runCCA: Canonical Correspondence Analysis and Redundancy Analysis
In microbiome/mia: Microbiome analysis

runCCA

R Documentation

Canonical Correspondence Analysis and Redundancy Analysis

Description

These functions perform Canonical Correspondence Analysis on data stored in a SummarizedExperiment.

Usage

calculateCCA(x, ...)

runCCA(x, ...)

calculateRDA(x, ...)

runRDA(x, ...)

## S4 method for signature 'ANY'
calculateCCA(x, ...)

## S4 method for signature 'SummarizedExperiment'
calculateCCA(
  x,
  formula,
  variables,
  test.signif = TRUE,
  assay.type = assay_name,
  assay_name = exprs_values,
  exprs_values = "counts",
  scores = "wa",
  ...
)

## S4 method for signature 'SingleCellExperiment'
runCCA(x, formula, variables, altexp = NULL, name = "CCA", ...)

## S4 method for signature 'ANY'
calculateRDA(x, ...)

## S4 method for signature 'SummarizedExperiment'
calculateRDA(
  x,
  formula,
  variables,
  test.signif = TRUE,
  assay.type = assay_name,
  assay_name = exprs_values,
  exprs_values = "counts",
  scores = "wa",
  ...
)

## S4 method for signature 'SingleCellExperiment'
runRDA(x, formula, variables, altexp = NULL, name = "RDA", ...)

Arguments

`x`	For `calculate` a `SummarizedExperiment` or a numeric matrix with columns as samples For `run` a `SingleCellExperiment` or a derived object.
`...`	additional arguments passed to vegan::cca or vegan::dbrda and other internal functions. `method` a dissimilarity measure to be applied in dbRDA and possible following homogeneity test. (By default: `method="euclidean"`) `scale` a logical scalar, should the expression values be standardized? `scale` is disabled when using `*RDA` functions. Please scale before performing RDA. (By default: `scale=TRUE`) `full` a logical scalar, should all the results from the significance calculations be returned. When `full=FALSE`, only summary tables are returned. (By default: `full=FALSE`) `homogeneity.test` a single `character` value for specifying the significance test used to analyse `vegan::betadisper` results. Options include 'permanova' (`vegan::permutest`), 'anova' (`stats::anova`) and 'tukeyhsd' (`stats::TukeyHSD`). (By default: `homogeneity.test="permanova"`)
`formula`	If `x` is a `SummarizedExperiment` a formula can be supplied. Based on the right-hand side of the given formula `colData` is subset to `variables`. `variables` and `formula` can be missing, which turns the CCA analysis into a CA analysis and dbRDA into PCoA/MDS.
`variables`	When `x` is a `SummarizedExperiment`, `variables` can be used to specify variables from `colData`. When `x` is a matrix, `variables` is a `data.frame` or an object coercible to one containing the variables to use. All variables are used. Please subset, if you want to consider only some of them. `variables` and `formula` can be missing, which turns the CCA analysis into a CA analysis and dbRDA into PCoA/MDS.
`test.signif`	a logical scalar, should the PERMANOVA and analysis of multivariate homogeneity of group dispersions be performed. (By default: `test.signif = TRUE`)
`assay.type`	a single `character` value for specifying which assay to use for calculation.
`assay_name`	a single `character` value for specifying which assay to use for calculation. (Please use `assay.type` instead. At some point `assay_name` will be disabled.)
`exprs_values`	a single `character` value for specifying which assay to use for calculation. (Please use `assay.type` instead.)
`scores`	A string specifying scores to be returned. Must be 'wa' (site scores found as weighted averages (cca) or weighted sums (rda) of v with weights Xbar, but the multiplying effect of eigenvalues removed) or 'u' ((weighted) orthonormal site scores). (By default: `scores='wa'`)
`altexp`	String or integer scalar specifying an alternative experiment containing the input data.
`name`	String specifying the name to be used to store the result in the reducedDims of the output.

Details

*CCA functions utilize vegan:cca and *RDA functions vegan:dbRDA. By default dbRDA is done with euclidean distances which equals to RDA.

Significance tests are done with vegan:anova.cca (PERMANOVA). Group dispersion, i.e., homogeneity within groups is analyzed with vegan:betadisper (multivariate homogeneity of groups dispersions (variances)) and statistical significance of homogeneity is tested with a test specified by homogeneity.test parameter.

Value

For calculateCCA a matrix with samples as rows and CCA dimensions as columns. Attributes include calculated cca/rda object and significance analysis results.

For runCCA a modified x with the results stored in reducedDim as the given name.

Examples

library(scater)
data(GlobalPatterns)
GlobalPatterns <- runCCA(GlobalPatterns, data ~ SampleType)
plotReducedDim(GlobalPatterns,"CCA", colour_by = "SampleType")

# Fetch significance results
attr(reducedDim(GlobalPatterns, "CCA"), "significance")

GlobalPatterns <- runRDA(GlobalPatterns, data ~ SampleType)
plotReducedDim(GlobalPatterns,"CCA", colour_by = "SampleType")

# Specify dissimilarity measure
GlobalPatterns <- transformAssay(GlobalPatterns, method = "relabundance")
GlobalPatterns <- runRDA(
    GlobalPatterns, data ~ SampleType, assay.type = "relabundance",  method = "bray")

# To scale values when using *RDA functions, use transformAssay(MARGIN = "features", ...) 
tse <- GlobalPatterns
tse <- transformAssay(tse, MARGIN = "features", method = "z")
# Data might include taxa that do not vary. Remove those because after z-transform
# their value is NA
tse <- tse[ rowSums( is.na( assay(tse, "z") ) ) == 0, ]
# Calculate RDA
tse <- runRDA(tse, formula = data ~ SampleType,
              assay.type = "z", name = "rda_scaled", na.action = na.omit)
# Plot
plotReducedDim(tse,"rda_scaled", colour_by = "SampleType")
# A common choice along with PERMANOVA is ANOVA when statistical significance
# of homogeneity of groups is analysed. Moreover, full signficance test results
# can be returned.
 tse <- runRDA(tse, data ~ SampleType, homogeneity.test = "anova", full = TRUE)

microbiome/mia documentation built on April 27, 2024, 4:04 a.m.