cluster cells using kmeans algorithm
doKmeans(
gobject,
feat_type = NULL,
spat_unit = NULL,
expression_values = c("normalized", "scaled", "custom"),
feats_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 = TRUE,
seed_number = 1234
)
giotto object
feature type (e.g. "cell")
spatial unit (e.g. "rna", "dna", "protein")
expression values to use (e.g. "normalized", "scaled", "custom")
subset of features to use
dimension reduction to use (e.g. "cells", "pca", "umap", "tsne")
dimensions reduction name, default to "pca"
dimensions to use, default = 1:10
distance method (e.g. "original", "pearson", "spearman", "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski")
number of final clusters, default = 10
kmeans maximum iterations, default = 100
kmeans nstart, default = 1000
kmeans algorithm, default to "Hartigan-Wong"
name for kmeans clustering, default to "kmeans"
boolean: return giotto object (default = TRUE)
set seed (default = TRUE)
number for seed
giotto object with new clusters appended to cell metadata
Description on how to use Kmeans clustering method.
g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> cell_spatInfo_spatVector.shp
#> cell
#>
#> 3.2 read Giotto spatial centroid information
#> cell
#>
#> 3.3 read Giotto spatial overlap information
#> No overlaps were found, overlap loading will be
#> skipped
#>
#> 4. read Giotto image information
#> a giotto python environment was found
#> Using python path:
#> "/Users/yuanlab/Library/r-miniconda/envs/giotto_env/bin/pythonw"
doKmeans(g)
#> An object of class giotto
#> >Active spat_unit: cell
#> >Active feat_type: rna
#> [SUBCELLULAR INFO]
#> polygons : cell
#> [AGGREGATE INFO]
#> expression -----------------------
#> [cell][rna] raw normalized scaled
#> spatial locations ----------------
#> [cell] raw
#> spatial networks -----------------
#> [cell] Delaunay_network spatial_network
#> spatial enrichments --------------
#> [cell][rna] cluster_metagene DWLS
#> dim reduction --------------------
#> [cell][rna] pca custom_pca umap custom_umap tsne
#> nearest neighbor networks --------
#> [cell][rna] sNN.pca custom_NN
#> attached images ------------------
#> images : alignment image
#>
#>
#> Use objHistory() to see steps and params used