sb_corr {structToolbox} | R Documentation

## Signal/batch correction for mass spectrometry data

### Description

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`.

### Details

This object makes use of functionality from the following packages:

- `pmp`

### 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

### References

Jankevics A, Lloyd GR, Weber RJM (2021). pmp: Peak Matrix Processing and signal batch correction for metabolomics datasets. R package version 1.5.0.

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='run_order',batch_col='batch_no',qc_col='class')

[Package structToolbox version 1.6.1 Index]