Package index • inferCSN

Inferring cell-specific gene regulatory network

inferCSN-package: inferCSN: inferring cell-type specific gene regulatory network

inferCSN_logo(): inferCSN logo

inferCSN(): inferring cell-type specific gene regulatory network

Network processing

network_sift(): Sifting network

weight_sift(): Weight sift

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

Network visualization

plot_contrast_networks(): Plot contrast networks

plot_dynamic_networks(): Plot dynamic networks

plot_network_heatmap(): Plot network heatmap

plot_static_networks(): Plot dynamic networks

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

Calculate gene rank

calculate_gene_rank(): Rank TFs and genes in network

Sparse regression model

single_network(): Construct network for single target gene

fit_srm(): Sparse regression model

sparse_regression(): Fit a sparse regression model

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

filter_sort_matrix(): Filter and sort matrix

log_message(): Print diagnostic message

matrix_to_table(): Switch matrix to network table

meta_cells(): Detection of metacells from single-cell gene expression matrix

network_format(): Format network table

normalization(): Normalize numeric vector

parallelize_fun(): Parallelize a function

pearson_correlation(): Correlation and covariance calculation for sparse 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

r_square(): \(R^2\) (coefficient of determination)

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.

simulate_sparse_matrix(): Generate a simulated sparse matrix for single-cell data testing

subsampling(): Subsampling function

table_to_matrix(): Switch network table to matrix

Example data

example_matrix: Example matrix data

example_meta_data: Example meta data

example_ground_truth: Example ground truth data