normalized_data_batch_qc: normalized_data_batch_qc

In RAMClustR: Mass Spectrometry Metabolomics Feature Clustering and Interpretation

normalized_data_batch_qc

Description

normalize data using batch.qc

Usage

normalized_data_batch_qc(
  data = NULL,
  batch = NULL,
  order = NULL,
  qc = NULL,
  qc.inj.range = 20,
  output.plot = FALSE
)

Arguments

data	feature in ms/msms level data
batch	integer vector with length equal to number of injections in xset or csv file or dataframe
order	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
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

normalized data.

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