Research on visualization possibilities for TSS comparison - Integrative-Transcriptomics/tss-prediction-comparison GitHub Wiki

1. Data Preparation:

  • Data Sources:
    • TSS Predictions: This includes lists of genomic positions where TSS are predicted by different predators.
    • GFF File Data: Contains annotations for genomic features such as genes, exons, and regulatory elements, each with start and end positions.
  • Integrate and Preprocess Data:
    • Parse the GFF file to extract relevant features and their positions.
    • Align TSS predictions with these genomic features to provide meaningful context for visualization.

2. Choosing the Right Visualization Tool:

  • Why Choose Plotly?

    • Interactive Features: Plotly supports interactive plots with functionalities like zooming, panning, and tooltips, which are crucial for exploring complex genomic data.
    • Variety of Chart Types: It provides numerous chart types that can effectively handle genomic data visualizations, such as scatter plots, line plots, and heatmaps.
    • Seamless React Integration: With react-plotly.js, Plotly integrates well into React applications, making it easy to incorporate interactive charts within React components.

  • Alternatives:

    • D3.js: Offers the highest flexibility and control for creating custom visualizations but requires more effort to implement and integrate into React.
    • Recharts: Simple to use and integrates nicely with React. It’s suitable for creating standard chart types.
    • Chart.js (with react-chartjs-2): Lightweight and user-friendly for basic visualizations, though it offers less interactivity compared to Plotly.
    • ECharts (with echarts-for-react): Supports rich interactive features and is powerful for complex visualizations, similar to Plotly.
    • Visx: Combines D3.js's data manipulation power with React's component architecture, offering a modular and high-performance approach.

3. Types of Visualizations for TSS Predictions:

  • Scatter Plot: Displays TSS positions along the genome, with different colors or symbols for predictions from different models. This is ideal for direct comparison of TSS positions.

  • Line Plot: Useful if the TSS predictions include a confidence score or other metrics, showing how these values change along the genome.

  • Histogram: Depicts the distribution of TSS predictions across the genome, highlighting regions with high densities of TSS.

  • Overlapping Tracks: Similar to genomic tracks used in bioinformatics, where predictions from different models are displayed as separate rows or bands.

  • Integrating GFF Data:

    • Annotations: Add shapes or markers to represent genomic features from the GFF file, such as gene regions.
    • Background Highlights: Use shaded areas or colored backgrounds to highlight significant genomic features like genes or regulatory regions.
    • Layered Tracks: Display GFF features as separate tracks or layers alongside TSS predictions for a comprehensive view.

4. Enhancing Interactivity and User Experience:

  • Interactivity Enhancements:
    • Tooltips: Show additional information when hovering over TSS points or genomic features.
    • Zoom and Pan: Enable users to zoom in on specific regions and explore data details.
    • Clickable Features: Make TSS points or genomic features clickable to reveal more information or link to external resources.

5. Implementing Plotly in a React Application:

  • Set Up Plotly:

    • Install necessary Plotly libraries (plotly.js and react-plotly.js).
    • Create a React component that uses Plotly to render the TSS predictions and GFF data, passing these datasets as props.
    • Customize the plot using Plotly’s layout and data configuration options.

  • Integration in React:

    • Embed the Plotly component within your React component.
    • Pass the prepared TSS prediction and GFF data to this component to visualize them interactively.