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Benchmarking Statistical and Machine-Learning Methods for Single-cell RNA Sequencing Data

Benchmarking Statistical and Machine-Learning Methods for Single-cell RNA Sequencing Data
Author: Nan Xi
Publisher:
Total Pages: 203
Release: 2021
Genre:
ISBN:
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The large-scale, high-dimensional, and sparse single-cell RNA sequencing (scRNA-seq) data have raised great challenges in the pipeline of data analysis. A large number of statistical and machine learning methods have been developed to analyze scRNA-seq data and answer related scientific questions. Although different methods claim advantages in certain circumstances, it is difficult for users to select appropriate methods for their analysis tasks. Benchmark studies aim to provide recommendations for method selection based on an objective, accurate, and comprehensive comparison among cutting-edge methods. They can also offer suggestions for further methodological development through massive evaluations conducted on real data. In Chapter 2, we conduct the first, systematic benchmark study of nine cutting-edge computational doublet-detection methods. In scRNA-seq, doublets form when two cells are encapsulated into one reaction volume by chance. The existence of doublets, which appear as but are not real cells, is a key confounder in scRNA-seq data analysis. Computational methods have been developed to detect doublets in scRNA-seq data; however, the scRNA-seq field lacks a comprehensive benchmarking of these methods, making it difficult for researchers to choose an appropriate method for their specific analysis needs. Our benchmark study compares doublet-detection methods in terms of their detection accuracy under various experimental settings, impacts on downstream analyses, and computational efficiency. Our results show that existing methods exhibited diverse performance and distinct advantages in different aspects. In Chapter 3, we develop an R package DoubletCollection to integrate the installation and execution of different doublet-detection methods. Traditional benchmark studies can be quickly out-of-date due to their static design and the rapid growth of available methods. DoubletCollection addresses this issue in benchmarking doublet-detection methods for scRNA-seq data. DoubletCollection provides a unified interface to perform and visualize downstream analysis after doublet-detection. Additionally, we created a protocol using DoubletCollection to execute and benchmark doublet-detection methods. This protocol can automatically accommodate new doublet-detection methods in the fast-growing scRNA-seq field. In Chapter 4, we conduct the first comprehensive empirical study to explore the best modeling strategy for autoencoder-based imputation methods specific to scRNA-seq data. The autoencoder-based imputation method is a family of promising methods to denoise sparse scRNA-seq data; however, the design of autoencoders has not been formally discussed in the literature. Current autoencoder-based imputation methods either borrow the practice from other fields or design the model on an ad hoc basis. We find that the method performance is sensitive to the key hyperparameter of autoencoders, including architecture, activation function, and regularization. Their optimal settings on scRNA-seq are largely different from those on other data types. Our results emphasize the importance of exploring hyperparameter space in such complex and flexible methods. Our work also points out the future direction of improving current methods.

Development and Benchmarking of Imputation Methods for Micriobome and Single-cell Sequencing Data

