Download or read book Computational Methods for Studying Cellular Differentiation Using Single cell RNA sequencing written by Hui Ting Grace Yeo and published by . This book was released on 2020 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Single-cell RNA-sequencing (scRNA-seq) enables transcriptome-wide measurements of single cells at scale. As scRNA-seq datasets grow in complexity and size, more complex computational methods are required to distill raw data into biological insight. In this thesis, we introduce computational methods that enable analysis of novel scRNA-seq perturbational assays. We also develop computational models that seek to move beyond simple observations of cell states toward more complex models of underlying biological processes. In particular, we focus on cellular differentiation, which is the process by which cells acquire some specific form or function. First, we introduce barcodelet scRNA-seq (barRNA-seq), an assay which tags individual cells with RNA ‘barcodelets’ to identify them based on the treatments they receive. We apply barRNA-seq to study the effects of the combinatorial modulation of signaling pathways during early mESC differentiation toward germ layer and mesodermal fates. Using a data-driven analysis framework, we identify combinatorial signaling perturbations that drive cells toward specific fates. Second, we describe poly-adenine CRISPR gRNA-based scRNA-seq (pAC-seq), a method that enables the direct observation of guide RNAs (gRNAs) in scRNA-seq. We apply it to assess the phenotypic consequences of CRISPR/Cas9-based alterations of gene cis-regulatory regions. We find that power to detect transcriptomic effects depend on factors such as rate of mono/biallelic loss, baseline gene expression, and the number of cells per target gRNA. Third, we propose a generative model for analyzing scRNA-seq containing unwanted sources of variation. Using only weak supervision from a control population, we show that the model enables removal of nuisance effects from the learned representation without prior knowledge of the confounding factors. Finally, we develop a generative modeling framework that learns an underlying differentiation landscape from population-level time-series data. We validate the modeling framework on an experimental lineage tracing dataset, and show that it is able to recover the expected effects of known modulators of cell fate in hematopoiesis.

Download or read book Computational Methods for Single Cell Data Analysis written by Guo-Cheng Yuan and published by Humana Press. This book was released on 2019-02-14 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis.

Download or read book Computational Methods for the Analysis of Single Cell RNA Seq Data written by Marmar Moussa and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Single cell transcriptional profiling is critical for understanding cellular heterogeneity and identification of novel cell types and for studying growth and development of tissues and tumors. Leveraging recent advances in single cell RNA sequencing (scRNA-Seq) technology requires novel methods that are robust to high levels of technical and biological noise and scale to datasets of millions of cells. In this work, we address several challenges in the analysis work-flow of scRNA-Seq data: First, we propose novel computational approaches for unsupervised clustering of scRNA-Seq data based on Term Frequency - Inverse Document Frequency (TF-IDF) transformation that has been successfully used in text analysis. Here, we present empirical experimental results showing that TF-IDF methods consistently outperform commonly used scRNA-Seq clustering approaches. Second, we study the so called 'drop-out' effect that is considered one of the most notable challenges in scRNA-Seq analysis, where only a fraction of the transcriptome of each cell is captured. The random nature of drop-outs, however, makes it possible to consider imputation methods as means of correcting for drop-outs. In this part we study existing scRNA-Seq imputation methods and propose a novel iterative imputation approach based on efficiently computing highly similar cells. We then present results of a comprehensive assessment of existing and proposed methods on real scRNA-Seq datasets with varying per cell sequencing depth. Third, we present a computational method for assigning and/or ordering cells based on their cell-cycle stages from scRNA-Seq. And finally, we present a web-based interactive computational work-flow for analysis and visualization of scRNA-seq data.

Download or read book Computational Stem Cell Biology written by Patrick Cahan and published by Humana. This book was released on 2019-05-07 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume details methods and protocols to further the study of stem cells within the computational stem cell biology (CSCB) field. Chapters are divided into four sections covering the theory and practice of modeling of stem cell behavior, analyzing single cell genome-scale measurements, reconstructing gene regulatory networks, and metabolomics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Stem Cell Biology: Methods and Protocols will be an invaluable guide to researchers as they explore stem cells from the perspective of computational biology.

