ViolinPlotQCSummary: Graph violin plots to visualize distribution of QC features...

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

Description

The function takes a dataframe and list of QC features of interest as input and graphs violin plots for distribution of these features. The results are returned as a list of ggplot objects. Each element in the list corresponds to a mass spectrometry run in the input dataframe.

Usage

ViolinPlotQCSummary(data, runs = "all", features = "all", labels = NULL,
  response.var = c("Status"), description.columns = c("Notes"),
  font.size = 50, ...)

Arguments

data	A dataframe that contains columns for features of interest and FileName where FileName refers to names of individual mass spectrometry runs included in the input. This function is mainly used in conjunction with the MakeDataSet function. data.set$data.merged is used as the input where data.set is the output of MakeDataSet.
runs	List of indivodual mass spectrometry runs to be plotted. Default is "all".
features	List of QC features of interest to be plotted. Default is "all".
labels	Name of the column that holds the annotated labels for each peak. When labels are provided, the violin plots will be overlaid with dot plots, where the dots are colored based on their corresponding label.
response.var	If the input dataframe contains columns corresponding to response variables (such as Status or Status.prediction), it should be indicated here. Response and description columns as well as identifier columns will be removed from the data before creating the violin plots.
description.columns	If the input dataframe contains columns corresponding to description variables (Such as Notes), it should be indicated here. Response and description columns as well as identifier columns will be removed from the data before creating the violin plots.
font.size	Font size of text in the plot

Value

List of plots of class ggplot for each run in the input data

Examples

feature.set <- c("Area2SumRatioCV_standard","TransitionJaggedness_standard","TransitionSymmetry_standard","TransitionFWHM2base_standard","TransitionFWHM_standard","TransitionModality_standard","TransitionShift_standard")
violin.plots = ViolinPlotQCSummary(data.set.CSF$data.merged,runs = "all",features = feature.set,font.size = 15, labels = "Status")
violin.plots[[1]]

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