Get the mzrt profile and group information as a mzrt list and/or save them as csv or rds for further analysis.

Usage

getmzrt(
  xset,
  name = NULL,
  mzdigit = 4,
  rtdigit = 1,
  method = "medret",
  value = "into",
  eic = FALSE,
  type = "o"
)

Arguments

xset: xcmsSet/XCMSnExp objects
name: file name for csv and/or eic file, default NULL
mzdigit: m/z digits of row names of data frame, default 4
rtdigit: retention time digits of row names of data frame, default 1
method: parameter for groupval or featureDefinitions function, default medret
value: parameter for groupval or featureDefinitions function, default into
eic: logical, save xcmsSet and xcmsEIC objects for further investigation with the same name of files, you will need raw files in the same directory as defined in xcmsSet to extract the EIC based on the binned data. You could use `plot` to plot EIC for specific peaks. For example, `plot(xcmsEIC,xcmsSet,groupidx = 'M123.4567T278.9')` could show the EIC for certain peaks with m/z 206 and retention time 2789. default F
type: csv format for further analysis, m means Metaboanalyst, a means xMSannotator, p means Mummichog(NA values are imputed by `getimputation`, and F test is used here to generate stats and p value), o means full information csv (for `pmd` package), default o. mapo could output all those format files.

Value

mzrt object, a list with mzrt profile and group information

References

Smith, C.A., Want, E.J., O’Maille, G., Abagyan, R., Siuzdak, G., 2006. XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification. Anal. Chem. 78, 779–787.

Examples

if (FALSE) { # \dontrun{
library(faahKO)
cdfpath <- system.file('cdf', package = 'faahKO')
xset <- getdata(cdfpath, pmethod = ' ')
getmzrt(xset, name = 'demo', type = 'mapo')
} # }