Download or read book The Mouse Nervous System written by Charles Watson and published by Academic Press. This book was released on 2011-11-28 with total page 815 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Mouse Nervous System provides a comprehensive account of the central nervous system of the mouse. The book is aimed at molecular biologists who need a book that introduces them to the anatomy of the mouse brain and spinal cord, but also takes them into the relevant details of development and organization of the area they have chosen to study. The Mouse Nervous System offers a wealth of new information for experienced anatomists who work on mice. The book serves as a valuable resource for researchers and graduate students in neuroscience. Systematic consideration of the anatomy and connections of all regions of the brain and spinal cord by the authors of the most cited rodent brain atlases A major section (12 chapters) on functional systems related to motor control, sensation, and behavioral and emotional states A detailed analysis of gene expression during development of the forebrain by Luis Puelles, the leading researcher in this area Full coverage of the role of gene expression during development and the new field of genetic neuroanatomy using site-specific recombinases Examples of the use of mouse models in the study of neurological illness

Download or read book Introduction to Single Cell Omics written by Xinghua Pan and published by Frontiers Media SA. This book was released on 2019-09-19 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Single-cell omics is a progressing frontier that stems from the sequencing of the human genome and the development of omics technologies, particularly genomics, transcriptomics, epigenomics and proteomics, but the sensitivity is now improved to single-cell level. The new generation of methodologies, especially the next generation sequencing (NGS) technology, plays a leading role in genomics related fields; however, the conventional techniques of omics require number of cells to be large, usually on the order of millions of cells, which is hardly accessible in some cases. More importantly, harnessing the power of omics technologies and applying those at the single-cell level are crucial since every cell is specific and unique, and almost every cell population in every systems, derived in either vivo or in vitro, is heterogeneous. Deciphering the heterogeneity of the cell population hence becomes critical for recognizing the mechanism and significance of the system. However, without an extensive examination of individual cells, a massive analysis of cell population would only give an average output of the cells, but neglect the differences among cells. Single-cell omics seeks to study a number of individual cells in parallel for their different dimensions of molecular profile on genome-wide scale, providing unprecedented resolution for the interpretation of both the structure and function of an organ, tissue or other system, as well as the interaction (and communication) and dynamics of single cells or subpopulations of cells and their lineages. Importantly single-cell omics enables the identification of a minor subpopulation of cells that may play a critical role in biological process over a dominant subpolulation such as a cancer and a developing organ. It provides an ultra-sensitive tool for us to clarify specific molecular mechanisms and pathways and reveal the nature of cell heterogeneity. Besides, it also empowers the clinical investigation of patients when facing a very low quantity of cell available for analysis, such as noninvasive cancer screening with circulating tumor cells (CTC), noninvasive prenatal diagnostics (NIPD) and preimplantation genetic test (PGT) for in vitro fertilization. Single-cell omics greatly promotes the understanding of life at a more fundamental level, bring vast applications in medicine. Accordingly, single-cell omics is also called as single-cell analysis or single-cell biology. Within only a couple of years, single-cell omics, especially transcriptomic sequencing (scRNA-seq), whole genome and exome sequencing (scWGS, scWES), has become robust and broadly accessible. Besides the existing technologies, recently, multiplexing barcode design and combinatorial indexing technology, in combination with microfluidic platform exampled by Drop-seq, or even being independent of microfluidic platform but using a regular PCR-plate, enable us a greater capacity of single cell analysis, switching from one single cell to thousands of single cells in a single test. The unique molecular identifiers (UMIs) allow the amplification bias among the original molecules to be corrected faithfully, resulting in a reliable quantitative measurement of omics in single cells. Of late, a variety of single-cell epigenomics analyses are becoming sophisticated, particularly single cell chromatin accessibility (scATAC-seq) and CpG methylation profiling (scBS-seq, scRRBS-seq). High resolution single molecular Fluorescence in situ hybridization (smFISH) and its revolutionary versions (ex. seqFISH, MERFISH, and so on), in addition to the spatial transcriptome sequencing, make the native relationship of the individual cells of a tissue to be in 3D or 4D format visually and quantitatively clarified. On the other hand, CRISPR/cas9 editing-based In vivo lineage tracing methods enable dynamic profile of a whole developmental process to be accurately displayed. Multi-omics analysis facilitates the study of multi-dimensional regulation and relationship of different elements of the central dogma in a single cell, as well as permitting a clear dissection of the complicated omics heterogeneity of a system. Last but not the least, the technology, biological noise, sequence dropout, and batch effect bring a huge challenge to the bioinformatics of single cell omics. While significant progress in the data analysis has been made since then, revolutionary theory and algorithm logics for single cell omics are expected. Indeed, single-cell analysis exert considerable impacts on the fields of biological studies, particularly cancers, neuron and neural system, stem cells, embryo development and immune system; other than that, it also tremendously motivates pharmaceutic RD, clinical diagnosis and monitoring, as well as precision medicine. This book hereby summarizes the recent developments and general considerations of single-cell analysis, with a detailed presentation on selected technologies and applications. Starting with the experimental design on single-cell omics, the book then emphasizes the consideration on heterogeneity of cancer and other systems. It also gives an introduction of the basic methods and key facts for bioinformatics analysis. Secondary, this book provides a summary of two types of popular technologies, the fundamental tools on single-cell isolation, and the developments of single cell multi-omics, followed by descriptions of FISH technologies, though other popular technologies are not covered here due to the fact that they are intensively described here and there recently. Finally, the book illustrates an elastomer-based integrated fluidic circuit that allows a connection between single cell functional studies combining stimulation, response, imaging and measurement, and corresponding single cell sequencing. This is a model system for single cell functional genomics. In addition, it reports a pipeline for single-cell proteomics with an analysis of the early development of Xenopus embryo, a single-cell qRT-PCR application that defined the subpopulations related to cell cycling, and a new method for synergistic assembly of single cell genome with sequencing of amplification product by phi29 DNA polymerase. Due to the tremendous progresses of single-cell omics in recent years, the topics covered here are incomplete, but each individual topic is excellently addressed, significantly interesting and beneficial to scientists working in or affiliated with this field.

