Package 'TPP'

Description

Performs the whole analysis workflow for 2D-TPP experiment by invoking routines for data import, data processing, fold change computation, median normalization, TPP-CCR curve fitting, plotting and production of the result table.

Usage

analyze2DTPP(
  configTable,
  data = NULL,
  resultPath = NULL,
  idVar = "gene_name",
  fcStr = NULL,
  intensityStr = "signal_sum_",
  naStrs = c("NA", "n/d", "NaN", "<NA>"),
  methods = "doseResponse",
  qualColName = "qupm",
  compFc = TRUE,
  normalize = TRUE,
  addCol = NULL,
  nCores = 1,
  nonZeroCols = "qssm",
  fcTolerance = 0.1,
  r2Cutoff = 0.8,
  fcCutoff = 1.5,
  slopeBounds = c(1, 50),
  fractAbund = FALSE,
  xlsxExport = TRUE,
  plotAll = FALSE,
  plotAllR2 = FALSE,
  plotSingle = FALSE,
  trRef = NULL,
  refFcStr = "norm_rel_fc_",
  addInfo = FALSE,
  createReport = "none",
  paletteName = "Spectral",
  configFile
)

Arguments

Details

Invokes the following steps:

1. Import data using the tpp2dImport function.

2. Remove zero sumionarea values.

3. Compute fold changes from raw data (sumionarea)

4. Perform normalization by fold change medians (optional) using the tpp2dNormalize function. To perform normalization, set argument normalize=TRUE.

paletteName specifies the color palette to be used by the brewer.pal function from the RColorBrewer package to assign a separate color to each concentration.

Value

A data frame in which the model results (slopes and pEC50 values) are stored row-wise for each protein and administered temperatures.

References

Becher, I., Werner, T., Doce, C., Zaal, E. A., Berkers, C. R., T"ogel, I., Salzer, E., Bantscheff, M., Savitski, M. M. (2016) Thermal profiling reveals phenylalanine hydroxylase as an off-target of panobinostat. Nature Chemical Biology, 12(11), 908-910.

Examples

data(panobinostat_2DTPP_smallExample)
config_tpp2d <- panobinostat_2DTPP_config
data_tpp2d <- panobinostat_2DTPP_data
tpp2dResults <- analyze2DTPP(configTable = config_tpp2d, 
                             data = data_tpp2d,
                             methods=c("doseResponse"),
                             createReport="none",
                             nCores=1,
                             idVar = "representative",
                             addCol = "clustername",
                             intensityStr = "sumionarea_protein_",
                             nonZeroCols = "qusm")

Description

Performs analysis of a TPP-CCR experiment by invoking routines for data import, data processing, normalization, curve fitting, and production of the result table.

Usage

analyzeTPPCCR(
  configTable,
  data = NULL,
  resultPath = NULL,
  idVar = "gene_name",
  fcStr = "rel_fc_",
  naStrs = c("NA", "n/d", "NaN", "<NA>"),
  qualColName = "qupm",
  normalize = TRUE,
  ggplotTheme = tppDefaultTheme(),
  nCores = "max",
  nonZeroCols = "qssm",
  r2Cutoff = 0.8,
  fcCutoff = 1.5,
  slopeBounds = c(1, 50),
  plotCurves = TRUE,
  verbose = FALSE,
  xlsxExport = TRUE,
  fcTolerance = 0.1
)

Arguments

Details

Invokes the following steps:

1. Import data using the tppccrImport function.

2. Perform normalization by fold change medians (optional) using the tppccrNormalize function. To perform normalization, set argument normalize=TRUE.

3. Fit and analyze dose response curves using the tppccrCurveFit function.

4. Export results to Excel using the tppExport function.

The default settings are tailored towards the output of the python package isobarQuant, but can be customized to your own dataset by the arguments idVar, fcStr, naStrs, qualColName.

If resultPath is not specified, result files are stored at the path defined in the first entry of configTable$Path. If the input data are not specified in configTable, no result path will be set. This means that no output files or dose response curve plots are produced and analyzeTPPCCR just returns the results as a data frame.

The function analyzeTPPCCR reports intermediate results to the command line. To suppress this, use suppressMessages.

The dose response curve plots will be stored in a subfolder with name DoseResponse_Curves at the location specified by resultPath.

Only proteins with fold changes bigger than [fcCutoff * (1 - fcTolerance) or smaller than 1/(fcCutoff * (1 - fcTolerance))] will be used for curve fitting. Additionally, the proteins fulfilling the fcCutoff criterion without tolerance will be marked in the output column meets_FC_requirement.

Value

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

References

Savitski, M. M., Reinhard, F. B., Franken, H., Werner, T., Savitski, M. F., Eberhard, D., ... & Drewes, G. (2014). Tracking cancer drugs in living cells by thermal profiling of the proteome. Science, 346(6205), 1255784.

Franken, H, Mathieson, T, Childs, D. Sweetman, G. Werner, T. Huber, W. & Savitski, M. M. (2015), Thermal proteome profiling for unbiased identification of drug targets and detection of downstream effectors. Nature protocols 10(10), 1567-1593.

See Also

tppDefaultTheme

Examples

data(hdacCCR_smallExample)
tppccrResults <- analyzeTPPCCR(configTable=hdacCCR_config, 
                               data=hdacCCR_data, nCores=1)

Description

Performs analysis of a TPP-TR experiment by invoking routines for data import, data processing, normalization, curve fitting, and production of the result table.

Usage

analyzeTPPTR(
  configTable,
  data = NULL,
  resultPath = NULL,
  methods = c("meltcurvefit", "splinefit"),
  idVar = "gene_name",
  fcStr = "rel_fc_",
  ciStr = NULL,
  naStrs = c("NA", "n/d", "NaN", "<NA>"),
  qualColName = "qupm",
  normalize = TRUE,
  normReqs = tpptrDefaultNormReqs(),
  ggplotTheme = tppDefaultTheme(),
  nCores = "max",
  startPars = c(Pl = 0, a = 550, b = 10),
  splineDF = c(3:7),
  maxAttempts = 500,
  plotCurves = TRUE,
  fixedReference = NULL,
  pValMethod = "robustZ",
  pValFilter = list(minR2 = 0.8, maxPlateau = 0.3),
  pValParams = list(binWidth = 300),
  verbose = FALSE,
  xlsxExport = TRUE
)

Arguments

Details

Invokes the following steps:

1. Import data using the tpptrImport function.

2. Perform normalization (optional) using the tpptrNormalize function. To perform normalization, set argument normalize=TRUE. The normalization will be filtered according to the criteria specified in the normReqs argument (also see the documentation of tpptrNormalize and tpptrDefaultNormReqs for further information).

3. Fit melting curves using the function tpptrCurveFit.

4. Produce result table using the function tpptrAnalyzeMeltingCurves.

5. Export results to Excel using the function tppExport.

The default settings are tailored towards the output of the python package isobarQuant, but can be customized to your own dataset by the arguments idVar, fcStr, naStrs, qualColName.

If resultPath is not specified, the location of the first input file specified in configTable will be used. If the input data are not specified in configTable, no result path will be set. This means that no output files or melting curve plots are produced and analyzeTPPTR just returns the results as a data frame.

