Machine leanring algorithms for single-cell data analysis

Peng, Jianhao

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https://hdl.handle.net/2142/115567 Copy

Description

Title

Machine leanring algorithms for single-cell data analysis

Author(s)

Peng, Jianhao

Issue Date

2022-04-22

Director of Research (if dissertation) or Advisor (if thesis)

Milenkovic, Olgica

Doctoral Committee Chair(s)

Milenkovic, Olgica

Committee Member(s)

• Ochoa, Idoia
• Raginsky, Maxim
• Shormonoy, Ilan

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• single cell
• online algorithm
• network analysis
• gene regulatory network

Abstract

In this thesis, we proposed various machine learning algorithms for analyzing different types of single cell sequencing data. Starting with the most common single cell RNA-seq data in Chapter 2, we proposed an online convex matrix factorization algorithm named online cvxMF that can efficiently learn representatives and interpretable lower-dimension basis vectors for each cell type. In Chapter 3, we introduced ChIA-Drop, a new type of network-structured data for chromatin interaction analysis, and extended our online cvxMF algorithm to a novel online convex network dictionary learning method that includes MCMC sampling and Gene Ontology enrichment analysis. The newly proposed method, online cvxNDL, is able to accurately reconstruct the original ChIA-Drop network and provide network dictionaries associated with biological functions. Lastly in Chapter 4, we proposed SimiC, a single cell gene regulatory network (GRN) inference algorithm that can jointly learn several GRNs from related cell phenotypes. Combined with regulon activity scores and regulatory dissimilarity scores for each of the driver genes across different phenotypes, SimiC is able to capture regulatory dynamics that are missed by previous methods.

Graduation Semester

2022-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/115567

Copyright and License Information

Copyright 2022 Jianhao Peng

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