pre_mQTL: Statistical Recoupling of variables for mQTL analysis
In mQTL.NMR: Metabolomic Quantitative Trait Locus Mapping for 1H NMR data

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

Makes use of SRV to preprocess metabolomic data for dimensionality reduction by statistical recoupling of variables

1	pre_mQTL(infile, outfile, RedMet="SRV", met="sum", corrT = 0.9, BinWidth=0.01)

`infile`	metabolomic datafile in csvs format
`outfile`	reduced metabolomic datafile in csvs format
`met`	a charcater specifying the used statistical summary
`RedMet`	a charcater indicating the used dimensionality reduction method: Redmet="SRV" for statistical recoupling of variables and Redmet="bin" to apply the bining approach
`corrT`	a numerical parameter indicating correlation threshold
`BinWidth`	a numerical parameter indicating the bining width

mQTL-NMR package implements two dimensionality reduction methods. The first one concerns the SRV algorithm which forms clusters of variables using a measure of a local spectral dependency. The second one concerns the classical bining method which divides the spectra into evenly spaced windows (bins) whose width commonly ranges between 0.001 and 0.05 ppm.

variables are associated into a series of clusters (or bins). This function provides in output the paramaters of the clusters (min and max borders, mean,...)

Lyamine Hedjazi

- Blaise,B. et al (2009) Statistical recoupling prior to significance testing in nuclear magnetic resonance based metabonomics, Anal. Chem., 81(15), 6242-6251. - S praul, M.; Neidig, P.; Klauck, U.; Kessler, P.; Holmes, E.; Nicholson, J. K.; Sweatman, B.C.; Salman, S.R.; Farrant, R.D.; Rahr, E.; et al. J.Pharm. Biomed. Anal. 1994, 12, 1215-1225.

SRV,post_mQTL

# Download data files

load_datafiles()

# Format data

format_mQTL(phenofile,genofile,physiodat,cleandat,cleangen)

# Constant Sum normlisation
nmeth<-'CS'
normalise_mQTL(cleandat,CSnorm,nmeth)

# Alignment
align_mQTL(CSnorm,aligdat)

# Dimensionality reduction
met="rectangle" # choose the statistical summarizing measure ("max","sum","trapez",...)
RedMet="SRV" # reduction method ("SRV" or "bin")

pre_mQTL(aligdat, reducedF, RedMet="SRV",met, corrT=0.9)