trainModel {MetaNeighbor} | R Documentation |
When comparing clusters to a large reference dataset, this function summarizes the gene-by-cell matrix into a much smaller highly variable gene-by-cluster matrix which can be fed as training data into MetaNeighborUS, resulting in substantial time and memory savings.
trainModel(var_genes, dat, i = 1, study_id, cell_type)
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 |
The output is a gene-by-cluster matrix that contains all the information necessary to run MetaNeighborUS from a pre-trained model.
data(mn_data) var_genes = variableGenes(dat = mn_data, exp_labels = mn_data$study_id) trained_model = trainModel(var_genes = var_genes, dat = mn_data, study_id = mn_data$study_id, cell_type = mn_data$cell_type) celltype_NV = MetaNeighborUS(trained_model = trained_model, dat = mn_data, study_id = mn_data$study_id, cell_type = mn_data$cell_type) celltype_NV