Nagarajan Vijayaraj, Shi Guangpu, Arunkumar Samyuktha, Liu Chunhong, Gopalakrishnan Jaanam, Nath Pulak R, Jang Junseok, Caspi Rachel R
Laboratory of Immunology, National Eye Institute, NIH, Bethesda, MD 20892, United States.
Neuro-Immune Regulome Unit (Alumni), National Eye Institute, NIH, Bethesda, MD 20892, United States.
Bioinformatics. 2025 Aug 2;41(8). doi: 10.1093/bioinformatics/btaf402.
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.
The SCassist package, along with the detailed tutorials, is available at GitHub. https://github.com/NIH-NEI/SCassist.
单细胞RNA测序（scRNA-seq）数据分析通常涉及复杂的迭代工作流程，需要大量专业知识和时间。为应对这种复杂性，我们开发了SCassist，这是一个R包，它利用大语言模型（LLM）的能力来指导和增强scRNA-seq分析。SCassist将LLM集成到关键工作流程步骤中，以分析用户数据，并为过滤、归一化和聚类参数提供相关建议。它还提供LLM指导的对可变特征和主成分的深刻解释，以及细胞类型注释和富集分析。SCassist使用谷歌的Gemini、OpenAI的GPT和Meta的Llama3等流行的LLM提供智能辅助，使各级研究人员都能进行scRNA-seq分析。
SCassist包以及详细教程可在GitHub上获取。https://github.com/NIH-NEI/SCassist 。
