Gene Set Enrichment Analysis (GSEA) Service
Understanding the functional implications of genes is paramount. As the keys to various biological processes, genes interact in complex networks, and their functions aid in elucidating mechanisms underlying diseases, developmental biology, and many other areas of scientific research. CD ComputaBio offers a sophisticated Gene set enrichment analysis (GSEA) service that empowers researchers to interpret and analyze gene functions effectively. Our state-of-the-art computational modeling tools and expertise enable researchers to glean insights from genomic data to accelerate their scientific discoveries.
Introduction to Gene Set Enrichment Analysis (GSEA) Service
Gene set enrichment analysis (GSEA) is a bioinformatics method used to determine whether a set of genes shares common biological functions, pathways, or cellular locations more than would be expected by chance. This analysis is crucial for interpreting high-throughput data, such as that obtained from microarray and RNA-Seq experiments.
Figure 1. Gene set enrichment analysis (GSEA) service. (Koh C W T, et al. 2023)
Our Service
CD ComputaBio provides a comprehensive suite of services tailored to meet the unique needs of each research project.
Gene Set Development
Recognizing that one size does not fit all, we offer customized gene set development services. Our experts work closely with clients to curate and refine gene sets that align with specific hypotheses, experimental conditions, or biological contexts, ensuring the relevance of the analysis to the research goals.
Downstream Functional Analysis
We offer a range of downstream analysis tools, including gene ontology enrichment analysis, pathway analysis, and network analysis. These tools can help you identify the biological functions and pathways that are associated with your significant gene sets, providing you with more actionable insights.
Pathway Integration and Visualization
To enhance the understanding of GSEA results, we integrate pathway databases, enabling the visualization of enriched pathways alongside statistical data. Our visualizations provide intuitive representations of complex data, allowing for easy interpretation and communication of results.
Advanced Statistical Analysis
Our statistical analysis also takes into account the complex nature of genomic data, including batch effects, confounding factors, and technical noise. By using advanced statistical methods, we can reduce the false positive rate and increase the power of your analysis, providing you with more confident results.
Approaches to Gene set enrichment analysis (GSEA) Service
Over-Representation Analysis (ORA)
Over-representation analysis is a classical approach that identifies whether the presence of a gene set is statistically significant in the context of specific biological categories. This method highlights functional enrichments that warrant further investigation.
Network-based Approaches
Network-based methods take into account the complex interactions among genes. This approach reveals functional modules or clusters within the data, which can provide deeper insights into cellular mechanisms.
Statistical Modeling Approaches
CD ComputaBio employs statistical models such as permutation-based methods. For example, in GSEA, we use permutation tests to estimate the statistical significance of gene set enrichment.
Advantages of Our Services
Gene set enrichment analysis (GSEA) is a crucial step in understanding the biological significance of gene sets in genomics research. CD ComputaBio offers a comprehensive range of services in this area, using a combination of different approaches, reliable software tools, and a team of experts. Our focus on customized solutions, high-quality results, and timely delivery makes us an ideal partner for researchers and organizations involved in genomics-related studies. Contact us to learn more about our services.
References:
- Koh C W T, Ooi J S G, Ong E Z, et al. STAGEs: A web-based tool that integrates data visualization and pathway enrichment analysis for gene expression studies. Scientific Reports, 2023, 13(1): 7135.
- Ma Y, Sun S, Shang X, et al. Integrative differential expression and gene set enrichment analysis using summary statistics for scRNA-seq studies. Nature communications, 2020, 11(1): 1585.
* For Research Use Only.
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