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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Get a solid foundation for microarray data analysis.
a great book to read about microarray data analysis
Simple Great
Excellent book. Highly recommended!
Introduction to Statistical Data Analysis of Microarrays

Data Analysis Tools for DNA Microarrays
Sorin Draghici
Manufacturer: Chapman & Hall/CRC
ProductGroup: Book
Binding: Hardcover

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ASIN: 1584883154

Book Description

Technology today allows the collection of biological information at an unprecedented level of detail and in increasingly vast quantities. To reap real knowledge from the mountains of data produced, however, requires interdisciplinary skills-a background not only in biology but also in computer science and the tools and techniques of data analysis. To help meet the challenges of DNA research, Data Analysis Tools for DNA Microarrays builds the foundation in the statistics and data analysis tools needed by biologists and provides the overview of microarrays needed by computer scientists. It first presents the basics of microarray technology and more importantly, the specific problems the technology poses from the data analysis perspective. It then introduces the fundamentals of statistics and the details of the techniques most commonly used to analyze microarray data. The final chapter focuses on commercial applications with sections exploring various software packages from BioDiscovery, Insightful, SAS, and Spotfire. The book is richly illustrated with more than 230 figures in full color and comes with a CD-ROM containing full-feature trial versions of software for image analysis (ImaGene, BioDiscovery Inc.) and data analysis (GeneSight, BioDiscovery Inc. and S-Plus Array Analyzer, Insightful Inc.). Written in simple language and illustrated in full color, Data Analysis Tools for DNA Microarrays lowers the communication barrier between life scientists and analytical scientists. It prepares those charged with analyzing microarray data to make informed choices about the techniques to use in a given situation and contribute to further advances in the field.

Customer Reviews:

Get a solid foundation for microarray data analysis........2007-02-18

I'm more than 2/3 through the book and I've never encountered a topic that I feel could have been better presented. My definition of a Great book is that I can understand and follow it, and this definitely is a Great book! Thanks to the author for writing such readable text. This text has not made it to my bookshelf at work, it stays on my desk.

a great book to read about microarray data analysis.......2006-08-07

I have entered the area of microarray data analysis three years ago, having an engineering/machine learning background which includes good knowledge of statistics. After reading many journal papers about particular algorithms for microarray data analysis, I felt the need to read a book so that I could get the big picture of the field. At the beginning I was skeptical about reading Draghici's book because it was recommended to me as "excellent" by a biologist. I was pretty sure that given my background I will get bored of it quickly. My intuition failed me in this case because after reading it, I found it too as being far from ordinary, and answering my needs as well.

The book is an easy-to-follow introduction to the area of microarray data analysis covering areas from image analysis and preprocessing, to differential expression, clustering, and high level analysis such as ontological analysis. The book is particularly useful in underlying common pitfalls with microarray data. Examples include failing to correct for multiple testing in microarray experiments and the misuse or overuse of the clustering algorithms. Abounding examples and clear illustration are given to support every single aspect treated in the text. In my opinion, graduate level students in biology, bioinformatics and statistics can greatly benefit from the lecture of this book.

Another positive aspect is the fact that, with the exception of one chapter about the available commercial software, this book was written by just one author. This gives a continuity of ideas and a consistency of notations and terms throughout the entire book. This is usually not found in many other books on this topic as they are sometimes just edited collections of chapters written independently by different authors (see for instance the text by Berrar et. al which has about 40 contributors).

A great incentive for me in writing this review was reading an overzealous critique to this book, written by Eric Wu in this webpage. I found some of his comments to be particularly misleading and out of context. For instance he says "the book only deals with the bare minimum of data analysis". Compared with other books in the field, the topics about data analysis covered in the book are not only more numerous but much more thoroughly explained. This book does not expedite the reader to some references but cares about explaining the things. If this book is the "bare minimum" at 500 pages, how is Mr. Wu going to characterize the other well known books in the field such as Knudsen, Simon, Speed, Baldi, etc. which have at most half as many as this book has. Knudsen, for instance, takes the reader from absolute measurements to and including ANOVA in 17 pages. Draghici covers the same topics in 7 chapters or about 250 pages, and that would be without counting the chapters on the basic statistics or image analysis. Another example of biased assessment is when Mr. Wu says "Exploratory data visualizing and data mining algorithms are not covered thoroughly in this book. For example, principal component analysis (PCA) is presented as a subsection of a chapter." The PCA description in the book is more than just fine to me. The book is not supposed to be an encyclopedia of statistics. What the reader needs to know is how PCA can help with the visualization of these multidimensional data sets and not necessarily give all the details about PCA.
A last example I give of superficial judgment in Mr. Wu's view is the so called "inflation of Type I error rate". Mr. Wu says: "... if the probability of making a correct conclusion excludes the probability of making Type II errors, 1 - p should be stated as the probability of not making Type I errors".. In general, this statement would be true. However, the paragraph from the book to which Mr. Wu is referring to actually starts by saying: "When the t statistic for a gene is more extreme than the threshold..." etc. If the observed statistic is more extreme than the threshold, the statistical reasoning requires us to reject the null hypothesis. In this case type II errors (false negatives) CANNOT occur. Hence, in this case, the probability of drawing the correct conclusion is indeed 1-p, exactly as stated in the book.

