Brain Cell Classification Analysis

Machine learning approach to identify brain cell types and subtypes using dimensionality reduction and clustering

This project explores advanced machine learning techniques for analyzing brain cell data, focusing on identifying distinct cell types and their subtypes through dimensionality reduction, clustering, and supervised learning approaches.

The analysis demonstrates how computational methods can reveal biological insights from high-dimensional genomic data, supporting neuroscientific discoveries about brain cell classification.

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Project Overview

This comprehensive analysis tackles the challenge of classifying brain cells using machine learning techniques on high-dimensional biological data. The project encompasses multiple analytical approaches to understand cellular diversity and hierarchy in brain tissue.

Key Objectives:

  • Identify three main brain cell types through visualization
  • Discover cellular subtypes within each main category
  • Apply supervised learning for automated classification
  • Optimize hyperparameters for improved analysis accuracy

Methodology & Analysis

Brain Cell Type Identification

Using Principal Component Analysis (PCA) followed by t-SNE dimensionality reduction, I successfully identified three distinct brain cell types. The analysis involved:

  • Data preprocessing with log transformation
  • PCA projection on top 15 principal components
  • t-SNE visualization with perplexity=50 and complexity=40

The resulting visualization clearly demonstrates three separable cell populations, confirming the existence of distinct brain cell types as described in neuroscientific literature.

Subtype Discovery

To explore cellular diversity within main types, I implemented K-means clustering with 8 subcategories. This analysis revealed:

  • Multiple subcategories within each of the three main cell types
  • Clear hierarchical structure of brain cell classification
  • Distinct clustering patterns supporting biological cell type theory

Clustering Optimization

Using systematic evaluation methods, I determined optimal clustering parameters:

  • Elbow method and Silhouette Score analysis
  • Optimal cluster number: 7 subcategories
  • Clear separation and biological relevance of identified clusters

Supervised Learning Implementation

Applied logistic regression with regularization for automated cell classification:

  • Feature Selection: Top 100 features using SelectKBest
  • Regularization: L2 regularization with cross-validation
  • Performance: Mean cross-validation score of 96.35%
  • Feature Comparison: High-variance features significantly outperformed random selection

Results Comparison:

  • Random features: 50.99% accuracy
  • High-variance features: 90.34% accuracy, 99.83% AUC

Hyperparameter Analysis

Comprehensive evaluation of key parameters affecting analysis quality:

Principal Components Impact: Tested 10, 50, 100, 250, and 500 PCs

  • Higher PC counts led to sparser clustering
  • Optimal range: 10-250 PCs for clear visualization

t-SNE Parameter Optimization:

  • Perplexity: Effective range 20-50
  • Learning Rate: Optimal range 100-1500
  • Joint optimization revealed parameter interactions affecting clustering quality

Key Findings

The analysis successfully demonstrates that computational approaches can:

  • Accurately identify distinct brain cell types with 96%+ accuracy
  • Reveal cellular subtypes within major categories
  • Optimize analytical parameters for maximum biological insight
  • Validate biological theories through data-driven approaches

The hierarchical nature of brain cell classification becomes evident through systematic application of unsupervised and supervised learning techniques, providing computational support for neuroscientific understanding of cellular diversity.

Technical Impact

This project showcases the power of combining multiple machine learning approaches for biological data analysis:

  • Effective dimensionality reduction for high-dimensional genomic data
  • Robust clustering validation and optimization
  • Successful transfer from unsupervised to supervised learning
  • Systematic hyperparameter optimization for reproducible results

The methodology demonstrates how computational techniques can accelerate biological discovery and provide quantitative validation of scientific hypotheses.

Completed as part of MIT MicroMasters Program in Statistics and Data Science