returns
-------
self
"""
Y_path = os.path.join(out_dir, 'temp.Y.csv')
nor_Y_path = os.path.join(out_dir, 'temp.norY.csv')
ref_path = os.path.join(out_dir, 'temp.ref.csv')
gini_path = os.path.join(out_dir, 'temp.gini.csv')
ploidy_path = os.path.join(out_dir, 'temp.ploidy.csv')
scope_path = os.path.join(out_dir, 'run-scope.R')
utils.write_scope(scope_path)
command = 'Rscript {0} {1} {2} {3} {4} {5} {6} {7} {8} {9} {10} {11}'.format(scope_path, bam_dir, Y_path, ref_path, gini_path, ploidy_path, nor_Y_path,self.seq, self.reg, self.ref, self.mapq, self.bin_len)
code = os.system(command)
if code != 0:
sys.exit(1)
tasklogger.log_start('SCYN')
Y = pd.read_csv(Y_path, index_col=0)
nor_Y = pd.read_csv(nor_Y_path, index_col=0)
ref = pd.read_csv(ref_path, index_col=0)
gini = pd.read_csv(gini_path, index_col=0)
ploidy = pd.read_csv(ploidy_path, index_col=0)
self.meta_info = pd.DataFrame(index=['c_gini', 'c_ploidy'], columns=Y.columns)
self.meta_info.loc['c_gini'] = gini.T.iloc[0].values
self.meta_info.loc['c_ploidy'] = ploidy.T.iloc[0].values
self.meta_info = self.meta_info.T
self._cal_cnv(ref, Y, nor_Y)
self.bin_info = ref
# clean up temp files
utils.clean_up([Y_path, nor_Y_path, ref_path,
gini_path, ploidy_path, scope_path])
tasklogger.log_complete('SCYN')
return self
def fit(self, X, Y, q=None):
if hasattr(self, "phi_X"):
tasklogger.log_info("Using precomputed diffusion coordinates.")
else:
tasklogger.log_start("diffusion coordinates")
if q is None:
with parallel.ParallelQueue(n_jobs=min(2, self.n_jobs)) as q:
return self.fit(X, Y, q)
else:
q.queue(
math.diffusionCoordinates,
X,
decay=self.decay_X,
knn=self.knn_X,
n_pca=self.n_pca_X if self.n_pca_X is not None
and self.n_pca_X < min(X.shape) else None,
n_eigenvectors=self.n_eigenvectors,
n_jobs=max(self.n_jobs // 2, 1),
verbose=self.verbose,
random_state=self.random_state,
)
q.queue(
math.diffusionCoordinates,
Y,
decay=self.decay_Y,
knn=self.knn_Y,
n_pca=self.n_pca_Y if self.n_pca_Y is not None
and self.n_pca_Y < min(Y.shape) else None,
n_eigenvectors=self.n_eigenvectors,
n_jobs=max(self.n_jobs // 2, 1),
verbose=self.verbose,
random_state=self.random_state,
)
(phi_X, lambda_X), (phi_Y, lambda_Y) = q.run()
self.phi_X = phi_X
self.lambda_X = lambda_X
self.phi_Y = phi_Y
self.lambda_Y = lambda_Y
tasklogger.log_complete("diffusion coordinates")
return self
def PHATE(X,
*args,
is_graph=False,
knn_dist='euclidean',
solver='smacof',
verbose=0,
seed=None,
n_jobs=15,
**kwargs):
if knn_dist is None:
if is_graph:
knn_dist = 'precomputed'
tasklogger.log_start("PHATE")
Y = phate.PHATE(*args,
knn_dist=knn_dist,
verbose=verbose,
random_state=seed,
n_jobs=n_jobs,
mds_solver=solver,
**kwargs).fit_transform(X)
tasklogger.log_complete("PHATE")
return Y
def impute(self, data):
"""Main function of I-Impute
Parameters
----------
data : matrix, shape (m x n)
The raw reads count matrix
Returns
-------
imputed_data: matrix, shape (m x n)
The imputed matrix, pandas Dataframe object
"""
tasklogger.log_start('I-Impute')
imputed_data = None
if self.iteration:
exp_mse = 1
mse = 100
previous_imputed_data = data
iteration = 1
while mse > exp_mse:
tasklogger.log_info(
'iteratively impute for the {0}th time'.format(iteration))
current_imputed_data = self._cimpute(previous_imputed_data)
dist_matrix = (current_imputed_data - previous_imputed_data)**2
n_values = data.shape[0] * data.shape[1]
mse = np.sqrt(dist_matrix.values.sum() / n_values)
previous_imputed_data = current_imputed_data
iteration += 1
imputed_data = previous_imputed_data
else:
imputed_data = self._cimpute(data)
tasklogger.log_complete('I-Impute')
return imputed_data
def build_landmark_op(self):
"""Build the landmark operator
Calculates spectral clusters on the kernel, and calculates transition
probabilities between cluster centers by using transition probabilities
between samples assigned to each cluster.
