runTPP2D: Run complete TPP2D analysis

In nkurzaw/TPP2D: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP)

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)

nkurzaw/TPP2D documentation built on May 9, 2023, 10:04 a.m.