Cluster Cells

clusterCells()

Cluster cells using a variety of different methods.

clusterCells(
    gobject,
    cluster_method = c("leiden", "louvain_community", "louvain_multinet", "randomwalk",
        "sNNclust", "kmeans", "hierarchical"),
    name = "cluster_name",
    nn_network_to_use = "sNN",
    network_name = "sNN.pca",
    pyth_leid_resolution = 1,
    pyth_leid_weight_col = "weight",
    pyth_leid_part_type = c("RBConfigurationVertexPartition",
        "ModularityVertexPartition"),
    pyth_leid_init_memb = NULL,
    pyth_leid_iterations = 1000,
    pyth_louv_resolution = 1,
    pyth_louv_weight_col = NULL,
    python_louv_random = F,
    python_path = NULL,
    louvain_gamma = 1,
    louvain_omega = 1,
    walk_steps = 4,
    walk_clusters = 10,
    walk_weights = NA,
    sNNclust_k = 20,
    sNNclust_eps = 4,
    sNNclust_minPts = 16,
    borderPoints = TRUE,
    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"),
    km_centers = 10,
    km_iter_max = 100,
    km_nstart = 1000,
    km_algorithm = "Hartigan-Wong",
    hc_agglomeration_method = c("ward.D2", "ward.D", "single", "complete", "average",
        "mcquitty", "median", "centroid"),
    hc_k = 10,
    hc_h = NULL,
    return_gobject = TRUE,
    set_seed = T,
    seed_number = 1234
)

Arguments

Arguments passed on to select_NearestNetwork().

gobject

giotto object

cluster_method

community cluster method to use

name

name for new clustering result

nn_network_to_use

type of NN network to use (kNN vs sNN)

network_name

name of NN network to use

pyth_leid_resolution

resolution for leiden

pyth_leid_weight_col

column to use for weights

pyth_leid_part_type

partition type to use

pyth_leid_init_memb

initial membership

pyth_leid_iterations

number of iterations

pyth_louv_resolution

resolution for louvain

pyth_louv_weight_col

python louvain param: weight column

python_louv_random

python louvain param: random

python_path

specify specific path to python if required

louvain_gamma

louvain param: gamma or resolution

louvain_omega

louvain param: omega

walk_steps

randomwalk: number of steps

walk_clusters

randomwalk: number of clusters

walk_weights

randomwalk: weight column

sNNclust_k

SNNclust: k neighbors to use

sNNclust_eps

SNNclust: epsilon

sNNclust_minPts

SNNclust: min points

borderPoints

SNNclust: border points

expression_values

expression values to use

genes_to_use

= NULL,

dim_reduction_to_use

dimension reduction to use

dim_reduction_name

name of reduction ‘pca’,

dimensions_to_use

dimensions to use

distance_method

distance method

km_centers

kmeans centers

km_iter_max

kmeans iterations

km_nstart

kmeans random starting points

km_algorithm

kmeans algorithm

hc_agglomeration_method

hierarchical clustering method

hc_k

hierachical number of clusters

hc_h

hierarchical tree cutoff

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