cytoglm {CytoGLMM} | R Documentation |
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
tb_coef |
coefficent table |
df_samples_subset |
possibly subsampled df_samples_subset table |
protein_names |
input protein names |
condition |
input condition variable |
group |
input group names |
covariate_names |
input covariates |
cell_n_min |
input cell_n_min |
cell_n_subsample |
input cell_n_subsample |
unpaired |
true if unpaired samples were provided as input |
num_boot |
input num_boot |
num_cores |
input num_cores |
formula_str |
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