DANCE - Deregulation Analysis in Networks of Copy-number-driven Expression

Yes, we think we can DANCE! To understand the differences of regulatory mechanisms between two biological conditions/disease subtypes, we can use DANCE to infer a context-specific network. Our approach quantifies the context-specific impacts of copy-number alterations on gene expression under one condition.

An example of deregulation network, where copy-number loci (red nodes) drive expression (green nodes) differentially in breast cancer ER-negative subtype compared to the ER-positive subtype.

DANCE features several key ideas:

  1. Subtype-specific: The deregulation network is a context-specific network, with interactions between copy number and expressions that exist in one cancer subtype but not the other, eg. specific to breast cancer ER negative but not ER positive subtype.
  2. Efficient inference: The network inference is facilitated by sparse regression models (implemented based on R package lol), while special attention has been paid to the nature of DNA copy-number data.
  3. Cis- and trans- associations: Unlike previous methods, both cis- and trans- associations are considered, which means DANCE is able to infer the effect of a remote loci exerts on a transcript.

Download and Installation

DANCE has been submitted to Bioconductor





The current version of R package can be downloaded here

The package is submitted to Bioconductor.


Other packages

  • BaalChIP - allele-specific TF binding
  • BitPhylogeny - infer intra-tumor phylogenies
  • CRImage - tumor image analysis
  • DANCE - subtype specific drivers of cancer
  • GoIFISH - single cell IFISH analysis
  • HTSanalyzeR - functional annotation
  • lol - lots of lassos
  • MEDICC - intra-patient copy number comparison
  • nem - nested effects models
  • OncoNEM - oncogenetic NEMs
  • PAN - posterior associa- tion networks
  • RedeR - graph visualiza- tion and analysis
  • RTN - network recon- struction and analysis
  • SANTA - spatial analysis of network associations
Data @ Bioconductor