evaluateDimRed: evaluateDimRed

In pipeComp: pipeComp pipeline benchmarking framework

Description

Gathers evaluation statistics on a reduced space using known cell labels.

Usage

evaluateDimRed(x, clusters = NULL, n = c(10, 20, 50), covars)

Arguments

x	The matrix of the reduced space, with cells as rows and components as columns
clusters	The vector indicating each cell's cluster.
n	A numeric vector indiciating the number of top dimensions at which to gather statistics (default 'c(10,20,50)'). Will use all available dimensions if a higher number is given.
covars	A character vectors containing any additional covariates (column names of 'colData') to track during evalutation.

Value

A list with the following components: * silhouettes: a matrix of the silhouette for each cell-cluster pair at each value of 'n' * clust.avg.silwidth: a matrix of the cluster average width at each value of 'n' * R2: the proportion of variance in each component (up to 'max(n)') that is explained by the clusters (i.e. R-squared of a linear model).

Examples

# random data
library(scater)
sce <- runPCA(logNormCounts(mockSCE(ngenes = 500)))
sce <- addPerCellQC(sce)
# random population labels
sce$cluster <- sample(LETTERS[1:3], ncol(sce), replace=TRUE)
res <- evaluateDimRed(sce, sce$cluster, covars=c("sum","detected"))
# average silhouette widths:
res$clust.avg.silwidth
# adjusted R2 of covariates:
res$covar.adjR2

pipeComp documentation built on Nov. 8, 2020, 7:35 p.m.