tpptrFTest: Analyze spline fits to detect differential behavior over time

In TPP: Analyze thermal proteome profiling (TPP) experiments

Description

Analyze fitted natural spline models and look for differential behaviour between conditions by a moderated F-test.

Usage

tpptrFTest(fittedModels, doPlot = FALSE, resultPath = NULL)

Arguments

fittedModels	a table of fitted spline models (produced by tpptrFitSplines).
doPlot	boolean value indicating whether QC plots should be produced. Currently, QC plots comprise distributions of the F statistics, and the p-values before/ after Benjamini Hochberg adjustment.
resultPath	location where to store QC plots, if doPlot = TRUE.

Details

If doPlot is TRUE, but no resultPath is specified, the plots will be prompted to the active device.

The moderated F-statistic is calculated by the following equation: ...

Value

A long table containing the hypothesis test results per protein.

See Also

ns, squeezeVar

Examples

data(hdacTR_smallExample)
tpptrData <- tpptrImport(configTable = hdacTR_config, data = hdacTR_data)
normResults <- tpptrNormalize(data = tpptrData, normReqs = tpptrDefaultNormReqs())
normData_eSets <- normResults$normData
fitData <- tpptrTidyUpESets(normData_eSets)
fits <- tpptrFitSplines(data = fitData, factorsH1 = "condition", nCores = 1, splineDF = 4:5)
testResults <- tpptrFTest(fittedModels = fits)

TPP documentation built on Nov. 8, 2020, 5:55 p.m.