plot_correlation_between_samples: Plots the correlation between injections.

In peterblattmann/SWATH2stats: Transform and Filter SWATH Data for Statistical Packages

Plots the correlation between injections.

Description

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

Usage

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(s)

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)
}

peterblattmann/SWATH2stats documentation built on July 2, 2023, 9:42 p.m.