Self Contained Pipeline Construction

self-contained-pipeline-construction

Summary

Construct modular, reproducible metabolomics analysis pipelines by chaining maplet functions through pipe operators (magrittr's %>% or base R's |>) that operate on a shared SummarizedExperiment container. This approach eliminates temporary variables and improves readability while maintaining full traceability of data, analysis steps, and results.

When to use

When you have metabolomics datasets (loaded as SummarizedExperiment objects) and need to apply a sequence of data-processing and analytical steps (e.g., annotation, statistical analysis, visualization, reporting) in a linear, reproducible workflow without intermediate object assignments or manual state management.

When NOT to use

• Input data is already a feature table with no need for annotation or statistical analysis.
• Analysis requires non-linear or conditional branching logic (e.g., different downstream steps based on intermediate QC results); maplet pipelines are designed for linear chaining.
• Intermediate steps require manual inspection or interactive decision-making between pipeline stages; self-contained pipelines are best suited for fully scripted, deterministic workflows.

Inputs

• Raw metabolomics data (e.g., CSV, mzML, or other format supported by maplet loaders)
• SummarizedExperiment container (initialized or from prior pipeline step)
• Annotation reference data (optional, for annotation step)
• Statistical parameters or configuration (e.g., for analysis functions)

Outputs

• SummarizedExperiment object containing input data, analysis results, and pipeline metadata
• RDS file (serialized SummarizedExperiment)
• Visualizations (plots, reports) generated by maplet visualization and reporting functions
• Statistical test results and summaries stored in assays or colData/rowData of SummarizedExperiment

How to apply

Initialize a SummarizedExperiment container using maplet's data-loading functions, then chain one or more maplet functions (for annotation, statistical analysis, visualization, or reporting) using the %>% pipe operator from magrittr or the |> base R operator. Each pipe passes the entire SummarizedExperiment object forward, accumulating analysis steps and results within the same container. Execute the complete chained pipeline as a single script block, which preserves modularity and readability. Save the final SummarizedExperiment object using saveRDS() to archive the complete analysis history and outputs.

Related tools

• maplet (Provides data loading, annotation, statistical analysis, visualization, and reporting functions that operate on SummarizedExperiment containers and are designed to be chained via pipe operators.) — https://github.com/krumsieklab/maplet
• SummarizedExperiment (Bioconductor package that serves as the central container for storing metabolomics data, analysis steps, and results; all maplet functions read from and write to this object.) — https://bioconductor.org/packages/release/bioc/vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html
• magrittr (Provides the %>% pipe operator used to chain maplet functions without temporary variable assignments; enables smooth connections between pipeline steps.) — https://magrittr.tidyverse.org/

Examples

library(maplet)
se <- mt_load_metabolomics_data('data.csv') %>%
  mt_anno_lipidmaps() %>%
  mt_statistics_univariate() %>%
  mt_reporting_html()
saveRDS(se, 'pipeline_output.rds')

Evaluation signals

• The final RDS file contains a valid SummarizedExperiment object with populated assays, colData, and rowData reflecting all pipeline steps.
• All intermediate analysis results (e.g., statistical p-values, annotations, feature metadata) are present and consistent within the SummarizedExperiment container.
• The pipeline script runs without errors from start to finish, demonstrating that all chained functions are compatible and properly format their inputs/outputs.
• Re-running the same pipeline script on the same input data produces identical outputs (deterministic reproducibility).
• The maplet pipeline is shorter and more readable than an equivalent script using temporary variable assignments, confirming the modularity benefit.

Limitations

• maplet is in active development; commits without release tags are not guaranteed to be stable.
• Pipelines are inherently linear and do not support conditional branching or dynamic routing based on intermediate results.
• The skill requires all analysis steps to be compatible with the SummarizedExperiment structure; external analysis functions must be wrapped or adapted to this container model.
• No built-in changelog or version tracking is available to document changes between pipeline runs.

Evidence

• [full_text] maplet pipelines operate by chaining functions via pipe operators: "maplet pipelines operate by chaining functions via pipe operators (either %>% from magrittr or |> from base R) that connect pipeline steps without requiring temporary variables or multiple"
• [readme] SummarizedExperiment as central repository: "SummarizedExperiment (SE), which serves as a central repository for each pipeline's data, analysis steps, and results."
• [readme] maplet function categories: "maplet provides a suite of functions for interacting with this container including but not limited to data loading, annotation, statistical analysis, visualization, and reporting."
• [readme] Pipe operator benefit: "This operator allows for smooth connections between pipeline steps, without the need for temporary variables or multiple assignments."
• [readme] Pipeline properties: "The combination of these elements allows for the creation of pipelines which are simple to follow, highly modular, and easily reproducible."
• [readme] Active development caveat: "maplet is in active development. Any commit without a release tag is not guaranteed to be stable."