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The Statistics Of Rna Splicing In Single Cells
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Book Synopsis The Statistics of RNA Splicing in Single Cells by : Julia Eve Olivieri
Download or read book The Statistics of RNA Splicing in Single Cells written by Julia Eve Olivieri and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the amount of single-cell RNA-sequencing (scRNA-seq) data has exponentially increased in recent years, analysis of RNA splicing in these datasets remains virtually nonexistent, largely due to the sparsity and bias of the data. In this thesis, we introduce a new method of analyzing differential splicing at the single-cell level and apply this method to make new biological discoveries. We start by introducing the SpliZ, which quantifies the alternative splicing of each gene in a single number for each cell in the dataset. We verify the validity of the SpliZ through simulation, comparison with existing methods, and re-discovery of known true positives in the human lung. Next, we apply the SpliZ to over 200,000 cells from human, mouse, and mouse lemur to create a comprehensive atlas of cell-type-resolved alternative splicing, with experimental validation of two examples. We discover that unsupervised clustering of cells based only on the SpliZ scores of RPS24 and ATP5F1C accurately recapitulates division into stromal, immune, and epithelial compartments. Correlation of the SpliZ with developmental time reveals previously unknown conserved splicing changes throughout spermatogenesis. Finally, we apply the SpliZ to spatial transcriptomics data to discover spatially-resolved RNA splicing patterns in the mouse brain, which are more significantly localized than gene expression for Myl6 and Gng13. The SpliZ opens the door to widespread analysis of alternative splicing in scRNA-seq data.
Book Synopsis Statistical Methods for Bulk and Single-cell RNA Sequencing Data by : Wei Li
Download or read book Statistical Methods for Bulk and Single-cell RNA Sequencing Data written by Wei Li and published by . This book was released on 2019 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the invention of next-generation RNA sequencing (RNA-seq) technologies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies on bulk tissues. Recently, the emerging single-cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at a single-cell resolution, providing a chance to characterize stochastic heterogeneity within a cell population. The analysis of bulk and single-cell RNA-seq data at four different levels (samples, genes, transcripts, and exons) involves multiple statistical and computational questions, some of which remain challenging up to date. The first part of this dissertation focuses on the statistical challenges in the transcript-level analysis of bulk RNA-seq data. The next-generation RNA-seq technologies have been widely used to assess full-length RNA isoform structure and abundance in a high-throughput manner, enabling us to better understand the alternative splicing process and transcriptional regulation mechanism. However, accurate isoform identification and quantification from RNA-seq data are challenging due to the information loss in sequencing experiments. In Chapter 2, given the fast accumulation of multiple RNA-seq datasets from the same biological condition, we develop a statistical method, MSIQ, to achieve more accurate isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. The MSIQ method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples and allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy of MSIQ compared with alternative methods through both simulation and real data studies. In Chapter 3, we introduce a novel method, AIDE, the first approach that directly controls false isoform discoveries by implementing the statistical model selection principle. Solving the isoform discovery problem in a stepwise manner, AIDE prioritizes the annotated isoforms and precisely identifies novel isoforms whose addition significantly improves the explanation of observed RNA-seq reads. Our results demonstrate that AIDE has the highest precision compared to the state-of-the-art methods, and it is able to identify isoforms with biological functions in pathological conditions. The second part of this dissertation discusses two statistical methods to improve scRNA-seq data analysis, which is complicated by the excess missing values, the so-called dropouts due to low amounts of mRNA sequenced within individual cells. In Chapter 5, we introduce scImpute, a statistical method to accurately and robustly impute the dropouts in scRNA-seq data. The scImpute method automatically identifies likely dropouts, and only performs imputation on these values by borrowing information across similar cells. Evaluation based on both simulated and real scRNA-seq data suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropouts, enhance the clustering of cell subpopulations, and improve the accuracy of differential expression analysis. In Chapter 6, we propose a flexible and robust simulator, scDesign, to optimize the choices of sequencing depth and cell number in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information. It is the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings.
