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Algorithms

 

The following key algorithms developed in our group are integrated into Expander:

Algorithms marked with * are avialable also as stand-alone command line versions.

CLICK*:

CLICK is a novel clustering algorithm which is applicable to gene expression analysis as well as to other biological applications. No prior assumptions are made on the structure or the number of the clusters. The algorithm utilizes graph-theoretic and statistical techniques to identify tight groups of highly similar elements (kernels), which are likely to belong to the same true cluster.

SAMBA*:

SAMBA is a novel biclustering algorithm for the identification of modules of genes that exhibit similar behaviour under a subset of the examined biological conditions. SAMBA is an efficient way to discover statistically significant biclusters in large scale biological datasets, consisting of hundreds or thousands of diverse experiments. It extends the standard clustering approach by detecting subtle similarities between genes across subsets of the measured conditions and enabling genes to participate in several biclusters. Thus, it is more suitable for analyzing heterogeneous datasets.
 

TANGO*: 

TANGO tests whether the group of genes in each cluster is enriched for a particular function. The functions of the genes are determined according to GO annotation files. Since the GO functions are highly related, TANGO performs hyper-geometric enrichment tests and corrects for multiple testing by bootstrapping and estimating the empirical p-value distribution for the evaluated sets. 

PRIMA: 

PRIMA (PRomoter Integration in Microarray Analysis) is a program for finding transcription factors (TFs) whose binding sites are enriched in a given set of promoters. After identifying a group of co-regulated genes using clustering or biclustering, the promoters of the genes can be analyzed using PRIMA. By utilizing known models for binding sites (BSs) of TFs, PRIMA identifies TFs whose BSs are significantly over-represented in that set of promoters. Such TFs are candidate regulators of the corresponding set of genes.

FAME:

FAME is an algorithm which performs empirical tests using a sampling technique (random permutations) to estimate whether the group is enriched or depleted with the targets of some miRNA  families. This is done while accounting for biases in the 3' UTR sequences.

MATISSE*:

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 functional module is a group of genes that form a connected component in a protein interaction network and have similar gene expression patterns.

DEGAS*:

DEGAS (DysrEgulated Gene set Analysis via Subnetworks) is a method for identifying connected gene subnetworks significantly enriched for genes that are dysregulated in specimens of a disease. DEGAS receives as input expression profiles of the disease patients and of controls and a global network. The subnetworks identified by DEGAS can provide a signature of the disease potentially useful for diagnosis, pinpoint possible pathways affected by the disease, and suggest targets for drug intervention.