Calculate gene to gene spatial correlations (for meta cells)

This function implements the HotSpot gene <> gene local correlation functionality from HotSpot, see DeTomaso, et al. This version works on MetaCell counts which are stored in memory directly.

Three dense metacells x genes blocks are live at once, so keep genes_to_use to the panel actually of interest rather than the whole transcriptome.

Usage

rs_mc_hotspot_gene_cor(
  sparse_data,
  embd,
  knn_data,
  hotspot_params,
  cells_to_keep,
  genes_to_use,
  verbose,
  seed
)

Arguments

sparse_data: A named list that needs to have data, indptr, indices, nrow, ncol and cs_type. Shape is (metacells, genes) and the data are the raw counts.
embd: Numerical matrix. The embedding matrix from which to generate the kNN graph. Needs one row per entry of cells_to_keep.
knn_data: Optional list. This contains pre-computed kNN data (indices, dist, k) and the dist_metric it was built with. The user has to ensure consistency! If provided, this will be used rather than a graph built from the parameter list.
hotspot_params: List. The HotSpot parameter list. The kNN parameters are only read when no knn_data is provided; normalise is unused on this path.
cells_to_keep: Integer vector. 0-index vector indicating which meta cells to include in the analysis. Ensure that this is of same order/length as the embedding matrix.
genes_to_use: Integer vector. 0-index vector indicating which genes to include.
verbose: Integer. 0L - quiet; 1L - normal verbosity; 2L - detailed verbosity.
seed: Integer. Random seed for reproducibility.

Value

A list with the following elements.

cor - The genes x genes local correlation matrix, in the order of genes_to_use.
z - The Z-scores of these local correlations, same shape.

References

DeTomaso, et al., Cell Systems, 2021