iDEAWeight.fit: Integrative DE and GSEA based summary statistics and weight...

In xzhoulab/iDEA: Integrative Differential Expression and Gene Set Enrichment Analysis

Integrative DE and GSEA based summary statistics and weight for each gene

Description

Integrative DE and GSEA into a unfied framwork. The parameters of the model were infered via EM-MCMC algorithm.

Usage

iDEAWeight.fit(
  object,
  weight = NULL,
  fit_noGS = FALSE,
  init_beta = NULL,
  init_tau = c(-2, 0.5),
  min_degene = 5,
  em_iter = 15,
  mcmc_iter = 1000,
  fit.tol = 1e-05,
  verbose = TRUE,
  ...
)

Arguments

object	iDEA object
weight	The weight for each gene to construct the gene-gene interation
fit_noGS	Bool variable to indicate whether fitting the model without the annoation
init_beta	Initial value for gene effect size, beta
init_tau	Initial value for annotations, including the intercept
min_degene	The threshold for the number of detected DE genes. For some of extremely cases, the method does not work when the number of detected DE genes is 0.
em_iter	Maximum iteration for EM algorithm
mcmc_iter	Maximum iteration for MCMC algorithm
fit.tol	Tol for fitting the model
verbose	Print the progresses
...	Ignored

Value

Returns a iDEA object with EM-MCMC results stored in object@de.

xzhoulab/iDEA documentation built on Oct. 8, 2022, 8:54 a.m.