dolomite-spatial 0.1.1

Read and write Spatial Experiments to takane representations

Save and write SpatialExperiment's in Python

Introduction

The dolomite-spatial package is the Python counterpart to the alabaster.spatial R package, providing methods for saving/reading SpatialExperiment objects within the dolomite framework.

Quick start

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

from spatialexperiment import SpatialExperiment, construct_spatial_image_class

import biocframe
import numpy as np

spe = SpatialExperiment(
    assays={"counts": np.random.rand(1000, 200)},
    row_data=biocframe.BiocFrame(
        {"foo": np.random.rand(1000), "bar": np.random.rand(1000)}
    ),
    column_data=biocframe.BiocFrame(
        {"whee": np.random.rand(200), "stuff": np.random.rand(200), "sample_id": ["sample_1", "sample_2"] * 100}
    ),
    reduced_dims={"tsnooch": np.random.rand(200, 4)},
    spatial_coords = np.array(
        [
            np.random.uniform(low=0.0, high=100.0, size=200),
            np.random.uniform(low=0.0, high=100.0, size=200)
        ]
    ).transpose(),
    img_data = biocframe.BiocFrame({
        "sample_id": ["sample_1", "sample_1", "sample_2"],
        "image_id": ["aurora", "dice", "desert"],
        "data": [
            construct_spatial_image_class("biocpy/spatialexperiment/tests/images/sample_image1.jpg"),
            construct_spatial_image_class("biocpy/spatialexperiment/tests/images/sample_image2.png"),
            construct_spatial_image_class("biocpy/spatialexperiment/tests/images/sample_image3.jpg"),
        ],
        "scale_factor": [1, 1, 1],
    })
)

print(spe)

Now we can save it:

from dolomite_base import save_object
import dolomite_spatial
import os
from tempfile import mkdtemp

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

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

from dolomite_base import read_object

roundtrip = read_object(path)
print(roundtrip)

## output
class: SpatialExperiment
dimensions: (1000, 200)
assays(1): ['counts']
row_data columns(2): ['foo', 'bar']
row_names(0):  
column_data columns(3): ['whee', 'stuff', 'sample_id']
column_names(0):  
main_experiment_name:  
reduced_dims(1): ['tsnooch']
alternative_experiments(0): []
row_pairs(0): []
column_pairs(0): []
metadata(0): 
spatial_coords columns(0): []
img_data columns(4): ['sample_id', 'image_id', 'data', 'scale_factor']

Note

This project has been set up using BiocSetup and PyScaffold.