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 |