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scrnaseq-deep-learning

Analyze single-cell RNA-seq data with deep learning tools for batch correction and label transfer.

6|2|Updated Jun 11, 2026
Search Tags:#deep learning#single-cell RNA-seq#batch correction#label transfer#perturbation prediction
One-click install
npx skills add https://github.com/pradyumnasagar/open-research-skills --skill scrnaseq-deep-learning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scrnaseq-deep-learning
Source: https://github.com/pradyumnasagar/open-research-skills/tree/main/skills/machine-learning-bio/scrnaseq-deep-learning
Command: npx skills add https://github.com/pradyumnasagar/open-research-skills --skill scrnaseq-deep-learning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scvi-tools, transformers, torch-geometric, torch-lightning, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a deep learning framework for analyzing single-cell RNA-seq data, addressing the challenges of batch correction, label transfer, perturbation prediction, and foundation model fine-tuning.

Core Features & Use Cases

  • Batch Correction & Integration: Utilize scVI and scANVI for batch effect removal and integration of scRNA-seq data.
  • Label Transfer: Apply scANVI to predict cell types on new, unlabeled datasets based on a labeled reference dataset.
  • Perturbation Prediction: Employ scVI and scGPT to predict perturbation effects from baseline data.
  • Foundation Model Fine-tuning: Fine-tune scGPT and Geneformer models on user-specific datasets for downstream analysis.
  • Use Case: Suppose you have scRNA-seq data from a perturbation experiment. Use this Skill to correct batch effects, predict perturbation effects, and annotate cell types.

Quick Start

Run the 'analyze-scrnaseq' script to correct batch effects and perform label transfer on your single-cell RNA-seq data.

Frequently Asked Questions about scrnaseq-deep-learning

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

FAQPage Schema
How do I perform batch correction on single-cell RNA-seq data?▼

Label transfer predicts cell types on new, unlabeled single-cell RNA-seq datasets using scANVI. It leverages a labeled reference dataset to train a semi-supervised model, accurately annotating cell populations in your new data based on the learned representations.

Can I predict perturbation effects in scRNA-seq data using deep learning?▼

Fine-tuning scGPT and Geneformer foundation models on user-specific single-cell RNA-seq datasets is supported. This adapts pre-trained transformer models to your specific biological context, improving performance on downstream tasks like perturbation prediction.

Do I need PyTorch Lightning and scvi-tools to run single-cell deep learning workflows?▼

You need scvi-tools, transformers, torch-geometric, and torch-lightning to run these single-cell deep learning workflows. These dependencies provide the necessary neural network architectures, graph neural network support, and training loop management for model inference and fine-tuning.

What is the best way to integrate multiple single-cell RNA-seq datasets?▼

The best way to integrate multiple single-cell RNA-seq datasets is using scVI for batch effect removal. It utilizes a variational autoencoder framework to condition on batch covariates, yielding a corrected, integrated latent representation for joint analysis.

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