Next-generation Cell Type Annotation:
Integrating NLP and ML Techniques
My MSc thesis explores the integration of Natural Language Processing and Machine Learning for enhanced single-cell RNA sequencing classification using gene text embeddings with autoencoders.
Abstract
This thesis presents a novel approach to cell type annotation in single-cell RNA sequencing (scRNA-seq) data by integrating Natural Language Processing (NLP) techniques with traditional machine learning methods. The research focuses on leveraging gene text embeddings combined with autoencoders to enhance the accuracy and efficiency of cell type classification.
The methodology combines state-of-the-art text embedding techniques from NLP to capture semantic relationships between genes, with autoencoder architectures that learn compressed representations of single-cell expression profiles. This hybrid approach addresses the high-dimensional nature of scRNA-seq data while incorporating biological knowledge encoded in gene descriptions.
Key contributions include: (1) Development of a gene embedding framework specifically tailored for scRNA-seq analysis, (2) Integration of these embeddings with autoencoder architectures for dimensionality reduction, and (3) Demonstration of improved cell type classification accuracy compared to traditional methods.
Key Research Findings
Gene Text Embeddings
Developed novel gene text embedding techniques that capture biological relationships and functional similarities between genes based on their descriptions and annotations.
Autoencoder Integration
Successfully integrated autoencoder architectures with gene embeddings to create compressed representations that preserve biological information while reducing dimensionality.
Enhanced Classification
Achieved significant improvements in cell type annotation accuracy, demonstrating the effectiveness of combining NLP and ML techniques for bioinformatics applications.
Technical Implementation
Technologies Used
Key Innovations
Novel Embedding Approach
Created gene embeddings using biological descriptions and GO terms, capturing semantic relationships beyond expression values.
Hybrid Architecture
Combined traditional bioinformatics approaches with modern NLP techniques for improved biological interpretation.
Scalable Implementation
Designed for efficient processing of large-scale scRNA-seq datasets with millions of cells.
Research Materials
Full Thesis
Complete thesis document including all chapters, methodology, results, and appendices.
Defense Presentation
Thesis defense presentation slides with key findings and visualizations.
Interested in the implementation details or code?
Research Impact
This research contributes to the growing field of computational biology by demonstrating how techniques from natural language processing can enhance biological data analysis. The methods developed have potential applications in precision medicine, drug discovery, and understanding cellular heterogeneity in complex tissues.
"Key is getting information from data"
- My research philosophy