inferCSN-package | inferCSN: Inferring Cell-Specific Gene Regulatory Network |
acc.calculate | ACC calculate |
as_matrix | Attempts to turn a dgCMatrix into a dense matrix |
auc.calculate | AUC value calculate |
calculate.gene.rank | Calculate and rank TFs in network |
check.parameters | Check input parameters |
coef.SRM_fit | Extracts a specific solution in the regularization path |
coef.SRM_fit_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 |
inferCSN | Inferring Cell-Specific Gene Regulatory Network |
inferCSN-method | Inferring Cell-Specific Gene Regulatory Network |
model.fit | Fit a sparse regression model |
network.heatmap | The heatmap of network |
network_format | Format weight table |
network_sift | network_sift |
normalization | normalization |
parallelize_fun | Apply function over a List or Vector |
plot_contrast_networks | plot_contrast_networks |
plot_dynamic_networks | plot_dynamic_networks |
plot_scatter | plot_scatter |
plot_static_networks | Plot of dynamic networks |
predict.SRM_fit | Predict Response |
predict.SRM_fit_CV | Predict Response |
prepare.performance.data | prepare.performance.data |
print.SRM_fit | Prints a summary of model.fit |
print.SRM_fit_CV | Prints a summary of model.fit |
rse | Relative Squared Error |
r_square | R^2 (coefficient of determination) |
single.network | Construct network for single gene |
sparse.regression | Sparse regression model |
sse | Sum of Squared Errors |
table.to.matrix | Switch weight table to matrix |