dce_nb: Differential Causal Effects for negative binomial data

In cbg-ethz/dce: Pathway Enrichment Based on Differential Causal Effects

Differential Causal Effects for negative binomial data

Description

Shortcut for the main function to analyse negative binomial data

Usage

dce_nb(
  graph,
  df_expr_wt,
  df_expr_mt,
  solver_args = list(method = "glm.dce.nb.fit", link = "identity"),
  adjustment_type = "parents",
  effect_type = "total",
  p_method = "hmp",
  test = "wald",
  lib_size = FALSE,
  deconfounding = FALSE,
  conservative = FALSE,
  log_level = logger::INFO
)

Arguments

graph	valid object defining a directed acyclic graph
df_expr_wt	data frame with wild type expression values
df_expr_mt	data from with mutation type expression values
solver_args	additional arguments for the solver function
adjustment_type	character string for the method to define the adjustment set Z for the regression
effect_type	method of computing causal effects
p_method	character string. "mean", "sum" for standard summary functions, "hmp" for harmonic mean or any method from package 'metap', e.g., "meanp" or "sump".
test	either "wald" for testing significance with the wald test or "lr" for using a likelihood ratio test
lib_size	either a numeric vector of the same length as the sum of wild type and mutant samples or a logical. If TRUE, it is recommended that both data sets include not only the genes included in the graph but all genes available in the original data set.
deconfounding	indicates whether adjustment against latent confounding is used. If FALSE, no adjustment is used, if TRUE it adjusts for confounding by automatically estimating the number of latent confounders. The estimated number of latent confounders can be chosen manually by setting this variable to some number.
conservative	logical; if TRUE, does not use the indicator variable for the variables in the adjustment set
log_level	Control verbosity (logger::INFO, logger::DEBUG, ...)

Value

list of matrices with dces and corresponding p-value

Examples

dag <- create_random_DAG(30, 0.2)
X.wt <- simulate_data(dag)
dag.mt <- resample_edge_weights(dag)
X.mt <- simulate_data(dag)
dce_nb(dag,X.wt,X.mt)

cbg-ethz/dce documentation built on Oct. 29, 2022, 8:14 a.m.