plot_correlation_between_samples {SWATH2stats} | R Documentation

## 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)
    }

[Package SWATH2stats version 1.20.1 Index]