Run GRNBoost2-like regulatory network inference and return a standardized adjacency table with columns `TF`, `target`, and `importance`.
Usage
RunGRNBoost2(object, ...)
# S3 method for class 'Seurat'
RunGRNBoost2(
object,
assay = NULL,
layer = "counts",
regulators = NULL,
targets = NULL,
backend = c("cpp", "python"),
max_edges_per_target = Inf,
n_rounds = 5000,
learning_rate = 0.01,
max_depth = 3,
max_features = 0.1,
subsample = 0.9,
early_stop_window_length = 25,
exclude_self = TRUE,
importance_norm_power = 0,
correlation_fill = FALSE,
boost_weight = 0.5,
correlation_weight = 0.8,
covariance_weight = 0.4,
correlation_method = c("pearson", "spearman"),
output_file = NULL,
work_dir = tempdir(),
prefix = "grnboost2",
envname = "scenic_env",
conda = "auto",
prepare_env = FALSE,
cores = 1,
seed = 1234,
force = FALSE,
verbose = TRUE,
...
)
# S3 method for class 'matrix'
RunGRNBoost2(object, ...)
# Default S3 method
RunGRNBoost2(
object,
regulators = NULL,
targets = NULL,
genes_in = c("rows", "columns"),
backend = c("cpp", "python"),
max_edges_per_target = Inf,
n_rounds = 5000,
learning_rate = 0.01,
max_depth = 3,
max_features = 0.1,
subsample = 0.9,
early_stop_window_length = 25,
exclude_self = TRUE,
importance_norm_power = 0,
correlation_fill = FALSE,
boost_weight = 0.5,
correlation_weight = 0.8,
covariance_weight = 0.4,
correlation_method = c("pearson", "spearman"),
seed = 1234,
output_file = NULL,
work_dir = tempdir(),
prefix = "grnboost2",
envname = "scenic_env",
conda = "auto",
prepare_env = FALSE,
cores = 1,
force = FALSE,
verbose = TRUE,
...
)Arguments
- object
A Seurat object or expression matrix.
- ...
Additional backend-specific arguments.
- assay
Assay used when `object` is a Seurat object.
- layer
Assay layer used when `object` is a Seurat object.
- regulators
Candidate transcription factor genes.
- targets
Optional target genes. If `NULL`, all genes in the GRN matrix are considered as candidate targets.
- backend
Runtime backend. Supports `"cpp"` and `"python"`.
- max_edges_per_target
Maximum incoming regulator edges retained per target. The default `Inf` keeps all positive-importance links, matching arboreto GRNBoost2 output.
- n_rounds
Number of boosting rounds for GRNBoost2-like tree ensemble inference. The default follows arboreto `SGBM_KWARGS`.
- learning_rate
GRNBoost2-like tree ensemble learning rate.
- max_depth
Maximum depth of each regression tree.
- max_features
Fraction of candidate regulators sampled at each tree split.
- subsample
Fraction of cells sampled for each boosting round.
- early_stop_window_length
Out-of-bag improvement window used for GRNBoost2 early stopping.
- exclude_self
Whether native GRNBoost2-like inference excludes a target gene from its own regulator feature set.
- importance_norm_power
Power used to normalize native GRNBoost2-like edge importance by the total importance for each target. Set to `0` to disable normalization.
- correlation_fill
Whether native GRNBoost2-like inference should fill missing TF-target candidates with expression-correlation support before applying `max_edges_per_target`.
- boost_weight
Weight of the native boosting score used when correlation fill is enabled.
- correlation_weight
Weight of the correlation-fill score relative to native boosting importance.
- covariance_weight
Weight of the covariance score used when correlation fill is enabled.
- correlation_method
Correlation method used for native GRNBoost2-like candidate filling.
- output_file
Optional path where the adjacency table is written.
- work_dir
Working directory used by Python backends.
- prefix
Prefix for temporary Python backend files.
- envname
Python environment used by Python backends.
- conda
Conda-compatible executable used by Python backends.
- prepare_env
Whether to prepare the Python environment before running Python backends.
- cores
Number of workers used by native/Python GRNBoost2.
- seed
Random seed passed to supported backends.
- force
Whether to rebuild existing `output_file`.
- verbose
Whether to print progress messages.
- genes_in
Matrix orientation for matrix inputs. `"rows"` means genes x cells; `"columns"` means cells x genes.