spateo.tools.coarse_align#
Module Contents#
Functions#
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A port of MATLAB's procrustes function to Numpy. |
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Translate the x/y coordinates of data points by the translating the centroid to the origin. Then data will be |
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Use pca to rotate a coordinate matrix to reveal the largest variance on each dimension. |
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Coarsely align the slices based on the major axis, identified via PCA |
- spateo.tools.coarse_align.procrustes(X: numpy.ndarray, Y: numpy.ndarray, scaling: bool = True, reflection: str = 'best') Tuple[float, numpy.ndarray, dict][source]#
A port of MATLAB’s procrustes function to Numpy.
This function will need to be rewritten just with scipy.spatial.procrustes and scipy.linalg.orthogonal_procrustes later.
Procrustes analysis determines a linear transformation (translation, reflection, orthogonal rotation and scaling) of the points in Y to best conform them to the points in matrix X, using the sum of squared errors as the goodness of fit criterion.
d, Z, [tform] = procrustes(X, Y)
- Parameters:
- X
matrices of target and input coordinates. they must have equal numbers of points (rows), but Y may have fewer dimensions (columns) than X. scaling: if False, the scaling component of the transformation is forced to 1
- Y
matrices of target and input coordinates. they must have equal numbers of points (rows), but Y may have fewer dimensions (columns) than X. scaling: if False, the scaling component of the transformation is forced to 1
- reflection
if ‘best’ (default), the transformation solution may or may not include a reflection component, depending on which fits the data best. setting reflection to True or False forces a solution with reflection or no reflection respectively.
- Returns:
- the residual sum of squared errors, normalized according to a measure of the scale of X,
((X - X.mean(0))**2).sum()
Z: the matrix of transformed Y-values tform: a dict specifying the rotation, translation and scaling that maps X –> Y
- Return type:
d
- spateo.tools.coarse_align.AffineTrans(x: numpy.ndarray, y: numpy.ndarray, centroid_x: float, centroid_y: float, theta: Tuple[None, float], R: Tuple[None, numpy.ndarray]) Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray][source]#
Translate the x/y coordinates of data points by the translating the centroid to the origin. Then data will be rotated with angle theta.
- Parameters:
- x
x coordinates for the data points (bins). 1D np.array.
- y
y coordinates for the data points (bins). 1D np.array.
- centroid_x
x coordinates for the centroid of data points (bins).
- centroid_y
y coordinates for the centroid of data points (bins).
- theta
the angle of rotation. Unit is is in np.pi (so 90 degree is np.pi / 2 and value is defined in the clockwise direction.
- R
the rotation matrix. If R is provided, theta will be ignored.
- Returns:
The translation matrix used in affine transformation. T_r: The rotation matrix used in affine transformation. trans_xy_coord: The matrix that stores the translated and rotated coordinates.
- Return type:
T_t
- spateo.tools.coarse_align.pca_align(X: numpy.ndarray) Tuple[numpy.ndarray, numpy.ndarray][source]#
Use pca to rotate a coordinate matrix to reveal the largest variance on each dimension.
This can be used to correct, for example, embryo slices to the right orientation.
- Parameters:
- X
The input coordinate matrix.
- Returns:
The rotated coordinate matrix that has the major variances on each dimension. R: The rotation matrix that was used to convert the input X matrix to output Y matrix.
- Return type:
Y
- spateo.tools.coarse_align.align_slices_pca(adata: anndata.AnnData, spatial_key: str = 'spatial', inplace: bool = False, result_key: Tuple[None, str] = None) None[source]#
Coarsely align the slices based on the major axis, identified via PCA
- Parameters:
- adata
the input adata object that contains the spatial key in .obsm.
- spatial_key
the key in .obsm that points to the spatial information.
- inplace
whether the spatial coordinates will be inplace updated or a new key `spatial_.
- result_key
when inplace is False, this points to the key in .obsm that stores the corrected spatial coordinates.
- Returns:
Nothing but updates the spatial coordinates either inplace or with the result_key key based on the major axis identified via PCA.