Integrative Inference of De Novo Cis-Regulatory Modules
TF-gene regulation strength matrix
TF-gene regulation strength matrix sampled from the previous round
Regulation Strength Sampling Function
Inverse-gamma distribution hyper-parameter alpha
Gene baseline expression
Gene baseline expression sampled from the previous round.
Gene Baseline Expression Sampling Function
Inverse-gamma distribution hyper-parameter beta
BICORN Algorithm Function
Genes in the prior binding network
Prior TF-gene binding network
TFs in the prior binding network
TF-gene binding network
TF-gene binding network sampled from the previous round
Prior TF-gene binding network
cis-Regulatory Module Sampling Function
Data Initialization for BICORN
Gene expression data
Genes in the expression data
Regulation strength variance
Variance of baseline gene expression.
Variance of gene expression fitting residuals.
Transcription factor activity variance
Fitting Residule Variance Sampling Function
Transcription factr activity matrix
Transcription factr activity matrix sampled from the previous round
Transcription Factor Activity Sampling Function
Gene expression data used for module inference
Prior transcription factor binding knowledge and target gene expression data are integrated in a Bayesian framework for functional cis-regulatory module inference. Using Gibbs sampling, we iteratively estimate transcription factor associations for each gene, regulation strength for each binding event and the hidden activity for each transcription factor.