Machine Learning Methods for Single Cell RNA-Sequencing Data to Improve Clinical Oncology

Single-cell RNA sequencing (scRNA-seq) offers a detailed view of the cellular and phenotypic composition of healthy and diseased tissues. While machine learning (ML) methods are well-suited for the high-dimensional nature of scRNA-seq data, current computational tools face limitations, particularly when confronted with data from clinical oncology. This thesis presents the development and application of ML techniques for scRNA-seq data to address key computational challenges, with a focus on challenges in clinical oncology. It covers four key areas: identifying gene signatures and biomarkers in multiple myeloma, developing methods to account for somatic copy number variations in tumor samples, benchmarking large, pre-trained scRNA-seq foundation models, and creating a framework for predicting clinical outcomes using patient-level representations of single-cell data. Together, these studies aim to develop and evaluate novel ML algorithms for scRNA-seq data which can unlock actionable insights for personalized medicine.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Boiarsky, Rebecca

Advisors dc:contributor.advisor

• Sontag, David
• Getz, Gad

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/163710

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/163710