| name | bioconductor-multiassayexperiment |
| description | Harmonize data management of multiple experimental assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of st |
| when_to_use | Use when: 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 [, i. Not for: For sets of assays with the exact same information across all rows (e.g., identical genes or genomic ranges), use SummarizedExperiment instead. |
| user-invocable | false |
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
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