tiger: TIGER main function

In netZoo/netZooR: Unified methods for the inference and analysis of gene regulatory networks

TIGER main function

Description

TIGER main function

Usage

tiger(
  expr,
  prior,
  method = "VB",
  TFexpressed = TRUE,
  signed = TRUE,
  baseline = TRUE,
  psis_loo = FALSE,
  seed = 123,
  out_path = NULL,
  out_size = 300,
  a_sigma = 1,
  b_sigma = 1,
  a_alpha = 1,
  b_alpha = 1,
  sigmaZ = 10,
  sigmaB = 1,
  tol = 0.005
)

Arguments

expr	A normalized log-transformed gene expressison matrix. Rows are genes and columns are sampeles (cells).
prior	A prior regulatory network in adjacency matrix format. Rows are TFs and columns target genes.
method	Method used for Bayesian inference. "VB" or "MCMC". Defaults to "VB".
TFexpressed	TF mRNA needs to be expressed or not. Defaults to TRUE.
signed	Prior network is signed or not. Defaults to TRUE.
baseline	Include baseline or not. Defaults to TRUE.
psis_loo	Use pareto smoothed importance sampling leave-one-out cross validation to check model fitting or not. Defaults to FALSE.
seed	Seed for reproducible results. Defaults to 123.
out_path	(Optional) output path for CmdStanVB or CmdStanMCMC object. Defaults to NULL.
out_size	Posterior sampling size. Default = 300.
a_sigma	Hyperparameter of error term. Default = 1.
b_sigma	Hyperparameter of error term. Default = 1.
a_alpha	Hyperparameter of edge weight W. Default = 1.
b_alpha	Hyperparameter of edge weight W. Default = 1.
sigmaZ	Standard deviation of TF activity Z. Default = 10.
sigmaB	Standard deviation of baseline term. Default = 1.
tol	Convergence tolerance on ELBO.. Default = 0.005.

Value

A TIGER list object. * W is the estimated regulatory network, but different from prior network, rows are genes and columns are TFs. * Z is the estimated TF activities, rows are TFs and columns are samples. * TF.name, TG.name, and sample.name are the used TFs, target genes and samples. * If psis_loo is TRUE, loocv is a table of psis_loo result for model checking. * If psis_loo is TRUE, elpd_loo is the Bayesian LOO estimate of the expected log pointwise predictive density, which can be used for Bayesian stacking to handle multi-modality later.

Examples

data(TIGER_expr)
data(TIGER_prior)
tiger(TIGER_expr,TIGER_prior)

netZoo/netZooR documentation built on Oct. 16, 2024, 10:23 p.m.