def magic(adata):
from magic import MAGIC
X, libsize = scprep.normalize.library_size_normalize(
adata.obsm["train"], rescale=1, return_library_size=True)
X = scprep.transform.sqrt(X)
Y = MAGIC().fit_transform(X, genes="all_genes")
Y = scprep.utils.matrix_transform(Y, np.square)
Y = scprep.utils.matrix_vector_elementwise_multiply(Y, libsize, axis=0)
adata.obsm["denoised"] = Y
return adata
def run_magic(self,
n_pca_components=20,
random_pca=True,
t=6,
k=30,
ka=10,
epsilon=1,
rescale_percent=99):
MAGICed = [x for x in self.operation.history if 'MAGIC' in x]
if MAGICed:
print('already ran MAGIC on the current data')
return
else:
pca_data = self.run_pca(n_pca_components,
random_pca,
no_effect=True)
new_data = MAGIC.magic(self.data.values,
pca_data.data.values,
t=t,
k=k,
ka=ka,
epsilon=epsilon,
rescale=rescale_percent)
new_data = pd.DataFrame(new_data,
index=self.data.index,
columns=self.data.columns)
# Construct class object
par = '-'.join(
(str(n_pca_components), str(random_pca), str(t), str(k),
str(ka), str(epsilon), str(rescale_percent)))
self.data = new_data
self.operation.add('MAGIC', par)
def magic(
adata: AnnData,
name_list: Union[str, Sequence[str], None] = None,
k: int = 10,
a: int = 15,
t: str = 'auto',
n_pca: int = 100,
knn_dist: str = 'euclidean',
random_state: Optional[Union[int, RandomState]] = None,
n_jobs: Optional[int] = None,
verbose: bool = False,
copy: Optional[bool] = None,
**kwargs,
) -> Optional[AnnData]:
"""\
Markov Affinity-based Graph Imputation of Cells (MAGIC) API [vanDijk18]_.
MAGIC is an algorithm for denoising and transcript recover of single cells
applied to single-cell sequencing data. MAGIC builds a graph from the data
and uses diffusion to smooth out noise and recover the data manifold.
More information and bug reports
`here <https://github.com/KrishnaswamyLab/MAGIC>`__. For help, visit
<https://krishnaswamylab.org/get-help>.
Parameters
----------
adata
An anndata file with `.raw` attribute representing raw counts.
name_list
Denoised genes to return. The default `'all_genes'`/`None`
may require a large amount of memory if the input data is sparse.
Another possibility is `'pca_only'`.
k
number of nearest neighbors on which to build kernel
a
sets decay rate of kernel tails.
If None, alpha decaying kernel is not used
t
power to which the diffusion operator is powered.
This sets the level of diffusion. If 'auto', t is selected
according to the Procrustes disparity of the diffused data
n_pca
Number of principal components to use for calculating
neighborhoods. For extremely large datasets, using
n_pca < 20 allows neighborhoods to be calculated in
roughly log(n_samples) time.
knn_dist
recommended values: 'euclidean', 'cosine', 'precomputed'
Any metric from `scipy.spatial.distance` can be used
distance metric for building kNN graph. If 'precomputed',
`data` should be an n_samples x n_samples distance or
affinity matrix
random_state
Random seed. Defaults to the global `numpy` random number generator
n_jobs
Number of threads to use in training. All cores are used by default.
verbose
If `True` or an integer `>= 2`, print status messages.
If `None`, `sc.settings.verbosity` is used.
copy
If true, a copy of anndata is returned. If `None`, `copy` is True if
`genes` is not `'all_genes'` or `'pca_only'`. `copy` may only be False
if `genes` is `'all_genes'` or `'pca_only'`, as the resultant data
will otherwise have different column names from the input data.
kwargs
Additional arguments to `magic.MAGIC`
Returns
-------
If `copy` is True, AnnData object is returned.
If `subset_genes` is not `all_genes`, PCA on MAGIC values of cells are
stored in `adata.obsm['X_magic']` and `adata.X` is not modified.