"""
tasklogger.log_start("landmark operator")
is_sparse = sparse.issparse(self.kernel)
# spectral clustering
tasklogger.log_start("SVD")
_, _, VT = randomized_svd(self.diff_aff,
n_components=self.n_svd,
random_state=self.random_state)
tasklogger.log_complete("SVD")
tasklogger.log_start("KMeans")
kmeans = MiniBatchKMeans(self.n_landmark,
init_size=3 * self.n_landmark,
batch_size=10000,
random_state=self.random_state)
self._clusters = kmeans.fit_predict(self.diff_op.dot(VT.T))
# some clusters are not assigned
tasklogger.log_complete("KMeans")
# transition matrices
pmn = self._landmarks_to_data()
# row normalize
pnm = pmn.transpose()
pmn = normalize(pmn, norm='l1', axis=1)
pnm = normalize(pnm, norm='l1', axis=1)
landmark_op = pmn.dot(pnm) # sparsity agnostic matrix multiplication
if is_sparse:
# no need to have a sparse landmark operator
landmark_op = landmark_op.toarray()
# store output
self._landmark_op = landmark_op
self._transitions = pnm
tasklogger.log_complete("landmark operator")
def align(self,
X,
Y,
phi_X=None,
phi_Y=None,
lambda_X=None,
lambda_Y=None):
"""Harmonic alignment
Parameters
----------
X : array-like, shape=[n_samples, n_features]
Input dataset
Y : array-like, shape=[m_samples, n_features]
Input dataset
phi_{X,Y} : array-like, shape=[{n,m}_samples, {n,m}_samples], optional (default: None)
Precomputed Laplacian eigenvectors
lambda_{X,Y} : list-like, shape=[{n,m}_samples], optional (default: None)
Precomputed Laplacian eigenvalues
Returns
-------
XY_aligned : array-like, shape=[n_samples + m_samples, n_samples + m_samples - 1]
"""
tasklogger.log_start("Harmonic Alignment")
np.random.seed(self.random_state)
# normalized L with diffusion coordinates
with parallel.ParallelQueue(n_jobs=min(2, self.n_jobs)) as q:
if (phi_X is not None or phi_Y is not None or lambda_X is not None
or lambda_Y is not None):
if None in (phi_X, phi_Y, lambda_X, lambda_Y):
raise RuntimeError(
"If a precomputed eigensystem is provided, all of"
" `phi_X, phi_Y, lambda_X, lambda_Y` must be provided."
