Applies a transformation to a dataset from a MultiDataSet object

Applies a transformation to a dataset from a MultiDataSet object. Implemented transformations are: Variance Stabilising Normalisation (from the vsn package), Variance Stabilising Transformation (from the DESeq2 package - only for count data), and appropriate feature-wise normalisation through the BestNormalise package.

Usage

transform_dataset(
  mo_data,
  dataset,
  transformation,
  return_multidataset = FALSE,
  return_matrix_only = FALSE,
  verbose = TRUE,
  log_base = 2,
  pre_log_function = zero_to_half_min,
  method,
  ...
)

Arguments

mo_data: A MultiDataSet-class object.
dataset: Character, name of the dataset to transform.
transformation: Character, transformation to be applied. Possible values are: vsn, vst-deseq2, logx best-normalize-auto or best-normalize-manual. See Details.
return_multidataset: Logical, should a MultiDataSet object with the original data replaced by the transformed data returned? If FALSE, the output of the function depends on return_matrix_only. Default value is FALSE.
return_matrix_only: Logical, should only the transformed matrix be returned? If TRUE, the function will return a matrix. If FALSE, the function instead returns a list with the transformed data as well as other information relevant to the transformation. Ignored if return_multidataset is TRUE. Default value is FALSE.
verbose: Logical, should information about the transformation be printed? Default value is TRUE.
log_base: Numeric, the base with respect to which logarithms are computed. Default value is 2. Only used if transformation = 'logx'.
pre_log_function: Function that will be applied to the matrix before the log transformation (e.g. to apply an offset to the values to avoid issues with zeros). Default value is the zero_to_half_min() function. Only used if transformation = 'logx'.
method: Character, if transformation = 'best-normalize-manual', which normalisation method should be applied. See possible values in transform_bestNormalise_manual(). Ignored for other transformations.
...: Further arguments passed to the bestNormalize::bestNormalize() function or the method function from the bestNormalize package.

Value

if return_multidataset = TRUE: a MultiDataSet::MultiDataSet object, in which the original data for the transformed dataset has been replaced.
if return_multidataset = FALSE and return_matrix_only = TRUE: a matrix with the transformed data.
if return_multidataset = FALSE and return_matrix_only = FALSE: a list with two elements, transformed_data containing a matrix of transformed data, and info_transformation containing information about the transformation (depends on the transformation applied).

Details

Currently implemented transformations and recommendations based on dataset type:

vsn: Variance Stabilising normalisation, implemented in the vsn::justvsn() function from the vsn package. This method was originally developed for microarray intensities. This transformation is recommended for microarray, metabolome, chemical or other intensity-based datasets. In practice, applies the transform_vsn() function.
vst-deseq2: Variance Stabilising Transformation, implemented in the DESeq2::varianceStabilizingTransformation() function from the DESeq2 package. This method is applicable to count data only. This transformation is recommended for RNAseq or similar count-based datasets. In practice, applies the transform_vst() function.
logx: log-transformation (default to log2, but base can be specified). In practice, applies the transform_logx() function.
best-normalize-auto: most appropriate normalisation method automatically selected from a number of options, implemented in the bestNormalize::bestNormalize() function from the bestNormalize package. This transformation is recommended for phenotypes that are each measured on different scales (since the transformation method selected will potentially be different across the features), preferably with a reasonable number of features (less than 100) to avoid large computation times. In practice, applies the transform_bestNormalise_auto() function.
best-normalize-manual: performs the same transformation (specified through the method argument) to each feature of a dataset. This transformation is recommended for phenotypes data in which the different phenotypes are measured on the same scale. The different normalisation methods are:
- "arcsinh_x": data is transformed as log(x + sqrt(x^2 + 1));
- "boxcox": Box Cox transformation;
- "center_scale": data is centered and scaled;
- "exp_x": data is transformed as exp(x);
- "log_x": data is transformed as log_b(x+a) (a and b either selected automatically per variable or passed as arguments);
- "orderNorm": Ordered Quantile technique;
- "sqrt_x": data transformed as sqrt(x + a) (a selected automatically per variable or passed as argument),
- "yeojohnson": Yeo-Johnson transformation.