NormScaling: Normalisation methods based on scaling
In NormalizeMets: Analysis of Metabolomics Data

Description Usage Arguments Details Value Author(s) References See Also Examples

Normalise a metabolomic data matrix according to a specified scaling method.

1 2	NormScaling(featuredata, method = c("median", "mean", "sum", "ref"), refvec = NULL, saveoutput = FALSE, outputname = NULL, ...)

`featuredata`	featuredata A data frame in the featuredata format. This is a dataframe with metabolites in columns and samples in rows. Unique sample names should be provided as row names.
`method`	A character string indicating the required scaling-based normalization method. Must be one of "`median`", "`mean`", "`sum`", or "`ref`". See NormalizeMets Vignette for details.
`refvec`	A reference vector to be used with the method "`ref`".
`saveoutput`	A logical indicating whether the normalised data matrix should be saved as a .csv file.
`outputname`	The name of the output file if the output has to be saved.
`...`	Arguments to other functions

The normalisation methods based on scaling include normalisation to a total sum, or by the median or mean of each sample, and are denoted by "sum", "median", and "mean" respectively. The method "ref" normalises the metabolite abundances to a specified reference vector.

The result is an object of class alldata.

Alysha M De Livera, Gavriel Olshansky

De Livera, Alysha M De, M. Aho-Sysi, Laurent Jacob, J. Gagnon-Bartch, Sandra Castillo, J.A. Simpson, and Terence P. Speed. 2015. Statistical Methods for Handling Unwanted Variation in Metabolomics Data. Analytical Chemistry 87 (7). American Chemical Society: 3606-3615.

normFit.

   ## Reading the data
     data(mixdata)
     featuredata <- mixdata$featuredata
     sampledata<-mixdata$sampledata
     metabolitedata<-mixdata$metabolitedata
     refvec<-featuredata[,which(metabolitedata$type =="IS")[1]]
    
    ## Normalise by the median
    norm_med <- NormScaling(featuredata, method = "median")
    
    ## Normalise by a reference vector, in this case an internal standard
    norm_is <- NormScaling(featuredata, method = "ref", 
        refvec=refvec)

    ## Normalise by the sum
    norm_sum <- NormScaling(featuredata, method = "sum")

   ## Rla Plots after normalising by the median
    RlaPlots(norm_med$featuredata, group= sampledata$batch)