Book Synopsis Proceedings of the Twelfth Annual ACM-SIAM Symposium on Discrete Algorithms by : SIAM Activity Group on Discrete Mathematics
Download or read book Proceedings of the Twelfth Annual ACM-SIAM Symposium on Discrete Algorithms written by SIAM Activity Group on Discrete Mathematics and published by SIAM. This book was released on 2001-01-01 with total page 962 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains 130 papers, which were selected based on originality, technical contribution, and relevance. Although the papers were not formally refereed, every attempt was made to verify the main claims. It is expected that most will appear in more complete form in scientific journals. The proceedings also includes the paper presented by invited plenary speaker Ronald Graham, as well as a portion of the papers presented by invited plenary speakers Udi Manber and Christos Papadimitriou.
Book Synopsis Statistical Methods for Alternative Splicing Using RNA Sequencing by : Yu Hu
Download or read book Statistical Methods for Alternative Splicing Using RNA Sequencing written by Yu Hu and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The emergence of RNA-seq technology has made it possible to estimate isoform-specific gene expression and detect differential alternative splicing between conditions, thus providing us an effective way to discover disease susceptibility genes. Analysis of alternative splicing, however, is challenging because various biases present in RNA-seq data complicates the analysis, and if not appropriately corrected, will affect gene expression estimation and downstream modeling. Motivated by these issues, my dissertation focused on statistical problems related to the analysis of alternative splicing in RNA-seq data. In Part I of my dissertation, I developed PennSeq, a method that aims to account for non-uniform read distribution in isoform expression estimation. PennSeq models non-uniformity using the empirical read distribution in RNA-seq data. It is the first time that non-uniformity is modeled at the isoform level. Compared to existing approaches, PennSeq allows bias correction at a much finer scale and achieved higher estimation accuracy. In Part II of my dissertation, I developed PennDiff, a method that aims to detect differential alternative splicing by RNA-seq. This approach avoids multiple testing for exons originated from the same isoform(s) and is able to detect differential alternative splicing at both exon and gene level, with more flexibility and higher sensitivity than existing methods. In Part III of my dissertation, I focused on problems arising from single-cell RNA-seq (scRNA-seq), a newly developed technology that allows the measurement of cellular heterogeneity of gene expression in single cells. Compared to bulk tissue RNA-seq, analysis of scRNA-seq data is more challenging due to high technical variability across cells and extremely low sequencing depth. To overcome these challenges, I developed SCATS, a method that aims to detect differential alternative splicing with scRNA-seq data. SCATS employs an empirical Bayes approach to model technical noise by use of external RNA spike-ins and groups informative reads sharing the same isoform(s) to detect splicing change. SCATS showed superior performance in both simulation and real data analyses. In summary, methods developed in my dissertation provide biomedical researchers a set of powerful tools for transcriptomic data analysis and will aid novel scientific discovery.
Book Synopsis Statistical Models for RNA Biology by : Boyko Kakaradov
Download or read book Statistical Models for RNA Biology written by Boyko Kakaradov and published by . This book was released on 2014 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of RNA sequencing and other high-throughput molecular assays, RNA biology has recently transitioned from careful curation of single-hypothesis experiments to data-driven design of multi-hypothesis investigations. Fortunately, statistical advances and increasingly powerful computers have given rise to machine learning, a computational framework which can automatically distill perpetually growing datasets into predictive models of fundamental cellular and disease processes. Finally, recent advances in microfluidics have enabled the efficient capture and interrogation of individual cells by a variety of molecular assays. My research bridges theses fields by introducing predictive statistical models of RNA abundance and processing in single cells to uncover new insights into the regulation of RNA editing and splicing and their effects on cellular differentiation. This dissertation collects my contributions in single-cell and statistical genomics, from low-level details of data analysis to high-level principles of cellular identity and diversity. My early contributions concentrate on building error models of RNA sequencing data in order to extract biologically-relevant signals from experimental noise and sampling biases inherent in high-throughput sequencing technologies. Specifically, I describe statistical models of RNA splicing and editing that are robust to noise from PCR duplicates or sequencing errors and to uneven sampling from incomplete reverse transcription or cDNA fragmentation biases. I then evaluate the models' self-consistency and compare their accuracy relative to a gold standard. With a solid statistical foundation for sequencing data analysis established, my latest contributions focus on developing principled methods of constructing and evaluating compelling biological hypotheses in collaboration with domain experts. Specifically, I describe a Bayesian model of A-to-I RNA editing whose high specificity helped resolve the functional difference between the catalytically-active RNA binding protein ADR-2, and its inactive homolog ADR-1. In another collaboration, I used machine learning to resolve a long-standing question in immunology regarding the asymmetric specification of T cells into two functionally distinct lineages. Here, through one of the first applications of single-cell gene expression analysis of the immune system, I demonstrate that pathogen-activated T cells undergo an early bifurcation into effector- and memory-fated populations and help identify the genes whose asymmetric expression drive this phenomenon. Together all of these contributions establish a principled statistical framework for experimental design and analysis which integrates both hypothesis- and data-driven models to validate new findings and uncover novel principles of RNA biology.
