tpptrCurveFit: Fit melting curves to all proteins in a dataset.

Description

Fit melting curves to all proteins in a dataset.

Usage

tpptrCurveFit(data, dataCI = NULL, resultPath = NULL, ggplotTheme = tppDefaultTheme(), doPlot = TRUE, startPars = c(Pl = 0, a  = 550, b = 10), maxAttempts = 500, nCores = "max", verbose = FALSE)

Arguments

data

list of ExpressionSets with protein fold changes for curve fitting.

dataCI

list of ExpressionSets with protein fold change confidence intervals for curve fitting. Default to NULL.

resultPath

location where to store the melting curve plots.

ggplotTheme

ggplot theme for melting curve plots.

doPlot

boolan value indicating whether melting curves should be plotted, or whether just the curve parameters should be returned.

startPars

start values for the melting curve parameters. Will be passed to function nls for curve fitting.

maxAttempts

maximal number of curve fitting attempts if model does not converge.

nCores

either a numerical value given the desired number of CPUs, or 'max' to automatically assign the maximum possible number (default).

verbose

plot name of each fitted protein to the command lin as a means of progress report.

Value

A list of ExpressionSets storing the data together with the melting curve parameters for each experiment. Each ExpressionSet contains the measured fold changes, as well as row and column metadata. In each ExpressionSet S, the fold changes can be accessed by exprs(S). Protein expNames can be accessed by featureNames(S). Isobaric labels and the corresponding temperatures are returned by S$label and S$temperature.

Details

If the melting curve fitting procedure does not converge, it will be repeatedly started from perturbed starting parameters (maximum iterations defined by argument maxAttempts)

The melting curve plots will be stored in a subfolder with name Melting_Curves at the location specified by resultPath.

Examples

Run this code

data(hdacTR_smallExample)
tpptrData <- tpptrImport(configTable=hdacTR_config, data=hdacTR_data)
tpptrNorm <- tpptrNormalize(data=tpptrData, normReqs=tpptrDefaultNormReqs())
normalizedData <- tpptrNorm$normData
hdacSubsets <- lapply(normalizedData, 
                      function(d) d[grepl("HDAC", featureNames(d))])
tpptrFittedHDACs <- tpptrCurveFit(hdacSubsets, nCores=1)
# Show estimated parameters for vehicle and treatment experiments:
pData(featureData(tpptrFittedHDACs[["Vehicle_1"]]))
pData(featureData(tpptrFittedHDACs[["Panobinostat_1"]]))

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