Microarray Gene Expression Data Analysis: A Beginner's Guide

Book Description

Microarray technology is arguably the most important recent breakthrough in molecular biology. It enables researchers to obtain snapshots of gene expression for all the genes in a genome in a single experiment. Microarray experiments generate massive amounts of data that can be analysed to extract new knowledge about the underlying biological processes.This guide covers aspects of designing microarray experiments and analysing the data generated, and includes information on some of the tools that are available from non-commercial sources. Concepts and principles underpinning gene expression analysis are emphasised, and wherever possible the mathematics has been simplified. The guide is intended for use by graduates and researchers in bioinformatics and the life sciences and is also suitable for statisticians who are interested in the approaches currently used to study gene expression.

Customer Reviews:

A good introduction to the topic .......2007-05-20

Microarrays are a tool for monitoring gene expression levels for thousands of genes in parallel. This technology is very useful since patterns in the gene expression can be used for molecular characterization of phenomena that range from disease states and response to stimuli to the differences between cells of different types. The amount of information obtained from one microarray experiment can be large. These large amounts of information present new challenges in the areas of data storage, management, and analysis by biologists who are not accustomed to dealing with this much data. Also, the software used for data analysis is usually written by mathematicians and statisticians that have a minimum of training in biology.

This book addresses some of the issues faced by researchers who are beginning their first microarray experiments. It covers various aspects of designing and analyzing the results of microarray experiments. Microarrays are not limited to the study of gene expression, but this remains the most common use of the technology and therefore is the only use of arrays discussed here. This book attempts to explain the underlying concepts and principles routinely used in analysis of gene expression data. The book should be accessible by statisticians, computer scientists, and students of bioinformatics who want a grounding in the types of analysis currently used to study microarray data.

The book begins with an introductory chapter which is followed by three major chapters. As with any technology that has the capacity to detect small changes in a highly dynamic system, the underlying experimental design and the manner in which an experiment is conducted is critical for obtaining high quality data. Chapter two addresses these issues. The raw data from microarray experiments are images that must be transformed and organized into gene expression matrices. These transformations are the subject of chapter 3. Finally, in chapter 4, the common methods used for analyzing gene expression data matrices with the goal of obtaining new insights into biology are discussed. The book does a pretty good job of providing the reader with a general understanding of the nature of microarray data and how it can be analyzed. It was never meant to be a reference book or a comprehensive review, just a gentle introduction.

Well written, short explanations but nevertheless understandable.......2005-07-06

Certainly, this book can not give a complete description of microarrays, neither from an experimental nor a theoretical side. Nevertheless, the issues presented and discussed provide the reader with a solid basis for more advanced studies.

In my opinion, this book is well written, the explanations given are descriptive and understandable and its overall organization is plausible. I recommend this book as an introduction for the analysis of microarray data, because it provides a good overview of existing methods in this field. A warning: This does not mean, that all these methods are thorougly expained! It just provides an overview!! If you want to learn, e.g., clustering methods, you should consult another book (probably no other book about microarrays but a decent book dealing only with data analysis in general or clustering methods...)

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A Beginner's Guide to Microarrays

Manufacturer: Springer
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Binding: Hardcover

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ASIN: 1402074727

Book Description

Microarray technology is more accessible than ever, and an ever-widening field of scientists is using this technology. However, the manufacture, experimental design, and analysis of microarrays are not always straightforward, and researchers new to the field run into technical and theoretical roadblocks that can hinder progress with this powerful new technology.
A Beginner's Guide to Microarrays addresses two audiences - the core facility manager who produces, hybridizes, and scans arrays, and the basic research scientist who will be performing the analysis and interpreting the results. User friendly coverage and detailed protocols are provided for the technical steps and procedures involved in many facets of microarray technology, including:

-Cleaning and coating glass slides,
-Designing oligonucleotide probes,
-Constructing arrays for the detection and quantification of different bacterial species,
-Preparing spotting solutions,
-Troubleshooting spotting problems,
-Setting up and running a core facility,
-Normalizing background signal and controlling for systematic variance,
-Designing experiments for maximum effect,
-Analyzing data with statistical procedures,
-Clustering data with machine-learning protocols.

This book is addressed to researchers using microarrays for the first time. One faces a myriad of problems at the outset of such a task, and there is no need to 'reinvent the wheel' for each scientist that runs into these problems. Knowing the strengths and weaknesses of microarrays before research begins can save time, money, and resources.

DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling

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A stringing together of short essays.
Excellent book. Recommended.
By far the best book on DNA microarrays.

DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling
Pierre Baldi , G. Wesley Hatfield , and Wesley G. Hatfield
Manufacturer: Cambridge University Press
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Binding: Hardcover

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ASIN: 0521800226

Book Description

Massive data acquisition technologies--such as genome sequencing, high-throughput drug screening, and DNA arrays--are in the process of revolutionizing biology and medicine. This concise, user-friendly and interdisciplinary guide to DNA microarray technology is an introduction and a reference for both biologists and computational scientists. The authors describe the underlying technologies and offer an awareness of the "noise" and pitfalls present in the data generated. They also provide an idea of the different data mining techniques and algorithms that are available to interpret data, and the advantages and disadvantages of each in differing situations.

Customer Reviews:

A stringing together of short essays........2005-06-07

This book tries to combine a practical and theoretical point of view concering microarray expermiments and the data analyis thereof. This is a very honourable goal. Unfortunatelly, it fails. An indicator for this can already be seen in the low number of pages. This book has less than 140 pages (I exclude the last chapter and the appendix). It is clear, that it is impossible to discuss in detail this topic in this limited number of pages. Hence, during reading the chapters one gets the feeling, that one reads short essays which are stringed together. At no point the authors go into detail but give only a short idea and references.

I see no reason, why I should recommend this book to anyone. It is in its current form just immature. My prediction: There will be no second edition because even its basic substance is very weak.

Some words to the last chapter (systems biology). This is indeed the most interesting and best chapter of the book (35 pages) without going into details as the rest of the book. I think according to this chapter one realize under which premise this book was written. Unfortunatelly, combining buzz worlds in short essays is not enough for a good book. Sorry guys, I think you can do better!

Excellent book. Recommended........2002-11-12

This book has a good balance between experimental and computational methods. It provides a description of DNA microarray technologies, experimental protocols, and the multiple sources of noise and variability. The book contains an insightful overview of the computational issues and available algorithms for data analysis from differential expression, to dimensionality reduction and visualization (e.g. PCA), to clustering (e.g. hierarchical). New methods are described to gether with a good overview of available software, data bases, web sites, and other resources, as well as several "walk through" examples. I particularly enjoyed the last chapter on Systems Biology.

By far the best book on DNA microarrays........2002-10-14

"Very complete : covers both the experimental and the computational methods with specific examples. Written by two top scientists who have worked hard at complementing each other's strengths. I particularly enjoyed the last chapter on Systems Biology which provides a masterful overview of current resaerch trends."

Microarrays for an Integrative Genomics (Computational Molecular Biology)

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A helpful and informative overview
Well written
Not well written...
lots of important stuff
Amazing

Microarrays for an Integrative Genomics (Computational Molecular Biology)
Isaac S. Kohane , Alvin Kho , and Atul J. Butte
Manufacturer: The MIT Press
ProductGroup: Book
Binding: Paperback

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ASIN: 0262612100

Book Description

Functional genomics--the deconstruction of the genome to determine the biological function of genes and gene interactions--is one of the most fruitful new areas of biology. The growing use of DNA microarrays allows researchers to assess the expression of tens of thousands of genes at a time. This quantitative change has led to qualitative progress in our ability to understand regulatory processes at the cellular level.

This book provides a systematic introduction to the use of DNA microarrays as an investigative tool for functional genomics. The presentation is appropriate for readers from biology or bioinformatics. After presenting a framework for the design of microarray-driven functional genomics experiments, the book discusses the foundations for analyzing microarray data sets, genomic data-mining, the creation of standardized nomenclature and data models, clinical applications of functional genomics research, and the future of functional genomics.

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A helpful and informative overview.......2006-01-05

The authors of this book are very excited about the prospects of the field of functional genomics and DNA microarray technology. Their optimism however is tempered by a large degree of caution, for they make it clear in the first few paragraphs of the book that expression profiling using microarrays is still in its infancy and that there have been exaggerated reports of its success. They wrote this book with the intent of giving the reader a more realistic view of microarray technology and have succeeded in their goal. They target the book specifically to experienced biologists and bioinformaticians with limited experience in using microarrays, and to students who are entering the field of bioinformatics. Most importantly, they emphasize that functional genomics is an experimental science, and that highly sophisticated algorithms from data mining or other areas of artificial intelligence will be of no assistance if the experimental information is not there in the first place. They do encourage however further development of these algorithms, in order to be able to extract the data as it becomes available, and as microarray technology itself matures. Even with the current technology, enormous amounts of data are generated, and if sense is to be made of this data, one will have to develop more effective algorithms than what are currently available.

