GSgalgoR-package | GSgalgoR: A bi-objective evolutionary meta-heuristic to identify robust transcriptomic classifiers associated with patient outcome across multiple cancer types. |
calculate_distance | Functions to calculate distance matrices using cpu computing |
calculate_distance_euclidean_cpu | Functions to calculate distance matrices using cpu computing |
calculate_distance_pearson_cpu | Functions to calculate distance matrices using cpu computing |
calculate_distance_spearman_cpu | Functions to calculate distance matrices using cpu computing |
calculate_distance_uncentered_cpu | Functions to calculate distance matrices using cpu computing |
callback_base_report | Print basic info per generation |
callback_base_return_pop | A base callback function that returns a galgo.Obj |
callback_default | A default call_back function that does nothing. |
callback_no_report | Print minimal information to the user about galgo execution. |
classify_multiple | Classify samples from multiple centroids |
cluster_algorithm | Wrapper function to perform partition around medioids (PAM) for GalgoR |
cluster_classify | Distance to centroid classifier function |
cosine_similarity | Function for calculating the cosine similarity |
create_centroids | Create Centroids |
galgo | GSgalgoR main function |
galgo.Obj | Galgo Object class |
galgo.Obj-class | Galgo Object class |
GSgalgoR | GSgalgoR: A bi-objective evolutionary meta-heuristic to identify robust transcriptomic classifiers associated with patient outcome across multiple cancer types. |
k_centroids | Function to calculate the centroids of different groups (classes) |
non_dominated_summary | Summary of the non dominated solutions |
plot_pareto | Plot pareto front from an galgo.Obj |
select_distance | Functions to calculate distance matrices using cpu computing |
surv_fitness | Survival fitness function using the Restricted Mean Survival Time (RMST) of each group |
to_dataframe | Convert galgo.Obj to data.frame |
to_list | Convert galgo.Obj to list |