MetaNeighborUS: Runs unsupervised version of MetaNeighbor
In mm-shah/MetaNeighbor: Single cell replicability analysis

Description Usage Arguments Value Examples

View source: R/MetaNeighborUS.R

When it is difficult to know how cell type labels compare across datasets this function helps users to make an educated guess about the overlaps without requiring in-depth knowledge of marker genes

1 2	MetaNeighborUS(var_genes, dat, i = 1, study_id, cell_type, fast_version = FALSE)

`var_genes`	vector of high variance genes.
`dat`	SummarizedExperiment object containing gene-by-sample expression matrix.
`i`	default value 1; non-zero index value of assay containing the matrix data
`study_id`	a vector that lists the Study (dataset) ID for each sample
`cell_type`	a vector that lists the cell type of each sample
`fast_version`	default value FALSE; a boolean flag indicating whether to use the fast and low memory version of MetaNeighbor

The output is a cell type-by-cell type mean AUROC matrix, which is built by treating each pair of cell types as testing and training data for MetaNeighbor, then taking the average AUROC for each pair (NB scores will not be identical because each test cell type is scored out of its own dataset, and the differential heterogeneity of datasets will influence scores).

data(mn_data)
var_genes = variableGenes(dat = mn_data, exp_labels = mn_data$study_id)
celltype_NV = MetaNeighborUS(var_genes = var_genes,
                             dat = mn_data,
                             study_id = mn_data$study_id,
                             cell_type = mn_data$cell_type,
                             fast_version=FALSE)
celltype_NV