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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 (????). pmp: Peak Matrix Processing and signal batch correction for metabolomics datasets. R package version 1.15.1.

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')