scde (version 2.0.1)

pagoda.reduce.loading.redundancy: Collapse aspects driven by the same combinations of genes

Description

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.

Usage

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, ...)

Arguments

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

Value

• 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

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