Spatial Omics Solutions for Pathway Analysis

Spatial Omics Solutions for Pathway Analysis

Genetic variants associated with multiple diseases affect a limited number of common pathways or networks of interactions. The primary goal of pathway analysis is to determine the statistically significant association of biologically relevant genes with the target phenotype. Pathway analysis has become the preferred choice for gaining insight into the underlying biology of differentially expressed genes and proteins, as it reduces complexity and increases explanatory power.

How we fuse pathway analysis into the dataset obtained from the spatial-omics analysis

CD Genomics leverages spatial omics technologies to comprehensively characterize tissue and structure at single-cell or sub-cellular resolution. This information provides a solid foundation for understanding many biological processes in health and disease that are not available using traditional techniques.

We have added modules for pathway analysis to the solution and combined it with visualization analysis to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical bias. This plays an important role in extracting biological signals from raw data. Allowing our customers to view pathways and find regions, where alterations occur, plays a critical role in the interpretation of high-dimensional molecular data.

General process

  • Data Import. Import locally a list of genes and their ploidy changes or gene expression matrices, etc., from the spatial-omics analysis.
  • Parameter settings. In the parameter settings, you can select the pathways of interest, analysis methods, and method parameters.
  • Analysis and visualization. Visually and interactively explore and export analysis results.

Technical features and advantages

  • Helps to understand which biological pathways are involved in response to a specific disease phenotype or drug treatment process.
  • Discover associations between pathways that represent collections of molecular entities that share biological functions, as well as phenotypes of interest.
  • Based on topological information, we can explain biologically relevant differences between components by assigning more weight to genetic changes that have a greater impact on the pathway.
  • By considering different topological information, the same set of pathway components can be analyzed more precisely. This is because, under certain biological conditions, certain interactions may be different.
  • Identify specific protein functions, biological pathways, and physical interactions that are abundant in a given group.
For research use only, not intended for any clinical use.

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