Overall, I find that the value you get per dollar spent when buying this book is high, and thereby I would strongly recommend it.

Dr. Adi L. Tarca, Windsor (CANADA)

Simple Great.......2006-05-16

This book is a must to understand fundamental statistical analysis of microarray data. Must have it.

Excellent book. Highly recommended!.......2006-04-04

Being a book worm, as soon as I started working with microarrays I bought a bunch of books on the subject. After six months working with this technique and reading chapters on all the books I've bought I can say with certainty that Draghici's is the best introductory book on microarrays. Other books around are better at describing protocols or explaining the math involved in microarray data analysis but Draghici's book does a very good job at explaining how to analyse microarray data for the biologist (and maybe for other publics but statisticians). Everytime some friend ask me for hints on chapters or books to read for learning (or re-learning) statistics I suggest this book. The first chapters are an excellent review of the basics of statistics necessary for day to day practice. The only complain I have is that the shareware software that comes with the book does not work anymore (it's trial period has already expired and therefore it is not possible to install it even if you get a brand new book). I read this book from cover to cover and I think that, considering how readable it is, anyone could do it.

Introduction to Statistical Data Analysis of Microarrays.......2004-09-28

The targeted audience of this book is biologists who are eager to get an understanding of the analysis tools they use for microarrays. The book does an excellent job addressing this tier of audience.

The book has plenty of examples. Almost all the examples, whether fake or real, are microarray-related. Whenever needed, figures or charts are provided to illustrate ideas. A few chapters that introduce basic statistical concepts provide solved problems and exercises. All these efforts are worthwhile making difficult statistical concepts easy to understand in the context of microarrays and making the book especially valuable for biologists who do not have strong background in statistics.

This book has an emphasis on major statistical aspects of microarray data analysis. There are 17 chapters in this book. About 8 of them are directly related to statistics. Especially, there is one whole chapter devoted to multiple hypothesis testing, one chapter for ANOVA, and one chapter for experimental design. The above subjects are presented in a thorough, yet easy-to-follow style. Statistical issues are often not well addressed in published papers using microarrays. This book on microarray data analysis does an excellent job emphasizing this aspect.

The title of the book indicates "data analysis". However, since this is not a clearly defined term, you should be aware that the book only deals with "the bare minimum" of data analysis. That is routines, such as normalization, transformation, statistical testing, and clustering, that have to be carried out each and every time. Exploratory data visualizing and data mining algorithms are not covered thoroughly in this book. For example, principal component analysis (PCA) is presented as a subsection of a chapter. It does not provide explanations on concepts such as loading factors nor scree test. Series data (e.g. time series) are on two pages only and there is no mention of Fourier transformation. Support vector machine (SVM), which is widely used today as a supervised classification method, is not presented at all.

As I mentioned at the beginning, the targeted audience is biologists. If you are a statistician or a bioinformatician who wants to mathematically explore data analysis algorithms, you should look somewhere else. You may be disappointed that many concepts are not rigorously or accurately defined in this book. For example, the book uses capital letters to denote random variables. But the concept of random variables is not rigorously defined in the book. One of the consequences is the weak definition of mathematical expectation. Another example is the inflation of Type I error rate. On page 220, the author claims that the probability of "drawing the correct conclusion" is 1 - p, where p is the calculated probability of a statistic versus a parameter. However, if the probability of making a correct conclusion excludes the probability of making Type II errors, 1 - p should be stated as the probability of not making Type I errors.

In summary, this is a good book on microarray analysis tools for biologists using microarrays. However, people who are seeking in-depth descriptions of these algorithms should look somewhere else.

Statistics for Microarrays: Design, Analysis and Inference

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Very good, on the whole

Statistics for Microarrays: Design, Analysis and Inference
Ernst Wit , and John McClure
Manufacturer: Wiley
ProductGroup: Book
Binding: Hardcover

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ASIN: 0470849932

Book Description

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.

Download Description

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.

