rc.feature.normalize.qc {RAMClustR}

rc.feature.normalize.qc

Description

extractor for xcms objects in preparation for clustering

Usage

rc.feature.normalize.qc(
  ramclustObj = NULL,
  order = NULL,
  batch = NULL,
  qc = NULL,
  output.plot = FALSE,
  p.cut = 0.05,
  rsq.cut = 0.1,
  p.adjust = "none"
)

Arguments

ramclustObj

ramclustObj containing MSdata with optional MSMSdata (MSe, DIA, idMSMS)

order

integer vector with length equal to number of injections in xset or csv file

batch

integer vector with length equal to number of injections in xset or csv file

qc

logical vector with length equal to number of injections in xset or csv file or dataframe

output.plot

logical: if TRUE (default), plots are output to PDF.

p.cut

numeric when run order correction is applied, only features showing a run order vs signal with a linear p-value (after FDR correction) < p.cut will be adjusted. also requires r-squared < rsq.cut.

rsq.cut

numeric when run order correction is applied, only features showing a run order vs signal with a linear r-squared > rsq.cut will be adjusted. also requires p values < p.cut.

p.adjust

which p-value adjustment should be used? default = "none", see ?p.adjust

Details

This function offers normalization by run order, batch number, and QC sample signal intensity.

Each input vector should be the same length, and equal to the number of samples in the $MSdata set.

Input vector order is assumed to be the same as the sample order in the $MSdata set.

Value

ramclustR object with normalized data.

Author(s)

Corey Broeckling

References

Broeckling CD, Afsar FA, Neumann S, Ben-Hur A, Prenni JE. RAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data. Anal Chem. 2014 Jul 15;86(14):6812-7. doi: 10.1021/ac501530d. Epub 2014 Jun 26. PubMed PMID: 24927477.