`spateo.tools.cluster.utils`#

Module Contents#

Functions#

`compute_pca_components`(→ Tuple[Any, int, float])	Calculate the inflection point of the PCA curve to
`pca_spateo`(adata[, X_data, n_pca_components, pca_key, ...])	Do PCA for dimensional reduction.
`pearson_residuals`(adata[, n_top_genes, subset, theta, ...])	Preprocess UMI count data with analytic Pearson residuals.
`integrate`(→ anndata.AnnData)	Concatenating all anndata objects.
`ecp_silhouette`(→ float)	Here we evaluate the clustering performance by calculating the Silhouette Coefficient.
`spatial_adj`(adata[, spatial_key, pca_key, e_neigh, ...])	Calculate the adjacent matrix based on a neighborhood graph of gene expression space

Attributes#

to_dense_matrix

spateo.tools.cluster.utils.to_dense_matrix[source]#

spateo.tools.cluster.utils.compute_pca_components(matrix: numpy.ndarray | scipy.sparse.spmatrix, random_state: int | None = 1, save_curve_img: str | None = None) → Tuple[Any, int, float][source]#

Calculate the inflection point of the PCA curve to obtain the number of principal components that the PCA should retain.

Parameters:

matrix: A dense or sparse matrix.
save_curve_img: If save_curve_img != None, save the image of the PCA curve and inflection points.

Returns:

The number of principal components that PCA should retain. new_components_stored: Percentage of variance explained by the retained principal components.

Return type:

new_n_components

Do PCA for dimensional reduction.

Parameters:

adata: An Anndata object.
X_data: The user supplied data that will be used for dimension reduction directly.
n_pca_components: The number of principal components that PCA will retain. If none, will Calculate the inflection point of the PCA curve to obtain the number of principal components that the PCA should retain.
pca_key: Add the PCA result to obsm using this key.
genes: The list of genes that will be used to subset the data for dimension reduction and clustering. If None, all genes will be used.
layer: The layer that will be used to retrieve data for dimension reduction and clustering. If None, will use adata.X.

Returns:

The processed AnnData, where adata.obsm[pca_key] stores the PCA result.

Return type:

adata_after_pca

spateo.tools.cluster.utils.pearson_residuals(adata: anndata.AnnData, n_top_genes: int | None = 3000, subset: bool = False, theta: float = 100, clip: float | None = None, check_values: bool = True)[source]#

Preprocess UMI count data with analytic Pearson residuals.

Pearson residuals transform raw UMI counts into a representation where three aims are achieved:

1.Remove the technical variation that comes from differences in total counts between cells; 2.Stabilize the mean-variance relationship across genes, i.e. ensure that biological signal from both low and

high expression genes can contribute similarly to downstream processing

3.Genes that are homogeneously expressed (like housekeeping genes) have small variance, while genes that are: differentially expressed (like marker genes) have high variance

Parameters:

adata

An anndata object.

n_top_genes

Number of highly-variable genes to keep.

subset

Inplace subset to highly-variable genes if True otherwise merely indicate highly variable genes.

theta

The negative binomial overdispersion parameter theta for Pearson residuals. Higher values correspond to less overdispersion (var = mean + mean^2/theta), and theta=np.Inf corresponds to a Poisson model.

clip

Determines if and how residuals are clipped: * If None, residuals are clipped to the interval [-sqrt(n), sqrt(n)], where n is the number of cells

in the dataset (default behavior).

If any scalar c, residuals are clipped to the interval [-c, c]. Set clip=np.Inf for no clipping.

check_values

Check if counts in selected layer are integers. A Warning is returned if set to True.

Returns:

Updates adata with the field adata.obsm["pearson_residuals"], containing pearson_residuals.

spateo.tools.cluster.utils.integrate(adatas: List[anndata.AnnData], batch_key: str = 'slices', fill_value: int | float = 0) → anndata.AnnData[source]#

Concatenating all anndata objects.

Parameters:

adatas: AnnData matrices to concatenate with.
batch_key: Add the batch annotation to obs using this key.
fill_value: Scalar value to fill newly missing values in arrays with.

Returns:

The concatenated AnnData, where adata.obs[batch_key] stores a categorical variable labeling the batch.

Return type:

integrated_adata

spateo.tools.cluster.utils.ecp_silhouette(matrix: numpy.ndarray | scipy.sparse.spmatrix, cluster_labels: numpy.ndarray) → float[source]#

Here we evaluate the clustering performance by calculating the Silhouette Coefficient. The silhouette analysis is used to choose an optimal value for clustering resolution.

The Silhouette Coefficient is a widely used method for evaluating clustering performance, where a higher Silhouette Coefficient score relates to a model with better defined clusters and indicates a good separation between the celltypes.

Advantages of the Silhouette Coefficient:

The score is bounded between -1 for incorrect clustering and +1 for highly dense clustering. Scores around zero indicate overlapping clusters.
The score is higher when clusters are dense and well separated, which relates to a standard concept of a cluster.

Original Code Repository: https://scikit-learn.org/stable/modules/clustering.html#silhouette-coefficient

Parameters:

matrix: A dense or sparse matrix of feature.
cluster_labels: A array of labels for each cluster.

Returns:

Mean Silhouette Coefficient for all clusters.

Examples

>>> silhouette_score(matrix=adata.obsm["X_pca"], cluster_labels=adata.obs["leiden"].values)

spateo.tools.cluster.utils.spatial_adj(adata: anndata.AnnData, spatial_key: str = 'spatial', pca_key: str = 'pca', e_neigh: int = 30, s_neigh: int = 6, n_pca_components: int = 30)[source]#: Calculate the adjacent matrix based on a neighborhood graph of gene expression space and a neighborhood graph of physical space.

spateo.tools.cluster.utils#

Module Contents#

Functions#

Attributes#

`spateo.tools.cluster.utils`#