omero-integration — Инструмент разработчика для работы с OMERO API

--- 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. license: Unknown metadata: skill-author: K-Dense Inc. --- # 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 ```bash 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: ```python 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:** ```python 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: ```python 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