spateo.tdr.widgets.morphogenesis
#
Module Contents#
Functions#
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Obtain the optimal mapping relationship and developmental direction between cells for samples between continuous developmental stages. |
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Calculating and predicting the vector field during development by the Kernel method (sparseVFC). |
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Calculating and predicting the vector field during development by the Kernel method (sparseVFC). |
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Calculate the velocity for each cell with the reconstructed vector field function. |
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Calculate curvature for each cell with the reconstructed vector field function. |
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Calculate acceleration for each cell with the reconstructed vector field function. |
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Calculate curl for each cell with the reconstructed vector field function. |
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Calculate torsion for each cell with the reconstructed vector field function. |
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Calculate divergence for each cell with the reconstructed vector field function. |
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Calculate jacobian for each cell with the reconstructed vector field function. |
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Prediction of cell developmental trajectory based on reconstructed vector field. |
- spateo.tdr.widgets.morphogenesis.cell_directions(adatas: List[anndata.AnnData], layer: str = 'X', genes: Optional[Union[list, numpy.ndarray]] = None, spatial_key: str = 'align_spatial', key_added: str = 'mapping', alpha: float = 0.001, numItermax: int = 200, numItermaxEmd: int = 100000, dtype: str = 'float32', device: str = 'cpu', keep_all: bool = False, inplace: bool = True, **kwargs) Optional[List[anndata.AnnData]] [source]#
Obtain the optimal mapping relationship and developmental direction between cells for samples between continuous developmental stages.
- Parameters
- adatas
AnnData object of samples from continuous developmental stages.
- layer
If
'X'
, uses.X
to calculate dissimilarity between spots, otherwise uses the representation given by.layers[layer]
.- genes
Genes used for calculation. If None, use all common genes for calculation.
- spatial_key
The key in
.obsm
that corresponds to the spatial coordinate of each cell.- .uns. : The key that will be used for the vector field key in
- key_added
The key that will be used in
.obsm
.X_{key_added}
-TheX_{key_added}
that will be used for the coordinates of the cell that maps optimally in the next stage.V_{key_added}
-TheV_{key_added}
that will be used for the cell developmental directions.
- alpha
Alignment tuning parameter. Note: 0 <= alpha <= 1.
When
alpha = 0
only the gene expression data is taken into account, while whenalpha =1
only the spatial coordinates are taken into account.- numItermax
Max number of iterations for cg during FGW-OT.
- numItermaxEmd
Max number of iterations for emd during FGW-OT.
- dtype
The floating-point number type. Only
float32
andfloat64
.- device
Equipment used to run the program. You can also set the specified GPU for running.
E.g.: '0'
- keep_all
Whether to retain all the optimal relationships obtained only based on the pi matrix, If
keep_all
is False, the optimal relationships obtained based on the pi matrix and the nearest coordinates.- inplace
Whether to copy adata or modify it inplace.
- **kwargs
Additional parameters that will be passed to
pairwise_align
function.
- Returns
An
AnnData
object is updated/copied with theX_{key_added}
andV_{key_added}
in the.obsm
attribute.
- spateo.tdr.widgets.morphogenesis.morphofield_X(X: numpy.ndarray, V: numpy.ndarray, NX: Optional[numpy.ndarray] = None, grid_num: Optional[List[int]] = None, M: int = 100, lambda_: float = 0.02, lstsq_method: str = 'scipy', min_vel_corr: float = 0.8, restart_num: int = 10, restart_seed: Union[List[int], Tuple[int], numpy.ndarray] = (0, 100, 200, 300, 400), **kwargs) dict [source]#
Calculating and predicting the vector field during development by the Kernel method (sparseVFC).
- Parameters
- X
The spatial coordinates of each cell.
- V
The developmental direction of each cell.
- NX
The spatial coordinates of new data point (grid). If
NX
is None, generate grid based ongrid_num
.- grid_num
The number of grids in each dimension for generating the grid velocity. Default is
[50, 50, 50]
.- M
The number of basis functions to approximate the vector field.
- lambda
Represents the trade-off between the goodness of data fit and regularization. Larger Lambda_ put more weights on regularization.
- lstsq_method
The name of the linear least square solver, can be either
'scipy'
or'douin'
.- min_vel_corr
The minimal threshold for the cosine correlation between input velocities and learned velocities to consider as a successful vector field reconstruction procedure. If the cosine correlation is less than this threshold and
restart_num
> 1,restart_num
trials will be attempted with different seeds to reconstruct the vector field function. This can avoid some reconstructions to be trapped in some local optimal.- restart_num
The number of retrials for vector field reconstructions.
- restart_seed
A list of seeds for each retrial. Must be the same length as
restart_num
or None.- **kwargs
Additional parameters that will be passed to
SparseVFC
function.
