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edgeR & DESeq2 Analysis with ChatGPT (RNA-Seq Tutorial using R)

Calculate edgeR & DESeq2 P-values Using ChatGPT-Generated R Scripts | Subio Platform Workflow

This video demonstrates how to use ChatGPT to generate R scripts and perform differential expression analysis with edgeR and DESeq2. The same approach can also be applied to other R packages and Bioconductor tools.

Instead of writing complex code manually, you can describe the analysis in plain text, have ChatGPT generate the required R script, and execute it in R. If errors occur, ChatGPT can also help identify the cause and suggest corrections.

This video demonstrates the following workflow:

  • Exporting Gene Counts data from Subio Platform
  • Preparing the analysis data sheet
  • Generating an R script with ChatGPT
  • Calculating differential expression using edgeR and DESeq2
  • Troubleshooting and fixing errors
  • Importing the results back into Subio Platform
  • Visualizing and comparing P-values

This approach allows researchers to focus more on data interpretation rather than coding.

About the edgeR and DESeq2 analyses shown in this video

In this video, ChatGPT is instructed to generate R scripts based on the standard analysis workflows recommended for edgeR and DESeq2. However, because the purpose of this demonstration is to compare different statistical methods using a small-sample dataset (N=2 per group), differentially expressed genes (DEGs) are selected using P-value and Fold Change criteria rather than false discovery rate (FDR)-adjusted significance thresholds.

Are edgeR and DESeq2 sufficient on their own?

DESeq2 and edgeR are widely used and highly valuable methods for RNA-Seq differential expression analysis. However, no statistical method can automatically resolve issues such as data bias, batch effects, or biological and technical variability between samples. For this reason, we recommend visually inspecting and interpreting the data throughout the analysis process using Subio Platform, rather than relying solely on automated pipelines.

For a detailed discussion of the characteristics of edgeR, DESeq2, and t-tests, and the differences in the genes identified by each method, see: Choosing Between edgeR, DESeq2, and t-tests for RNA-Seq Differential Expression Analysis. The dataset used in this video is discussed in more detail as Case 1: Low-Variance, Small-Sample In Vitro Data.

Part of the RNA-Seq Data Analysis Tutorial Series

This page corresponds to 6-b. Differential Expression Analysis with edgeR and DESeq2 Using ChatGPT and R in our RNA-Seq data analysis tutorial series. The tutorial provides additional guidance on DEG selection strategies and interpretation of results.

If you would like to learn the complete RNA-Seq workflow, including data preparation, normalization, filtering, PCA, clustering, DEG analysis, and enrichment analysis, please see:
→ RNA-Seq Data Analysis Tutorial: Learning the Workflow and Interpretation Using GEO Data

Related Topics

Choosing Between edgeR, DESeq2, and t-tests for RNA-Seq Differential Expression Analysis

Are t-tests Inappropriate for RNA-Seq? Avoiding Overreliance on DESeq2, edgeR, and Statistical Models

A New Style of RNA-Seq Data Analysis - Analyze with R/Python × Visualize and Manage Data with Subio

Performing RNA-Seq Deconvolution Analysis in Consultation with AI - From Tool Selection and R Scripting to Result Interpretation

Getting Started

To follow this tutorial, first download the example dataset and install Subio Platform.

After downloading the SSA file and installing Subio Platform, watch the following 90-second introduction video before proceeding with the analysis tutorial.

Subio Platform the 90-Seconds Demo