Statistical Methods and Computational Tools for High-throughput Genomics Data
Creators: name: Zhu, Jiaqiang
Publisher: name: My University
Year: 2022
Subjects: subject: Biostatistics
subject: Genomics
subject: Mixed Models
subject: Spatial Transcriptomics
subject: Simulator
subject: Genetics
subject: FOS: Biological sciences
subject: Science (General)
subject: Statistics and Numeric Data
subject: Science

description: Recent advancements in various sequencing technologies have enabled the measurement of gene expression and other omics at a genome-wide scale, with potentially single-cell resolution and spatial information. These new technologies have created enormous statistical and computational challenges for various analytic tasks, including molecular trait heritability estimation, differential expression analysis, and characterization of the spatial transcriptomic landscape on the tissue. In this dissertation, I propose several statistical and computational methods to address the challenges from several new genomics technologies, facilitating data analysis and biological discovery. In Chapter II, I develop a method, PQLseq (Penalized Quasi-Likelihood for sequencing count data), enabling effective and efficient heritability estimation and differential analysis in bulk RNA sequencing (RNAseq) and bisulfite sequencing (BSseq) studies. PQLseq relies on a generalized linear mixed model framework to properly account for the count nature of the sequencing data while controlling for sample dependence. PQLseq fits a Poisson mixed model for modeling RNAseq data and a binomial mixed model for modeling BSseq data, with an efficient penalized quasi-likelihood algorithm for scalable computation. With extensive simulations, I show that PQLseq is well suited for differential analysis in large sequencing studies and can produce an unbiased heritability estimate for sequencing count data. In Chapter III, I focus on emerging spatially resolved transcriptomics (SRT) data and develop a method, SPARK (Spatial PAttern Recognition via Kernels), to identify genes that display spatial expression patterns in various SRT studies. SPARK directly models spatial count data through generalized linear spatial models. It relies on recently developed statistical formulas for hypothesis testing, effectively controlling type I errors and yielding high statistical power. With a computationally efficient algorithm based on penalized quasi-likelihood, SPARK is scalable to datasets with tens of thousands of genes measured on tens of thousands of spatial locations. In Chapter IV, I focus on large-scale SRT data with a substantial fraction of zero values and develop a method, SPARK-X (SPARK-eXpedited), to efficiently identify genes that display spatial expression patterns in large-scale SRT studies. SPARK-X builds upon a robust covariance test framework and extends it to incorporate a variety of spatial kernels for non-parametric spatial modeling of sparse count data from large spatial transcriptomic studies. With additional algebraic innovations, SPARK-X reduces the computational complexity and physical memory requirement for SE analysis from cubic to linear with respect to the number of spatial locations, resulting in several orders of computational speed improvements and physical memory savings as compared to existing approaches. In Chapter V, I develop SRTsim, an SRT-specific simulator for scalable, reproducible, and well-documented SRT simulation. SRTsim is motivated by the recent fast-paced development of computational methods for various SRT-specific tasks. These developed computational methods are often validated and evaluated in silico via synthetic SRT data, but existing SRT data simulations are often study-specific, poorly documented, not easily reproducible, and/or can fail to resemble real SRT data. In addition, existing simulators for single-cell RNA-seq data are not directly applicable for SRT simulation as these simulators do not make use of spatial information and can fail to preserve the spatial expression patterns in the SRT data. Therefore, I develop SRTsim, which not only captures various gene expression characteristics observed in the real data but also preserves spatial expression patterns in SRT. I demonstrate how SRTsim can be used for benchmarking methods for spatial clustering and expression pattern detection.