Getting Started

Welcome to the Raman Spectroscopy Analysis Application! This guide will help you get up and running quickly.

System Requirements

Minimum Requirements

• Operating System: Windows 10/11, macOS 10.14+, or Linux (Ubuntu 18.04+)

• Python: 3.12 or higher

• RAM: 4 GB (8 GB recommended for large datasets)

• Storage: 500 MB free space

• Display: 1280×720 resolution (1920×1080 recommended)

Recommended Requirements

• RAM: 8 GB or more

• Display: 1920×1080 or higher resolution

• GPU: NVIDIA GPU with CUDA support (for deep learning preprocessing)

Installation Options

There are three ways to install and run the application:

1. Source Installation (Recommended for Developers)

Best for users who want to:

• Contribute to development

• Customize the application

• Stay on the latest version

• Debug or extend functionality

Time: ~10 minutes
Difficulty: Intermediate (requires familiarity with Python)

See Installation Guide for detailed steps.

2. Portable Executable (Windows Only)

Best for users who want to:

• Run without installing Python

• Quick testing or evaluation

• Run from USB drive

• Avoid system modifications

Time: ~2 minutes
Difficulty: Easy

See Installation Guide for detailed steps.

3. Installer (Windows Only)

Best for users who want to:

• Professional installation experience

• Start Menu integration

• File associations for project files

• Standard Windows uninstallation

Time: ~5 minutes
Difficulty: Easy

See Installation Guide for detailed steps.

Quick Start Tutorial

Step 1: Launch the Application

From Source:

cd Raman-Spectroscopy-Analysis-Application
uv run python main.py

From Executable: Double-click RamanApp.exe

From Installer: Launch from Start Menu or desktop shortcut

Step 2: Create Your First Project

1. On the Home Page, click New Project

2. Enter a project name (e.g., “My First Analysis”)

3. Select a project folder location

4. Click Create

Step 3: Import Data

1. Navigate to the Data Package tab

2. Click Import Data

3. Select your Raman spectroscopy data files (CSV, TXT, ASC/ASCII, or PKL format)

4. Review the imported spectra in the preview panel

Step 4: Preprocess Your Data

1. Navigate to the Preprocessing tab

2. Add preprocessing steps:

   • Baseline Correction → Select “AsLS” (default settings)

   • Smoothing → Select “Savitzky-Golay” (window=11, poly=3)

   • Normalization → Select “Vector Normalization”

3. Preview the effects in real-time

4. Click Apply Pipeline to process all spectra

Step 5: Analyze Your Data

1. Navigate to the Analysis tab

2. Select an analysis method:

   • PCA for exploratory analysis

   • Band Ratio for specific biomarker analysis

   • Statistical Tests for group comparisons

3. Configure parameters

4. Click Run Analysis

5. View and export results

Step 6: Machine Learning (Optional)

1. Navigate to the Machine Learning tab

2. Configure your dataset:

   • Define groups (Control vs Disease)

   • Select validation method (GroupKFold recommended)

3. Choose an algorithm (Random Forest recommended for beginners)

4. Click Train Model

5. Evaluate results (ROC curves, confusion matrix, SHAP plots)

Next Steps

Now that you’ve completed your first analysis, explore these topics:

• Interface Overview - Learn about all interface elements

• Data Import Guide - Supported formats and data organization

• Preprocessing Tutorial - Detailed preprocessing pipeline guide

• Analysis Methods - Comprehensive method documentation

• Best Practices - Tips for Raman spectroscopy analysis

Getting Help

Documentation Resources

• User Guide - Comprehensive tutorials and walkthroughs

• Analysis Methods Reference - Theory and parameter guidance

• API Documentation - For developers and advanced users

• FAQ - Frequently asked questions

• Troubleshooting - Common issues and solutions

Community Support

• GitHub Issues: Report bugs or request features

• GitHub Discussions: Ask questions and share experiences

• Email: Contact the development team via @zerozedsc

Video Tutorials (Coming Soon)

We’re working on video tutorials covering:

• Complete installation walkthrough

• First project and data import

• Building a preprocessing pipeline

• Performing PCA analysis

• Training and evaluating ML models

Subscribe to updates on our GitHub repository to be notified when videos are available.

Feedback

Your feedback helps us improve! Please share:

• Feature requests

• Usability suggestions

• Documentation improvements

• Bug reports

Submit feedback via GitHub Issues.