The function analyzeTPPTR reports intermediate results to the command line. To suppress this, use suppressMessages.

The configTable argument is a dataframe, or the path to a spreadsheet (tab-delimited text-file or xlsx format). Information about each experiment is stored row-wise. It contains the following columns:

• Path:location of each datafile. Alternatively, data can be directly handed over by the data argument.

• Experiment: unique experiment names.

• Condition: experimental conditions of each dataset.

• Label columns: each isobaric label names a column that contains the temperatures administered for the label in the individual experiments.

The argument methods can be one of the following: More than one method can be specified. For example, parametric testing of melting points and nonparametric spline-based goodness-of-fit tests can be performed sequentially in the same analysis. The results are then written to separate columns of the output table.

If methods contains "meltcurvefit", melting curve plots will be stored in a subfolder with name Melting_Curves at the location specified by resultPath. If methods contains "splinefit", plots of the natural spline fits will be stored in a subfolder with name Spline_Fits at the location specified by resultPath.

The argument nCores could be either 'max' (use all available cores) or an upper limit of CPUs to be used.

If doPlot = TRUE, melting curve plots are generated separately for each protein and stored in separate pdfs. Each file is named by the unique protein identifier. Filenames are truncated to 255 characters (requirement by most operation systems). Truncated filenames are indicated by the suffix "_truncated[d]", where [d] is a unique number to avoid redundancies. All melting curve plots are stored in a subfolder with name Melting_Curves at the location specified by resultPath.

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

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 which the fit results are stored row-wise for each protein.

References

Savitski, M. M., Reinhard, F. B., Franken, H., Werner, T., Savitski, M. F., Eberhard, D., ... & Drewes, G. (2014). Tracking cancer drugs in living cells by thermal profiling of the proteome. Science, 346(6205), 1255784.

Franken, H, Mathieson, T, Childs, D. Sweetman, G. Werner, T. Huber, W. & Savitski, M. M. (2015), Thermal proteome profiling for unbiased identification of drug targets and detection of downstream effectors. Nature protocols 10(10), 1567-1593.

See Also

tppDefaultTheme, tpptrImport, tpptrNormalize, tpptrCurveFit, tpptrAnalyzeMeltingCurves

Examples

data(hdacTR_smallExample)
tpptrResults <- analyzeTPPTR(configTable = hdacTR_config, data = hdacTR_data, 
                methods = "meltcurvefit", nCores = 1)

The configuration table to analyze hdacCCR_data.

Description

The configuration table to analyze hdacCCR_data.

Details

hdacCCR_config is a data frame that specifies the experiment names, isobaric labels, and the administered drug concentrations at each label.

References

Franken, H, Mathieson, T, Childs, D. Sweetman, G. Werner, T. Huber, W. & Savitski, M. M. (2015), Thermal proteome profiling for unbiased identification of drug targets and detection of downstream effectors. Nature protocols 10(10), 1567-1593.

See Also

hdacCCR_smallExample, hdacCCR_data

Description

Example subset of a Panobinostat TPP-CCR dataset (replicates 1 and 2)

Details

A list with two subsets of a dataset obtained by TPP-CCR experiments to investigate drug effects for HDAC inhibitor Panobinostat. It contains 7 HDACs as well as a random selection of 493 further proteins.

You can use this dataset to explore the TPP package functionalities without invoking the whole time consuming analysis on the big dataset.

The original dataset is located in the folder 'example_data/CCR_example_data' in the package's installation directory. You can find it on your system by the R command system.file('example_data', package = 'TPP'). The measurements were generated by four separate multiplexed TMT experiments with 10 TMT labels each. Quantitative values per protein were obtained by the python software isobarQuant and converted to fold changes relative to the lowest temperature. The raw data before quantification can be found in the proteomicsDB database (http://www.proteomicsdb.org/#projects/4221/3102) with the following sample mapping:

• Panobinostat_1: MS-experiment numbers P97404B02-B10

• Panobinostat_2: MS-experiment numbers P97414B02-B10

References

Franken, H, Mathieson, T, Childs, D. Sweetman, G. Werner, T. Huber, W. & Savitski, M. M. (2015), Thermal proteome profiling for unbiased identification of drug targets and detection of downstream effectors. Nature protocols 10(10), 1567-1593.

See Also

hdacCCR_smallExample, hdacTR_config

Description

Example dataset obtained by TPP-CCR experiments for analysis by the TPP-package. It contains all necessary arguments to start the analysis (config table and list of data frames).

References

Franken, H, Mathieson, T, Childs, D. Sweetman, G. Werner, T. Huber, W. & Savitski, M. M. (2015), Thermal proteome profiling for unbiased identification of drug targets and detection of downstream effectors. Nature protocols 10(10), 1567-1593.

See Also

hdacCCR_data, hdacCCR_config

The configuration table to analyze hdacTR_data.

Description

The configuration table to analyze hdacTR_data.

Details

hdacTR_config is a data frame that specifies the experiment name, isobaric labels, and the administered temperatures at each label.

References

Franken, H, Mathieson, T, Childs, D. Sweetman, G. Werner, T. Huber, W. & Savitski, M. M. (2015), Thermal proteome profiling for unbiased identification of drug targets and detection of downstream effectors. Nature protocols 10(10), 1567-1593.

See Also

hdacTR_smallExample, hdacTR_data

Description

Example subset of a dataset obtained by TPP-TR experiments to investigate possible targets for HDAC inhibitor Panobinostat.

Details

hdacTR_data is a list of data frames that contain measurements for HDACs as well as a random selection of 500 further proteins.

You can use this dataset to explore the TPP package functionalities without invoking the whole time consuming analysis on the whole dataset.

The original dataset is located in the folder 'example_data/TR_example_data' in the package's installation directory. You can find it on your system by the R command system.file('example_data', package = 'TPP').

The measurements were generated by four separate multiplexed TMT experiments with 10 TMT labels each. Quantitative values per protein were obtained by the python software isobarQuant and converted to fold changes relative to the lowest temperature. The raw data before quantification can be found in the proteomicsDB database (http://www.proteomicsdb.org/#projects/4221/3101) with the following sample mapping:

• Panobinostat_1: MS-experiment numbers P85192B02-B10

• Panobinostat_2: MS-experiment numbers P85881B02-B10

• Vehicle_1: MS-experiment numbers P85202B02-B10

• Vehicle_2: MS-experiment numbers P85891B02-B10

References

Franken, H, Mathieson, T, Childs, D. Sweetman, G. Werner, T. Huber, W. & Savitski, M. M. (2015), Thermal proteome profiling for unbiased identification of drug targets and detection of downstream effectors. Nature protocols 10(10), 1567-1593.

See Also

hdacTR_smallExample, hdacTR_config

Description

Example of a TPP-TR result table.

Details

Contains the data object resultTable.

Description

Example dataset obtained by TPP-TR experiments for analysis by the TPP-package. It contains all necessary arguments to start the analysis (config table and list of data frames).

References

Franken, H, Mathieson, T, Childs, D. Sweetman, G. Werner, T. Huber, W. & Savitski, M. M. (2015), Thermal proteome profiling for unbiased identification of drug targets and detection of downstream effectors. Nature protocols 10(10), 1567-1593.

