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

DE Results

Extract, filter, and export differential expression results.

Required Libraries

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

Extracting DESeq2 Results

# 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

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

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

Filtering Significant Genes

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

# 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

# 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 n_sig
catsprintf n_up
catsprintf n_down


summarydecideTestsqlf

Adding Gene Annotations

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  
                              multiVals  

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

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   col.names  

Comparing Results Between Methods

# 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
catsprintf deseq2_only
catsprintf edger_only


libraryVennDiagram
venn.diagram
    x  DESeq2  deseq2_sig edgeR  edger_sig
    filename  
    fill   

Multiple Testing Correction

# 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

# 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

Column	Description
baseMean	Mean normalized counts
log2FoldChange	Log2 fold change
lfcSE	Standard error of LFC
stat	Wald statistic
pvalue	Raw p-value
padj	Adjusted p-value (BH)

edgeR

Column	Description
logFC	Log2 fold change
logCPM	Average log2 CPM
F	Quasi-likelihood F-statistic
PValue	Raw p-value
FDR	False discovery rate

Related Skills

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