One round of clustering in the iteractive clustering pipeline

onestep_clust(
  norm.dat,
  select.cells = colnames(norm.dat),
  counts = NULL,
  method = c("louvain", "leiden", "ward.D", "kmeans"),
  vg.padj.th = 0.5,
  dim.method = c("pca", "WGCNA"),
  max.dim = 20,
  rm.eigen = NULL,
  rm.th = 0.7,
  de.param = de_param(),
  merge.type = c("undirectional", "directional"),
  maxGenes = 3000,
  sampleSize = 4000,
  max.cl.size = 300,
  k.nn = 15,
  prefix = NULL,
  verbose = FALSE,
  regress.x = NULL
)

Arguments

norm.dat: normalized expression data matrix in log transform, using genes as rows, and cells and columns. Users can use log2(FPKM+1) or log2(CPM+1).
select.cells: The cells to be clustered. Default: columns of norm.dat
counts: Raw gene counts. Default NULL, inferred from norm.dat.
method: Clustering method. It can be "louvain", "hclust" and "kmeans". Default "louvain"
vg.padj.th: High variance gene adjusted pvalue cut off. Default 0.5.
dim.method: Dimension reduction techniques. Current options include "pca" and "WGCNA". Default "pca"
max.dim: The number of top dimensions retained. Default 20. Since clustering is performed iteratively, not all relevant dimensions need to be captured in one iterations.
rm.eigen: The reduced dimensions that need to be masked and removed. Default NULL.
rm.th: The cutoff for correlation between reduced dimensions and rm.eigen. Reduced dimensions with correlatin with any rm.eigen vectors are not used for clustering. Default 0.7
de.param: The differential gene expression threshold. See de_param() function for details.
maxGenes: Only used when dim.method=="WGCNA". The maximum number of genes to calculate gene modules.
sampleSize: The number of sampled cells to compute reduced dimensions.
max.cl.size: Sampled cluster size. This is to speed up limma DE gene calculation. Instead of using all cells, we randomly sampled max.cl.size number of cells for testing DE genes.
prefix: Used to keep track of intermediate results in "verbose" mode. Default NULL.
verbose: Default FALSE
type: Can either be "undirectional" or "directional". If "undirectional", the differential gene threshold de.param is applied to combined up-regulated and down-regulated genes, if "directional", then the differential gene threshold is applied to both up-regulated and down-regulated genes.

Value

Clustering result is returned as a list with two elements: cl: cluster membership for each cell markers: top markers that seperate clusters