tpptrAnalyzeMeltingCurves: Analyze fitted curve parameters to detect significant shifts...

In TPP: Analyze thermal proteome profiling (TPP) experiments

Description

Compute p-values for the pairwise comparisons of melting curve shifts between different conditions.

Usage

tpptrAnalyzeMeltingCurves(
  data,
  pValMethod = "robustZ",
  pValFilter = list(minR2 = 0.8, maxPlateau = 0.3),
  pValParams = list(binWidth = 300)
)

Arguments

data	list of ExpressionSets containing fold changes and metadata. Their featureData fields contain the fitted melting curve parameters.
pValMethod	Method for p-value computation. Currently restricted to 'robustZ' (see Cox & Mann (2008)).
pValFilter	optional list of filtering criteria to be applied before p-value computation.
pValParams	optional list of parameters for p-value computation.

Details

The pValParams argument is a list that can contain optional parameters for the chosen p-value computation pValMethod. The following options are available:

1. pValMethod = "robustZ":

   pValParams=list(binWidth=[your_binWidth]).

Value

A data frame in which the fit results are stored row-wise for each protein.

References

Cox, J., & Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized ppb-range mass accuracies and proteome-wide protein quantification. Nature biotechnology, 26(12), 1367-1372.

Examples

data(hdacTR_smallExample)
tpptrData <- tpptrImport(hdacTR_config, hdacTR_data)
tpptrNorm <- tpptrNormalize(data=tpptrData, 
                            normReqs=tpptrDefaultNormReqs())
normalizedData <- tpptrNorm$normData
## Not run: 
# Fit melting curves to each protein 
# (can take some time depending on device used):
 fittedData <- tpptrCurveFit(normalizedData, nCores=1)
 resultTable <- tpptrAnalyzeMeltingCurves(fittedData)
 subset(resultTable, fulfills_all_4_requirements)$Protein_ID

## End(Not run)

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