See Also

hdacTR_data, hdacTR_config

The configuration table to analyze panobinostat_2DTPP_data.

Description

The configuration table to analyze panobinostat_2DTPP_data.

Details

panobinostat_2DTPP_config is a data frame that specifies the experiment names, isobaric labels, and the administered drug concentrations at each label.

See Also

panobinostat_2DTPP_data, panobinostat_2DTPP_smallExample

Description

Example subset of a Panobinostat 2D-TPP dataset

Details

A list with two subsets of a dataset obtained by 2D-TPP experiments to investigate drug effects for HDAC inhibitor Panobinostat. The experiment was performed on living HepG2 cells (see Becher et al. (2016). Thermal profiling reveals phenylalanine hydroxylase as an off-target of panobinostat. Nature Chemical Biology, (September)) It contains 7 HDACs as well as a random selection of 493 further proteins.

You can use this dataset to explore the TPP package functionalities without invoking the whole time consuming analysis on the big dataset.

See Also

panobinostat_2DTPP_config, panobinostat_2DTPP_smallExample

Description

Example dataset obtained by 2D-TPP experiments for analysis by the TPP-package. It contains all necessary arguments to start the analysis (config table and list of data frames).

See Also

panobinostat_2DTPP_data, panobinostat_2DTPP_config

Description

Example of a TPP-TR result table.

Details

resultTable is a data frame that contains the measurements of several TPP-TR experiments, the fitted melting curve parameters, as well as p-values and the results of additional quality checks for each protein. It can be used as input for the function tppQCPlotsCorrelateExperiments.

Description

TPP is a toolbox for analyzing thermal proteome profiling (TPP) experiments.

Usage

.onLoad(libname, pkgname)

Arguments

Details

In order to start a TPP-TR analysis, use function analyzeTPPTR. For a TPP-CCR analysis, use function analyzeTPPCCR. See the vignette for detailed instructions.

Value

No return value defined for this document.

References

Savitski, M. M., Reinhard, F. B., Franken, H., Werner, T., Savitski, M. F., Eberhard, D., ... & Drewes, G. (2014). Tracking cancer drugs in living cells by thermal profiling of the proteome. Science, 346(6205), 1255784.

Franken, H, Mathieson, T, Childs, D. Sweetman, G. Werner, T. Huber, W. & Savitski, M. M. (2015), Thermal proteome profiling for unbiased identification of drug targets and detection of downstream effectors. Nature protocols 10(10), 1567-1593.

Description

These functions are defunct and no longer available.

Usage

tpp2dPlotCCRGoodCurves()

tpp2dPlotCCRAllCurves()

tpp2dPlotCCRSingleCurves()

tpp2dEvalConfigTable()

tpp2dRemoveZeroSias()

tpp2dReplaceColNames()

tpp2dCreateCCRConfigFile()

Details

Defunct functions are: tpp2dPlotCCRGoodCurves, tpp2dPlotCCRAllCurves, tpp2dPlotCCRSingleCurves, tpp2dEvalConfigTable, tpp2dRemoveZeroSias, tpp2dReplaceColNames, tpp2dCreateCCRConfigFile

Value

No value returned

Description

These functions are deprecated and no longer available.

Value

No value returned

Description

Adds additional info to 2D-TPP CCR output data, like counts on how often a certain protein was stabilized or destabilized

Usage

tpp2dAddAdditionalInfo(data, idVar = "gene_name")

Arguments

Value

A data frame to which additional data like how often a protein has been (de-)stabilized has been attached

Examples

load(system.file("example_data/2D_example_data/shortCCRresults.RData", package="TPP"))
shortCCRresults <- tpp2dAddAdditionalInfo(data = shortCCRresults, idVar="representative")

Description

Calculates fractional abundance and DMSO ratio of successive sumionareas and creates respective columns which are added two the data frame which is handed over

Usage

tpp2dCalcFractAbundance(
  configTable = NULL,
  data,
  intensityStr = NULL,
  idVar = NULL
)

Arguments

Value

Data frame that was handed over with additional columns of fractional abundance and DMSO1 vs DMSO2 ratio

Examples

data(panobinostat_2DTPP_smallExample)

# Import data:
datIn <- tpp2dImport(configTable = panobinostat_2DTPP_config,
                      data = panobinostat_2DTPP_data,
                      idVar = "representative",
                      addCol = "clustername",
                      intensityStr = "sumionarea_protein_",
                      nonZeroCols = "qusm")

# View attributes of imported data (experiment infos and import arguments):
attr(datIn, "importSettings") %>% unlist
attr(datIn, "configTable")

# Compute fractional abundance:
datDMSORatio <- tpp2dCalcFractAbundance(data = datIn)
colnames(datDMSORatio)

Description

Computes fold changes by calculating fold changes of the sumionarea relative to the reference column.

Usage

tpp2dComputeFoldChanges(
  configTable = NULL,
  data,
  intensityStr = NULL,
  fcStr = NULL,
  newFcStr = "rel_fc_"
)

Arguments

Value

A data.frame with additional columns with constitute fold changes calculated with respect to the intensity values of the zero treatment column

Examples

# Preparation:
data(panobinostat_2DTPP_smallExample)

# Import data:
datIn <- tpp2dImport(configTable = panobinostat_2DTPP_config,
                      data = panobinostat_2DTPP_data,
                      idVar = "representative",
                      addCol = "clustername",
                      intensityStr = "sumionarea_protein_",
                      nonZeroCols = "qusm")

# View attributes of imported data (experiment infos and import arguments):
attr(datIn, "importSettings") %>% unlist
attr(datIn, "configTable")

# Compute fold changes:
datFC <- tpp2dComputeFoldChanges(data = datIn)

# View updated attributes. Now contain field 'fcStrNorm' indicating prefix
# of the fold change columns after normalization.
attr(datFC, "importSettings")["fcStr"]

Description

Generates a list of dose response curve plots per protein and temperature point.

Usage

tpp2dCreateDRplots(
  data = NULL,
  type = "all",
  verbose = FALSE,
  paletteName = "Spectral"
)

Arguments

Details

data is a data frame in wide table format returned by function tpp2dCurveFit. Its attributes contain information about the experiment names, temperatures, isobaric labels, as well as instructions on how to find the relevant columns in the wide table.

type defines which curves to display per plot. Possible values are:

• "all": Create one plot per protein. This plot simultaneously displays the curves for all available temperatures for this protein (the default).

• "good": Create one plot per protein. This plot displays all dose response curves with a high goodness-of-fit. Choose this option to save runtime by focusing only on the reliable fits.

• "single": Create one separate plot per protein and temperature. This plot displays all dose response curves with a high goodness-of-fit.

paletteName specifies the color palette to be used by the brewer.pal function from the RColorBrewer package to assign a separate color to each concentration.

