MultiAssayExperiment

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

• Version: 1.38.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Depends: SummarizedExperiment
• Imports: Biobase, BiocBaseUtils, BiocGenerics, DelayedArray, GenomicRanges, IRanges, MatrixGenerics, S4Vectors, tidyr
• Install: BiocManager::install("MultiAssayExperiment")

When to Use

• Integrating multi-omics experiments (e.g., DNA mutations, RNA abundance) measured on the same biological specimens into a single object.
• Managing complex experimental designs where a single patient maps to multiple assays, missing assays, or technical replicates using a sampleMap.
• Subsetting multiple experimental assays simultaneously by patient IDs, genomic ranges, or column metadata using [, intersectColumns, or intersectRows.
• Reshaping multi-assay data into long or wide formats for downstream analysis using longFormat or wideFormat.

When NOT to Use

• For sets of assays with the exact same information across all rows (e.g., identical genes or genomic ranges), use SummarizedExperiment instead.

Data Requirements

• experiments: A named list or ExperimentList containing assay datasets (e.g., matrix, SummarizedExperiment, RangedSummarizedExperiment, RaggedExperiment).
• colData: A DataFrame containing primary patient/specimen metadata, where rownames are patient identifiers.
• sampleMap: A three-column DataFrame (assay, primary, colname) that unambiguously maps experimental observations to rows in colData.

Key Parameters

• experiments: A named list of experimental data objects provided to the MultiAssayExperiment constructor.
• colData: A DataFrame of primary metadata describing the biological units (e.g., patients).
• sampleMap: A DataFrame relating the primary data to the experimental assays.
• metadata: A list of study-wide metadata (e.g., citation information) attached to the object.

Best Practices

• Run the prepMultiAssay helper function before constructing the object to diagnose and resolve inconsistent names between the ExperimentList, colData, and sampleMap.
• Use the listToMap convenience function to easily convert a list of platform-specific data frames into a valid sampleMap.
• Store study-wide metadata, such as citation information, using the metadata slot at the MultiAssayExperiment level.
• Ensure all elements in the ExperimentList are named; unnamed elements will prompt an error during construction.

Common Pitfalls

• Unnamed ExperimentList: Passing an unnamed list of experiments to the constructor or prepMultiAssay causes an error. Fix: Assign names to the list elements (e.g., names(objlist) <- c("Affy", "Methyl")) before construction.
• Dropped Samples: Assay samples (colnames) that cannot be mapped to a corresponding row in colData via the sampleMap are silently dropped. Fix: Check the $drops element returned by prepMultiAssay to identify and fix unmatched identifiers.
• Mismatched sampleMap Columns: Providing a sampleMap without the exact column names assay, primary, and colname. Fix: Ensure the sampleMap strictly adheres to this three-column naming convention.

Alternatives

• SummarizedExperiment: Recommended for single-assay datasets or multiple assays that share the exact same row features (e.g., identical genomic ranges).

Citations

• Ramos, M., et al. (2017). Software for the Integration of Multi-Omics Experiments in Bioconductor. Cancer Research.

References

• Homepage: https://bioconductor.org/packages/MultiAssayExperiment
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.html

<!-- biomate-cta -->

---

Run this on BioMate

This skill is the knowledge layer — when, why, and how to use multiassayexperiment. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.

▶ Open multiassayexperiment on BioMate →