poisson_lognormal_mcle: Fit simplified Poisson log-normal model

In ChristofSeiler/cytoeffect: Regression Models with Multivariate Outcomes for Mass Cytometry Experiments

Fit simplified Poisson log-normal model

Description

poisson_lognormal uses maximum composite likelihood estimation to fit Poisson log-normal models to each sample.

Usage

poisson_lognormal_mcle(
  df_samples_subset,
  protein_names,
  condition,
  group,
  ncores = 1,
  slurm_settings = "slurm_batchtools.tmpl"
)

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
ncores	Number of CPU cores
slurm_settings	Path to slurm cluster template for batchtools

Value

A list of class cytoeffect_poisson_mcle containing

tb_args	output tibble of model fits
protein_names	input protein names
condition	input condition variable
group	input group names
df_samples_subset	input df_samples_subset table

Examples

set.seed(1)
df = simulate_data(n_cells = 10)
str(df)
fit = poisson_lognormal_mcle(df,
                             protein_names = names(df)[3:ncol(df)],
                             condition = "condition",
                             group = "donor",
                             ncores = 1)

ChristofSeiler/cytoeffect documentation built on Jan. 11, 2023, 1:05 p.m.