evaluateDimRed {pipeComp} | R Documentation

## evaluateDimRed

### Description

Gathers evaluation statistics on a reduced space using known cell labels. If using 'evaluteDimRed' in a custom pipeline, you will probably want to use 'pipeComp:::.aggregateDR' as the corresponding aggregation function.

### 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. If missing, will attempt to use default covariates. To disable, set 'covars=c()'.

### 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

[Package pipeComp version 1.4.0 Index]