Statistical Methods For Reliable Inference In Rna Seq Experiments To Facilitate Regenerative Medicine

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Statistical Methods for Reliable Inference in RNA-seq Experiments to Facilitate Regenerative Medicine

Author :
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (938 download)

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Book Synopsis Statistical Methods for Reliable Inference in RNA-seq Experiments to Facilitate Regenerative Medicine by :

Download or read book Statistical Methods for Reliable Inference in RNA-seq Experiments to Facilitate Regenerative Medicine written by and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last decade of genome research has led to major technological advances in sequencing, genotyping, and phenotyping. However, how best to derive useful information from them still remains to be explored by statistical scientists. In this dissertation, I develop, implement, evaluate and apply three statistical methods for high-dimensional data analysis to facilitate efforts in regenerative medicine. The first method is an empirical Bayes model called EBSeq for identifying differentially expressed (DE) genes and isoforms. Unlike microarrays, RNA-seq experiments allow for the identification of not only DE genes, but also their corresponding isoforms on a genome-wide scale. Taking advantage of the merits of empirical Bayesian methods, we developed EBSeq which models the uncertainty groups via different priors. Our results demonstrate substantially improved power and performance of EBSeq for identifying DE isoforms compared to other competing methods. The second method is an auto-regressive hidden Markov model called EBSeq-HMM for identifying expression changes across ordered conditions. With improvements in next-generation sequencing technologies and reductions in price, ordered RNA-seq experiments are becoming common. Of primary interest in these experiments is identifying genes that are changing over time or space, for example, and then characterizing the specific expression changes. In EBSeq-HMM, an autoregressive hidden Markov model is implemented to accommodate dependence in gene expression across ordered conditions. As demonstrated in simulation and case studies, the output proves useful in identifying DE genes, characterizing their changes over conditions, and classifying genes into particular expression paths. The third method is a statistical pipeline called Oscope for identifying oscillatory gene sets using unsynchronized single-cell RNA-seq data. Recent advance of single-cell RNA-seq enables precise quantification of gene expression among individual cells. This provides the potential to uncover oscillatory systems at single-cell level. However, methods to identify candidate oscillatory gene sets in an unsynchronized cell population are still lacking. Here we developed a statistical pipeline with 3 main modules - a paired-sine model to identify co-oscillating gene paires, a K-Medoid clustering module to group gene pairs into oscillatory gene sets, and an extended nearest insertion algorithm to recover base cycle profile of oscillatory genes.

Pre-processing and Statistical Inference Methods for High-throughput Genomic Data with Application to Biomarker Detection and Regenerative Medicine

Download Pre-processing and Statistical Inference Methods for High-throughput Genomic Data with Application to Biomarker Detection and Regenerative Medicine PDF Online Free

Author : Jeea Choi
Publisher :
ISBN 13 :
Total Pages : 125 pages
Book Rating : 4.:/5 (972 download)

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Book Synopsis Pre-processing and Statistical Inference Methods for High-throughput Genomic Data with Application to Biomarker Detection and Regenerative Medicine by : Jeea Choi

Download or read book Pre-processing and Statistical Inference Methods for High-throughput Genomic Data with Application to Biomarker Detection and Regenerative Medicine written by Jeea Choi and published by . This book was released on 2017 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome research advances of the last two decades allow us to obtain various forms of data, such as next-generation sequencing, genotyping, phenotyping, as well as clinical information. However, our ability to derive useful information from these data remains to be improved. This motivated me to develop a pipeline with new computational methods. In this dissertation, I develop, implement, evaluate, and apply statistical and computational methods for high-dimensional data analysis to facilitate efforts in regenerative medicine and to uncover novel insights in cancer genomics. The first method is an integrative pathway-index (IPI) model to identify a clinically actionable biomarker of high-risk advanced ovarian cancer patients. Despite improvements in operative management and therapies, overall survival rates in advanced ovarian cancer have remained largely unchanged over the past three decades. The IPI model is applied to messenger RNA expression and survival data collected on ovarian cancer patients as part of the Cancer Genome Atlas project. The approach identifies signatures that are strongly associated with overall and progression-free survival, and also identifies group of patients who may benefit from enhanced adjuvant therapy. The second method is called SCDC for removing increased variability due to oscillating genes in a snapshot scRNA-seq experiment. Single-cell RNA sequencing provides a new avenue for studying oscillatory gene expression. However, in many studies, oscillations (e.g., cell cycle) are not of interest, and the increased variability imposed by them masks the effects of interest. In bulk RNA-seq, the increase in variability caused by oscillatory genes is mitigated by averaging over thousands of cells. However, in typical unsynchronized scRNA-seq, this variability remains. Simulation and case studies demonstrate that by removing increased variability due to oscillations, both the power and accuracy of downstream analysis is increased. Finally, in this thesis, we have extended a data analysis pipeline for both single- cell and bulk RNA-seq data. In this pipeline, we review current standards and resources for (sc)RNA-seq data analysis and provide an extended pipeline that incorporates a quality control scheme and user friendly advanced statistical analysis software for visualization and projected principal component analysis (PCA).

