cluster_cell: Cluster single cells or combination of single cells and bulk

In jianhaizhang/spatialHeatmap: spatialHeatmap

Cluster single cells or combination of single cells and bulk

Description

Cluster only single cell data or combination of single cell and bulk data. Clusters are created by first building a graph, where nodes are cells and edges represent connections between nearest neighbors, then partitioning the graph. The cluster labels are stored in the cluster column of colData slot of SingleCellExperiment.

Usage

cluster_cell(
  sce,
  graph.meth = "knn",
  dimred = "PCA",
  knn.gr = list(),
  snn.gr = list(),
  cluster = "wt",
  wt.arg = list(steps = 4),
  fg.arg = list(),
  sl.arg = list(spins = 25),
  le.arg = list(),
  eb.arg = list()
)

Arguments

sce	The single cell data or combination of single cell and bulk data at log2 scale after dimensionality reduction in form of SingleCellExperiment.
graph.meth	Method to build a nearest-neighbor graph, snn (see buildSNNGraph) or knn (default, see buildKNNGraph). The clusters are detected by first creating a nearest neighbor graph using snn or knn then partitioning the graph.
dimred	A string of PCA (default) or UMAP specifying which reduced dimensions to use for creating a nearest neighbor graph.
knn.gr	Additional arguments in a named list passed to buildKNNGraph.
snn.gr	Additional arguments in a named list passed to buildSNNGraph.
cluster	The clustering method. One of wt (cluster_walktrap, default), fg (cluster_fast_greedy), le (cluster_leading_eigen), sl (cluster_fast_greedy), eb (cluster_edge_betweenness).
wt.arg, fg.arg, sl.arg, le.arg, eb.arg	A named list of arguments passed to wt, fg, le, sl, eb respectively.

Value

A SingleCellExperiment object.

Author(s)

Jianhai Zhang [e-mail]
Dr. Thomas Girke [e-mail]

References

Morgan M, Obenchain V, Hester J, Pagès H (2021). SummarizedExperiment: SummarizedExperiment container. R package version 1.24.0, https://bioconductor.org/packages/SummarizedExperiment. Amezquita R, Lun A, Becht E, Carey V, Carpp L, Geistlinger L, Marini F, Rue-Albrecht K, Risso D, Soneson C, Waldron L, Pages H, Smith M, Huber W, Morgan M, Gottardo R, Hicks S (2020). “Orchestrating single-cell analysis with Bioconductor.” Nature Methods, 17, 137–145. https://www.nature.com/articles/s41592-019-0654-x. Lun ATL, McCarthy DJ, Marioni JC (2016). “A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.” F1000Res., 5, 2122. doi: 10.12688/f1000research.9501.2. Csardi G, Nepusz T: The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006. https://igraph.org

Examples

library(scran); library(scuttle) 
sce <- mockSCE(); sce <- logNormCounts(sce)
# Modelling the variance.
var.stats <- modelGeneVar(sce)
sce.dimred <- denoisePCA(sce, technical=var.stats, subset.row=rownames(var.stats)) 

sce.clus <- cluster_cell(sce=sce.dimred, graph.meth='snn', dimred='PCA')
# Clusters.
table(colData(sce.clus)$label)


# See details in function "coclus_meta" by running "?coclus_meta".

jianhaizhang/spatialHeatmap documentation built on April 21, 2024, 7:43 a.m.