OMERO and Polarity-JaM

OMERO is a client-server software for visualizing, managing, and annotating microscope images. It is used by many research institutions to store and manage their microscopy data. PolarityJaM can be used to analyze images stored in OMERO. This notebook demonstrates how to connect to an OMERO server, load an image, and analyze it with Polarity-JaM.

Installation

To use PolarityJaM with OMERO, you need to install the OMERO Python client. You can install it with pip in your environment where Polarity-JaM is installed:

micromamba activate polarityjam
pip install omero-py

Connection to OMERO

You can use PolarityJaM with OMERO in the same way as with local images. You need to load the image from OMERO, convert it to a numpy array, and then analyze it with PolarityJaM.

You might need to adjust the connection parameters to your OMERO server.

### ADAPT ME ###
HOSTNAME = "myomero.my-domain.net"
PORT = int(4064)
### ADAPT ME ###

from omero.gateway import BlitzGateway
from getpass import getpass

# connect to omero
conn = BlitzGateway(
    input("Username: "), getpass("Password: "), host=HOSTNAME, port=PORT, secure=True
)
conn.connect()

User and Group Information

Sometimes you need to know which groups the user is a member of or switch the group to access the images. You can get this information with the following commands.

print("Groups the user is a member of:")
for g in conn.getGroupsMemberOf():
    print("Group: %s \tID: %s" % (g.getName(), g.getId()))
print(f"Current group: {conn.getGroupFromContext().getName()}")

# switch group
conn.setGroupForSession(203)
print(f"Current group: {conn.getGroupFromContext().getName()}")

Load Image from Omero

You can load an image from OMERO with the image ID. You can find the image ID in the OMERO web interface. Here we load our example image.

img_060721_EGM2_18dyn_02_omero_id = 30618

img_omero = conn.getObject("Image", img_060721_EGM2_18dyn_02_omero_id)

assert img_omero is not None, "Image not found"

Lets check the image shape and convert it to a numpy array.

img_omero

Image information ID Name Image 30618 060721_EGM2_18dyn_02.tif Dataset 3454 example_data Project 2002 Polarity-JaM

Dimensions (TCZYX): ( 1, 4, 1, 1024, 1024) Voxel/Pixel dimensions (ZYX): (na, na, na) Physical units: (na, na, na) Channel Names: ['0', '1', '2', '3']

import numpy as np
from pathlib import Path

# get image shape
shape = img_omero.getSizeZ(), img_omero.getSizeT(), img_omero.getSizeC(), img_omero.getSizeX(), img_omero.getSizeY()
print("Image shape: ZTCXY", shape)

# convert to numpy array
pxls = img_omero.getPrimaryPixels()
img = np.zeros([img_omero.getSizeX(), img_omero.getSizeY(), img_omero.getSizeC()])
for i in range(img_omero.getSizeC()):
    img[:, :, i] = pxls.getPlane(0, i, 0)

print("Numpy Image shape: ", img.shape)

Image shape: ZTCXY (1, 1, 4, 1024, 1024)
Numpy Image shape:  (1024, 1024, 4)

### ADAPT ME ###
path_root = Path("")
output_path = path_root.joinpath("polarityjam_out")
### ADAPT ME ###

input_file = img_omero.getName()
output_file_prefix = Path(input_file).stem

print("Input file: ", input_file)
print("Output path: ", output_path)
print("Output file prefix: ", output_file_prefix)

Analysis

Lets analyze the image with PolarityJaM. We first need to define the parameters for the image, runtime, and plotting. Then we can segment the image and extract features to finally plot the results.

Note

This part is similar to the local analysis. See the notebook PolarityJaM for more details.

# must run in an environment where polarityJaM and omero-py is installed
from polarityjam import Extractor, Plotter, PropertiesCollection
from polarityjam import RuntimeParameter, PlotParameter, ImageParameter
from polarityjam import PolarityJamLogger
from polarityjam import load_segmenter

plog = PolarityJamLogger("WARNING")

# describe your image with ImageParameter
params_image = ImageParameter()

# set the channels
params_image.channel_organelle = 0  # golgi channel
params_image.channel_nucleus = 2
params_image.channel_junction = 3
params_image.channel_expression_marker = 3
params_image.pixel_to_micron_ratio = 2.4089

# define other parameters, use default values
params_runtime = RuntimeParameter()
params_plot = PlotParameter()

# change some parameters
params_runtime.membrane_thickness = 6
params_runtime.min_organelle_size = 9

print(params_image)
print(params_runtime)
print(params_plot)

