Parse, extract and create Python representations for datasets stored in RDS files. It supports Bioconductor's SummarizedExperiment and SingleCellExperiment objects. This is possible because of Aaron's rds2cpp library.
The package uses memory views (except for strings) so that we can access the same memory from C++ space in Python (through Cython of course). This is especially useful for large datasets so we don't make copies of data.
Package is published to PyPI
pip install rds2pyIf you do not have an RDS object handy, feel free to download from single-cell-test-files.
from rds2py import as_SCE, read_rds
rObj = read_rds(<path_to_file>)This rObj contains the realized structure of the RDS file as a compatible dict object, it contains two keys
dataif atomic entities, contains the numpy view of the memory spaceattributes: additional properties available for the object.
The package provides friendly functions to easily convert some R representations to useful python representations.
from rds2py import as_spase_matrix, as_SCE
# to convert an robject to a sparse matrix
sp_mat = as_sparse(rObj)
# to convert an robject to SCE
sce = as_SCE(rObj)For more use cases reading data.frame, dgCMatrix, dgRMatrix etc, checkout the documentation
This project uses Cython to provide bindings from C++ to Python and tries to use the same memory space (except for strings) instead of making copy of the data.
Steps to properly setup
- git submodules is initialized in
extern/rds2cpp cmake .inextern/rds2cppdirectory to download dependencies, especially thebytemelibrary
First one needs to build the extern library, this would generate a shared object file to src/rds2py/core-[*].so
python setup.py build_ext --inplaceFor typical development workflows, run
python setup.py build_ext --inplace && toxThis project has been set up using PyScaffold 4.3. For details and usage information on PyScaffold see https://pyscaffold.org/.