Date of Award

Summer 8-17-2023

Author's School

Graduate School of Arts and Sciences

Author's Department


Degree Name

Master of Arts (AM/MA)

Degree Type



In recent years, many cell type deconvolution methods based on DNA methylation data and gene expression data have been developed. Both of these two methods have its special advantages and disadvantages, e.g., DNA methylation-based methods’ data source is usually more stable than gene expression and DNA methylation is easier to measure in FFPE tissues or formalin-fixed paraffin-embedded, while some gene-expression data like scRNA-seq data usually has high cost and complexity. On the other hand, gene expression-based deconvolution methods currently have many more available methods than DNA methylation-based deconvolution methods, which leads to DNA methylation-based methods in many cases can learn from the existing gene expression-based methods, e.g., the EMeth learns from ICeD-T while the MethylCIBERSORT learns from CIBERSORT. Since both of these two kinds of different data-based methods are powerful tools to realize the purpose of cell type-specific deconvolution and may could benefit each other’s development, as well as they have been still rapidly developing in recent years with believably more coming new methods in the future. It may be well worth looking back and comparing some recent gene expression data-based and DNA methylation-based deconvolution methods to get some comprehensive sense of this field’s development and directions on both two different data-based deconvolution methods


English (en)

Chair and Committee

Professor Feres Renato

Committee Members

Professor Xiang Tang, Professor Soumendra Lahiri

Construction+of+Hypothesis+Testing+for+Covariance+Matrices+of+Mixed+RNA-seq+Samples+Based+on+Cumulant+(Semi-Invariants)(1).pdf (636 kB)
(Some notes and Future Work which aim at solving Two-Sample Covariance Testing Problem in Bulk Gene Expression Based Deconvolution Model

Master Thesis.pdf (1076 kB)

Included in

Biostatistics Commons