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rapids-singlecell-complete

Accelerate single-cell RNA-seq preprocessing and clustering on AnnData with RAPIDS.

1|Updated Dec 3, 2025
Search Tags:#gpu#single-cell#scRNA-seq#annData#rapids#gpu-accelerated
One-click install
npx skills add https://github.com/Ketomihine/my_skills --skill rapids-singlecell-complete
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rapids-singlecell-complete
Source: https://github.com/Ketomihine/my_skills/tree/main/rapids-singlecell-complete
Command: npx skills add https://github.com/Ketomihine/my_skills --skill rapids-singlecell-complete

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

RAPIDS Single-Cell Complete provides GPU-accelerated tooling and documentation to speed up large-scale single-cell RNA-seq analyses on AnnData objects, enabling faster preprocessing, dimensionality reduction, clustering, and downstream interpretation.

Core Features & Use Cases

  • GPU-accelerated preprocessing: QC metrics, normalization, HVG selection, and scaling on GPU with RAPIDS.
  • Dimensionality reduction & clustering: PCA, UMAP, t-SNE, Louvain/Leiden with GPU acceleration for large datasets.
  • Advanced workflows: Multi-GPU workflows with Dask-CUDA, out-of-core processing, and spatial analysis (ligand-receptor interactions) with RAPIDS ecosystem integration.
  • Use Case: Process a 1-2 million cell scRNA-seq dataset by moving data to the GPU, performing batch correction, and generating embeddings and clusters within minutes rather than hours.

Quick Start

Install RAPIDS and this skill, load your AnnData object, move data to the GPU, and run the standard GPU-accelerated workflow: QC metrics, HVG detection, normalization, log1p, HVG filtering, PCA, neighbors, and UMAP/TSNE, followed by clustering and visualization.

Frequently Asked Questions about rapids-singlecell-complete

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I accelerate scRNA-seq analysis on large AnnData datasets?▼

To accelerate scRNA-seq analysis on large AnnData datasets, move data to the GPU and use RAPIDS APIs like cuDF and cuML for preprocessing, dimensionality reduction, and clustering within minutes.

Can I use Dask-CUDA for multi-GPU workflows in single-cell RNA-seq analysis?▼

Yes, you can use Dask-CUDA for multi-GPU workflows in single-cell RNA-seq analysis to scale out-of-core processing and handle massive datasets across multiple GPUs using the RAPIDS ecosystem.

What is GPU-accelerated single-cell analysis and when do I need it?▼

GPU-accelerated single-cell analysis speeds up large-scale scRNA-seq processing using RAPIDS. You need it when processing 1-2 million cell datasets takes hours instead of minutes on standard CPUs.

Does this toolkit support spatial analysis and ligand-receptor interactions?▼

Yes, the toolkit supports spatial analysis including ligand-receptor interactions by integrating the broader RAPIDS ecosystem to accelerate these advanced workflows on GPU hardware.

What is the best way to perform clustering on a million-cell scRNA-seq dataset?▼

The best way to cluster a million-cell scRNA-seq dataset is using GPU-accelerated Louvain or Leiden algorithms via RAPIDS, which computes embeddings and clusters significantly faster than CPU-based methods.

Do I need specific hardware to run GPU-accelerated preprocessing for AnnData?▼

Yes, you need NVIDIA GPU hardware to run GPU-accelerated preprocessing for AnnData, as the toolkit relies on RAPIDS libraries like cuDF, cuML, and CuPy to execute QC, normalization, and scaling.

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