Python package to download and convert datasets from the METASPACE knowledge base to common formats for single-cell and spatial omics analysis. Datasets can be directly downloaded to AnnData and SpatialData objects.
AnnData is the underlying data format of many packages of the scverse such as scanpy for single-cell data analysis and squidpy for spatial omics analysis.
Another supported format that is part of the scverse is SpatialData for storing, aligning, and processing spatial omics data. This enables users to easily align and integrate METASPACE datasets to other spatial omics modalities.
Additionally, the commonly used colocalization analysis for spatial metabolomics can be performed with the package.
If you encounter any bugs or have suggestions for new features, please open an issue in the GitHub repository.
Our package requires python >= 3.9.
You can install the package directly from PyPI:
pip install metaspace-converterThe full documentation for the package can be found here: https://metaspace2020.github.io/metaspace-converter/
The METASPACE-converter package uses the python client to download datasets from METASPACE. It serves as a wrapper that downloads and converts datasets directly to AnnData and SpatialData objects.
If you also need to upload or modify datasets on METASPACE, please check the METASPACE python client documentation.
Datasets can be downloaded to AnnData objects using the metaspace_to_anndata function.
This allows downstream analysis e.g. with scanpy
or squidpy.
from metaspace_converter import metaspace_to_anndata
import scanpy as sc
# Download data and convert to an AnnData object
adata = metaspace_to_anndata(
dataset_id="2022-08-05_17h28m56s",
fdr=0.1,
database=("BraChemDB", "2018-01"),
)
# Visualization with ScanPy
sc.pl.spatial(
adata,
# Choose the first ion for visualization
color=adata.var.index[0],
img_key=None,
spot_size=1,
)
Optical images can also be downloaded, if available, and saved in the AnnData object. Squidpy allows for an easy overlay of ion images and the optical image.
from metaspace_converter import metaspace_to_anndata
import squidpy as sq
# Download dataset with optical background image
adata = metaspace_to_anndata(
dataset_id="2022-08-05_17h28m56s",
fdr=0.1,
database=("BraChemDB", "2018-01"),
add_optical_image=True,
)
sq.pl.spatial_scatter(
adata, color=adata.var.index[0], shape="square", img=True, size=15, alpha=0.5
)
If you want to work with the ion images as numpy arrays, the function anndata_to_image_array can
convert previously downloaded AnnData objects to numpy arrays.
from metaspace_converter import metaspace_to_anndata, anndata_to_image_array
# Download data
adata2 = metaspace_to_anndata(dataset_id="2023-11-14_21h58m39s", fdr=0.1)
ion_images = anndata_to_image_array(adata2)
# 20 ion images of shape 130x143
print(ion_images.shape)
# > (20, 130, 143)Download to the SpatialData format equally easy and
can be done with the metaspace_to_spatialdata function.
Here using a reversed colormap which better represents intense values on a bright background.
from metaspace_converter import metaspace_to_spatialdata
import spatialdata_plot # noqa: Not directly used but extends spatialdata
# Download dataset with optical background image
sdata = metaspace_to_spatialdata(
dataset_id="2022-08-05_17h28m56s",
fdr=0.1,
database=("BraChemDB", "2018-01"),
)
# Workaround: spatialdata-plot currently does not use points transformation
sdata.points["maldi_points"] = sdata.transform_element_to_coordinate_system(
sdata.points["maldi_points"], "global"
)
(
sdata.pl.render_images("optical_image")
.pl.render_points(
"maldi_points",
color=sdata.table.var.index[0],
alpha=1,
size=2,
cmap="viridis_r",
)
.pl.show(title=sdata.table.var.index[0], coordinate_systems="global")
)
Unless specified otherwise in file headers or LICENSE files present in subdirectories, all files are licensed under the Apache 2.0 license.