pagoda.cluster.cells {scde} | R Documentation

## 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)

[Package scde version 2.16.0 Index]