runTPP2D {TPP2D} | R Documentation

## Run complete TPP2D analysis

### Description

Run complete TPP2D analysis

### Usage

    runTPP2D(df = NULL, configTable = NULL, data = NULL,
      idVar = "protein_id", intensityStr = "signal_sum_",
      fcStr = "rel_fc_", nonZeroCols = "qusm", geneNameVar = "gene_name",
      addCol = NULL, qualColName = "qupm", naStrs = c("NA", "n/d",
      "NaN"), concFactor = 1e+06, medianNormalizeFC = TRUE,
      filterContaminants = TRUE, recomputeSignalRatios = FALSE,
      minObs = 20, independentFiltering = FALSE, fcThres = 1.5,
      optim_fun_h0 = .min_RSS_h0, optim_fun_h1 = .min_RSS_h1_slope_pEC50,
      optim_fun_h1_2 = NULL, gr_fun_h0 = NULL, gr_fun_h1 = NULL,
      gr_fun_h1_2 = NULL, slopEC50 = TRUE, maxit = 750,
      BPPARAM = BiocParallel::SerialParam(progressbar = TRUE), B = 20,
      byMsExp = TRUE, alpha = 0.1)

### Arguments

`df` | tidy data_frame retrieved after import of a 2D-TPP dataset, potential filtering and addition of a column "nObs" containing the number of observations per protein
`configTable` | character string of a file path to a config table
`data` | possible list of datasets from different MS runs corresponding to a 2D-TPP dataset, circumvents loading datasets referencend in config table, default is NULL
`idVar` | character string indicating which data column provides the unique identifiers for each protein.
`intensityStr` | character string indicating which columns contain raw intensities measurements
`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.
`nonZeroCols` | column like default qssm that should be imported and requested to be non-zero in analyzed data
`geneNameVar` | character string of the column name that describes the gene name of a given protein in the raw data files
`addCol` | character string indicating additional column to import
`qualColName` | character string indicating which column can be used for additional quality criteria when deciding between different non-unique protein identifiers.
`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`.
`concFactor` | numeric value that indicates how concentrations need to be adjusted to yield total unit e.g. default mmol - 1e6
`medianNormalizeFC` | perform median normalization (default: TRUE).
`filterContaminants` | logical variable indicating whether data should be filtered to exclude contaminants (default: TRUE).
`recomputeSignalRatios` | logical variable indicaiting whether signals should be recomputed from relative fold changes, recommended if Isobarquant was used for protein quantification
`minObs` | number of minimal observations per protein to include it in the analysis
`independentFiltering` | logical variable indicating whether independent filtering should be performed based on minimal fold changes per protein profile
`fcThres` | numeric value of minimal fold change (or inverse fold change) a protein has to show to be kept upon independent filtering
`optim_fun_h0` | optimization function that should be used for fitting the H0 model
`optim_fun_h1` | optimization function that should be used for fitting the H1 model
`optim_fun_h1_2` | optional additional optimization function that will be run with paramters retrieved from optim_fun_h1 and should be used for fitting the H1 model with the trimmed sum model, default is NULL
`gr_fun_h0` | optional gradient function for optim_fun_h0, default is NULL
`gr_fun_h1` | optional gradient function for optim_fun_h1, default is NULL
`gr_fun_h1_2` | optional gradient function for optim_fun_h1_2, default is NULL
`slopEC50` | logical flag indicating whether the h1 model is fitted with a linear model describing the shift od the pEC50 over temperatures
`maxit` | maximal number of iterations the optimization should be given, default is set to 500
`BPPARAM` | = BiocParallel::SerialParam(progressbar = TRUE),
`B` | numeric value indicating number of rounds of bootstraps that should be performed to estimate the null distribution
`byMsExp` | logical indicating whether bootstrapping should be performed within MS experiments
`alpha` | FDR level that should be controlled

### Value

a tpp2dExperiment object

### Examples

    data("simulated_cell_extract_df")
    runTPP2D(df = simulated_cell_extract_df %>%
       filter(representative %in% 1:3),
       B = 1)

[Package TPP2D version 1.4.1 Index]