NetResponse: probabilistic algorithms for functional network analysis
Leo Lahti*, Olli-Pekka Huovilainen, António Gusmão and Juuso Parkkinen
(*) University of Helsinki, Finland; corresponding author
NetResponse provides probabilistic tools for functional analysis of
interaction networks, and provides a global map of network activation
patterns across versatile contexts. The implementations are based on
probabilistic models and variational learning to deal rigorously with
inherent uncertainty in real-life data, and to allow incorporation of
prior information in the analysis. Currently available tools:
Applicability of the models has been demonstrated by case studies in
et al., 2010, Parkkinen and Kaski, 2010), where the algorithms
have been used to investigate the structure and context-specific
transcriptional activity of genome-scale interaction networks in human
The techniques are implemented
in R, an open source
environment for statistical computing. For installation instructions,
see the project
page at BioConductor. For further documentation, see
source code for the NetResponse algorithm¹ is available.
to detect and characterize local, context-specific activation patterns
and to construct global functional maps of large interaction networks
by utilizing functional information of the network based on
non-parametric analysis with variational Dirichlet process Gaussian
- Interaction Component Model (ICMg³): to discover
functional network modules, or communities, taking into account
the uncertainty in network structure. Module discovery can be
supervised by functional information of the network.
- Visualization: Preliminary tools are available to
visualize netresponse output. Install
visualization package and check the package vignette (in package
source, inst/doc/) for examples.
Licensed under an open source software license (GNU GPL >=2). Your
feedback and contributions are welcome.
- Leo Lahti et al. (2010). Global
modeling of transcriptional responses in interaction
networks. Bioinformatics. (abstract; advanced online-access (pdf); preprint with supplementary figures (pdf)
- Kenichi Kurihara et al. (2007). Accelerated variational
Dirichlet process mixtures. In B. Schölkopf, J. Platt, and
T. Hoffman, eds., Advances in Neural Information Processing
Systems 19, 761-768. MIT Press, Cambridge, MA.
- Juuso Parkkinen and Samuel Kaski.
Searching for functional gene modules with interaction component models.
BMC Systems Biology 2010, 4:4.