plot_correlation_between_samples: Plots the correlation between injections.
In SWATH2stats: Transform and Filter SWATH Data for Statistical Packages

Description Usage Arguments Value Author(s) Examples

View source: R/plot_correlation_between_samples.R

This function plots the Pearson's and Spearman correlation between samples. If decoys are present these are removed before plotting.

plot_correlation_between_samples(
  data,
  column_values = "Intensity",
  comparison = transition_group_id ~ Condition + BioReplicate,
  fun_aggregate = NULL,
  label = TRUE,
  ...
)

`data`	Data frame that is produced by the OpenSWATH/pyProphet workflow.
`column_values`	Indicates the columns for which the correlation 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 over the different Condition and 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 correlation value in the plot.
`...`	Further arguments passed to methods.

Plots in Rconsole a correlation heatmap and returns the data frame used to do the plotting.

Peter Blattmann

{
 data("OpenSWATH_data", package="SWATH2stats")
 data("Study_design", package="SWATH2stats")
 data <- sample_annotation(OpenSWATH_data, Study_design)
 information <- plot_correlation_between_samples(data)
}