pagoda.reduce.redundancy: Collapse aspects driven by similar patterns (i.e. separate...

In hms-dbmi/scde: Single Cell Differential Expression

Collapse aspects driven by similar patterns (i.e. separate the same sets of cells)

Description

Examines PC loading vectors underlying the identified aspects and clusters aspects based on score correlation. Clusters of aspects driven by the same patterns are determined based on the distance.threshold.

Usage

pagoda.reduce.redundancy(tamr, distance.threshold = 0.2,
  cluster.method = "complete", distance = NULL,
  weighted.correlation = TRUE, plot = FALSE, top = Inf, trim = 0,
  abs = FALSE, ...)

Arguments

tamr	output of pagoda.reduce.loading.redundancy()
distance.threshold	similarity threshold for grouping interdependent aspects
cluster.method	one of the standard clustering methods to be used (fastcluster::hclust is used if available or stats::hclust)
distance	distance matrix
weighted.correlation	Boolean of whether to use a weighted correlation in determining the similarity of patterns
plot	Boolean of whether to show plot
top	Restrict output to the top n aspects of heterogeneity
trim	Winsorization trim to use prior to determining the top aspects
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)
tamr2 <- pagoda.reduce.redundancy(tamr, distance.threshold = 0.9, plot = TRUE, labRow = NA, labCol = NA, box = TRUE, margins = c(0.5, 0.5), trim = 0)

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