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April 24th, 2025
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National Institutes of Health
bioinformatics
biorxiv

SCassist: An AI Based Workflow Assistant for Single-Cell Analysis

Nagarajan, V.Open in Google Scholar•Shi, G.Open in Google Scholar•Arunkumar, S.Open in Google Scholar•Liu, C.Open in Google Scholar•Gopalakrishnan, J.Open in Google Scholar•Nath, P. R.Open in Google Scholar•Jang, J.Open in Google Scholar•Caspi, R.Open in Google Scholar

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 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

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