Package index • inferCSN

Inferring cell-specific gene regulatory network
`inferCSN-package`	inferCSN: infer cell-type-specific gene regulatory network
`inferCSN_logo()`	inferCSN logo
`inferCSN()`	inferring cell-type specific gene regulatory network
Network visualization
`plot_dynamic_networks()`	Plot dynamic networks
`plot_static_networks()`	Plot dynamic networks
`plot_contrast_networks()`	Plot contrast networks
`plot_network_heatmap()`	Plot network heatmap
`plot_detail_network()`	Plot the dynamic differential network but colored by communities and optionally faded by igraph::betweenness
`plot_diffnet_detail()`	Plot the dynamic differential network but colored by communities and optionally faded by betweenness
`plot_dyn_diffnet()`	Plot the dynamic differential network
`plot_dynamic_network()`	quick plot of dynamic networks
`plot_gof()`	Plot goodness-of-fit metrics.
`plot_heatmap_by_treatment()`	Useful plotting function to plot heatmap with pre-split matrix
`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_targets_and_regulators()`	Updated plot of top regulators given targets in dynamic networks based on a weight column. Top regulators computed for each epoch, but maintained in plot across epochs if present in epoch subnetwork.
`plot_targets_with_top_regulators_detail()`	quick plot of top regulators given targets in dynamic networks based on reconstruction weight, colored by expression and interaction type
`plot_tf_network()`	Plot sub-network centered around one TF.
`plot_top_features()`	quick plot of top regulators in dynamic networks
`heatmap_by_treatment_group()`	Useful plotting function to plot heatmap of module expression across time with pre-split matrix
`hm_dyn()`	plots results of findDynGenes
`hm_dyn_clust()`	plots results of findDynGenes
`hm_dyn_epoch()`	heatmap
Plotting functions
`plot_coefficient()`	Plot coefficients
`plot_coefficients()`	Plot coefficients for multiple targets
`plot_edges_comparison()`	Network Edge Comparison Visualization
`plot_embedding()`	Plot embedding
`plot_histogram()`	Plot histogram
`plot_scatter()`	Plot expression data in a scatter plot
Plotting theme
`no_legend()`	Removes legend from plot.
`no_margin()`	Removes margins from plot.
`no_x_text()`	Removes x axis text.
`no_y_text()`	Removes y axis text.
Network evaluation
`calculate_metrics()`	Calculate Network Prediction Performance Metrics
`calculate_accuracy()`	Calculate Accuracy
`calculate_auc()`	Calculate AUC Metrics
`calculate_auroc()`	Calculate AUROC Metric
`calculate_auprc()`	Calculate AUPRC Metric
`calculate_precision()`	Calculate Precision Metric
`calculate_recall()`	Calculate Recall Metric
`calculate_f1()`	Calculate F1 Score
`calculate_si()`	Calculate Set Intersection
`calculate_ji()`	Calculate Jaccard Index
`calculate_degree_distribution()`	calculate_degree_distribution
`JI_across_topregs()`	Computes Jaccard similarity between top regulators in two sets of networks across a range of top X regulators
Sparse regression model
`single_network()`	Construct network for single target gene
`fit_srm()`	Sparse regression model
`fit_srm2()`	Fit a sparse regression model
`sparse_regression()`	Fit a sparse regression model
`fit_cvglmnet()`	Cross-validation for regularized generalized linear models
`fit_cvsrm()`	Cross-validation for sparse regression models
`fit_glm()`	Fit generalized linear model
`fit_glmnet()`	Fit regularized generalized linear model
`fit_xgb()`	Fit a gradient boosting regression model with XGBoost
`fit_model()`	Fit model
`fit_models()`	Fit models for gene expression
`fit_susie()`	Fit a SuSiE regression model
`format_coefs()`	Format network coefficients
vector functions
`infer_vector()`	infer VECTOR
`vector_auto_center()`	Title
`vector_build_grid()`	Title
`vector_build_net()`	Title
`vector_build_value()`	Title
`vector_draw_arrow()`	Title
`vector_grid_value()`	Title
`vector_rank_pca()`	rank umap
RNA data processing
`aggregate_assay()`	Aggregate Seurat assay over groups
`aggregate_matrix()`	Aggregate matrix over groups
`expression_ksmooth()`	expression_ksmooth
Peak data processing
