Cluster cells using a SNN cluster approach.
doSNNCluster(
gobject,
name = "sNN_clus",
nn_network_to_use = "kNN",
network_name = "kNN.pca",
k = 20,
eps = 4,
minPts = 16,
borderPoints = TRUE,
return_gobject = TRUE,
set_seed = FALSE,
seed_number = 1234
)giotto object
name for cluster, default to "sNN_clus"
type of NN network to use (only works on kNN), default to "kNN"
name of kNN network to use, default to "kNN.pca"
Neighborhood size for nearest neighbor sparsification to create the shared NN graph, default = 20
Two objects are only reachable from each other if they share at least eps nearest neighbors, default = 4
minimum number of points that share at least eps nearest neighbors for a point to be considered a core points, default = 16
should borderPoints be assigned to clusters like in DBSCAN? (default = TRUE)
boolean: return giotto object (default = TRUE)
set seed (default = FALSE)
number for seed
giotto object with new clusters appended to cell metadata
See sNNclust from dbscan package
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
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> python already initialized in this session
#> active environment : '/usr/bin/python3'
#> python version : 3.10
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#> "/usr/bin/python3"
g <- createNearestNetwork(g, type = "kNN")
doSNNCluster(g)
#> An object of class giotto
#> >Active spat_unit: cell
#> >Active feat_type: rna
#> dimensions : 634, 624 (features, cells)
#> [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 kNN.pca
#> attached images ------------------
#> images : alignment image
#>
#>
#> Use objHistory() to see steps and params used