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Summarised workflow

We have detailed the various steps in a differential expression analysis workflow, providing theory with example code. To provide a more succinct reference for the code needed to run a DGE analysis, we have summarized the steps in an analysis below:



Obtaining gene-level counts from your preprocessing and create DESeq object

If you have a traditional raw count matrix

Load data and metadata

data <- read_table("../Data/Mov10_full_counts.txt") 

meta <- read_table("../Data/Mov10_full_meta.txt")

Check that the row names of the metadata equal the column names of the raw counts data

all(colnames(data)[-1] == meta$sample)

Create DESeq2Dataset object

dds <- DESeqDataSetFromMatrix(countData = data %>% column_to_rownames("GeneSymbol"), 
                              colData = meta %>% column_to_rownames("sample"), 
                              design = ~ condition)

If you have pseudocounts

Load samplesheet with all our metadata from our pipeline

# Load data, metadata and tx2gene and create a txi object
meta <- read_csv("/work/Intro_bulkRNAseq/Data/samplesheet.csv")

Create a list of salmon results

dir <- "/work/Intro_bulkRNAseq/Data/salmon"
tx2gene <- read_table(file.path(dir,"salmon_tx2gene.tsv"), col_names = c("transcript_ID","gene_ID","gene_symbol"))

# Get all salmon results files
files <- file.path(dir, meta$sample, "quant.sf")
names(files) <- meta$sample

Create txi object

txi <- tximport(files, type="salmon", tx2gene=tx2gene, countsFromAbundance = "lengthScaledTPM", ignoreTxVersion = TRUE)

Create dds object

dds <- DESeqDataSetFromTximport(txi,
                                   colData = meta %>% column_to_rownames("sample"), 
                              design = ~ condition)

Exploratory data analysis

Prefiltering low count genes + PCA & hierarchical clustering - identifying outliers and sources of variation in the data:

Prefiltering low count genes

keep <- rowSums(counts(dds)) > 0
dds <- dds[keep,]

Rlog transformation

# Transform counts for data visualization
rld <- rlog(dds, 


Plot PCA



Extract the rlog matrix from the object

rld_mat <- assay(rld)
rld_cor <- cor(rld_mat) # Pearson correlation betweeen samples
rld_dist <- as.matrix(dist(t(assay(rld)))) #distances are computed by rows, so we need to transponse the matrix

Plot heatmap of correlations

         annotation = meta %>% column_to_rownames("sample") %>% select("condition"))

Plot heatmap of distances with a new color range

heat.colors <- brewer.pal(6, "Blues") # Colors from the RColorBrewer package (only 6)
heat.colors <- colorRampPalette(heat.colors)(100) # Interpolate 100 colors

         annotation = meta %>% column_to_rownames("sample") %>% select("condition"),
         color = heat.colors)

Run DESeq2:

Optional step - Re-create DESeq2 dataset if the design formula has changed after QC analysis in include other sources of variation using

# dds <- DESeqDataSetFromMatrix(data, colData = meta, design = ~ covariate + condition)

Run DEseq2

# Run DESeq2 differential expression analysis
dds <- DESeq(dds)

Optional step - Output normalized counts to save as a file to access outside RStudio using

normalized_counts <- counts(dds, normalized=TRUE)

Check the fit of the dispersion estimates

Plot dispersion estimates


Create contrasts to perform Wald testing or the shrunken log2 fold changes between specific conditions

Formal LFC calculation

# Specify contrast for comparison of interest
contrast <- c("condition", "MOV10_overexpression", "control")

# Output results of Wald test for contrast
res <- results(dds, 
               contrast = contrast, 
               alpha = 0.05)


# Get name of the contrast you would like to use

# Shrink the log2 fold changes to be more accurate
res <- lfcShrink(dds, 
                 coef = "condition_MOV10_overexpression_vs_control", 
                 type = "apeglm")

Output significant results:

# Set thresholds
padj.cutoff <- 0.05

# Turn the results object into a tibble for use with tidyverse functions
res_tbl <- res %>%
  data.frame() %>%
  rownames_to_column(var="gene") %>% 

# Subset the significant results
sig_res <- filter(res_tbl, 
                  padj < padj.cutoff)

Visualize results: volcano plots, heatmaps, normalized counts plots of top genes, etc.

