Machine leanring algorithms for single-cell data analysis

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Peng, Jianhao

Contributors dc:contributor

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

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

Statement dc:rights

• Copyright 2022 Jianhao Peng

Language dc:language

en, eng

Handle dc:identifier

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

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F115567