rc.feature.normalize.batch.qc: rc.feature.normalize.batch.qc

In RAMClustR: Mass Spectrometry Metabolomics Feature Clustering and Interpretation

rc.feature.normalize.batch.qc

Description

normalize data using batch.qc

Usage

rc.feature.normalize.batch.qc(
  order = NULL,
  batch = NULL,
  qc = NULL,
  ramclustObj = NULL,
  qc.inj.range = 20,
  output.plot = FALSE
)

Arguments

order	integer vector with length equal to number of injections in xset or csv file or dataframe
batch	integer vector with length equal to number of injections in xset or csv file or dataframe
qc	logical vector with length equal to number of injections in xset or csv file or dataframe
ramclustObj	ramclustObj containing MSdata with optional MSMSdata (MSe, DIA, idMSMS)
qc.inj.range	integer: how many injections around each injection are to be scanned for presence of QC samples when using batch.qc normalization? A good rule of thumb is between 1 and 3 times the typical injection span between QC injections. i.e. if you inject QC ever 7 samples, set this to between 7 and 21. smaller values provide more local precision but make normalization sensitive to individual poor outliers (though these are first removed using the boxplot function outlier detection), while wider values provide less local precision in normalization but better stability to individual peak areas.
output.plot	logical set to TRUE to store plots

Value

ramclustR object with normalized data.

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