Clustering and dimensionality analysis of single-molecule localization microscopy data in T.cruzi (Escalante et al.)

This repository contains all code and data processing pipelines used in the paper:

"Distinct nanoscale organizations of GPI-anchored mucins and trans-sialidases in Trypanosoma cruzi" by Escalante et al.

.
├── Experimental/                          # Processed localization data
├── Area_Picks/                            # Parameters of selected circular areas
├── Clustering.py                          # DBSCAN clustering analysis
├── Experimental_analysis.py               # Figure 1 processing
├── Simulation.py                          # Data randomization
├── Experimental_vs_randomized.py          # Figure 2 analysis
├── Shuffling.py                           # Figure 3 cross-distance analysis
├── Non_clustered_analysis.py              # Figure 4 free localization analysis
├── Fibrillar_compartments_simulations.py  # Figure 5 modeling
└── Circular_compartments_simulations.py   # Figure 5 modeling

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🔬 Data Processing Workflow
1. Initial Data Preparation
Circular areas were exported from pre-processed localizations (drift-corrected, filtered, with aligned mucin and trans-sialidase channels) using Picasso [1] super-resolution analysis software.
- Location: Experimental/ folder contains all localization data.

2. Area Selection Parameters
All parameters for selected circular areas are exported in:
- Location: Area_Picks/ folder

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🛠️ Clustering Analysis
Script: Clustering.py

Method: DBSCAN clustering of mucins and trans-sialidases across all areas + DBCV [2] validation method (from hdbscan implementation in python)

Output: Cluster data used in multiple figures

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📊 Analysis Scripts
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🟢 Figure 1 Analysis
Script: Experimental_analysis.py
Processes: Experimental data to generate Figure 1 results
Output: Parameters and quantitative results for Figure 1

🔵 Figure 2 Analysis
Simulation.py - Generates randomized data
Experimental_vs_randomized.py - Compares experimental vs randomized distances
Output: Figure 2 comparative analysis

🟡 Figure 3 Analysis
Script: Shuffling.py
Processes:
- Cross-distances between mucins and trans-sialidases
- Cross-randomization
- Cluster overlap calculations
Output: All Figure 3 data metrics

🔴 Figure 4 Analysis
Script: Non_clustered_analysis.py
Processes: Analysis of free/non-clustered localizations
Output: Figure 4 results

🟣 Figure 5 Analysis
Fibrillar_compartments_simulations.py - Fibrillar compartment modeling
Circular_compartments_simulations.py - Circular compartment modeling
Output: Compartmentalization models and dimensionality analysis

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[1] Schnitzbauer, J., Strauss, M. T., Schlichthaerle, T., Schueder, F. & Jungmann, R.
Super-resolution microscopy with DNA-PAINT. Nat. Protoc. 12, 1198–1228 (2017).
DOI: 10.1038/nprot.2017.119

[2] Moulavi, D., Jaskowiak, P.A., Campello, R.J., Zimek, A. and Sander, J., 2014. Density-Based Clustering Validation. In SDM (pp. 839-847).