rc.feature.filter.blanks: rc.feature.filter.blanks

In RAMClustR: Mass Spectrometry Metabolomics Feature Clustering and Interpretation

rc.feature.filter.blanks

Description

used to remove features which are found at similar intensity in blank samples

Usage

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

Arguments

ramclustObj	ramclustObj containing MSdata with optional MSMSdata (MSe, DIA, idMSMS)
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 MSdata and MSMSdata sets after they are used to filter contaminant features.

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.

RAMClustR documentation built on Oct. 20, 2023, 5:08 p.m.