pagoda.reduce.redundancy {scde} | R Documentation

## 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)

[Package scde version 2.20.0 Index]