- Returns
- X: Current state.
valid_ind: The indices of cells that have finite velocity values. X_ctrl: Sample control points of current state. ctrl_idx: Indices for the sampled control points. Y: Velocity estimates in delta t. beta: Parameter of the Gaussian Kernel for the kernel matrix (Gram matrix). V: Prediction of velocity of X. C: Finite set of the coefficients for the P: Posterior probability Matrix of inliers. VFCIndex: Indexes of inliers found by sparseVFC. sigma2: Energy change rate. grid: Grid of current state. grid_V: Prediction of velocity of the grid. iteration: Number of the last iteration. tecr_vec: Vector of relative energy changes rate comparing to previous step. E_traj: Vector of energy at each iteration. method: The method of learning vector field. Here method == ‘sparsevfc’.
Here the most important results are X, V, grid and grid_V.
X: Cell coordinates of the current state. V: Developmental direction of the X. grid: Grid coordinates of current state. grid_V: Prediction of developmental direction of the grid.
- Return type
A dictionary which contains
- spateo.tdr.widgets.morphogenesis.morphofield(adata: anndata.AnnData, spatial_key: str = 'align_spatial', V_key: str = 'V_mapping', key_added: str = 'VecFld_morpho', NX: Optional[numpy.ndarray] = None, grid_num: Optional[List[int]] = None, M: int = 100, lambda_: float = 0.02, lstsq_method: str = 'scipy', min_vel_corr: float = 0.8, restart_num: int = 10, restart_seed: Union[List[int], Tuple[int], numpy.ndarray] = (0, 100, 200, 300, 400), inplace: bool = True, **kwargs) Optional[anndata.AnnData] [source]#
Calculating and predicting the vector field during development by the Kernel method (sparseVFC).
- Parameters
- adata
AnnData object that contains the cell coordinates of the two states after alignment.
- spatial_key
The key from the
.obsm
that corresponds to the spatial coordinates of each cell.- V_key
The key from the
.obsm
that corresponds to the developmental direction of each cell.- key_added
The key that will be used for the vector field key in
.uns
.- NX
The spatial coordinates of new data point. If NX is None, generate new points based on grid_num.
- grid_num
The number of grids in each dimension for generating the grid velocity. Default is
[50, 50, 50]
.- M
The number of basis functions to approximate the vector field.
- lambda
Represents the trade-off between the goodness of data fit and regularization. Larger Lambda_ put more weights on regularization.
- lstsq_method
The name of the linear least square solver, can be either
'scipy'
or'douin'
.- min_vel_corr
The minimal threshold for the cosine correlation between input velocities and learned velocities to consider as a successful vector field reconstruction procedure. If the cosine correlation is less than this threshold and
restart_num
> 1,restart_num
trials will be attempted with different seeds to reconstruct the vector field function. This can avoid some reconstructions to be trapped in some local optimal.- restart_num
The number of retrials for vector field reconstructions.
- restart_seed
A list of seeds for each retrial. Must be the same length as
restart_num
or None.- inplace
Whether to copy adata or modify it inplace.
- **kwargs
Additional parameters that will be passed to
SparseVFC
function.
- Returns
An
AnnData
object is updated/copied with thekey_added
dictionary in the.uns
attribute.The
key_added
dictionary which contains:X: Current state. valid_ind: The indices of cells that have finite velocity values. X_ctrl: Sample control points of current state. ctrl_idx: Indices for the sampled control points. Y: Velocity estimates in delta t. beta: Parameter of the Gaussian Kernel for the kernel matrix (Gram matrix). V: Prediction of velocity of X. C: Finite set of the coefficients for the P: Posterior probability Matrix of inliers. VFCIndex: Indexes of inliers found by sparseVFC. sigma2: Energy change rate. grid: Grid of current state. grid_V: Prediction of velocity of the grid. iteration: Number of the last iteration. tecr_vec: Vector of relative energy changes rate comparing to previous step. E_traj: Vector of energy at each iteration. method: The method of learning vector field. Here method == ‘sparsevfc’.
Here the most important results are X, V, grid and grid_V.
X: Cell coordinates of the current state. V: Developmental direction of the X. grid: Grid coordinates of current state. grid_V: Prediction of developmental direction of the grid.
- spateo.tdr.widgets.morphogenesis._generate_vf_class(adata: anndata.AnnData, vf_key: str)[source]#
- spateo.tdr.widgets.morphogenesis.morphofield_velocity(adata: anndata.AnnData, vf_key: str = 'VecFld_morpho', key_added: str = 'velocity', inplace: bool = True) Optional[anndata.AnnData] [source]#
Calculate the velocity for each cell with the reconstructed vector field function.
- Parameters
- adata
AnnData object that contains the reconstructed vector field.
