Reference

Main workflow Functions executing the main Pando workflow
initiate_grn()	Initiate the RegulatoryNetwork object.
find_motifs()	Scan for motifs in candidate regions.
infer_grn()	Infer a Gene Regulatory Network with Pando
find_modules()	Find TF modules in regulatory network
Visualization Functions to visualize the GRN
get_network_graph()	Compute network graph embedding using UMAP.
get_tf_network()	Get sub-network centered around one TF.
get_umap()	Compute UMAP embedding
plot_gof()	Plot goodness-of-fit metrics.
plot_module_metrics()	Plot module metrics number of genes, number of peaks and number of TFs per gene.
plot_network_graph()	Plot network graph.
plot_tf_network()	Plot sub-network centered around one TF.
Object interaction Functions to interact with Seurat and Network objects
GetGRN()	Get network data
GetNetwork()	Get network
Network-class	The Modules class
NetworkFeatures()	Get network features
NetworkGraph()	Get network parameters
NetworkModules()	Get TF modules
NetworkParams()	Get network parameters
NetworkRegions()	Get network regions
NetworkTFs()	Get network TFs
Params()	Get GRN inference parameters
coef(<SeuratPlus>) coef(<RegulatoryNetwork>) coef(<Network>)	Get fitted coefficients
gof()	Get goodness-of-fit info
print(<RegulatoryNetwork>) print(<Network>) print(<Modules>) print(<Regions>)	Print RegulatoryNetwork objects
Regions-class	The Regions class
RegulatoryNetwork-class	The RegularotyNetwork class
SeuratPlus-class	The SeuratPlus class
GetAssaySummary()	Get summary of seurat assay
Model fitting Functions to fit models
fit_bagging_ridge()	Fit a bagging ridge regression model as implemented in scikit-learn (python)
fit_bayesian_ridge()	Fit a bayesian ridge regression model as implemented in scikit-learn (python)
fit_brms()	Fit a Bayesian regression model with brms and Stan
fit_cvglmnet()	Cross-validation for regularized generalized linear models
fit_glm()	Fit generalized linear model
fit_glmnet()	Fit regularized generalized linear model
fit_grn_models()	Fit models for gene expression
fit_model()	Fit (regularized) generalized linear model
fit_xgb()	Fit a gradient boosting regression model with XGBoost
Utilities Utility functions
aggregate_matrix()	Aggregate matrix over groups
aggregate_assay()	Aggregate Seurat assay over groups
map_par()	Apply function over a List or Vector (in parallel)
sparse_cor()	Safe correlation function which returns a sparse matrix without missing values
find_peaks_near_genes()	Find peaks or regions near gene body or TSS