NeuroWave - Exploring Microwave exposure effects on glial cells

A comprehensive computational pipeline for analyzing cellular responses to microwave stimulation across multiple modalities: morphology, calcium dynamics, cytotoxicity, Thermal profiling and transcriptomics.

📊 Overview

This repository contains a complete analysis suite for studying cellular responses to microwave stimulation. The pipeline integrates data from:

• Morphological analysis (CellProfiler outputs)
• Calcium imaging (GCaMP fluorescence traces)
• Cytotoxicity assays (LDH measurements)
• Transcriptomics (Bulk RNA-seq data)

Each analysis module is designed to be modular, reproducible, and produces publication-ready visualizations.

📁 Repository Structure

microwave_analysis/
├── README.md                     # This file
├── LICENSE                       # License information
├── .gitignore                    # Git ignore file
├── setup_environment.R           # Environment setup script
├── morphological_analysis/       # Cell morphology analysis
│   ├── Astrocyte_morphological_analysis.Rmd
│   ├── Microglia_morphological_analysis.Rmd
│   ├── GBM_morphological_analysis.Rmd
├── calcium_imaging/              # Calcium dynamics analysis
│   ├── Calcium_Signalling_Analysis.Rmd
├── cytotoxicity_assay/           # LDH assay analysis
│   ├── Ldh_Cytotoxicity_Analysis.Rmd
├── transcriptomics/              # RNA-seq analysis
│   ├── Transcriptomics_BulkSeq_Analysis.Rmd
│   ├── msigdb_databases/         # Pathway databases
├── Thermal Analysis/              # Temprature measurements
│   ├── Thermal_analysis.Rmd
└──── 

🚀 Quick Start

Prerequisites

1. R (version 4.1.0 or higher)
2. RStudio (recommended)
3. Required system libraries:
   • macOS: brew install libpng cairo
   • Ubuntu: sudo apt-get install libcurl4-openssl-dev libssl-dev libxml2-dev
   • Windows: Install Rtools

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/microwave-analysis.git
   cd microwave-analysis

2. Set up R environment: Open R/RStudio and run:

   # Run setup script
   source("setup_environment.R")
   
   # Or manually install packages
   install.packages(c("tidyverse", "ggplot2", "ggpubr", "patchwork", "rmarkdown"))

3. Test installation:

   # Check if all required packages are installed
   source("utilities/check_installation.R")

📋 Step-by-Step Usage

1. Morphological Analysis

For analyzing cell morphology from CellProfiler outputs:

Step 1: Prepare your data

• Place CellProfiler CSV outputs in a structured folder:

  your_data/
  ├── primary_objects.csv    # Soma measurements
  └── secondary_objects.csv  # Whole cell measurements
  

2. Calcium Imaging Analysis

For analyzing GCaMP calcium traces:

Step 1: Organize calcium trace files

calcium_data/
├── experiment1.csv
├── experiment2.csv
└── metadata.csv    # Optional: experimental conditions

Step 2: Configure analysis parameters

Edit the calcium analysis script:

# Set your parameters
data_folder <- "path/to/your/calcium/data"
results_folder <- "path/to/results"
recording_time_min <- 3        # Recording duration in minutes
cutoff_fixed <- 0.2            # Activity threshold
delta_multiplier <- 2          # Peak detection sensitivity

3. LDH Cytotoxicity Assay

For analyzing LDH release data:

Step 1: Prepare your LDH data

Create a CSV file with columns:

• Condition (e.g., "G0", "G1", etc.)
• Lum_score (raw luminescence values)
• Replicate (optional)

Step 2: Interpret results

Output includes:

• Normalized cytotoxicity values
• Statistical comparisons
• Dose-response visualizations
• Comparative analysis across cell types

4. Transcriptomics Analysis

For analyzing RNA-seq data:

Step 1: Prepare count data

• Count matrix (genes × samples)
• Metadata file (samples × conditions)

Step 2: Explore results

The pipeline produces:

• Differential expression results
• Volcano plots
• Pathway enrichment analysis
• Interactive visualizations

🔧 Configuration Guide

Experimental Design Mapping

Map your experimental conditions to the analysis codes:

