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Statistical Hurdle Models For Single Cell Gene Expression
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Book Synopsis Statistical Hurdle Models for Single Cell Gene Expression by : Andrew McDavid
Download or read book Statistical Hurdle Models for Single Cell Gene Expression written by Andrew McDavid and published by . This book was released on 2016 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation describes a set of statistical methods developed for analysis of single cell gene expression. A characteristic of single cell expression is bimodal expression, in which two clusters of expression are present. In any given transcript, the null cluster corresponds to cells without detectable expression (hence a non-zero measurement reflects measurement error) while the signal cluster contains cells with a positive, detectable level of expression. Statistical models that accommodate this characteristic are considered. • In Chapter 1, motivation and history of single cell gene expression is considered. Scientific and statistical questions addressable through single cell expression are discussed, and some statistical frameworks for bulk and single cell expression are described. • In Chapter 2, I consider data generated from replicates of single cells and 100 cell aggregates that were assayed through single cell reverse-transcriptase qPCR (rt-qPCR). In rt-qPCR the null cluster manifests as bona-fide zeros, so expression is characterized by zero-inflation of otherwise continuous values. The average expression from single cells and 100-cell replicates is compared to develop quality control metrics that optimize the single-cell, 100-cell concordance. A Hurdle model is proposed, which accounts for the fact that genes at the single-cell level can be on (and a continuous expression measure is recorded) or dichotomously off (and the recorded expression is zero). Based on this model, I derive a combined likelihood-ratio test for differential expression that incorporates both the discrete and continuous components. This chapter was originally published in McDavid et al. [2013]. • In Chapter 3, I consider application of the hurdle model to single cell RNA sequencing (scRNAseq). In these technologies, the binary zero-inflation described found in rt- qPCR-based assays manifests itself as continuous, bimodal expression, motivating a clustering and thresholding procedure to assign expression to a cluster. The Hurdle model, extended and cast as a vector generalized linear model (vGLM), is provided as an R package named MAST. The cellular detection rate (CDR) is defined as the number of expressed genes found in a cell. It is identified as an important latent factor in single cell experiments, and is argued to measure size and efficiency variations among cells. Gene set enrichment analysis using the Hurdle model, and use of residuals defined through such models are discussed. Parts of this chapter were originally published in Finak et al. [2015], McDavid et al. [2014]. • In Chapter 4, the Hurdle model is generalized to model multivariate dependences between cells, permitting the parametrization of graphical models. A neighborhood selection-based method is proposed to leverage group-l1 penalized regression. Networks estimated on single-cell and multi-cell experiments are contrasted and found to be very distinct. In order to synthesize graphs estimated on transcriptome-scale data, a test for enrichment of connections between and within gene ontology categories is proposed.
Book Synopsis Statistical Methods for Single Cell Gene Expression by : Satabdi Saha
Download or read book Statistical Methods for Single Cell Gene Expression written by Satabdi Saha and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation elucidates a set of statistical methods developed for analysis of single cell gene expression datasets. Gene expression profiling of single cells has led to unprecedented progress in understanding normal physiology, disease progression and developmental processes. However, despite many improvements in high throughput sequencing, various technical factors including cell-cycle heterogeneity, library size differences, amplification bias, and low RNA capture per cell lead to high noise in scRNA-seq experiments. A primary characteristic of these datasets is the presence of high number of zeroes which represents the undetectable level of expression for a transcript. Statistical methods capable of modelling novel single cell experiments are developed and new estimation strategies are proposed and validated using simulated and real data experiments. 0́Ø In Chapter 1, the motivation and underlying philosophies of single cell gene expression is reviewed. Methods for analysis of dose response experiments and gene co-expression networks are reviewed and novel statistical hypothesis to be investigated using single cell experiments are discussed. 0́Ø In Chapter 2, I analyze a unique in vivo dose response hepatic scRNAseq dataset consisting of 9 dose groups with 3 biological replicates for 11 distinct liver cell types for greater than 100K cells. A Hurdle model for multiple group data is proposed, which models the bimodality of single cell gene expression within multiple groups. Based on the model assumptions, I derive a fit for purpose Bayesian test for simultaneously testing the differences in mean gene expression and zero proportions for multiple dose groups. For comparison the counterpart likelihood-ratio test for differential expression that incorporates testing for both components is also derived. This chapter was originally published in [1]. 0́Ø In Chapter 3, dose response curve estimation for single cell experiments is studied. Current protocols for genomic dose response modelling are only capable of modelling bulk and microarray datasets. A semiparametric regression model for joint dose response curve estimation for multiple cell-types while accounting for confounding covariates is proposed. A novel, scalable and efficient optimization algorithm using the MM philosophy is proposed for the estimation of both monotone and non-monotone curves. Two relevant tests of hypothesis are discussed and the proposed methods are validated using several simulated datasets. 0́Ø In Chapter 4, co-expression network estimation is studied using graph signal processing. A kernelized signed graph learning approach is developed for learning single cell gene co-expression networks, based on the assumption of smoothness of gene expressions over activating edges. Performance is assessed using real human and mouse embryonic datasets. This chapter was originally published in [2].
