MA-plot for Differential Analysis Results.

MAPlot(
  deres,
  signif = "padj",
  signif.threshold = 0.05,
  l2fc.threshold = 1,
  point.alpha = 0.6,
  point.size.vec = c(2, 4),
  linetype = 2,
  point.color.vec = c("blue", "grey", "red"),
  legend.pos = "top",
  label.num = NULL,
  label.df = NULL,
  label.key = NULL,
  label.color = NULL
)

Arguments

deres	Data frame contains all genes/peaks.
signif	Significance criterion. For DESeq2 results, can be chosen from padj, pvalue. For edgeR results, can be chosen from FDR, PValue. Default: padj.
signif.threshold	Significance threshold to get differentially expressed genes or accessible/binding peaks. Default: 0.05.
l2fc.threshold	Log2 fold change threshold to get differentially expressed genes or accessible/binding peaks. Default: 1.
point.alpha	Opacity of a geom. Default: 0.6.
point.size.vec	Point size for regular (all points) and(or) labeled points. Default: 2 for regular and 4 for labeled points.
linetype	Threshold linetype. Default: 2.
point.color.vec	Point color for Down, Not, Up regulated genes or peaks. Default: red for Up, grey for Not and blue for Down.
legend.pos	Legend position. Default: top.
label.num	Gene/Peak number to label, choose according to log2FoldChange. When `label.df` is set NULL, use this to determine genes to label. Default: NULL.
label.df	Label data frame, at least contains Gene column. Default: NULL(use `label.num`). When provided, the second column should not be in `deres`.
label.key	Which column to use as label. Default: NULL (use rownames of `deres` or Gene column of `gene.df`).
label.color	Color vector for labels. Default: NULL.

Value

A ggplot2 object.

Examples

library(DESeq2)
library(DEbPeak)
count.file <- system.file("extdata", "snon_count.txt", package = "DEbPeak")
meta.file <- system.file("extdata", "snon_meta.txt", package = "DEbPeak")
count.matrix <- read.table(file = count.file, header = TRUE, sep = "\t")
meta.info <- read.table(file = meta.file, header = TRUE)
dds <- DESeq2::DESeqDataSetFromMatrix(countData = count.matrix, colData = meta.info, design = ~condition)
#> Warning: some variables in design formula are characters, converting to factors
keep.genes <- rowSums(DESeq2::counts(dds, normalized = FALSE)) >= 10
dds <- dds[keep.genes, ]
dds$condition <- relevel(dds$condition, ref = "WT")
dds <- DESeq(dds)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
dds.results <- results(dds, contrast = c("condition", "KO", "WT"))
dds.results.ordered <- dds.results[order(dds.results$log2FoldChange, decreasing = TRUE), ]
MAPlot(dds.results.ordered, signif = "pvalue", l2fc.threshold = 0.3, label.num = 2, point.alpha = 0.8, label.color = c("purple", "green"))
#> Differential expression analysis with DESeq2!