/bio-de-results

name: bio-de-results
description: Extract, filter, annotate, and export differential expression results from DESeq2 or edgeR. Use for identifying significant genes, applying multiple testing corrections, adding gene annotations, and preparing results for downstream analysis. Use when filtering and exporting DE analysis results.
tool_type: r
primary_tool: DESeq2

Version Compatibility

Reference examples tested with: DESeq2 1.42+, edgeR 4.0+

Before using code patterns, verify installed versions match. If versions differ:

• R: `packageVersion('<pkg>')` then `?function_name` to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

DE Results

Extract, filter, and export differential expression results.

Required Libraries

library(DESeq2)  # or library(edgeR)
library(dplyr)   # For data manipulation

Extracting DESeq2 Results

**Goal:** Retrieve DE statistics from a fitted DESeq2 model as a usable data frame.

**Approach:** Call results() with optional shrinkage, then convert to a data frame with gene identifiers.

# Basic results
res <- results(dds)

# With specific alpha (adjusted p-value threshold)
res <- results(dds, alpha = 0.05)

# With log fold change shrinkage
res <- lfcShrink(dds, coef = 'condition_treated_vs_control', type = 'apeglm')

# Convert to data frame
res_df <- as.data.frame(res)
res_df$gene <- rownames(res_df)

Extracting edgeR Results

**Goal:** Retrieve DE statistics from a fitted edgeR model as a data frame.

**Approach:** Use topTags with n=Inf to extract all gene-level results.

# Get all results
results <- topTags(qlf, n = Inf)$table

# Add gene column
results$gene <- rownames(results)

Filtering Significant Genes

**Goal:** Identify genes meeting statistical significance and biological effect size criteria.

**Approach:** Subset results by adjusted p-value, fold change magnitude, and expression level thresholds.

**"Get the significant differentially expressed genes"** → Filter DE results by adjusted p-value and fold change cutoffs to produce up- and down-regulated gene lists.

By Adjusted P-value

# DESeq2
sig_genes <- subset(res, padj < 0.05)

# edgeR
sig_genes <- subset(results, FDR < 0.05)

# Using dplyr
sig_genes <- res_df %>%
    filter(padj < 0.05) %>%
    arrange(padj)

By Fold Change

# Absolute log2 fold change > 1 (2-fold change)
sig_genes <- subset(res, padj < 0.05 & abs(log2FoldChange) > 1)

# Up-regulated only
up_genes <- subset(res, padj < 0.05 & log2FoldChange > 1)

# Down-regulated only
down_genes <- subset(res, padj < 0.05 & log2FoldChange < -1)

Combined Filters

# Stringent filtering
sig_genes <- res_df %>%
    filter(padj < 0.01,
           abs(log2FoldChange) > 1,
           baseMean > 10) %>%
    arrange(padj)

Ordering Results

**Goal:** Rank DE genes by statistical significance or biological effect size.

**Approach:** Sort results by adjusted p-value, absolute fold change, or mean expression.

# By adjusted p-value (most significant first)
res_ordered <- res[order(res$padj), ]

# By absolute fold change (largest changes first)
res_ordered <- res[order(abs(res$log2FoldChange), decreasing = TRUE), ]

# By base mean expression
res_ordered <- res[order(res$baseMean, decreasing = TRUE), ]

# Combined: significant genes ordered by fold change
sig_ordered <- res_df %>%
    filter(padj < 0.05) %>%
    arrange(desc(abs(log2FoldChange)))

Summary Statistics

**Goal:** Quantify the number of up- and down-regulated genes at chosen thresholds.

**Approach:** Count genes passing significance filters and report directional breakdown.

# DESeq2 summary
summary(res)

# Manual counts
n_tested <- sum(!is.na(res$padj))
n_sig <- sum(res$padj < 0.05, na.rm = TRUE)
n_up <- sum(res$padj < 0.05 & res$log2FoldChange > 0, na.rm = TRUE)
n_down <- sum(res$padj < 0.05 & res$log2FoldChange < 0, na.rm = TRUE)

cat(sprintf('Tested: %d genes\n', n_tested))
cat(sprintf('Significant (padj < 0.05): %d genes\n', n_sig))
cat(sprintf('Up-regulated: %d genes\n', n_up))
cat(sprintf('Down-regulated: %d genes\n', n_down))

# edgeR summary
summary(decideTests(qlf))

Adding Gene Annotations

**Goal:** Enrich DE results with gene symbols, descriptions, and cross-database identifiers.

**Approach:** Map Ensembl or Entrez IDs to human-readable annotations using org.db, biomaRt, or custom files.

**"Add gene names to my DE results"** → Map gene identifiers to symbols and descriptions using annotation databases, then merge with the results table.

From Bioconductor Annotation Package

library(org.Hs.eg.db)  # Human; use org.Mm.eg.db for mouse

# If gene IDs are Ensembl
res_df$symbol <- mapIds(org.Hs.eg.db,
                         keys = rownames(res_df),
                         column = 'SYMBOL',
                         keytype = 'ENSEMBL',
                         multiVals = 'first')

res_df$entrez <- mapIds(org.Hs.eg.db,
                         keys = rownames(res_df),
                         column = 'ENTREZID',
                         keytype = 'ENSEMBL',
                         multiVals = 'first')

res_df$description <- mapIds(org.Hs.eg.db,
                              keys = rownames(res_df),
                              column = 'GENENAME',
                              keytype = 'ENSEMBL',
                              multiVals = 'first')

From BioMart

library(biomaRt)

mart <- useMart('ensembl', dataset = 'hsapiens_gene_ensembl')

annotations <- getBM(
    attributes = c('ensembl_gene_id', 'external_gene_name', 'description'),
    filters = 'ensembl_gene_id',
    values = rownames(res_df),
    mart = mart
)

# Merge with results
res_annotated <- merge(res_df, annotations,
                        by.x = 'row.names', by.y = 'ensembl_gene_id',
                        all.x = TRUE)

From Custom File

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