DOI: 10.5281/zenodo.16325107
title: Clustering and dimensionality analysis of single-molecule localization microscopy data in T.cruzi (Escalante et al.)

description: 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 Workflow1. Initial Data PreparationCircular 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 ParametersAll parameters for selected circular areas are exported in:- Location: Area_Picks/ folder

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🛠️ Clustering AnalysisScript: 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____________________________________________

🟢 Figure 1 AnalysisScript: Experimental_analysis.pyProcesses: Experimental data to generate Figure 1 resultsOutput: Parameters and quantitative results for Figure 1

🔵 Figure 2 AnalysisSimulation.py - Generates randomized dataExperimental_vs_randomized.py - Compares experimental vs randomized distancesOutput: Figure 2 comparative analysis

🟡 Figure 3 AnalysisScript: Shuffling.pyProcesses:- Cross-distances between mucins and trans-sialidases- Cross-randomization- Cluster overlap calculationsOutput: All Figure 3 data metrics

🔴 Figure 4 AnalysisScript: Non_clustered_analysis.pyProcesses: Analysis of free/non-clustered localizationsOutput: Figure 4 results

🟣 Figure 5 AnalysisFibrillar_compartments_simulations.py - Fibrillar compartment modelingCircular_compartments_simulations.py - Circular compartment modelingOutput: 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).