feature_detection

Feature detection identifies persistent signals (chromatographic peaks) in LC-MS data. Feature linking combines features across multiple samples for quantitative comparison.

Skills
Scientific
0

What it does and how to use it

A skill that teaches Claude Code to do scientific tasks well. It is packaged knowledge the model uses whenever it is relevant.

Install the skill (command above).
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Install (Claude Code)

mkdir -p .claude/skills/feature-detection && curl -fsSL https://doutorai.pt/data/lib/skills__feature-detection.md -o .claude/skills/feature-detection/SKILL.md

Paste this command into the terminal, in your project folder, and the component installs automatically.

Content

# Feature Detection and Linking

## Overview

Feature detection identifies persistent signals (chromatographic peaks) in LC-MS data. Feature linking combines features across multiple samples for quantitative comparison.

## Feature Detection Basics

A feature represents a chromatographic peak characterized by:
- m/z value (mass-to-charge ratio)
- Retention time (RT)
- Intensity
- Quality score
- Convex hull (spatial extent in RT-m/z space)

## Feature Finding

### Feature Finder Multiples (FFM)

Standard algorithm for feature detection in centroided data:

```python
import pyopenms as ms

# Lo

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