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Signal/batch correction for mass spectrometry data

Source: R/sb_corr.R
sb_corr.Rd

Applies Quality Control Robust Spline (QC-RSC) method to correct for signal drift and batch differences in mass spectrometry data.

Usage

sb_corr(
  order_col,
  batch_col,
  qc_col,
  smooth = 0,
  use_log = TRUE,
  min_qc = 4,
  qc_label = "QC",
  spar_lim = c(-1.5, 1.5),
  ...
)

Arguments

order_col

(character) The column name of sample_meta indicating the run order of the samples.

batch_col

(character) The column name of sample_meta indicating the batch each sample was measured in.

qc_col

(character) The column name of sample_meta indicating the group each sample is a member of.

smooth

(numeric) The amount of smoothing applied (0 to 1). If set to 0 the smoothing parameter will be estimated using leave-one-out cross-validation. The default is 0.

use_log

(logical) Log tranformation. Allowed values are limited to the following:

  • "TRUE": The data is log transformed prior to performing signal correction.

  • "FALSE": Signal correction is applied to the input data.

The default is TRUE.

min_qc

(numeric) The minimum number of QC samples required for signal correction. The default is 4.

qc_label

(character) The label used to identify QC samples. The default is "QC".

spar_lim

(numeric) A two element vector specifying the upper and lower limits when spar = 0. Allows the value of spar to be constrained within these limits to prevent overfitting. The default is c(-1.5, 1.5).

...

Additional slots and values passed to struct_class.

Value

A sb_corr object with the following output slots:

corrected(DatasetExperiment) The DatasetExperiment after signal/batch correction has been applied.
fitted(data.frame) The fitted splines for each feature.

struct object

Details

This object makes use of functionality from the following packages:

  • pmp

Inheritance

A sb_corr object inherits the following struct classes:

[sb_corr] >> [model] >> [struct_class]

References

Jankevics A, Lloyd GR, Weber RJM (2025). pmp: Peak Matrix Processing and signal batch correction for metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.22.1, https://bioconductor.org/packages/pmp.

Kirwan JA, Broadhurst DI, Davidson RL, Viant MR (2013). "Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow." Analytical and Bioanalytical Chemistry, 405(15), 5147-5157.

Examples

M = sb_corr(
      order_col = character(0),
      batch_col = character(0),
      qc_col = character(0),
      smooth = 0,
      use_log = FALSE,
      min_qc = 4,
      qc_label = "QC",
      spar_lim = c(-1.5, 1.5))

M = sb_corr(order_col='run_order',batch_col='batch_no',qc_col='class')