rds2py

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.

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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 for the latest usage guidelines. Previous versions may have incompatible APIs.

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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

pip install rds2py

# or install optional dependencies
pip install rds2py[optional]

Usage

If you do not have an RDS object handy, feel free to download one from single-cell-test-files.

Basic Usage

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.

Write-your-own-reader

In addition, the package provides the dictionary representation of the RDS file, allowing users to write their own custom readers into appropriate Python representations.

from rds2py import parse_rds

data = parse_rds("path/to/file.rds")
print(data)

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.

from rds2py.read_granges import read_genomic_ranges

gr = read_genomic_ranges(data)

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/.