Introduction to the Pathway graph

Pathway graph offers a unique space to explore Reactome.org-annotated biological pathways within the context of your expression data. In this graph, proteins, transcripts, and other entities are plotted as nodes (appearing as circles) and edges (the connections between nodes represented by arrows).

Pathway graphs, also known as network graphs or knowledge graphs, represent a collection of interlinked entities organized into contexts through linking and semantic metadata. They are used to build a framework for data integration and analysis, providing context around metrics derived from a network system.

Using pathway graphs, you can simplify your extensive data by investigating differential expression within a biological context. This approach provides immediate insights into potential system regulations relevant to your study.

Discover connections between biological systems annotated in your data and see if your differential expression analysis suggests co-regulation.

Suggested workflows in Pathway graph

1. Discover the effect on your pathway of interest

If the nodes (e.g., proteomics) in your analysis can be annotated to your pathways of interest, you can select them from the Select pathways menu. For example, you can choose pathways such as "Assembly of the HIV Virion."

Use the filters to zoom in on your data and regulate the pathway. Available filters include:

• Node filters
• Search for nodes
• Select and exclude nodes

Additionally, you can select key regulated expression or use tools like Volcano plots or PCA plots to assess the general trend and variance of the pathway across selected comparisons of interest.

2. Combine pathways for system biology insight

To gain system biology insights, you can combine multiple pathways. Here's how:

1. Select multiple pathways: Choose the pathways you are interested in studying together.
2. Assess interactions: Evaluate existing or new hypotheses on how these pathways interact.
3. Discover co-regulation: Investigate if co-regulation is suggested by the data.

Use interaction databases to check for known protein-protein interactions. See if different databases indicate similar or differing interactions between pathways.

Finally, assess whether the regulation patterns align with the reaction information about interaction partners or molecular functions, which can be found in the Annotations section.

3. Identify and explore your key regulators/interests

You can easily identify and explore your key regulators or interests using data-driven insights. Follow these steps:

1. Search for New Nodes: Expand your search using the Search for new nodes function.
2. Save your candidates: Save your candidates to a list you created in My Lists.
3. Explore Information: Explore the available information on your candidates provided in DB Lookup.
4. Evaluate Biomarkers: Evaluate your biomarkers based on the insights gained.

This process helps you efficiently pinpoint and analyze important elements within your data.