Workflow Overview

For in-depth information, visit the following sections:

1. Raw Data

  • The user selects a raw data file using a compatible reader plugin (e.g., ChimeraReader, BinaryReader).

  • Available metadata (duration, number of channels, sampling rate, etc.) is extracted and displayed.

  • Users choose the desired channel(s), time ranges, and optionally apply filters for preprocessing the data.

  • Live previews allow validation of selected parameters before further analysis.

2. Event Analysis

  • Users initiate event detection within one or more time ranges for selected channels.

  • Event-finder plugins (e.g., ClassicBlockageFinder) scan the filtered signal to identify translocation events or blockages.

  • Progress is tracked through per-task progress bars, and results are dynamically updated within the view.

  • The system supports multithreaded execution, allowing multiple event-finding operations to run in parallel.

  • Metadata (e.g., voltage, baseline current, etc.) is stored and can be used for downstream tasks like fitting or classification.

3. Metadata Handling

  • Metadata related to fitted events can be viewed, filtered, and exported.

  • Users can create visualizations such as histograms, scatterplots, density plots, and heatmaps.

  • SQL-like filters enable interactive data subsetting.

  • Plots can be saved, configurations reloaded, and filtered subsets exported for external use.

4. Clustering

  • Users can group events into clusters based on selected metadata features.

  • Two clustering algorithms are supported:

    • HDBSCAN: - Requires Cluster Size (minimum cluster size to form a group),``Min Points`` (minimum samples to define a core point), and Sensitivity (controls how aggressively clusters are split).

    • Gaussian Mixtures: - Requires only Number of Clusters to define how many Gaussian components will be used.

  • Features must be explicitly added by selecting a column and clicking the ➕ button. Only added features are included in clustering.

  • Optional filters can be applied to restrict the input data using SQL-like syntax.

  • Users can visualize clusters in 2D or 3D, merge selected clusters manually, and commit results back to the database as new columns (cluster_label, cluster_confidence).

Need a visual guide through Poriscope?

Check out the Tutorial and Documentation for a full walkthrough of the interface and workflow.