Statistical Analysis of Next Generation Sequencing Data

Author : Somnath Datta
Publisher : Springer
ISBN 13 : 3319072129
Total Pages : 438 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis Statistical Analysis of Next Generation Sequencing Data by : Somnath Datta

Download or read book Statistical Analysis of Next Generation Sequencing Data written by Somnath Datta and published by Springer. This book was released on 2014-07-03 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine. About the editors: Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics. Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics.

Statistical Methods for the Analysis of Genomic Data

Author : Hui Jiang
Publisher : MDPI
ISBN 13 : 3039361406
Total Pages : 136 pages
Book Rating : 4.0/5 (393 download)

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Book Synopsis Statistical Methods for the Analysis of Genomic Data by : Hui Jiang

Download or read book Statistical Methods for the Analysis of Genomic Data written by Hui Jiang and published by MDPI. This book was released on 2020-12-29 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.

Design of Experiments and Advanced Statistical Techniques in Clinical Research

Author : Basavarajaiah D. M.
Publisher : Springer Nature
ISBN 13 : 9811582106
Total Pages : 380 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Design of Experiments and Advanced Statistical Techniques in Clinical Research by : Basavarajaiah D. M.

Download or read book Design of Experiments and Advanced Statistical Techniques in Clinical Research written by Basavarajaiah D. M. and published by Springer Nature. This book was released on 2020-11-05 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent Statistical techniques are one of the basal evidence for clinical research, a pivotal in handling new clinical research and in evaluating and applying prior research. This book explores various choices of statistical tools and mechanisms, analyses of the associations among different clinical attributes. It uses advanced statistical methods to describe real clinical data sets, when the clinical processes being examined are still in the process. This book also discusses distinct methods for building predictive and probability distribution models in clinical situations and ways to assess the stability of these models and other quantitative conclusions drawn by realistic experimental data sets. Design of experiments and recent posthoc tests have been used in comparing treatment effects and precision of the experimentation. This book also facilitates clinicians towards understanding statistics and enabling them to follow and evaluate the real empirical studies (formulation of randomized control trial) that pledge insight evidence base for clinical practices. This book will be a useful resource for clinicians, postgraduates scholars in medicines, clinical research beginners and academicians to nurture high-level statistical tools with extensive scope.

Statistical Methods for Improving Data Quality in Modern Rna Sequencing Experiments

Author : Zijian Ni (Ph.D.)
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (136 download)

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Book Synopsis Statistical Methods for Improving Data Quality in Modern Rna Sequencing Experiments by : Zijian Ni (Ph.D.)

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

Statistical Methods for Bulk and Single-cell RNA Sequencing Data

Author : Wei Li
Publisher :
ISBN 13 :
Total Pages : 207 pages
Book Rating : 4.:/5 (11 download)

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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.

Statistical Methods for the Analysis of RNA Sequencing Data

Author : Man-Kee Maggie Chu
Publisher :
ISBN 13 :
Total Pages : 340 pages
Book Rating : 4.:/5 (16 download)

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Book Synopsis Statistical Methods for the Analysis of RNA Sequencing Data by : Man-Kee Maggie Chu

Download or read book Statistical Methods for the Analysis of RNA Sequencing Data written by Man-Kee Maggie Chu and published by . This book was released on 2014 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: The next generation sequencing technology, RNA-sequencing (RNA-seq), has an increasing popularity over traditional microarrays in transcriptome analyses. Statistical methods used for gene expression analyses with these two technologies are di erent because the array-based technology measures intensities using continuous distributions, whereas RNA-seq provides absolute quantification of gene expression using counts of reads. There is a need for reliable statistical methods to exploit the information from the rapidly evolving sequencing technologies and limited work has been done on expression analysis of time-course RNA-seq data. Functional clustering is an important method for examining gene expression patterns and thus discovering co-expressed genes to better understand the biological systems. Clusteringbased approaches to analyze repeated digital gene expression measures are in demand. In this dissertation, we propose a model-based clustering method for identifying gene expression patterns in time-course RNA-seq data. Our approach employs a longitudinal negative binomial mixture model to postulate the over-dispersed time-course gene count data. The e ectiveness of the proposed clustering method is assessed using simulated data and is illustrated by real data from time-course genomic experiments. Due to the complexity and size of genomic data, the choice of good starting values is an important issue to the proposed clustering algorithm. There is a need for a reliable initialization strategy for cluster-wise regression specifically for time-course discrete count data. We modify existing common initialization procedures to suit our model-based clustering algorithm and the procedures are evaluated through a simulation study on artificial datasets and are applied to real genomic examples to identify the optimal initialization method. Another common issue in gene expression analysis is the presence of missing values in the datasets. Various treatments to missing values in genomic datasets have been developed but limited work has been done on RNA-seq data. In the current work, we examine the performance of various imputation methods and their impact on the clustering of time-course RNA-seq data. We develop a cluster-based imputation method which is specifically suitable for dealing with missing values in RNA-seq datasets. Simulation studies are provided to assess the performance of the proposed imputation approach.

