CAMERA (version 1.28.0)

groupCorr: EIC correlation grouping of LC/ESI-MS data

Description

Peak grouping after correlation information into pseudospectrum groups for an xsAnnotate object. Return an xsAnnotate object with grouping information.

Usage

groupCorr(object,cor_eic_th=0.75, pval=0.05, graphMethod="hcs", calcIso = FALSE, calcCiS = TRUE, calcCaS = FALSE, psg_list=NULL, xraw=NULL,  cor_exp_th=0.75, ...)

Arguments

object

The xsAnnotate object

cor_eic_th

Correlation threshold for EIC correlation

pval

p-value threshold for testing correlation of significance

graphMethod

Clustering method for resulting correlation graph. See calcPC for more details.

calcIso

Include isotope detection informationen for graph clustering

calcCiS

Calculate correlation inside samples

calcCaS

Calculate correlation accross samples

psg_list

Vector of pseudospectra indices. The correlation analysis will be only done for those groups

xraw

Optional xcmsRaw object, which should be used for raw data extraction

cor_exp_th

Threshold for intensity correlations across samples

...

Additional parameter

Details

The algorithm calculates different informations for group peaks into so called pseudospectra. This pseudospectra contains peaks, with have a high correlation between each other. So far three different kind of information are available. Correlation of intensities across samples (need more than 3 samples), EIC correlation between peaks inside a sample and additional the informationen about recognized isotope cluster can be included. After calculation of all these informations, they are combined as edge value into a graph object. A following graph clustering algorithm separate the peaks (nodes in the graph) into the pseudospectra.

See Also

calcCiS calcCaS calcPC xsAnnotate-class

Examples

 library(CAMERA)
 file        <- system.file('mzdata/MM14.mzdata', package = "CAMERA");
 xs          <- xcmsSet(file, method="centWave", ppm=30, peakwidth=c(5, 10));
 an          <- xsAnnotate(xs);
 an.group    <- groupFWHM(an);
 an.iso      <- findIsotopes(an.group); #optional step for using isotope information
 an.grp.corr <- groupCorr(an.iso, calcIso=TRUE);
 
 #For csv output
 # write.csv(file="peaklist_with_isotopes.csv",getPeaklist(an))

 #Multiple sample 
 library(faahKO)
 xs.grp       <- group(faahko)
 
 #With selected sample
 xsa          <- xsAnnotate(xs.grp, sample=1)
 xsa.group    <- groupFWHM(xsa)
 xsa.iso      <- findIsotopes(xsa.group) #optional step
 xsa.grp.corr <- groupCorr(xsa.iso, calcIso=TRUE)

 #With automatic selection
 xsa.auto     <- xsAnnotate(xs.grp)
 xsa.grp      <- groupFWHM(xsa.auto)
 xsa.iso      <- findIsotopes(xsa.grp) #optional step
 index        <- c(1,4) #Only group one and four will be calculate
 #We use also correlation across sample
 xsa.grp.corr <- groupCorr(xsa.iso, psg_list=index, calcIso=TRUE, calcCaS=TRUE)
 #Note: Group 1 and 4 have no subgroups