spateo.tdr.morphometrics.morphofield

Submodules

• spateo.tdr.morphometrics.morphofield.gaussian_process
• spateo.tdr.morphometrics.morphofield.sparsevfc
• spateo.tdr.morphometrics.morphofield.trajectory

Functions

morphofield_gp(→ Optional[anndata.AnnData])	Calculating and predicting the vector field during development by the Gaussian Process method.
cell_directions(→ Tuple[Optional[anndata.AnnData], ...)	Obtain the optimal mapping relationship and developmental direction between cells for samples between continuous developmental stages.
morphofield_sparsevfc(, inplace, ...)	Calculating and predicting the vector field during development by the Kernel method (sparseVFC).
morphopath(→ Optional[anndata.AnnData])	Prediction of cell developmental trajectory based on reconstructed vector field.

Package Contents

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, nonrigid_only: bool = False, inplace: bool = True) → anndata.AnnData | None

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].

nonrigid_only

If True, only the nonrigid part of the vector field will be calculated.

inplace

Whether to copy adata or modify it inplace.

Returns:

An AnnData object is updated/copied with the key_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]

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}-The X_{key_added} that will be used for the coordinates of the cell that maps optimally in the next stage.

• V_{key_added}-The V_{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 when alpha =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 and float64.

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 the X_{key_added} and V_{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

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 the key_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, nonrigid_only: bool = False, inplace: bool = True, **kwargs) → anndata.AnnData | None

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 and prediction 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 or both 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 or trajectory. If origin used, the average expression state from the init_cells will be calculated and the fate prediction is based on this state. If trajectory used, the average expression states of all cells predicted from the vector field function at each time point will be used. If average is False, 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.

nonrigid_only

If True, only the nonrigid part of the vector field will be calculated.

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 the key_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.