Bio De Results

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

# Load annotation file
gene_info <- read.csv('gene_annotations.csv')

# Merge with results
res_annotated <- merge(res_df, gene_info, by = 'gene', all.x = TRUE)

Exporting Results

Goal: Save DE results in formats suitable for sharing, publication, or downstream tools.

Approach: Write filtered and annotated results to CSV, Excel workbooks, or ranked gene lists for pathway analysis.

To CSV

# All results
write.csv(res_df, file = 'deseq2_all_results.csv', row.names = FALSE)

# Significant only
sig_genes <- res_df %>% filter(padj < 0.05)
write.csv(sig_genes, file = 'deseq2_significant.csv', row.names = FALSE)

To Excel

library(openxlsx)

# Create workbook with multiple sheets
wb <- createWorkbook()

addWorksheet(wb, 'All Results')
writeData(wb, 'All Results', res_df)

addWorksheet(wb, 'Significant')
writeData(wb, 'Significant', sig_genes)

addWorksheet(wb, 'Up-regulated')
writeData(wb, 'Up-regulated', up_genes)

addWorksheet(wb, 'Down-regulated')
writeData(wb, 'Down-regulated', down_genes)

saveWorkbook(wb, 'de_results.xlsx', overwrite = TRUE)

Gene Lists for Pathway Analysis

# Just gene IDs for GO/KEGG analysis
sig_gene_list <- rownames(subset(res, padj < 0.05))
write.table(sig_gene_list, file = 'significant_genes.txt',
            quote = FALSE, row.names = FALSE, col.names = FALSE)

# With fold changes for GSEA
gsea_input <- res_df %>%
    filter(!is.na(log2FoldChange)) %>%
    select(gene, log2FoldChange) %>%
    arrange(desc(log2FoldChange))
write.table(gsea_input, file = 'gsea_input.rnk',
            sep = '\t', quote = FALSE, row.names = FALSE, col.names = FALSE)

Comparing Results Between Methods

Goal: Assess concordance between DESeq2 and edgeR results to identify robust DE genes.

Approach: Compute set overlaps and visualize with a Venn diagram.

# Get significant genes from both methods
deseq2_sig <- rownames(subset(deseq2_res, padj < 0.05))
edger_sig <- rownames(subset(edger_results, FDR < 0.05))

# Overlap
common <- intersect(deseq2_sig, edger_sig)
deseq2_only <- setdiff(deseq2_sig, edger_sig)
edger_only <- setdiff(edger_sig, deseq2_sig)

cat(sprintf('DESeq2 significant: %d\n', length(deseq2_sig)))
cat(sprintf('edgeR significant: %d\n', length(edger_sig)))
cat(sprintf('Common: %d\n', length(common)))
cat(sprintf('DESeq2 only: %d\n', length(deseq2_only)))
cat(sprintf('edgeR only: %d\n', length(edger_only)))

# Venn diagram
library(VennDiagram)
venn.diagram(
    x = list(DESeq2 = deseq2_sig, edgeR = edger_sig),
    filename = 'de_overlap.png',
    fill = c('steelblue', 'coral')
)

Multiple Testing Correction

Goal: Apply or compare multiple testing correction methods for DE p-values.

Approach: Use Benjamini-Hochberg (default), Bonferroni, or IHW for adjusted p-values.

# DESeq2 uses Benjamini-Hochberg by default
# To use different methods:

# Independent Hypothesis Weighting (more powerful)
library(IHW)
res_ihw <- results(dds, filterFun = ihw)

# Manual p-value adjustment
res_df$padj_bonferroni <- p.adjust(res_df$pvalue, method = 'bonferroni')
res_df$padj_bh <- p.adjust(res_df$pvalue, method = 'BH')
res_df$padj_fdr <- p.adjust(res_df$pvalue, method = 'fdr')

Handling NA Values

Goal: Understand and handle missing values in DE results caused by filtering or outlier detection.

Approach: Identify the source of NAs (zero counts, independent filtering, outliers) and remove or investigate them.

# Count NAs
sum(is.na(res$padj))

# Remove genes with NA padj
res_complete <- res[!is.na(res$padj), ]

# Understand why NAs occur
# - baseMean = 0: No counts
# - NA only in padj: Outlier or low count filtered by independent filtering

# Check outliers
res[which(is.na(res$pvalue) & res$baseMean > 0), ]

Quick Reference: Result Columns

DESeq2

edgeR

Related Skills

• deseq2-basics - Run DESeq2 analysis
• edger-basics - Run edgeR analysis
• de-visualization - Visualize results
• pathway-analysis - Use gene lists for enrichment