Technology focus: COSMX

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

Loading the data

Please download the data from here: CosMx dataset and adjust the variable containing the location of the .zarr file.

cosmx_zarr_path = "./cosmx.zarr"

import spatialdata as sd

cosmx_sdata = sd.read_zarr(cosmx_zarr_path)
cosmx_sdata

SpatialData object with:
├── Images
│     ├── '1_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '2_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '3_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '4_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '5_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '6_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '7_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '8_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '9_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '10_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '11_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '12_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '13_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '14_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '15_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '16_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '17_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '18_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '19_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '20_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '21_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '22_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '23_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '24_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '25_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '26_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '27_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '28_image': SpatialImage[cyx] (3, 3648, 5472)
│     ├── '29_image': SpatialImage[cyx] (3, 3648, 5472)
│     └── '30_image': SpatialImage[cyx] (3, 3648, 5472)
├── Labels
│     ├── '1_labels': SpatialImage[yx] (3648, 5472)
│     ├── '2_labels': SpatialImage[yx] (3648, 5472)
│     ├── '3_labels': SpatialImage[yx] (3648, 5472)
│     ├── '4_labels': SpatialImage[yx] (3648, 5472)
│     ├── '5_labels': SpatialImage[yx] (3648, 5472)
│     ├── '6_labels': SpatialImage[yx] (3648, 5472)
│     ├── '7_labels': SpatialImage[yx] (3648, 5472)
│     ├── '8_labels': SpatialImage[yx] (3648, 5472)
│     ├── '9_labels': SpatialImage[yx] (3648, 5472)
│     ├── '10_labels': SpatialImage[yx] (3648, 5472)
│     ├── '11_labels': SpatialImage[yx] (3648, 5472)
│     ├── '12_labels': SpatialImage[yx] (3648, 5472)
│     ├── '13_labels': SpatialImage[yx] (3648, 5472)
│     ├── '14_labels': SpatialImage[yx] (3648, 5472)
│     ├── '15_labels': SpatialImage[yx] (3648, 5472)
│     ├── '16_labels': SpatialImage[yx] (3648, 5472)
│     ├── '17_labels': SpatialImage[yx] (3648, 5472)
│     ├── '18_labels': SpatialImage[yx] (3648, 5472)
│     ├── '19_labels': SpatialImage[yx] (3648, 5472)
│     ├── '20_labels': SpatialImage[yx] (3648, 5472)
│     ├── '21_labels': SpatialImage[yx] (3648, 5472)
│     ├── '22_labels': SpatialImage[yx] (3648, 5472)
│     ├── '23_labels': SpatialImage[yx] (3648, 5472)
│     ├── '24_labels': SpatialImage[yx] (3648, 5472)
│     ├── '25_labels': SpatialImage[yx] (3648, 5472)
│     ├── '26_labels': SpatialImage[yx] (3648, 5472)
│     ├── '27_labels': SpatialImage[yx] (3648, 5472)
│     ├── '28_labels': SpatialImage[yx] (3648, 5472)
│     ├── '29_labels': SpatialImage[yx] (3648, 5472)
│     └── '30_labels': SpatialImage[yx] (3648, 5472)
├── Points
│     ├── '1_points': DataFrame with shape: (581747, 9) (2D points)
│     ├── '2_points': DataFrame with shape: (1928480, 9) (2D points)
│     ├── '3_points': DataFrame with shape: (1469614, 9) (2D points)
│     ├── '4_points': DataFrame with shape: (1762072, 9) (2D points)
│     ├── '5_points': DataFrame with shape: (1940851, 9) (2D points)
│     ├── '6_points': DataFrame with shape: (0, 9) (2D points)
│     ├── '7_points': DataFrame with shape: (1984798, 9) (2D points)
│     ├── '8_points': DataFrame with shape: (1793758, 9) (2D points)
│     ├── '9_points': DataFrame with shape: (1161574, 9) (2D points)
│     ├── '10_points': DataFrame with shape: (1377729, 9) (2D points)
│     ├── '11_points': DataFrame with shape: (1730901, 9) (2D points)
│     ├── '12_points': DataFrame with shape: (1719410, 9) (2D points)
│     ├── '13_points': DataFrame with shape: (1155204, 9) (2D points)
│     ├── '14_points': DataFrame with shape: (998577, 9) (2D points)
│     ├── '15_points': DataFrame with shape: (1220996, 9) (2D points)
│     ├── '16_points': DataFrame with shape: (1972482, 9) (2D points)
│     ├── '17_points': DataFrame with shape: (1567489, 9) (2D points)
│     ├── '18_points': DataFrame with shape: (1124222, 9) (2D points)
│     ├── '19_points': DataFrame with shape: (969657, 9) (2D points)
│     ├── '20_points': DataFrame with shape: (1303179, 9) (2D points)
│     ├── '21_points': DataFrame with shape: (1146492, 9) (2D points)
│     ├── '22_points': DataFrame with shape: (943272, 9) (2D points)
│     ├── '23_points': DataFrame with shape: (1106228, 9) (2D points)
│     ├── '24_points': DataFrame with shape: (884838, 9) (2D points)
│     ├── '25_points': DataFrame with shape: (961553, 9) (2D points)
│     ├── '26_points': DataFrame with shape: (1265701, 9) (2D points)
│     ├── '27_points': DataFrame with shape: (1332922, 9) (2D points)
│     ├── '28_points': DataFrame with shape: (1766990, 9) (2D points)
│     ├── '29_points': DataFrame with shape: (1462793, 9) (2D points)
│     └── '30_points': DataFrame with shape: (1653046, 9) (2D points)
└── Table
      └── AnnData object with n_obs × n_vars = 106660 × 980
    obs: 'fov', 'Area', 'AspectRatio', 'Width', 'Height', 'Mean.MembraneStain', 'Max.MembraneStain', 'Mean.PanCK', 'Max.PanCK', 'Mean.CD45', 'Max.CD45', 'Mean.CD3', 'Max.CD3', 'Mean.DAPI', 'Max.DAPI', 'fov_labels', 'cell_ID'
    uns: 'spatialdata_attrs'
    obsm: 'global', 'spatial': AnnData (106660, 980)
with coordinate systems:
▸ '1', with elements:
