neighborVoting {MetaNeighbor} | R Documentation |
The function performs cell type identity prediction based on 'guilt by association' using cross validation. Performance is evaluated by calculating the AUROC for each cell type.
neighborVoting( exp_labels, cell_labels, network, means = TRUE, node_degree_normalization = TRUE )
exp_labels |
A vector that indicates the dataset source of each sample |
cell_labels |
sample by cell type matrix that indicates the cell type of each sample (0-absent; 1-present) |
network |
sample by sample adjacency matrix, ranked and standardized between 0-1 |
means |
default |
node_degree_normalization |
default |
If means = TRUE
(default) a vector containing the mean of
AUROC values across cross-validation folds will be returned. If FALSE a list
is returned containing a cell type by dataset matrix of AUROC scores, for
each fold of cross-validation. Default is over-ridden when more than one cell
type is assessed.
data("mn_data") data("GOmouse") library(SummarizedExperiment) AUROC_scores = MetaNeighbor(dat = mn_data, experiment_labels = as.numeric(factor(mn_data$study_id)), celltype_labels = metadata(colData(mn_data))[["cell_labels"]], genesets = GOmouse, bplot = TRUE) AUROC_scores