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Build the final regulons

build_regulons.Rd

Turns the TF to gene table into the gene sets you hand to aucell_sc(). tf_to_genes_motif_enrichment() only flags which pairs survived the CisTarget leading edge, it does not remove anything, so this is the step that applies that filter.

Following SCENIC, the TF is added back to its own target list and regulons below min_genes are dropped. Note the CisTarget leading edge usually cuts hard: a module of a few hundred candidate targets typically ends up as a regulon of a few dozen.

Usage

build_regulons(
  x,
  use_leading_edge = TRUE,
  add_tf = TRUE,
  min_genes = 10L,
  mode = c("activating", "repressing", "both"),
  .verbose = TRUE
)

# S3 method for class 'ScenicGrn'
build_regulons(
  x,
  use_leading_edge = TRUE,
  add_tf = TRUE,
  min_genes = 10L,
  mode = c("activating", "repressing", "both"),
  .verbose = TRUE
)

Arguments

x

ScenicGrn object. You need to have run tf_to_genes_motif_enrichment() for the leading edge filter to do anything.

use_leading_edge

Boolean. Shall only the TF to gene pairs inside the CisTarget leading edge be kept. Defaults to TRUE.

add_tf

Boolean. Shall the TF be added to its own regulon. Defaults to TRUE, following SCENIC.

min_genes

Integer. Regulons with fewer genes than this are dropped. Defaults to 10L, the SCENIC value.

mode

String. Which links to keep, based on the cor_sign column added by tf_to_genes_correlations(). One of c("activating", "repressing", "both"). With "both" the regulon names get a "_pos" or "_neg" suffix so the two stay distinct. Defaults to "activating".

.verbose

Boolean. Controls verbosity of the function.

Value

A named list of character vectors, one per regulon.

References

Aibar, et al., Nat Methods, 2017

Examples

# turn the signed TF to gene table into regulon gene sets
sc <- demo_single_cells()
grn <- scenic_grn_sc(
  sc,
  tf_ids = sprintf("gene_%02d", 1:5),
  scenic_params = params_scenic(
    min_counts = 1L,
    learner_params = list(n_trees = 20L)
  ),
  .verbose = FALSE
)
grn <- identify_tf_to_genes(
  grn,
  method = "top_k",
  k_tfs = 3L,
  .verbose = FALSE
)
grn <- tf_to_genes_correlations(grn, object = sc, .verbose = FALSE)
lengths(build_regulons(
  grn,
  use_leading_edge = FALSE,
  min_genes = 3L,
  .verbose = FALSE
))
#> gene_01 gene_02 gene_03 gene_04 gene_05 
#>      10       4      17      13       5 

unlink(sc@dir_data, recursive = TRUE, force = TRUE)