Customer Reviews:

Very good, on the whole.......2004-08-02

This is the best introduction I know for anyone trying to learn the bioinformatics of microarrays. It starts with a brief description of the DNA microarrays, their chemistry, and the sources of uncertainty in their measurements, just enough for a non-biologist to get the general ideas. It skips the steps of scanning and spot recognition, mostly, and jumps right into analysis of the array of spot readings.

That is where the text comes into its own. One happy surprise is the book's emphasis on quality control and error management. Quality issues are addressed first by themselves, then as they affect the design and analysis of an experiment's biological meaning. This covers a wide variety of issues, including dye swaps, array background correction, and inference in the presence of low-quality data. There are soft spots in the discussion, especially in handling of missing data. That fits the general tone of the book, though, by stressing understanding over rigor.

This book comes with a macro package for the R environment, an open-source system somewhat like Matlab or Mathematica. That is both the strength and the weakness of this book. The strength of course, is the working code. It lets you see a real implementation of the algorithms that the authors describe. The weakness is that the implementations don't explain how the algorithms were developed, why they work, or how to recognize when they've been pushed past their breaking points. If you need more than rote recitation of the authors' implementation, you may find this frustrating. Also, the book uses five data sets for concrete discussion, but the software kit seems to include only one.

Microarray data sets (a few individual with thousands of measurements each) are very different from standard statistical data sets (lots of individuals with few measurements each). Despite the dramatic improvements of the last few years, the processing of the arrays themselves still varis widely under even the tightest control. Microarrays really do need different kinds of analysis and experimental design. This is a very readable explanation of why and how those procedures are used. I just wish the procedures themselves were presented in a little more depth.

//wiredweird

Computational Molecular Biology: An Algorithmic Approach (Computational Molecular Biology)

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An excellent conversational review
Readable and practical
The title says it...
Good book, but the back cover lies....
computational

Computational Molecular Biology: An Algorithmic Approach (Computational Molecular Biology)
Pavel A. Pevzner
Manufacturer: The MIT Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 0262161974

Book Description

In one of the first major texts in the emerging field of computational molecular biology, Pavel Pevzner covers a broad range of algorithmic and combinatorial topics and shows how they are connected to molecular biology and to biotechnology. The book has a substantial "computational biology without formulas" component that presents the biological and computational ideas in a relatively simple manner. This makes the material accessible to computer scientists without biological training, as well as to biologists with limited background in computer science.

Computational Molecular Biology series
Computer science and mathematics are transforming molecular biology from an informational to a computational science. Drawing on computational, statistical, experimental, and technological methods, the new discipline of computational molecular biology is dramatically increasing the discovery of new technologies and tools for molecular biology. The new MIT Press Computational Molecular Biology series provides a unique venue for the rapid publication of monographs, textbooks, edited collections, reference works, and lecture notes of the highest quality.

Customer Reviews:

An excellent conversational review.......2005-08-16

Dr. Pevzner writes with a very lucid and conversational style about very complex and seemingly inscrutable topics. As a biologist who works primarily with computational tools in the field of genomics, this resource has helped to provide me with more than a rudimentary understanding of the algorithms and logic lurking in the methods of sequence analysis. Explaining dynamic programming to a biologist with rudimentary programming skills is a daunting task. However, his description of sequence alignment algorithms (including dynamic programming) in chapter 6 is quite readable and the information is very accessible. I highly recommend this book if you want a comprehensive understanding of the computational biologists toolkit.

Readable and practical.......2005-02-04

Pevzner has written a very useful book on bioinformatics algorithms, and one that seems reasonably up to date. The table of contents follows a classic plan: restriction maps, assembly and sequencing, 2- and N- way string comparisons, and analysis of rearrangements. There's a good but brief section on mass spec analysis - unfortunately, that chapter is called "Proteomics" even though the term covers a lot more than MS. Other sections skim the surface of hidden Markov models and Gibbs sampling for finding patterns ("motifs") in DNA.

A few chapters have unusual strengths. The "Conway Equation" gives more insight in analysis of motif significance than other introductory books do. The section in sequence comparison pays a lot more attention to BLAST-like algorithms than other books do, also - modern material you'd normally see only in the journals. Also, the section on rearrangements gives some ideas about using rearrangement data for phylogenetic analysis. That really gives the material meaning. Rearrangements aren't just string operations, they're features of evolution, and they can be compared to each other. No matter what the discussion, Pevzner keeps maintains a readable and enjoyably informal tone.