Book Synopsis Statistical Methods in Single Cell and Spatial Transcriptomics Data by : Roopali Singh
Download or read book Statistical Methods in Single Cell and Spatial Transcriptomics Data written by Roopali Singh and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Single cell RNA-sequencing (scRNA-seq) allows one to study the transcriptomics of different cell types in heterogeneous samples (e.g. tissues) at a single cell level. Most scRNA-seq protocols experience high levels of dropout due to the small amount of starting material, leading to a majority of reported expression levels being zero. Though missing data contain information about reproducibility, they are often excluded in the reproducibility assessment, potentially generating misleading assessments. In the first part of my dissertation, we develop a copula-based regression model to assess how the reproducibility of high-throughput experiments is affected by the choices of operational factors (e.g., platform or sequencing depth) when a large number of measurements are missing. Simulations show that our method is more accurate in detecting differences in reproducibility than existing measures of reproducibility. We illustrate the usefulness of our method by comparing the reproducibility of different library preparation platforms and studying the effect of sequencing depth on reproducibility, thereby determining the cost-effective sequencing depth that is required to achieve sufficient reproducibility. The spatial locations of these single cells are lost in scRNA-seq data. A recently emerging technology, Spatial Transcriptomics (ST), measures the gene expression in a tissue slice in situ, maintaining cells' spatial information in the tissue. However, they do not have a single-cell resolution but rather produce a group of potentially heterogeneous cells at each spot, which needs to be deconvolved to learn cell composition at each spot. In the second part of my dissertation, we develop a reference-free deconvolution method, based on Bayesian non-negative matrix factorization, to infer the cell type composition of each spot. Unlike the existing deconvolution methods, which all take reference-based approaches, our approach does not rely on scRNA-seq references. Simulations show that our method is more accurate in detecting the cell-type compositions than existing deconvolution techniques in case of varying spot size, heterogeneity, and imperfect single-cell reference. We illustrate the usefulness of our method using Mouse Brain Cerebellum data and Human Intestine Developmental data.
Book Synopsis Statistical Inference of a Single Cell Gene Expression Model for Heterogeneous Tissues by : Graham Heimberg
Download or read book Statistical Inference of a Single Cell Gene Expression Model for Heterogeneous Tissues written by Graham Heimberg and published by . This book was released on 2018 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis we present new statistical analyses for analyzing gene expression distributions within tissue samples and comparing them across tissue samples. These analyses share a common approach, to exploit a natural property of transcriptional systems to reduce the complexity of this data. Through a survey of over 500 datasets, we find that global gene expression profiles can be accurately represented as a linear combination of a relatively small number of gene expression "programs".
Book Synopsis Statistical Models for Gene Expression Data by : Helene Høgsbro Thygesen
Download or read book Statistical Models for Gene Expression Data written by Helene Høgsbro Thygesen and published by . This book was released on 2006 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Models for Single-cell Genetics by : Tobias Heinen
Download or read book Statistical Models for Single-cell Genetics written by Tobias Heinen and published by . This book was released on 2024* with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :National Academies of Sciences, Engineering, and Medicine Publisher :National Academies Press ISBN 13 :0309676738 Total Pages :201 pages Book Rating :4.3/5 (96 download)
Book Synopsis Next Steps for Functional Genomics by : National Academies of Sciences, Engineering, and Medicine
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.
Book Synopsis A First Course in Bayesian Statistical Methods by : Peter D. Hoff
Download or read book A First Course in Bayesian Statistical Methods written by Peter D. Hoff and published by Springer Science & Business Media. This book was released on 2009-06-02 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
Book Synopsis Statistical Methods and Software for High-throughput Gene Expression Experiments by : James Hudson Bullard
Download or read book Statistical Methods and Software for High-throughput Gene Expression Experiments written by James Hudson Bullard and published by . This book was released on 2009 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Handbook of Statistical Bioinformatics by : Henry Horng-Shing Lu
Download or read book Handbook of Statistical Bioinformatics written by Henry Horng-Shing Lu and published by Springer Nature. This book was released on 2022-12-08 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
Download or read book Mixed Models written by Eugene Demidenko and published by John Wiley & Sons. This book was released on 2013-08-05 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the First Edition “This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one’s personal library.” —Journal of the American Statistical Association Mixed modeling is a crucial area of statistics, enabling the analysis of clustered and longitudinal data. Mixed Models: Theory and Applications with R, Second Edition fills a gap in existing literature between mathematical and applied statistical books by presenting a powerful examination of mixed model theory and application with special attention given to the implementation in R. The new edition provides in-depth mathematical coverage of mixed models’ statistical properties and numerical algorithms, as well as nontraditional applications, such as regrowth curves, shapes, and images. The book features the latest topics in statistics including modeling of complex clustered or longitudinal data, modeling data with multiple sources of variation, modeling biological variety and heterogeneity, Healthy Akaike Information Criterion (HAIC), parameter multidimensionality, and statistics of image processing. Mixed Models: Theory and Applications with R, Second Edition features unique applications of mixed model methodology, as well as: Comprehensive theoretical discussions illustrated by examples and figures Over 300 exercises, end-of-section problems, updated data sets, and R subroutines Problems and extended projects requiring simulations in R intended to reinforce material Summaries of major results and general points of discussion at the end of each chapter Open problems in mixed modeling methodology, which can be used as the basis for research or PhD dissertations Ideal for graduate-level courses in mixed statistical modeling, the book is also an excellent reference for professionals in a range of fields, including cancer research, computer science, and engineering.
