This repository contains the code and case studies from our research on the application of Large Language Models (LLMs) in developing reviewable and supportable bioinformatics processe. The repository includes original examples RNA-seq and scRNA-seq analysis and the updated code generated by various LLMs: Claude3.5 ChatGPT-4 Turbo (o1-PREVIEW), Unify AI and Gemini.
Background: I am a bioinformatician and have programmed this code, bulkRNA_CaseStudy.R, for my bulkRNA-seq analysis. Could you guide me by answering the following questions?
Prompt 1: Could you assist in writing good code following best practices?
Prompt 2: What are the best practices for developing an efficient code?
Prompt 3: What are the crucial checkpoints they should follow in the reviewing process?
Bulk RNA-seq: R script for processing bulk RNA-seq data, from reading raw count tables per sample to the normalization process, including filtering steps. The original script is titled bulkRNA_CaseStudy.R.
- bulkRNAseq_Claude.R: Updated R script refined by Claude 3.5.
- bulkRNAseq_chatGPT.R: Updated R script refined by ChatGPT-4 Turbo (o1-PREVIEW).
- bulkRNAseq_UnifyAI.R: Updated R script refined by UnifyAI.
- bulkRNAseq_Gemini.R: Updated R script refined by Gemini.
Single-cell RNA-seq: R script for processing scRNA-seq data, starting from the creation of a Seurat object to identifying the most highly variable genes, including normalization step. The script is named scRNA_CaseStudy.R.
- scRNAseq_Claude.R: Updated R script refined by Claude 3.5.
- scRNAseq_chatGPT.R: Updated R script refined by ChatGPT-4 Turbo (o1-PREVIEW).
- scRNAseq_UnifyAI.R: Updated R script refined by UnifyAI.
- scRNAseq_Gemini.R: Updated R script refined by Gemini.