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")
| 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 |
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")