Gene Expresion or Peak Accessibility/Binding Plot.

GenePlot(
  deobj,
  deres,
  group.key = NULL,
  ref.group = NULL,
  base = 10,
  fill.color = c("blue", "red"),
  fill.alpha = 0.6,
  gene.df = NULL,
  label.key = NULL,
  gene.num = 2,
  plot.col = 2,
  scales = "free",
  signif = "padj",
  signif.threshold = 0.05,
  l2fc.threshold = 1,
  legend.pos = "top"
)

Arguments

deobj	Object created by DESeq2 or edgeR.
deres	Data frame contains all genes/peaks.
group.key	Sample group information. When set NULL, select first column of metadata. Default: NULL.
ref.group	Reference group name. When set NULL, select first element of groups. Default: NULL.
base	A positive or complex number: the base with respect to which logarithms are computed. Default: 10.
fill.color	Color for box,
fill.alpha	Opacity of a geom. Default: 0.6.
gene.df	Gene data frame, at least contains Gene column. Default: NULL. When set NULL, use `gene.num`.
label.key	Column name in `gene.df` or `deres` to use as gene plot title. Default: NULL. When set NULL, use Gene column.
gene.num	Gene/Peak number to plot, choose according to log2FoldChange. When `gene.df` is set NULL, use this to determine genes/peak to plot. Default: NULL.
plot.col	Column number of final plot. Default: 2.
scales	Scales same as `facet_wrap`.
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.
legend.pos	Legend position. Default: top.

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), ]
GenePlot(deobj = dds, deres = dds.results.ordered, group.key = "condition", ref.group = "WT", fill.color = c("red", "blue"), fill.alpha = 0.8, gene.num = 2, signif = "pvalue", l2fc.threshold = 0.3)
#> Differential expression analysis with DESeq2!