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Export an experiment to a Bioconductor SummarizedExperiment

Source: R/data-summarizedexperiment.R
save_dataset_summarizedexperiment.Rd

Converts an MRMhubExperiment to a SummarizedExperiment, the Bioconductor container for feature x sample data, and optionally to a LipidomicsExperiment for use with lipidr. This opens the experiment to the Bioconductor ecosystem - limma for differential abundance, POMA and pmp for preprocessing, ComplexHeatmap for visualization.

Usage

save_dataset_summarizedexperiment(
  data = NULL,
  path = NULL,
  variable = NULL,
  as = c("SummarizedExperiment", "LipidomicsExperiment"),
  filter_data = FALSE,
  overwrite = TRUE
)

Arguments

data

An MRMhubExperiment object.

path

Optional file path. When given, the object is written there with saveRDS() and returned invisibly; a .rds extension is appended if missing. When NULL (default) the object is returned.

variable

Feature variables to export as assays, e.g. "conc" or c("intensity", "conc"). NULL (default) exports every feature_* variable present in the data.

as

Class to produce. "SummarizedExperiment" (default) or "LipidomicsExperiment", which additionally requires the lipidr package and is only meaningful for lipidomics data.

filter_data

Use QC-filtered data (dataset_filtered, see filter_features_qc()) instead of the full dataset. Default FALSE.

overwrite

Overwrite an existing file at path. Default TRUE.

Value

A SummarizedExperiment (or LipidomicsExperiment). Returned invisibly when path is given.

Details

Layout. Features are rows and analyses are columns, following the SummarizedExperiment convention. Each feature variable becomes one assay, so feature_intensity, feature_norm_intensity and feature_conc sit side-by-side in the same object and are addressed with SummarizedExperiment::assay(se, "conc"). Assay names drop the feature_ prefix. annot_features becomes rowData(), annot_analyses becomes colData(), and the processing state (status, flags, concentration unit) becomes metadata().

Everything is exported. Internal standards, QC samples, blanks and calibrants are all included and flagged rather than dropped, because downstream tools need them: lipidr requires the istd annotation and pmp's blank filter needs blanks present. Subset when you need to:

se[!rowData(se)$is_istd, se$qc_type == "SPL"]

Most statistical tools will otherwise happily include blanks and calibrants and return nonsense.

QC metrics are not written to rowData(). To filter features by QC criteria, use filter_features_qc() and export with filter_data = TRUE. save_feature_qc_metrics() exports the metrics themselves.

References

Morgan M, Obenchain V, Hester J, & Pagès H (2026). SummarizedExperiment: A container (S4 class) for matrix-like assays. R package version 1.42.0. doi:10.18129/B9.bioc.SummarizedExperiment https://bioconductor.org/packages/SummarizedExperiment

Mohamed A, Molendijk J, & Hill MM (2020). lipidr: A Software Tool for Data Mining and Analysis of Lipidomics Datasets. Journal of Proteome Research, 19(7), 2890-2897. doi:10.1021/acs.jproteome.0c00082

See also

save_dataset_csv(), save_dataset_mztab(), save_report_xlsx()

Examples

mexp <- normalize_by_istd(lipidomics_dataset)
#> ! Interfering features defined in metadata, but no correction was applied. Use `correct_interferences()` to correct.
#> ✔ 20 features normalized with 9 ISTDs in 499 analyses.
mexp <- quantify_by_istd(mexp)
#> ✔ 20 feature concentrations calculated based on 9 ISTDs and sample amounts of 499 analyses.
#> ℹ Concentrations are given in μmol/L.

se <- save_dataset_summarizedexperiment(mexp)
SummarizedExperiment::assayNames(se)
#> [1] "rt"             "area"           "height"         "fwhm"          
#> [5] "width"          "intensity"      "norm_intensity" "pmol_total"    
#> [9] "conc"          

# study samples only, internal standards dropped
se[!SummarizedExperiment::rowData(se)$is_istd, se$qc_type == "SPL"]
#> class: SummarizedExperiment 
#> dim: 20 374 
#> metadata(11): title analysis_type ... var_batch_corrected
#>   mrmhub_version
#> assays(9): rt area ... pmol_total conc
#> rownames(20): CE 18:1 Cer d18:1/16:0 ... TG 48:2 [-18:1] TG 48:2 [SIM]
#> rowData names(17): feature_id feature_class ... remarks feature_label
#> colnames(374): Longit_batch1_1 Longit_batch1_2 ... Longit_batch6_52
#>   Longit_batch6_53
#> colData names(13): analysis_order analysis_id ... annot_order_num
#>   remarks