DecisionMap: A function to create heatmaps to compare performance with...

In eralpdogu/MSstatsQC: Longitudinal system suitability monitoring and quality control for proteomic experiments

Description

A function to create heatmaps to compare performance with user defined performance criteria

Usage

DecisionMap(
  data = NULL,
  method = "XmR",
  peptideThresholdRed = 0.7,
  peptideThresholdYellow = 0.5,
  L = 1,
  U = 5,
  type = "mean",
  title = "heatmap plot",
  listMean = NULL,
  listSD = NULL
)

Arguments

data	Comma-separated (*.csv), QC file format. It should contain a Precursor column and the metrics columns.
method	It is either "CUSUM" or "XmR"
peptideThresholdRed	Is a threshold that marks percentage of peptides above it red on the heatmap. Defaults to 0.7
peptideThresholdYellow	Is a threshold that marks percentage of peptides above it and below the peptideThresholdRed, yellow on the heatmap. Defaults to 0.5
L	Lower bound of the giude set. Defaults to 1
U	Upper bound of the guide set. Defaults to 5
type	can take two values, "mean" or "dispersion". Defaults to "mean"
title	the title of the plot. Defaults to "heatmap plot"
listMean	List of the means for the metrics. If you don't know the means leave it as NULL and they will be calculated automatically by using L and U. The default is NULL.
listSD	List of the standard deviations for the metrics. If you don't know the standard deviations leave it as NULL and they will be calculated automatically by using L and U. The default is NULL.

Value

A heatmap to aggregate results per metric generated from heatmap.DataFrame data frame.

Examples

# First process the data to make sure it's ready to use
sampleData <- DataProcess(S9Site54)
head(sampleData)
# Draw Decision maker plot
DecisionMap(data = sampleData, method = "CUSUM")
DecisionMap(data = sampleData, method = "CUSUM", type = "variability")
DecisionMap(data = sampleData, method = "XmR")
DecisionMap(data = sampleData, method = "XmR", type = "variability")

eralpdogu/MSstatsQC documentation built on April 17, 2020, 7:33 a.m.