- vf_key
The key in
.uns
that corresponds to the reconstructed vector field.- key_added
The key that will be used for the velocity key in
.obsm
.- inplace
Whether to copy adata or modify it inplace.
- Returns
An
AnnData
object is updated/copied with thekey_added
in the.obsm
attribute which contains velocities.
- spateo.tdr.widgets.morphogenesis.morphofield_curvature(adata: anndata.AnnData, vf_key: str = 'VecFld_morpho', key_added: str = 'curvature', formula: int = 2, method: str = 'analytical', inplace: bool = True) Optional[anndata.AnnData] [source]#
Calculate curvature for each cell with the reconstructed vector field function.
- Parameters
- adata
AnnData object that contains the reconstructed vector field.
- vf_key
The key in
.uns
that corresponds to the reconstructed vector field.- key_added
The key that will be used for the curvature key in
.obs
and.obsm
.- formula
Which formula of curvature will be used, there are two formulas, so formula can be either
{1, 2}
. By default it is 2 and returns both the curvature vectors and the norm of the curvature. The formula one only gives the norm of the curvature.- method
The method that will be used for calculating curvature field, either
'analytical'
or'numerical'
.'analytical'
method uses the analytical expressions for calculating curvature while'numerical'
method uses numdifftools, a numerical differentiation tool, for computing curvature.'analytical'
method is much more efficient.- inplace
Whether to copy adata or modify it inplace.
- Returns
An
AnnData
object is updated/copied with thekey_added
in the.obs
and.obsm
attribute.The
key_added
in the.obs
which contains curvature. Thekey_added
in the.obsm
which contains curvature vectors.
- spateo.tdr.widgets.morphogenesis.morphofield_acceleration(adata: anndata.AnnData, vf_key: str = 'VecFld_morpho', key_added: str = 'acceleration', method: str = 'analytical', inplace: bool = True) Optional[anndata.AnnData] [source]#
Calculate acceleration for each cell with the reconstructed vector field function.
- Parameters
- adata
AnnData object that contains the reconstructed vector field.
- vf_key
The key in
.uns
that corresponds to the reconstructed vector field.- key_added
The key that will be used for the acceleration key in
.obs
and.obsm
.- method
The method that will be used for calculating acceleration field, either
'analytical'
or'numerical'
.'analytical'
method uses the analytical expressions for calculating acceleration while'numerical'
method uses numdifftools, a numerical differentiation tool, for computing acceleration.'analytical'
method is much more efficient.- inplace
Whether to copy adata or modify it inplace.
- Returns
An
AnnData
object is updated/copied with thekey_added
in the.obs
and.obsm
attribute.The
key_added
in the.obs
which contains acceleration. Thekey_added
in the.obsm
which contains acceleration vectors.
- spateo.tdr.widgets.morphogenesis.morphofield_curl(adata: anndata.AnnData, vf_key: str = 'VecFld_morpho', key_added: str = 'curl', method: str = 'analytical', inplace: bool = True) Optional[anndata.AnnData] [source]#
Calculate curl for each cell with the reconstructed vector field function.
- Parameters
- adata
AnnData object that contains the reconstructed vector field.
- vf_key
The key in
.uns
that corresponds to the reconstructed vector field.- key_added
The key that will be used for the torsion key in
.obs
.- method
The method that will be used for calculating torsion field, either
'analytical'
or'numerical'
.'analytical'
method uses the analytical expressions for calculating torsion while'numerical'
method uses numdifftools, a numerical differentiation tool, for computing torsion.'analytical'
method is much more efficient.- inplace
Whether to copy adata or modify it inplace.
- Returns
An
AnnData
object is updated/copied with thekey_added
in the.obs
and.obsm
attribute.The
key_added
in the.obs
which contains magnitude of curl. Thekey_added
in the.obsm
which contains curl vectors.
- spateo.tdr.widgets.morphogenesis.morphofield_torsion(adata: anndata.AnnData, vf_key: str = 'VecFld_morpho', key_added: str = 'torsion', method: str = 'analytical', inplace: bool = True) Optional[anndata.AnnData] [source]#
Calculate torsion for each cell with the reconstructed vector field function.
- Parameters
- adata
AnnData object that contains the reconstructed vector field.
- vf_key
The key in
.uns
that corresponds to the reconstructed vector field.- key_added
The key that will be used for the torsion key in
.obs
and.obsm
.- method
The method that will be used for calculating torsion field, either
'analytical'
or'numerical'
.'analytical'
method uses the analytical expressions for calculating torsion while'numerical'
method uses numdifftools, a numerical differentiation tool, for computing torsion.'analytical'
method is much more efficient.- inplace
Whether to copy adata or modify it inplace.
