MATISSE - identifying modules using gene expression and interaction networks MATISSE (Module Analysis via Topology of Interactions and Similarity SEts) is a program for detection of functional modules using interaction networks and expression data. A functioncal module is a group of cellular components and their interactions that can be attributed a specific biological function. MATISSE implements several algorithmic engines that analyze together expression and network data. Each of the algorithms addresses a different research question and scenario. The software is activated using a friendly graphical user interface. MATISSE currently supports the analysis of data from the species H. sapiens, M. musculus, S. cerevisiae, D. melanogaster, and C. elegans.

[Download] [Screenshots] [Manual] [Methods] [References] [Developers] [Contact and support] [Sample input files and modules] [Additional details]

Download MATISSE v1.1 for Windows and Linux (beta)

• MATISSE requires an installation of Java JRE, version 5.0 or higher. It is recommended to use the latest version.
• At least 1GB of memory.
• Currently, only Windows platform is supported.

• Display and interactive layout of modules:
  
• Specifying algorithm parameters:
  
• Displaying network and expression data side-by-side:
  
• Computing and visualizing annotation enrichments:
  

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  Manual

  A full manual of MATISSE is available here.

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  Methods implemented in the program:

  • The [MATISSE] algorithm for extraction of co-expressed gene modules.
  • [deMATISSE] algorithm for extraction of co-expressed gene modules which are also correlated with some sample parameter.
  • The [CEZANNE] algorithm for extraction of confidently connected co-expressed gene modules
  • The [DEGAS] algorithm for analysis of case-control data.
  • [Co-clustering] of network and expression data.
  • The [CAST] clustering algorithm for network data
  • K-means clustering of gene expression data

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  Developers of MATISSE

  MATISSE is developed in the School of Computer Science at the Tel Aviv University by Igor Ulitsky and Ron Shamir.
  Some parts of the program were developed as parts of other projects in our group:
  • Expander
  • SPIKE
  • SIMBA

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  Contact and support

  For any questions please e-mail Igor Ulitsky

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  Sample input files and modules:

  [July 2009 updated network files] [S. cerevisiae response to osmotic shock modules]
  [Human cell cycle modules]

  July 2009 updated sample network files

  • Human protein-protein interaction network in Binary and Zipped tab-delimited formats.
  • S. cerevisae protein-protein interaction network in Binary and Zipped tab-delimited formats.

  S. cerevisiae response to osmotic shock

  S. cerevisiae protein interaction network
  Publication sources for the interaction network
  Processed expression data
  MATISSE modules

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  Human HeLa cell lines cell cycle

  Human protein interaction network
  Publication sources for the interaction network
  Processed expression data
  MATISSE modules

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  Supplementary details

  Adapted EM algorithm for learning Gaussian mixtures
  

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  References

  • MATISSE:
    Identification of functional modules using network topology and high-throughput data
    I. Ulitsky and R. Shamir
    BMC Systems Biology, Vol. 1, No. 8 (2007)
    
    
  • deMATISSE:
    Regulatory networks define phenotypic classes of human stem cell lines
    F.J. Mueller, D. Kostka, L. Laurent, I. Ulitsky, R. Williams, C. Lu, I.H. Park, M.S. Rao, R. Shamir, P.H. Schwartz, N.O. Schmidt and J.F. Loring.
    Nature, 455(7211) 401-405 (2008)

    Detecting pathways transcriptionally correlated with clinical parameters
    I. Ulitsky and R. Shamir.
    Proceedings of CSB 2008 249-258, Imperial College Press, London, UK (2008)
    
    

  • CEZANNE:
    Identifying functional modules using expression profiles and confidence-scored protein interactions
    I. Ulitsky and R. Shamir.
    Bioinformatics, 25(9):1158-1164 (2009) .
    
    
  • DEGAS:
    Detecting Disease-Specific Dysregulated Pathways Via Analysis of Clinical Expression Profiles
    I. Ulitsky, R.M. Karp and R. Shamir
    Proceedings of RECOMB 2008, pp. 347--359, LNBI 4955, Springer, Berlin, (2008).
    
    
  • Co-Clustering:
    Co-clustering of biological networks and gene expression data
    D. Hanisch, A. Zien, R. Zimmer and T. Lengauer
    Bioinformatics Vol. 18 Supp. 1 (2002)
    
    
  • CAST:
    Clustering gene expression patterns
    A. Ben-Dor, R. Shamir and Z. Yakhini
    Journal of Computational Biology 6(3-4) (1999)
    
    

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