ImageParameter:
channel_junction              3
channel_nucleus               2
channel_organelle             0
channel_expression_marker     3
pixel_to_micron_ratio         2.4089

RuntimeParameter:
extract_group_features        False
membrane_thickness            6
junction_threshold            -1
feature_of_interest           cell_area
min_cell_size                 50
min_nucleus_size              10
min_organelle_size            9
dp_epsilon                    5
cue_direction                 0
connection_graph              True
segmentation_algorithm        CellposeSegmenter
remove_small_objects_size     10
clear_border                  True
save_sc_image                 False
keyfile_condition_cols        ['short_name']

PlotParameter:
plot_junctions                True
plot_polarity                 True
plot_elongation               True
plot_circularity              True
plot_marker                   True
plot_ratio_method             True
plot_shape_orientation        True
plot_foi                      True
plot_sc_image                 False
plot_threshold_masks          None
plot_sc_partitions            False
show_statistics               False
show_polarity_angles          True
show_graphics_axis            False
show_scalebar                 True
outline_width                 2
length_scalebar_microns       20.0
graphics_output_format        ['png']
dpi                           300
graphics_width                5
graphics_height               5
membrane_thickness            5
fontsize_text_annotations     6
font_color                    w
marker_size                   2
alpha                         0.7
alpha_cell_outline            1.0

print("Used algorithm for segmentation: %s " % params_runtime.segmentation_algorithm)

Used algorithm for segmentation: CellposeSegmenter

# Now define your segmenter and segment your image with the default algorithm and default parameters.
cellpose_segmentation, _ = load_segmenter(params_runtime)

# prepare your image for segmentation
img_prepared, img_prepared_params = cellpose_segmentation.prepare(img, params_image)

# now segment your prepared image to get the masks
mask = cellpose_segmentation.segment(img_prepared, input_file)

# plot segmentation mask to check the quality
plotter = Plotter(params_plot)
plotter.plot_mask(mask, img_prepared, img_prepared_params, output_path, output_file_prefix);

# feature extraction
collection = PropertiesCollection()
extractor = Extractor(params_runtime)
extractor.extract(img, params_image, mask, output_file_prefix, output_path, collection);

collection.dataset.head()

	filename	img_hash	label	cell_X	cell_Y	cell_shape_orientation_rad	cell_shape_orientation_deg	cell_major_axis_length	cell_minor_axis_length	cell_eccentricity	...	junction_perimeter	junction_protein_area	junction_mean_expression	junction_protein_intensity	junction_interface_linearity_index	junction_interface_occupancy	junction_intensity_per_interface_area	junction_cluster_density	junction_cue_directional_intensity_ratio	junction_cue_axial_intensity_ratio
1	060721_EGM2_18dyn_02	99b1548d1d7368b24afd215aa00c0506a0f31434	11.0	721.049684	44.831045	3.115089	178.481461	148.218110	57.849774	0.920687	...	356.551299	2243.0	317.373711	995720.0	1.050856	0.578093	256.628866	443.923317	0.076623	0.516027
2	060721_EGM2_18dyn_02	99b1548d1d7368b24afd215aa00c0506a0f31434	12.0	57.170727	55.504237	0.007508	0.430191	99.033516	76.261338	0.637976	...	315.563492	1793.0	242.299719	619673.0	1.085897	0.559788	193.466438	345.606804	-0.282458	0.587275
3	060721_EGM2_18dyn_02	99b1548d1d7368b24afd215aa00c0506a0f31434	13.0	619.520117	70.120408	2.742737	157.147232	102.321920	89.083348	0.491959	...	334.534055	2079.0	267.149868	736250.0	1.070077	0.610932	216.353218	354.136604	-0.131366	0.428645
4	060721_EGM2_18dyn_02	99b1548d1d7368b24afd215aa00c0506a0f31434	14.0	176.148852	105.566653	0.422114	24.185359	187.633346	145.493784	0.631451	...	609.126984	2496.0	227.167310	1017159.0	1.065744	0.401544	163.635618	407.515625	-0.262354	0.540647
5	060721_EGM2_18dyn_02	99b1548d1d7368b24afd215aa00c0506a0f31434	15.0	822.559345	71.767302	2.477095	141.927109	75.120572	52.348251	0.717211	...	224.651804	1793.0	389.570198	816943.0	1.055555	0.772179	351.827304	455.629113	-0.024172	0.541412

5 rows × 66 columns

collection.dataset.to_csv(output_path.joinpath('features.csv'))

# plot the cell orientation
plotter.plot_shape_orientation(collection, "060721_EGM2_18dyn_02");  # image is automatically saved in output_path