Merge Clusters¶
-
mergeClusters()
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
)
Arguments¶
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 |
Value¶
Giotto object
Details¶
Merge selected clusters based on pairwise correlation scores and size of the cluster. To avoid the merging of large clusters, 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 which are 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.
Examples¶
data("mini_giotto_single_cell")
pDataDT(mini_giotto_single_cell)
#> cell_ID nr_genes perc_genes total_expr leiden_clus cell_types
#> 1: cell_2 13 65 111.98320 3 cell C
#> 2: cell_7 15 75 115.73030 3 cell C
#> 3: cell_12 11 55 95.49802 1 cell A
#> 4: cell_15 12 60 99.94782 3 cell C
#> 5: cell_17 13 65 111.32963 2 cell B
#> 6: cell_30 11 55 96.64302 3 cell C
#> 7: cell_37 6 30 57.77777 2 cell B
#> 8: cell_40 9 45 82.84693 2 cell B
#> 9: cell_44 9 45 79.93838 2 cell B
#> 10: cell_53 9 45 82.40747 1 cell A
#> 11: cell_64 8 40 73.06345 1 cell A
#> 12: cell_74 11 55 93.04295 3 cell C
#> 13: cell_85 8 40 73.72574 1 cell A
#> 14: cell_86 14 70 115.75186 1 cell A
#> 15: cell_90 11 55 93.02181 1 cell A
#> 16: cell_95 6 30 59.55714 1 cell A
#> 17: cell_96 10 50 88.31757 1 cell A
#> 18: cell_107 16 80 130.62640 1 cell A
#> 19: cell_113 12 60 99.83100 2 cell B
#> 20: cell_118 14 70 117.63523 2 cell B
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)
#> cell_ID nr_genes perc_genes total_expr leiden_clus cell_types
#> 1: cell_2 13 65 111.98320 3 cell C
#> 2: cell_7 15 75 115.73030 3 cell C
#> 3: cell_12 11 55 95.49802 1 cell A
#> 4: cell_15 12 60 99.94782 3 cell C
#> 5: cell_17 13 65 111.32963 2 cell B
#> 6: cell_30 11 55 96.64302 3 cell C
#> 7: cell_37 6 30 57.77777 2 cell B
#> 8: cell_40 9 45 82.84693 2 cell B
#> 9: cell_44 9 45 79.93838 2 cell B
#> 10: cell_53 9 45 82.40747 1 cell A
#> 11: cell_64 8 40 73.06345 1 cell A
#> 12: cell_74 11 55 93.04295 3 cell C
#> 13: cell_85 8 40 73.72574 1 cell A
#> 14: cell_86 14 70 115.75186 1 cell A
#> 15: cell_90 11 55 93.02181 1 cell A
#> 16: cell_95 6 30 59.55714 1 cell A
#> 17: cell_96 10 50 88.31757 1 cell A
#> 18: cell_107 16 80 130.62640 1 cell A
#> 19: cell_113 12 60 99.83100 2 cell B
#> 20: cell_118 14 70 117.63523 2 cell B
#> merged_cluster
#> 1: m_1
#> 2: m_1
#> 3: m_1
#> 4: m_1
#> 5: 2
#> 6: m_1
#> 7: 2
#> 8: 2
#> 9: 2
#> 10: m_1
#> 11: m_1
#> 12: m_1
#> 13: m_1
#> 14: m_1
#> 15: m_1
#> 16: m_1
#> 17: m_1
#> 18: m_1
#> 19: 2
#> 20: 2
plotUMAP_2D(mini_giotto_single_cell, cell_color = 'merged_cluster', point_size = 3)
