fitH1Model: Fit H1 model and evaluate fit statistics

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

Fit H1 model and evaluate fit statistics

Description

Fit H1 model and evaluate fit statistics

Usage

fitH1Model(
  df,
  maxit = 500,
  optim_fun = .min_RSS_h1_slope_pEC50,
  optim_fun_2 = NULL,
  gr_fun = NULL,
  gr_fun_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	optimization function that should be used for fitting the H0 model
optim_fun_2	optional secound optimization function for fitting the H1 model that should be used based on the fitted parameters of the optimizationfor based on optim_fun
gr_fun	optional gradient function for optim_fun, default is NULL
gr_fun_2	optional gradient function for optim_fun_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 H1 model characteristics for each protein

Examples

data("simulated_cell_extract_df")
temp_df <- simulated_cell_extract_df %>% 
  filter(clustername %in% paste0("protein", 1:5)) %>% 
  group_by(representative) %>% 
  mutate(nObs = n()) %>% 
  ungroup
  
fitH1Model(temp_df)

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