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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 |
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annotate_clusters()
|
annotate_clusters functionally annotates the identified clusters |
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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 |
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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 |