University of Illinois Urbana-Champaign

Single-cell multi-omic data analysis with mathematical and statistical methods

Zhang, Shuyi

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ZHANG-DISSERTATION-2022.pdf

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https://hdl.handle.net/2142/116075

Description

Title
Single-cell multi-omic data analysis with mathematical and statistical methods
Author(s)
Zhang, Shuyi
Issue Date
2022-07-13
Director of Research (if dissertation) or Advisor (if thesis)
Song, Jun S
Doctoral Committee Chair(s)
Golding, Ido
Committee Member(s)
  • Kim, Sangjin
  • Zhao, Sihai Dave
Department of Study
Physics
Discipline
Physics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Sequencing analysis
  • Stochastic processes
  • Information geometry
  • Spectral graph theory
Abstract
Recent advances in next-generation sequencing-based single-cell technologies have allowed high-throughput quantitative detection of cell-surface proteins along with the transcriptome in individual cells, extending our understanding of the heterogeneity of cell populations in diverse tissues that are in different diseased states or under different experimental conditions. From the cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) technology, in particular, count data of surface proteins allow for immunophenotyping of cells yet pose new computational challenges; there is currently a dearth of rigorous mathematical tools for analyzing the data. In this thesis, we seek to address three issues in data analysis for CITE-seq, namely, removing the systematic biases between samples, calling true signals from noise, and merging information from multiple modalities. First, we utilize concepts and ideas from Riemannian geometry to remove batch effects between samples. Subsequently, we develop a framework for distinguishing positive signals from background noise using statistical inference and multiple testing. Lastly, we use the ideas of Hamiltonian operators and density matrices from physics and introduce a unified graph-based learning scheme for effectively merging information from multiple modalities. The strengths of these approaches are demonstrated on CITE-seq data sets of mouse and human tissue samples. The geometrical methods for batch correction, the statistical methods for signal detection, and the graph-based methods for effectively merging the multiple modalities that we introduce in this thesis provide promising frameworks based on ideas from mathematics, statistics, and physics for analyzing the multi-omic data generated using the CITE-seq technology.
Graduation Semester
2022-08
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/116075
Copyright and License Information
Copyright 2022 Shuyi Zhang

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