Download or read book Computational Methods for the Analysis of Genomic Data and Biological Processes written by Francisco A. Gómez Vela and published by MDPI. This book was released on 2021-02-05 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.

Download or read book Computational Methods for Transcriptome based Cellular Phenotyping written by Matthew Nathan Bernstein and published by . This book was released on 2019 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the basic chemical mechanisms of cellular biology are now well-known, we are still a long way from understanding how phenotypes emerge from these basic mechanisms. Within the last decade, RNA-sequencing (RNA-seq) has become a ubiquitous technology for measuring the transcriptome, which provides a snapshot of gene expression across the entire genome. An improvement in our ability to predict how phenotypes emerge from the complex patterns of gene expression, a task we refer to as transcriptome-based cellular phenotyping (TBCP), would lead to considerable medical and technological advancements. Machine learning promises to be an apt approach for TBCP due to its ability to overcome noise inherent in RNA-seq data and because it does not require a priori knowledge regarding the rules and patterns that lead from gene expression to phenotype. Furthermore, there exist large, public databases of RNA-seq data that promise to be a valuable source of training data for developing machine learning algorithms to perform TBCP. Unfortunately, this opportunity is impeded by a number of challenges inherent in these databases including poorly structured metadata and data heterogeneity. In this thesis, I present three projects that push the state-of-the-art in the ability to leverage the trove of publicly available gene expression data for TBCP. In the first project, we address the problem of poorly structured metadata that exist in public genomics databases. We specifically focus on the Sequence Read Archive (SRA), which is the premiere repository of raw RNA-seq data curated by the National Institutes of Health; however, our work generalizes to other databases. Existing approaches treat metadata normalization as a named entity recognition problem where the goal is to tag metadata with terms from controlled vocabularies when that term is mentioned in the metadata. We reframe this problem as an inference task, in which we tag the metadata with only those terms that describe the underlying biology of the described sample rather than with all mentioned terms. By doing so, we achieve much higher precision than that achieved by existing methods, and maintain a competitive recall. In the second project, we leverage the normalized metadata produced by the first project in order to train predictive models of phenotype from RNA-seq derived gene expression data. We specifically focus on the cell type prediction task: given an RNA-seq sample, we wish to predict the cell type from which the sample was derived. Cell type prediction is an important step in many transcriptomic analyses, including that of annotating cell types in single-cell RNA-seq datasets. This work represents the first effort towards a cell type prediction task that utilizes the full potential of publicly available RNA-seq data. Finally, in the third project, we build on the second project in order to address the task of cell type prediction on sparse single-cell RNA-seq data (scRNA-seq) produced by novel droplet-based technologies. These droplet-based scRNA-seq technologies are enabling the sequencing of higher numbers of cells at the cost of a lower read-depth per cell. Such low read-depths result in fewer genes with detected expression per cell. We explore the effects of applying cell type classifiers trained on dense, bulk RNA-seq data to sparse scRNA-seq data and propose a novel probabilistic generative model for adapting the bulk-trained classifiers to sparse input data.

