PlotQCReport: Draws violin plots to show distribution of QC features for a...

In shadieshghi/TargetedMSQC: TargetedMSQC for Automated Quality Assessment of Targeted Mass Spectrometry Data

Description

A longer description of how the function works.

Usage

PlotQCReport(response.data, response.var = c("Status.prediction"),
  report.path = "", min.ok.transitions.threshold = 3, plot.prob = FALSE,
  flag.class.prob.var = c("flag.prob.prediction"), ...)

Arguments

response.data	A dataframe with identifier columns (File, FileName, PeptideModifiedSequence, FragmentIon, PrecursorCharge ,ProductCharge) and the response variable column (Status.prediction by default). Usually, the input dataframe is the output of the ApplyQCModel function.
response.var	The name of the column in the input data that contains the response variable to be used for creating the dataset QC report. This variable is usually the predicted status for each peak generated by the ApplyQCModel function.
report.path	The path to directory where the pdf file of the QC report should be saved.
min.ok.transitions.threshold	Minimum number of transitions that should pass QC for a peptide to pass QC in each sample.
plot.prob	A logical value to indicate whether the class probabilities should be used in heatmaps. If plot.prob = FALSE, of the class probabilities, the assigned binary class will be used.
flag.class.prob.var	The name of the response.data column that holds class probabilities for the "flag" class. This parameter is used only if plot.prob = TRUE.

Value

Saves a .pdf file of the QC report in report.path

Examples

response.data <- ApplyQCModel(data.set.CSF$feature.data,
                              model.rrf.CSF,
                              response.var = c("Status"),
                              description.columns = c("Notes"),
                              flag.prob.threshold = 0.5,
                              type = "prob")
PlotQCReport(response.data,report.path = "",response.var = c("Status.prediction"),plot.prob = TRUE)

shadieshghi/TargetedMSQC documentation built on May 13, 2019, 12:20 p.m.