Differential miRNA Expression
Load miRNA Count Data
library(DESeq2)
# Load miRge3 or miRDeep2 counts
counts <- read.csv('miR.Counts.csv', row.names = 1)
# Create sample metadata
coldata <- data.frame(
sample = colnames(counts),
condition = factor(c('control', 'control', 'treated', 'treated')),
row.names = colnames(counts)
)
DESeq2 Analysis
# Create DESeq2 dataset
dds <- DESeqDataSetFromMatrix(
countData = round(counts), # DESeq2 requires integers
colData = coldata,
design = ~ condition
)
# Filter low-expressed miRNAs
# miRNAs typically have fewer total counts than mRNAs
# Keep miRNAs with at least 10 reads across samples
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep, ]
# Run DESeq2
dds <- DESeq(dds)
# Get results
res <- results(dds, contrast = c('condition', 'treated', 'control'))
res <- res[order(res$padj), ]
Apply Shrinkage for Effect Sizes
# apeglm shrinkage for more accurate log2 fold changes
# Particularly important for low-count miRNAs
library(apeglm)
res_shrunk <- lfcShrink(
dds,
coef = 'condition_treated_vs_control',
type = 'apeglm'
)
Filter Significant miRNAs
# Standard thresholds for miRNA DE
# padj < 0.05: FDR-corrected significance
# |log2FC| > 1: 2-fold change minimum
sig <- subset(res_shrunk, padj < 0.05 & abs(log2FoldChange) > 1)
sig <- sig[order(sig$padj), ]
# Separate up and down-regulated
up <- subset(sig, log2FoldChange > 0)
down <- subset(sig, log2FoldChange < 0)
cat('Upregulated:', nrow(up), '\n')
cat('Downregulated:', nrow(down), '\n')
edgeR Alternative
library(edgeR)
# Create DGEList
dge <- DGEList(counts = counts, group = coldata$condition)
# Filter low expression
keep <- filterByExpr(dge)
dge <- dge[keep, , keep.lib.sizes = FALSE]
# Normalize
dge <- calcNormFactors(dge)
# Design matrix
design <- model.matrix(~ condition, data = coldata)
# Estimate dispersion
dge <- estimateDisp(dge, design)
# Fit model and test
fit <- glmQLFit(dge, design)
qlf <- glmQLFTest(fit, coef = 2)
# Get results
res_edger <- topTags(qlf, n = Inf)$table
Visualization
library(ggplot2)
library(EnhancedVolcano)
# Volcano plot
EnhancedVolcano(
res_shrunk,
lab = rownames(res_shrunk),
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.05,
FCcutoff = 1,
title = 'Differential miRNA Expression'
)
# MA plot
plotMA(res_shrunk, ylim = c(-4, 4))
Heatmap of DE miRNAs
library(pheatmap)
# Get normalized counts
vsd <- vst(dds, blind = FALSE)
# Select significant miRNAs
sig_mirnas <- rownames(sig)
mat <- assay(vsd)[sig_mirnas, ]
# Z-score scale rows
mat_scaled <- t(scale(t(mat)))
pheatmap(
mat_scaled,
annotation_col = coldata['condition'],
cluster_rows = TRUE,
cluster_cols = TRUE,
show_rownames = nrow(mat) < 50
)
Export Results
# Full results with normalized counts
res_df <- as.data.frame(res_shrunk)
res_df$miRNA <- rownames(res_df)
res_df$baseMean_norm <- rowMeans(counts(dds, normalized = TRUE)[rownames(res_df), ])
write.csv(res_df, 'DE_miRNAs_full.csv', row.names = FALSE)
# Significant only
write.csv(as.data.frame(sig), 'DE_miRNAs_significant.csv')
Related Skills
- mirge3-analysis - Get miRNA counts
- mirdeep2-analysis - Alternative quantification
- target-prediction - Predict targets of DE miRNAs
- differential-expression - General DE analysis concepts
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