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 )
| 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 |
giotto object with new clusters appended to cell metadata
Wrapper for the different clustering methods.