meanSDplot: Mean-SD plot

In MSstatsSampleSize: Simulation tool for optimal design of high-dimensional MS-based proteomics experiment

Description

Draw the plot for the mean protein abundance vs standard deviation in each condition. The ‘lowess’ function is used to fit the LOWESS smoother between mean protein abundance and standard deviation.

Usage

meanSDplot(
  data,
  x.axis.size = 10,
  y.axis.size = 10,
  smoother_size = 1,
  xlimUp = 30,
  ylimUp = 3,
  height = 4,
  width = 4,
  address = ""
)

Arguments

data	A list with mean protein abundance matrix and standard deviation matrix. It should be the output of estimateVar function.
x.axis.size	Size of x-axis labeling in Mean-SD Plot. Default is 10.
y.axis.size	Size of y-axis labels. Default is 10.
smoother_size	Size of lowess smoother. Default is 1.
xlimUp	The upper limit of x-axis for mean-SD plot. Default is 30.
ylimUp	The upper limit of y-axis for mean-SD plot. Default is 3.
height	Height of the saved pdf file. Default is 4.
width	Width of the saved pdf file. Default is 4.
address	The name of folder that will store the results. Default folder is the current working directory. The other assigned folder has to be existed under the current working directory. An output pdf file is automatically created with the default name of ‘MeanSDPlot.pdf’. The command address can help to specify where to store the file as well as how to modify the beginning of the file name. If address=FALSE, plot will be not saved as pdf file but showed in window.

Value

meanSDplot is the plot for the mean protein abundance (X-axis) vs standard deviation (Y-axis) in each condition.

Author(s)

Ting Huang, Meena Choi, Olga Vitek

Examples

data(OV_SRM_train)
data(OV_SRM_train_annotation)

variance_estimation <- estimateVar(data = OV_SRM_train,
                                   annotation = OV_SRM_train_annotation)

meanSDplot(variance_estimation)

MSstatsSampleSize documentation built on Nov. 8, 2020, 4:53 p.m.