clusteringFromDistance: clusteringFromDistance

In WilsonImmunologyLab/LinQView: Integrate information from both transcriptome and cell surface protein signals to better identify cell heterogeneity

Description

Run clustering method (implemented by Seurat package) to identify cell populations using cell-cell pairwise distances

Usage

clusteringFromDistance(
  object,
  assay = "All",
  resolution = c(0.6, 0.6, 0.6),
  graph.k.param = 20,
  graph.compute.SNN = TRUE,
  graph.prune.SNN = 1/15,
  graph.nn.eps = 0,
  graph.force.recalc = FALSE,
  cluster.modularity.fxn = 1,
  cluster.initial.membership = NULL,
  cluster.node.sizes = NULL,
  cluster.algorithm = 1,
  cluster.n.start = 10,
  cluster.n.iter = 10,
  cluster.random.seed = 0,
  cluster.group.singletons = TRUE,
  cluster.temp.file.location = NULL,
  cluster.edge.file.name = NULL
)

Arguments

object	Seurat object
assay	run UMAP for which assay, choose from RNA, ADT, Joint or All
resolution	resolution for 1) Joint, 2) RNA and 3) ADT clustering. if assay = All, user should provide all three solutions. otherwise user only need to provide one resolution for selected assay.
graph.k.param	parameter for Seurat function FindNeighbors. Defines k for the k-nearest neighbor algorithm
graph.compute.SNN	parameter for Seurat function FindNeighbors. also compute the shared nearest neighbor graph
graph.prune.SNN	parameter for Seurat function FindNeighbors. Sets the cutoff for acceptable Jaccard index when computing the neighborhood overlap for the SNN construction. Any edges with values less than or equal to this will be set to 0 and removed from the SNN graph. Essentially sets the strigency of pruning (0 — no pruning, 1 — prune everything).
graph.nn.eps	parameter for Seurat function FindNeighbors. Error bound when performing nearest neighbor seach using RANN; default of 0.0 implies exact nearest neighbor search
graph.force.recalc	parameter for Seurat function FindNeighbors. Force recalculation of SNN.
cluster.modularity.fxn	parameter for Seurat function FindClusters.Modularity function (1 = standard; 2 = alternative).
cluster.initial.membership	parameter for Seurat function FindClusters.Parameters to pass to the Python leidenalg function.
cluster.node.sizes	parameter for Seurat function FindClusters.Parameters to pass to the Python leidenalg function.
cluster.algorithm	parameter for Seurat function FindClusters.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.
cluster.n.start	parameter for Seurat function FindClusters.Number of random starts.
cluster.n.iter	parameter for Seurat function FindClusters.Maximal number of iterations per random start.
cluster.random.seed	parameter for Seurat function FindClusters.Seed of the random number generator.
cluster.group.singletons	parameter for Seurat function FindClusters.Group singletons into nearest cluster. If FALSE, assign all singletons to a "singleton" group
cluster.temp.file.location	parameter for Seurat function FindClusters.Directory where intermediate files will be written. Specify the ABSOLUTE path.
cluster.edge.file.name	parameter for Seurat function FindClusters.Edge file to use as input for modularity optimizer jar.

WilsonImmunologyLab/LinQView documentation built on Jan. 3, 2022, 10 p.m.