Extract the TF to gene data from the ScenicGrn object

Usage

get_cistarget_res(x)

# S3 method for class 'ScenicGrn'
get_cistarget_res(x)

Arguments

x: ScenicGrn object from which to extract the TF to gene data.table.

Value

data.table with TF to gene information

Examples

# CisTarget against a synthetic ranking database
sc <- demo_single_cells()
tfs <- sprintf("gene_%02d", 1:5)
grn <- scenic_grn_sc(
  sc,
  tf_ids = tfs,
  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
)
genes <- get_gene_names(sc)
targets <- split(get_tf_to_gene(grn)$gene, get_tf_to_gene(grn)$tf)
rankings <- matrix(
  vapply(
    tfs,
    function(tf) {
      as.integer(rank(!(genes %in% targets[[tf]]), ties.method = "first"))
    },
    integer(length(genes))
  ),
  nrow = length(genes),
  dimnames = list(genes, sprintf("motif_%s", tfs))
)
annot <- data.table::data.table(
  motif = sprintf("motif_%s", tfs),
  TF = tfs,
  annotationSource = factor(
    c(rep("directAnnotation", 4), "inferredBy_MotifSimilarity")
  )
)
grn <- tf_to_genes_motif_enrichment(
  grn,
  motif_rankings = rankings,
  annot_data = annot,
  cis_target_params = params_cistarget(
    auc_threshold = 1,
    nes_threshold = 1,
    high_conf_cats = "directAnnotation",
    low_conf_cats = "inferredBy_MotifSimilarity"
  ),
  .verbose = FALSE
)
get_cistarget_res(grn)[, c("gs_name", "motif", "nes")]
#> Key: <motif>
#>    gs_name         motif      nes
#>     <char>        <char>    <num>
#> 1: gene_01 motif_gene_01 1.243565
#> 2: gene_02 motif_gene_02 1.666598
#> 3: gene_03 motif_gene_03 1.038259
#> 4: gene_04 motif_gene_04 1.063332
#> 5: gene_05 motif_gene_05 1.608758

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