Unveiling Phenotype–Genotype Interplay with Deep
Learning Foundation Models for scRNA-seq: A
Quantitative Perspective
Name
thadawasin-big536-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
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4.37 MB
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Author(s)
Thadawasin, Pakaphol
Advisor(s)
Edelman, Elazer R.
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
Foundation models have emerged as powerful tools for analyzing single-cell RNA sequencing (scRNA-seq) data, leveraging large-scale pretraining to capture complex gene expression patterns. However, a comprehensive quantitative framework for understanding the interplay between phenotypes and genotypes remains underdeveloped. Such a framework is critical not only for validating model performance but also for uncovering previously unrecognized biological relationships. In this work, we present both traditional and deep learning-based quantitative analysis pipelines for PolyGene [1], a transformer-based scRNA-seq foundation model, aimed at disentangling the complex phenotype–genotype relationship. First, we implement a top-k classification and entropy evaluation pipeline to serve as a primary validation framework. Our results demonstrate that the pretrained PolyGene [1] is robust in top-k classification metrics and provides meaningful insights into the entropy landscape of human cells across different life stages. Second, we propose a novel deep learning gradientbased gene selection method designed to address limitations in traditional feature selection approaches, such as poor scalability and sensitivity to heterogeneity in high-dimensional data. Through empirical evaluations on benchmark scRNA-seq datasets, we show that our method enhances model interpretability and improves downstream performance, offering a more scalable and biologically relevant alternative to existing techniques. Overall, this work introduces a set of quantitative analysis tools that fill a critical gap in evaluating and interpreting scRNA-seq foundation models, contributing to a deeper understanding of the genotype–phenotype interplay through modern deep learning techniques.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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