spateo.tools.cluster.utils¶
Attributes¶
Functions¶
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Calculate the inflection point of the PCA curve to |
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Do PCA for dimensional reduction. |
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Preprocess UMI count data with analytic Pearson residuals. |
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Concatenating all anndata objects. |
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Here we evaluate the clustering performance by calculating the Silhouette Coefficient. |
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Calculate the adjacent matrix based on a neighborhood graph of gene expression space |
Module Contents¶
- 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
- spateo.tools.cluster.utils.pca_spateo(adata: anndata.AnnData, X_data: numpy.ndarray | None = None, n_pca_components: int | None = None, pca_key: str | None = 'X_pca', genes: list | None = None, layer: str | None = None, random_state: int | None = 1)[source]¶
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.