tpptrAnalyzeMeltingCurves {TPP} | R Documentation

## 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 = "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)

[Package TPP version 3.8.5 Index]