rc.cmpd.filter.blanks {RAMClustR}

rc.cmpd.filter.blanks

Description

used to remove compounds which are found at similar intensity in blank samples. Only applied after clustering. see also rc.feature.filter.blanks for filtering at the feature level (only done before clustering).

Usage

rc.cmpd.filter.blanks(
  ramclustObj = NULL,
  qc.tag = "QC",
  blank.tag = "blank",
  sn = 3,
  remove.blanks = TRUE
)

Arguments

ramclustObj

ramclustObj containing SpecAbund dataframe.

qc.tag

character vector of length one or two. If length is two, enter search string and factor name in $phenoData slot (i.e. c("QC", "sample.type"). If length one (i.e. "QC"), will search for this string in the 'sample.names' slot by default.

blank.tag

see 'qc.tag' , but for blanks to use as background.

sn

numeric defines the ratio for 'signal'. i.e. sn = 3 indicates that signal intensity must be 3 fold higher in sample than in blanks, on average, to be retained.

remove.blanks

logical. TRUE by default. this removes any recognized blanks samples from the SpecAbund sets after they are used to filter contaminant compounds

Details

This function removes compounds which contain signal in QC samples comparable to blanks.

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.