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 ================================================================================== 🔬 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 ================================================================================== 🛠️ 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 ================================================================================== 📊 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 ================================================================================== [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).