        1_image (Images), 1_labels (Labels), 1_points (Points)
▸ '2', with elements:
        2_image (Images), 2_labels (Labels), 2_points (Points)
▸ '3', with elements:
        3_image (Images), 3_labels (Labels), 3_points (Points)
▸ '4', with elements:
        4_image (Images), 4_labels (Labels), 4_points (Points)
▸ '5', with elements:
        5_image (Images), 5_labels (Labels), 5_points (Points)
▸ '6', with elements:
        6_image (Images), 6_labels (Labels), 6_points (Points)
▸ '7', with elements:
        7_image (Images), 7_labels (Labels), 7_points (Points)
▸ '8', with elements:
        8_image (Images), 8_labels (Labels), 8_points (Points)
▸ '9', with elements:
        9_image (Images), 9_labels (Labels), 9_points (Points)
▸ '10', with elements:
        10_image (Images), 10_labels (Labels), 10_points (Points)
▸ '11', with elements:
        11_image (Images), 11_labels (Labels), 11_points (Points)
▸ '12', with elements:
        12_image (Images), 12_labels (Labels), 12_points (Points)
▸ '13', with elements:
        13_image (Images), 13_labels (Labels), 13_points (Points)
▸ '14', with elements:
        14_image (Images), 14_labels (Labels), 14_points (Points)
▸ '15', with elements:
        15_image (Images), 15_labels (Labels), 15_points (Points)
▸ '16', with elements:
        16_image (Images), 16_labels (Labels), 16_points (Points)
▸ '17', with elements:
        17_image (Images), 17_labels (Labels), 17_points (Points)
▸ '18', with elements:
        18_image (Images), 18_labels (Labels), 18_points (Points)
▸ '19', with elements:
        19_image (Images), 19_labels (Labels), 19_points (Points)
▸ '20', with elements:
        20_image (Images), 20_labels (Labels), 20_points (Points)
▸ '21', with elements:
        21_image (Images), 21_labels (Labels), 21_points (Points)
▸ '22', with elements:
        22_image (Images), 22_labels (Labels), 22_points (Points)
▸ '23', with elements:
        23_image (Images), 23_labels (Labels), 23_points (Points)
▸ '24', with elements:
        24_image (Images), 24_labels (Labels), 24_points (Points)
▸ '25', with elements:
        25_image (Images), 25_labels (Labels), 25_points (Points)
▸ '26', with elements:
        26_image (Images), 26_labels (Labels), 26_points (Points)
▸ '27', with elements:
        27_image (Images), 27_labels (Labels), 27_points (Points)
▸ '28', with elements:
        28_image (Images), 28_labels (Labels), 28_points (Points)
▸ '29', with elements:
        29_image (Images), 29_labels (Labels), 29_points (Points)
▸ '30', with elements:
        30_image (Images), 30_labels (Labels), 30_points (Points)
▸ 'global', with elements:
        1_image (Images), 2_image (Images), 3_image (Images), 4_image (Images), 5_image (Images), 6_image (Images), 7_image (Images), 8_image (Images), 9_image (Images), 10_image (Images), 11_image (Images), 12_image (Images), 13_image (Images), 14_image (Images), 15_image (Images), 16_image (Images), 17_image (Images), 18_image (Images), 19_image (Images), 20_image (Images), 21_image (Images), 22_image (Images), 23_image (Images), 24_image (Images), 25_image (Images), 26_image (Images), 27_image (Images), 28_image (Images), 29_image (Images), 30_image (Images), 1_labels (Labels), 2_labels (Labels), 3_labels (Labels), 4_labels (Labels), 5_labels (Labels), 6_labels (Labels), 7_labels (Labels), 8_labels (Labels), 9_labels (Labels), 10_labels (Labels), 11_labels (Labels), 12_labels (Labels), 13_labels (Labels), 14_labels (Labels), 15_labels (Labels), 16_labels (Labels), 17_labels (Labels), 18_labels (Labels), 19_labels (Labels), 20_labels (Labels), 21_labels (Labels), 22_labels (Labels), 23_labels (Labels), 24_labels (Labels), 25_labels (Labels), 26_labels (Labels), 27_labels (Labels), 28_labels (Labels), 29_labels (Labels), 30_labels (Labels), 1_points (Points), 2_points (Points), 3_points (Points), 4_points (Points), 5_points (Points), 6_points (Points), 7_points (Points), 8_points (Points), 9_points (Points), 10_points (Points), 11_points (Points), 12_points (Points), 13_points (Points), 14_points (Points), 15_points (Points), 16_points (Points), 17_points (Points), 18_points (Points), 19_points (Points), 20_points (Points), 21_points (Points), 22_points (Points), 23_points (Points), 24_points (Points), 25_points (Points), 26_points (Points), 27_points (Points), 28_points (Points), 29_points (Points), 30_points (Points)
▸ 'global_only_image', with elements:
        1_image (Images), 2_image (Images), 3_image (Images), 4_image (Images), 5_image (Images), 6_image (Images), 7_image (Images), 8_image (Images), 9_image (Images), 10_image (Images), 11_image (Images), 12_image (Images), 13_image (Images), 14_image (Images), 15_image (Images), 16_image (Images), 17_image (Images), 18_image (Images), 19_image (Images), 20_image (Images), 21_image (Images), 22_image (Images), 23_image (Images), 24_image (Images), 25_image (Images), 26_image (Images), 27_image (Images), 28_image (Images), 29_image (Images), 30_image (Images)
▸ 'global_only_labels', with elements:
        1_labels (Labels), 2_labels (Labels), 3_labels (Labels), 4_labels (Labels), 5_labels (Labels), 6_labels (Labels), 7_labels (Labels), 8_labels (Labels), 9_labels (Labels), 10_labels (Labels), 11_labels (Labels), 12_labels (Labels), 13_labels (Labels), 14_labels (Labels), 15_labels (Labels), 16_labels (Labels), 17_labels (Labels), 18_labels (Labels), 19_labels (Labels), 20_labels (Labels), 21_labels (Labels), 22_labels (Labels), 23_labels (Labels), 24_labels (Labels), 25_labels (Labels), 26_labels (Labels), 27_labels (Labels), 28_labels (Labels), 29_labels (Labels), 30_labels (Labels), 1_points (Points), 2_points (Points), 3_points (Points), 4_points (Points), 5_points (Points), 6_points (Points), 7_points (Points), 8_points (Points), 9_points (Points), 10_points (Points), 11_points (Points), 12_points (Points), 13_points (Points), 14_points (Points), 15_points (Points), 16_points (Points), 17_points (Points), 18_points (Points), 19_points (Points), 20_points (Points), 21_points (Points), 22_points (Points), 23_points (Points), 24_points (Points), 25_points (Points), 26_points (Points), 27_points (Points), 28_points (Points), 29_points (Points), 30_points (Points)