Function to get gene_IDs based on gene names. The function will take as input a vector of gene names of interest, the tx2gene dataframe and the dds object that you analyzed.

lookup <- function(gene_name, tx2gene, dds){
  hits <- tx2gene %>% select(gene_symbol, gene_ID) %>% distinct() %>% 
    filter(gene_symbol %in% gene_name & gene_ID %in% rownames(dds))

lookup(gene_name = "MOV10", tx2gene = tx2gene, dds = dds)

Plot expression for single gene

plotCounts(dds, gene="ENSG00000155363", intgroup="condition")



Volcano plot with labels (top N genes)

## Obtain logical vector where TRUE values denote padj values < 0.05 and fold change > 1.5 in either direction
res_tbl <- res_tbl %>% 
mutate(threshold = padj < 0.05 & abs(log2FoldChange) >= 0.58)
## Create an empty column to indicate which genes to label
res_tbl <- res_tbl %>% mutate(genelabels = "")

## Sort by padj values 
res_tbl <- res_tbl %>% arrange(padj)

## Populate the genelabels column with contents of the gene symbols column for the first 10 rows, i.e. the top 10 most significantly expressed genes
res_tbl$genelabels[1:10] <- as.character(res_tbl$gene[1:10])

ggplot(res_tbl, aes(x = log2FoldChange, y = -log10(padj))) +
  geom_point(aes(colour = threshold)) +
  geom_text_repel(aes(label = genelabels)) +
  ggtitle("Mov10 overexpression") +
  xlab("log2 fold change") + 
  ylab("-log10 adjusted p-value") +
  theme(legend.position = "none",
        plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = rel(1.25))) 

Heatmap of differentially expressed genes

# filter significant results from normalized counts
norm_sig <- normalized_counts %>% as_tibble(rownames = "gene") %>%
  dplyr::filter(gene %in% sig_res$gene) %>% column_to_rownames(var="gene")
         cluster_rows = T, #cluster by expression pattern
         scale = "row", # scale by gene so expression pattern is visible
         treeheight_row = 0, # dont show the row dendogram
         show_rownames = F, # remove rownames so it is more clear
         annotation = meta %>% column_to_rownames(var = "sample") %>% dplyr::select("condition")

Perform analysis to extract functional significance of results: GO or KEGG enrichment, GSEA, etc.

Annotate with annotables

ids <- grch37 %>% dplyr::filter(ensgene %in% res_tbl$gene) 
res_ids <- inner_join(res_tbl, ids, by=c("gene"="ensgene"))

Perform enrichment analysis of GO terms (can be done as well with KEGG pathways)

# Create background dataset for hypergeometric testing using all genes tested for significance in the results
all_genes <- dplyr::filter(res_ids, ! %>% 
  pull(gene) %>% 

# Extract significant results
sig <- dplyr::filter(res_ids, padj < 0.05 & !

sig_genes <- sig %>% 
  pull(gene) %>% 
# Perform enrichment analysis
ego <- enrichGO(gene = sig_genes, 
                universe = all_genes,
                keyType = "ENSEMBL",
                OrgDb =, 
                ont = "BP", 
                pAdjustMethod = "BH", 
                qvalueCutoff = 0.05, 
                readable = TRUE)
ego <- enrichplot::pairwise_termsim(ego)

Visualize result

dotplot(ego, showCategory=50)
emapplot(ego, showCategory = 50)


## To color genes by log2 fold changes, we need to extract the log2 fold changes from our results table creating a named vector
sig_foldchanges <- sig$log2FoldChange

names(sig_foldchanges) <- sig$gene
## Cnetplot details the genes associated with one or more terms - by default gives the top 5 significant terms (by padj)
         showCategory = 5, 

Perform GSEA analysis of KEGG pathways (can be done as well with GO terms)

# Extract entrez IDs. IDs should not be duplicated or NA
res_entrez <- dplyr::filter(res_ids, entrez != "NA" & entrez != "NULL" & duplicated(entrez)==F)

## Extract the foldchanges
foldchanges <- res_entrez$log2FoldChange

## Name each fold change with the corresponding Entrez ID
names(foldchanges) <- res_entrez$entrez

## Sort fold changes in decreasing order
foldchanges <- sort(foldchanges, decreasing = TRUE)
# Run GSEA of KEGG
gseaKEGG <- gseKEGG(geneList = foldchanges, # ordered named vector of fold changes (Entrez IDs are the associated names)
              organism = "hsa", # supported organisms listed below
              pvalueCutoff = 0.05, # padj cutoff value
              verbose = FALSE)

gseaKEGG_results <- gseaKEGG@result
## Plot the GSEA plot for a single enriched pathway:
gseaplot(gseaKEGG, geneSetID = gseaKEGG_results$ID[1])
## Output images for a single significant KEGG pathway
pathview( = foldchanges,
     = gseaKEGG_results$ID[1],
              species = "hsa",
              limit = list(gene = 2, # value gives the max/min limit for foldchanges
              cpd = 1))

Make sure to output the versions of all tools used in the DE analysis:


Last update: July 12, 2023
Created: July 12, 2023