" Got phi_X={}, phi_Y={}, lambda_X={}, lambda_Y={}".
format(phi_X, phi_Y, lambda_X, lambda_Y))
else:
self.phi_X, self.phi_Y = phi_X, phi_Y
self.lambda_X, self.lambda_Y = lambda_X, lambda_Y
self.fit(X, Y, q)
# evaluate wavelets over data in the spectral domain
tasklogger.log_start("wavelets")
transform = build_wavelet_transform(
X,
self.phi_X,
self.lambda_X,
Y,
self.phi_Y,
self.lambda_Y,
self.n_filters,
self.overlap,
q=q,
)
tasklogger.log_complete("wavelets")
# compute transformed data
tasklogger.log_start("transformed data")
self.phi_combined, self.lambda_combined = combine_eigenvectors(
transform, self.phi_X, self.phi_Y, self.lambda_X, self.lambda_Y)
E = self.phi_combined @ np.diag(self.lambda_combined**self.t)
# build the joint diffusion map
tasklogger.log_start("graph Laplacian")
self.graph = graphtools.Graph(
E,
knn=self.knn_XY,
decay=self.decay_XY,
n_pca=self.n_pca_XY if self.n_pca_XY is not None
and self.n_pca_XY < min(E.shape) else None,
use_pygsp=True,
thresh=1e-4,
anisotropy=1,
lap_type="normalized",
n_jobs=self.n_jobs,
verbose=self.verbose,
random_state=self.random_state,
)
tasklogger.log_complete("graph Laplacian")
tasklogger.log_complete("transformed data")
tasklogger.log_complete("Harmonic Alignment")
return self.graph
def test_tasks():
logger = tasklogger.log_start("test")
assert time.time() - logger.tasks['test'] < 0.01
time.sleep(logger.min_runtime)
tasklogger.log_complete("test")
assert 'test' not in logger.tasks
def fit(self, X):
"""Computes the diffusion operator
Parameters
----------
X : array, shape=[n_samples, n_features]
input data with `n_samples` samples and `n_features`
dimensions. Accepted data types: `numpy.ndarray`,
`scipy.sparse.spmatrix`, `pd.DataFrame`, `anndata.AnnData`.
Returns
-------
magic_operator : MAGIC
The estimator object
"""
if self.knn_dist == 'precomputed':
if isinstance(X, sparse.coo_matrix):
X = X.tocsr()
if X[0, 0] == 0:
precomputed = "distance"
else:
precomputed = "affinity"
tasklogger.log_info(
"Using precomputed {} matrix...".format(precomputed))
n_pca = None
else:
precomputed = None
if self.n_pca is None or X.shape[1] <= self.n_pca:
n_pca = None
else:
n_pca = self.n_pca
if self.graph is not None:
if self.X is not None and not \
utils.matrix_is_equivalent(X, self.X):
"""
If the same data is used, we can reuse existing kernel and
diffusion matrices. Otherwise we have to recompute.
"""
self.graph = None
else:
try:
self.graph.set_params(decay=self.a,
knn=self.k + 1,
distance=self.knn_dist,
precomputed=precomputed,
n_jobs=self.n_jobs,
verbose=self.verbose,
n_pca=n_pca,
thresh=1e-4,
random_state=self.random_state)
tasklogger.log_info(
"Using precomputed graph and diffusion operator...")
except ValueError as e:
# something changed that should have invalidated the graph
tasklogger.log_debug("Reset graph due to {}".format(
str(e)))
self.graph = None
self.X = X
if utils.has_empty_columns(X):
warnings.warn("Input matrix contains unexpressed genes. "
"Please remove them prior to running MAGIC.")