`find_peaks_near_genes()`	Find peaks or regions near gene body or TSS
Dynamic object
`dynamic_genes()`	Get dynamic genes
`dynamic_genes_new()`	find genes expressed dynamically
`findDynGenes()`	find genes expressed dynamically
`get_pseudotime()`	Get pseudotime information
`get_embedding()`	Get dimensional information
`compileDynGenes()`	compileDynGenes
Network processing
`export_csn()`	Export network from CSN object
`get_attribute()`	Get any attribute from a CSNObject object
`metrics()`	Get metrics
`print(<Network>)` `print(<Modules>)` `print(<Regions>)`	Print Network objects
`simulate_sparse_matrix()`	Generate a simulated sparse matrix for single-cell data testing
`subsampling()`	Subsampling function
`weight_sift()`	Weight sift
`network_sift()`	Sifting network
`add_interaction_type()`	add_interaction_type
`add_type()`	Adds interaction type to dynamic differential network
`DefaultNetwork()`	Get active network
`GetAssay()`	Get seurat assay
`GetAssaySummary()`	Get summary of seurat assay
`GetNetwork()`	Get network
`LayerData()`	Get layer data from CSNObject
`NetworkGraph()`	Get network graph
`NetworkModules()`	Get network modules
`NetworkParams()`	Get network parameters
`NetworkRegions()`	Get network regions
`NetworkTFs()`	Get network TFs
`Params()`	Get GRN inference parameters
`VariableFeatures()`	Get variable features from CSNObject
`define_epochs()`	Define epochs
`define_epochs_new()`	Define epochs
`density_points()`	density_points
`diffnet_community_detection()`	Perform community detection on a dynamic network
`dynamic_windowing()`	dynamic_windowing
`dynamic_difference_network()`	Compute a dynamic difference network
`dynamic_shortest_path()`	Function to return shortest path from 1 regulator to 1 target in a dynamic network
`dynamic_shortest_path_multiple()`	Function to return shortest path from multiple TFs to multiple targets in a dynamic network
`edge_uniqueness()`	Function to compute edge differences between networks
`epochGRN()`	Divides grnDF into epochs, filters interactions between genes not in same or consecutive epochs
`compile_epochs()`	compile_epochs
`get_network_graph()`	Compute network graph embedding using UMAP.
`get_tf_network()`	Get sub-network centered around one TF.
`assign_epochs_simple()`	Assigns genes to epochs just based on which mean is maximal
`assign_epochs()`	Assigns genes to epochs
`assign_epochs_new1()`	Assigns genes to epochs
`assign_network()`	Assigns genes to epochs
`subnets()`	Function to assign nodes to communities via Louvain clustering
`split_epochs_by_group()`	Splits data into epochs manually
`split_epochs_by_pseudotime()`	Splits data into epochs
`biglist_compute_betweenness_degree()`	Computes betweenness and degree of each regulator for each network in a list of networks
`compute_JI_topregs()`	Computes Jaccard similarity between top regulators in two sets of networks
`compute_betweenness_degree()`	Function to compute betweenness and degree
`compute_frobenius_distance()`	Computes frobenius distance in a pairwise manner between two sets of networks
`cor_and_add_action()`	Adds an extra column to the result of dynamic_shortest_path_multiple that predicts overall action based on correlation between "from" and "to"
`find_communities()`	find_communities
`find_cuts_by_clustering()`	Returns cuts to define epochs
`find_cuts_by_similarity()`	find_cuts_by_similarity
`find_modules()`	Find TF modules in regulatory network
`find_motifs()`	Scan for motifs in candidate regions
`find_targets()`	Finds binding targets given list of dataframes containing binding info for effectors
`static_shortest_path()`	Function to return shortest path from 1 regulator to 1 target in a static network
Calculate gene rank
`calculate_gene_rank()`	Calculate gene rank
`compute_pagerank()`	Function to compute page rank of TF+target networks
`calculate_page_rank()`	Calculate PageRank
`plot_gene_rank()`	Plot gene ranks and network properties
Network perturbation
`Perturbation-class`	The Perturbation class
`SetNetwork()`	Set Network object
`calculate_trajectory()`	Calculate cell trajectory in embedding space
`embedPerturbation()`	Embed perturbation results
