User Guide

Welcome to the comprehensive user guide for the Raman Spectroscopy Analysis Application. This guide covers everything you need to know to effectively use the software for your research.

Guide Structure

This user guide is organized into the following sections:

1. Interface Overview

Learn about the application’s user interface, including:

• Main window layout and navigation

• Tab system (Home, Data, Preprocessing, Analysis, ML)

• Common UI elements and controls

2. Data Import and Management

Master data organization and import:

• Supported file formats (CSV, TXT, ASC/ASCII, PKL)

• Data structure requirements

• Creating and managing datasets

• Grouping spectra for analysis

• Metadata management

• Batch import operations

3. Preprocessing Pipeline

Build effective preprocessing workflows:

• Understanding the preprocessing pipeline

• Step-by-step preprocessing guide

• All 40+ preprocessing methods explained

• Parameter selection guidelines

• Real-time preview system

• Saving and loading pipelines

• Best practices for Raman data

4. Analysis Methods

Perform comprehensive spectral analysis:

• Exploratory analysis (PCA, UMAP, t-SNE, clustering)

• Statistical comparisons (t-tests, ANOVA, correlations)

• Visualization methods (heatmaps, waterfall plots)

• Band ratio analysis

• Spectral unmixing (MCR-ALS, NMF)

• Interpreting results

5. Machine Learning

Train and evaluate classification models:

• Dataset preparation

• Algorithm selection (SVM, RF, XGBoost, etc.)

• Validation strategies (GroupKFold, LOPOCV)

• Training and evaluation

• Interpreting model results (ROC, confusion matrix, SHAP)

• Exporting trained models

• Avoiding common pitfalls

6. Best Practices

Learn research best practices:

• Data quality control and validation

• Avoiding data leakage

• Sample size considerations

• Publication-ready figures

• Reproducible workflows

• Documentation and record-keeping

• Common mistakes and how to avoid them

Quick Navigation

By Task

I want to…

• Import my data → Data Import Guide

• Remove baseline → Preprocessing Guide

• Compare two groups → Analysis Guide

• Visualize group separation → Analysis Guide

• Train a classifier → Machine Learning Guide

• Interpret my model → Machine Learning Guide

• Export results → Analysis Guide

By Research Goal

My research involves…

• Cancer detection → Best Practices

• Multiple myeloma vs MGUS → Best Practices

• Cell classification → Best Practices

• Biomarker discovery → Analysis Guide

• Method validation → Best Practices

Typical Workflows

Workflow 1: Quality Control and Exploratory Analysis

        graph TD
    A[Import Data] --> B[Visual Inspection]
    B --> C[Basic Preprocessing]
    C --> D[PCA Analysis]
    D --> E{Groups Separate?}
    E -->|Yes| F[Identify Key Bands]
    E -->|No| G[Check for Outliers]
    G --> C
    F --> H[Statistical Tests]
    H --> I[Export Results]
    

Recommended Sections:

1. Data Import

2. Preprocessing

3. Analysis

4. Analysis

Workflow 2: Classification Model Development

        graph TD
    A[Import & Group Data] --> B[Quality Control]
    B --> C[Preprocessing Pipeline]
    C --> D[Train/Test Split]
    D --> E[Model Training]
    E --> F[Cross-Validation]
    F --> G{Performance OK?}
    G -->|No| H[Adjust Preprocessing/Model]
    H --> E
    G -->|Yes| I[Interpret Model]
    I --> J[External Validation]
    J --> K[Export Model]
    

Recommended Sections:

1. Data Import

2. Preprocessing

3. Machine Learning

4. Best Practices

Workflow 3: Spectral Unmixing

        graph TD
    A[Import Mixed Spectra] --> B[Preprocessing]
    B --> C[Estimate Components]
    C --> D[MCR-ALS Analysis]
    D --> E[Validate Endmembers]
    E --> F{Physical Meaning?}
    F -->|No| G[Adjust Constraints]
    G --> D
    F -->|Yes| H[Interpret Contributions]
    H --> I[Export Components]
    

Recommended Sections:

1. Analysis

2. Analysis

Common Questions

When Should I Use Each Analysis Method?

Analysis Goal	Recommended Method	Section
Explore group separation	PCA	Analysis Guide
Test if groups differ	Statistical tests	Analysis Guide
Classify new samples	Machine Learning	ML Guide
Find biomarkers	Band ratio + stats	Analysis Guide
Decompose mixtures	MCR-ALS	Analysis Guide
Visualize high-dimensional data	UMAP or t-SNE	Analysis Guide

What Preprocessing Should I Use?

Minimum preprocessing for Raman data:

1. Baseline correction (AsLS or AirPLS)

2. Smoothing (Savitzky-Golay)

3. Normalization (Vector or SNV)

See Preprocessing Guide for specific use cases.

How Do I Ensure Valid Results?

Key validation steps:

• Data quality: Remove outliers and cosmic rays

• Preprocessing: Validate each step with preview

• Statistics: Use appropriate tests and corrections

• Machine learning: Use proper validation (GroupKFold, external test set)

• Reproducibility: Document all parameters and steps

See Best Practices for complete checklist.

Getting Help

Documentation Resources

• FAQ - Frequently asked questions

• Troubleshooting - Common issues and solutions

• Analysis Methods Reference - Detailed method documentation

• API Documentation - For developers

Community Support

• GitHub Discussions - Ask questions

• GitHub Issues - Report bugs

Video Tutorials (Coming Soon)

We’re creating video tutorials for:

• Complete walkthrough of the interface

• Building preprocessing pipelines

• Performing PCA analysis with interpretation

• Training and evaluating ML models

• Real-world case studies

Contributing to This Guide

Found an error or want to improve this guide?

1. Visit the GitHub repository

2. Fork the repository

3. Edit the relevant markdown file in docs/user-guide/

4. Submit a pull request

Your contributions help the entire community!