Skip to contents

Calculate VISION scores (with auto-correlation scores)

vision_w_autocor_sc.Rd

Calculates VISION-type scores for pathways based on DeTomaso, et al. Compared to other score types, you can also calculate delta-type scores between positive and negative gene indices, think epithelial vs mesenchymal gene signature, etc. Additionally, this function also calculates the auto- correlation values, answering the question if a given signature shows non- random enrichment on the kNN graph. The kNN graph (and distance measures) will be generated on-the-fly based on the embedding you wish to use.

Usage

vision_w_autocor_sc(
  object,
  gs_list,
  embd_to_use,
  no_embd_to_use = NULL,
  vision_params = params_sc_vision(),
  streaming = FALSE,
  random_seed = 42L,
  .verbose = TRUE
)

Arguments

object

SingleCells class.

gs_list

Named nested list. The elements have the gene identifiers of the respective gene sets and have the option to have a "pos" and "neg" gene sets. The names need to be part of the variables of the SingleCells class.

embd_to_use

String. The embedding to use. Whichever you chose, it needs to be part of the object.

no_embd_to_use

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

vision_params

List with vision parameters, see params_sc_vision() with the following elements:

  • n_perm - Integer. Number of random permutations

  • n_cluster - Integer. Number of random clusters to generate to associate each set with.

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

streaming

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

random_seed

Integer. The random seed.

.verbose

Boolean. Controls the verbosity of the function.

Value

Matrix of cells x signatures with the VISION pathway scores as values.

References

DeTomaso, et al., Nat. Commun., 2019