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Omero Integration

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name: omero-integration description: "Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows."

OMERO Integration

Overview

OMERO is an open-source platform for managing, visualizing, and analyzing microscopy images and metadata. Access images via Python API, retrieve datasets, analyze pixels, manage ROIs and annotations, for high-content screening and microscopy workflows.

When to Use This Skill

This skill should be used when: - Working with OMERO Python API (omero-py) to access microscopy data - Retrieving images, datasets, projects, or screening data programmatically - Analyzing pixel data and creating derived images - Creating or managing ROIs (regions of interest) on microscopy images - Adding annotations, tags, or metadata to OMERO objects - Storing measurement results in OMERO tables - Creating server-side scripts for batch processing - Performing high-content screening analysis

Core Capabilities

This skill covers eight major capability areas. Each is documented in detail in the references/ directory:

1. Connection & Session Management

File: references/connection.md

Establish secure connections to OMERO servers, manage sessions, handle authentication, and work with group contexts. Use this for initial setup and connection patterns.

Common scenarios: - Connect to OMERO server with credentials - Use existing session IDs - Switch between group contexts - Manage connection lifecycle with context managers

2. Data Access & Retrieval

File: references/data_access.md

Navigate OMERO's hierarchical data structure (Projects → Datasets → Images) and screening data (Screens → Plates → Wells). Retrieve objects, query by attributes, and access metadata.

Common scenarios: - List all projects and datasets for a user - Retrieve images by ID or dataset - Access screening plate data - Query objects with filters

3. Metadata & Annotations

File: references/metadata.md

Create and manage annotations including tags, key-value pairs, file attachments, and comments. Link annotations to images, datasets, or other objects.

Common scenarios: - Add tags to images - Attach analysis results as files - Create custom key-value metadata - Query annotations by namespace

4. Image Processing & Rendering

File: references/image_processing.md

Access raw pixel data as NumPy arrays, manipulate rendering settings, create derived images, and manage physical dimensions.

Common scenarios: - Extract pixel data for computational analysis - Generate thumbnail images - Create maximum intensity projections - Modify channel rendering settings

5. Regions of Interest (ROIs)

File: references/rois.md

Create, retrieve, and analyze ROIs with various shapes (rectangles, ellipses, polygons, masks, points, lines). Extract intensity statistics from ROI regions.

Common scenarios: - Draw rectangular ROIs on images - Create polygon masks for segmentation - Analyze pixel intensities within ROIs - Export ROI coordinates

6. OMERO Tables

File: references/tables.md

Store and query structured tabular data associated with OMERO objects. Useful for analysis results, measurements, and metadata.

Common scenarios: - Store quantitative measurements for images - Create tables with multiple column types - Query table data with conditions - Link tables to specific images or datasets

7. Scripts & Batch Operations

File: references/scripts.md

Create OMERO.scripts that run server-side for batch processing, automated workflows, and integration with OMERO clients.

Common scenarios: - Process multiple images in batch - Create automated analysis pipelines - Generate summary statistics across datasets - Export data in custom formats

8. Advanced Features

File: references/advanced.md

Covers permissions, filesets, cross-group queries, delete operations, and other advanced functionality.

Common scenarios: - Handle group permissions - Access original imported files - Perform cross-group queries - Delete objects with callbacks

Installation

uv pip install omero-py

Requirements: - Python 3.7+ - Zeroc Ice 3.6+ - Access to an OMERO server (host, port, credentials)

Quick Start

Basic connection pattern:

from omero.gateway import BlitzGateway

# Connect to OMERO server
conn = BlitzGateway(username, password, host=host, port=port)
connected = conn.connect()

if connected:
    # Perform operations
    for project in conn.listProjects():
        print(project.getName())

    # Always close connection
    conn.close()
else:
    print("Connection failed")

Recommended pattern with context manager:

from omero.gateway import BlitzGateway

with BlitzGateway(username, password, host=host, port=port) as conn:
    # Connection automatically managed
    for project in conn.listProjects():
        print(project.getName())
    # Automatically closed on exit

Selecting the Right Capability

For data exploration: - Start with references/connection.md to establish connection - Use references/data_access.md to navigate hierarchy - Check references/metadata.md for annotation details

For image analysis: - Use references/image_processing.md for pixel data access - Use references/rois.md for region-based analysis - Use references/tables.md to store results

For automation: - Use references/scripts.md for server-side processing - Use references/data_access.md for batch data retrieval

For advanced operations: - Use references/advanced.md for permissions and deletion - Check references/connection.md for cross-group queries

Common Workflows

Workflow 1: Retrieve and Analyze Images

  1. Connect to OMERO server (references/connection.md)
  2. Navigate to dataset (references/data_access.md)
  3. Retrieve images from dataset (references/data_access.md)
  4. Access pixel data as NumPy array (references/image_processing.md)
  5. Perform analysis
  6. Store results as table or file annotation (references/tables.md or references/metadata.md)

Workflow 2: Batch ROI Analysis

  1. Connect to OMERO server
  2. Retrieve images with existing ROIs (references/rois.md)
  3. For each image, get ROI shapes
  4. Extract pixel intensities within ROIs (references/rois.md)
  5. Store measurements in OMERO table (references/tables.md)

Workflow 3: Create Analysis Script

  1. Design analysis workflow
  2. Use OMERO.scripts framework (references/scripts.md)
  3. Access data through script parameters
  4. Process images in batch
  5. Generate outputs (new images, tables, files)

Error Handling

Always wrap OMERO operations in try-except blocks and ensure connections are properly closed:

from omero.gateway import BlitzGateway
import traceback

try:
    conn = BlitzGateway(username, password, host=host, port=port)
    if not conn.connect():
        raise Exception("Connection failed")

    # Perform operations

except Exception as e:
    print(f"Error: {e}")
    traceback.print_exc()
finally:
    if conn:
        conn.close()

Additional Resources

  • Official Documentation: https://omero.readthedocs.io/en/stable/developers/Python.html
  • BlitzGateway API: https://omero.readthedocs.io/en/stable/developers/Python.html#omero-blitzgateway
  • OMERO Model: https://omero.readthedocs.io/en/stable/developers/Model.html
  • Community Forum: https://forum.image.sc/tag/omero

Notes

  • OMERO uses group-based permissions (READ-ONLY, READ-ANNOTATE, READ-WRITE)
  • Images in OMERO are organized hierarchically: Project > Dataset > Image
  • Screening data uses: Screen > Plate > Well > WellSample > Image
  • Always close connections to free server resources
  • Use context managers for automatic resource management
  • Pixel data is returned as NumPy arrays for analysis
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