Visualization for TSS comparison
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Simplicity and User-Friendliness:
- Plotly is renowned for its user-friendly API, making it easy to create interactive charts.
- Ideal for quickly implementing standard charts without extensive configuration.
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Interactive Charts:
- Plotly provides comprehensive interactivity by default, including zooming, panning, and hover effects.
- These interactive features make Plotly an excellent choice for dashboards that require deep data interactions.
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Extensive Documentation and Community Support:
- Plotly boasts extensive documentation and a large user community, facilitating learning and troubleshooting.
- It supports a wide range of chart types, including complex 3D charts.
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Seamless Integration with Dash:
- Plotly integrates seamlessly with Dash, a web application for building dashboards, also developed by Plotly.
- This integration simplifies creating complex dashboards in Python.
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Performance:
- Plotly can become slower in rendering and interactivity with very large datasets.
- This might be a limitation for very large sets of data to visualize.
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Performance and Speed:
- ECharts is known for its high performance, especially when handling and visualizing large datasets.
- It uses WebGL for 3D rendering, beneficial for complex visualizations.
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Flexibility and Customization:
- ECharts offers extensive flexibility and customization options, ideal for tailored visualizations.
- It supports a wide array of visual effects and layouts that can be finely tuned.
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Modern and Attractive Aesthetics:
- ECharts provides stylish and modern charts that can be more visually appealing.
- The default styles are generally more refined and require less tweaking.
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Open Source and Free:
- ECharts is fully open-source and free to use, even for commercial projects.
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Complexity and Learning Curve:
- ECharts' API can be more complex, presenting a steeper learning curve, especially for beginners.
- Its high flexibility often requires more configuration to create simple charts.
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Documentation and Support:
- Although ECharts has good documentation, it can be less intuitive compared to Plotly.
- It can be more challenging to find specific information for certain use cases.
After weighing the pros and cons of both libraries, we have decided to proceed with Plotly. Here are our reasons:
- User-Friendliness: Plotly allows us to quickly create interactive charts without extensive setup. This speeds up development time and makes it easier for new team members to get up to speed.
- Interactive Features: Plotly's high level of built-in interactivity is ideal for our use cases, which require users to deeply engage with the data.
- Integration with Dash: As we plan to build dashboards that integrate seamlessly with Python, Plotly's integration with Dash provides a significant advantage.
- Documentation and Support: Plotly's comprehensive documentation and large community make development and troubleshooting much easier.
- can be taken directly from the .wig file
- Issue:
- Might not be helpful visually if the amount of bases is too big
- vertical lines indicate TSSs
- bins might visualize TSSs better from a user perspective
- Issues:
- How to scale the graph? What if the user wants to look at say a 1000 bases at time?
-> Zooming in and out cant change the number of bins too much because then we would end up with the graph above
-> instead more values should make up one bin the more zoomed out the graph is
- How to scale the graph? What if the user wants to look at say a 1000 bases at time?
- Graph with complete sequence and TSSs of TSSpredator and our TSSs in different colors for direct comparison
- Demonstrating the commonalities and differences between our prediction and TSSpredators in numbers for example
- total number of TSSs