The book does have some weaknesses, though. It's a bit advanced for an undergrad intro, but bottoms out before the Baum-Welch algorithm, for example. Discussion of microarrays for sequencing seems dated. Pevnzer describes their use in sequencing, a rarity now, but skips their use in functional gneomics, where they are used most often. Illustration style is erratic and many diagrams are oddly stretched (3.5, 5.7, 8.3, and others, some much worse). Formal analysis of the algorithms is weak, but Pevzner somewhat makes up for that with better statistical analysis than many authors give. Also, even though the book was reprinted in 2001, it still estimates 100K genes in the human genome.

This is a good second book, maybe the one to read after Pevzner's newer "Introduction". It covers most of the basics and gives fairly usable pseudocode. Most of all, it always keeps the biology in mind. That, by itself, makes this book stand out.

//wiredweird

The title says it..........2004-01-12

An excellent book for studying computational molecular biology from an algorithmic perspective. (But if you never took mathematics seriously, you are forewarned.)

Good book, but the back cover lies...........2002-11-21

As others have noted, the premise that this book is for beginners from either the computational or the biological field is flawed...unless one's definition of beginner is a lot more advanced than mine.

For example even chapter one throws out terms like "recombination" and electrophoresis. without enough explanation for the biology newbie, IMO. Heck, for someone truly new to biology, a bit of time explaining what a chromosome is is probably time well spent.

And for the person coming from a pure biology background, some of the mathematics will definitely be a problem unless they have a decent understanding of combinatorics and discrete mathematics. And that "computational biology without formulas" blurb on the back cover should be read as "not as many formulas as I could have included if I really wanted", rather than "no formulas at all". There are equations galore in this book, rest assured of that.

That said, if a person *does* have the necessary background to make the material accessbile, then the book is definitely worth the purchase. The book's failure is in defining its target audience, not in the material presented.

computational.......2000-12-22

While this is certainly the do-loop of computational biology the reader would question the assertion that this book provides a common link (no pun) between the biologists need for computational expertise and the programmer's need for biological insight. In either case a solid basis in Discrete Mathematics goes along way here (usually a required course for computer science majors). This reader thinks a similar required course in genetics should be made for engineers to reduce their reductionistic tendencies. However the distinction between these lines grows narrower with each new computer chip. None the less the book is well written, and easy to read (as Discrete Math stuff goes). This book is not for beginners in either Combinatorics or genetics and the last part of the book poses many current questions that as the author says, "are just currently being answered". This book already assumes you know about such things as NIH, PDB, Chime, Isis, NCIB, docking, etc. For those less adapt at programming (myself) the following alternatives are fun, useful and to the point. Both trees and networks can be easily set up in MathCad using their built in resource center add-ins for Combinatorics and Set Theory. They also provide a Traveling Salesman routine in Numerical Recipes that can be applied directly to the problems in Pevzner's book. (Although remembering that most optimization algorithms provide only the most probable 100 out of 2 million it is still fun!). Most of the mappings and node process familiar to Discrete Math can be solved using Mathcad and some sort of adjacency matrix combination. (Including the four-color mapping problem). This provides the basis for most nodal mappings. For the more daring the adjacency matrices can be run through Matlab's GUI's decompositions and analyzed using their optimization toolbox. Currently I'm investigating the Hidden Markovian chains using the Frame advance feature of Mathcad applied to 2D cspline- intercept graphing and updating by frame iteration. This book is for the serious student or solid course material in a related field, and while probably not rated in top ten novels of 2000 certainly rates five mouse clicks from this reader.

Design and Analysis of DNA Microarray Investigations

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Design and Analysis of DNA Microarray Investigations
Richard M. Simon , Edward L. Korn , Lisa M. McShane , Michael D. Radmacher , George W. Wright , and Yingdong Zhao
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 0387001352

Book Description

This book is targeted to biologists with limited statistical background and to statisticians and computer scientists interested in being effective collaborators on multi-disciplinary DNA microarray projects. State-of-the-art analysis methods are presented with minimal mathematical notation and a focus on concepts. This book is unique because it is authored by statisticians at the National Cancer Institute who are actively involved in the application of microarray technology.

Many laboratories are not equipped to effectively design and analyze studies that take advantage of the promise of microarrays. Many of the software packages available to biologists were developed without involvement of statisticians experienced in such studies and contain tools that may not be optimal for particular applications. This book provides a sound preparation for designing microarray studies that have clear objectives, and for selecting analysis tools and strategies that provide clear and valid answers. The book offers an in depth understanding of the design and analysis of experiments utilizing microarrays and should benefit scientists regardless of what software packages they prefer. In order to provide all readers with hands on experience in data analysis, it includes an Appendix tutorial on the use of BRB-ArrayTools and step by step analyses of several major datasets using this software which is freely available from the National Cancer Institute for non-commercial use.

