spateo.tdr.morphometrics.morphofield
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Submodules#
Package Contents#
Functions#
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Calculating and predicting the vector field during development by the Gaussian Process method. |
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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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Prediction of cell developmental trajectory based on reconstructed vector field. |
- spateo.tdr.morphometrics.morphofield.morphofield_gp(adata: anndata.AnnData, spatial_key: str = 'align_spatial', vf_key: str = 'VecFld_morpho', NX: numpy.ndarray | None = None, grid_num: List[int] | None = None, inplace: bool = True) anndata.AnnData | None [source]#
Calculating and predicting the vector field during development by the Gaussian Process method.
- 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.- vf_key
The key in
.uns
that corresponds to the reconstructed vector field.- 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]
.- inplace
Whether to copy adata or modify it inplace.
- Returns:
An
AnnData
object is updated/copied with thekey_added
dictionary in the.uns
attribute.The
key_added
dictionary which contains: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. method: The method of learning vector field. Here method == ‘gaussian_process’.
- spateo.tdr.morphometrics.morphofield.cell_directions(adataA: anndata.AnnData, adataB: anndata.AnnData, layer: str = 'X', genes: list | numpy.ndarray | None = 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) Tuple[anndata.AnnData | None, numpy.ndarray] [source]#
Obtain the optimal mapping relationship and developmental direction between cells for samples between continuous developmental stages.
- Parameters:
- adataA
AnnData object of sample A from continuous developmental stages.
- adataB
AnnData object of sample B 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 of sample A is updated/copied with theX_{key_added}
andV_{key_added}
in the.obsm
attribute. A pi metrix.
- spateo.tdr.morphometrics.morphofield.morphofield_sparsevfc(adata: anndata.AnnData, spatial_key: str = 'align_spatial', V_key: str = 'V_mapping', key_added: str = 'VecFld_morpho', NX: numpy.ndarray | None = None, grid_num: List[int] | None = None, M: int = 100, lambda_: float = 0.02, lstsq_method: str = 'scipy', min_vel_corr: float = 0.8, restart_num: int = 10, restart_seed: List[int] | Tuple[int] | numpy.ndarray = (0, 100, 200, 300, 400), inplace: bool = True, **kwargs) anndata.AnnData | None [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.morphometrics.morphofield.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: int | float | None = None, average: bool = False, cores: int = 1, inplace: bool = True, **kwargs) anndata.AnnData | None [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.