designSampleSizePCAplot: PCA plot for each simulation

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

Description

PCA plot for each simulation

Usage

designSampleSizePCAplot(
  simulations,
  which.PCA = "all",
  x.axis.size = 10,
  y.axis.size = 10,
  dot.size = 3,
  legend.size = 7,
  width = 6,
  height = 5,
  address = ""
)

Arguments

simulations	A list of simulated datasets. It should be the output of simulateDataset function.
which.PCA	Select one PCA plot to show. It can be "all", "allonly", or "simulationX". X should be index of simulation, such as "simulation1" or "simulation5". Default is "all", which generates all the plots. "allonly" generates the PCA plot for the whole input dataset. "simulationX" generates the PCA plot for a specific simulated dataset (given by index).
x.axis.size	size of x-axis labeling in PCA Plot. Default is 10.
y.axis.size	size of y-axis labels. Default is 10.
dot.size	size of dots in PCA plot. Default is 3.
legend.size	size of legend above Profile plot. Default is 7.
width	width of the saved pdf file. Default is 6.
height	height of the saved pdf file. Default is 5.
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 ‘PCAPlot.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.

Details

This function draws PCA plot for the whole input dataset and each simulated dataset in ‘simulations’ (input for this function). It outputs the number of simulations plus 1 of PCA plots. The first page shows a PCA plot for the input preliminary dataset. Each of the following pages shows a PCA plot under one simulation. x-axis of PCA plot is the first component and y-axis is the second component. This function can be used to validate whether the simulated dataset looks consistent with the input dataset.

Value

PCA plot : x-axis of PCA plot is the first component and y-axis is the second component.

Author(s)

Ting Huang, Meena Choi, Olga Vitek

Examples

data(OV_SRM_train)
data(OV_SRM_train_annotation)

# num_simulations = 10: simulate 10 times
# expected_FC = "data": fold change estimated from OV_SRM_train
# select_simulated_proteins = "proportion":
# select the simulated proteins based on the proportion of total proteins
# simulate_valid = FALSE: use input OV_SRM_train as validation set
# valid_samples_per_group = 50: 50 samples per condition
simulated_datasets <- simulateDataset(data = OV_SRM_train,
                                      annotation = OV_SRM_train_annotation,
                                      num_simulations = 10,
                                      expected_FC = "data",
                                      list_diff_proteins =  NULL,
                                      select_simulated_proteins = "proportion",
                                      protein_proportion = 1.0,
                                      protein_number = 1000,
                                      samples_per_group = 50,
                                      simulate_valid = FALSE,
                                      valid_samples_per_group = 50)

# output a PDF file with multiple PCA plots
designSampleSizePCAplot(simulated_datasets)

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