Download or read book Computational Biology for Stem Cell Research written by Pawan Raghav and published by Elsevier. This book was released on 2024-01-12 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Biology for Stem Cell Research is an invaluable guide for researchers as they explore HSCs and MSCs in computational biology. With the growing advancement of technology in the field of biomedical sciences, computational approaches have reduced the financial and experimental burden of the experimental process. In the shortest span, it has established itself as an integral component of any biological research activity. HSC informatics (in silico) techniques such as machine learning, genome network analysis, data mining, complex genome structures, docking, system biology, mathematical modeling, programming (R, Python, Perl, etc.) help to analyze, visualize, network constructions, and protein-ligand or protein-protein interactions. This book is aimed at beginners with an exact correlation between the biomedical sciences and in silico computational methods for HSCs transplantation and translational research and provides insights into methods targeting HSCs properties like proliferation, self-renewal, differentiation, and apoptosis. Modeling Stem Cell Behavior: Explore stem cell behavior through animal models, bridging laboratory studies to real-world clinical allogeneic HSC transplantation (HSCT) scenarios. Bioinformatics-Driven Translational Research: Navigate a path from bench to bedside with cutting-edge bioinformatics approaches, translating computational insights into tangible advancements in stem cell research and medical applications. Interdisciplinary Resource: Discover a single comprehensive resource catering to biomedical sciences, life sciences, and chemistry fields, offering essential insights into computational tools vital for modern research.

Download or read book Statistical Simulation and Analysis of Single cell RNA seq Data written by Tianyi Sun and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Kernel Methods for Pattern Analysis written by John Shawe-Taylor and published by Cambridge University Press. This book was released on 2004-06-28 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description

Download or read book Revealing Translational and Fundamental Insights Via Computational Analysis of Single cell Sequencing Data written by Jessica Lu Zhou and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Single-cell sequencing has emerged as a powerful tool for dissecting cellular heterogeneity and providing cell type-specific biological insights. Single-cell sequencing technologies have rapidly proliferated over the last decade, leading to an explosion of data generated from such experiments. However, several challenges exist in the computational analysis of single-cell sequencing data due to its large and complex nature, including the need for sophisticated statistical methods to distinguish biologically meaningful signals from noise, the integration of single-cell sequencing data with other types of biological information, and the development of scalable and reproducible computational pipelines that can handle the large and complex nature of the data. In this dissertation, I present two distinct projects analyzing single-cell sequencing data. The first is of an analytical nature and tackles a translational question. In this project, I built computational pipelines for processing and analyzing single-nucleus RNA- and ATAC-sequencing datasets generated from the amygdalae of genetically diverse heterogenous stock rats, which were subjected to a behavioral protocol for studying addiction-like behaviors following cocaine self-administration. In doing so, I provide a standard reference for analyzing such data as well as reveal cell type-specific insights into the molecular underpinnings of cocaine addiction. The second project is oriented towards methods development and seeks to understand the fundamental biological question of transcriptional regulation. Here, I developed a statistical framework for simulating and modeling data from single-cell CRISPR regulatory screens and used it to perform a genome-wide interrogation of epistatic-like interactions between enhancer pairs. I found that multiple enhancers act together in a multiplicative fashion with little evidence for interactive effects between them. This work revealed novel insights into the collective behavior of multiple regulatory elements and provides a tool that can be applied to future datasets generated from such experiments. This dissertation exemplifies how computational methods can be applied in different contexts to extract meaning from a variety of single-cell sequencing modalities. By tackling both a translational and fundamental biological question, I have showcased the breadth of what can be revealed by studying single-cell sequencing data and the computational methods necessary to extract this information.