Condition Code	MW Status	Exposure Time	Recovery Time
G0, A0, M0	ON	5 min	0 h
G1, A1, M1	OFF	5 min	0 h
G2, A2, M2	ON	30 min	0 h
G3, A3, M3	OFF	30 min	0 h
G4, A4, M4	ON	5 min	24 h
G5, A5, M5	OFF	5 min	24 h
G6, A6, M6	ON	30 min	24 h
G7, A7, M7	OFF	30 min	24 h

Customizing Analysis Parameters

Each analysis module has configurable parameters:

Morphological Analysis

# In the R Markdown files, adjust:
company_colors <- c("#E50000", "#008A8A", ...)  # Color palette
window_size <- 50                               # Rolling window size
optimal_k <- 6                                  # Number of clusters
pval_threshold <- 0.05                          # Statistical threshold

Calcium Imaging

# In calcium_imaging_analysis.Rmd:
recording_time_min <- 3        # Recording duration
cutoff_fixed <- 0.2            # Activity threshold
delta_multiplier <- 2          # Peak detection sensitivity
skip_correction <- TRUE        # Skip baseline correction

Statistical Methods

• Morphology: KS tests, Wilcoxon tests, Chi-square tests
• Calcium: Peak detection algorithms, firing rate calculations
• Cytotoxicity: t-tests, ANOVA, dose-response modeling
• Transcriptomics: limma-voom, DESeq2, GSEA

📈 Output Interpretation

Morphological Analysis Outputs

1. Cell Density Plots: Normalized cell counts per condition
2. Parameter Distributions: Box plots of morphological features
3. Cluster Analysis: UMAP/t-SNE visualizations of phenotypes
4. Phenotype Proportions: Bar plots of morphological classifications

Calcium Imaging Outputs

1. Trace Visualizations: Raw and processed calcium traces
2. Event Detection: Raster plots of calcium events
3. Firing Rate Analysis: Statistical comparisons of activity
4. Waveform Analysis: Average calcium transient shapes

Statistical Significance Codes

• * p < 0.05
• ** p < 0.01
• *** p < 0.001
• ns not significant

🔍 Troubleshooting

Common Issues and Solutions

Issue 1: Package Installation Failures

# If Bioconductor packages fail to install:
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(version = "3.16")  # Specify version

Issue 2: Memory Errors

# Increase memory limit
memory.limit(size = 16000)  # On Windows

# Use 64-bit R
# Filter data early in pipeline
# Process in chunks for large datasets

Issue 3: File Path Errors

# Use relative paths
data_path <- file.path("data", "your_file.csv")

# Check file existence
if (!file.exists(data_path)) {
  stop("File not found: ", data_path)
}

# Set working directory properly
setwd("/full/path/to/your/project")

Issue 4: Missing Dependencies

# Check and install missing packages
required <- c("tidyverse", "ggplot2", "dplyr")
missing <- required[!(required %in% installed.packages()[,"Package"])]
if(length(missing)) install.packages(missing)

Debugging Tips

1. Run in stages: Test each analysis module separately
2. Check data formats: Ensure CSV files are properly formatted
3. Review log files: Check R console output for warnings/errors
4. Use example data: Test with provided example datasets first

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Code Style Guidelines

• Use meaningful variable names
• Comment complex sections
• Follow tidyverse style guide
• Include example data for new features
• Update documentation accordingly

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📞 Support

For questions, issues, or feature requests:

1. Check the documentation: Review this README and inline documentation
2. Search existing issues: Your question might already be answered
3. Open a new issue: Provide a minimal reproducible example
4. Contact maintainers: [Vatsal D. Jariwala]

📊 Citation

If you use this software in your research, please cite:

@software{microwave_analysis_suite,
  author = Vatsal D. Jariwala},
  title = {Microwave Stimulation Analysis Suite},
  year = {2025},
  url = {https://github.com/VatsJari/NeuroWave}
}

🎯 Key Features

• Modular Design: Each analysis module works independently
• Reproducible: Complete documentation and version control
• Scalable: Handles datasets from small to large scale
• Customizable: Easily adjust parameters for specific needs
• Visualization-Rich: Publication-ready plots and figures
• Statistical Rigor: Multiple testing corrections and effect sizes

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Happy Analyzing! 🧬🔬📊

For best results, start with the example datasets and gradually replace them with your own data. Don't hesitate to open issues if you encounter problems or have suggestions for improvements.