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()

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/3.13.5/x64/lib/python3.13/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.
  warnings.warn("Transforming to str index.", ImplicitModificationWarning)

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

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']