if self.graph is None:
# reset X_magic in case it was previously set
self.X_magic = None
tasklogger.log_start("graph and diffusion operator")
self.graph = graphtools.Graph(X,
n_pca=n_pca,
knn=self.k + 1,
decay=self.a,
thresh=1e-4,
n_jobs=self.n_jobs,
verbose=self.verbose,
random_state=self.random_state)
tasklogger.log_complete("graph and diffusion operator")
return self
def impute(self,
data,
t_max=20,
plot=False,
ax=None,
max_genes_compute_t=500,
threshold=0.001):
"""Peform MAGIC imputation
Parameters
----------
data : graphtools.Graph, graphtools.Data or array-like
Input data
t_max : int, optional (default: 20)
Maximum value of t to consider for optimal t selection
plot : bool, optional (default: False)
Plot the optimal t selection graph
ax : matplotlib.Axes, optional (default: None)
Axis on which to plot. If None, a new axis is created
max_genes_compute_t : int, optional (default: 500)
Above this number, genes will be subsampled for
optimal t selection
threshold : float, optional (default: 0.001)
Threshold after which Procrustes disparity is considered
to have converged for optimal t selection
Returns
-------
X_magic : array-like, shape=[n_samples, n_pca]
Imputed data
"""
if not isinstance(data, graphtools.base.Data):
data = graphtools.base.Data(data, n_pca=self.n_pca)
data_imputed = data.data_nu
if data_imputed.shape[1] > max_genes_compute_t:
subsample_genes = np.random.choice(data_imputed.shape[1],
max_genes_compute_t,
replace=False)
else:
subsample_genes = None
if hasattr(data, "data_pca"):
weights = None # data.data_pca.explained_variance_ratio_
else:
weights = None
if self.t == 'auto':
_, data_prev = self.calculate_error(
data_imputed,
data_prev=None,
weights=weights,
subsample_genes=subsample_genes)
error_vec = []
t_opt = None
else:
t_opt = self.t
tasklogger.log_start("imputation")
# classic magic
# the diffusion matrix is powered when t has been specified by
# the user, and the dimensions of the diffusion matrix are lesser
# than those of the data matrix. (M^t) * D
if (t_opt is not None) and \
(self.diff_op.shape[1] < data_imputed.shape[1]):
diff_op_t = np.linalg.matrix_power(self.diff_op, t_opt)
data_imputed = diff_op_t.dot(data_imputed)
# fast magic
# a while loop is used when the dimensions of the diffusion matrix
# are greater than those of the data matrix, or when t is not specified
# (so as to allow for the calculation of the optimal t value)
else:
i = 0
while (t_opt is None and i < t_max) or \
(t_opt is not None and i < t_opt):
i += 1
data_imputed = self.diff_op.dot(data_imputed)
if self.t == 'auto':
error, data_prev = self.calculate_error(
data_imputed,
data_prev,
weights=weights,
subsample_genes=subsample_genes)
error_vec.append(error)
tasklogger.log_debug("{}: {}".format(i, error_vec))
if error < threshold and t_opt is None:
t_opt = i + 1
tasklogger.log_info(
"Automatically selected t = {}".format(t_opt))
tasklogger.log_complete("imputation")
if plot:
# continue to t_max
tasklogger.log_start("optimal t plot")
if t_opt is None:
# never converged
warnings.warn("optimal t > t_max ({})".format(t_max),
RuntimeWarning)
else:
data_overimputed = data_imputed
while i < t_max:
i += 1
data_overimputed = self.diff_op.dot(data_overimputed)
error, data_prev = self.calculate_error(
data_overimputed,
data_prev,
weights=weights,
subsample_genes=subsample_genes)
error_vec.append(error)
# create axis
if ax is None:
fig, ax = plt.subplots()
show = True
else:
show = False
# plot
x = np.arange(len(error_vec)) + 1
ax.plot(x, error_vec)
if t_opt is not None:
ax.plot(
t_opt,
error_vec[t_opt - 1],
'ro',
markersize=10,
)
ax.plot(x, np.full(len(error_vec), threshold), 'k--')
ax.set_xlabel('t')