The raw counts are stored in `.raw` attribute of AnnData object.
Examples
--------
>>> import scanpy as sc
>>> import scanpy.external as sce
>>> adata = sc.datasets.paul15()
>>> sc.pp.normalize_per_cell(adata)
>>> sc.pp.sqrt(adata) # or sc.pp.log1p(adata)
>>> adata_magic = sce.pp.magic(adata, name_list=['Mpo', 'Klf1', 'Ifitm1'], k=5)
>>> adata_magic.shape
(2730, 3)
>>> sce.pp.magic(adata, name_list='pca_only', k=5)
>>> adata.obsm['X_magic'].shape
(2730, 100)
>>> sce.pp.magic(adata, name_list='all_genes', k=5)
>>> adata.X.shape
(2730, 3451)
"""
try:
from magic import MAGIC
except ImportError:
raise ImportError(
'Please install magic package via `pip install --user '
'git+git://github.com/KrishnaswamyLab/MAGIC.git#subdirectory=python`'
)
start = logg.info('computing PHATE')
all_or_pca = isinstance(name_list, (str, type(None)))
if all_or_pca and name_list not in {"all_genes", "pca_only", None}:
raise ValueError("Invalid string value for `name_list`: "
"Only `'all_genes'` and `'pca_only'` are allowed.")
if copy is None:
copy = not all_or_pca
elif not all_or_pca and not copy:
raise ValueError(
"Can only perform MAGIC in-place with `name_list=='all_genes' or "
f"`name_list=='pca_only'` (got {name_list}). Consider setting "
"`copy=True`")
adata = adata.copy() if copy else adata
n_jobs = settings.n_jobs if n_jobs is None else n_jobs
X_magic = MAGIC(
k=k,
a=a,
t=t,
n_pca=n_pca,
knn_dist=knn_dist,
random_state=random_state,
n_jobs=n_jobs,
verbose=verbose,
**kwargs,
).fit_transform(adata, genes=name_list)
logg.info(
' finished',
time=start,
deep=("added\n 'X_magic', PCA on MAGIC coordinates (adata.obsm)"
if name_list == "pca_only" else ''))
# update AnnData instance
if name_list == "pca_only":
# special case - update adata.obsm with smoothed values
adata.obsm["X_magic"] = X_magic.X
elif copy:
# just return X_magic
X_magic.raw = adata
adata = X_magic
else:
# replace data with smoothed data
adata.raw = adata
adata.X = X_magic.X
if copy:
return adata
def magic(
adata: AnnData,
name_list: Union[Literal['all_genes', 'pca_only'], Sequence[str], None] = None,
*,
knn: int = 5,
decay: Optional[float] = 1,
knn_max: Optional[int] = None,
t: Union[Literal['auto'], int] = 3,
n_pca: Optional[int] = 100,
solver: Literal['exact', 'approximate'] = 'exact',
knn_dist: str = 'euclidean',
random_state: Optional[Union[int, RandomState]] = None,
n_jobs: Optional[int] = None,
verbose: bool = False,
copy: Optional[bool] = None,
**kwargs,
) -> Optional[AnnData]:
"""\
Markov Affinity-based Graph Imputation of Cells (MAGIC) API [vanDijk18]_.
MAGIC is an algorithm for denoising and transcript recover of single cells
applied to single-cell sequencing data. MAGIC builds a graph from the data
and uses diffusion to smooth out noise and recover the data manifold.
The algorithm implemented here has changed primarily in two ways
compared to the algorithm described in [vanDijk18]_. Firstly, we use
the adaptive kernel described in Moon et al, 2019 [Moon17]_ for
improved stability. Secondly, data diffusion is applied
in the PCA space, rather than the data space, for speed and
memory improvements.
More information and bug reports
`here <https://github.com/KrishnaswamyLab/MAGIC>`__. For help, visit
<https://krishnaswamylab.org/get-help>.
