Hierarchical Cluster Analysis (HCA)

Hierarchical Cluster Analysis (HCA)

In statistics and data mining, hierarchical clustering is a useful method of cluster analysis which aims to build a hierarchy of clusters. This method produces a hierarchical decomposition of a given set of data objects. In the biology field, clustering is established based on the assumption that compounds involved in a biological processes behave similarly under the control of the same regulatory network. HCA assumes that a metabolic compound with an unknown function has similar changes to a known metabolite from a defined metabolic pathway, it can be inferred that the unknown element may also be involved in the same pathway. Thus, a clustering that groups a number of metabolites suggests that they can be connected within a common metabolic pathway. This pathway-based approach to identify metabolic features can yield a variety of biological information. Creative Proteomics has applied HCA to the correlation matrix composed of metabolic intermediates in the glycolytic pathway, and performed a detailed examination of the patterns within clusters to determine memberships that may be useful for known metabolic pathways.

Figure 1. Hierarchical clustering of cortical Aβ deposition (A) and glucose metabolism (B) patterns. (Da An, Z.; et al. 2022)

Our Services

Creative Proteomics has developed a novel metabolic flux analysis platform to provide hierarchical cluster analysis (HCA) service in a competitive fashion. We can offer a wide range of services to support all research and development activities. At Creative Proteomics, when evaluating a clustering solution, our experts validate each group returned by a clustering algorithm through manual analysis and visual inspection.

• The H NMR spectra are analyzed using principal component analysis to identify metabolites responsible for gender differences
• Various metabolites are identified that are responsible for the observed separations
• Our approach is capable of identifying the metabolites that are important for the discrimination of classes of individuals of similar physiological conditions

Figure 2. Hierarchical clustering analysis of metabolic profiles. (Nobu, M, K.; et al. 2020)

Features of Our MFA Platform

• Developed based on the most updated knowledge of biology, bioinformatics and software development
• Widely applicable to a wide range of metabolic system
• Professional bioinformatics teams & personalized bioinformatics analysis services.
• Advanced instrument platform
• Integrated quantitative methodologies and comprehensive solutions for metabolomics

Based on high-performance quantitative techniques and advanced equipment, Creative Proteomics has constantly updating our metabolic flux analysis platform and is committed to offering professional, rapid and high-quality services of hierarchical cluster analysis (HCA) at competitive prices for global customers. Our personalized and comprehensive services can satisfy any innovative scientific study demands, please <>contact our specialists to discuss your specific needs. We are looking forward to cooperating with you!

References

1. Da An, Z.; et al. Spatial Distribution and Hierarchical Clustering of β-Amyloid and Glucose Metabolism in Alzheimer’s Disease. Frontiers in Aging Neuroscience. 2022. 14: 788567.
2. Aggrey, S. E.; et al. Effect of host genotype and Eimeria acervulina infection on the metabolome of meat-type chickens. Public Library of Science (PLoS). 2019(10).