To perform successful experiments, the authors describe a `functional genomics pipeline', and list the characteristics that it must have, consisting of both `wet' (laboratory) and `dry' (computational) steps. They devote a lot of space in the book describing how to develop an effective genomic experiment. Crucial to such investigations they say is a design that maximizes the possibility of observing relevant gene expression patterns, and the `experiment design space', which encapsulates all possible conditions that a particular biological system could be influenced by. Also important to the design is the `expression space', which is the collection of all potential expression values of all genes in a given genome. One could view the expression space as a vector space of high dimension, with each dimension corresponding to a single gene. Of great interest, and widely discussed in the general bioinformatics literature under the guise of the new field of `systems biology' is a subset of the expression space called the `transcriptome.' This subset models the expression of a cellular system under all stimuli. Considering that one might have to deal with 30,000 genes in the case of a human, the characterization of the transcriptome will be a formidable project. Interactions between the genes will complicate the analysis even further. The authors view each experiment as being an exploration of the space of all possible expression patterns, and describe good experimentation as being the `maximal exercise' of the genome. This consists of finding those correlations between the genes that have the greatest impact on the process under scrutiny.

A book on microarrays would not be complete if it did not discuss how they actually function. This is done in a fair detail in chapter three of the book. The authors do not favor a particular vendor but rather discuss what biological assumptions all microarray technology is based on. One of these assumptions is, as expected, that there is a direct connection between mRNA transcription and the protein translation associated with it.

In any laboratory experiment one has to deal with experimental uncertainty or "noise." This involves the influence of unknown external perturbations that result in variability in the outcomes of the experiment. As further evidence that the authors are careful experimenters, they discuss noise in detail, noting first that expression experiments deal with information that is both digital (DNA sequence information) and analog (mRNA expression levels). They distinguish between `intra-chip' noise, which arises when one probe feature influences another, improper scanning techniques, and manufacturing defects, and `inter-chip' noise, which arises from sample variation. Normalization issues are also discussed. Readers should take particular attention to the discussion on fold calculation and significance because of its connection with statistical analysis and because it sets the tone for the rest of the book. In particular, this discussion leads to the very important topic of dissimilarity and similarity measures. This part of the book is more sophisticated mathematically than what has been encountered so far, dealing for example with the concept of a metric space, which may appear to be somewhat abstract by readers who are not mathematically astute. Linear correlation and mutual information are two examples of metrics that are discussed.

Data mining is of course heavily discussed in the book, along with the new field of `ontological engineering' and how the latter is used functional genomics. Data mining is of course a vast field, but the authors give the reader a good taste of how some of its techniques can be applied to analyze microarray experiments. Both unsupervised and supervised learning is discussed, along with `self-organizing maps.' The authors end the book with their vision of future developments. Naturally they point to further refinements in microarray technology, the need for educating a new generation of bioinformaticists, and the push towards the development of new data mining algorithms. Certainly all of these are important, and one can expect other technological developments to occur in the coming years that may prove superior to microarrays in their application to functional genomics. In addition, and there are indications of this even at the present time, one can expect technologies that fully automate the study of gene expression. This includes the generation of hypotheses that characterize scientific investigation, the development and construction of the experiments themselves, and the analysis of the resulting data.

Well written.......2004-06-29

This is a well written book that gives an overview of the technology of microarrays and their use as investigative tools in functional genomics experiments. I found the technical and analytical descriptions very easy to follow. This is still the only book around that can bring any investigator with little knowledge of molecular biology, data analysis, and/or microarrays up to speed in the field. It is also a good text book for a graduate level course on microarray data analysis.

Not well written..........2004-02-27

I am not an informatics researcher, however I hold a doctorate in biotechnology related areas, as well a law degree. I routinely purchase books and journals to keep up. However, the problem with this book is its presentation. It is written in an almost stereotypically pretentious manner to the extent that it clearly detracts from the subject matter's presentation. Did you know that a tissue or cell type may be "interrogated"? Coincedentally, I happened upon a brief review article by the same author in Nature Biotech. Again the writing was such that it was too much of an effort to extract what was being said. For those who feel drawn to this book, check the internal pages on Amazon's site.

lots of important stuff.......2004-01-21

This book contains lots of important topical information on the design and analysis of microarray experiments. It calls attention to a lot of important but sometimes subtle issues that many biologists appear to be overlooking. It appears to be a must-read for researchers who want to avoid expensive dead ends. But it's not perfect...

