competeModels: Compete H0 and H1 models per protein and obtain F statistic

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

Compete H0 and H1 models per protein and obtain F statistic

Description

Compete H0 and H1 models per protein and obtain F statistic

Usage

competeModels(
  df,
  fcThres = 1.5,
  independentFiltering = FALSE,
  minObs = 20,
  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,
  maxit = 750
)

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
fcThres	numeric value of minimal fold change (or inverse fold change) a protein has to show to be kept upon independent filtering
independentFiltering	boolean flag indicating whether independent filtering should be performed based on minimal fold changes per protein profile
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
maxit	maximal number of iterations the optimization should be given, default is set to 500

Value

data frame summarising the fit characteristics of H0 and H1 models and therof resulting computed F statistics per protein

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>% 
  filter(clustername %in% paste0("protein", 1:10)) %>% 
  group_by(representative) %>% 
  mutate(nObs = n()) %>% 
  ungroup 
competeModels(temp_df)  

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