c2v.tl.graph_associations#
- c2v.tl.graph_associations(adata, layer=None, graph_key='connectivities', n_pcs=50, adj_method='fdr_bh', key_added='X_gPCA')#
Computes the supervised principal components analysis (sPCA) of the data [10.1016/j.patcog.2010.12.015]. Shortly, it finds the axes that maximize the correlation between the gene expression and the graph Laplacian (one can see it as an autocorrelation-aware PCA).
- Parameters:
- adata sc.AnnData
AnnData object containing the data.
- layer None | str
Layer to use for the analysis.
- graph_key str
Key to use for the graph.
- n_pcs int
Number of principal components to compute.
- adj_method str
Method for adjusting p-values.
- key_added str
Key to use for adding the results to the AnnData object.
- Return type:
- Returns:
None