Plots the correlation between injections.

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,
  ...
)

Arguments

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.

Value

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

Author

Peter Blattmann

Examples

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