HCS Plates¶

This is a minimal example of how to work with OME-Zarr Plates using ngio.

Show what's in the plate¶

In [1]:

from pathlib import Path

from ngio import open_ome_zarr_plate
from ngio.utils import download_ome_zarr_dataset

# Download the dataset
download_dir = Path(".").absolute().parent.parent / "data"

hcs_path = download_ome_zarr_dataset("CardiomyocyteTinyMip", download_dir=download_dir)
hcs_zarr = open_ome_zarr_plate(hcs_path)
print(hcs_zarr)
print(f"Rows: {hcs_zarr.rows}, Columns: {hcs_zarr.columns}")

# Get all the images in the plate
hcs_zarr.get_images()

from pathlib import Path from ngio import open_ome_zarr_plate from ngio.utils import download_ome_zarr_dataset # Download the dataset download_dir = Path(".").absolute().parent.parent / "data" hcs_path = download_ome_zarr_dataset("CardiomyocyteTinyMip", download_dir=download_dir) hcs_zarr = open_ome_zarr_plate(hcs_path) print(hcs_zarr) print(f"Rows: {hcs_zarr.rows}, Columns: {hcs_zarr.columns}") # Get all the images in the plate hcs_zarr.get_images()

/opt/hostedtoolcache/Python/3.13.11/x64/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Unzipping contents of '/home/runner/work/ngio/ngio/data/20200812-CardiomyocyteDifferentiation14-Cycle1-tiny-mip.zarr.zip' to '/home/runner/work/ngio/ngio/data/tmp'

Plate([rows x columns] (1 x 1))
Rows: ['B'], Columns: ['03']

Out[1]:

{'B/03/0': OmeZarrContainer(levels=5, labels=['nuclei'], #tables=4)}

Aggregate tables across all images¶

In [2]:

# Aggregate all table across all images

table = hcs_zarr.concatenate_image_tables(name="nuclei")
table.dataframe

# Aggregate all table across all images table = hcs_zarr.concatenate_image_tables(name="nuclei") table.dataframe

/opt/hostedtoolcache/Python/3.13.11/x64/lib/python3.13/functools.py:934: ImplicitModificationWarning: Transforming to str index.
  return dispatch(args[0].__class__)(*args, **kw)

Out[2]:

	area	bbox_area	equivalent_diameter	max_intensity	mean_intensity	min_intensity	standard_deviation_intensity	row	column	path_in_well
label
1	1360.0	1664.0	13.746119	268.0	184.579407	125.0	24.502481	B	03	0
2	2464.0	2992.0	16.757685	461.0	273.246765	132.0	51.794971	B	03	0
3	1968.0	2688.0	15.548035	429.0	277.291656	143.0	41.822422	B	03	0
4	5120.0	6400.0	21.384073	413.0	279.035156	118.0	44.379074	B	03	0
5	288.0	352.0	8.193409	341.0	243.315979	147.0	41.861721	B	03	0
...	...	...	...	...	...	...	...	...	...	...
1493	1248.0	1440.0	13.357914	385.0	269.291656	108.0	45.967396	B	03	0
1494	528.0	704.0	10.027941	475.0	315.710236	128.0	70.174896	B	03	0
1495	608.0	768.0	10.510781	377.0	281.544403	122.0	34.698723	B	03	0
1496	288.0	480.0	8.193409	356.0	261.003479	122.0	44.835232	B	03	0
1497	432.0	528.0	9.379111	454.0	336.101837	172.0	53.799728	B	03	0

1497 rows × 10 columns

Save the table in the HCS plate¶

In [3]:

# Save the tabvle in the HCS plate
hcs_zarr.add_table(name="nuclei", table=table)

# Read the table back for sanity check
hcs_zarr.get_table("nuclei").dataframe

# Save the tabvle in the HCS plate hcs_zarr.add_table(name="nuclei", table=table) # Read the table back for sanity check hcs_zarr.get_table("nuclei").dataframe

/opt/hostedtoolcache/Python/3.13.11/x64/lib/python3.13/site-packages/anndata/_io/zarr.py:44: UserWarning: Writing zarr v2 data will no longer be the default in the next minor release. v3 data will be written by default. If you are explicitly setting this configuration, consider migrating to the zarr v3 file format.
  f = open_write_group(store)

Out[3]:

	area	bbox_area	equivalent_diameter	max_intensity	mean_intensity	min_intensity	standard_deviation_intensity	row	column	path_in_well
label
1	1360.0	1664.0	13.746119	268.0	184.579407	125.0	24.502481	B	03	0
2	2464.0	2992.0	16.757685	461.0	273.246765	132.0	51.794971	B	03	0
3	1968.0	2688.0	15.548035	429.0	277.291656	143.0	41.822422	B	03	0
4	5120.0	6400.0	21.384073	413.0	279.035156	118.0	44.379074	B	03	0
5	288.0	352.0	8.193409	341.0	243.315979	147.0	41.861721	B	03	0
...	...	...	...	...	...	...	...	...	...	...
1493	1248.0	1440.0	13.357914	385.0	269.291656	108.0	45.967396	B	03	0
1494	528.0	704.0	10.027941	475.0	315.710236	128.0	70.174896	B	03	0
1495	608.0	768.0	10.510781	377.0	281.544403	122.0	34.698723	B	03	0
1496	288.0	480.0	8.193409	356.0	261.003479	122.0	44.835232	B	03	0
1497	432.0	528.0	9.379111	454.0	336.101837	172.0	53.799728	B	03	0

1497 rows × 10 columns

Create a new empty Plate¶

In [4]:

from ngio import ImageInWellPath, create_empty_plate

test_plate = create_empty_plate(
    store="./data/empty_plate.zarr",
    name="Test Plate",
    images=[
        ImageInWellPath(row="A", column="01", path="0"),
        ImageInWellPath(row="A", column="02", path="0"),
        ImageInWellPath(row="A", column="02", path="1", acquisition_id=1),
    ],
    overwrite=True,
)

print(test_plate)
print(f"Rows: {test_plate.rows}, Columns: {test_plate.columns}")

from ngio import ImageInWellPath, create_empty_plate test_plate = create_empty_plate( store="./data/empty_plate.zarr", name="Test Plate", images=[ ImageInWellPath(row="A", column="01", path="0"), ImageInWellPath(row="A", column="02", path="0"), ImageInWellPath(row="A", column="02", path="1", acquisition_id=1), ], overwrite=True, ) print(test_plate) print(f"Rows: {test_plate.rows}, Columns: {test_plate.columns}")

Plate([rows x columns] (1 x 2))
Rows: ['A'], Columns: ['01', '02']

In [ ]: