Reconstructing Cell Lineage Trees From Phenotypic Features With Metric Learning Da Kuang

Format:

Book

Thesis/Dissertation

Author/Creator:

Kuang, Da, author.

Contributor:

University of Pennsylvania. Computer and Information Science., degree granting institution.

Language:

English

Subjects (All):

0306.

0307.

0715.

0984.

Local Subjects:

0306.

0307.

0715.

0984.

Physical Description:

1 electronic resource (120 pages)

Contained In:

Dissertations Abstracts International 87-09B

Place of Publication:

Ann Arbor : ProQuest Dissertations and Theses, 2025

Language Note:

English

Summary:

How a single fertilized cell gives rise to a complex array of specialized cell types in development is a central question in biology. The cells grow, divide, and acquire differentiated characteristics through poorly understood molecular processes. A key approach to studying developmental processes is to infer the tree graph of cell lineage division and differentiation histories, providing an analytical framework for dissecting individual cells' molecular decisions during replication and differentiation. Although genetically engineered lineage-tracing methods have advanced the field, they are either infeasible or ethically constrained in many organisms. In contrast, modern single-cell technologies can measure high-content molecular profiles (e.g., transcriptomes) in a wide range of biological systems.Here, we introduce CellTreeQM, a novel deep learning method based on transformer architectures that learns an embedding space with geometric properties optimized for tree-graph inference. By formulating lineage reconstruction as a tree-metric learning problem, we have systematically explored supervised, weakly supervised, and unsupervised training settings. We present a cell lineage reconstruction benchmark, CellTreeBench, to facilitate a comprehensive evaluation of our learning method. We benchmarked the method on (1) synthetic data modeled via Brownian motion with independent noise and spurious signals, and (2) lineage-resolved single-cell RNA sequencing datasets. Experimental results show that CellTreeQM recovers lineage structures with minimal supervision and limited data, offering a scalable framework for uncovering cell lineage relationships in challenging animal models. To our knowledge, this is the first method to cast cell lineage inference explicitly as a metric learning task, paving the way for future computational models aimed at uncovering the molecular dynamics of cell lineage.

Notes:

Advisors: Kim, Junhyong; Wang, Li-San Committee members: Ives, Zachary G.; Ungar, Lyle; Warnow, Tandy

Source: Dissertations Abstracts International, Volume: 87-09, Section: B.

Ph.D. University of Pennsylvania 2025

Vendor supplied data

Local Notes:

School code: 0175

ISBN:

9798277442197

Access Restriction:

Restricted for use by site license