pagoda.reduce.loading.redundancy: Collapse aspects driven by the same combinations of genes
In scde: Single Cell Differential Expression

Description Usage Arguments Value Examples

Examines PC loading vectors underlying the identified aspects and clusters aspects based on a product of loading and score correlation (raised to corr.power). Clusters of aspects driven by the same genes are determined based on the distance.threshold and collapsed.

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pagoda.reduce.loading.redundancy(tam, pwpca, clpca = NULL, plot = FALSE,
  cluster.method = "complete", distance.threshold = 0.01, corr.power = 4,
  n.cores = detectCores(), abs = TRUE, ...)

`tam`	output of pagoda.top.aspects()
`pwpca`	output of pagoda.pathway.wPCA()
`clpca`	output of pagoda.gene.clusters() (optional)
`plot`	whether to plot the resulting clustering
`cluster.method`	one of the standard clustering methods to be used (fastcluster::hclust is used if available or stats::hclust)
`distance.threshold`	similarity threshold for grouping interdependent aspects
`corr.power`	power to which the product of loading and score correlation is raised
`n.cores`	number of cores to use during processing
`abs`	Boolean of whether to use absolute correlation
`...`	additional arguments are passed to the pagoda.view.aspects() method during plotting

a list structure analogous to that returned by pagoda.top.aspects(), but with addition of a $cnam element containing a list of aspects summarized by each row of the new (reduced) $xv and $xvw

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
tamr <- pagoda.reduce.loading.redundancy(tam, pwpca)