Canonical Correspondence Analysis and Redundancy Analysis

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

getCCA(x, ...)

addCCA(x, ...)

getRDA(x, ...)

addRDA(x, ...)

calculateCCA(x, ...)

runCCA(x, ...)

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

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

calculateRDA(x, ...)

runRDA(x, ...)

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

# S4 method for class '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 class '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. (By default: method="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 full=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). (By default: homogeneity.test="permanova")
permutations a numeric value specifying the number of permutations for significance testing in vegan::anova.cca. (By default: permutations=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 column of the colData where results will be stored. (Default: "CCA")

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.

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.

Examples

library(miaViz)
#> Loading required package: ggraph
#> 
#> Attaching package: ‘miaViz’
#> The following object is masked from ‘package:mia’:
#> 
#>     plotNMDS
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")
#> Too few points to calculate an ellipse
#> Too few points to calculate an ellipse


# Fetch significance results
attr(reducedDim(tse, "CCA"), "significance")
#> $permanova
#>                Df ChiSquare         F Pr(>F) Total variance Explained variance
#> Model           4  0.150386 0.7379888  0.676         1.8825         0.07988631
#> ClinicalStatus  4  0.150386 0.7379888  0.651         1.8825         0.07988631
#> Residual       34  1.732114        NA     NA         1.8825         0.92011369
#> 
#> $homogeneity
#>                Df    Sum Sq    Mean Sq        F N.Perm Pr(>F) Total variance
#> ClinicalStatus  4 0.2184213 0.05460533 1.757098    999  0.152       1.275039
#>                Explained variance
#> ClinicalStatus          0.1713056
#> 

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
   )
#> Warning: Certain samples are removed from the result because they did not include sufficient metadata.

# Plot RDA
plotRDA(tse, "rda_scaled", colour_by = "ClinicalStatus")
#> Too few points to calculate an ellipse
#> Too few points to calculate an ellipse


# 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
    )

Canonical Correspondence Analysis and Redundancy Analysis

Arguments

Value

Details

See also

Examples