transformAssay: Transform assay

In microbiome/mia: Microbiome analysis

Transform assay

Description

Variety of transformations for abundance data, stored in assay. See details for options.

Usage

transformAssay(x, ...)

## S4 method for signature 'SummarizedExperiment'
transformAssay(
  x,
  assay.type = "counts",
  assay_name = NULL,
  method = c("alr", "chi.square", "clr", "css", "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 'SingleCellExperiment'
transformAssay(x, altexp = altExpNames(x), ...)

Arguments

x	TreeSummarizedExperiment.
...	additional arguments passed e.g. on to vegan:decostand. reference: Character scalar. Used to to fill reference sample's column in returned assay when calculating alr. (Default: NA) ref_vals Deprecated. Use reference instead. percentile: Numeric scalar or NULL (css). Used to set the percentile value that calculates the scaling factors in the css normalization. If NULL, percentile is estimated from the data by calculating the portion of samples that exceed the threshold. (Default: NULL) scaling: Numeric scalar. Adjusts the normalization scale by dividing the calculated scaling factors, effectively changing the magnitude of the normalized counts. (Default: 1000). threshold: Numeric scalar. Specifies relative difference threshold and determines the first point where the relative change in differences between consecutive quantiles exceeds this threshold. (Default: 0.1).
assay.type	Character scalar. Specifies which assay to use for calculation. (Default: "counts")
assay_name	Deprecated. Use assay.type instead.
method	Character scalar. Specifies the transformation method.
MARGIN	Character scalar. Determines whether the transformation is applied sample (column) or feature (row) wise. (Default: "samples")
name	Character scalar. A name for the column of the colData where results will be stored. (Default: "method")
pseudocount	Logical scalar or numeric scalar. When TRUE, automatically adds half of the minimum positive value of assay.type (missing values ignored by default: na.rm = TRUE). When FALSE, does not add any pseudocount (pseudocount = 0). Alternatively, a user-specified numeric value can be added as pseudocount. (Default: FALSE).
altexp	Character vector or NULL. Specifies the names of alternative experiments to which the transformation should also be applied. If NULL, the transformation is only applied to the main experiment. (Default: altExpNames(x)).

Details

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.

• 'css': Cumulative Sum Scaling (CSS) can be used to normalize count data by accounting for differences in library sizes. By default, the function determines the normalization percentile for summing and scaling counts. If you want to specify the percentile value, good default value might be 0.5.The method is inspired by the CSS methods in metagenomeSeq package.

• '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.

Value

transformAssay returns the input object x, with a new transformed abundance table named name added in the assay.

References

Paulson, J., Stine, O., Bravo, H. et al. (2013) Differential abundance analysis for microbial marker-gene surveys Nature Methods 10, 1200–1202. doi:10.1038/nmeth.2658

Examples

data(GlobalPatterns)
tse <- GlobalPatterns

# 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"))

# Perform CSS normalization.
tse <- transformAssay(tse, method = "css")
head(assay(tse, "css"))

# 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)  

# Using altexp parameter. First agglomerate the data and then apply
# transformation.
tse <- GlobalPatterns
tse <- agglomerateByRanks(tse)
tse <- transformAssay(tse, method = "relabundance")
# The transformation is applied to all alternative experiments
altExp(tse, "Species")

microbiome/mia documentation built on Nov. 20, 2024, 1:12 a.m.