Fit GLM with bootstrap resampling
cytoglm( df_samples_subset, protein_names, condition, group = "donor", covariate_names = NULL, cell_n_min = Inf, cell_n_subsample = 0, num_boot = 100, num_cores = 1 )
df_samples_subset | Data frame or tibble with proteins counts, cell condition, and group information |
---|---|
protein_names | A vector of column names of protein to use in the analysis |
condition | The column name of the condition variable |
group | The column name of the group variable |
covariate_names | The column names of covariates |
cell_n_min | Remove samples that are below this cell counts threshold |
cell_n_subsample | Subsample samples to have this maximum cell count |
num_boot | Number of bootstrap samples |
num_cores | Number of computing cores |
A list of class cytoglm
containing
coefficent table
possibly subsampled df_samples_subset table
input protein names
input condition variable
input group names
input covariates
input cell_n_min
input cell_n_subsample
true if unpaired samples were provided as input
input num_boot
input num_cores
formula use in the regression model
set.seed(23) df <- generate_data() protein_names <- names(df)[3:12] df <- dplyr::mutate_at(df, protein_names, function(x) asinh(x/5)) glm_fit <- CytoGLMM::cytoglm(df, protein_names = protein_names, condition = "condition", group = "donor", num_boot = 10) # in practice >=1000 glm_fit #> #> ####################### #> ## paired analysis #### #> ####################### #> #> number of bootstrap samples: 10 #> #> number of cells per group and condition: #> control treatment #> 1 1000 1000 #> 2 1000 1000 #> 3 1000 1000 #> 4 1000 1000 #> 5 1000 1000 #> 6 1000 1000 #> 7 1000 1000 #> 8 1000 1000 #> #> proteins included in the analysis: #> m01 m02 m03 m04 m05 m06 m07 m08 m09 m10 #> #> condition compared: condition #> grouping variable: donor