neighborVoting {MetaNeighbor} | R Documentation

## Runs the neighbor voting algorithm.

### Description

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.

### Usage

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

### Arguments

`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 `TRUE`, determines output formatting
`node_degree_normalization` | default `TRUE`, should predictions be divided by node degree?

### Value

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.

### See Also

`MetaNeighbor`

### Examples

    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

[Package MetaNeighbor version 1.10.0 Index]