- Returns
An
AnnData
object is updated/copied with thekey_added
in the.obs
and.uns
attribute.The
key_added
in the.obs
which contains torsion. Thekey_added
in the.uns
which contains torsion matrix.
- spateo.tdr.widgets.morphogenesis.morphofield_divergence(adata: anndata.AnnData, vf_key: str = 'VecFld_morpho', key_added: str = 'divergence', method: str = 'analytical', vectorize_size: Optional[int] = 1000, inplace: bool = True) Optional[anndata.AnnData] [source]#
Calculate divergence for each cell with the reconstructed vector field function.
- Parameters
- adata
AnnData object that contains the reconstructed vector field.
- vf_key
The key in
.uns
that corresponds to the reconstructed vector field.- key_added
The key that will be used for the acceleration key in
.obs
and.obsm
.- method
The method that will be used for calculating acceleration field, either
'analytical'
or'numerical'
.'analytical'
method uses the analytical expressions for calculating acceleration while'numerical'
method uses numdifftools, a numerical differentiation tool, for computing acceleration.'analytical'
method is much more efficient.- vectorize_size
vectorize_size is used to control the number of samples computed in each vectorized batch.
If vectorize_size = 1, there’s no vectorization whatsoever.
If vectorize_size = None, all samples are vectorized.
- inplace
Whether to copy adata or modify it inplace.
- Returns
An
AnnData
object is updated/copied with thekey_added
in the.obs
attribute.The
key_added
in the.obs
which contains divergence.
- spateo.tdr.widgets.morphogenesis.morphofield_jacobian(adata: anndata.AnnData, vf_key: str = 'VecFld_morpho', key_added: str = 'jacobian', method: str = 'analytical', inplace: bool = True) Optional[anndata.AnnData] [source]#
Calculate jacobian for each cell with the reconstructed vector field function.
- Parameters
- adata
AnnData object that contains the reconstructed vector field.
- vf_key
The key in
.uns
that corresponds to the reconstructed vector field.- key_added
The key that will be used for the jacobian key in
.obs
and.obsm
.- method
The method that will be used for calculating jacobian field, either
'analytical'
or'numerical'
.'analytical'
method uses the analytical expressions for calculating jacobian while'numerical'
method uses numdifftools, a numerical differentiation tool, for computing jacobian.'analytical'
method is much more efficient.- inplace
Whether to copy adata or modify it inplace.
- Returns
An
AnnData
object is updated/copied with thekey_added
in the.obs
and.uns
attribute.The
key_added
in the.obs
which contains jacobian. Thekey_added
in the.uns
which contains jacobian tensor.
- spateo.tdr.widgets.morphogenesis.morphopath(adata: anndata.AnnData, vf_key: str = 'VecFld_morpho', key_added: str = 'fate_morpho', layer: str = 'X', direction: str = 'forward', interpolation_num: int = 250, t_end: Optional[Union[int, float]] = None, average: bool = False, cores: int = 1, inplace: bool = True, **kwargs) Optional[anndata.AnnData] [source]#
Prediction of cell developmental trajectory based on reconstructed vector field.
- Parameters
- adata
AnnData object that contains the reconstructed vector field function in the
.uns
attribute.- vf_key
The key in
.uns
that corresponds to the reconstructed vector field.- key_added
The key under which to add the dictionary Fate (includes
t
andprediction
keys).- layer
Which layer of the data will be used for predicting cell fate with the reconstructed vector field function.
- direction
The direction to predict the cell fate. One of the
forward
,backward
orboth
string.- interpolation_num
The number of uniformly interpolated time points.
- t_end
The length of the time period from which to predict cell state forward or backward over time.
- average
The method to calculate the average cell state at each time step, can be one of
origin
ortrajectory
. Iforigin
used, the average expression state from the init_cells will be calculated and the fate prediction is based on this state. Iftrajectory
used, the average expression states of all cells predicted from the vector field function at each time point will be used. Ifaverage
isFalse
, no averaging will be applied.- cores
Number of cores to calculate path integral for predicting cell fate. If cores is set to be > 1, multiprocessing will be used to parallel the fate prediction.
- inplace
Whether to copy adata or modify it inplace.
- **kwargs
Additional parameters that will be passed into the
fate
function.
- Returns
An
AnnData
object is updated/copied with thekey_added
dictionary in the.uns
attribute.The
key_added
dictionary which contains:t: The time at which the cell state are predicted. prediction: Predicted cells states at different time points. Row order corresponds to the element order in
t. If init_states corresponds to multiple cells, the expression dynamics over time for each cell is concatenated by rows. That is, the final dimension of prediction is (len(t) * n_cells, n_features). n_cells: number of cells; n_features: number of genes or number of low dimensional embeddings. Of note, if the average is set to be True, the average cell state at each time point is calculated for all cells.