Run DoRothEA transcription factor activity inference

Usage

RunDorothea(
  srt,
  assay = NULL,
  layer = "data",
  species = c("Homo_sapiens", "Mus_musculus"),
  input_species = NULL,
  geneID_from_IDtype = "symbol",
  geneID_to_IDtype = "symbol",
  homolog_params = list(),
  confidence = c("A", "B", "C"),
  regulons = NULL,
  method = c("ulm", "viper", "wmean"),
  minsize = 5,
  options = list(),
  assay_name = "dorothea",
  new_assay = TRUE,
  add_meta = TRUE,
  verbose = TRUE
)

Arguments

srt: A Seurat object.
assay: Which assay to use. If NULL, the default assay of the Seurat object will be used. When the object also contains ChromatinAssay, the default assay and additional ChromatinAssay will be preprocessed sequentially.
layer: Assay layer used as the expression matrix.
species: Species used to select bundled DoRothEA regulons. DoRothEA only provides human and mouse regulons. For other input species, set input_species and project expression values to this regulon species through homologous gene conversion before activity inference.
input_species: Species of the input expression features. If NULL, the input is assumed to use the same gene namespace as species. When this differs from species, expression features are converted with ConvertHomologs before DoRothEA activity inference.
geneID_from_IDtype, geneID_to_IDtype: Gene identifier types passed to ConvertHomologs for cross-species projection. For bundled DoRothEA regulons, geneID_to_IDtype should normally remain "symbol".
homolog_params: Additional named arguments passed to ConvertHomologs when input_species differs from species, such as Ensembl_version, biomart, mirror, max_tries, multi_mapping, and collapse_fun.
confidence: DoRothEA confidence levels to keep.
regulons: Optional regulon table with tf, target, mor, and confidence columns. If NULL, bundled dorothea_hs or dorothea_mm data are loaded from the dorothea package.
method: Activity inference backend from decoupleR.
minsize: Minimum regulon size passed to decoupleR.
options: Additional named options passed to the selected decoupleR function.
assay_name: Name of the assay used to store TF activity scores.
new_assay: Whether to store TF activity scores as a new assay.
add_meta: Whether to also write TF activity scores to srt@meta.data with the assay_name prefix for direct plotting with FeatureDimPlot().
verbose: Whether to print the message. Default is TRUE.

Value

A Seurat object with DoRothEA results stored in srt@tools[["Dorothea"]], optionally TF activity scores stored in srt@meta.data, and optionally a TF activity assay when new_assay = TRUE. For cross-species runs, the homolog projection summary is stored in srt@tools[["Dorothea"]]$homolog_conversion.

References

Garcia-Alonso, L., Holland, C.H., Ibrahim, M.M., Turei, D., and Saez-Rodriguez, J. (2019). Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Research, 29, 1363-1375. doi:10.1101/gr.240663.118

Badia-i-Mompel, P., Velez Santiago, J., Braunger, J., Geiss, C., Dimitrov, D., Muller-Dott, S., Taus, P., Dugourd, A., Holland, C.H., Ramirez Flores, R.O., and Saez-Rodriguez, J. (2022). decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinformatics Advances, 2, vbac016. doi:10.1093/bioadv/vbac016

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(
  pancreas_sub,
  verbose = FALSE
)
#> ℹ [2026-07-02 09:37:03] Skip `log1p()` because `layer = data` is not "counts"

pancreas_sub <- RunDorothea(
  pancreas_sub,
  layer = "counts",
  species = "Mus_musculus",
  confidence = c("A", "B", "C"),
  method = "ulm",
  minsize = 5,
  new_assay = FALSE
)
#> ℹ [2026-07-02 09:37:10] Run DoRothEA/decoupleR with 12895 regulon edges
#> ℹ [2026-07-02 09:37:21] DoRothEA TF activity scores stored in <Seurat> metadata

pancreas_sub@tools$Dorothea$regulon_summary
#>   n_tfs n_targets n_edges confidence
#> 1   273      5130   12895      A,B,C
head(pancreas_sub@tools$Dorothea$result)
#>   statistic source        condition     score      p_value
#> 1       ulm     Ar AAACCTGAGCCTTGAT 1.9230738 0.0544885134
#> 2       ulm     Ar AAACCTGGTAAGTGGC 3.8375556 0.0001247429
#> 3       ulm     Ar AAACGGGAGATATGGT 2.3175899 0.0204841737
#> 4       ulm     Ar AAACGGGCAAAGAATC 0.9565534 0.3388071679
#> 5       ulm     Ar AAACGGGGTACAGTTC 0.6864447 0.4924426684
#> 6       ulm     Ar AAACGGGTCAGCTCTC 1.0246312 0.3055527415

activity_cols <- head(
  grep("^dorothea_", colnames(pancreas_sub@meta.data), value = TRUE),
  2
)
head(pancreas_sub@meta.data[, activity_cols, drop = FALSE])
#>                  dorothea_Ar dorothea_Arid2
#> AAACCTGAGCCTTGAT   1.9230738     -0.5482112
#> AAACCTGGTAAGTGGC   3.8375556     -0.2029179
#> AAACGGGAGATATGGT   2.3175899     -0.1763966
#> AAACGGGCAAAGAATC   0.9565534     -0.5784380
#> AAACGGGGTACAGTTC   0.6864447     -0.2284842
#> AAACGGGTCAGCTCTC   1.0246312     -0.6278209

FeatureDimPlot(
  pancreas_sub,
  features = activity_cols,
  reduction = "StandardUMAP2D",
  ncol = 2
)