cluster cells using kmeans algorithm
doKmeans( gobject, expression_values = c("normalized", "scaled", "custom"), genes_to_use = NULL, dim_reduction_to_use = c("cells", "pca", "umap", "tsne"), dim_reduction_name = "pca", dimensions_to_use = 1:10, distance_method = c("original", "pearson", "spearman", "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"), centers = 10, iter_max = 100, nstart = 1000, algorithm = "Hartigan-Wong", name = "kmeans", return_gobject = TRUE, set_seed = T, seed_number = 1234 )
Arguments
| gobject | giotto object |
|---|---|
| expression_values | expression values to use |
| genes_to_use | subset of genes to use |
| dim_reduction_to_use | dimension reduction to use |
| dim_reduction_name | dimensions reduction name |
| dimensions_to_use | dimensions to use |
| distance_method | distance method |
| centers | number of final clusters |
| iter_max | kmeans maximum iterations |
| nstart | kmeans nstart |
| algorithm | kmeans algorithm |
| name | name for kmeans clustering |
| return_gobject | boolean: return giotto object (default = TRUE) |
| set_seed | set seed |
| seed_number | number for seed |
Value
giotto object with new clusters appended to cell metadata
Details
Description on how to use Kmeans clustering method.
See also
Examples
data(mini_giotto_single_cell) mini_giotto_single_cell = doKmeans(mini_giotto_single_cell, centers = 4, name = 'kmeans_clus') plotUMAP_2D(mini_giotto_single_cell, cell_color = 'kmeans_clus', point_size = 3)
