scde (version 2.0.1)

pagoda.cluster.cells: 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)