Metadata-Version: 2.1
Name: dolomite-sce
Version: 0.2.0
Summary: Save and load single-cell experiments in the dolomite framework!
Home-page: https://github.com/ArtifactDB/dolomite-sce
Author: LTLA
Author-email: infinite.monkeys.with.keyboards@gmail.com
License: MIT
Project-URL: Documentation, https://github.com/ArtifactDB/dolomite-sce
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Requires-Python: >=3.8
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
License-File: LICENSE.txt
Requires-Dist: dolomite-base >=0.3.0
Requires-Dist: dolomite-se >=0.2.0
Requires-Dist: singlecellexperiment >=0.4.5
Requires-Dist: importlib-metadata ; python_version < "3.8"
Provides-Extra: testing
Requires-Dist: setuptools ; extra == 'testing'
Requires-Dist: pytest ; extra == 'testing'
Requires-Dist: pytest-cov ; extra == 'testing'

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# Save and write `SingleCellExperiments` in Python

## Introduction

The **dolomite-sce** package is the Python counterpart to the [**alabaster.sce**](https://github.com/ArtifactDB/alabaster.sce) R package,
providing methods for saving/reading `SingleCellExperiment` objects within the [**dolomite** framework](https://github.com/ArtifactDB/dolomite-base).

## Quick start

Let's mock up a `SingleCellExperiment` that contains reduced dimensions and alternative experiments,

```python
from singlecellexperiment import SingleCellExperiment
import biocframe
import numpy

sce = SingleCellExperiment(
     assays={"counts": numpy.random.rand(1000, 200)},
     row_data=biocframe.BiocFrame(
          {"foo": numpy.random.rand(1000), "bar": numpy.random.rand(1000)}
     ),
     column_data=biocframe.BiocFrame(
          {"whee": numpy.random.rand(200), "stuff": numpy.random.rand(200)}
     ),
     reduced_dims={"tsnooch": numpy.random.rand(200, 4)},
     alternative_experiments={
          "very_useful_modality": SummarizedExperiment(
               {"counts": numpy.random.rand(100, 200)}
          )
     },
)
```

Now we can save it:

```python
from dolomite_base import save_object
import dolomite_sce
import os
from tempfile import mkdtemp

path = os.path.join(mkdtemp(), "test")
save_object(se, path)
```

And load it again, e,g., in a new session:

```python
from dolomite_base import read_object

roundtrip = read_object(path)
## Class SingleCellExperiment with 1000 features and 200 cells
##   assays: ['counts']
##   row_data: ['foo']
##   column_data: ['whee']
```
