SCassist: An AI Based Workflow Assistant for Single-Cell Analysis
Nagarajan, V.; Shi, G.; Arunkumar, S.; Liu, C.; Gopalakrishnan, J.; Nath, P. R.; Jang, J.; Caspi, R.
Show abstract
SummarySingle-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 (LLMs) to guide and enhance scRNA-seq analysis. SCassist integrates LLMs 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 LLMs like Googles Gemini, OpenAIs GPT and Metas Llama3, making scRNA-seq analysis accessible to researchers at all levels. Availability and implementationThe SCassist package, along with the detailed tutorials, is available at GitHub. https://github.com/NIH-NEI/SCassist
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.