What features should be used to generate the UMAP plot

Oct 13, 2025 by recoveryshake.com 9 views

Overview

The virome section currently displays a graph of virome components (edges vs. nodes) and allows users to select viromes via a dropdown. To improve this section, we propose adding a UMAP plot for visualizing virome relationships and a local vs. global context toggle to enable detailed and broad-scale analysis. These enhancements will be part of a new “Hypervirome” module, providing users with more advanced tools for exploring virome data.

Background / Context

The current virome section has several limitations that hinder its usability and analytical power. Limited Visualization is a major concern, as the graph of virome components does not show relationships between viromes or broader patterns in the dataset. Additionally, the Lack of Context makes it difficult for users to compare selected viromes to the entire dataset or explore high-level trends. Furthermore, Missed Opportunities arise from not utilizing advanced techniques like UMAP, which could provide valuable insights into virome relationships and clusters.

By adding a UMAP plot and local vs. global context toggle, we can address these limitations and provide users with a more comprehensive and interactive experience. This will enable users to visualize and explore relationships between viromes, provide both detailed (local) and broad-scale (global) views of virome data, and enhance the overall usability and analytical power of the virome section.

Hypothesis

Adding a UMAP plot and local/global context toggle will improve the virome section’s ability to reveal patterns and relationships in the data, leading to a more engaging and informative user experience. This hypothesis is based on the assumption that users will benefit from a more interactive and visual representation of virome data, allowing them to explore and analyze the data in a more intuitive and effective manner.

Experiment

To test our hypothesis, we will implement the following features:

1. UMAP Plot Implementation

We will generate a UMAP plot based on features like viral family composition, contig counts, or ecological metadata. This plot will provide a two-dimensional representation of the virome data, allowing users to visualize relationships and clusters among viromes. Interactive elements, such as hover for details and click to filter viromes, will be added to enhance the user experience.

2. Local vs. Global Context Toggle

We will add a toggle to switch between two modes:

Local Context: Detailed views of selected viromes, including graphs and statistics, will be displayed.
Global Context: The UMAP plot will show selected viromes in relation to all others, providing a broad-scale view of the virome data.

3. Integration into Hypervirome Module

We will combine the UMAP plot and local/global context toggle into the existing virome section, ensuring that the new features are intuitive and easy to use. This will involve designing a user-friendly interface that allows users to easily switch between local and global contexts.

4. Testing

We will test the new features with a variety of virome datasets to ensure accuracy and usability. User feedback will be gathered to refine the implementation and ensure that the new features meet the needs of users.

Expected Outcome

The expected outcome of this experiment is a UMAP plot that visualizes relationships and clusters among viromes, a local vs. global context toggle that enables detailed and broad-scale analysis, and an enhanced virome section that provides users with more advanced tools for exploring virome data.

Open Questions

Several open questions arise from this experiment:

What features should be used to generate the UMAP plot (e.g., viral family composition, contig counts, ecological metadata)?
Are there performance considerations when generating UMAP plots for large datasets?

References

[placeholder]

Implementation Details

To implement the UMAP plot and local/global context toggle, we will use the following tools and technologies:

UMAP library: We will use the UMAP library to generate the UMAP plot.
Interactive visualization tools: We will use interactive visualization tools, such as Plotly or Bokeh, to create the UMAP plot and add interactive elements.
Front-end framework: We will use a front-end framework, such as React or Angular, to design the user interface and integrate the UMAP plot and local/global context toggle into the existing virome section.

Conclusion

Q: What is the current limitation of the virome section?

A: The current virome section has several limitations, including limited visualization, lack of context, and missed opportunities. The graph of virome components does not show relationships between viromes or broader patterns in the dataset, making it difficult for users to compare selected viromes to the entire dataset or explore high-level trends.

Q: What is a UMAP plot, and how will it enhance the virome section?

A: A UMAP plot is a two-dimensional representation of high-dimensional data, such as virome data. It will provide a visual representation of relationships and clusters among viromes, allowing users to explore and analyze the data in a more intuitive and effective manner. The UMAP plot will be generated based on features like viral family composition, contig counts, or ecological metadata.

Q: What is the local vs. global context toggle, and how will it enhance the virome section?

A: The local vs. global context toggle will allow users to switch between two modes:

Local Context: Detailed views of selected viromes, including graphs and statistics, will be displayed.
Global Context: The UMAP plot will show selected viromes in relation to all others, providing a broad-scale view of the virome data.

Q: How will the UMAP plot and local/global context toggle be integrated into the existing virome section?

A: The UMAP plot and local/global context toggle will be integrated into the existing virome section using a front-end framework, such as React or Angular. The user interface will be designed to be intuitive and easy to use, allowing users to easily switch between local and global contexts.

Q: What features should be used to generate the UMAP plot?

A: The features used to generate the UMAP plot will depend on the specific needs of the user and the characteristics of the virome data. Some possible features include:

Viral family composition: The proportion of different viral families in the virome data.
Contig counts: The number of contigs (small DNA fragments) in the virome data.
Ecological metadata: Information about the environment in which the virome data was collected, such as temperature, pH, or salinity.

Q: Are there performance considerations when generating UMAP plots for large datasets?

A: Yes, generating UMAP plots for large datasets can be computationally intensive and may require significant resources. However, the UMAP library is designed to handle large datasets efficiently, and the plot can be generated in a reasonable amount of time.

Q: How will user feedback be gathered to refine the implementation?

A: User feedback will be gathered through a variety of channels, including:

Surveys: Users will be asked to complete surveys to provide feedback on the new features.
User testing: Users will be asked to test the new features and provide feedback on their usability and effectiveness.
Bug reports: Users will be able to report any bugs or issues they encounter with the new features.

Q: What are the expected outcomes of this experiment?

A: The expected outcomes of this experiment are:

A UMAP plot that visualizes relationships and clusters among viromes.
A local vs. global context toggle that enables detailed and broad-scale analysis.
An enhanced virome section that provides users with more advanced tools for exploring virome data.

Q: What are the next steps in implementing the UMAP plot and local/global context toggle?

A: The next steps in implementing the UMAP plot and local/global context toggle are:

Designing the user interface: The user interface will be designed to be intuitive and easy to use.
Implementing the UMAP plot: The UMAP plot will be implemented using the UMAP library.
Testing the new features: The new features will be tested with a variety of virome datasets to ensure accuracy and usability.