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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 explains how to generate R scripts using ChatGPT and calculate P-values with edgeR and DESeq2. This approach can also be applied to other R libraries and Bioconductor tools.

Instead of writing complex code yourself, you define the analysis in text, ask ChatGPT to generate the script, and then run it in R. If errors occur, ChatGPT can also help identify the cause and suggest corrections.

In this video, we demonstrate the following workflow:

  • Export Gene Counts data from Subio Platform
  • Prepare the data sheet for analysis
  • Generate an R script with ChatGPT
  • Calculate P-values with edgeR and DESeq2
  • Fix errors
  • Import the results back into Subio Platform
  • Visualize and compare P-values

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

Are edgeR and DESeq2 Enough?

DESeq2 and edgeR are widely used and important methods for RNA-Seq differential expression analysis. However, no statistical method can automatically solve problems such as data bias, batch effects, or variability among samples.

For this reason, P-values and FDR values calculated in R should be imported back into Subio Platform and interpreted while visually checking expression patterns, relationships among samples, and differences among analysis methods.

Differences in the genes identified as significant by edgeR, DESeq2, and t-tests are explained in the related case study: Characteristics of Genes Identified as Significant by edgeR, DESeq2, and t-tests No.421.

edgeR and DESeq2 accept Gene Counts as input and then perform normalization and statistical processing automatically. However, in this process, batch effects and sample-to-sample bias may become less visible, making it more difficult to notice factors that can lead to false positives. For details, please see the related case study: When Samples Appear Separated in RNA-Seq PCA No.403.

This page is part of the RNA-Seq data analysis tutorial

This page corresponds to “6-b. Differential expression analysis with edgeR and DESeq2 using ChatGPT and R” in the RNA-Seq data analysis tutorial. The tutorial page explains in more detail how to choose a DEG extraction strategy according to your purpose and how to interpret the results.

If you would like to learn the full workflow, including data preparation, normalization, filtering, PCA, clustering, DEG extraction, and enrichment analysis, please see the tutorial below.
→  RNA-Seq Data Analysis Tutorial: Learn the Workflow and How to Interpret Results Using GEO Data

Related Topics

A Case Study Comparing edgeR, DESeq2, and t-test in RNA-Seq Differential Expression Analysis (1): Low-Variance, Small-Sample In Vitro Data

Are t-tests Really Inappropriate for RNA-Seq? DESeq2, edgeR, and Why We Should Not Overtrust Statistical Models

When Samples Appear Separated in RNA-Seq PCA: What to Check Before Calling It a Batch Effect