Visualise the data

As we can see, the data contains 30 slides which would make plotting very cumbersome. Thereofore, we will only focus on one coordinate systems for now (2) but then also later provide a plot of all coordinate systems. Let’s visualise some of the data contained in the spatialdata object. For this, we will need to load the spatialdata-plot library.

import spatialdata_plot

cosmx_sdata.pl.render_images().pl.show(coordinate_systems=["2"])

cosmx_sdata.pl.render_labels().pl.show(coordinate_systems=["2"])

cosmx_sdata.pl.render_points().pl.show(coordinate_systems=["2"])

We can see that the points layer features too many points to properly visualise in a small’ish plot. Therefore, we reduce their number to only 1 % of the total.

cosmx_sdata.points["2_points"] = cosmx_sdata.points["2_points"].sample(frac=0.01)
cosmx_sdata.pl.render_points().pl.show(coordinate_systems=["2"])

Furthermore, we are going to convert the CellComp column of the points layer to a categorical column. This will allow us to colour the points by their cell type.

import pandas as pd

cat_type = pd.CategoricalDtype(cosmx_sdata.points["2_points"]["CellComp"].unique())
cosmx_sdata.points["2_points"]["CellComp"] = cosmx_sdata.points["2_points"]["CellComp"].astype(cat_type)

cosmx_sdata.pl.render_points(color="CellComp").pl.show(coordinate_systems=["2"])

We can further overlay that information with the cell_labels. This will allow us to see which points actually lay inside the borders of an annotated cell.

import matplotlib.pyplot as plt

fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(12, 12))

(
    cosmx_sdata.pl.render_points(color="CellComp", alpha=0.5)
    .pl.render_labels(fill_alpha=0, outline=True, contour_px=15)
    .pl.show(coordinate_systems=["2"], ax=ax)
)

We can see, that the 0 label locates outside of the annotated cells. Finally, let’s visualise all images contained in the data.

cosmx_sdata.pl.render_images().pl.show()

INFO     Dropping coordinate system 'global_only_labels' since it doesn't have relevant elements.