tpptrSplineFitAndTest: Perform spline fitting and analyze by moderated F-test

Description Usage Arguments Details Value See Also Examples

View source: R/tpptrSplineFitAndTest.R

Description

A wrapper function around the functions tpptrFitSplines, tpptrFTest, tpptrPlotSplines, which fits natural splines to all proteins in a dataset and detect differential behavior between conditions by a moderated F-test. The results are formatted as a wide table with one row per protein. This table contains all the original data, the test results, and (optionally) additional annotation columns for each protein.

Usage

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tpptrSplineFitAndTest(data, factorsH1, factorsH0 = character(),
  resultPath = NULL, doPlot = TRUE, nCores = "max", splineDF = 3:7,
  additionalCols = NULL, verbose = NULL, ggplotTheme = NULL)

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?

resultPath

location where to store the spline plots per protein.

doPlot

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

nCores

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

splineDF

degrees of freedom for natural spline fitting.

additionalCols

additional annotation per protein to append to the result table.

verbose

DEPRECATED

ggplotTheme

DEPRECATED.

Details

Plots of the natural spline fits will be stored in a subfolder with name Spline_Fits at the location specified by resultPath.

Argument data can either be long table, or a list of expressionSets as returned by tpptrImport. If a long table, it needs to contain the following columns: 'uniqueID' (identifier), 'x' (independent variable for fitting, usually the temperature) and 'y' (dependent variable for fitting, usually the relative concentration).

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 data frame in wide format with one row per protein. It contains the smoothing spline parameters and F-test results obtained by comparing the null and alternative models.

See Also

ns, AICc

Examples

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data(hdacTR_smallExample)
tpptrData <- tpptrImport(configTable = hdacTR_config, data = hdacTR_data)
fitData <- tpptrTidyUpESets(tpptrData)
hdacSplineFits <- tpptrSplineFitAndTest(data = fitData,
                                        factorsH1 = "condition",
                                        nCores = 1,
                                        splineDF = 4:5,
                                        doPlot = FALSE)
# Show estimated splines for HDAC1:
filter(hdacSplineFits, Protein_ID == "HDAC1")
# -> Which proteins showed significant condition effects?
hdacSplineFits %>% filter(p_adj_NPARC <= 0.01) %>% select(Protein_ID, p_adj_NPARC)
# Quality control: test for replicate-specific effects:
 testResults <- tpptrSplineFitAndTest(data = fitData,
                                     factorsH1 = "replicate",
                                     nCores = 1,
                                     splineDF = 4,
                                     doPlot = FALSE)
# -> Which proteins showed significant replicate effects?
testResults %>% filter(p_adj_NPARC <= 0.01) %>% select(Protein_ID, p_adj_NPARC)

Bioconductor-mirror/TPP documentation built on July 7, 2017, 11:01 p.m.