Run SCENIC gene regulatory network analysis

Usage

RunSCENIC(
  srt,
  assay = NULL,
  layer = "counts",
  ranking_dbs = NULL,
  motif_annotations = NULL,
  regulators = NULL,
  targets = NULL,
  work_dir = "scenic_output/",
  species = c("Homo_sapiens", "Mus_musculus", "Drosophila_melanogaster"),
  genome = NULL,
  data_dir = NULL,
  prefix = "scenic",
  min_expr_cells = 3,
  min_regulon_size = 10,
  include_negative_regulons = FALSE,
  backend = c("cpp", "python"),
  n_rounds = 5000,
  learning_rate = 0.01,
  max_depth = 3,
  max_features = 0.1,
  subsample = 0.9,
  early_stop_window_length = 25,
  cores = 1,
  seed = 1234,
  force = FALSE,
  assay_name = "scenic",
  tool_name = "SCENIC",
  return_seurat = TRUE,
  envname = NULL,
  conda = "auto",
  prepare_env = 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 count matrix.
ranking_dbs: Character vector of cisTarget ranking feather files. If NULL, the gene-based v10 cisTarget ranking databases are prepared from species.
motif_annotations: Motif annotation table used by scenic ctx. If NULL, the v10 motif2tf table is prepared from species.
regulators: Transcription factors used as candidate regulators in GRNBoost2. This can be a character vector of gene names or one text file. If NULL, the cisTarget TF list is prepared from species.
targets: Optional target genes used to restrict the GRN output. This can be a character vector of gene names or one text file. Regulator expression is still kept as predictor input for GRNBoost2, and the adjacency table passed to scenic ctx is filtered to these targets.
work_dir: Directory used for SCENIC input and output files.
species: Species used to select cisTarget reference files when ranking_dbs, motif_annotations, or regulators is NULL. Supported values are "Homo_sapiens", "Mus_musculus", and "Drosophila_melanogaster".
genome: Genome build used to select cisTarget reference files when automatic references are prepared. Human supports "hg38" (default) and "hg19". Mouse and fly currently use "mm10" and "dm6", respectively.
data_dir: Directory used to cache automatically prepared SCENIC reference files. If NULL, files are stored under tools::R_user_dir("scop", "data")/SCENIC/<species>.
prefix: Prefix for SCENIC output files.
min_expr_cells: Minimum number of cells where a gene must be detected before GRNBoost2. To run SCENIC on metacells, first create a metacell-level object with RunMetaCell() and pass that object to RunSCENIC().
min_regulon_size: Minimum regulon size kept after scenic ctx.
include_negative_regulons: Whether the C++ backend should also build negatively correlated regulons and label them as TF(-). The default matches pySCENIC's positive-regulon workflow and labels C++ regulons as TF(+).
backend: SCENIC backend. "cpp" uses the R/C++ path and "python" uses the Python pySCENIC path. The selected backend controls GRN, cisTarget pruning, and AUCell scoring together.
n_rounds: Number of boosting rounds used by GRNBoost2.
learning_rate: Learning rate used by GRNBoost2.
max_depth: Maximum tree depth used by GRNBoost2.
max_features: Fraction of features sampled by GRNBoost2.
subsample: Row subsampling fraction used by GRNBoost2.
early_stop_window_length: Early-stopping window used by GRNBoost2.
cores: Number of workers used by GRNBoost2, scenic ctx, and AUCell scoring. If multicore execution is not supported, this is automatically reduced to one core.
seed: Random seed used by GRNBoost2 and Seurat overclustering.
force: Whether to rebuild existing SCENIC outputs.
assay_name: Name of the assay used to store regulon activity scores.
tool_name: Name of the srt@tools entry.
return_seurat: Whether to return the modified Seurat object. If FALSE, a result list is returned.
envname: Python environment used for SCENIC. If NULL, the isolated "scenic_env" environment is used.
conda: The path or command name of a conda-compatible executable.
prepare_env: Whether to prepare and configure the SCENIC Python environment before running.
verbose: Whether to print the message. Default is TRUE.

Value

A Seurat object with SCENIC results, or a result list when return_seurat = FALSE.

Examples

if (FALSE) { # \dontrun{
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
pancreas_sub <- RunSCENIC(
  pancreas_sub,
  species = "Mus_musculus"
)
} # }