plot_correlation_between_samples {SWATH2stats} | R Documentation |
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 method. |
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) plot_correlation_between_samples(data)