Parameters
----------
adata
An anndata file with `.raw` attribute representing raw counts.
name_list
Denoised genes to return. The default `'all_genes'`/`None`
may require a large amount of memory if the input data is sparse.
Another possibility is `'pca_only'`.
knn
number of nearest neighbors on which to build kernel.
decay
sets decay rate of kernel tails.
If None, alpha decaying kernel is not used.
knn_max
maximum number of nearest neighbors with nonzero connection.
If `None`, will be set to 3 * `knn`.
t
power to which the diffusion operator is powered.
This sets the level of diffusion. If 'auto', t is selected
according to the Procrustes disparity of the diffused data.
n_pca
Number of principal components to use for calculating
neighborhoods. For extremely large datasets, using
n_pca < 20 allows neighborhoods to be calculated in
roughly log(n_samples) time. If `None`, no PCA is performed.
solver
Which solver to use. "exact" uses the implementation described
in van Dijk et al. (2018) [vanDijk18]_. "approximate" uses a faster
implementation that performs imputation in the PCA space and then
projects back to the gene space. Note, the "approximate" solver may
return negative values.
knn_dist
recommended values: 'euclidean', 'cosine', 'precomputed'
Any metric from `scipy.spatial.distance` can be used
distance metric for building kNN graph. If 'precomputed',
`data` should be an n_samples x n_samples distance or
affinity matrix.
random_state
Random seed. Defaults to the global `numpy` random number generator.
n_jobs
Number of threads to use in training. All cores are used by default.
verbose
If `True` or an integer `>= 2`, print status messages.
If `None`, `sc.settings.verbosity` is used.
copy
If true, a copy of anndata is returned. If `None`, `copy` is True if
`genes` is not `'all_genes'` or `'pca_only'`. `copy` may only be False
if `genes` is `'all_genes'` or `'pca_only'`, as the resultant data
will otherwise have different column names from the input data.
kwargs
Additional arguments to `magic.MAGIC`.
Returns
-------
If `copy` is True, AnnData object is returned.
If `subset_genes` is not `all_genes`, PCA on MAGIC values of cells are
stored in `adata.obsm['X_magic']` and `adata.X` is not modified.
The raw counts are stored in `.raw` attribute of AnnData object.
Examples
--------
>>> import scanpy as sc
>>> import scanpy.external as sce
>>> adata = sc.datasets.paul15()
>>> sc.pp.normalize_per_cell(adata)
>>> sc.pp.sqrt(adata) # or sc.pp.log1p(adata)
>>> adata_magic = sce.pp.magic(adata, name_list=['Mpo', 'Klf1', 'Ifitm1'], knn=5)
>>> adata_magic.shape
(2730, 3)
>>> sce.pp.magic(adata, name_list='pca_only', knn=5)
>>> adata.obsm['X_magic'].shape
(2730, 100)
>>> sce.pp.magic(adata, name_list='all_genes', knn=5)
>>> adata.X.shape
(2730, 3451)
"""
try:
from magic import MAGIC, __version__
except ImportError:
raise ImportError(
'Please install magic package via `pip install --user '
'git+git://github.com/KrishnaswamyLab/MAGIC.git#subdirectory=python`'
)
else:
if not version.parse(__version__) >= version.parse(MIN_VERSION):
raise ImportError(
'scanpy requires magic-impute >= '
f'v{MIN_VERSION} (detected: v{__version__}). '
'Please update magic package via `pip install --user '
'--upgrade magic-impute`'
)
start = logg.info('computing MAGIC')
all_or_pca = isinstance(name_list, (str, type(None)))
if all_or_pca and name_list not in {"all_genes", "pca_only", None}:
raise ValueError(
"Invalid string value for `name_list`: "
"Only `'all_genes'` and `'pca_only'` are allowed."
