Package: GSgalgoR 1.23.0

Overview | Algorithm | Installation | GSgalgoR library | Examples datasets | Examples | Loading data | Data tidying and preparation | Drop duplicates and NA's | Expand probesets that map for multiple genes | Rescale expression matrix | Survival Object | Run galgo() | Setting parameters | Run Galgo algorithm | Galgo Object | Solutions | ParetoFront | to_list() function | to_dataframe() function | plot_pareto() | Case study | Data Preprocessing | Breast cancer classification | Survival of UPP patients | Survival of TRANSBIG patients | Find breast cancer gene signatures with GSgalgoR | Set configuration parameters | Analyzing Galgo results | Pareto front | Summary of the results | Select best performing solutions | Create prototypic centroids | Test Galgo signatures in a test set | Classify train and test set into GSgalgoR subtypes | Calculate train and test set C.Index | Calculate C.Index for training and test set using the prediction models | Evaluate prediction survival of Galgo signatures | Comparison of Galgo vs PAM50 classifier | Session info

Introduction | Example 1: A simple custom callback function definition | Example 2: Saving partial population pool using custom callback function | Callbacks implemented in GSgalgoR | Session info