dolomite-mae 0.2.0
Save and load multi-assay experiments in the dolomite framework!
License: MIT
Requires Python: >=3.9

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# Save and load `MultiAssayExperiments` in Python

## Introduction

The **dolomite-mae** package is the Python counterpart to the [**alabaster.mae**](https://github.com/ArtifactDB/alabaster.mae) R package,
providing methods for saving/reading `MultiAssayExperiment` objects within the [**dolomite** framework](https://github.com/ArtifactDB/dolomite-base).
All components of the `MultiAssayExperiment` - column_data, sample map and experiments - are saved to their respective file representations,
which can be loaded in a new R/Python environment for cross-language analyses.

## Quick start

Let's mock up a `MultiAssayExperiment`:

```python
from multiassayexperiment import MultiAssayExperiment
from singlecellexperiment import SingleCellExperiment
from summarizedexperiment import SummarizedExperiment
import biocframe
import numpy

x = numpy.random.rand(1000, 200)
x2 = (numpy.random.rand(1000, 200) * 10).astype(numpy.int32)

sce = SingleCellExperiment(
     {"logcounts": x, "counts": x2},
     main_experiment_name="aaron's secret modality",
     row_data=biocframe.BiocFrame(
          {"foo": numpy.random.rand(1000), "bar": numpy.random.rand(1000)},
          row_names=["gene_sce_" + str(i) for i in range(1000)],
     ),
     column_data=biocframe.BiocFrame(
          {"whee": numpy.random.rand(200), "stuff": numpy.random.rand(200)},
          row_names=["cell_sce" + str(i) for i in range(200)],
     ),
)

se = SummarizedExperiment(
     {"counts": numpy.random.rand(100, 200)},
     row_data=biocframe.BiocFrame(
          {"foo": numpy.random.rand(100), "bar": numpy.random.rand(100)},
          row_names=["gene_se_" + str(i) for i in range(100)],
     ),
     column_data=biocframe.BiocFrame(
          {"whee": numpy.random.rand(200), "stuff": numpy.random.rand(200)},
          row_names=["cell_se" + str(i) for i in range(200)],
     ),
)

mae = MultiAssayExperiment(experiments={"jay_expt": sce, "aarons_expt": se})
```

Now we can save it:

```python
from dolomite_base import save_object
import dolomite_se
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)
```