scde (version 2.0.1)

pagoda.reduce.redundancy: 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)