工作流程
计算机科学
规范化(社会学)
聚类分析
钥匙(锁)
情报检索
数据挖掘
数据库
人工智能
人类学
计算机安全
社会学
作者
Vijayaraj Nagarajan,Guangpu Shi,Samyuktha Arunkumar,Chunhong Liu,Jaanam Gopalakrishnan,Pulak Ranjan Nath,Jun‐Ho Jang,Rachel R Caspi
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2025-07-12
卷期号:41 (8)
被引量:8
标识
DOI:10.1093/bioinformatics/btaf402
摘要
SUMMARY: Single-cell RNA sequencing (scRNA-seq) data analysis often involves complex iterative workflow, requiring significant expertise and time. To navigate this complexity, we have developed SCassist, an R package that leverages the power of the large language models (LLM's) to guide and enhance scRNA-seq analysis. SCassist integrates LLM's into key workflow steps, to analyze user data and provide relevant recommendations for filtering, normalization and clustering parameters. It also provides LLM guided insightful interpretations of variable features and principal components, along with cell type annotations and enrichment analysis. SCassist provides intelligent assistance using popular LLM's like Google's Gemini, OpenAI's GPT and Meta's Llama3, making scRNA-seq analysis accessible to researchers at all levels. AVAILABILITY AND IMPLEMENTATION: The SCassist package, along with the detailed tutorials, is available at GitHub. https://github.com/NIH-NEI/SCassist.
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