estimateNetwork

model

cutoff for marginal posterior probabilities

thresh

"marginal": select regulators based on marginal posterior probabilities; "MAP": select regulators based on MAP configuration

select

algorithm used for NEM based network inference, see <code><a rd-options="" href="/link/nem?package=birte&version=1.8.1" data-mini-rdoc="birte::nem">nem</a></code>

method

de.genes

optional: number of bootstrap replicates to draw (non-parameteric bootstrap)

bootstrap

typeII


Given a biRte model, this function makes posterior inference about possible upstream-downstream relationships between active regulators. This is done based on observed differential expression of putative target genes. The idea is that regulator A acts upstream of regulator B, if differentially expressed targets of B are a subset of those of A.


Expression levels of mRNA molecules are regulated by
different processes, comprising inhibition or activation by
transcription factors and post-transcriptional degradation by
microRNAs. biRte uses regulatory networks of TFs, miRNAs and
possibly other factors, together with mRNA, miRNA and other
available expression data to predict the relative influence of
a regulator on the expression of its target genes. Inference is
done in a Bayesian modeling framework using
Markov-Chain-Monte-Carlo. A special feature is the possibility
for follow-up network reverse engineering between active
regulators.

Holger Froehlich

birte

Bayesian Inference of Regulatory Influence on Expression (biRte)

estimateNetwork function

algorithm used for NEM based network inference, see <code><a href="nem" rd-options="">nem</a></code>

Given a biRte model, this function makes posterior inference about possible upstream-downstream relationships between active regulators. This is done based on observed differential expression of putative target genes. The idea is that regulator A acts upstream of regulator B, if differentially expressed targets of B are a subset of those of A.

estimateNetwork: Estimate network between active regulators using Nested Effects Models (NEMs).

Description

Usage

Arguments

Value

Examples