A well-informed computer scientist will recognize that quite a few computational statements are just plain wrong (e.g., p 180,
"[Dendrograms] require the comprehensive precomputation of the dissimilarity measure for all pairs of genes, which grows on the order of N^2" Wrong! Try bucketing. Or p 139, a dissimilarity function based on linear correlation coefficients is "definite". No! If x is a vector and C is a scalar, then clearly x=/=Cx, but d(x,Cx)=0, contrary to the definition of "definite". The "pseudocode" in Chapter 4 is not any clearer than the text, and it is not structured in a way that would allow it to be elaborated into well-engineered code. So rely on this book for big ideas and references, not for details. The book also reinforces my preconception that MIT Press doesn't employ editors... 'way too many typos, for starters.

You have to know the basics of molecular biology for this book, and it wouldn't hurt to have a basic understanding of DNA chips as well. It's definitely not the first step for a mathematical scientist hoping to become a bioinformatician. (But why should it be? :c)

Amazing.......2002-10-14

This is the book we have all been waiting for. The authors do an amazing job of describing, in understandable terms, how to perform meaningful microarray experiments. I highly recommend this seminal work.

Analyzing Microarray Gene Expression Data (Wiley Series in Probability and Statistics)

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Analyzing Microarray Gene Expression Data (Wiley Series in Probability and Statistics)
Geoffrey J. McLachlan , Kim-Anh Do , and Christophe Ambroise
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Hardcover

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ASIN: 0471226165

Book Description

Emphasis on clustering of data of gene tissues
* Includes new research findings and activities in molecular biology
* Highlights the important general field of bioinformatics and genomics and discusses the impact of microarray analysis on both

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Microarray Data Analysis: Methods and Applications (Methods in Molecular Biology)

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Microarray Data Analysis: Methods and Applications (Methods in Molecular Biology)

Manufacturer: Humana Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 1588295400

Book Description

In this new volume, renowned authors contribute fascinating, cutting-edge insights into microarray data analysis. This innovative book includes in-depth presentations of genomic signal processing, artificial neural network use for microarray data analysis, signal processing and design of microarray time series experiments, application of regression methods, gene expression profiles and prognostic markers for primary breast cancer, and factors affecting the cross-correlation of gene expression profiles. Also detailed are use of tiling arrays for large genome analysis, comparative genomic hybridization data on cDNA microarrays, integrated high-resolution genome-wide analysis of gene dosage and gene expression in human brain tumors, gene and MeSH ontology, and survival prediction in follicular lymphoma using tissue microarrays.

Advanced Analysis of Gene Expression Microarray Data (Science, Engineering, and Biology Informatics)

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Advanced Analysis of Gene Expression Microarray Data (Science, Engineering, and Biology Informatics)
Aidong Zhang
Manufacturer: World Scientific Publishing Company
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ASIN: 9812566457

Statistical Analysis of Gene Expression Microarray Data

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Outstanding survey
Excellent book for data analyst

Statistical Analysis of Gene Expression Microarray Data

Manufacturer: Chapman & Hall/CRC
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Binding: Hardcover

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ASIN: 1584883278

Book Description

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. And there is arguably no group better qualified to do so than the authors of this book. Statistical Analysis of Gene Expression Microarray Data promises to become the definitive basic reference in the field. Under the editorship of Terry Speed, some of the world's most pre-eminent authorities have joined forces to present the tools, features, and problems associated with the analysis of genetic microarray data. These include:: · Model-based analysis of oligonucleotide arrays, including expression index computation, outlier detection, and standard error applications · Design and analysis of comparative experiments involving microarrays, with focus on \ two-color cDNA or long oligonucleotide arrays on glass slides · Classification issues, including the statistical foundations of classification and an overview of different classifiers · Clustering, partitioning, and hierarchical methods of analysis, including techniques related to principal components and singular value decomposition Although the technologies used in large-scale, high throughput assays will continue to evolve, statistical analysis will remain a cornerstone of their success and future development. Statistical Analysis of Gene Expression Microarray Data will help you meet the challenges of large, complex datasets and contribute to new methodological and computational advances.

Customer Reviews:

Outstanding survey.......2004-09-12

Microarray studies are becoming the preferred research tools in many areas, including cancer research, development studies, and studies in organisms' responses to their environments. Because of differences between organisms or between experiments, microarray data is always statistical in nature. The problem is that the data aren't well suited to traditional statistics. Instead of studying a few characteristics in large numbers of individuals, microarray studies typically yield thousands of data values for a few dozen samples.

That mismatch, between current statistical practice and microarray analysis requirements, seem to be driving many innovations in statistical analysis. This book is a brief survey of four of those areas of analysis: model-based analysis, experimental design, classification, and clustering.

