Count QC plot.

CountQC(
  deobj,
  group.key = NULL,
  type = c("saturation", "cpm"),
  min.count = 0,
  ndepth = 10,
  cat.colors = NULL,
  ...
)

Arguments

deobj	Object created by DESeq2 or edgeR.
group.key	Sample group information. When set NULL, select first column of metadata. Default: NULL.
type	QC plot type, chosen from saturation and cpm. Default: saturation.
min.count	A feature is considered to be detected if the corresponding number of read counts is > `min.count`. By default, `min.count` = 0. . This parameter is used by type "saturation".
ndepth	Number of different sequencing depths to be simulated and plotted apart from the real depth. Default: 10. This parameter is only used by type "saturation".
cat.colors	Color used for different CPM Threshold groups or samples. Default: NULL (auto-selection).
...	Parameters for `dat`.

Value

Count QC plot

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
CountQC(deobj = dds, group.key = "condition", type = "cpm")
#> Differential expression analysis with DESeq2!
#> [1] "Warning: 25096 features with 0 counts in all samples are to be removed for this analysis."
#> [1] "Count distributions are to be computed for:"
#>  [1] "KO1" "KO2" "KO3" "KO4" "KO5" "KO6" "WT1" "WT2" "WT3" "WT4" "WT5" "WT6"
CountQC(deobj = dds, group.key = "condition", type = "saturation")
#> Differential expression analysis with DESeq2!