This function corrects lipidomics feature intensities by subtracting interference (e.g., isotope overlap or in-source fragments). The correction is applied using the following formula: $$value\_corrected = value\_raw - value\_raw\_interfering\_feature \times proportion\_interference$$
The interfering features and their relative contributions must be defined in the feature metadata.
By default, sequential series of interferences (e.g., isotopic M+2
interferences of PC 34:2 > PC 34:“1 > PC 34:0) will be corrected in a
sequential manner. This means that the correction will applied iteratively,
starting with the most downstream feature in the series. To disable this
behavior, basing each correction on the raw signal of the interfering feature
set sequential_correction = FALSE
Usage
correct_interferences(
data = NULL,
variable = "feature_intensity",
sequential_correction = TRUE,
neg_to_na = FALSE
)Arguments
- data
MidarExperiment object containing lipidomics data.
- variable
Name of the variable to be corrected. Default:
feature_intensity.- sequential_correction
A logical indicating whether to apply corrections sequentially, starting with the most downstream feature. If
FALSE, corrections are based on the raw signal of the interfering features. IfFALSE, the correction will be based on the raw signal of the interfering feature.- neg_to_na
If
TRUE, negative or zero values after correction will be replaced withNA. Default:FALSE.
Details
For isotopic interference correction of MRM/PRM data, the relative isotope abundances needed for the calculation ('proportion_interference') can be calculated using the LICAR application (Gao et al., 2021), see below..
References
Gao L., Ji S, Burla B, Wenk MR, Torta F, Wenk MR, & Cazenave-Gassiot A (2021). LICAR: An Application for Isotopic Correction of Targeted Lipidomic Data Acquired with Class-Based Chromatographic Separations Using Multiple Reaction Monitoring. Analytical Chemistry, 93(6), 3163-3171. https://doi.org/10.1021/acs.analchem.0c04565