neighborVoting: Runs the neighbor voting algorithm.
In mm-shah/MetaNeighbor: Single cell replicability analysis

Description Usage Arguments Value See Also Examples

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.

1	neighborVoting(exp_labels, cell_labels, network, means = TRUE)

`exp_labels`	numerical 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 `TRUE`, determines output formatting

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.

MetaNeighbor

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