# A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation)

## Documentation for package ‘scGPS’ version 1.25.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

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
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'
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
PCA | PCA
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
PrinComp_cpp | Principal component analysis
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
subset_cpp | Subset a matrix
sub_clustering | sub_clustering for selected cells
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
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
tSNE | tSNE
var_cpp | Calculate variance