| name | bioconductor-mudata |
| description | Save MultiAssayExperiments to h5mu files supported by muon and mudata. Muon is a Python framework for multimodal omics data analysis. It uses an HDF5-based format for data storage. |
MuData
Workflows
Standard Workflow
Save MultiAssayExperiments to h5mu files supported by muon and mudata. Muon is a Python framework for multimodal omics data analysis. It uses an HDF5-based format for data storage.
library(MuData)
library(MultiAssayExperiment)
writeH5MU(mae, "citefuse_example.h5mu")
mae_backed <- readH5MU("citefuse_example.h5mu", backed = TRUE)
Input: A MultiAssayExperiment object containing multimodal single-cell data. Output: An HDF5-based .h5mu file written to disk, or a MultiAssayExperiment object read from disk.
When to Use
- To save multimodal datasets represented as
MultiAssayExperiment objects into .h5mu files using writeH5MU for cross-platform sharing with Python frameworks like muon or mudata.
- To read
.h5mu files into R as MultiAssayExperiment objects using readH5MU.
- To load large-scale multimodal datasets on-disk without loading them fully into memory by setting
backed = TRUE in readH5MU to return a DelayedMatrix.
When NOT to Use
- For storing unimodal datasets, use standard
AnnData formats or writeH5AD because MuData is specifically designed for multimodal data.
- For saving complex R-specific objects that have no HDF5/Python equivalent, use
saveRDS because writeH5MU only exports standard modalities and metadata.
- For performing downstream single-cell analysis (like clustering or differential expression) directly on the
.h5mu file, use packages like scran or Seurat because MuData is strictly an I/O interface.
Data Requirements
- Input format:
MultiAssayExperiment containing experiments of class SingleCellExperiment, SummarizedExperiment, or matrix.
- Structure: Multiple modalities (e.g.,
RNA, ADT, HTO) stored as distinct experiments within the MultiAssayExperiment.
- Normalization state: Can store both raw counts (e.g., in
counts assay) and normalized counts (e.g., in logcounts or clr assays).
Key Parameters
- backed (FALSE): If set to
TRUE in readH5MU, keeps matrices on disk and loads them as DelayedMatrix objects to save memory.
Best Practices
- Ensure all modalities are properly aligned and harmonized within a
MultiAssayExperiment before writing.
- Store normalized assays (e.g.,
logcounts or clr) in the corresponding experiment layers alongside raw counts.
- Use
backed = TRUE when reading very large .h5mu files to prevent out-of-memory errors.
Common Pitfalls
- Attempting to read unsupported Python-specific structures (like pairwise graphs or multimodal embeddings) into R:
readH5MU will skip these because MultiAssayExperiment does not natively support them.
- Writing mixed data types or unharmonized sample maps: Ensure the
MultiAssayExperiment is harmonized using standard constructor checks before calling writeH5MU.
Alternatives
SingleCellMultiModal for downloading pre-packaged multimodal datasets.rhdf5 for low-level HDF5 file manipulation in R.Seurat for an alternative single-cell multimodal object representation and I/O.
Citations
- Ramos M, Schiffer L, Re A, Azhar R, Basunia A, Cabrera CR, Chan T, Chapman P, Davis S, Gomez-Cabrero D, Culhane AC, Haibe-Kains B, Hansen K, Kodali H, Louis MS, Mer AS, Reister M, Morgan M, Carey V, Waldron L (2017). "Software For The Integration Of Multi-Omics Experiments In Bioconductor." Cancer Research, 77(21); e39-42.
References
- Homepage: bioconductor.org/packages/mudata
- Vignette: bioconductor.org/packages/release/bioc/vignettes/mudata/inst/doc/mudata.html
