QCplot: Generate QC plots

In umiAnalyzer: Tools for Analyzing Sequencing Data with Unique Molecular Identifiers

Description

Visualize the UMI count for each selected assay and sample for a given consensus depth. This is useful to detect differences in coverage, especially for multiplexed assays.

Usage

QCplot(
  object,
  group.by = "sample",
  plotDepth = 3,
  assays = NULL,
  samples = NULL,
  theme = "classic",
  option = "viridis",
  direction = "default",
  toggle_mean = TRUE,
  center = "mean",
  line_col = "blue",
  angle = 0,
  plotly = FALSE
)

Arguments

object	Requires a UMI sample or UMI experiment object
group.by	String. Which variable should be used as a factor on the x-axis. Default is sample
plotDepth	Which consensus depth to plot
assays	(Optional) user-supplied list of assays to plot. Default is all.
samples	(Optional) user-supplied list of samples to plot. Default is all.
theme	ggplot theme to use.
option	Color palette to use, either ggplot default or viridis colors.
direction	If viridis colors are used, choose orientation of color scale.
toggle_mean	Show mean or median
center	Choose mean or median
line_col	Choose color for mean/median line
angle	Angle of labels on x-axis.
plotly	Should plotly be used for rendering?

Value

A ggplot object

Examples

library(umiAnalyzer)

main = system.file('extdata', package = 'umiAnalyzer')
samples <- list.dirs(path = main, full.names = FALSE, recursive = FALSE)
simsen <- createUmiExperiment(experimentName = 'example',mainDir = main,sampleNames = samples)

depth_plot <- QCplot(simsen)

umiAnalyzer documentation built on Nov. 25, 2021, 9:07 a.m.