tpptrFitSplines: Perform spline fitting

In TPP: Analyze thermal proteome profiling (TPP) experiments

Description

Fit natural splines to all proteins in a dataset.

Usage

tpptrFitSplines(
  data,
  factorsH1,
  factorsH0 = character(0),
  splineDF = 3:7,
  computeAUC = NULL,
  returnModels = TRUE,
  nCores = "max"
)

Arguments

data	the data to be fitted
factorsH1	which factors should be included in the alternative model?
factorsH0	which factors should be included in the null model?
splineDF	degrees of freedom for natural spline fitting.
computeAUC	DEPRECATED
returnModels	should the linear models be returned in a column of the result table? Activation increases memory requirements.
nCores	either a numerical value given the desired number of CPUs, or 'max' to automatically assign the maximum possible number (default). Argument splineDF specifies the degrees of freedom for natural spline fitting. As a single numeric value, it is directly passed on to the splineDF argument of splines::ns. Experience shows that splineDF = 4 yields good results for TPP data sets with 10 temperature points. It is also possible to provide a numeric vector. In this case, splines are fitted for each entry and the optimal value is chosen per protein using Akaike's Information criterion.

Value

A table containing the fitted models per protein

See Also

ns, AICc

Examples

data(hdacTR_smallExample)
tpptrData <- tpptrImport(configTable = hdacTR_config, data = hdacTR_data)
normResults <- tpptrNormalize(data = tpptrData, 
               normReqs = tpptrDefaultNormReqs())
normData_eSets <- normResults$normData
normData_longTable <- tpptrTidyUpESets(normData_eSets)
hdacSubset <- subset(normData_longTable, grepl("HDAC", uniqueID))
hdacSplineFits <- tpptrFitSplines(data = hdacSubset, 
                                  factorsH1 = c("condition"), 
                                  nCores = 1)

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