mergeClusters | R Documentation |
Merge selected clusters based on pairwise correlation scores and size of cluster.
mergeClusters(
gobject,
expression_values = c("normalized", "scaled", "custom"),
cluster_column,
cor = c("pearson", "spearman"),
new_cluster_name = "merged_cluster",
min_cor_score = 0.8,
max_group_size = 20,
force_min_group_size = 10,
max_sim_clusters = 10,
return_gobject = TRUE,
verbose = TRUE
)
gobject |
giotto object |
expression_values |
expression values to use |
cluster_column |
name of column to use for clusters |
cor |
correlation score to calculate distance |
new_cluster_name |
new name for merged clusters |
min_cor_score |
min correlation score to merge pairwise clusters |
max_group_size |
max cluster size that can be merged |
force_min_group_size |
size of clusters that will be merged with their most similar neighbor(s) |
max_sim_clusters |
maximum number of clusters to potentially merge to reach force_min_group_size |
return_gobject |
return giotto object |
verbose |
be verbose |
Merge selected clusters based on pairwise correlation scores and size of cluster.
To avoid large clusters to merge the max_group_size can be lowered. Small clusters can
be forcibly merged with their most similar pairwise cluster by adjusting the
force_min_group_size parameter. Clusters smaller than this value will be merged
independent on the provided min_cor_score value. The force_min_group_size might not always
be reached if clusters have already been merged before
A giotto object is returned by default, if FALSE then the merging vector will be returned.
Giotto object
data("mini_giotto_single_cell")
pDataDT(mini_giotto_single_cell)
mini_giotto_single_cell = mergeClusters(mini_giotto_single_cell,
cluster_column = 'leiden_clus',
min_cor_score = 0.7,
force_min_group_size = 4)
pDataDT(mini_giotto_single_cell)
plotUMAP_2D(mini_giotto_single_cell, cell_color = 'merged_cluster', point_size = 3)
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