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

Description Usage Arguments Value Examples

Description

Compete H0 and H1 models per protein and obtain F statistic

Usage

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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

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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 Aug. 17, 2019, 11:26 a.m.