pagoda.cluster.cells: Determine optimal cell clustering based on the genes driving...

In hms-dbmi/scde: Single Cell Differential Expression

Determine optimal cell clustering based on the genes driving the significant aspects

Description

Determines cell clustering (hclust result) based on a weighted correlation of genes underlying the top aspects of transcriptional heterogeneity. Branch orientation is optimized if 'cba' package is installed.

Usage

pagoda.cluster.cells(tam, varinfo, method = "ward.D",
  include.aspects = FALSE, verbose = 0, return.details = FALSE)

Arguments

tam	result of pagoda.top.aspects() call
varinfo	result of pagoda.varnorm() call
method	clustering method ('ward.D' by default)
include.aspects	whether the aspect patterns themselves should be included alongside with the individual genes in calculating cell distance
verbose	0 or 1 depending on level of desired verbosity
return.details	Boolean of whether to return just the hclust result or a list containing the hclust result plus the distance matrix and gene values

Value

hclust result

Examples

data(pollen)
cd <- clean.counts(pollen)

knn <- knn.error.models(cd, k=ncol(cd)/4, n.cores=10, min.count.threshold=2, min.nonfailed=5, max.model.plots=10)
varinfo <- pagoda.varnorm(knn, counts = cd, trim = 3/ncol(cd), max.adj.var = 5, n.cores = 1, plot = FALSE)
pwpca <- pagoda.pathway.wPCA(varinfo, go.env, n.components=1, n.cores=10, n.internal.shuffles=50)
tam <- pagoda.top.aspects(pwpca, return.table = TRUE, plot=FALSE, z.score=1.96)  # top aspects based on GO only
hc <- pagoda.cluster.cells(tam, varinfo)
plot(hc)

hms-dbmi/scde documentation built on April 19, 2023, 10:21 p.m.