This is a re-organized version of PhD-exp1-data-analysis. Please note that this repository is still being updated. Older versions or missing files can be found in the original repository.
Code used for Adriano Wanderlingh's Lasius niger Group Size Effects Experiment. 🐜 🦠
Testing Social Immunity role in disease hindering dynamics in societies, using ant colonies interaction networks and behaviour | experimental epidemiology.
R and C++ scripts for:
- Tracking Data Post-processing
- Geometrical capsules assignment
- Interactions detection
- Machine Learning Behavioural Classification
- Personal immunity investment and pathogen load quantification
- RTqPCR data pre-processing pipeline with MNAR data simulations
- extra related material
The repository is organized into two main folders: Scripts
and Data
. The Scripts
folder contains R and C++ code for data processing, analysis, and visualization. The Data
folder contains raw and processed data files.
.
├── Scripts
│ ├── PhD-Ant_Colonies_Tracking_Analysis
│ │ ├── Automated_Behavioural_Inference
│ │ └── molecular_bio_analysis
│ └── code_Social_Network_Plasticity_Exp_2018_AW
│ ├── 1_data_post_processing
│ │ └── source
│ └── 2_statistics_and_plotting
│ └── source
└── Data
└── PhD-Ant_Colonies_Tracking_Analysis
├── Automated_Behavioural_Inference
└── molecular_bio_analysis
For more information, read the folder README and the pre-processing_Adriano_June2022 guide.
For more information, read the folder README
This folder contains scripts for molecular biology analysis, including qPCR and RT-qPCR analysis.
This folder contains scripts for data post-processing and statistical analysis of ant behavioural interactions, adapted from Stroeymeyt et al., 2018.
Mean_ant_length_per_TrackingSystem.txt
: [Information needed]
Adriano_RTqPCR_immune_genes_MASTER_REPORT.csv
: [Information needed]Adriano_qPCR_pathogen_load_MASTER_REPORT.csv
: [Information needed]
- Code written by Adriano Wanderlingh, Nathalie Stroeymeyt and Tom Richardson with contributions by Enrico Gavagnign. Ant Epidemiology Stroeymeyt Lab, University of Bristol, UK
This file was generated by extracting information on the repo with README-AI and using find . d > dirs.txt
to get the folder structure, then feeding both to GPT-4, which was prompted to write a README.md file.