bio-imaging-mass-cytometry-interactive-annotation
Interactive cell type annotation for IMC data. Covers napari-based annotation, marker-guided labeling, training data generation, and annotation validation. Use when manually annotating cell types for training classifiers or validating automated phenotyping results.
npx skills add majiayu000/claude-skill-registry --skill interactive-annotation-gptomics-bioskills-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Interactive Annotation ## Napari-Based Annotation ```python import napari import numpy as np from skimage import io import pandas as pd # Load IMC image stack image_stack = io.imread('imc_image.tiff') # (C, H, W) segmentation_mask = io.imread('cell_segmentation.tiff') # Create napari viewer viewer = napari.Viewer() # Add channels as separate layers for visualization channel_names = ['CD45', 'CD3', 'CD68', 'panCK', 'DNA'] for i, name in enumerate(channel_names): viewer.add_image(image_stack[i], name=name, visible=False, colormap='gray', blending='additive') # Add segmentation viewer.add_labels(segmentation_mask, name='Cells') # Add annotation layer (start empty) annotation_layer = viewer.add_labels( np.zeros_like(segmentation_mask), name='Cell_Types' ) # Define cell types cell_type_mapping = {1: 'T_cell', 2: 'Macrophage', 3: 'Epithelial', 4: 'Stromal', 5: 'Other'} ``` ## Marker-Guided Annotation ```python def create_marker_overlay(image_stack, channel_indices, colors): '''Create RGB overlay of selected markers for easier annotation.''' h, w = image_stack.shape[1:] overlay = np.zeros((h, w, 3), dtype=np.float32) for idx, color in zip(channel_indices, colors): channel = image_stack[
- Napari-Based Annotation
- Marker-Guided Annotation
- Training Data Generation
- Semi-Automated Annotation
- Annotation Validation
- Napari Plugin Interface
- Batch Annotation Workflow
- Related Skills
What does the bio-imaging-mass-cytometry-interactive-annotation skill do?
Interactive cell type annotation for IMC data. Covers napari-based annotation, marker-guided labeling, training data generation, and annotation validation. Use when manually annotating cell types for training classifiers or validating automated phenotyping results.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill interactive-annotation-gptomics-bioskills-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.