View source: R/perform.seurat.subclustering.R
perform.graph.subclustering | R Documentation |
Seurat subclustering
perform.graph.subclustering( object, assay, clust.method, column, clusters, neighbours, algorithm = 1, res = 0.6, verbose = FALSE, seed = 1234, ... )
object |
An IBRAP S4 class object |
assay |
Character. Which assay within the object to access |
clust.method |
Character. Which cluster_assignments dataframe to access |
column |
Character. Which column to access within the cluster_assignment dataframe |
clusters |
Which cluster(s) would you like to subcluster |
neighbours |
Character. String indicating which neighbourhood graphs should be used. |
algorithm |
Numerical. Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm; 4 = Leiden algorithm). Leiden requires the leidenalg python. Default = 1 Default = NULL |
res |
Numerical vector. Which resolution to run the clusterign algorithm at, a smaller and larger value identified less and more clusters, respectively. Default = c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5) |
verbose |
Logical Should function messages be printed? |
seed |
Numeric. What should the seed be set as. Default = 1234 |
... |
arguments to be passed to Seurat::FindClusters |
cluster.df.name |
Character. What to call the df contained in clusters. Default = 'seurat |
A new column within the defined cluster_assignment dataframe containing original and new subclusters
object <- perform.graph.subclustering(object = object, assay = 'SCT', clust.method = 'pca', column = 'neighbourhood_graph_res.0.7', clusters = c(1,5,9), neighbours = 'pca_nn:', algorithm = 1)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.