AI-assisted RNA-seq analysis with Bioconductor

A quick primer before the walkthrough:

In plain English: Take a raw RNA-seq count matrix through DESeq2 differential expression and clusterProfiler pathway analysis, using an LLM to help annotate and interpret results.

Key terms:

• RNA-seq — sequencing that measures gene expression across the whole transcriptome.
• Count matrix — a table of gene × sample read counts.
• DESeq2 / clusterProfiler — R/Bioconductor packages for stats and pathway enrichment.

What you'll need: R + Bioconductor installed, an existing count matrix, and access to an LLM service (such as OpenAI, Anthropic, or a locally hosted model).

Comfort level: Comfortable with R and standard bioinformatics workflows. Not a starting point for AI or RNA-seq newcomers — see the beginner primers first.

New to this? The workflow is: a table of raw gene counts goes in, a list of genes that differ between your conditions comes out, and the AI helps you check the design and explain the results in words. The AI never touches the statistics — DESeq2 does that. If R is new to you, run each block one line at a time and read what prints.

What you'll build

An AI-assisted RNA-seq differential expression workflow in R using Bioconductor's DESeq2, with an LLM helping you interpret gene lists and draft methods text.

Prerequisites

• R 4.3+ and RStudio (or Positron)
• A count matrix (genes × samples) and a sample metadata table
• An LLM interface suitable for your research environment. Always verify your organization's current data governance policy and vendor agreements before sharing data, as terms change over time.

1. Install the core stack

if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install(c("DESeq2", "tximeta", "apeglm", "EnhancedVolcano", "clusterProfiler", "org.Hs.eg.db"))

2. Load counts and build the DESeq dataset

library(DESeq2)
counts <- as.matrix(read.csv("counts.csv", row.names = 1))
coldata <- read.csv("samples.csv", row.names = 1)
stopifnot(all(colnames(counts) == rownames(coldata)))

dds <- DESeqDataSetFromMatrix(counts, coldata, design = ~ condition)

# Filtering threshold is illustrative; adjust according to your assay depth and SOPs
dds <- dds[rowSums(counts(dds)) >= 10, ]
dds <- DESeq(dds)
res <- lfcShrink(dds, coef = "condition_treated_vs_control", type = "apeglm")

Note: Filtering thresholds and experimental design settings are example-only and should be tailored to your specific assay design, library depth, and study SOPs.

3. Ask the LLM to sanity-check your design

To protect sample privacy and avoid sharing indirect attributes (such as sex, age, or cohort details), do not paste raw sample metadata tables into hosted models. Instead, share a redacted schema or synthetic metadata structure alongside the output of summary(res).

Prompt:

"Given this experimental design summary and redacted metadata schema, what potential confounders should I evaluate? Suggest PCA or batch-correction steps to consider before I interpret results."

Common areas to evaluate with your team: batch aliased with condition, uneven library sizes, or unmodeled covariates. Always evaluate model suggestions using your own expert judgment and study protocols.

4. Enrichment + AI interpretation

When performing enrichment analysis, include the tested gene universe to help prevent statistical bias in the results.

library(clusterProfiler)
library(org.Hs.eg.db)

# Significance thresholds are example values; adapt based on your study plan
sig_genes <- rownames(subset(res, padj < 0.05 & abs(log2FoldChange) > 1))
bg_universe <- rownames(res)

ego <- enrichGO(
  gene          = sig_genes,
  universe      = bg_universe,
  OrgDb         = org.Hs.eg.db,
  keyType       = "SYMBOL",
  ont           = "BP"
)
head(ego)

Feed the top 20 enriched terms to the LLM: "Group these GO terms into 3–5 biological themes and flag any that look redundant or driven by a single pathway." This yields an initial thematic summary for scientific review.

5. Draft methods with the model

Prompt: "Write a Methods paragraph (past tense, ~150 words) describing DESeq2 with apeglm shrinkage, padj < 0.05, |log2FC| > 1, and clusterProfiler GO-BP enrichment. Cite package versions." Then verify every parameter and version string against your sessionInfo() log and protocol records.

Pitfalls

• Protect sensitive metadata: Do not upload direct or indirect patient attributes to hosted LLMs. It is your responsibility to comply with your institution's data privacy policies and review current vendor terms.
• Verify biological assertions: Do not accept AI-suggested gene functions or literature citations without primary source verification (such as PubMed lookups); generative models may introduce hallucinated details.
• Document execution state: Save sessionInfo() alongside every run to support provenance. Note that environment tracking supports, but does not satisfy, formal validated-records or regulatory compliance requirements.

Next steps

Wrap the pipeline in a Quarto document — a plain-text notebook format that mixes code, output, and narrative — so you can regenerate the report whenever the underlying count tables are updated.

If something goes wrong

• DESeqDataSetFromMatrix errors about row names or column names. The column order of your counts matrix must match the row order of your sample table exactly. Print both and compare before anything else.
• Almost no genes are significant. Check replication and grouping first: too few replicates, or a batch that lines up perfectly with your condition, will flatten real differences.
• Thousands of genes are significant. Look at the PCA plot. A large split usually means a batch, timepoint, or handling effect is dominating rather than your treatment.
• The enrichment terms read like a horoscope. Broad terms ("metabolic process") are often meaningless. Tighten the gene list, use a proper background set, and rely on the AI only to draft wording, not to decide biology.
• The AI-drafted methods paragraph states a version or parameter you did not use. Always paste your real sessionInfo() and thresholds back in and correct the draft; unverified methods text is a publication risk.

Plain-English glossary

• Counts matrix — genes as rows, samples as columns, values are reads assigned to each gene.
• Normalization — adjusting for the fact that some samples were sequenced more deeply.
• Log2 fold change — how much a gene changed; 1 means doubled, −1 means halved.
• p-value — how surprising the difference would be if there were no real effect.
• FDR / adjusted p-value — a p-value corrected for testing thousands of genes at once; use this one.
• Enrichment analysis — asking whether your gene list contains more members of a pathway than chance would give.