Value

A list of successfully generated plot objects of class 'ggplot'

See Also

tpp2dCurveFit brewer.pal

Examples

data(panobinostat_2DTPP_smallExample)

# Import data:
datIn <- tpp2dImport(configTable = panobinostat_2DTPP_config,
                      data = panobinostat_2DTPP_data,
                      idVar = "representative",
                      addCol = "clustername",
                      intensityStr = "sumionarea_protein_",
                      nonZeroCols = "qusm")

# Compute fold changes:
fcData2d <- tpp2dComputeFoldChanges(data = datIn)
normData2d <- tpp2dNormalize(data = fcData2d)
ccr2dResults <- tpp2dCurveFit(data = normData2d)
allCurves <- tpp2dCreateDRplots(data = ccr2dResults, type = "all")
allCurves[["HDAC1"]]

Description

Creates a markdown pdf file that summarizes the 2D-TPP analysis by reporting e.g. R version and package versions used

Usage

tpp2dCreateReport(
  data = NULL,
  configFile = NULL,
  resultPath = NULL,
  documentType = "html_document",
  configTable = NULL,
  normalize = TRUE,
  methods = c(""),
  idVar = "gene_name",
  fcStr = "rel_fc_",
  fcStrUpdated = "norm_rel_fc_",
  intensityStr = "signal_sum_",
  addCol = NULL,
  fcTolerance = NA,
  r2Cutoff = NA,
  fcCutoff = NA,
  slopeBounds = c(NA, NA),
  fTest = FALSE,
  trRef = "none"
)

Arguments

Value

A pdf or html report which summarizes all parameters that were set

Description

Performs a reference analysis of a TPP-TR experiment and generates boxplots for the distribution of fold changes at the different temperatures if desired.

Usage

tpp2dCreateTPPTRreference(
  trConfigTable = NULL,
  trDat = NULL,
  resultPath = NULL,
  outputName = NULL,
  createFCboxplots = FALSE,
  idVar = "gene_name",
  fcStr = "rel_fc_",
  qualColName = "qupm",
  normalize = TRUE
)

Arguments

Value

A TPP-TR reference object for a certain cell line with different supporting files in a desired output directory. The main object which is of interest for further analysis is the trRefData.RData file. This is the file to which a referencing system path has to be indicated when a function as tpp2dSplineFitAndTest require to input a TPP-TR reference object. The RData file consists of list carrying four different items:

1. tppCfgTable: the TPP-TR configtable which was used for generating this object

2. sumResTable a list of two elements 1. detail: the exact result data from the TR analysis and 2. summary. a summary of the analyzed TR data comprising the median and standard deviation values of the measurements at the different temperatures (encoded by the isobaric labels)

3. temperatures a table listing the temperatures which were used in the TR experiment in the different replicates

4. lblsByTemp a table matching each temperature to an isobaric label

Description

Performs analysis of a TPP-CCR experiment by invoking the routine for TPP-CCR curve fitting for each temperature of the sample.

Usage

tpp2dCurveFit(
  configFile = NULL,
  data,
  nCores = 1,
  naStrs = NULL,
  fcStr = NULL,
  idVar = NULL,
  nonZeroCols = NULL,
  r2Cutoff = 0.8,
  fcCutoff = 1.5,
  slopeBounds = c(1, 50),
  fcTolerance = 0.1
)

Arguments

Value

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

Examples

# Preparation:
data(panobinostat_2DTPP_smallExample)

# Import data:
datIn <- tpp2dImport(configTable = panobinostat_2DTPP_config,
                      data = panobinostat_2DTPP_data,
                      idVar = "representative",
                      addCol = "clustername",
                      intensityStr = "sumionarea_protein_",
                      nonZeroCols = "qusm")

# Compute fold changes:
datFC <- tpp2dComputeFoldChanges(data = datIn)

# Perform median normalization:
datNorm <- tpp2dNormalize(data = datFC)

# View updated attributes. Now contain field 'fcStrNorm' indicating prefix
# of the fold change columns after normalization.
attr(datNorm, "importSettings")["fcStrNorm"]

# Perform dose response curve fitting and pEC50 calculation:
datFit <- tpp2dCurveFit(data = datNorm)

Description

Produce Excel table of 2D-TPP experiment analysis results.

Usage

tpp2dExport(
  configTable = NULL,
  tab,
  resultPath = NULL,
  idVar = NULL,
  fcStr = NULL,
  intensityStr = NULL,
  outPath,
  addCol = NULL,
  normalizedData = NULL,
  trRef = NULL,
  addPlotColumns = TRUE
)

Arguments

Value

Creates excel file of the TPP-CCR analysis of the 2D-TPP data.

Examples

data(panobinostat_2DTPP_smallExample)
load(system.file("example_data/2D_example_data/shortData2d.RData", package="TPP"))
# tpp2dExport(configTable = panobinostat_2DTPP_config, tab=shortData2d, 
#             outPath=getwd(), 
#             idVar="representative", fcStr="norm_rel_fc_protein_", 
#             intensityStr="sumionarea_protein_", addCol=NULL)

data(panobinostat_2DTPP_smallExample)
# cfgRaw <- panobinostat_2DTPP_config
# datRaw <- panobinostat_2DTPP_data
# datIn <- tpp2dImport(cfgIn, datRaw, fcStr = NULL)
# datFC <- tpp2dComputeFoldChanges(data = datIn)
# datNorm <- tpp2dNormalize(data = datFC)
# cfgCCR <- convert_2D_cfgTable_to_CCR_cfgTable(cfgIn)
# datFitted <- tpp2dCurveFit(datNorm, nCores = 2)
# tpp2dCreateReport(getwd(), cfgIn, resultTable = datFitted, idVar = "representative", 
#                   intensityStr = "sumionarea_protein_")
# tpp2dExport(tab = datFitted, outPath = getwd(), addPlotColumns = FALSE)

Description

Exports plots into plots/ directory in the resultPath

Usage

tpp2dExportPlots(plotList, resultPath, type = "none")

Arguments

Details

Creates pdf files of the afore created plots by plot_2D_data_on_temperature_range or tpp2dCreateDRplots

Value

None

Description

Imports data from 2D-TPP experiments by parsing a configTable and reading in corresponding data file or data frames containing raw data (sumionarea values) and creating a big data frame comprising all samples with respective fold changes

Usage

tpp2dImport(
  configTable = NULL,
  data = NULL,
  idVar = "gene_name",
  addCol = NULL,
  intensityStr = "signal_sum_",
  qualColName = "qupm",
  nonZeroCols = "qssm",
  fcStr = NULL
)

Arguments

Value

A dataframe comprising all experimental data

Examples

# Preparation:
data(panobinostat_2DTPP_smallExample)

# Import data:
datIn <- tpp2dImport(configTable = panobinostat_2DTPP_config,
                      data = panobinostat_2DTPP_data,
                      idVar = "representative",
                      addCol = "clustername",
                      intensityStr = "sumionarea_protein_",
                      nonZeroCols = "qusm")

# View attributes of imported data (experiment infos and import arguments):
attr(datIn, "importSettings") %>% unlist
attr(datIn, "configTable")

Description

Merges 2D-TPP result data with TPP-TR reference data to generate a big table including both results

Usage

tpp2dMerge2dRef(
  resultTable_2D,
  referenceDataSummary,
  refIDVar = "Protein_ID",
  idVar = NULL,
  data = NULL,
  trRef = NULL
)

Arguments

Details

referenceSummary contains summary statistics like median fold changes and is produced by the function tpp2dCreateTPPTRreference. It summarizes the results of a TPP-TR analysis of a reference data set. A reference data set is the a output of a TR experiment without drug treatment on the same cell line as resultTable_2D.

