# cut_groups

Source: `R/cut_groups.R`
`cut_groups.Rd`

This step follows projection_visualization. After hclust() to cluster data and heatmap.2() to draw the heatmap in the projection_visualization step, this step uses cutree() to get subclusters, i.e., the major cell types in the samples.

    cut_groups(object, K, method = "ward.D")

## Arguments

object | A dmatch class object
K | Number of cell types to cut
method | The agglomeration method used by hclust() in the projection_visualization step for clustering data

## Value

A dmatch class object which have slots storing raw.data, batch.id, PCA, and more information. Specifically, cut_group slot stores information of celltypes for cells in the pairwise samples ("CellType")