runCCA: Canonical Correspondence Analysis and Redundancy Analysis
In FelixErnst/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

getCCA(x, ...)

addCCA(x, ...)

getRDA(x, ...)

addRDA(x, ...)

calculateCCA(x, ...)

runCCA(x, ...)

## S4 method for signature 'ANY'
getCCA(x, formula, data, ...)

## S4 method for signature 'SummarizedExperiment'
getCCA(
  x,
  formula = NULL,
  col.var = variables,
  variables = NULL,
  test.signif = TRUE,
  assay.type = assay_name,
  assay_name = exprs_values,
  exprs_values = "counts",
  ...
)

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

calculateRDA(x, ...)

runRDA(x, ...)

## S4 method for signature 'ANY'
getRDA(x, formula, data, ...)

## S4 method for signature 'SummarizedExperiment'
getRDA(
  x,
  formula = NULL,
  col.var = variables,
  variables = NULL,
  test.signif = TRUE,
  assay.type = assay_name,
  assay_name = exprs_values,
  exprs_values = "counts",
  ...
)

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

Arguments

`x`	`TreeSummarizedExperiment`.
`...`	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. (Default: `"euclidean"`) `scale`: `Logical scalar`. Should the expression values be standardized? `scale` is disabled when using `*RDA` functions. Please scale before performing RDA. (Default: `TRUE`) `na.action`: `function`. Action to take when missing values for any of the variables in `formula` are encountered. (Default: `na.fail`) `full` `Logical scalar`. Should all the results from the significance calculations be returned. When `FALSE`, only summary tables are returned. (Default: `FALSE`) `homogeneity.test`: `Character scalar`. Specifies the significance test used to analyse `vegan::betadisper` results. Options include 'permanova' (`vegan::permutest`), 'anova' (`stats::anova`) and 'tukeyhsd' (`stats::TukeyHSD`). (Default: `"permanova"`) `permutations` a numeric value specifying the number of permutations for significance testing in `vegan::anova.cca`. (Default: `999`)
`formula`	`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 `col.var`. `col.var` and `formula` can be missing, which turns the CCA analysis into a CA analysis and dbRDA into PCoA/MDS.
`data`	`data.frame` or coarcible to one. The covariance table including covariates defined by `formula`.
`col.var`	`Character scalar`. When `x` is a `SummarizedExperiment`,`col.var` can be used to specify variables from `colData`.
`variables`	Deprecated. Use `col.var` instead.
`test.signif`	`Logical scalar`. Should the PERMANOVA and analysis of multivariate homogeneity of group dispersions be performed. (Default: `TRUE`)
`assay.type`	`Character scalar`. Specifies which assay to use for calculation. (Default: `"counts"`)
`assay_name`	Deprecated. Use `assay.type` instead.
`exprs_values`	Deprecated. Use `assay.type` instead.
`altexp`	`Character scalar` or `integer scalar`. Specifies an alternative experiment containing the input data.
`name`	`Character scalar`. A name for the `reducedDim()` where results will be stored. (Default: `"CCA"`)

Details

*CCA functions utilize vegan:cca and *RDA functions vegan:dbRDA. By default, dbRDA is done with euclidean distances, which is equivalent to RDA. col.var and formula can be missing, which turns the CCA analysis into a CA analysis and dbRDA into PCoA/MDS.

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 getCCA a matrix with samples as rows and CCA dimensions as columns. Attributes include output from scores, eigenvalues, the cca/rda object and significance analysis results.

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

Examples

library(miaViz)
data("enterotype", package = "mia")
tse <- enterotype

# Perform CCA and exclude any sample with missing ClinicalStatus
tse <- addCCA(
    tse,
    formula = data ~ ClinicalStatus,
    na.action = na.exclude
    )

# Plot CCA
plotCCA(tse, "CCA", colour_by = "ClinicalStatus")

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

tse <- transformAssay(tse, method = "relabundance")

# Specify dissimilarity measure
tse <- addRDA(
    tse,
    formula = data ~ ClinicalStatus,
    assay.type = "relabundance",
    method = "bray",
    name = "RDA_bray",
    na.action = na.exclude
    )

# To scale values when using *RDA functions, use
# transformAssay(MARGIN = "features", ...) 
tse <- transformAssay(tse, method = "standardize", MARGIN = "features")

# 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, "standardize"))) == 0, ]

# Calculate RDA
tse <- addRDA(
   tse,
   formula = data ~ ClinicalStatus,
   assay.type = "standardize",
   name = "rda_scaled",
   na.action = na.omit
   )

# Plot RDA
plotRDA(tse, "rda_scaled", colour_by = "ClinicalStatus")

# A common choice along with PERMANOVA is ANOVA when statistical significance
# of homogeneity of groups is analysed. Moreover, full significance test
# results can be returned.
tse <- addRDA(
    tse,
    formula = data ~ ClinicalStatus,
    homogeneity.test = "anova",
    full = TRUE
    )

# Example showing how to pass extra parameters, such as 'permutations',
# to anova.cca
tse <- addRDA(
    tse,
    formula = data ~ ClinicalStatus,
    permutations = 500
    )

FelixErnst/mia documentation built on Nov. 18, 2024, 5:02 a.m.