Perform multi-dimensional scaling (MDS)

Source: R/AllGenerics.R, R/getMDS.R

Perform multi-dimensional scaling (MDS) also know as Principal Coordinate Analysis (PCoA). These functions are wrappers for scater::calculateMDS.

getMDS(x, ...)

addMDS(x, ...)

# S4 method for class 'SingleCellExperiment'
addMDS(x, name = "MDS", ...)

# S4 method for class 'SingleCellExperiment'
getMDS(x, assay.type = "counts", ...)

# S4 method for class 'TreeSummarizedExperiment'
getMDS(x, assay.type = "counts", ...)

Arguments

x

a SummarizedExperiment object.

...

additional arguments.

  • FUN: Function. A function that is applied to calculate dissimilarity. (Default: getDissimilarity)

  • subset.result: Logical result. Specifies whether to subset x to match the result if some samples were removed during calculation. (Default: TRUE)

  • ntop: Numeric scalar. Number of the most variable features used. (Default: Inf)

name

Character scalar. A name for the reducedDim() where results will be stored. (Default: "MDS")

assay.type

Character scalar. Specifies the name of assay used in calculation. (Default: "counts")

Value

getMDS returns a MDS results. addMDS returns a x with MDS results added to its reducedDim(x, name).

Details

These functions are wrappers for scater::calculateMDS and scater::runMDS. While getMDS returns the results, addMDS adds them to reducedDim(x). The difference is that these functions apply microbiome-specific options such as getDissimilarity function by default.

Unlike scater::calculateMDS, which uses the 500 most variable features by default, these functions use all features (ntop = Inf). With rarefaction, read counts are summed over the features used, so filtering features can drop samples whose depth falls below sample.

See scater::calculateMDS for details.

Examples

library(mia)
library(scater)
library(patchwork)

data(GlobalPatterns)
tse <- GlobalPatterns

# Calculate PCoA with Bray-Curtis dissimilarity
tse <- transformAssay(tse, method = "relabundance")
tse <- addMDS(tse, assay.type = "relabundance", method = "bray")

# Calculate PCoA with Unifrac and rarefaction. (Note: increase iterations)
tse <- addMDS(tse, method = "unifrac", name = "unifrac")

# Calculate PCoA with Unifrac and rarefaction. (Note: increase iterations)
tse <- addMDS(tse, method = "unifrac", name = "unifrac_rare", niter = 2L)

# Use only the 500 most variable features
tse <- addMDS(
    tse, assay.type = "relabundance", method = "bray", ntop = 500,
    name = "MDS_top500")

# Visualize results
p1 <- plotReducedDim(tse, "unifrac", colour_by = "SampleType") +
    labs(title = "Not rarefied")
p2 <- plotReducedDim(tse, "unifrac_rare", colour_by = "SampleType") +
    labs(title = "Rarefied")
p1 + p2