plot_variation: Plots the coefficient of variation for different replicates.

In SWATH2stats: Transform and Filter SWATH Data for Statistical Packages

Description

This function plots the coefficient of variation within replicates for a given value. If decoys are present these are removed before plotting.

Usage

plot_variation(
  data,
  column.values = "Intensity",
  comparison = transition_group_id + Condition ~ BioReplicate,
  fun_aggregate = NULL,
  label = FALSE,
  title = "cv across conditions",
  boxplot = TRUE,
  ...
)

Arguments

data	Data frame that is produced by the OpenSWATH/pyProphet workflow.
column.values	Indicates the columns for which the variation is assessed. This can be the Intensity or Signal, but also the retention time.
comparison	The comparison for assessing the variability. Default is to assess the variability per transition_group_id and Condition over the different Replicates. Comparison is performed using the dcast() function of the reshape2 package.
fun_aggregate	If for the comparison values have to be aggregated one needs to provide the function here.
label	Option to print value of median cv.
title	Title of plot. Default: "cv across conditions"
boxplot	Logical. If boxplot should be plotted. Default: TRUE
...	further arguments passed to method.

Value

Returns a list with the data and calculated cv and a table that summarizes the mean, median and mode cv per Condition (if Condition is contained in the comparison). In addition it plots in Rconsole a violin plot with the observed coefficient of variations.

Author(s)

Peter Blattmann

Examples

{
 data("OpenSWATH_data", package="SWATH2stats")
 data("Study_design", package="SWATH2stats")
 data <- sample_annotation(OpenSWATH_data, Study_design)
 var_summary <- plot_variation(data)
 }

Example output

SWATH2stats documentation built on April 17, 2021, 6:01 p.m.