NormQcsamples: Normalisation methods based on quality control samples
In NormalizeMets: Analysis of Metabolomics Data

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

This function is based on the quality control sample based robust LOESS (locally estimated scatterplot smoothing) signal correction (QC-RLSC) method as described by Dunn et al. (2011) and impletemented in statTarget: Luan H (2017).

1
2
3

NormQcsamples(featuredata, sampledata, method = c("rlsc"), span = 0,
  deg = 2, lg = TRUE, saveoutput = FALSE,
  outputname = "qcsample_results", ...)

`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.
`sampledata`	A dataframe that contains sample specific information. Unique sample names should be provided as row names. For this function, this should have, the batch number, the class and the run order, with column names 'batch', 'class' and 'order' respectively. For the QCs samples, 'class' should be allocated as 0.
`method`	A character string indicating the required normalization method. In this case, only "`rlsc` method as described by Dunn et al. (2011).
`span`	The smooting parameter. The default is 0.75.
`deg`	The degree for the polynomial fit. Must be either 0, 1, or 2. Defaults to degree 2 for local polynomial fits.
`lg`	A logical indicating whether the final normalized data matrix needs to be log transformed after rlsc method. Defaults to TRUE.
`saveoutput`	A logical indicating whether the normalised data matrix and related plots should be saved.
`outputname`	The name of the output file if the output has to be saved.
`...`	Extra input for the `statTarget::shiftCor` function.

The result is an object of class alldata.

Alysha M De Livera, Gavriel Olshansky

Luan H (2017). statTarget: Statistical Analysis of Metabolite Profile. R package version 1.6.0, https://github.com/13479776/statTarget.

Dunn,W.B., Broadhurst,D., Begley,P., Zelena,E., Francis-McIntyre,S., Anderson,N., Brown,M., Knowles,J.D., Halsall,A., Haselden,J.N. et al. (2011) Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc., 6, 1060-1083

statTarget::shiftCor.

##Reading the data
data(Didata)
NormQcsamples(sampledata=Didata$sampledata[order(Didata$sampledata$order),],
              featuredata=Didata$featuredata[order(Didata$sampledata$order),],
              saveoutput=FALSE)