Inferring Cell-Specific Gene Regulatory Network


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Documentation for package ‘inferCSN’ version 1.1.7

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inferCSN-package _*inferCSN*_: *infer*ring *C*ell-*S*pecific gene regulatory *N*etwork
%ss% Value selection operator
as_matrix Convert sparse matrix into dense matrix
calculate_accuracy Calculate Accuracy
calculate_auc Calculate AUC Metrics
calculate_auprc Calculate AUPRC Metric
calculate_auroc Calculate AUROC Metric
calculate_f1 Calculate F1 Score
calculate_gene_rank Rank TFs and genes in network
calculate_ji Calculate Jaccard Index
calculate_metrics Calculate Network Prediction Performance Metrics
calculate_precision Calculate Precision Metric
calculate_recall Calculate Recall Metric
calculate_si Calculate Set Intersection
check_sparsity Check sparsity of matrix
coef.srm Extracts a specific solution in the regularization path
coef.srm_cv Extracts a specific solution in the regularization path
example_ground_truth Example ground truth data
example_matrix Example matrix data
example_meta_data Example meta data
filter_sort_matrix Filter and sort matrix
fit_srm Sparse regression model
inferCSN *infer*ring *C*ell-*S*pecific gene regulatory *N*etwork
inferCSN-method *infer*ring *C*ell-*S*pecific gene regulatory *N*etwork
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
network_sift Sifting network
normalization Normalize numeric vector
parallelize_fun Parallelize a function
pearson_correlation Correlation and covariance calculation for sparse matrix
plot_coefficient Plot coefficients
plot_coefficients Plot coefficients for multiple targets
plot_contrast_networks Plot contrast networks
plot_dynamic_networks Plot dynamic networks
plot_edges_comparison Network Edge Comparison Visualization
plot_embedding Plot embedding
plot_histogram Plot histogram
plot_network_heatmap Plot network heatmap
plot_scatter Plot expression data in a scatter plot
plot_static_networks Plot dynamic networks
predict.srm Predicts response for a given sample
predict.srm_cv Predicts response for a given sample
print.srm Prints a summary of 'sparse_regression'
print.srm_cv Prints a summary of 'sparse_regression'
r_square R^2 (coefficient of determination)
simulate_sparse_matrix Generate a simulated sparse matrix for single-cell data testing
single_network Construct network for single target gene
sparse_cor Safe correlation function which returns a sparse matrix without missing values
sparse_cov_cor Fast correlation and covariance calcualtion for sparse matrices
sparse_regression Fit a sparse regression model
split_indices Split indices.
subsampling Subsampling function
table_to_matrix Switch network table to matrix
weight_sift Weight sift