Calculate the local auto-correlation of a gene

This method implements the HotSpot approach (see DeTomaso, et al.) to calculate the auto-correlation of a given gene in the kNN graph based on the chosen embedding. This can be used to identify genes that have strong local correlations and vary across the kNN graph.

Usage

hotspot_autocor_sc(
  object,
  embd_to_use = "pca",
  hotspot_params = params_sc_hotspot(),
  no_embd_to_use = NULL,
  cells_to_take = NULL,
  genes_to_take = NULL,
  streaming = FALSE,
  random_seed = 42L,
  .verbose = TRUE
)

Arguments

object

SingleCells class.

embd_to_use

String. The embedding to use. Defaults to "pca".

hotspot_params

List with hotspot parameters, see params_sc_hotspot() with the following elements:

• model - String. Which of the available models to use for the gene expression. Choices are one of c("danb", "normal", "bernoulli").

• normalise - Boolean. Shall the data be normalised.

• knn - List of kNN parameters. See params_knn_defaults() for available parameters and their defaults.

no_embd_to_use

Optional integer. Number of embedding dimensions to use. If NULL all will be used.

cells_to_take

Optional string vector. If you want to only use selected cells. If NULL will default to all cells_to_keep in the class.

genes_to_take

Optional string vector. If you wish to limit the search to a subset of genes. If NULL will default to all genes in the class.

streaming

Boolean. Shall the data be streamed in. Useful for larger data sets.

random_seed

Integer. Used for reproducibility.

.verbose

Boolean. Controls verbosity of the function.

Value

A data.table with the auto-correlations on a per gene basis and various statistics.

Details

Should a gene not be found in sufficient cells, the gene will be automatically filtered out from the results. This can occur for example if you have filtered out the cells that contain a given gene. The underlying genes are still available, but the cells that might contain them are not included.