Pluripotent Stem Cell Therapy for Diabetes

Author : Lorenzo Piemonti
Publisher : Springer Nature
ISBN 13 : 303141943X
Total Pages : 597 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Pluripotent Stem Cell Therapy for Diabetes by : Lorenzo Piemonti

Download or read book Pluripotent Stem Cell Therapy for Diabetes written by Lorenzo Piemonti and published by Springer Nature. This book was released on with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Statistical Methods for Whole Transcriptome Sequencing

Author : Cheng Jia
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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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.

Statistical Methods for RNA-sequencing Data

Author : Rhonda Bacher
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (123 download)

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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].

Statistical Methods for Multi-sample Analysis of RNA-SEQ and DNA Copy Number Data

Author : Saran Vardhanabhuti
Publisher :
ISBN 13 :
Total Pages : 129 pages
Book Rating : 4.:/5 (793 download)

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Book Synopsis Statistical Methods for Multi-sample Analysis of RNA-SEQ and DNA Copy Number Data by : Saran Vardhanabhuti

Download or read book Statistical Methods for Multi-sample Analysis of RNA-SEQ and DNA Copy Number Data written by Saran Vardhanabhuti and published by . This book was released on 2011 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Evaluation of Statistical Methods for Normalization and Differential Expression in MRNA-Seq Experiments

Author : Applied Research Applied Research Press
Publisher :
ISBN 13 : 9781517392260
Total Pages : 42 pages
Book Rating : 4.3/5 (922 download)

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Book Synopsis Evaluation of Statistical Methods for Normalization and Differential Expression in MRNA-Seq Experiments by : Applied Research Applied Research Press

Download or read book Evaluation of Statistical Methods for Normalization and Differential Expression in MRNA-Seq Experiments written by Applied Research Applied Research Press and published by . This book was released on 2015-09-16 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data. Our results have significant practical and methodological implications for the design and analysis of mRNA-Seq experiments. They highlight the importance of appropriate statistical methods for normalization and DE inference, to account for features of the sequencing platform that could impact the accuracy of results. They also reveal the need for further research in the development of statistical and computational methods for mRNA-Seq.

Statistical Methods for the Analysis of Genomic Data

Author : Hui Jiang
Publisher :
ISBN 13 : 9783039361410
Total Pages : 136 pages
Book Rating : 4.3/5 (614 download)

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Book Synopsis Statistical Methods for the Analysis of Genomic Data by : Hui Jiang

Download or read book Statistical Methods for the Analysis of Genomic Data written by Hui Jiang and published by . This book was released on 2020 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.

Computational Stem Cell Biology

Author : Patrick Cahan
Publisher : Humana
ISBN 13 : 9781493992232
Total Pages : 0 pages
Book Rating : 4.9/5 (922 download)

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Book Synopsis Computational Stem Cell Biology by : Patrick Cahan

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.

Algorithms for Minimization Without Derivatives

Author : Richard P. Brent
Publisher : Courier Corporation
ISBN 13 : 0486143686
Total Pages : 210 pages
Book Rating : 4.4/5 (861 download)

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Book Synopsis Algorithms for Minimization Without Derivatives by : Richard P. Brent

Download or read book Algorithms for Minimization Without Derivatives written by Richard P. Brent and published by Courier Corporation. This book was released on 2013-06-10 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: DIVOutstanding text for graduate students and research workers proposes improvements to existing algorithms, extends their related mathematical theories, and offers details on new algorithms for approximating local and global minima. /div

Systems Genetics

Author : Florian Markowetz
Publisher : Cambridge University Press
ISBN 13 : 131638098X
Total Pages : 287 pages
Book Rating : 4.3/5 (163 download)

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Book Synopsis Systems Genetics by : Florian Markowetz

Download or read book Systems Genetics written by Florian Markowetz and published by Cambridge University Press. This book was released on 2015-07-02 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whereas genetic studies have traditionally focused on explaining heritance of single traits and their phenotypes, recent technological advances have made it possible to comprehensively dissect the genetic architecture of complex traits and quantify how genes interact to shape phenotypes. This exciting new area has been termed systems genetics and is born out of a synthesis of multiple fields, integrating a range of approaches and exploiting our increased ability to obtain quantitative and detailed measurements on a broad spectrum of phenotypes. Gathering the contributions of leading scientists, both computational and experimental, this book shows how experimental perturbations can help us to understand the link between genotype and phenotype. A snapshot of current research activity and state-of-the-art approaches to systems genetics are provided, including work from model organisms such as Saccharomyces cerevisiae and Drosophila melanogaster, as well as from human studies.