runDPCoA | R Documentation |
Double Principal Correspondance analysis is made available via the
ade4
package in typical fashion. Results are stored in the
reducedDims
and are available for all the expected functions.
calculateDPCoA(x, y, ...)
## S4 method for signature 'ANY,ANY'
calculateDPCoA(
x,
y,
ncomponents = 2,
ntop = NULL,
subset_row = NULL,
scale = FALSE,
transposed = FALSE,
...
)
## S4 method for signature 'TreeSummarizedExperiment,missing'
calculateDPCoA(
x,
...,
assay.type = assay_name,
assay_name = exprs_values,
exprs_values = "counts",
tree_name = "phylo"
)
runDPCoA(x, ..., altexp = NULL, name = "DPCoA")
x |
For For |
y |
a |
... |
Currently not used. |
ncomponents |
Numeric scalar indicating the number of DPCoA dimensions to obtain. |
ntop |
Numeric scalar specifying the number of features with the highest
variances to use for dimensionality reduction. Alternatively |
subset_row |
Vector specifying the subset of features to use for dimensionality reduction. This can be a character vector of row names, an integer vector of row indices or a logical vector. |
scale |
Logical scalar, should the expression values be standardized? |
transposed |
Logical scalar, is x transposed with cells in rows? |
assay.type |
a single |
assay_name |
a single |
exprs_values |
a single |
tree_name |
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. |
In addition to the reduced dimension on the features, the reduced dimension
for samples are returned as well as sample_red
attribute.
eig
, feature_weights
and sample_weights
are
returned as attributes as well.
For calculateDPCoA
a matrix with samples as rows and CCA dimensions as
columns
For runDPCoA
a modified x
with the results stored in
reducedDim
as the given name
plotReducedDim
reducedDims
data(esophagus)
dpcoa <- calculateDPCoA(esophagus)
head(dpcoa)
esophagus <- runDPCoA(esophagus)
reducedDims(esophagus)
library(scater)
plotReducedDim(esophagus, "DPCoA")
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