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 method.

### 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)
    plot_correlation_between_samples(data)

[Package SWATH2stats version 1.14.0 Index]