| name | bioconductor-biomformat |
| description | This is an R package for interfacing with the BIOM format. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object (which is more complex than a single table), as well as limi |
| when_to_use | Use when: Reading BIOM format files (JSON or HDF5) into R using read_biom().; Extracting core observation data (OTU tables) as sparse matrices using biom_data().; Converting BIOM objects to SummarizedExperiment objects using biom_to_SummarizedExperiment().; Constructing a BIOM object from standard R matrices and data frames using make_biom().; Converting BIOM data into a tidy long-format data frame for down. Not for: For statistical analysis of microbiome data, use phyloseq or DESeq2 because biomformat is strictly an I/O and utility package.; For raw sequence processing, use dada2 because biomformat operates on already-constructed contingency tables. |
| user-invocable | false |
biomformat
Dependencies & Environment
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
- Version: 1.40.0 · Bioconductor: 3.23 · R: ≥ 4.6
- Imports: jsonlite, Matrix
- Install:
BiocManager::install("biomformat")
When to Use
- Reading BIOM format files (JSON or HDF5) into R using
read_biom().
- Extracting core observation data (OTU tables) as sparse matrices using
biom_data().
- Converting BIOM objects to
SummarizedExperiment objects using biom_to_SummarizedExperiment().
- Constructing a BIOM object from standard R matrices and data frames using
make_biom().
- Converting BIOM data into a tidy long-format data frame for downstream analysis using
as_tibble.biom().
When NOT to Use
- For statistical analysis of microbiome data, use
phyloseq or DESeq2 because biomformat is strictly an I/O and utility package.
- For raw sequence processing, use
dada2 because biomformat operates on already-constructed contingency tables.
Data Requirements
- BIOM format files (v1 JSON or v2 HDF5).
- For manual construction via
make_biom(), a count matrix (features in rows, samples in columns) and optional data.frame objects for observation and sample metadata.
Key Parameters
- data (default): The count matrix when using
make_biom().
- observation_metadata (default): Taxonomy or feature metadata
data.frame when using make_biom().
- matrix_element_type ("int"): The data type of the matrix elements in
make_biom().
- rows (default): Character vector to subset features directly in
biom_data().
- columns (default): Character vector to subset samples directly in
biom_data().
Best Practices
- Use
read_biom() which automatically detects and routes HDF5 files to read_hdf5_biom().
- Use
as_tibble.biom() to convert data into a tidy long-format for downstream dplyr and purrr workflows.
- For large datasets (>2GB), use
write_hdf5_biom() instead of write_biom() to avoid JSON serialization limits.
Common Pitfalls
- Attempting to print a massive BIOM object streams too much data; fix this by relying on the brief summary printed by default or using
biom_data() to inspect subsets.
- Functions expecting standard matrices fail on sparse matrix classes; fix this by coercing with
as(biom_data(x), "matrix").
- Writing very large tables with
write_biom() fails due to JSON string limits; fix this by using write_hdf5_biom() for HDF5 format.
Alternatives
phyloseq: Has many more utilities for interacting with and analyzing this kind of data.SummarizedExperiment: The standard Bioconductor container for rectangular feature-by-sample assay data (though biomformat interoperates with it).
Citations
- Paul J. McMurdie and Joseph N Paulson (2015). biomformat: An interface package for the BIOM file format. R/Bioconductor package version 1.0.0.
References
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