Improving Statistical Rigor in Single-cell and Spatial Omics

The recent technological revolution in single-cell and spatial omics has provided unprecedented multi-modal views of individual cells, transforming our understanding of cell biology in health and disease. Numerous computational methods have been developed to analyze data generated from these technologies. However, the statistical rigor of existing computational methods is often questionable: many computational methods are complicated "black-box'" algorithms (e.g., deep-learning-based methods). Therefore, it remains challenging to obtain correct statistical interpretation (e.g., well-calibrated p-values), and to avoid misinterpretation of observed data (e.g., exaggerated false discoveries). Due to the existence of numerous methods, the field crucially needs precise, statistically robust, and interpretable methods. During my Ph.D., I have been focusing on combining statistics and computational biology to provide accurate statistical interpretation to computational analyses in single-cell and spatial omics. This dissertation aims to address this statistical rigor issue through three main themes.My first theme concentrates on the probabilistic generative models for high-dimensional single-cell and spatial multi-omics data. The realistic simulation of single-cell and spatial multi-omics data plays a critical role in both evaluating the performance of computational tools and facilitating the exploration of experimental designs. However, the complex topology of cells and the high-dimension features pose significant challenges to this endeavor. To overcome this challenge, I developed scDesign3, the first unified framework for realistic in silico data generation of both single-cell and spatial omics.My second theme focuses on differential expression (DE) tests and false discovery rate (FDR) control based on inferred covariates. Identifying differentially expressed (DE) genes between or between cell states is a crucial task in investigating the underlying molecular mechanisms in cells. However, in single-cell RNA sequencing (scRNA-seq) analysis, the latent cell states are usually inferred from the data (e.g., inferred cell types by clustering or continuous trajectories by pseudotime inference). Therefore, conventional statistical tests can behave incorrectly if we ignore the fact that the covariates are inferred rather than observed. Hence, I developed PseudotimeDE, a robust DE method that accounts for pseudotime inference uncertainty and yields well-calibrated p-values. Separately, post-clustering DE was another related issue that drew our attention. This two-step procedure uses the same data twice: once to define cell clusters as potential cell types, and then to identify DE genes as potential cell-type marker genes. This practice, often known as "double dipping," can lead to the erroneous identification of false-positive cell-type marker genes, particularly when the cell clusters themselves are not well-defined. To overcome this challenge, I proposed ClusterDE, a post-clustering DE method for controlling the FDR of identified DE genes regardless of the clustering quality by using "synthetic null data."My third theme aims at feature selection and subsampling in large-scale scRNA-seq data. The large number of genes (~20,000) and increasing number of measured cells (> 1 million) in scRNA-seq datasets remain a challenge for data analysis. A practical solution to this computational bottleneck involves the strategic selection of a subset of cells or genes. We developed scSampler, a fast diversity-preserving cell subsampling inspired by space-filling design in the field of experimental design. scSampler selects a small subset of cells to accurately represent the primary variability in the entire dataset. In addition, we developed scPNMF, an unsupervised gene selection method through matrix factorization. This method effectively selects a significantly smaller subset of genes (~100) while still achieving robust discrimination in cell-type identification. We developed scGTM, a flexible and interpretable model that captures the trend of gene expression along the pseudotime of cells to select genes with specific expression patterns

Particle swarm optimization in constrained maximum likelihood estimation a case study

The aim of paper is to apply two types of particle swarm optimization, global best andlocal best PSO to a constrained maximum likelihood estimation problem in pseudotime anal-ysis, a sub-field in bioinformatics. The results have shown that particle swarm optimizationis extremely useful and efficient when the optimization problem is non-differentiable and non-convex so that analytical solution can not be derived and gradient-based methods can not beapplied.Comment: 11 pages, 7 figure

Review of computational methods for estimating cell potency from single-cell RNA-seq data, with a detailed analysis of discrepancies between method description and code implementation

In single-cell RNA sequencing (scRNA-seq) data analysis, a critical challenge is to infer hidden dynamic cellular processes from measured static cell snapshots. To tackle this challenge, many computational methods have been developed from distinct perspectives. Besides the common perspectives of inferring trajectories (or pseudotime) and RNA velocity, another important perspective is to estimate the differentiation potential of cells, which is commonly referred to as "cell potency." In this review, we provide a comprehensive summary of 11 computational methods that estimate cell potency from scRNA-seq data under different assumptions, some of which are even conceptually contradictory. We divide these methods into three categories: mean-based, entropy-based, and correlation-based methods, depending on how a method summarizes gene expression levels of a cell or cell type into a potency measure. Our review focuses on the key similarities and differences of the methods within each category and between the categories, providing a high-level intuition of each method. Moreover, we use a unified set of mathematical notations to detail the 11 methods' methodologies and summarize their usage complexities, including the number of ad-hoc parameters, the number of required inputs, and the existence of discrepancies between the method description in publications and the method implementation in software packages. Realizing the conceptual contradictions of existing methods and the difficulty of fair benchmarking without single-cell-level ground truths, we conclude that accurate estimation of cell potency from scRNA-seq data remains an open challenge

Recent Progress of Remediating Heavy Metal Contaminated Soil Using Layered Double Hydroxides as Super-Stable Mineralizer

Heavy metal contamination in soil, which is harmful to both ecosystem and mankind, has attracted worldwide attention from the academic and industrial communities. However, the most-widely used remediation technologies such as electrochemistry, elution, and phytoremediation. suffer from either secondary pollution, long cycle time or high cost. In contrast, in situ mineralization technology shows great potential due to its universality, durability and economical efficiency. As such, the development of mineralizers with both high efficiency and low-cost is the core of in situmineralization. In 2021, the concept of ‘Super-Stable Mineralization’ was proposed for the first time by Kong et al.[1] The layered double hydroxides (denoted as LDHs), with the unique host–guest intercalated structure and multiple interactions between the host laminate and the guest anions, are considered as an ideal class of materials for super-stable mineralization. In this review, we systematically summarize the application of LDHs in the treatment of heavy metal contaminated soil from the view of: 1) the structure–activity relationship of LDHs in in situ mineralization, 2) the advantages of LDHs in mineralizing heavy metals, 3) the scale-up preparation of LDHs-based mineralizers and 4) the practical application of LDHs in treating contaminated soil. At last, we highlight the challenges and opportunities for the rational design of LDH-based mineralizer in the future