Topology-based Clustering Research Articles

Topology-Based Clustering Techniques for Graph Partitioning Applied to the Italian Transmission Network

Topology-Based Clustering Techniques for Graph Partitioning Applied to the Italian Transmission Network

Two-Tier Mapper, an unbiased topology-based clustering method for enhanced global gene expression analysis.

Unbiased clustering methods are needed to analyze growing numbers of complex datasets. Currently available clustering methods often depend on parameters that are set by the user, they lack stability, and are not applicable to small datasets. To overcome these shortcomings we used topological data analysis, an emerging field of mathematics that discerns additional feature and discovers hidden insights on datasets and has a wide application range. We have developed a topology-based clustering method called Two-Tier Mapper (TTMap) for enhanced analysis of global gene expression datasets. First, TTMap discerns divergent features in the control group, adjusts for them, and identifies outliers. Second, the deviation of each test sample from the control group in a high-dimensional space is computed, and the test samples are clustered using a new Mapper-based topological algorithm at two levels: a global tier and local tiers. All parameters are either carefully chosen or data-driven, avoiding any user-induced bias. The method is stable, different datasets can be combined for analysis, and significant subgroups can be identified. It outperforms current clustering methods in sensitivity and stability on synthetic and biological datasets, in particular when sample sizes are small; outcome is not affected by removal of control samples, by choice of normalization, or by subselection of data. TTMap is readily applicable to complex, highly variable biological samples and holds promise for personalized medicine. TTMap is supplied as an R package in Bioconductor. Supplementary data are available at Bioinformatics online.

Topology-based clustering using polar self-organizing map.

Cluster analysis of unlabeled data sets has been recognized as a key research topic in varieties of fields. In many practical cases, no a priori knowledge is specified, for example, the number of clusters is unknown. In this paper, grid clustering based on the polar self-organizing map (PolSOM) is developed to automatically identify the optimal number of partitions. The data topology consisting of both the distance and density is exploited in the grid clustering. The proposed clustering method also provides a visual representation as PolSOM allows the characteristics of clusters to be presented as a 2-D polar map in terms of the data feature and value. Experimental studies on synthetic and real data sets demonstrate that the proposed algorithm provides higher clustering accuracy and lower computational cost compared with six conventional methods.

GraphCrunch 2: Software tool for network modeling, alignment and clustering

BackgroundRecent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI) data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype.ResultsWe introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL") for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other existing tool. Finally, GraphCruch 2 implements an algorithm for clustering nodes within a network based solely on their topological similarities. Using GraphCrunch 2, we demonstrate that eukaryotic and viral PPI networks may belong to different graph model families and show that topology-based clustering can reveal important functional similarities between proteins within yeast and human PPI networks.ConclusionsGraphCrunch 2 is a software tool that implements the latest research on biological network analysis. It parallelizes computationally intensive tasks to fully utilize the potential of modern multi-core CPUs. It is open-source and freely available for research use. It runs under the Windows and Linux platforms.

In Silico Study of Aro Genes Involved in the Ehrlich Pathway: Comparison between Saccharomyces Cerevisiae and Kluyveromyces Lactis

ABSTRACTAt the present state of the art, genome sequencing techniques are advancing with a faster pace than the experimental proof of predicted genes and their functions. Many approaches for clustering annotated genes based on the degree of their sequence homology have emerged during the last decade. In the current comparative genomic study a parallel has been drawn between a set of ARO genes involved in the aromatic alcohol synthesis in the model yeast organism Saccharomyces cerevisiae and their orthologs in Klyuveromyces lactis. Through a combination of sequence comparison, online resources for homology and topology-based clustering, and a tool for prediction of the cellular localisation of proteins, it has been shown that the genes involved in the first two steps of the 2-phenylethanol synthesis in K. lactis are generally conserved when compared to their S. cerevisiae orthologs. They show 40–62% identity of the encoded protein as well as conserved synteny and cellular localisation. It is also likely that these species utilise the same type of regulation mechanisms for the process. The presence of a second ARO8 ortholog in K. lactis suggests a more environmentallyflexible and also effective production of 2-phenylethanol. This comparison aims to facilitate the design of further experiments that can reveal the regulation of fusel alcohol formation in K. lactis and improve their production.

Transcriptional responses to fatty acid are coordinated by combinatorial control

In transcriptional regulatory networks, the coincident binding of a combination of factors to regulate a gene implies the existence of complex mechanisms to control both the gene expression profile and specificity of the response. Unraveling this complexity is a major challenge to biologists. Here, a novel network topology-based clustering approach was applied to condition-specific genome-wide chromatin localization and expression data to characterize a dynamic transcriptional regulatory network responsive to the fatty acid oleate. A network of four (predicted) regulators of the response (Oaf1p, Pip2p, Adr1p and Oaf3p) was investigated. By analyzing trends in the network structure, we found that two groups of multi-input motifs form in response to oleate, each controlling distinct functional classes of genes. This functionality is contributed in part by Oaf1p, which is a component of both types of multi-input motifs and has two different regulatory activities depending on its binding context. The dynamic cooperation between Oaf1p and Pip2p appears to temporally synchronize the two different responses. Together, these data suggest a network mechanism involving dynamic combinatorial control for coordinating transcriptional responses.