Value

A data frame with results merged from 2D-TPP and TPP-TR reference

See Also

tpp2dCreateTPPTRreference

Examples

data(panobinostat_2DTPP_smallExample)
config_tpp2d <- panobinostat_2DTPP_config
data_tpp2d <- panobinostat_2DTPP_data
tpp2dResults <- analyze2DTPP(configTable = config_tpp2d, 
                             data = data_tpp2d,
                             methods=c("doseResponse"),
                             createReport="none",
                             nCores=1,
                             idVar = "representative",
                             addCol = "clustername",
                             intensityStr = "sumionarea_protein_",
                             nonZeroCols = "qusm") 

trRef <- file.path(system.file("data", package="TPP"), 
"TPPTR_reference_results_HepG2.RData")

annotatedTable <- tpp2dMerge2dRef(resultTable_2D = tpp2dResults,
                                  referenceDataSummary = trRef)

Description

Normalizes fold changes retrieved from 2D-TPP experiment by dividing by the median fold change

Usage

tpp2dNormalize(configTable = NULL, data, fcStr = NULL)

Arguments

Value

A dataframe identical to the input dataframe except that the columns containing the fold change values have been normalized by their median.

Examples

# Preparation:
data(panobinostat_2DTPP_smallExample)

# Import data:
datIn <- tpp2dImport(configTable = panobinostat_2DTPP_config,
                      data = panobinostat_2DTPP_data,
                      idVar = "representative",
                      addCol = "clustername",
                      intensityStr = "sumionarea_protein_",
                      nonZeroCols = "qusm")

# Compute fold changes:
datFC <- tpp2dComputeFoldChanges(data = datIn)

# Perform median normalization:
datNorm <- tpp2dNormalize(data = datFC)

# View updated attributes. Now contain field 'fcStrNorm' indicating prefix
# of the fold change columns after normalization.
attr(datNorm, "importSettings")["fcStrNorm"]

Description

Plots quality control histograms of pEC50 values of reference dataset and indicates the pEC50 values of the 2D-TPP experiment

Usage

tpp2dPlotQChist(
  configFile = NULL,
  resultTable = NULL,
  resultPath = NULL,
  trRef = NULL,
  fcStr = "rel_fc_",
  idVar = "gene_name",
  qualColName = "qupm"
)

Arguments

Value

A pdf with various quality control plots for a specified 2D-TPP data set

Description

Plots quality control plots which indicate at which temperatures the pEC50 values of the treatment curves lie in comparison to those of the reference data

Usage

tpp2dPlotQCpEC50(
  resultTable = NULL,
  resultPath = NULL,
  trRef = NULL,
  idVar = "gene_name"
)

Arguments

Value

A folder with plots for each identified protein that compare melting points in the reference data set with the 2D-TPP data set

Description

Fit splines through TR reference dataset and extrapolates relative 2D-TPP datapoints, then compares spline fits of different treatments with non-treatment with an f-test

Usage

tpp2dSplineFitAndTest(
  data_2D = NULL,
  data,
  trRefDataPath = NULL,
  dataRef,
  refIDVar = "Protein_ID",
  refFcStr = "norm_rel_fc_",
  resultPath = NULL,
  doPlot = TRUE,
  verbose = FALSE,
  nCores = "max",
  ggplotTheme = NULL
)

Arguments

Details

dataRef can either be a tidy data frame of TPP-TR reference data, a list with TPP-TR reference data and additional information produced by tpp2dCreateTPPTRreference, or a character string with a link to the data in one of the described formats.

Value

None

Examples

data(panobinostat_2DTPP_smallExample)
config_tpp2d <- panobinostat_2DTPP_config
data_tpp2d <- panobinostat_2DTPP_data
trRef <- file.path(system.file("data", package="TPP"), 
  "TPPTR_reference_results_HepG2.RData")
datIn <- tpp2dImport(configTable = config_tpp2d,
                      data = data_tpp2d,
                      idVar = "representative",
                      addCol = "clustername",
                      intensityStr = "sumionarea_protein_",
                      nonZeroCols = "qusm")
fcData2d <- tpp2dComputeFoldChanges(data = datIn)
normData2d <- tpp2dNormalize(data = fcData2d)
analysisResults <- tpp2dSplineFitAndTest(data = normData2d,
                                         dataRef = trRef,
                                         refIDVar = "Protein_ID",
                                         refFcStr = "norm_rel_fc_protein_",
                                         doPlot = FALSE,
                                         nCores = 1)

Description

Fit splines through TR reference dataset and extrapolates relative 2D-TPP datapoints, then compares spline fits of different treatments with non-treatment with an f-test

Usage

tpp2dSplinePlot(
  data_2D = NULL,
  trRef = NULL,
  fcStr = NULL,
  idVar = NULL,
  refIdVar = "Protein_ID",
  methods = c("doseResponse", "splineFit"),
  refFcStr = "norm_rel_fc_protein_",
  verbose = FALSE
)

Arguments

Value

A list of ggplots which can be accessed via the unique protein ids in the idVar column

Examples

load(system.file("example_data/2D_example_data/shortData2d.RData", package="TPP"))
trRef <- system.file("example_data/2D_example_data/referenceNormData.RData", package="TPP")

Description

Definition of a TPP-TR reference object

Usage

tpp2dTRReferenceObject(
  tppRefData = NULL,
  tppRefPath = NULL,
  fcStr = "norm_rel_fc_",
  qualColName = "qupm"
)

Arguments

Value

A TPP-TR reference object

Examples

trRef <- system.file("example_data/2D_example_data/referenceNormData.RData", package="TPP")
tpp2dTRReferenceObject(tppRefPath=trRef)

Description

tppccrCurveFit fits logistic dose response curves to fold change measurements of a TPP-CCR experiment.

Usage

tppccrCurveFit(
  data = NULL,
  fcTable = NULL,
  cpdEffects = NULL,
  slopeBounds = c(1, 50),
  nCores = "max",
  verbose = FALSE
)

Arguments

Details

data is a list of expressionSet objects created by tppccrImport. If desired, it can be already preprocessed by tppccrNormalize or tppccrTransform. It contains the isobaric labels and administered drug concentrations in the phenoData and user-defined protein properties in the featureData. Protein IDs are stored in the featureNames.

Measurements and compound effects for curve fitting can be provided by the arguments fcTable and cpdEffects, instead of being stored in expressionSets in data.

If specified, fcTable needs to be a long table with column names "id" (the protein names), "concentration" (the fold changes), "labelName" (the isobaric label to each measurement), and "experiment" (e.g. "Vehicle_1" or "Panobinostat_1").

If specified, cpdEffects needs to be a long table with column names "id" (the protein names), "cpdEff" (character vector of compound effects, may contain NAs), and "experiment" (e.g. "Vehicle_1" or "Panobinostat_1").

Value

A list of expressionSet objects storing fold changes, the fitted curve parameters, as well as row and column metadata. In each expressionSet S, the fold changes can be accessed by Biobase::exprs(S). Protein expNames can be accessed by featureNames(S). Isobaric labels and the corresponding concentrations are returned by S$label and S$concentration. The fitted curve parameters are stored in codefeatureData(S).

