Differential m6A Analysis

exomePeak2 Differential Analysis

library(exomePeak2)

Define sample design

condition: factor for comparison

design <- data.frame( condition = factor(c('ctrl', 'ctrl', 'treat', 'treat')) )

Differential peak calling

result <- exomePeak2( bam_ip = c('ctrl_IP1.bam', 'ctrl_IP2.bam', 'treat_IP1.bam', 'treat_IP2.bam'), bam_input = c('ctrl_Input1.bam', 'ctrl_Input2.bam', 'treat_Input1.bam', 'treat_Input2.bam'), gff = 'genes.gtf', genome = 'hg38', experiment_design = design )

Get differential sites

diff_sites <- results(result, contrast = c('condition', 'treat', 'ctrl'))

QNB for Differential Methylation

library(QNB)

Requires count matrices from peak regions

IP and input counts per sample

qnb_result <- qnbtest( IP_count_matrix, Input_count_matrix, group = c(1, 1, 2, 2) # 1=ctrl, 2=treat )

Filter significant

padj < 0.05, |log2FC| > 1

sig <- qnb_result[qnb_result$padj < 0.05 & abs(qnb_result$log2FC) > 1, ]

Visualization

library(ggplot2)

Volcano plot

ggplot(diff_sites, aes(x = log2FoldChange, y = -log10(padj))) + geom_point(aes(color = padj < 0.05 & abs(log2FoldChange) > 1)) + geom_hline(yintercept = -log10(0.05), linetype = 'dashed') + geom_vline(xintercept = c(-1, 1), linetype = 'dashed')

Related Skills

• m6a-peak-calling - Identify peaks first

• differential-expression/de-results - Similar statistical concepts

• modification-visualization - Plot differential sites