Statistics For Microarrays
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Statistics For Microarrays
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Author : Ernst Wit
language : en
Publisher: John Wiley & Sons
Release Date : 2004-07-23
Statistics For Microarrays written by Ernst Wit and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-07-23 with Mathematics categories.
Interest in microarrays has increased considerably in the last ten years. This increase in the use of microarray technology has led to the need for good standards of microarray experimental notation, data representation, and the introduction of standard experimental controls, as well as standard data normalization and analysis techniques. Statistics for Microarrays: Design, Analysis and Inference is the first book that presents a coherent and systematic overview of statistical methods in all stages in the process of analysing microarray data – from getting good data to obtaining meaningful results. Provides an overview of statistics for microarrays, including experimental design, data preparation, image analysis, normalization, quality control, and statistical inference. Features many examples throughout using real data from microarray experiments. Computational techniques are integrated into the text. Takes a very practical approach, suitable for statistically-minded biologists. Supported by a Website featuring colour images, software, and data sets. Primarily aimed at statistically-minded biologists, bioinformaticians, biostatisticians, and computer scientists working with microarray data, the book is also suitable for postgraduate students of bioinformatics.
Statistical Analysis Of Gene Expression Microarray Data
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Author : Terry Speed
language : en
Publisher: CRC Press
Release Date : 2003-03-26
Statistical Analysis Of Gene Expression Microarray Data written by Terry Speed and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-03-26 with Mathematics categories.
Although less than a decade old, the field of microarray data analysis is now thriving and growing at a remarkable pace. Biologists, geneticists, and computer scientists as well as statisticians all need an accessible, systematic treatment of the techniques used for analyzing the vast amounts of data generated by large-scale gene expression studies
Dna Microarrays Part B Databases And Statistics
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Author :
language : en
Publisher: Elsevier
Release Date : 2006-08-28
Dna Microarrays Part B Databases And Statistics written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-08-28 with Science categories.
Modern DNA microarray technologies have evolved over the past 25 years to the point where it is now possible to take many million measurements from a single experiment. These two volumes, Parts A & B in the Methods in Enzymology series provide methods that will shepard any molecular biologist through the process of planning, performing, and publishing microarray results. Part A starts with an overview of a number of microarray platforms, both commercial and academically produced and includes wet bench protocols for performing traditional expression analysis and derivative techniques such as detection of transcription factor occupancy and chromatin status. Wet-bench protocols and troubleshooting techniques continue into Part B. These techniques are well rooted in traditional molecular biology and while they require traditional care, a researcher that can reproducibly generate beautiful Northern or Southern blots should have no difficulty generating beautiful array hybridizations. Data management is a more recent problem for most biologists. The bulk of Part B provides a range of techniques for data handling. This includes critical issues, from normalization within and between arrays, to uploading your results to the public repositories for array data, and how to integrate data from multiple sources. There are chapters in Part B for both the debutant and the expert bioinformatician. Provides an overview of platforms Includes experimental design and wet bench protocols Presents statistical and data analysis methods, array databases, data visualization and meta analysis
Microarray Data
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Author : Shailaja R. Deshmukh
language : en
Publisher: Alpha Science International, Limited
Release Date : 2007
Microarray Data written by Shailaja R. Deshmukh and has been published by Alpha Science International, Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Business & Economics categories.
Functional Genomics, a branch of bioinformatics, is essentially an interdisciplinary subject in which biologists, statisticians and computer experts interact to analyze the microarray data. This book caters to the needs of all the three disciplines. For biologists and computer scientists, it explains concepts of statistics and statistical inference. For Biologists and Statisticians, it provides annotated R programs to analyze microarray data. For Statisticians and Computer scientists, it explains basics of biology relevant to microarray experiment. Thus, the book will be useful to scientists from all the three disciplines, with not much knowledge of other disciplines, to analyze microarray data and interpret the results.
Statistical Analysis Of Gene Expression Data From Dna Microarrays Based On Partial Least Squares And Related Dimension Reduction Methods
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Author : Danh V. Nguyen (Ph. D. in statistics)
language : en
Publisher:
Release Date : 2000
Statistical Analysis Of Gene Expression Data From Dna Microarrays Based On Partial Least Squares And Related Dimension Reduction Methods written by Danh V. Nguyen (Ph. D. in statistics) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with categories.
Statistics And Data Analysis For Microarrays Using R And Bioconductor
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Author : Sorin Draghici
language : en
Publisher: CRC Press
Release Date : 2016-04-19
Statistics And Data Analysis For Microarrays Using R And Bioconductor written by Sorin Draghici and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Computers categories.
Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on,
Analysis Of Microarray Gene Expression Data
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Author : Mei-Ling Ting Lee
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-08
Analysis Of Microarray Gene Expression Data written by Mei-Ling Ting Lee and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-08 with Science categories.
After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. Microarray technology provides a systematic way to survey DNA and RNA variation. With the abundance of data produced from microarray studies, however, the ultimate impact of the studies on biology will depend heavily on data mining and statistical analysis. The contribution of this book is to provide readers with an integrated presentation of various topics on analyzing microarray data.
Statistical Methods For Microarray Data Analysis
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Author : Andrei Y. Yakovlev
language : en
Publisher: Humana Press
Release Date : 2016-08-23
Statistical Methods For Microarray Data Analysis written by Andrei Y. Yakovlev and has been published by Humana Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-23 with Medical categories.
Microarrays for simultaneous measurement of redundancy of RNA species are used in fundamental biology as well as in medical research. Statistically,a microarray may be considered as an observation of very high dimensionality equal to the number of expression levels measured on it. In Statistical Methods for Microarray Data Analysis: Methods and Protocols, expert researchers in the field detail many methods and techniques used to study microarrays, guiding the reader from microarray technology to statistical problems of specific multivariate data analysis. Written in the highly successful Methods in Molecular BiologyTM series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory. Thorough and intuitive, Statistical Methods for Microarray Data Analysis: Methods and Protocols aids scientists in continuing to study microarrays and the most current statistical methods.
High Dimensional Microarray Data Analysis
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Author : Shuichi Shinmura
language : en
Publisher: Springer
Release Date : 2019-05-14
High Dimensional Microarray Data Analysis written by Shuichi Shinmura and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-14 with Medical categories.
This book shows how to decompose high-dimensional microarrays into small subspaces (Small Matryoshkas, SMs), statistically analyze them, and perform cancer gene diagnosis. The information is useful for genetic experts, anyone who analyzes genetic data, and students to use as practical textbooks. Discriminant analysis is the best approach for microarray consisting of normal and cancer classes. Microarrays are linearly separable data (LSD, Fact 3). However, because most linear discriminant function (LDF) cannot discriminate LSD theoretically and error rates are high, no one had discovered Fact 3 until now. Hard-margin SVM (H-SVM) and Revised IP-OLDF (RIP) can find Fact3 easily. LSD has the Matryoshka structure and is easily decomposed into many SMs (Fact 4). Because all SMs are small samples and LSD, statistical methods analyze SMs easily. However, useful results cannot be obtained. On the other hand, H-SVM and RIP can discriminate two classes in SM entirely. RatioSV is the ratio of SV distance and discriminant range. The maximum RatioSVs of six microarrays is over 11.67%. This fact shows that SV separates two classes by window width (11.67%). Such easy discrimination has been unresolved since 1970. The reason is revealed by facts presented here, so this book can be read and enjoyed like a mystery novel. Many studies point out that it is difficult to separate signal and noise in a high-dimensional gene space. However, the definition of the signal is not clear. Convincing evidence is presented that LSD is a signal. Statistical analysis of the genes contained in the SM cannot provide useful information, but it shows that the discriminant score (DS) discriminated by RIP or H-SVM is easily LSD. For example, the Alon microarray has 2,000 genes which can be divided into 66 SMs. If 66 DSs are used as variables, the result is a 66-dimensional data. These signal data can be analyzed to find malignancy indicators by principal component analysis and cluster analysis.
Methods Of Microarray Data Analysis Ii
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Author : Simon M. Lin
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-08
Methods Of Microarray Data Analysis Ii written by Simon M. Lin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-08 with Science categories.
Microarray technology is a major experimental tool for functional genomic explorations, and will continue to be a major tool throughout this decade and beyond. The recent explosion of this technology threatens to overwhelm the scientific community with massive quantities of data. Because microarray data analysis is an emerging field, very few analytical models currently exist. Methods of Microarray Data Analysis II is the second book in this pioneering series dedicated to this exciting new field. In a single reference, readers can learn about the most up-to-date methods, ranging from data normalization, feature selection, and discriminative analysis to machine learning techniques. Currently, there are no standard procedures for the design and analysis of microarray experiments. Methods of Microarray Data Analysis II focuses on a single data set, using a different method of analysis in each chapter. Real examples expose the strengths and weaknesses of each method for a given situation, aimed at helping readers choose appropriate protocols and utilize them for their own data set. In addition, web links are provided to the programs and tools discussed in several chapters. This book is an excellent reference not only for academic and industrial researchers, but also for core bioinformatics/genomics courses in undergraduate and graduate programs.