Book Synopsis Computational Methods for Single-Cell Data Analysis by : Guo-Cheng Yuan
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.
Book Synopsis Handbook of Statistical Genomics by : David J. Balding
Download or read book Handbook of Statistical Genomics written by David J. Balding and published by John Wiley & Sons. This book was released on 2019-07-09 with total page 1828 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.
Book Synopsis The Mouse Nervous System by : Charles Watson
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
Author :Andras Nagy Publisher :Cold Spring Harbor, N.Y. : Cold Spring Harbor Laboratory Press ISBN 13 : Total Pages :784 pages Book Rating :4.3/5 (91 download)
Book Synopsis Manipulating the Mouse Embryo by : Andras Nagy
Download or read book Manipulating the Mouse Embryo written by Andras Nagy and published by Cold Spring Harbor, N.Y. : Cold Spring Harbor Laboratory Press. This book was released on 2003 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides background information and detailed protocols for developing a mouse colony and using the animals in transgenic and gene-targeting experiments. The protocols list the animals, equipment, and reagents required and step-by-step procedures. Topics include in vitro culture of preimplantation embryos, surgical procedures, the production of chimeras, and the analysis of genome alterations. The third edition adds protocols for cloning mice, modifying embryonic stem cells, intracytoplasmic sperm injection, and cryopreservation of embryos.
Book Synopsis Molecular Biology of The Cell by : Bruce Alberts
Download or read book Molecular Biology of The Cell written by Bruce Alberts and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Tumor Immunology and Immunotherapy - Cellular Methods Part B by :
Download or read book Tumor Immunology and Immunotherapy - Cellular Methods Part B written by and published by Academic Press. This book was released on 2020-01-29 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tumor Immunology and Immunotherapy – Cellular Methods Part B, Volume 632, the latest release in the Methods in Enzymology series, continues the legacy of this premier serial with quality chapters authored by leaders in the field. Topics covered include Quantitation of calreticulin exposure associated with immunogenic cell death, Side-by-side comparisons of flow cytometry and immunohistochemistry for detection of calreticulin exposure in the course of immunogenic cell death, Quantitative determination of phagocytosis by bone marrow-derived dendritic cells via imaging flow cytometry, Cytofluorometric assessment of dendritic cell-mediated uptake of cancer cell apoptotic bodies, Methods to assess DC-dependent priming of T cell responses by dying cells, and more. Contains content written by authorities in the field Provides a comprehensive view on the topics covered Includes a high level of detail
Book Synopsis Single-cell Analysis of MRNA Splicing Variants by : Jacob Potts
Download or read book Single-cell Analysis of MRNA Splicing Variants written by Jacob Potts and published by . This book was released on 2020 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Single-cell RNA sequencing (scRNA-seq) and imaging technologies have revealed the transcriptomic diversity of single cells, the study of which is key to understanding biological processes and disease states. Alternative splicing of RNA contributes towards this diversity, yielding variable transcriptional profiles across different cell types within the human body. This diversity is challenging to visualize in situ because current isoform-sensitive RNA imaging methods are inefficient or are only applicable to genes with large isoform-specific exons. Here, we attempt to expand the toolbox of spatially informed RNA visualization methods by optimizing click-amplifying fluorescent in situ hybridization (clampFISH) to visualize alternatively-spliced isoforms and describe some of the challenges we encountered. We conducted a meta-analysis of combined short- and long-read sequencing data to select optimal targets for splicing analysis in human cell lines. We developed several single molecule FISH (smFISH) probe sets to evaluate efficacy of isoform-specific clampFISH probes in future experiments. Next, we modified clampFISH probes to include more landing pads (binding sites for subsequent probes) with the aim of achieving more rapid signal amplification with fewer primary probes. We observed higher target signal intensity with more landing pads, but this effect created higher off-target signal intensity as well. Finally, we tested whether adding magnesium ions to hybridization conditions would increase clampFISH probe specificity. The tested magnesium ion concentrations reduced nonspecific binding but also reduced specific binding. Our results lay the foundation for future experiments that will inform isoform distributions at a single-cell level"--Author's abstract.
