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Zhongwei Li
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skills/omero-integration/SKILL.md
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skills/omero-integration/SKILL.md
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name: omero-integration
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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."
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---
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# OMERO Integration
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## Overview
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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.
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## When to Use This Skill
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This skill should be used when:
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- Working with OMERO Python API (omero-py) to access microscopy data
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- Retrieving images, datasets, projects, or screening data programmatically
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- Analyzing pixel data and creating derived images
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- Creating or managing ROIs (regions of interest) on microscopy images
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- Adding annotations, tags, or metadata to OMERO objects
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- Storing measurement results in OMERO tables
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- Creating server-side scripts for batch processing
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- Performing high-content screening analysis
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## Core Capabilities
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This skill covers eight major capability areas. Each is documented in detail in the references/ directory:
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### 1. Connection & Session Management
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**File**: `references/connection.md`
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Establish secure connections to OMERO servers, manage sessions, handle authentication, and work with group contexts. Use this for initial setup and connection patterns.
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**Common scenarios:**
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- Connect to OMERO server with credentials
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- Use existing session IDs
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- Switch between group contexts
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- Manage connection lifecycle with context managers
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### 2. Data Access & Retrieval
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**File**: `references/data_access.md`
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Navigate OMERO's hierarchical data structure (Projects → Datasets → Images) and screening data (Screens → Plates → Wells). Retrieve objects, query by attributes, and access metadata.
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**Common scenarios:**
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- List all projects and datasets for a user
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- Retrieve images by ID or dataset
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- Access screening plate data
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- Query objects with filters
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### 3. Metadata & Annotations
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**File**: `references/metadata.md`
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Create and manage annotations including tags, key-value pairs, file attachments, and comments. Link annotations to images, datasets, or other objects.
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**Common scenarios:**
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- Add tags to images
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- Attach analysis results as files
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- Create custom key-value metadata
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- Query annotations by namespace
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### 4. Image Processing & Rendering
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**File**: `references/image_processing.md`
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Access raw pixel data as NumPy arrays, manipulate rendering settings, create derived images, and manage physical dimensions.
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**Common scenarios:**
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- Extract pixel data for computational analysis
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- Generate thumbnail images
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- Create maximum intensity projections
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- Modify channel rendering settings
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### 5. Regions of Interest (ROIs)
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**File**: `references/rois.md`
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Create, retrieve, and analyze ROIs with various shapes (rectangles, ellipses, polygons, masks, points, lines). Extract intensity statistics from ROI regions.
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**Common scenarios:**
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- Draw rectangular ROIs on images
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- Create polygon masks for segmentation
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- Analyze pixel intensities within ROIs
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- Export ROI coordinates
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### 6. OMERO Tables
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**File**: `references/tables.md`
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Store and query structured tabular data associated with OMERO objects. Useful for analysis results, measurements, and metadata.
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**Common scenarios:**
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- Store quantitative measurements for images
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- Create tables with multiple column types
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- Query table data with conditions
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- Link tables to specific images or datasets
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### 7. Scripts & Batch Operations
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**File**: `references/scripts.md`
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Create OMERO.scripts that run server-side for batch processing, automated workflows, and integration with OMERO clients.
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**Common scenarios:**
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- Process multiple images in batch
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- Create automated analysis pipelines
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- Generate summary statistics across datasets
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- Export data in custom formats
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### 8. Advanced Features
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**File**: `references/advanced.md`
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Covers permissions, filesets, cross-group queries, delete operations, and other advanced functionality.
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**Common scenarios:**
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- Handle group permissions
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- Access original imported files
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- Perform cross-group queries
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- Delete objects with callbacks
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## Installation
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```bash
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uv pip install omero-py
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```
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**Requirements:**
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- Python 3.7+
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- Zeroc Ice 3.6+
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- Access to an OMERO server (host, port, credentials)
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## Quick Start
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Basic connection pattern:
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```python
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from omero.gateway import BlitzGateway
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# Connect to OMERO server
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conn = BlitzGateway(username, password, host=host, port=port)
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connected = conn.connect()
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if connected:
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# Perform operations
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for project in conn.listProjects():
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print(project.getName())
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# Always close connection
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conn.close()
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else:
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print("Connection failed")
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```
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**Recommended pattern with context manager:**
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```python
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from omero.gateway import BlitzGateway
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with BlitzGateway(username, password, host=host, port=port) as conn:
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# Connection automatically managed
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for project in conn.listProjects():
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print(project.getName())
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# Automatically closed on exit
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```
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## Selecting the Right Capability
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**For data exploration:**
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- Start with `references/connection.md` to establish connection
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- Use `references/data_access.md` to navigate hierarchy
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- Check `references/metadata.md` for annotation details
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**For image analysis:**
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- Use `references/image_processing.md` for pixel data access
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- Use `references/rois.md` for region-based analysis
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- Use `references/tables.md` to store results
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**For automation:**
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- Use `references/scripts.md` for server-side processing
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- Use `references/data_access.md` for batch data retrieval
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**For advanced operations:**
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- Use `references/advanced.md` for permissions and deletion
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- Check `references/connection.md` for cross-group queries
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## Common Workflows
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### Workflow 1: Retrieve and Analyze Images
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1. Connect to OMERO server (`references/connection.md`)
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2. Navigate to dataset (`references/data_access.md`)
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3. Retrieve images from dataset (`references/data_access.md`)
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4. Access pixel data as NumPy array (`references/image_processing.md`)
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5. Perform analysis
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6. Store results as table or file annotation (`references/tables.md` or `references/metadata.md`)
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### Workflow 2: Batch ROI Analysis
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1. Connect to OMERO server
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2. Retrieve images with existing ROIs (`references/rois.md`)
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3. For each image, get ROI shapes
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4. Extract pixel intensities within ROIs (`references/rois.md`)
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5. Store measurements in OMERO table (`references/tables.md`)
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### Workflow 3: Create Analysis Script
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1. Design analysis workflow
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2. Use OMERO.scripts framework (`references/scripts.md`)
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3. Access data through script parameters
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4. Process images in batch
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5. Generate outputs (new images, tables, files)
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## Error Handling
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Always wrap OMERO operations in try-except blocks and ensure connections are properly closed:
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```python
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from omero.gateway import BlitzGateway
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import traceback
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try:
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conn = BlitzGateway(username, password, host=host, port=port)
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if not conn.connect():
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raise Exception("Connection failed")
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# Perform operations
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except Exception as e:
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print(f"Error: {e}")
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traceback.print_exc()
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finally:
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if conn:
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conn.close()
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```
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## Additional Resources
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- **Official Documentation**: https://omero.readthedocs.io/en/stable/developers/Python.html
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- **BlitzGateway API**: https://omero.readthedocs.io/en/stable/developers/Python.html#omero-blitzgateway
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- **OMERO Model**: https://omero.readthedocs.io/en/stable/developers/Model.html
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- **Community Forum**: https://forum.image.sc/tag/omero
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## Notes
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- OMERO uses group-based permissions (READ-ONLY, READ-ANNOTATE, READ-WRITE)
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- Images in OMERO are organized hierarchically: Project > Dataset > Image
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- Screening data uses: Screen > Plate > Well > WellSample > Image
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- Always close connections to free server resources
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- Use context managers for automatic resource management
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- Pixel data is returned as NumPy arrays for analysis
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