Development and Benchmarking of Imputation Methods for Micriobome and Single-cell Sequencing Data
Author: Ruochen Jiang
Publisher:
Total Pages: 175
Release: 2021
Genre:
ISBN:
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Next generation sequencing (NGS) has revolutionized biomedical research and has a broad impact and applications. Since its advent around 15 years ago, this high scalable DNA sequencing technology has generated numerous biological data with new features and brought new challenges to data analysis. For example, researchers utilize RNA sequencing (RNA-seq) technology to more accurately quantify the gene expression levels. However, the NGS technology involves many processing steps and technical variations when measuring the expression values in the biological samples. In other words, the NGS data researchers observed could be biased due to the randomness and constraints in the NGS technology. This dissertation will mainly focus on microbiome sequencing data and single-cell RNA-seq (scRNA-seq) data. Both of them are highly sparse matrix-form count data. The zeros could either be biological or non-biological, and the high sparsity in the data have brought challenges to data analysis. Missing data imputation problem has been studied in statistics and social science as the survey data often experience non-response to some of the survey questions and those unresponded questions will be marked as "NA" or missing values in the data. Imputation methods are used to provide a sophisticated guess for the missing values, and the purpose is to avoid discarding the collected samples and for the ease of using the state-of-the-art statistical methods. In machine learning, the famous Netflix data challenge regarding film recommendation system also falls into the missing data imputation problem category. Netflix wants to find a way to predict users' fondness of the movies they have not watched. The potential scores these users would give to the unwatched films are regarded as missing values in the data. NGS data imputation problem is different from the previous two cases in that the missing values in the NGS data are not so well-defined. The zeros in the NGS data could either come from the biological origin (should not be regarded as missing values) or non-biological origin (due to the limitation of the sequencing technology and should be regarded as missing values). The size (number of samples and features) of the NGS matrix data is usually larger than the size of survey data but smaller than the size of the recommendation system data. In addition, in most cases, the percentage of missing values in the survey data is less than the percentage of zeros in the NGS data, and the missing values in the film recommendation system data have the highest percentage (> 99.9%). As a result, the commonly used missing data imputation methods in statistics and machine learning are not directly applicable to NGS data. In recent years, numerous imputation methods have been proposed to deal with the highly sparse scRNA-seq data. In light of this, this dissertation aims to address two questions. First, the microbiome sequencing data, having additional information comparing to the scRNA-seq data, lacks an imputation method. Secondly, whether to use imputation or not in scRNA-seq data analysis is still a controversial problem. The first part of this dissertation focuses on the first imputation method developed for the microbiome sequencing data: mbImpute. Microbiome studies have gained increased attention since many discoveries revealed connections between human microbiome compositions and diseases. A critical challenge in microbiome data analysis is the existence of many non-biological zeros, which distort taxon abundance distributions, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method for microbiome data---mbImpute---to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. Comprehensive simulations verify that mbImpute achieves better imputation accuracy under multiple metrics, compared with five state-of-the-art imputation methods designed for non-microbiome data. In real data applications, we demonstrate that mbImpute improves the power of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer, and mbImpute preserves non-zero distributions of taxa abundances. The second part of this dissertation focuses on how to deal with high sparsity in the scRNA-seq data. ScRNA-seq technologies have revolutionized biomedical sciences by enabling genome-wide profiling of gene expression levels at an unprecedented single-cell resolution. A distinct characteristic of scRNA-seq data is the vast proportion of zeros unseen in bulk RNA-seq data. Researchers view these zeros differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as false signals or missing data to be corrected. As a result, the scRNA-seq field faces much controversy regarding how to handle zeros in data analysis. We first discuss the sources of biological and non-biological zeros in scRNA-seq data. Second, we evaluate the impacts of non-biological zeros on cell clustering and differential gene expression analysis. Third, we summarize the advantages, disadvantages, and suitable users of three input data types: observed counts, imputed counts, and binarized counts and evaluate the performance of downstream analysis on these three input data types. Finally, we discuss the open questions regarding non-biological zeros, the need for benchmarking, and the importance of transparent analysis.

Gene Expression Data Analysis

Gene Expression Data Analysis
Author: Pankaj Barah
Publisher: CRC Press
Total Pages: 379
Release: 2021-11-21
Genre: Computers
ISBN: 1000425738
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Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and biological sciences

Graph Representation Learning

Graph Representation Learning
Author: William L. William L. Hamilton
Publisher: Springer Nature
Total Pages: 141
Release: 2022-06-01
Genre: Computers
ISBN: 3031015886
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Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Statistical Simulation and Analysis of Single-cell RNA-seq Data

Statistical Simulation and Analysis of Single-cell RNA-seq Data
Author: Tianyi Sun
Publisher:
Total Pages: 0
Release: 2023
Genre:
ISBN:
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The recent development of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized transcriptomic studies by revealing the genome-wide gene expression levels within individual cells. In contrast to bulk RNA sequencing, scRNA-seq technology captures cell-specific transcriptome landscapes, which can reveal crucial information about cell-to-cell heterogeneity across different tissues, organs, and systems and enable the discovery of novel cell types and new transient cell states. According to search results from PubMed, from 2009-2023, over 5,000 published studies have generated datasets using this technology. Such large volumes of data call for high-quality statistical methods for their analysis. In the three projects of this dissertation, I have explored and developed statistical methods to model the marginal and joint gene expression distributions and determine the latent structure type for scRNA-seq data. In all three projects, synthetic data simulation plays a crucial role. My first project focuses on the exploration of the Beta-Poisson hierarchical model for the marginal gene expression distribution of scRNA-seq data. This model is a simplified mechanistic model with biological interpretations. Through data simulation, I demonstrate three typical behaviors of this model under different parameter combinations, one of which can be interpreted as one source of the sparsity and zero inflation that is often observed in scRNA-seq datasets. Further, I discuss parameter estimation methods of this model and its other applications in the analysis of scRNA-seq data. My second project focuses on the development of a statistical simulator, scDesign2, to generate realistic synthetic scRNA-seq data. Although dozens of simulators have been developed before, they lack the capacity to simultaneously achieve the following three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill in this gap, scDesign2 is developed as a transparent simulator that achieves all three goals and generates high-fidelity synthetic data for multiple scRNA-seq protocols and other single-cell gene expression count-based technologies. Compared with existing simulators, scDesign2 is advantageous in its transparent use of probabilistic models and is unique in its ability to capture gene correlations via copula. We verify that scDesign2 generates more realistic synthetic data for four scRNA-seq protocols (10x Genomics, CEL-Seq2, Fluidigm C1, and Smart-Seq2) and two single-cell spatial transcriptomics protocols (MERFISH and pciSeq) than existing simulators do. Under two typical computational tasks, cell clustering and rare cell type detection, we demonstrate that scDesign2 provides informative guidance on deciding the optimal sequencing depth and cell number in single-cell RNA-seq experimental design, and that scDesign2 can effectively benchmark computational methods under varying sequencing depths and cell numbers. With these advantages, scDesign2 is a powerful tool for single-cell researchers to design experiments, develop computational methods, and choose appropriate methods for specific data analysis needs. My third project focuses on deciding latent structure types for scRNA-seq datasets. Clustering and trajectory inference are two important data analysis tasks that can be performed for scRNA-seq datasets and will lead to different interpretations. However, as of now, there is no principled way to tell which one of these two types of analysis results is more suitable to describe a given dataset. In this project, we propose two computational approaches that aim to distinguish cluster-type vs. trajectory-type scRNA-seq datasets. The first approach is based on building a classifier using eigenvalue features of the gene expression covariance matrix, drawing inspiration from random matrix theory (RMT). The second approach is based on comparing the similarity of real data and simulated data generated by assuming the cell latent structure as clusters or a trajectory. While both approaches have limitations, we show that the second approach gives more promising results and has room for further improvements.