The first section, on model-based analysis, is brief. Mostly, it seems to establish the language used in later sections. The next, on experimental design, deals with ways for getting the most information out of the fewest samples. The costs of arrays and processing are dropping, but still high. More analysis on less data makes good economic sense. The DNA samples analyzed also have costs - some can only be prepared in minute amounts, others must be extracted surgically from human patients. Either way, it's important to maximize the knowledge harvested from limited amounts of biologcal material.

The next section, on discrimination, is a bit longer. It briefly summarizes a wide variety of techniques for deciding which category best represents any one sample. This section gives a good review of analytic approaches: Fisher classifiers and their descendants, principal components, support vectors, and decision trees. Within trees, the authors note that the number of missing values in typical microarray data may interfere with standard analysis, and that surrogate variables may be needed in many cases. AI and data mining techniques aren't broadly represented, but this chapter is still very informative.

The final section, on clustering, was shorter. It was reasonably informative, and I gleaned a few new facts from it. Mostly, though, it seemed to present techniques that are already well known.

This book is a survey, so it emphasizes breadth over depth. Many algorithms described only briefly, and some are just mentioned by name. The developer will need to chase references to find an implementable level of detail. Still, the book has value as an index to references and as a comparison of techniques.

//wiredweird

Excellent book for data analyst.......2004-03-06

Thorough converage of statistics involved in microarray data analysis. It presents important knowledge for biologists who use data analysis tools but would like to know what is behind the scene. Understanding the book needs some statistical background and hence not a easy book for biologists and genetists who do not have that knowledge.
I would like to emphasize that experiment design issue is presented in a very clear way and should be read by all who plan to start project related to gene expression. Clustering and classification are two major analysis methods for microarray data, and the comprehensive discussion of the statistical mechanisms for each method in the last two chapters will help analysts to choose the right methods when mining the data. The first chapter seems to be a little out of the place, because it mainly discusses model-based genechip data analysis. This chapter touches a little about preprocessing and gene selection but far from complete.
A chapter with thorough discussion of pre-processing techniques and gene selection techniques would make this a prefect book. Overall it is a great reference for anyone who is interested in microarray data analysis!

Average customer rating:

Not the best but still good
Maybe good, just not for me

The Analysis of Gene Expression Data

Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 0387955771

Book Description

This book presents practical approaches for the analysis of data from gene expression microarrays. Each chapter describes the conceptual and methodological underpinning for a statistical tool and its implementation in software. Methods cover all aspects of statistical analysis of microarrays, from annotation and filtering to clustering and classification. Chapters are written by the developers of the software. All software packages described are free to academic users. The book includes coverage of various packages that are part of the Bioconductor project and several related R tools. The materials presented cover a range of software tools designed for varied audiences. Some chapters describe simple menu-driven software in a user-friendly fashion, and are designed to be accessible to microarray data analysts without formal quantitative training. Most chapters are directed at microarray data analysts with master-level training in computer science, biostatistics or bioinformatics. A minority of more advanced chapters are intended for doctoral students and researchers. The team of editors is from the Johns Hopkins Schools of Medicine and Public Health and has been involved with developing methods and software for microarray data analysis since the inception of this technology. Giovanni Parmigiani is Associate Professor of Oncology, Pathology and Biostatistics. He is the author of the book on "Modeling in Medical decision Making," a fellow of the ASA, and a recipient of the Savage Awards for Bayesian statistics. Elizabeth S. Garrett is Assistant Professor of Oncology and Biostatistics, and recipient of the Abbey Award for statistical education. Rafael A Irizarry is Assistant Professor of Biostatistics, and recipient of the Noether Award for non-parametric statistics. Scott L. Zeger is Professor and chair of Biostatistics. He is co-author of the book "Longitudinal Data Analysis," a fellow of the ASA and recipient of the Spiegelman Award for public health statistics.

Customer Reviews:

Not the best but still good.......2004-05-11

Some of the software commands in this book are already outdated, esp. in bioconductor. If you need to analyze a microarray data and you do not know much about the algorithms, this book gives an overview what is available. To get more insights about the theory, can check out EXPLORATORY AND ANALYSIS OF DNA MICROARRAY AND PROTEIN ARRAY or the research papers published by the authors. There is no one best method for analyzing microarray data, so don't expect a miracle from this book or any others.

Maybe good, just not for me.......2004-01-16

I thumbed through a copy of this book, but I admit I haven't read it. It just doesn't meet my needs.

It's about a number of software packages available for analyzing microarray and related kinds of data. Different sections of the book seemed to range from brief surveys to fairly detailed how-to chapters, by different authors, describing different packages.

I'm interested in microarray data analysis, but I want the more theoretical material - the basic computations and the principles behind them. Details of statistical and analytic techniques were too scattered, if present at all, to hold my interest.

Book Description

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.

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