API

Import SpatialDM as:

import spatialdm as sdm

Functions

spatialdm.stats.rbfweight(X_loc, l=None, cutoff=0.1, n_neighbors=None, n_neighbor_layers=6, single_cell=False, eff_dist=None)

Compute weight matrix based on radial basis function. cutoff & n_neighbors are two alternative options to restrict signaling range.

Parameters:

  • l – radial basis function parameter, need to be customized for optimal weight gradient and to restrain the range of signaling before downstream processing.

  • cutoff – (for secreted signaling) minimum weight to be kept from the rbf weight matrix. Weight below cutoff will be made zero

  • n_neighbors – (for secreted signaling) number of neighbors per spot from the rbf weight matrix.

  • n_neighbor_layers – (for adjacent signaling) number of neighbor layers per spot from the rbf weight matrix. Non-neighbors will be made 0

  • single_cell – if single_cell, diagonal will be made 0.

  • eff_dist – the alternative way to set l parameter by restricting the effective distance.

Returns:

secreted signaling weight matrix: W_spatial, and adjacent signaling weight matrix: KNN_connectivities

Examples

>>> import numpy as np
>>> from spatialdm.stats import rbfweight
>>> X_loc = np.vstack([np.repeat(range(10), 10), np.tile(range(10), 10)]).T
>>> spatial_W, KNN_connect = rbfweight(X_loc, l=1.2, n_neighbors=16)

>>> import matplotlib.pyplot as plt
>>> plt.scatter(X_loc[:, 0], X_loc[:, 1], c=spatial_W.toarray()[35], s=100)
>>> plt.show()

(Source code, png)

_images/api-1.png
spatialdm.stats.Moran_R(X, Y, spatial_W, standardise=True, nproc=1)

Computing Moran’s R for pairs of variables

Parameters:

  • X – Variable 1, (n_sample, n_variables) or (n_sample, )

  • Y – Variable 2, (n_sample, n_variables) or (n_sample, )

  • spatial_W – spatial weight matrix, sparse or dense, (n_sample, n_sample)

  • nproc – default to 1. Numpy may use more without much speedup.

Returns:

(Moran’s R, z score and p values)

Examples

>>> import numpy as np
>>> from spatialdm.stats import rbfweight, Moran_R
>>> np.random.seed(0)
>>> X1 = np.random.rand(100, 5)
>>> X2 = np.random.rand(100, 5)
>>> X2[:-1, 0], X2[:, 1], X2[1:, 2] = X1[1:, 0], X1[:, 1], X1[:-1, 2]
>>> X2 = X2 + 0.01 * np.random.rand(100, 5)
>>> X_loc = np.vstack([np.repeat(range(10), 10), np.tile(range(10), 10)]).T
>>> spatial_W, KNN_connect = rbfweight(X_loc, l=1.2, n_neighbors=16)
>>> R, z, p = Moran_R(X1, X2, spatial_W)
>>> print(R, z, p)

>>> import matplotlib.pyplot as plt
>>> fig = plt.figure(figsize=(6, 3))
>>> plt.subplot(1, 2, 1)
>>> plt.scatter(X_loc[:, 0], X_loc[:, 1], c=X1[:, 0], s=100)
>>> plt.subplot(1, 2, 2)
>>> plt.scatter(X_loc[:, 0], X_loc[:, 1], c=X2[:, 0], s=100)
>>> plt.tight_layout()
>>> plt.show()

(Source code, png)

_images/api-2.png

Datasets

The spatialdm.datasets module provides functions for loading and accessing spatial transcriptomics datasets. The following datasets are currently available:

  • dataset.melanoma(): Sample 1 rep 2 human melanoma slide from Thrane’s melanoma dataset.

  • dataset.SVZ(): Mouse sub-ventricular zone (SVZ) from Eng’s seqfish+ dataset.

  • dataset.A1(): Adult colon with colorectal cancer or IBD, pcw: Adult.

  • dataset.A2(): Adult colon with colorectal cancer or IBD, pcw: Adult.

  • dataset.A3(): Fetal colon, pcw: 12PCW.

  • dataset.A4(): Fetal colon, pcw: 19PCW.

  • dataset.A6(): Fetal small intestine, pcw: 12PCW.

  • dataset.A7(): Fetal small intestine, pcw: 12PCW.

  • dataset.A8(): Fetal small intestine, pcw: 12PCW.

  • dataset.A9(): Fetal small intestine, pcw: 12PCW.

Usage

To use the spatialdm.datasets module, simply import it as follows:

from spatialdm.datasets import dataset

Then, you can load a dataset using the corresponding function. For example, to load the melanoma dataset:

adata = dataset.melanoma()

This will return an anndata object containing the expression data for the melanoma dataset in .X, the cell type decomposition values in .obs, and the spatial coordinates in .obsm[‘spatial’].