runCCA {mia} | R Documentation |
These functions perform Canonical Correspondance Analysis on data stored
in a SummarizedExperiment
.
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")
x |
For For |
... |
additional arguments not used. |
formula |
If |
variables |
a |
scale |
Logical scalar, should the expression values be standardized? |
exprs_values |
a single |
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. |
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
For more details on the actual implementation see cca
and rda
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")