analyzeTPPCCR {TPP} | R Documentation

## Analyze TPP-CCR experiment

### 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

`configTable` | dataframe, or character object with the path to a file, that specifies important details of the TPP-CCR experiment. See Section `details` for instructions how to create this object.
`data` | single dataframe, containing fold change measurements and additional annotation columns to be imported. Can be used instead of specifying the file path in the `configTable` argument.
`resultPath` | location where to store dose-response curve plots and results table.
`idVar` | character string indicating which data column provides the unique identifiers for each protein.
`fcStr` | character string indicating which columns contain the actual fold change values. Those column names containing the suffix `fcStr` will be regarded as containing fold change values.
`naStrs` | character vector indicating missing values in the data table. When reading data from file, this value will be passed on to the argument `na.strings` in function `read.delim`.
`qualColName` | character string indicating which column can be used for additional quality criteria when deciding between different non-unique protein identifiers.
`normalize` | perform median normalization (default: TRUE).
`ggplotTheme` | ggplot theme for dose response curve plots.
`nCores` | either a numerical value given the desired number of CPUs, or 'max' to automatically assign the maximum possible number (default).
`nonZeroCols` | character string indicating a column that will be used for filtering out zero values.
`r2Cutoff` | Quality criterion on dose response curve fit.
`fcCutoff` | Cutoff for highest compound concentration fold change.
`slopeBounds` | Bounds on the slope parameter for dose response curve fitting.
`plotCurves` | boolean value indicating whether dose response curves should be plotted. Deactivating plotting decreases runtime.
`verbose` | print name of each fitted or plotted protein to the command line as a means of progress report.
`xlsxExport` | produce results table in xlsx format and store at the location specified by the `resultPath` argument.
`fcTolerance` | tolerance for the fcCutoff parameter. See details.

### 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)

[Package TPP version 3.20.1 Index]