cytoglm: Fit GLM with bootstrap resampling

In ChristofSeiler/CytoGLMM: Conditional Differential Analysis for Flow and Mass Cytometry Experiments

Fit GLM with bootstrap resampling

Description

Fit GLM with bootstrap resampling

Usage

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
)

Arguments

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

Value

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

Examples

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

ChristofSeiler/CytoGLMM documentation built on April 21, 2023, 3:38 a.m.