MATISSE

Category Cross-Omics>Pathway Analysis/Tools

Abstract MATISSE (Module Analysis via Topology of Interactions and Similarity SEts) is a software program for the detection of functional modules using interaction networks and expression data.

A functional module is a group of cellular components and their interactions that can be attributed to a specific biological function.

MATISSE implements several algorithmic engines (methods) that analyze expression data and network data together (see below).

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.

The manufacturer has developed a novel computational technique for the integrated analysis of network and similarity data.

The method is aimed to dissect together topological properties of gene or protein networks and other high-throughput data.

The manufacturer has used the method to analyze large-scale protein interaction networks and genome-wide transcription profiles in yeast and human.

The method was shown to identify functionally sound modules, i.e., connected sub-networks with highly coherent expression showing significant functional enrichment.

In comparison to the extant Co-clustering method, which aims to integrate similar data, the manufacturer's method demonstrated substantial improvement in solution quality.

Comparison to solutions produced by clustering, highlights the advantage of utilizing topological connectivity in the hunt for functionally sound modules.

By construction, the manufacturer's method is specifically advanced in detection of ‘regulatory modules’, and less fit for detection of ‘metabolic modules’.

The manufacturer's technique, implemented in MATISSE, is efficient and can analyze genome-scale interaction and expression data within minutes, according to the manufacturer.

The algorithm is very flexible and – unlike Co-clustering – can handle situations where Not all genes in the network have similarity information or expression patterns.

In particular, MATISSE can determine the subset on which similarity is computed using various criteria, e.g., initial probe filtering, differential expression confidence values, etc.

As the manufacturer has previously demonstrated, even when only a modest fraction of the overall network genes have expression/similarity information, the method finds meaningful modules successfully.

The requirement for network connectivity as proposed in the manufacturers method can be viewed as problematic due to a high rate of false negative interactions.

A natural extension of MATISSE which the manufacturer intends to pursue is to take into account the interaction confidence.

As a first step towards this goal, the manufacturer assessed the composition of the interactions in the reported sub-networks as follows:

The manufacturer compared the observed and expected number of interactions within the sub-networks, from each of the publications used as interaction sources in the S. cerevisiae interactions network.

The manufacturer found a clear enrichment for interactions from recent experiments, opposed to an under-representation of interactions from older experiments.

Since the current coverage of the protein interaction network is limited, the manufacturer suggests performing MATISSE analysis in addition to standard clustering analysis.

The framework described in this process is directly applicable to any kind of pairwise similarity data where the probabilistic assumptions hold.

Methods implemented in MATISSE:

1) The MATISSE algorithm for extraction of co-expressed gene modules.

2) The CEZANNE algorithm for extraction of confidently connected co- expressed gene modules.

3) The DEGAS algorithm for analysis of case-control data.

4) CO-CLUSTERING of network and expression data.

5) The CAST clustering algorithm for network data

6) K-means clustering of gene expression data.

For additional info, see the following references:

MATISSE: Identification of functional modules using network topology and high-throughput data I. Ulitsky and R. Shamir; BMC Systems Biology 2007, Vol. 1, No. 8.

CEZANNE: Identifying functional modules using expression profiles and confidence-scored protein interactions I. Ulitsky and R. Shamir; Submitted.

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)

Note: Some parts of MATISSE were developed as parts of other software projects:

EXPANDER (EXpression Analyzer and DisplayER, see G6G Abstract Number 20149);

SPIKE (Signaling Pathway Integrated Knowledge Engine, see G6G Abstract Number 20127) and SIMBA.

System Requirements

MATISSE requires the installation of JAVA JRE, version 5.0 or higher. It is recommended that you use the latest version.

At least 1GB of memory is recommended.

Currently, only the Windows platform is supported.

Manufacturer

Manufacturer Web Site MATISSE

Price Contact manufacturer.

G6G Abstract Number 20335

G6G Manufacturer Number 102304