runCCA {mia}R Documentation

Canonical Correspondance Analysis

Description

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

Usage

calculateCCA(x, ...)

runCCA(x, ...)

calculateRDA(x, ...)

runRDA(x, ...)

## S4 method for signature 'ANY'
calculateCCA(x, formula, variables, scale = TRUE)

## S4 method for signature 'SummarizedExperiment'
calculateCCA(x, formula, ..., exprs_values = "counts")

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

## S4 method for signature 'ANY'
calculateRDA(x, formula, variables, scale = TRUE)

## S4 method for signature 'SummarizedExperiment'
calculateRDA(x, formula, ..., exprs_values = "counts")

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

Arguments

x

For calculateCCA a numeric matrix with columns as samples or a SummarizedExperiment.

For runCCA a SingleCellExperiment or a derived object.

...

additional arguments not used.

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

a data.frame or an object coercible to one containing the variables to use. Can be missing, which turns the CCA analysis into a CA analysis. All variables are used. Please subset, if you want to consider only some of them.

scale

Logical scalar, should the expression values be standardized?

exprs_values

a single character value for specifying which assay to use for calculation.

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.

Value

For calculateCCA a matrix with samples as rows and CCA dimensions as columns

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

See Also

For more details on the actual implementation see cca and rda

Examples

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

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

[Package mia version 1.1.13 Index]