Technology focus: MERFISH

This notebook will present a rough overview of the plotting functionalities that spatialdata implements for MERFISH data.

Please download the merfish data from https://spatialdata.scverse.org/en/stable/tutorials/notebooks/datasets/README.html and adjust the variable containing the location of the .zarr file.

Information regarding data licensing and attribution for the dataset listed above is available at: https://github.com/scverse/spatialdata-notebooks/tree/main/datasets.

merfish_zarr_path = "./merfish.zarr"

import spatialdata as sd
import spatialdata_plot  # noqa: F401

merfish_sdata = sd.read_zarr(merfish_zarr_path)
merfish_sdata

SpatialData object, with associated Zarr store: /Users/macbook/embl/projects/basel/spatialdata-sandbox/merfish/data.zarr
├── Images
│     └── 'rasterized': DataArray[cyx] (1, 522, 575)
├── Points
│     └── 'single_molecule': DataFrame with shape: (<Delayed>, 3) (2D points)
├── Shapes
│     ├── 'anatomical': GeoDataFrame shape: (6, 1) (2D shapes)
│     └── 'cells': GeoDataFrame shape: (2389, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2389, 268)
with coordinate systems:
    ▸ 'global', with elements:
        rasterized (Images), single_molecule (Points), anatomical (Shapes), cells (Shapes)

Visualise the data

We’re going to create a naiive visualisation of the data, overlaying the annotated anatomical regions contained in anatomical and the tissue image. For this, we need to load the spatialdata_plot library which extends the sd.SpatialData object with the .pl module. Furthermore, we will only select the elements we want to plot using pp.get_elements().

merfish_sdata.subset(["anatomical", "rasterized"]).pl.render_images().pl.render_shapes(
    fill_alpha=0.5, outline=True
).pl.show()

The MERFISH data also contains points which we have so far not visualised. This can be done with the pl.render_points() function. However, since we have over 3 million points, we will only render 1 % of them as to not overplot the image.

sampled_points = merfish_sdata.points["single_molecule"].sample(frac=0.01)
# fix attrs (see https://github.com/scverse/spatialdata/issues/1035)
sampled_points.attrs = merfish_sdata.points["single_molecule"].attrs
merfish_sdata.points["single_molecule"] = sampled_points

merfish_sdata.pl.render_points(color="black").pl.show()

INFO     Value for parameter 'color' appears to be a color, using it as such.                                      
INFO     Using 'datashader' backend with 'None' as reduction method to speed up plotting. Depending on the         
         reduction method, the value range of the plot might change. Set method to 'matplotlib' do disable this    
         behaviour.

Furthermore, we can overlay all 3 layers and color the points by an annotation.

import matplotlib.pyplot as plt

fig, ax = plt.subplots(ncols=1, figsize=(6, 6))

(
    merfish_sdata.subset(["anatomical", "rasterized", "single_molecule"])
    .pl.render_images(cmap="gray")
    .pl.render_points(color="cell_type", size=1)
    .pl.render_shapes(fill_alpha=0.5, outline=True)
    .pl.show(ax=ax)
)

INFO     Using 'datashader' backend with 'None' as reduction method to speed up plotting. Depending on the         
         reduction method, the value range of the plot might change. Set method to 'matplotlib' do disable this    
         behaviour.