Download or read book Computational Methods for Analyzing RNA Sequencing to Study Post Transcriptional Gene Regulation written by Ashley Anne Cass and published by . This book was released on 2018 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the completion of the Human Genome Project in 2003, massive DNA sequencing efforts enabled gene mapping and enhanced our understanding of genetic variation. However, exactly how the same DNA sequence in every cell of one individual leads to vast biological variation is still not fully understood. In particular, the DNA sequence does not directly contain information regarding which genes are expressed in different cell types, tissues, and disease states. With the advent of high-throughput RNA sequencing (RNA-Seq), gene expression and RNA isoform variation can be assayed cost- and time-efficiently in different conditions. In this work, we aimed to develop computational methods to analyze RNA-Seq for the purpose of elucidating mechanisms of post-transcriptional gene regulation. The first chapter briefly introduces RNA biology, including co- and post-transcriptional gene regulation concepts. The second chapter describes the identification of small cleavage-inducing RNAs and their RNA targets for degradation through bioinformatic integration of small RNA sequencing and Degradome Sequencing, the latter capturing RNA degradation products. This work revealed an expanded repertoire of small cleavage-inducing RNAs (sciRNAs) and their targets, suggesting that small RNA-mediated cleavage is more widespread than previously appreciated. Post-transcriptional regulation is often mediated by cis-regulatory elements in 5' and 3' untranslated regions (UTRs), including sciRNA target motifs. Thus, alternative transcription start sites (ATSS) and alternative polyadenylation (APA) often impact post-transcriptional gene regulation through the inclusion or exclusion of cis-regulatory elements in UTRs. In chapter three, we describe mountainClimber, a novel method that overcomes several limitations of existing approaches to identify ATSS and APA from RNA-Seq. In chapter four, we applied mountainClimber to thousands of RNA-Seq datasets derived from many human tissues in the largest study of ATSS and APA to date. In chapter five, we applied mountainClimber to chromatin-associated and poly(A)-selected RNA-Seq in murine macrophages with or without previous exposure to an endotoxin. This analysis revealed ATSS, APA, and alternative transcription end sites associated with tolerization of macrophages to endotoxins. Finally, we summarize our conclusions in chapter six.

Download or read book Bioinformatics Analysis of Single Cell Sequencing Data and Applications in Precision Medicine written by Jialiang Yang and published by Frontiers Media SA. This book was released on 2020-02-27 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical and Computational Methods for Analysis of Spatial Transcriptomics Data written by Dylan Maxwell Cable and published by . This book was released on 2020 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial transcriptomic technologies measure gene expression at increasing spatial resolution, approaching individual cells. One limitation of current technologies is that spatial measurements may contain contributions from multiple cells, hindering the discovery of cell type-specific spatial patterns of localization and expression. In this thesis, I will explore the development of Robust Cell Type Decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA sequencing data to decompose mixtures, such as those observed in spatial transcriptomic technologies. Our RCTD approach accounts for platform effects introduced by systematic technical variability inherent to different sequencing modalities. We demonstrate RCTD provides substantial improvement in cell type assignment in Slide-seq data by accurately reproducing known cell type and subtype localization patterns in the cerebellum and hippocampus. We further show the advantages of RCTD by its ability to detect mixtures and identify cell types on an assessment dataset. Finally, we show how RCTD’s recovery of cell type localization uniquely enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD has the potential to enable the definition of spatial components of cellular identity, uncovering new principles of cellular organization in biological tissue.

Download or read book Next Steps for Functional Genomics written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2020-12-18 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the holy grails in biology is the ability to predict functional characteristics from an organism's genetic sequence. Despite decades of research since the first sequencing of an organism in 1995, scientists still do not understand exactly how the information in genes is converted into an organism's phenotype, its physical characteristics. Functional genomics attempts to make use of the vast wealth of data from "-omics" screens and projects to describe gene and protein functions and interactions. A February 2020 workshop was held to determine research needs to advance the field of functional genomics over the next 10-20 years. Speakers and participants discussed goals, strategies, and technical needs to allow functional genomics to contribute to the advancement of basic knowledge and its applications that would benefit society. This publication summarizes the presentations and discussions from the workshop.

Download or read book Benchmarking Statistical and Machine Learning Methods for Single cell RNA Sequencing Data written by Nan Xi and published by . This book was released on 2021 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.