Book Synopsis Introduction to High-Dimensional Statistics by : Christophe Giraud
Download or read book Introduction to High-Dimensional Statistics written by Christophe Giraud and published by CRC Press. This book was released on 2021-08-25 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the first edition: "[This book] succeeds singularly at providing a structured introduction to this active field of research. ... it is arguably the most accessible overview yet published of the mathematical ideas and principles that one needs to master to enter the field of high-dimensional statistics. ... recommended to anyone interested in the main results of current research in high-dimensional statistics as well as anyone interested in acquiring the core mathematical skills to enter this area of research." —Journal of the American Statistical Association Introduction to High-Dimensional Statistics, Second Edition preserves the philosophy of the first edition: to be a concise guide for students and researchers discovering the area and interested in the mathematics involved. The main concepts and ideas are presented in simple settings, avoiding thereby unessential technicalities. High-dimensional statistics is a fast-evolving field, and much progress has been made on a large variety of topics, providing new insights and methods. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this new edition: Offers revised chapters from the previous edition, with the inclusion of many additional materials on some important topics, including compress sensing, estimation with convex constraints, the slope estimator, simultaneously low-rank and row-sparse linear regression, or aggregation of a continuous set of estimators. Introduces three new chapters on iterative algorithms, clustering, and minimax lower bounds. Provides enhanced appendices, minimax lower-bounds mainly with the addition of the Davis-Kahan perturbation bound and of two simple versions of the Hanson-Wright concentration inequality. Covers cutting-edge statistical methods including model selection, sparsity and the Lasso, iterative hard thresholding, aggregation, support vector machines, and learning theory. Provides detailed exercises at the end of every chapter with collaborative solutions on a wiki site. Illustrates concepts with simple but clear practical examples.
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 Modeling Biological Responses Using Gene Expression Profiling and Linear Dynamical Statistical Models by : Claudia Rangel Escareño
Download or read book Modeling Biological Responses Using Gene Expression Profiling and Linear Dynamical Statistical Models written by Claudia Rangel Escareño and published by . This book was released on 2003 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Modeling and Interpreting Interactive Hypotheses in Regression Analysis by : Robert Franzese
Download or read book Modeling and Interpreting Interactive Hypotheses in Regression Analysis written by Robert Franzese and published by University of Michigan Press. This book was released on 2009-09-23 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social scientists study complex phenomena about which they often propose intricate hypotheses tested with linear-interactive or multiplicative terms. While interaction terms are hardly new to social science research, researchers have yet to develop a common methodology for using and interpreting them. Modeling and Interpreting Interactive Hypotheses in Regression Analysis provides step-by-step guidance on how to connect substantive theories to statistical models and how to interpret and present the results. "Kam and Franzese is a must-have for all empirical social scientists interested in teasing out the complexities of their data." ---Janet M. Box-Steffensmeier, Ohio State University "Kam and Franzese have written what will become the definitive source on dealing with interaction terms and testing interactive hypotheses. It will serve as the standard reference for political scientists and will be one of those books that everyone will turn to when helping our students or doing our work. But more than that, this book is the best text I have seen for getting students to really think about the importance of careful specification and testing of their hypotheses." ---David A. M. Peterson, Texas A&M University "Kam and Franzese have given scholars and teachers of regression models something they've needed for years: a clear, concise guide to understanding multiplicative interactions. Motivated by real substantive examples and packed with valuable examples and graphs, their book belongs on the shelf of every working social scientist." ---Christopher Zorn, University of South Carolina "Kam and Franzese make it easy to model what good researchers have known for a long time: many important and interesting causal effects depend on the presence of other conditions. Their book shows how to explore interactive hypotheses in your own research and how to present your results. The book is straightforward yet technically sophisticated. There are no more excuses for misunderstanding, misrepresenting, or simply missing out on interaction effects!" ---Andrew Gould, University of Notre Dame Cindy D. Kam is Assistant Professor, Department of Political Science, University of California, Davis. Robert J. Franzese Jr. is Associate Professor, Department of Political Science, University of Michigan, and Research Associate Professor, Center for Political Studies, Institute for Social Research, University of Michigan. For datasets, syntax, and worksheets to help readers work through the examples covered in the book, visit: www.press.umich.edu/KamFranzese/Interactions.html
Book Synopsis The Enteric Nervous System by : John Barton Furness
Download or read book The Enteric Nervous System written by John Barton Furness and published by . This book was released on 1987 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:
