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Computational Methods For The Analysis Of Single Cell Rna Seq Data
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Book Synopsis Computational Methods for the Analysis of Single-Cell RNA-Seq Data by : Marmar Moussa
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.
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 RNA-seq Data Analysis by : Eija Korpelainen
Download or read book RNA-seq Data Analysis written by Eija Korpelainen and published by CRC Press. This book was released on 2014-09-19 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: The State of the Art in Transcriptome AnalysisRNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. RNA-seq Data Analysis: A Practical Approach enables researchers to examine differential expression at gene, exon, and transcript le
Book Synopsis Computational Methods for the Analysis of Genomic Data and Biological Processes by : Francisco A. Gómez Vela
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.
Book Synopsis Statistical Simulation and Analysis of Single-cell RNA-seq Data by : Tianyi Sun
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.
Book Synopsis Statistical and Computational Methods for Single-cell Transcriptome Sequencing and Metagenomics by : Fanny Perraudeau
Download or read book Statistical and Computational Methods for Single-cell Transcriptome Sequencing and Metagenomics written by Fanny Perraudeau and published by . This book was released on 2018 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: I propose statistical methods and software for the analysis of single-cell transcriptome sequencing (scRNA-seq) and metagenomics data. Specifically, I present a general and flexible zero-inflated negative binomial-based wanted variation extraction (ZINB-WaVE) method, which extracts low-dimensional signal from scRNA-seq read counts, accounting for zero inflation (dropouts), over-dispersion, and the discrete nature of the data. Additionally, I introduce an application of the ZINB-WaVE method that identifies excess zero counts and generates gene and cell-specific weights to unlock bulk RNA-seq differential expression pipelines for zero-inflated data, boosting performance for scRNA-seq analysis. Finally, I present a method to estimate bacterial abundances in human metagenomes using full-length 16S sequencing reads.
Book Synopsis Computational Methods for Large-scale Single-cell RNA-seq and Multimodal Data by : Văn Hoàn Đỗ
Download or read book Computational Methods for Large-scale Single-cell RNA-seq and Multimodal Data written by Văn Hoàn Đỗ and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Methods for Studying Cellular Differentiation Using Single-cell RNA-sequencing by : Hui Ting Grace Yeo
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.
Book Synopsis Benchmarking Statistical and Machine-Learning Methods for Single-cell RNA Sequencing Data by : Nan Xi
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.
Book Synopsis Bioinformatics Analysis of Single Cell Sequencing Data and Applications in Precision Medicine by : Jialiang Yang
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:
Book Synopsis Revealing Translational and Fundamental Insights Via Computational Analysis of Single-cell Sequencing Data by : Jessica Lu Zhou
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.
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
Book Synopsis Statistical Methods for RNA-sequencing Data by : Rhonda Bacher
Download or read book Statistical Methods for RNA-sequencing Data written by Rhonda Bacher and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Major methodological and technological advances in sequencing have inspired ambitious biological questions that were previously elusive. Addressing such questions with novel and complex data requires statistically rigorous tools. In this dissertation, I develop, evaluate, and apply statistical and computational methods for analysis of high-throughput sequencing data. A unifying theme of this work is that all these methods are aimed at RNA-seq data. The first method focuses on characterizing gene expression in RNA-seq experiments with ordered conditions. The second focuses on single-cell RNA-seq data, where we develop a method for normalization to account for a previously unknown technical artifact in the data. Finally, we develop a simulation in order to recapitulate the source of the artifact [in silico].
Book Synopsis Statistical and Computational Methods for Analysis of Spatial Transcriptomics Data by : Dylan Maxwell Cable
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.
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 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 Computational Problems for RNA-seq Data Analysis by : Shunfu Mao
Download or read book Computational Problems for RNA-seq Data Analysis written by Shunfu Mao and published by . This book was released on 2020 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: High throughput sequencing of RNA (RNA-seq) has become a staple in modern molecular biology, with a wide range of applications including RNA transcripts assembly, variants detection, and gene expression estimation for downstream cellular analysis. RNA-seq data is therefore able to provide us with unprecedented insights into cellular organisms. However, they have also introduced a new set of computational challenges because of the nature of the sequenced RNA transcripts and an ever increasing number of RNA-seq experiments. For instance, the RNA transcripts have different expression levels, making the sequenced reads potentially unable to fully cover some lowly expressed gene regions. In addition, the RNA transcripts also share many repetitive patterns, making it ambiguous to determine the regions where some RNA-seq reads are actually sampled. Moreover, there are still many laborious procedures in the RNA-seq data analysis, making it difficult to keep pace with the constantly produced large amounts of RNA-seq data. There is an urgent need for better computational methods that are able to analyze the RNA-seq data more accurately and efficiently. Motivated by this, in the thesis, we have presented novel computational solutions for three computational problems for RNA-seq data analysis: Firstly, we have developed RefShannon - a new genome-guided RNA transcripts (transcriptome) assembly software. RefShannon reconstructs RNA transcripts, based on the alignments of RNA-seq reads onto a reference genome. It exploits the pair-end linking information of RNA-seq reads, and the varying expressions of RNA transcripts, in enabling an accurate reconstruction of the transcripts. Experiments demonstrate RefShannon has superior assembly performance over the state-of-art genome-guided assembly tools. Next, we have developed abSNP - a new RNA-seq SNP calling software. AbSNP detects SNPs in expressed gene regions, based on the alignments of RNA-seq reads onto a reference transcriptome. It exploits the mapping quality scores of RNA-seq reads, and the varying expressions of different genes. AbSNP is a cost-effective method as it requires no additional DNA-seq. It is also able to call SNPs with significantly improved sensitivity in repetitive gene regions, while other RNA-seq SNP callers are unable to make any calls in such regions. Finally, we have developed CellMeSH - a new web server and API package for automatic cell-type identification in single-cell RNA-seq (scRNA-seq) analysis. CellMeSH predicts cell types, based on a set of marker genes as query input. CellMeSH builds its database in a scalable and easy-to-update way using prior literature, and adopts a novel probabilistic method to better query the database. Through a variety of experiments on human and mouse scRNA-seq datasets, CellMeSH has demonstrated richer gene and cell-type information in its database, robust query method, and an overall superior annotation performance.
