Sum up feature intensities per analyte

This function sums up feature intensities per analyte_id.

This is useful when you have multiple features (e.g. adducts, isotopes, in-source fragments) or isomers that you want to combine into a single analyte intensity value, such as LPC sn1 and sn2 species.

Usage

data_sum_features(data, qualifier_action = "include")

Arguments

data

MRMhubExperiment object

qualifier_action

Character. How to handle qualifier features. To sum them up separately select "separate", to include them in the sum if quantifier select "include", to not sum them up select "exclude".

Value

MRMhubExperiment object

Details

Only raw signal variables are aggregated across the transitions of an analyte: feature_intensity, feature_height and feature_area are summed, and feature_rt is averaged. feature_fwhm and feature_width are set to NA for merged analytes: the constituents are separate chromatographic peaks, so no aggregate of their peak widths describes the merged quantity.

Summing transitions redefines feature_intensity, so all values derived from the pre-merge intensities are invalidated and removed: normalized intensities, concentrations, drift/batch correction results and QC metrics. Re-run normalize_by_istd() and the quantitation/correction steps after merging. A message reports this when such values were present.

A merged analyte inherits the feature metadata (feature_class, feature_label, istd_feature_id) of its first constituent transition. A warning is issued when the constituents disagree, since the value that wins is then arbitrary – for istd_feature_id it silently decides which internal standard the merged analyte is normalized against.

is_quantifier is not inherited but determined by the merge: the merged analyte is a quantifier if any of its constituents is one. A quantifier combined with either a qualifier or another quantifier therefore yields a quantifier, whereas qualifiers merged among themselves remain a qualifier.

Experimental

This function is experimental and its behaviour may change. It overwrites the feature_id of features sharing an analyte_id in both the dataset and the analysis metadata, and the original feature_id is not backed up anywhere. It is intended to run early (before normalization/quantitation); running it on a processed object drops the derived variables (see Details). Use with caution and check the results carefully.