Infer a Gene Regulatory Network with Pando

Infer a Gene Regulatory Network with Pando

infer_grn(object, ...)

# S3 method for SeuratPlus
infer_grn(
  object,
  genes = NULL,
  network_name = paste0(method, "_network"),
  peak_to_gene_method = c("Signac", "GREAT"),
  upstream = 1e+05,
  downstream = 0,
  extend = 1e+06,
  only_tss = FALSE,
  parallel = FALSE,
  tf_cor = 0.1,
  peak_cor = 0,
  aggregate_rna_col = NULL,
  aggregate_peaks_col = NULL,
  method = c("glm", "glmnet", "cv.glmnet", "brms", "xgb", "bagging_ridge",
    "bayesian_ridge"),
  alpha = 0.5,
  family = "gaussian",
  interaction_term = ":",
  adjust_method = "fdr",
  scale = FALSE,
  verbose = TRUE,
  ...
)

Arguments

...	Other parameters for the model fitting function.
genes	A character vector with the target genes to consider for GRN inference. Takes all VariableFeatures in the object per default.
peak_to_gene_method	Character specifying the method to link peak overlapping motif regions to nearby genes. One of 'Signac' or 'GREAT'.
upstream	Integer defining the distance upstream of the gene to consider as potential regulatory region.
downstream	Integer defining the distance downstream of the gene to consider as potential regulatory region.
extend	Integer defining the distance from the upstream and downstream of the basal regulatory region. Only used of peak_to_gene_method = 'GREAT'.
only_tss	Logical. Measure distance from the TSS (TRUE) or from the entire gene body (FALSE).
parallel	Logical. Whether to parallelize the computation with foreach.
tf_cor	Threshold for TF - target gene correlation.
peak_cor	Threshold for binding peak - target gene correlation.
method	A character string indicating the method to fit the model. 'glm' - Generalized Liner Model with stats. 'glmnet', 'cv.glmnet' - Regularized Generalized Liner Model with glmnet. 'brms' - Bayesian Regression Models using brms. 'xgb' - Gradient Boosting Regression using xgboost. 'bagging_ridge' - Bagging Ridge Regression using scikit-learn via reticulate. 'bayesian_ridge' - Bayesian Ridge Regression using scikit-learn via reticulate.
alpha	The elasticnet mixing parameter. See glmnet for details.
family	A description of the error distribution and link function to be used in the model. See stats for mode details.
interaction_term	The interaction term to use in the model between TF and binding site. '+' for additive interaction. ':' for 'multiplicative' interaction. '*' for crossing interaction, i.e. additive AND 'multiplicative'. For more info, see stats
adjust_method	Method for adjusting p-values.
scale	Logical. Whether to z-transform the expression and accessibility matrices.
verbose	Logical. Display messages. Set verbose to '2' to print errors for all model fits.

Value

A SeuratPlus object.