scembed

scEmbed trains Region2Vec models on single-cell ATAC-seq datasets to generate cell embeddings for clustering and analysis. It provides an unsupervised machine learning framework for representing and analyzing scATAC-seq data in…

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).
In Claude Code, describe what you need - the skill turns on by itself when the context fits.
No commands to memorize: you talk in plain language and the skill does the rest.

Install (Claude Code)

mkdir -p .claude/skills/scembed && curl -fsSL https://doutorai.pt/data/lib/skills__scembed.md -o .claude/skills/scembed/SKILL.md

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

Content

# scEmbed: Single-Cell Embedding Generation

## Overview

scEmbed trains Region2Vec models on single-cell ATAC-seq datasets to generate cell embeddings for clustering and analysis. It provides an unsupervised machine learning framework for representing and analyzing scATAC-seq data in low-dimensional space.

## When to Use

Use scEmbed when working with:
- Single-cell ATAC-seq (scATAC-seq) data requiring clustering
- Cell-type annotation tasks
- Dimensionality reduction for single-cell chromatin accessibility
- Integration with scanpy workflows for downstream analysis

## Workflow

### Step 1:

- 🔒 Full content on the Pro plan (€7.99/month) -

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