See Also

tppccrImport, tppccrNormalize, tppccrTransform

Examples

data(hdacCCR_smallExample)
tppccrData <- tppccrImport(configTable=hdacCCR_config, 
                           data=hdacCCR_data)
tppccrNorm <- tppccrNormalize(data=tppccrData)
tppccrTransformed <- tppccrTransform(data=tppccrNorm)
tppccrFitted <- tppccrCurveFit(data=tppccrTransformed, nCores=1)

Import TPP-CCR dataset for analysis by the TPP package.

Description

tppccrImport imports a table of protein fold changes and stores them in an ExpressionSet for use in the TPP package.

Usage

tppccrImport(
  configTable,
  data = NULL,
  idVar = "gene_name",
  fcStr = "rel_fc_",
  naStrs = c("NA", "n/d", "NaN", "<NA>"),
  qualColName = "qupm",
  nonZeroCols = "qssm"
)

Arguments

Details

The imported dataset has to contain measurements obtained by a TPP-CCR experiment. Fold changes need to be pre-computed using the lowest concentration as reference.

The dataset can be specified by filename in the configTable argument, or given directly in the data argument

The default settings are adjusted to analyze data of the python package isobarQuant. You can also customize them for your own dataset.

The configTable argument is a dataframe, or the path to a spreadsheet (tab-delimited text-file without quoted strings, or xlsx format). Information about each experiment is stored row-wise. It contains the following columns:

• Path: location of the datafile. Alternatively, data can be directly handed over by the data argument.

• Experiment: unique experiment name.

• Label columns: each isobaric label names a column that contains the concentration administered for the label in the individual experiments.

During data import, proteins with NAs in the data column specified by idVar receive unique generic IDs so that they can be processed by the package.

Value

ExpressionSet object storing the measured fold changes, as well as row and column metadata. In each ExpressionSet S, the fold changes can be accessed by Biobase::exprs(S). Protein expNames can be accessed by featureNames(S). Isobaric labels and the corresponding concentrations are returned by S$label and S$concentration.

See Also

tpptrImport, tppccrCurveFit

Examples

data(hdacCCR_smallExample)
tppccrData <- tppccrImport(configTable=hdacCCR_config, 
data = hdacCCR_data)

Description

Normalize each fold change column by its median.

Usage

tppccrNormalize(data)

Arguments

Value

List of expressionSet objects storing the normalized fold changes, as well as row and column metadata. In each expressionSet S, the fold changes can be accessed by Biobase::exprs(S). Protein names can be accessed by featureNames(S). Isobaric labels and the corresponding concentrations are returned by S$label and S$concentration.

Examples

data(hdacCCR_smallExample)
tppccrData <- tppccrImport(configTable=hdacCCR_config, data = hdacCCR_data)
tppccrNorm <- tppccrNormalize(data=tppccrData)
head(Biobase::exprs(tppccrNorm[[1]]))

Description

Normalize fold changes of TPP-CCR experiment to a reference column (usually that with the lowest concentration) to ensure that the transformation by tppccrTransform yields values between 0 and 1.

Usage

tppccrNormalizeToReference(data, refCol = NULL)

Arguments

Value

List of expressionSet objects storing the normalized fold changes, as well as row and column metadata. In each expressionSet S, the fold changes can be accessed by Biobase::exprs(S). Protein expNames can be accessed by featureNames(S). Isobaric labels and the corresponding concentrations are returned by S$label and S$concentration.

Examples

data(hdacCCR_smallExample)
tppccrData <- tppccrImport(configTable=hdacCCR_config, data = hdacCCR_data)
tppccrNorm <- tppccrNormalize(data=tppccrData)
# Normalize to lowest concentration (in the first column):
tppccrNormToRef <- tppccrNormalizeToReference(data=tppccrNorm, refCol=1)
# Obtain results per replicate:
refTransf_replicate1 <- tppccrNormToRef$Panobinostat_1
head(Biobase::exprs(refTransf_replicate1))
# Perform transformation:
tppccrTransformed <- tppccrTransform(data=tppccrNormToRef)
# Obtain transformed measurements per replicate:
transf_replicate1 <- tppccrTransformed$Panobinostat_1
transf_replicate2 <- tppccrTransformed$Panobinostat_2
# Inspect transformed data in replicate 1:
effects_replicate1 <- Biobase::featureData(transf_replicate1)$compound_effect
newData_repl1 <- data.frame(Biobase::exprs(transf_replicate1), 
                              Type=effects_replicate1)[!is.na(effects_replicate1),]

Description

tppccrPlotCurves plots the logistic dose response curves, as well as the underlying fold change measurements for each TPP-CCR experiment in a study.

Usage

tppccrPlotCurves(
  data = NULL,
  fcTable = NULL,
  curvePars = NULL,
  resultPath = NULL,
  ggplotTheme = tppDefaultTheme(),
  nCores = "max",
  verbose = FALSE
)

Arguments

Details

data is a list of expressionSet objects created by tppccrCurveFit. It contains the isobaric labels and administered drug concentrations in the phenoData and user-defined protein properties (including dose response curve parameters) in the featureData. Protein IDs are stored in the featureNames.

Measurements and compound effects for curve fitting can be provided by the arguments fcTable and cpdEffects, instead of being stored in expressionSets in data.

If specified, fcTable needs to be a long table with column names "id" (the protein names), "concentration" (the fold changes), "labelName" (the isobaric label to each measurement), and "experiment" (e.g. "Vehicle_1" or "Panobinostat_1").

If specified, curvePars needs to be a long table with column names "id" (the protein names), "param" (curve parameter per protein and experiment, see TPP:::drCurveParamNames(names=TRUE, info=FALSE) for possibilities), and "experiment" (e.g. "Vehicle_1" or "Panobinostat_1").

The dose response curve plots will be stored in a subfolder with name DoseResponse_Curves at the location specified by resultPath.

Value

A list of expressionSet objects storing fold changes, as well as row and column metadata. In each expressionSet S, the fold changes can be accessed by Biobase::exprs(S). Protein expNames can be accessed by featureNames(S). Isobaric labels and the corresponding concentrations are returned by S$label and S$concentration. Paths to the produced plots are stored in codefeatureData(S)$plot.

See Also

tppccrCurveFit,tppDefaultTheme

Examples

data(hdacCCR_smallExample)
tppccrData <- tppccrImport(configTable=hdacCCR_config, 
                           data=hdacCCR_data)
tppccrNorm <- tppccrNormalize(data=tppccrData)
tppccrTransformed <- tppccrTransform(data=tppccrNorm)
tppccrFitted <- tppccrCurveFit(data=tppccrTransformed, nCores=1)
hdacSubset <- sapply(tppccrFitted, function(d)d[grepl("HDAC", rownames(d)),])
tppccrPlotted <- tppccrPlotCurves(hdacSubset, resultPath=getwd(), nCores = 1)

Description

tppccrResultTable summarizes the outcomes of a TPP-CCR study in a results table and includes quality information about the estimated dose response curves.

Usage

tppccrResultTable(data, r2Cutoff = 0.8)

Arguments

Value

A data frame in which the results are stored row-wise for each protein, together with the original annotation from the input files.