`plotPerturbation()`	Visualize perturbation results
`plotPerturbationTrajectory()`	Plot perturbation trajectory
`predictPerturbation()`	Predict gene expression under TF perturbation
Utils functions
`%ss%`	Value selection operator
`as_matrix()`	Convert sparse matrix into dense matrix
`check_sparsity()`	Check sparsity of matrix
`coef(<srm>)` `coef(<srm_cv>)`	Extracts a specific solution in the regularization path
`coef(<Network>)`	Get fitted coefficients
`coef(<CSNObject>)`	Get fitted coefficients
`dMcast()`	Copy of the dMcast function from the Matrix.utils package, since this is off CRAN and does not seem to be maintained anymore
`edge_rank()`	Function to compute edge differences
`fast_aggregate()`	Copy of the aggregate.Matrix function from the Matrix.utils package, since this is off CRAN and does not seem to be maintained anymore
`filter_sort_matrix()`	Filter and sort matrix
`get_umap()`	Compute UMAP embedding
`ig_NiceGraph()`	returns a pretty graph given a grnTab and expression data
`ig_convertLarge()`	change igraph attributes so that it is suitable for plotting a small network
`ig_convertMedium()`	change igraph attributes so that it is suitable for plotting a medium network
`ig_convertSmall()`	change igraph attributes so that it is suitable for plotting a small network
`wn_ig_tabToIgraph()`	convert a table to an igraph
`ig_convertTFs()`	change igraph attributes so that it is suitable for plotting a network of only regs
`ig_exemplars()`	make a graph of the regulators, top targets, selecting only top XX targets each
`ig_scaleV()`	return a vector of scaled sizes for a vector of verticies
`ig_tabToIgraph()`	convert a table to an igraph
`matrix_to_table()`	Switch matrix to network table
`mean_module_expression()`	Computes mean expression of groups of genes
`mean_subnetwork_expression()`	Computes mean expression of groups of genes in a dynamic network
`meta_cells()`	Detection of metacells from single-cell gene expression matrix
`network_format()`	Format network table
`normalization()`	Normalize numeric vector
`table_to_matrix()`	Switch network table to matrix
`order_genes()`	Function that orders genes based on peak expression
`parallelize_fun()`	Parallelize a function
`pearson_correlation()`	Correlation and covariance calculation for sparse matrix
`pre_pseudotime_matrix()`	pre_pseudotime_matrix
`print(<logo>)`	print logo
`print(<srm>)` `print(<srm_cv>)`	Prints a summary of `sparse_regression`
`predict(<srm>)` `predict(<srm_cv>)`	Predicts response for a given sample
`rough_hierarchy()`	rough_hierarchy
`sample_and_epoch_reconstruct()`	Function to bootstrap Epoch reconstruction
`score_targets()`	Function to score targets of effectors
`SetNetwork()`	Set Network object
`sparse_cor()`	Safe correlation function which returns a sparse matrix without missing values
`sparse_cov_cor()`	Fast correlation and covariance calcualtion for sparse matrices
`split_indices()`	Split indices.
`log_message()`	Print diagnostic message
`r_square()`	\(R^2\) (coefficient of determination)
Class
`initiate_object()`	Initiate the `CSNObject` object
`CSNObject-class`	The CSNObject class
`Modules-class`	The Modules class
`Network-class`	The Network class
`Regions-class`	The Regions class
Example data and datasets
`simulate_csn_data()`	Simulate data for testing inferCSN
`create_seurat_object()`	Create a Seurat object from simulated data
`example_matrix`	Example matrix data
`example_ground_truth`	Example ground truth data
`example_meta_data`	Eexample_meta_data
`EnsDb.Hsapiens.v93.annot.UCSC.hg38`	EnsDb.Hsapiens.v93.annot.UCSC.hg38
`SCREEN.ccRE.UCSC.hg38`	SCREEN.ccRE.UCSC.hg38
`phastConsElements20Mammals.UCSC.hg38`	phastConsElements20Mammals.UCSC.hg38
`motif2tf`	motif2tf
`motifs`	motifs

Reference

Inferring cell-specific gene regulatory network

Network visualization

Plotting functions

Plotting theme

Network evaluation

Sparse regression model

vector functions

RNA data processing

Peak data processing

Dynamic object

Network processing

Calculate gene rank

Network perturbation

Utils functions

Class

Example data and datasets