)
if copy is None:
copy = not all_or_pca
elif not all_or_pca and not copy:
raise ValueError(
"Can only perform MAGIC in-place with `name_list=='all_genes' or "
f"`name_list=='pca_only'` (got {name_list}). Consider setting "
"`copy=True`"
)
adata = adata.copy() if copy else adata
n_jobs = settings.n_jobs if n_jobs is None else n_jobs
X_magic = MAGIC(
knn=knn,
decay=decay,
knn_max=knn_max,
t=t,
n_pca=n_pca,
solver=solver,
knn_dist=knn_dist,
random_state=random_state,
n_jobs=n_jobs,
verbose=verbose,
**kwargs,
).fit_transform(adata, genes=name_list)
logg.info(
' finished',
time=start,
deep=(
"added\n 'X_magic', PCA on MAGIC coordinates (adata.obsm)"
if name_list == "pca_only"
else ''
),
)
# update AnnData instance
if name_list == "pca_only":
# special case – update adata.obsm with smoothed values
adata.obsm["X_magic"] = X_magic.X
elif copy:
# just return X_magic
X_magic.raw = adata
adata = X_magic
else:
# replace data with smoothed data
adata.raw = adata
adata.X = X_magic.X
if copy:
return adata
def magic(adata,
name_list=None,
k=10,
a=15,
t='auto',
n_pca=100,
knn_dist='euclidean',
random_state=None,
n_jobs=None,
verbose=False,
copy=None,
**kwargs):
"""Markov Affinity-based Graph Imputation of Cells (MAGIC) API [vanDijk18]_.
MAGIC is an algorithm for denoising and transcript recover of single cells
applied to single-cell sequencing data. MAGIC builds a graph from the data
and uses diffusion to smooth out noise and recover the data manifold.
More information and bug reports
`here <https://github.com/KrishnaswamyLab/MAGIC>`__. For help, visit
<https://krishnaswamylab.org/get-help>.
Parameters
----------
adata : :class:`~scanpy.api.AnnData`
An anndata file with `.raw` attribute representing raw counts.
name_list : `list`, `'all_genes'`, or `'pca_only'`, optional (default: `'all_genes'`)
Denoised genes to return. Default is all genes, but this
may require a large amount of memory if the input data is sparse.
k : int, optional, default: 10
number of nearest neighbors on which to build kernel
a : int, optional, default: 15
sets decay rate of kernel tails.
If None, alpha decaying kernel is not used
t : int, optional, default: 'auto'
power to which the diffusion operator is powered.
This sets the level of diffusion. If 'auto', t is selected
according to the Procrustes disparity of the diffused data
n_pca : int, optional, default: 100
Number of principal components to use for calculating
neighborhoods. For extremely large datasets, using
n_pca < 20 allows neighborhoods to be calculated in
roughly log(n_samples) time.
knn_dist : string, optional, default: 'euclidean'
recommended values: 'euclidean', 'cosine', 'precomputed'
Any metric from `scipy.spatial.distance` can be used
distance metric for building kNN graph. If 'precomputed',
`data` should be an n_samples x n_samples distance or
affinity matrix
random_state : `int`, `numpy.RandomState` or `None`, optional (default: `None`)
Random seed. Defaults to the global `numpy` random number generator
n_jobs : `int` or None, optional. Default: None
Number of threads to use in training. All cores are used by default.
verbose : `bool`, `int` or `None`, optional (default: `sc.settings.verbosity`)
If `True` or an integer `>= 2`, print status messages.
If `None`, `sc.settings.verbosity` is used.
copy : `bool` or `None`, optional. Default: `None`.
If true, a copy of anndata is returned. If `None`, `copy` is True if
`genes` is not `'all_genes'` or `'pca_only'`. `copy` may only be False
if `genes` is `'all_genes'` or `'pca_only'`, as the resultant data
will otherwise have different column names from the input data.
kwargs : additional arguments to `magic.MAGIC`
Returns
-------
If `copy` is True, AnnData object is returned.
If `subset_genes` is not `all_genes`, PCA on MAGIC values of cells are stored in
`adata.obsm['X_magic']` and `adata.X` is not modified.