Handbook of Statistical Bioinformatics

Handbook of Statistical Bioinformatics
Author: Henry Horng-Shing Lu
Publisher: Springer Nature
Total Pages: 406
Release: 2022-12-08
Genre: Science
ISBN: 3662659026
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Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.

Statistical Methods for Improving Data Quality in Modern Rna Sequencing Experiments

Statistical Methods for Improving Data Quality in Modern Rna Sequencing Experiments
Author: Zijian Ni (Ph.D.)
Publisher:
Total Pages: 0
Release: 2022
Genre:
ISBN:
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RNA sequencing (RNA-seq) has revolutionized the possibility of measuring transcriptome-wide gene expression in the last two decades. Modern RNA sequencing techniques such as single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have been developed in recent years, allowing researchers to quantify gene expression in single-cell resolution or to profile gene activity patterns in 2-dimensional space across tissue. While useful, data collected from these techniques always come with noise, and appropriate filtering and cleaning are required for reliable downstream analyses. In this dissertation, I investigate multiple quality-related issues in scRNA-seq and ST experiments, and I develop, implement, evaluate and apply statistical methods to adjust for them. A unifying theme of this work is that all these methods aim at improving data quality and allowing for better power and precision in downstream analyses. For scRNA-seq data, the quality issue we discuss in this dissertation is distinguishing barcodes associated with real cells from those binding background noise. In droplet-based scRNA-seq experiments, raw data contains both cell barcodes that should be retained for downstream analysis as well as background barcodes that are uninformative and should be filtered out. Due to ambient RNAs presenting in all the barcodes, cell barcodes are not easily distinghished from background barcodes. Both misclassified background barcodes and cell barcodes induce misleading results in downstream analyses. Existing filtering methods test barcodes individually and consequently do not leverage the strong cell-to-cell correlation present in most datasets. To improve cell detection, we introduce CB2, a cluster-based approach for distinguishing real cells from background barcodes. As demonstrated in simulated and case study datasets, CB2 has increased power for identifying real cells which allows for the identification of novel subpopulations and improves downstream differential expression analyses. We then present a benchmark study to evaluate the performance of cell detection methods, including CB2, on public scRNA-seq datasets covering a variety of experiment protocols. In recent years, variants of scRNA-seq techniques have been developed for specialized biological tasks. While the data structures remain the same as the standard scRNA-seq experiment, the underlying data properties can alter a lot. Here, we propose the first benchmark study to provide a thorough comparison across existing cell detection methods in scRNA-seq data, and to guide users to choose the appropriate methods for their experiments. Evaluation metrics include power, precision, computational efficiency, robustness, and accessibility. In addition, we provide investigation and guidance on appropriately choosing filtering parameters in order to improve data quality. For ST data, we uncover, for the first time, a novel quality issue that genes expressed at one tissue region bleed out and contaminate nearby tissue regions. ST is a powerful and widely-used approach for profiling transcriptome-wide gene expression across a tissue with emerging applications in molecular medicine and tumor diagnostics. Recent ST experiments utilize slides containing thousands of spots with spot-specific barcodes that bind RNAs. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case owing to bleed from nearby spots, an artifact we refer to as spot swapping. We design a creative human-mouse chimeric ST experiment to validate the existence of spot swapping. Spot swapping hinders inferences of region-specific gene activities and tissue annotations. In order to decontaminate ST data, we propose SpotClean, a probabilistic model that measures the spot swapping effect and estimates gene expression using EM algorithm. SpotClean is shown to provide a more accurate estimation of the underlying gene expression, increase the specificity of marker gene signals, and, more importantly, allow for improved tumor diagnostics.