bootstrapNullAlternativeModel {TPP2D} | R Documentation

## Bootstrap null distribution of F statistics for FDR estimation based on resampling alternative model residuals

### Description

Bootstrap null distribution of F statistics for FDR estimation based on resampling alternative model residuals

### Usage

    bootstrapNullAlternativeModel(df, params_df, maxit = 500,
      independentFiltering = FALSE, fcThres = 1.5, minObs = 20,
      optim_fun_h0 = TPP2D:::.min_RSS_h0,
      optim_fun_h1 = TPP2D:::.min_RSS_h1_slope_pEC50,
      optim_fun_h1_2 = NULL, gr_fun_h0 = NULL, gr_fun_h1 = NULL,
      gr_fun_h1_2 = NULL, BPPARAM = BiocParallel::SerialParam(progressbar =
      TRUE), B = 20, byMsExp = TRUE, verbose = FALSE)

### 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
`params_df` | data frame listing all null and alternative model parameters as obtained by 'getModelParamsDf'
`maxit` | maximal number of iterations the optimization should be given, default is set to 500
`independentFiltering` | boolean flag 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
`minObs` | numeric value of minimal number of observations that should be required per protein
`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
`BPPARAM` | BiocParallel parameter for optional parallelization of null distribution generation through bootstrapping, default: BiocParallel::SerialParam()
`B` | numeric value of rounds of bootstrap, default: 20
`byMsExp` | boolean flag indicating whether resampling of residuals should be performed separately for data generated by different MS experiments, default TRUE, recommended
`verbose` | logical indicating whether to print each protein while its profile is boostrapped

### Value

data frame containing F statistics of proteins with permuted 2D thermal profiles that are informative on the Null distribution of F statistics

### Examples

    data("simulated_cell_extract_df")
    temp_df <- simulated_cell_extract_df %>%
      filter(clustername %in% paste0("protein", 1:3)) %>%
      group_by(representative) %>%
      mutate(nObs = n()) %>%
      ungroup
    temp_params_df <- getModelParamsDf(temp_df)
    boot_df <- bootstrapNullAlternativeModel(
      temp_df, params_df = temp_params_df, B = 2)

[Package TPP2D version 1.4.1 Index]