Browsing by Author "Xie, Jichun"

Recent advances in sequencing technologies have enabled the measurement of gene expression and other omics profiles at multi-cell, single-cell or subcellular resolution. However, these advances also posed challenges for data analysis, such as identifying differentially expressed feature gene sets with high accuracy and benchmarking computational methods for various analysis topics on data with complex heterogeneity. In my dissertation, we have focused on developing novel statistical and computational methods to address these challenges.

In project 1, we developed SifiNet, a versatile pipeline to identify cell-subpopulation specific feature genes, annotate cell subpopulations, and reveal their relationships. The major advantage of SifiNet is that it bypasses cell clustering and thus avoids possible bias introduced by inaccurate clustering; thus, SifiNet achieves significantly higher accuracy in feature gene identification and cell annotation than tranditional two-step methods relying on clustering. SifiNet can analyze both single cell RNA sequencing (scRNA-seq) and single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, providing insight into multiomic cellular profile.

In project 2, we developed GeneScape, a novel scRNA-seq data simulator that can simulate complex cellular heterogeneity. Existing scRNA-seq data simulators are limited in their abilities to simulate data with complex or subtle cellular heterogeneities, especially for those cells exhibit both cell type and cell state differences (such as differences in cell cycles, senescence levels, and DNA-damage levels). GeneScape can successfully simulate gene expressions for cells with complex heterogeneity structures.

In project 3, we developed GeneScape-S (GeneScape-Spatial), a simulator for spatially resolved transcriptomics (SRT) data. Existing SRT-specific simulators cannot fulfill customized needs such as simulating multi-layer data, mimicking local tissue heterogeneity, and accommodating mixing cell-type structures in low-resolution spots. To fill these gaps, we propose GeneScape-S, which preserves the expression and spatial patterns of real SRT data, and offers specially designed functions tailored to fulfill customized needs. GeneScape-S also incorporates the features in GeneScape to simulate complex heterogeneities.

In my dissertation, we have developed statistical and computational tools to evaluate and interpret machine learning outputs in genomics data. The first two projects focus on single-cell RNA-sequencing (scRNA-seq) data. In project 1, we evaluated the fitting of widely-used distribution families on scRNA-seq UMI counts and concluded that UMI counts of polyclonal cells following gene-specific cell-type-specific NB distributions without zero- inflation. Based on this modeling, we proposed the working dispersion score (WDS) to select genes that differentially express across cell types. In project 2, we developed a new internal (unsupervised) index, Clustering Deviation Index (CDI), to evaluate cell label sets obtained from clustering algorithms. We conducted in silico and experimental scRNA-seq studies to show that CDI can select the optimal clustering label set. We also benchmarked CDI by comparing it with other internal indices in terms of the agreement with external indices using high-quality benchmark label sets. In addition, we demonstrated that CDI is more computationally efficient than other internal indices, especially for million-scale datasets. In project 3, we proposed a model-agnostic hypothesis testing framework to interpret feature interactions underneath complex machine learning models. The simulation study results demonstrated large power while controlling the type I error rate.

In my dissertation, I have developed computational methods for high dimensional inference, motivated by the analysis of omics data. This dissertation is divided into two parts. The first part of this dissertation is motivated by flow cytometry data analysis, where a key goal is to identify sparse cell subpopulations that differ be- tween two groups. I have developed an algorithm called multiple Testing Embedded on an Aggregation tree Method (TEAM) to locate where distributions differ between two samples. Regions containing differences can be identified in layers along the tree: the first layer searches for regions containing short-range, strong distributional differences, and higher layers search for regions containing long-range, weak distributional differences. TEAM is able to pinpoint local differences and under mild assumptions, asymptotically control the layer-specific and overall false discovery rate (FDR). Simulations verify our theoretical results. When applied to real flow cytometry data, TEAM captures cell subtypes that are overexpressed in cytomegalovirus stimulation vs. control. In addition, I have extended the TEAM algorithm so that it can incorporate information from more than one cell attribute, allowing for more robust conclusions. The second part of this dissertation is motivated by rare variant association studies, where a key goal is to identify regions of rare variants, which are associated with disease. This problem is addressed via a flexible method called stochastic aggregation tree-embedded testing (SATET). SATET embeds testing of genomic regions onto an aggregation tree, which provides a way to test association at various resolutions. The rejection rule at each layer depends on the previous layer, and leads to a procedure that controls the layer-specific FDR. Compared to methods that search for rare-variant association over large regions, such as protein domains, SATET can pinpoint sub-genic regions associated with disease. Numerical experiments show FDR control for different genetic architectures and superior per- formance compared to domain-based analyses. When applied to a case-control study in amyotrophic lateral sclerosis (ALS), SATET identified sub-genic regions in known ALS-related genes, while implicating regions in new genes not previously captured by domain-based analyses.

In my dissertation, I develop three powerful hierarchical multiple testing methods by accounting for ancillary information of data. In my first project, we develop a multiple testing framework named Distance Assisted Recursive Testing (DART). DART assumes there exists some informative distance information in the data. Through rigorous proof and extensive simulations, we justified the false discovery rate (FDR) control and sensitivity improvement of DART. As an illustration, we apply our method to a clinical trial in leukemia patients receiving hematopoietic cell transplantation to identify the gut microbiota whose abundance will be impacted by the after-transplant care. The second project is motivated by the flow cytometry analysis in immunology study. The analysis can be translated into a statistical problem which is trying to pinpoint the regions where two density functions differ. By partitioning the sample space into small bins and conducting testing on each bin, we model the analysis into a multiple testing problem. We provide theoretical justification that the procedure achieves the statistical goal of pinpointing the regions with differential density with high sensitivity and precision. My third project is motivated by the rare variant association study. We develop a multiple testing framework named DATED (Dynamic Aggregation and Tree-Embedded testing) to pinpoint the disease-associated rare-variant regions hierarchically and dynamically. To accommodate the application objective, DATED adopts a rare variant region-level FDR weighted by the proportions of the neutral rare-variant. Extensive numerical simulations demonstrate the superior performance of DATED under various scenarios compared to the existing methods. We illustrate DATED by applying it to an amyotrophic lateral sclerosis (ALS) study for identifying pathogenic rare variants.