High-throughput genomic technologies make it possible to generate massive amounts of data for the study of biological mechanisms or disease etiology. Such high-dimensional genomic data can typically be represented as matrices, where each column represents a sample (e.g., patient, cell type, experimental condition, etc.), and each row represents a genomic feature (e.g., gene, genomic locus, etc.). By computationally analyzing these high-dimensional data matrices using dimensionality reduction (e.g., principal component analysis, PCA) or clustering methods, it is possible to understand the characteristic information in a sample and identify key features between samples to interrogate biological functions.
Creative BioMart Consulting offers a wide range of services for the design and analysis of high-dimensional genomic data experiments to support your drug discovery and preclinical studies. Applications include biomarker discovery and characterization, drug repositioning, pathway analysis, predictive toxicology, toxicogenomics, and pharmacogenomics.
The design of high-dimensional genomic data experiments is complex and requires consideration of numerous factors, including experimental objectives, research hypotheses, data processing, statistical analysis, and data visualization. We have a team of consultants who specialize in high-dimensional genomic data and can better advise and assist you. We encourage you to work with us early in the conceptual phase of your project, as we can provide extensive first-hand knowledge of how to use statistically supported designs to answer your questions.
Creative BioMart Consulting is committed to providing clients with the benefits of expertise and technical support, optimizing experimental design and data analysis, facilitating collaboration and communication, and providing industry-leading information. The analysis of high-dimensional genomic data experiments requires consideration of the complexity and high-dimensional nature of the data. We support consulting on data normalization, feature selection, data visualization, machine learning algorithms, and gene network analysis. We aim to provide strong support for our clients to achieve efficient research and innovation. If you are interested in our services, please feel free to contact us..