Book Synopsis RNA-Seq Analysis: Methods, Applications and Challenges by : Filippo Geraci
Download or read book RNA-Seq Analysis: Methods, Applications and Challenges written by Filippo Geraci and published by Frontiers Media SA. This book was released on 2020-06-08 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Methods for Whole Transcriptome Sequencing by : Cheng Jia
Download or read book Statistical Methods for Whole Transcriptome Sequencing written by Cheng Jia and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: RNA-Sequencing (RNA-Seq) has enabled detailed unbiased profiling of whole transcriptomes with incredible throughput. Recent technological breakthroughs have pushed back the frontiers of RNA expression measurement to single-cell level (scRNA-Seq). With both bulk and single-cell RNA-Seq analyses, modeling of the noise structure embedded in the data is crucial for drawing correct inference. In this dissertation, I developed a series of statistical methods to account for the technical variations specific in RNA-Seq experiments in the context of isoform- or gene- level differential expression analyses. In the first part of my dissertation, I developed MetaDiff (https://github.com/jiach/MetaDiff ), a random-effects meta-regression model, that allows the incorporation of uncertainty in isoform expression estimation in isoform differential expression analysis. This framework was further extended to detect splicing quantitative trait loci with RNA-Seq data. In the second part of my dissertation, I developed TASC (Toolkit for Analysis of Single-Cell data; https://github.com/scrna-seq/TASC), a hierarchical mixture model, to explicitly adjust for cell-to-cell technical differences in scRNA-Seq analysis using an empirical Bayes approach. This framework can be adapted to perform differential gene expression analysis. In the third part of my dissertation, I developed, TASC-B, a method extended from TASC to model transcriptional bursting- induced zero-inflation. This model can identify and test for the difference in the level of transcriptional bursting. Compared to existing methods, these new tools that I developed have been shown to better control the false discovery rate in situations where technical noise cannot be ignored. They also display superior power in both our simulation studies and real world applications.
Book Synopsis Single Cell Transcriptomics by : Raffaele A. Calogero
Download or read book Single Cell Transcriptomics written by Raffaele A. Calogero and published by Springer Nature. This book was released on 2022-12-10 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides up-to-date methods on single cell wet and bioinformatics protocols based on the researcher experiment requirements. Chapters detail basic analytical procedures, single-cell data QC, dimensionality reduction, clustering, cluster-specific features selection, RNA velocity, multi-modal data integration, and single cell RNA editing. 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. Cutting-edge and comprehensive, Single Cell Transcriptomics: Methods and Protocols aims to be a valuable resource for all researchers interested in learning more about this important and developing field.
Book Synopsis Alternative Splicing and Single-cell RNA-sequencing by : Jennifer Westoby
Download or read book Alternative Splicing and Single-cell RNA-sequencing written by Jennifer Westoby and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning in Single-Cell RNA-seq Data Analysis by : Khalid Raza
Download or read book Machine Learning in Single-Cell RNA-seq Data Analysis written by Khalid Raza and published by Springer Nature. This book was released on with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt:
