glog_transformation {pmp} | R Documentation

## Variance stabilising generalised logarithm (glog) transformation

### Description

Performs glog transformation on the data set. QC samples can be used to estimate technical variation in the data set and calculate transformation parameter λ (lambda). QC samples usually comprise a pool of aliquots taken from all other samples in the study and analysed repeatedly throughout an analytical batch.

### Usage

    glog_transformation(df, classes, qc_label, lambda = NULL)

### Arguments

`df` | A matrix-like (e.g. an ordinary matrix, a data frame) or RangedSummarizedExperiment-class object with all values of class `numeric()` or `integer()` of peak intensities, areas or other quantitative characteristic.
`classes` | `character()`, vector of class labels. Must be the same length as the number of sample in the input peak table. If input is `SummarizedExperiment` object, use `SummarizedExperiment_object$meta_data_column_name`.
`qc_label` | `character(1)` or `NULL`, class label used to identify QC samples.
`lambda` | `NULL` or `numeric(1)`, if not `NULL` will use provided value for glog lambda.

### Details

Many univariate and multivariate statistical tests require homogeneity and n ormality of dataset variance. Real-world metabolomics datasets often fail to meet these criteria due to asymmetric (i.e. non-'normal') and/or heteroscedatic (i.e. non-homogenous) variance structure. To address this issue, `glog` data transformations may be applied.
For each cell within the data matrix, transform the raw value (x) according to: log10(x + sqrt(x^2 + λ)). The parameter λ is typically calculated using quality control (QC) samples analysed throughout an analysis batch.

### Value

Object of class `SummarizedExperiment`. If input data are a matrix-like (e.g. an ordinary matrix, a data frame) object, function returns the same R data structure as input with all value of data type `numeric()`.

### References

Parsons HM et. al., BMC Bionf., 8(234), 2007. https://doi.org/10.1186/1471-2105-8-234

### Examples

    df <- MTBLS79[, MTBLS79$Batch == 1]
    out <- mv_imputation(df=df, method="knn")
    out <- glog_transformation (df=out, classes=df$Class,
        qc_label="QC")

[Package pmp version 1.0.0 Index]