https://github.com/biocpy/rds2py
Read RDS files, in Python
https://github.com/biocpy/rds2py
python r
Last synced: 19 days ago
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Read RDS files, in Python
- Host: GitHub
- URL: https://github.com/biocpy/rds2py
- Owner: BiocPy
- License: mit
- Created: 2022-11-05T07:13:25.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2025-04-14T16:28:57.000Z (28 days ago)
- Last Synced: 2025-04-14T17:33:44.395Z (28 days ago)
- Topics: python, r
- Language: Python
- Homepage: https://biocpy.github.io/rds2py/
- Size: 1.03 MB
- Stars: 22
- Watchers: 2
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Authors: AUTHORS.md
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# rds2py
Parse and construct Python representations for datasets stored in RDS files. `rds2py` supports various base classes from R, and Bioconductor's `SummarizedExperiment` and `SingleCellExperiment` S4 classes. **_For more details, check out [rds2cpp library](https://github.com/LTLA/rds2cpp)._**
---
**Version 0.5.0** brings major changes to the package,
- Complete overhaul of the codebase using pybind11
- Streamlined readers for R data types
- Updated API for all classes and methods
Please refer to the [documentation](https://biocpy.github.io/rds2py/) for the latest usage guidelines. Previous versions may have incompatible APIs.
---
The package provides:
- Efficient parsing of RDS files with _minimal_ memory overhead
- Support for R's basic data types and complex S4 objects
- Vectors (numeric, character, logical)
- Factors
- Data frames
- Matrices (dense and sparse)
- Run-length encoded vectors (Rle)
- Conversion to appropriate Python/NumPy/SciPy data structures
- dgCMatrix (sparse column matrix)
- dgRMatrix (sparse row matrix)
- dgTMatrix (sparse triplet matrix)
- Preservation of metadata and attributes from R objects
- Integration with BiocPy ecosystem for Bioconductor classes
- SummarizedExperiment
- RangedSummarizedExperiment
- SingleCellExperiment
- GenomicRanges
- MultiAssayExperiment
## Installation
Package is published to [PyPI](https://pypi.org/project/rds2py/)
```shell
pip install rds2py
# or install optional dependencies
pip install rds2py[optional]
```
By default, the package does not install packages to convert python representations to BiocPy classes. Please consider installing all optional dependencies.
## Usage
If you do not have an RDS object handy, feel free to download one from [single-cell-test-files](https://github.com/jkanche/random-test-files/releases).
```python
from rds2py import read_rds
r_obj = read_rds("path/to/file.rds")
```
The returned `r_obj` either returns an appropriate Python class if a parser is already implemented or returns the dictionary containing the data from the RDS file.
In addition, the package provides the dictionary representation of the RDS file.
```python
from rds2py import parse_rds
robject_dict = parse_rds("path/to/file.rds")
print(robject_dict)
```
### Write-your-own-reader
Reading RDS files as dictionary representations allows users to write their own custom readers into appropriate Python representations.
```python
from rds2py import parse_rds
robject = parse_rds("path/to/file.rds")
print(robject)
```
if you know this RDS file contains an `GenomicRanges` object, you can use the built-in reader or write your own reader to convert this dictionary.
```python
from rds2py.read_granges import read_genomic_ranges
gr = read_genomic_ranges(robject)
print(gr)
```
## Type Conversion Reference
| R Type | Python/NumPy Type |
| ---------- | ------------------------------------ |
| numeric | numpy.ndarray (float64) |
| integer | numpy.ndarray (int32) |
| character | list of str |
| logical | numpy.ndarray (bool) |
| factor | list |
| data.frame | BiocFrame |
| matrix | numpy.ndarray or scipy.sparse matrix |
| dgCMatrix | scipy.sparse.csc_matrix |
| dgRMatrix | scipy.sparse.csr_matrix |
## Developer Notes
This project uses pybind11 to provide bindings to the rds2cpp library. Please make sure necessary C++ compiler is installed on your system.
## Note
This project has been set up using PyScaffold 4.5. For details and usage
information on PyScaffold see https://pyscaffold.org/.