CORE_bagging            Main clustering SCORE (CORE V2.0) Stable
                        Clustering at Optimal REsolution with bagging
                        and bootstrapping
CORE_clustering         Main clustering CORE V2.0 updated
CORE_subcluster         sub_clustering (optional) after running CORE
                        'test'
PCA                     PCA
PrinComp_cpp            Principal component analysis
add_import              add_import
annotate_clusters       annotate_clusters functionally annotates the
                        identified clusters
bootstrap_parallel      BootStrap runs for both scGPS training and
                        prediction with parallel option
bootstrap_prediction    BootStrap runs for both scGPS training and
                        prediction
calcDist                Compute Euclidean distance matrix by rows
calcDistArma            Compute Euclidean distance matrix by rows
clustering              HC clustering for a number of resolutions
clustering_bagging      HC clustering for a number of resolutions
day_2_cardio_cell_sample
                        One of the two example single-cell count
                        matrices to be used for training 'scGPS' model
day_5_cardio_cell_sample
                        One of the two example single-cell count
                        matrices to be used for 'scGPS' prediction
distvec                 Compute Distance between two vectors
find_markers            find marker genes
find_optimal_stability
                        Find the optimal cluster
find_stability          Calculate stability index
mean_cpp                Calculate mean
new_scGPS_object        new_scGPS_object
new_summarized_scGPS_object
                        new_summarized_scGPS_object
plot_CORE               Plot dendrogram tree for CORE result
plot_optimal_CORE       plot one single tree with the optimal
                        clustering result
plot_reduced            plot reduced data
predicting              Main prediction function applying the optimal
                        ElasticNet and LDA models
rand_index              Calculate rand index
rcpp_Eucl_distance_NotPar
                        Function to calculate Eucledean distance matrix
                        without parallelisation
rcpp_parallel_distance
                        distance matrix using C++
reformat_LASSO          summarise bootstrap runs for Lasso model, from
                        'n' bootstraps
sub_clustering          sub_clustering for selected cells
subset_cpp              Subset a matrix
summary_accuracy        get percent accuracy for Lasso model, from 'n'
                        bootstraps
summary_deviance        get percent deviance explained for Lasso model,
                        from 'n' bootstraps
summary_prediction_lasso
                        get percent deviance explained for Lasso model,
                        from 'n' bootstraps
summary_prediction_lda
                        get percent deviance explained for LDA model,
                        from 'n' bootstraps
tSNE                    tSNE
top_var                 select top variable genes
tp_cpp                  Transpose a matrix
training                Main model training function for finding the
                        best model that characterises a subpopulation
training_gene_sample    Input gene list for training 'scGPS', e.g.
                        differentially expressed genes
var_cpp                 Calculate variance
