cytoflexmix: Logistic mixture regression

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

Logistic mixture regression

Description

Logistic mixture regression

Usage

cytoflexmix(
  df_samples_subset,
  protein_names,
  condition,
  group = "donor",
  cell_n_min = Inf,
  cell_n_subsample = 0,
  ks = seq_len(10),
  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
cell_n_min	Remove samples that are below this cell counts threshold
cell_n_subsample	Subsample samples to have this maximum cell count
ks	A vector of cluster sizes
num_cores	Number of computing cores

Value

A list of class cytoglm containing

flexmixfits	list of flexmix objects
df_samples_subset	possibly subsampled df_samples_subset table
protein_names	input protein names
condition	input condition variable
group	input group names
cell_n_min	input cell_n_min
cell_n_subsample	input cell_n_subsample
ks	input ks
num_cores	input num_cores

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))
mix_fit <- CytoGLMM::cytoflexmix(df,
                                 protein_names = protein_names,
                                 condition = "condition",
                                 group = "donor",
                                 ks = 2)
mix_fit

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