See Also

tppccrCurveFit,tppccrPlotCurves

Examples

data(hdacCCR_smallExample)
tppccrData <- tppccrImport(configTable=hdacCCR_config, 
                           data=hdacCCR_data)
tppccrNorm <- tppccrNormalize(data=tppccrData)
tppccrTransformed <- tppccrTransform(data=tppccrNorm)
tppccrFitted <- tppccrCurveFit(data=tppccrTransformed, nCores=1)
tppccrResults <- tppccrResultTable(data=tppccrFitted)
subset(tppccrResults, passed_filter_Panobinostat_1 & passed_filter_Panobinostat_2)

Description

Transform fold changes of TPP-CCR experiment to prepare them for dose response curve fitting.

Usage

tppccrTransform(data, fcCutoff = 1.5, fcTolerance = 0.1)

Arguments

Details

Only proteins with fold changes bigger than [fcCutoff * (1 - fcTolerance) or smaller than 1/(fcCutoff * (1 - fcTolerance))] will be used for curve fitting. Additionally, the proteins fulfilling the fcCutoff criterion without tolerance will be marked in the output column meets_FC_requirement.

Value

List of expressionSet objects storing the transformed fold changes, as well as row and column metadata. In each expressionSet S, the fold changes can be accessed by Biobase::exprs(S). Protein expNames can be accessed by featureNames(S). Isobaric labels and the corresponding concentrations are returned by S$label and S$concentration.

Examples

data(hdacCCR_smallExample)
tppccrData <- tppccrImport(configTable=hdacCCR_config, data = hdacCCR_data)
tppccrNorm <- tppccrNormalize(data=tppccrData)
# Perform transformation:
tppccrTransformed <- tppccrTransform(data=tppccrNorm)
# Obtain transformed measurements per replicate:
transf_replicate1 <- tppccrTransformed$Panobinostat_1
transf_replicate2 <- tppccrTransformed$Panobinostat_2
# Inspect transformed data in replicate 1:
effects_replicate1 <- Biobase::featureData(transf_replicate1)$compound_effect
newData_repl1 <- data.frame(Biobase::exprs(transf_replicate1), 
                              Type=effects_replicate1)[!is.na(effects_replicate1),]

Description

Default theme to be passed to the gplots produced by the TPP package.

Usage

tppDefaultTheme()

Details

Internally, the theme is used as an argument for the function ggplot2::theme_set in order specify the appearance of the melting curve plots.

The specified plot properties include bold font and increased font size for axis labels and title, as well as a 90 degree angle for y axis labels.

Value

ggplot theme with default settings for melting plot appearance.

Examples

# Import data:
data(hdacTR_smallExample)
tpptrData <- tpptrImport(configTable=hdacTR_config, data=hdacTR_data)
# Obtain template with default settings:
normRequirements <- tpptrDefaultNormReqs()
print(normRequirements)
# Relax filter on the 10th fold change column for 
# normalization set production:
normRequirements$fcRequirements[3,3] <- 0.25
# Perform normalization:
tpptrNorm <- tpptrNormalize(data=tpptrData, normReqs=)

Description

Produce Excel table of TPP-TR or TPP-CCR experiment out of the data frame returned by tpptrAnalyzeMeltingCurves

Usage

tppExport(tab, file, expNames = NULL, expColors = NULL)

Arguments

Value

No value returned.

Examples

data(hdacTR_resultsTable_smallExample)
tppExport(resultTable, "tpptr_example_results.xlsx")

Description

Plot pairwise relationships between the proteins in different TPP experiments.

Usage

tppQCPlotsCorrelateExperiments(
  tppData,
  annotStr = "",
  path = NULL,
  ggplotTheme = tppDefaultTheme()
)

Arguments

Value

List of plots for each experiment.

See Also

tppDefaultTheme

Examples

data(hdacTR_smallExample)
tpptrData <- tpptrImport(configTable=hdacTR_config, data=hdacTR_data)
# Quality control (QC) plots BEFORE normalization:
tppQCPlotsCorrelateExperiments(tppData=tpptrData, 
annotStr="Non-normalized Fold Changes")
# Quality control (QC) plots AFTER normalization:
tpptrNorm <- tpptrNormalize(data=tpptrData, normReqs=tpptrDefaultNormReqs())
tpptrDataNormalized <- tpptrNorm$normData
tppQCPlotsCorrelateExperiments(tppData=tpptrDataNormalized, 
annotStr="Normalized Fold Changes")

Description

Reference dataset obtained by TPP-TR experiments without drug treatment on HepG2 cell lines.

Details

tppRefData is a list of data frames that contains TPP-TR measurements for a large number of proteins in wide format. The experiments were performed in two replicates. It can be used as a reference for normalization of 2D-TPP data. See the vignette for the 2D workflow for details.

Description

Reference dataset obtained by TPP-TR experiments without drug treatment on HepG2 cell lines.

Details

Contains the data object tppRefData.

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

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)

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

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)

If doPlot = TRUE, melting curves are be plotted in individual files per protein. Each file is named by its unique identifier. Filenames are truncated to 255 characters (requirement by most operation systems). Truncated filenames are indicated by the suffix "_truncated[d]", where [d] is a unique number to avoid redundancies.

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

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 Biobase::exprs(S). Protein expNames can be accessed by featureNames(S). Isobaric labels and the corresponding temperatures are returned by S$label and S$temperature.

See Also

tppDefaultTheme

Examples

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", Biobase::featureNames(d))])
tpptrFittedHDACs <- tpptrCurveFit(hdacSubsets, nCores=1)
# Show estimated parameters for vehicle and treatment experiments:
Biobase::pData(Biobase::featureData(tpptrFittedHDACs[["Vehicle_1"]]))
Biobase::pData(Biobase::featureData(tpptrFittedHDACs[["Panobinostat_1"]]))

Description

Filter criteria as described in the publication.

Usage

tpptrDefaultNormReqs()

Value

List with two entries: 'fcRequirements' describes filtering requirements on fold change columns, 'otherRequirements' contains criteria on additional metadata columns.

Examples

data(hdacTR_smallExample)
tpptrData <- tpptrImport(configTable=hdacTR_config, data=hdacTR_data)
tpptrNorm <- tpptrNormalize(data=tpptrData, normReqs=tpptrDefaultNormReqs())

Description

Fit natural splines to all proteins in a dataset.

Usage

tpptrFitSplines(
  data,
  factorsH1,
  factorsH0 = character(0),
  splineDF = 3:7,
  computeAUC = NULL,
  returnModels = TRUE,
  nCores = "max"
)

Arguments

Value

A table containing the fitted models per protein

See Also

ns, AICc

Examples

data(hdacTR_smallExample)
tpptrData <- tpptrImport(configTable = hdacTR_config, data = hdacTR_data)
normResults <- tpptrNormalize(data = tpptrData, 
               normReqs = tpptrDefaultNormReqs())
normData_eSets <- normResults$normData
normData_longTable <- tpptrTidyUpESets(normData_eSets)
hdacSubset <- subset(normData_longTable, grepl("HDAC", uniqueID))
hdacSplineFits <- tpptrFitSplines(data = hdacSubset, 
                                  factorsH1 = c("condition"), 
                                  nCores = 1)

Description

Analyze fitted natural spline models and look for differential behaviour between conditions by a moderated F-test.

