fitAndEvalDataset: Fit H0 and H1 model to 2D thermal profiles of proteins and...

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

Description

Fit H0 and H1 model to 2D thermal profiles of proteins and compute F statistic

Usage

fitAndEvalDataset(
  df,
  maxit = 500,
  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,
  ec50_lower_limit = NULL,
  ec50_upper_limit = NULL,
  slopEC50 = TRUE
)

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
maxit	maximal number of iterations the optimization should be given, default is set to 500
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
ec50_lower_limit	lower limit of ec50 parameter
ec50_upper_limit	lower limit of ec50 parameter
slopEC50	logical flag indicating whether the h1 model is fitted with a linear model describing the shift od the pEC50 over temperatures

Value

data frame with H0 and H1 model characteristics for each protein and respectively computed F statistics

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>% 
  group_by(representative) %>% 
  mutate(nObs = n()) %>% 
  ungroup 
fitAndEvalDataset(temp_df)  

TPP2D documentation built on Nov. 8, 2020, 4:54 p.m.