transformAssay | R Documentation |
Variety of transformations for abundance data, stored in assay
.
See details for options.
transformAssay(
x,
assay.type = "counts",
assay_name = NULL,
method = c("alr", "chi.square", "clr", "frequency", "hellinger", "log", "log10",
"log2", "max", "normalize", "pa", "range", "rank", "rclr", "relabundance", "rrank",
"standardize", "total", "z"),
MARGIN = "samples",
name = method,
pseudocount = FALSE,
...
)
## S4 method for signature 'SummarizedExperiment'
transformAssay(
x,
assay.type = "counts",
assay_name = NULL,
method = c("alr", "chi.square", "clr", "frequency", "hellinger", "log", "log10",
"log2", "max", "normalize", "pa", "range", "rank", "rclr", "relabundance", "rrank",
"standardize", "total", "z"),
MARGIN = "samples",
name = method,
pseudocount = FALSE,
...
)
x |
|
assay.type |
|
assay_name |
Deprecated. Use |
method |
|
MARGIN |
|
name |
|
pseudocount |
|
... |
additional arguments passed on to
|
transformAssay
function provides a variety of options for
transforming abundance data. The transformed data is calculated and stored
in a new assay
.
The transformAssay
provides sample-wise (column-wise) or feature-wise
(row-wise) transformation to the abundance table
(assay) based on specified MARGIN
.
The available transformation methods include:
'alr', 'chi.square', 'clr', 'frequency', 'hellinger', 'log', 'normalize', 'pa', 'rank', 'rclr'
'relabundance', 'rrank', 'standardize', 'total': please refer to
decostand
for details.
'log10': log10 transformation can be used for reducing the skewness of the data.
log10 = \log_{10} x
where x
is a single value of data.
'log2': log2 transformation can be used for reducing the skewness of the data.
log2 = \log_{2} x
where x
is a single value of data.
transformAssay
returns the input object x
, with a new
transformed abundance table named name
added in the assay
.
Leo Lahti and Tuomas Borman. Contact: microbiome.github.io
data(esophagus, package="mia")
tse <- esophagus
# By specifying 'method', it is possible to apply different transformations,
# e.g. compositional transformation.
tse <- transformAssay(tse, method = "relabundance")
# The target of transformation can be specified with "assay.type"
# Pseudocount can be added by specifying 'pseudocount'.
# Perform CLR with smallest positive value as pseudocount
tse <- transformAssay(tse, assay.type = "relabundance", method = "clr",
pseudocount = TRUE
)
head(assay(tse, "clr"))
# With MARGIN, you can specify the if transformation is done for samples or
# for features. Here Z-transformation is done feature-wise.
tse <- transformAssay(tse, method = "standardize", MARGIN = "features")
head(assay(tse, "standardize"))
# Name of the stored table can be specified.
tse <- transformAssay(tse, method="hellinger", name="test")
head(assay(tse, "test"))
# pa returns presence absence table.
tse <- transformAssay(tse, method = "pa")
head(assay(tse, "pa"))
# rank returns ranks of taxa.
tse <- transformAssay(tse, method = "rank")
head(assay(tse, "rank"))
# In order to use other ranking variants, modify the chosen assay directly:
assay(tse, "rank_average", withDimnames = FALSE) <- colRanks(
assay(tse, "counts"), ties.method = "average", preserveShape = TRUE)
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