ax.set_ylabel('disparity(data_{t}, data_{t-1})')
ax.set_xlim([1, len(error_vec)])
plt.tight_layout()
tasklogger.log_complete("optimal t plot")
if show:
plt.show(block=False)
return data_imputed
layer_ids = np.tile(data['layer'], n)
epoch = np.repeat(np.arange(n), m)
digit_ids = np.repeat(np.arange(10), 10)
digit_activity = np.array([
np.sum(np.abs(trace[:, :, digit_ids == digit]), axis=2)
for digit in np.unique(digit_ids)
])
most_active_digit = np.argmax(digit_activity, axis=0).flatten()
tasklogger.log_start("Naive DR")
trace_flat = trace.reshape(-1, trace.shape[-1])
tasklogger.log_start("PHATE")
phate_naive_op = phate.PHATE(verbose=0)
phate_naive = phate_naive_op.fit_transform(trace_flat)
tasklogger.log_complete("PHATE")
tasklogger.log_start("DM")
dm_naive = m_phate.kernel.DM(phate_naive_op.graph)
tasklogger.log_complete("DM")
tasklogger.log_start("t-SNE")
tsne_naive = TSNE().fit_transform(trace_flat)
tasklogger.log_complete("t-SNE")
tasklogger.log_start("ISOMAP")
isomap_naive = Isomap().fit_transform(trace_flat)
tasklogger.log_complete("ISOMAP")
tasklogger.log_complete("Naive DR")
tasklogger.log_start("Multislice DR")
tasklogger.log_start("M-PHATE")
m_phate_op = m_phate.M_PHATE(verbose=0)
m_phate_data = m_phate_op.fit_transform(trace)
def transform(self, X=None, t_max=100, plot_optimal_t=False, ax=None):
"""Computes the position of the cells in the embedding space
Parameters
----------
X : array, optional, shape=[n_samples, n_features]
input data with `n_samples` samples and `n_dimensions`
dimensions. Not required, since PHATE does not currently embed
cells not given in the input matrix to `PHATE.fit()`.
Accepted data types: `numpy.ndarray`,
`scipy.sparse.spmatrix`, `pd.DataFrame`, `anndata.AnnData`. If
`knn_dist` is 'precomputed', `data` should be a n_samples x
n_samples distance or affinity matrix
t_max : int, optional, default: 100
maximum t to test if `t` is set to 'auto'
plot_optimal_t : boolean, optional, default: False
If true and `t` is set to 'auto', plot the Von Neumann
entropy used to select t
ax : matplotlib.axes.Axes, optional
If given and `plot_optimal_t` is true, plot will be drawn
on the given axis.
Returns
-------
embedding : array, shape=[n_samples, n_dimensions]
The cells embedded in a lower dimensional space using PHATE
"""
if self.graph is None:
raise NotFittedError("This PHATE instance is not fitted yet. Call "
"'fit' with appropriate arguments before "
"using this method.")
elif X is not None and not utils.matrix_is_equivalent(X, self.X):
# fit to external data
warnings.warn(
"Pre-fit PHATE should not be used to transform a "
"new data matrix. Please fit PHATE to the new"
" data by running 'fit' with the new data.", RuntimeWarning)
if isinstance(self.graph, graphtools.graphs.TraditionalGraph) and \
self.graph.precomputed is not None:
raise ValueError("Cannot transform additional data using a "
"precomputed distance matrix.")
else:
if self.embedding is None:
self.transform()
transitions = self.graph.extend_to_data(X)
return self.graph.interpolate(self.embedding, transitions)
else:
diff_potential = self._calculate_potential(
t_max=t_max, plot_optimal_t=plot_optimal_t, ax=ax)
if self.embedding is None:
tasklogger.log_start("{} MDS".format(self.mds))
self.embedding = mds.embed_MDS(diff_potential,
ndim=self.n_components,
how=self.mds,
distance_metric=self.mds_dist,
n_jobs=self.n_jobs,
seed=self.random_state,
verbose=max(
self.verbose - 1, 0))
tasklogger.log_complete("{} MDS".format(self.mds))
if isinstance(self.graph, graphtools.graphs.LandmarkGraph):
tasklogger.log_debug("Extending to original data...")
return self.graph.interpolate(self.embedding)
else:
return self.embedding