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Chlamydomonas Motility and Evolutionary Analysis

This repository contains reproducible scripts and notebooks for a quantitative and evolutionary analysis of motility and phototaxis in Chlamydomonas reinhardtii. The project integrates experimental measurements, statistical testing, and evolutionary modeling to examine how mutational variance and fitness relate to phototactic performance across mutation accumulation (MA) and natural isolate (NI) strains.

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Repository Structure

motility_analysis/
│
├── 01_preprocessing/          # Data cleaning and formatting
│   ├── 01_preprocess_motility.py
│
├── 02_visualization/          # Exploratory plots and summary figures
│   ├── 02_plot_motility.py
│
├── 03_statistics/             # Parametric and non-parametric tests
│   ├── 03a_stats_strainwise.py
│   ├── 03b_stats_typewise_alltypes.py
│   ├── 03c_stats_typewise_comparisons/
│       ├── 03d_stats_NI_vs_CC2344MA.py
│       ├── 03e_stats_NI_vs_CC2931MA.py
│       ├── 03f_stats_CC2344MA_vs_CC2344ANC.py
│       ├── 03g_stats_CC2931MA_vs_CC2931ANC.py
│       └── ...
│
├── 04_evolutionary/           # Linking phototaxis to fitness and mutation
│   ├── 04_evolutionary_stats.py
│
├── 05_candidate_genes/        # GO-based candidate gene exploration
│   ├── Candidate_Genes_458.ipynb
│
└── README.md

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Analysis Overview

• Data preprocessing: Cleaning and harmonizing phototaxis assay outputs.
• Visualization: Generating summary figures of distance metrics by strain and week.
• Statistical testing: Comparing motility across types using ANOVA, mixed-effects models, and permutation tests.
• Evolutionary modeling: Testing associations between phototaxis, mutational load, and fitness.
• Candidate gene exploration: Integrating GO term and literature data to highlight vision- and motility-related genes.

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Software Environment

Component	Description
Python	3.9–3.11 (tested locally in JupyterLab)
Core Packages	pandas, numpy, scipy, statsmodels, seaborn, matplotlib, natsort, itertools, statsmodels.formula.api, multipletests (from statsmodels.stats.multitest)
Optional (for GO analysis)	gffutils, Bio (Biopython), requests, re
R (optional)	tidyverse, ggplot2 — used for supplementary nested ANOVA validation
Environment	JupyterLab or VS Code (recommended for running scripts interactively)

If you wish to replicate the environment:

pip install pandas numpy scipy statsmodels seaborn matplotlib natsort biopython gffutils

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Reproducibility

To reproduce or extend the analysis:

1. Clone this repository:

   git clone https://github.com/fretbret/chlamy-motility-analysis.git
   cd chlamy-motility-analysis

2. Open JupyterLab or VSCode.

3. Run each script sequentially or explore the notebook interactively:

   jupyter lab 05_candidate_genes/Candidate_Genes_458.ipynb

4. All generated plots and CSV outputs will appear in the respective directories.

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Citation and Attribution

Developed as part of undergraduate research in the Ness Lab, Department of Biology, University of Toronto Mississauga.

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