The raw counts are stored in `.raw` attribute of AnnData object.
Examples
--------
>>> import scanpy.api as sc
>>> import magic
>>> adata = sc.datasets.paul15()
>>> sc.pp.normalize_per_cell(adata)
>>> sc.pp.sqrt(adata) # or sc.pp.log1p(adata)
>>> adata_magic = sc.pp.magic(adata, name_list=['Mpo', 'Klf1', 'Ifitm1'], k=5)
>>> adata_magic.shape
(2730, 3)
>>> sc.pp.magic(adata, name_list='pca_only', k=5)
>>> adata.obsm['X_magic'].shape
(2730, 100)
>>> sc.pp.magic(adata, name_list='all_genes', k=5)
>>> adata.X.shape
(2730, 3451)
"""
try:
from magic import MAGIC
except ImportError:
raise ImportError(
'Please install magic package via `pip install --user '
'git+git://github.com/KrishnaswamyLab/MAGIC.git#subdirectory=python`'
)
logg.info('computing PHATE', r=True)
needs_copy = not (name_list is None or
(isinstance(name_list, str)
and name_list in ["all_genes", "pca_only"]))
if copy is None:
copy = needs_copy
elif needs_copy and not copy:
raise ValueError(
"Can only perform MAGIC in-place with `name_list=='all_genes' or "
"`name_list=='pca_only'` (got {}). Consider setting "
"`copy=True`".format(name_list))
adata = adata.copy() if copy else adata
verbose = settings.verbosity if verbose is None else verbose
if isinstance(verbose, (str, int)):
verbose = _settings_verbosity_greater_or_equal_than(2)
n_jobs = settings.n_jobs if n_jobs is None else n_jobs
X_magic = MAGIC(k=k,
a=a,
t=t,
n_pca=n_pca,
knn_dist=knn_dist,
random_state=random_state,
n_jobs=n_jobs,
verbose=verbose,
**kwargs).fit_transform(adata, genes=name_list)
logg.info(
' finished',
time=True,
end=' ' if _settings_verbosity_greater_or_equal_than(3) else '\n')
# update AnnData instance
if name_list == "pca_only":
# special case - update adata.obsm with smoothed values
adata.obsm["X_magic"] = X_magic.X
logg.hint('added\n'
' \'X_magic\', PCA on MAGIC coordinates (adata.obsm)')
elif copy:
# just return X_magic
X_magic.raw = adata
adata = X_magic
else:
# replace data with smoothed data
adata.raw = adata
adata.X = X_magic.X
if copy:
return adata
if not isinstance(args.num_actions, (list, tuple)): # single action case
args.num_actions = [args.num_actions]
args.dim_actions = env.dim_actions
args.num_inputs = num_inputs
parse_action_args(args)
if args.seed == -1:
args.seed = np.random.randint(0, 10000)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
print(args)
policy_net = MAGIC(args, num_inputs)
if not args.display:
display_models([policy_net])
# share parameters among threads, but not gradients
for p in policy_net.parameters():
p.data.share_memory_()
disp_trainer = Trainer(args, policy_net, data.init(args.env_name, args, False))
disp_trainer.display = True
def disp():
x = disp_trainer.get_episode()
args.num_actions = env.num_actions
# Multi-action
if not isinstance(args.num_actions, (list, tuple)): # single action case
args.num_actions = [args.num_actions]
args.dim_actions = env.dim_actions
parse_action_args(args)
if args.seed == -1:
args.seed = np.random.randint(0, 10000)
torch.manual_seed(args.seed)
print(args)
policy_net = MAGIC(args)
if not args.display:
display_models([policy_net])
# share parameters among threads, but not gradients
for p in policy_net.parameters():
p.data.share_memory_()
disp_trainer = Trainer(args, policy_net, data.init(args.env_name, args, False))
disp_trainer.display = True
def disp():
x = disp_trainer.get_episode()