Usage

tpptrFTest(fittedModels, doPlot = FALSE, resultPath = NULL)

Arguments

Details

If doPlot is TRUE, but no resultPath is specified, the plots will be prompted to the active device.

The moderated F-statistic is calculated by the following equation: ...

Value

A long table containing the hypothesis test results per protein.

See Also

ns, squeezeVar

Examples

data(hdacTR_smallExample)
tpptrData <- tpptrImport(configTable = hdacTR_config, data = hdacTR_data)
normResults <- tpptrNormalize(data = tpptrData, normReqs = tpptrDefaultNormReqs())
normData_eSets <- normResults$normData
fitData <- tpptrTidyUpESets(normData_eSets)
fits <- tpptrFitSplines(data = fitData, factorsH1 = "condition", nCores = 1, splineDF = 4:5)
testResults <- tpptrFTest(fittedModels = fits)

Import TPP-TR datasets for analysis by the TPP package.

Description

tpptrImport imports several tables of protein fold changes and stores them in a list of ExpressionSets for use in the TPP package.

Usage

tpptrImport(
  configTable,
  data = NULL,
  idVar = "gene_name",
  fcStr = "rel_fc_",
  naStrs = c("NA", "n/d", "NaN"),
  qualColName = "qupm",
  outputFormat = "eSetList"
)

Arguments

Details

The imported datasets have to contain measurements obtained by TPP-TR experiments. Fold changes need to be pre-computed using the lowest temperature as reference.

An arbitrary number of datasets can be specified by filename in the Path-column of the configTable argument, or given directly as a list of dataframes in the data argument. They can differ, for example, by biological replicate or by experimental condition (for example, treatment versus vehicle). Their names are defined uniquely by the Experiment column in configTable. Experimental conditions can be specified by an optional column in configTable.

The default settings are adjusted to analyze data of the python package isobarQuant. You can also customize them for your own dataset.

The configTable argument is a dataframe, or the path to a spreadsheet (tab-delimited text-file without quoted strings, or xlsx format). Information about each experiment is stored row-wise. It contains the following columns:

• Path:location of each datafile. Alternatively, data can be directly handed over by the data argument.

• Experiment: unique experiment names.

• Condition: experimental conditions of each dataset.

• Label columns: each isobaric label names a column that contains the temperatures administered for the label in the individual experiments.

Proteins with NAs in the data column specified by idVar receive unique generic IDs so that they can be processed by the package.

Value

A list of ExpressionSets storing the imported data for 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 Biobase::exprs(S). Protein expNames can be accessed by featureNames(S). Isobaric labels and the corresponding temperatures are returned by S$label and S$temperature

See Also

tppccrImport

Examples

data(hdacTR_smallExample)
tpptrData <- tpptrImport(hdacTR_config, hdacTR_data)

Description

Normalizes fold changes determined by TPP-TR experiments over different experimental groups.

Usage

tpptrNormalize(
  data,
  normReqs = tpptrDefaultNormReqs(),
  qcPlotTheme = tppDefaultTheme(),
  qcPlotPath = NULL,
  startPars = c(Pl = 0, a = 550, b = 10),
  maxAttempts = 1,
  fixedReference = NULL
)

Arguments

Details

Performs normalization of all fold changes in a given list of ExpressionSets. The normalization procedure is described in detail in Savitski et al. (2014). Whether normalization needs to be performed and what method is best suited depends on the experiment. Here we provide a reasonable solution for the data at hand.

We distinguish between filtering conditions on fold changes and on additional annotation columns. Correspondingly, normReqs contains two fields, fcFilters and otherFilters. Each entry contains a data frame with three columns specifying the column to be filtered, as well as upper and lower bounds. An example is given by tpptrDefaultNormReqs.

Value

A list of ExpressionSets storing the normalized data 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 Biobase::exprs(S). Protein expNames can be accessed by featureNames(S). Isobaric labels and the corresponding temperatures are returned by S$label and S$temperature

References

Savitski, M. M., Reinhard, F. B., Franken, H., Werner, T., Savitski, M. F., Eberhard, D., ... & Drewes, G. (2014). Tracking cancer drugs in living cells by thermal profiling of the proteome. Science, 346(6205), 1255784.

Franken, H, Mathieson, T, Childs, D. Sweetman, G. Werner, T. Huber, W. & Savitski, M. M. (2015), Thermal proteome profiling for unbiased identification of drug targets and detection of downstream effectors. Nature protocols 10(10), 1567-1593.

See Also

tpptrImport

Examples

data(hdacTR_smallExample)
tpptrData <- tpptrImport(hdacTR_config, hdacTR_data)
tpptrNorm <- tpptrNormalize(data=tpptrData, normReqs=tpptrDefaultNormReqs())
names(tpptrNorm)

Description

Plot spline fits per protein

Usage

tpptrPlotSplines(
  data,
  factorsH1 = NULL,
  factorsH0 = NULL,
  fittedModels,
  testResults,
  resultPath = NULL,
  individual = TRUE,
  overview = FALSE,
  returnPlots = FALSE,
  control = list(nCores = "max", maxRank = 500, highlightBelow = 0.05),
  maxRank = NULL,
  highlightBelow = NULL,
  plotIndividual = NULL,
  plotAlphabetical = NULL
)

Arguments

Details

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

Exporting each plot to individual files (individual = TRUE) can cost runtime and the resulting files can be tedious to browse. If you just want to browse the results, use overview = TRUE instead.

If overview = TRUE, two summary PDFs are created that enable quick browsing through all results. They contain the plots in alphacetical order (splineFit_alphabetical.pdf), or ranked by p-values (splineFit_top_xx.pdf, where xx is the maximum rank defined by overviewSettings$maxRank).

Value

None

See Also

ns, AICc, tpptrFitSplines, tpptrFTest

Examples

data(hdacTR_smallExample)
tpptrData <- tpptrImport(configTable = hdacTR_config, data = hdacTR_data)
tidyData <- tpptrTidyUpESets(tpptrData)
splineFits <- tpptrFitSplines(data = tidyData, nCores = 1, splineDF = 4:5,
                              factorsH1 = "condition", returnModels = TRUE)
testResults <- tpptrFTest(fittedModels = splineFits, doPlot = FALSE)
tpptrPlotSplines(data = tidyData, fittedModels = splineFits,
                 individual = FALSE,
                 testResults = testResults, resultPath = getwd())

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

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

Arguments

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

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)

Description

Convert list of expressionSets (intermediate output of several TPP-TR functions) to tidy tables.

Usage

tpptrTidyUpESets(tppESetList, returnType = "exprs")

Arguments

Details

expressionSet lists are for example produced by tpptrImport, tpptrNormalize, tpptrCurveFit.

Value

Either the fold changes per protein across all experiments (if returnType = "exprs"), or the additional annotation per protein and experiment (if returnType = "featureData"). For example, the peptide counts per identified protein can be found here.

Examples

data(hdacTR_smallExample)
tpptrData <- tpptrImport(configTable = hdacTR_config, data = hdacTR_data)
concentrations <- tpptrTidyUpESets(tpptrData)
additionalInfos <- tpptrTidyUpESets(tpptrData, returnType = "featureData")
summary(concentrations)