tpptrNormalize {TPP} | R Documentation

## Normalize protein fold changes

### 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

`data` | List of `ExpressionSet`s with protein fold changes to be normalized.
`normReqs` | List of filtering criteria for construction of the normalization set.
`qcPlotTheme` | ggplot theme for the created plots
`qcPlotPath` | location where plots of the curves fitted to the normalization set medians should be stored.
`startPars` | start values for the melting curve parameters. Will be passed to function `nls` for curve fitting.
`maxAttempts` | maximal number of curve attempts to fit melting curve to fold change medians when computing normalization factors.
`fixedReference` | name of a fixed reference experiment for normalization. If NULL (default), the experiment with the best R2 when fitting a melting curve through the median fold changes is chosen as the reference.

### 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)

[Package TPP version 3.15.3 Index]