Document Type
Dissertation
Date of Award
12-31-2025
Degree Name
Doctor of Philosophy in Computing Sciences - (Ph.D.)
Department
Computer Science
First Advisor
Zhi Wei
Second Advisor
James M. Calvin
Third Advisor
William Evan Johnson
Fourth Advisor
Gao Zhang
Fifth Advisor
Yao Ma
Abstract
Single-cell and multi-omic technologies have transformed the dissection of cellular heterogeneity and regulatory dynamics in health and disease. However, the high dimensionality, technical variability, and biological complexity of these datasets present significant challenges for integration, annotation, and interpretation. In this dissertation, a suite of computational approaches is introduced to address key problems in single-cell and multi-omic data analysis through model-based innovations and applied statistical frameworks.
First, a constrained deep learning framework for single-cell data integration, label transfer, and clustering is proposed. By incorporating biologically motivated constraints into the training process, robust performance is achieved across simulated and benchmark datasets spanning diverse cell type compositions and batch effects.
Second, integrative statistical analyses are applied to matched primary and metastatic lung cancer samples profiled by multi-omic technologies. Through these analyses, consistent metabolic reprogramming in brain metastases is revealed, particularly in oxidative phosphorylation and lipid metabolism pathways, thereby highlighting potential therapeutic vulnerabilities.
Finally, a U-test-based statistical framework is developed for detecting dysregulated, cell-type-specific gene regulatory networks from single-nucleus RNA-seq data. Enhanced performance is demonstrated in real and simulated datasets, and scalable solutions for single-cell genomics are provided.
Together, these contributions integrate deep learning, statistical modeling, and systems biology, yielding a flexible computational toolkit for the analysis of high-throughput single-cell and multi-omic datasets.
Recommended Citation
Ren, Jianlan, "Computational methods for single-cell and multi-omic data integration and regulatory network inference" (2025). Dissertations. 1867.
https://digitalcommons.njit.edu/dissertations/1867
