TPP (version 2.2.3)

tpptrAnalyzeMeltingCurves: Analyze fitted curve parameters to detect significant shifts in melting points.

Description

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

Usage

tpptrAnalyzeMeltingCurves(data, pValMethod = "maxQuant", 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 'maxQuant' (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.

Value

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

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 = "maxQuant": pValParams=list(binWidth=[your_binWidth]).

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)