The authors are current or former members of the Biometric Research Branch at the National Cancer Institute. They have collaborated on major biomedical studies utilizing microarrays and in the development of statistical methodology for the design and analysis of microarray investigations. Dr. Simon, chief of the branch, is also the architect of BRB-ArrayTools.

Average customer rating:

Great Introduction to Microarray Analysis
Neat little book on microarrays
If you are new to microarray, get this book.
an intro. for biologists
A Good Book for Microarray Bioinformatics

Microarray Bioinformatics
Dov Stekel
Manufacturer: Cambridge University Press
ProductGroup: Book
Binding: Paperback

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ASIN: 052152587X

Book Description

DNA microarrays have revolutionized molecular biology and are becoming a standard tool in the field. Dov Stekel's book is a comprehensive guide to the mathematics, statistics, and computing required to use microarrays successfully. Unlike traditional molecular biology, the successful use of DNA microarrays requires the application of statistics and computing to design the arrays and experiments, and to analyze and manage the data. This book is written for researchers, clinicians, and laboratory managers.

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This book is a comprehensive guide to all of the mathematics, statistics and computing you will need to successfully operate DNA microarray experiments. It is written for researchers, clinicians, laboratory heads and managers, from both biology and bioinformatics backgrounds, who work with, or who intend to work with microarrays. The book covers all aspects of microarray bioinformatics, giving you the tools to design arrays and experiments, to analyze your data, and to share your results with your organisation or with the international community. There are chapters covering sequence databases, oligonucleotide design, experimental design, image processing, normalisation, identifying differentially expressed genes, clustering, classification and data standards. The book is based on the highly successful Microarray Bioinformatics course at Oxford University, and therefore is ideally suited for teaching the subject at postgraduate or professional level.

Customer Reviews:

Great Introduction to Microarray Analysis.......2006-05-12

This is an excellent introduction to microarray analysis. It is great at explaining the theory behind normalization, clustering, and dimensionality reduction without getting hung up on the statistics behind it. If you are looking for an exhaustive statistical treatment on the topic, this is not the book. But it will give you excellent background on these techniques that make reading statistical papers on the topic much easier for the non-statistics biologist.

Highly recommended.

Neat little book on microarrays.......2006-03-24

Without question this short paperback is a nifty little text. What it does is provide the beginner with a basic brief overview in covering all major aspects of microarrays.

What you have to keep in mind is this book is intended for those who want a brief overview of all aspects of microarrays. Its a "forest for the trees" book on microarrays. The writing is very good and easy to follow, and its a great introductory text and reasonably priced.

Regardless of ones formal training, (e.g. Biology, Statistics, Computer Science, ... , health science) I think it would make an excellent little basic reference on ones bookshelf or to just have around in the lab for undergraduates/beginning graduate students.

Bottomline: If you prefer to learn things by starting at the start and not at the end then consider this book; Indeed its a great starter book to get your feet a little wet before jumping in over your head to the more gnarly stuff.

If you are new to microarray, get this book........2005-05-16

This book describes basic concepts and procedures for those who are new to microarray. I'd recommend that a reader should use this book to grasp what microarray is. You won't be able to know anything in depth from this book but it will be nice to have this if you have trouble in understanding a more challenging book. Once you read this book, please go ahead and read another book since this book doesn't tell you everything about microarray. It's just a basic overview... i was glad that I used this book as my first microarray textbook....

an intro. for biologists.......2004-09-08

This book is written clearly, which also means it doesn't touch too deep. I believe it's mainly useful for biologists who want to get a brief and application oriented introduction, but not for the researchers that want to improve the technology.

A Good Book for Microarray Bioinformatics.......2004-01-04

I rate this book a 5 star because I believe this book is one of best bioinformatics books which make it possible for the biologists to understand the bioinformatic tools inside of microarray technology. For me the most useful chapters include Sequence Databases for Microarrays, Computer Design of Oligonucleotide Probes, Normalisation, Measuring and Quantifying Microarray Variability, Analysis of Differentially Expressed Genes. As a three-years microarray user, I still get a lot information after I read this book. However, no any bioinformatic books are perfect and complete. There are also some limitations in this book. The author sometimes did not provide detailed information on some biostatistic analysis tools and only provided some references for reading. Since a lot of bioinformatic tools are still in the trial stage and need to be improved, we can not blame the author for the incompleteness.
As a 250-pages bioinformatics book, I believe, this book is very informative and useful for microarray users and biologists who are tired of understanding the abstract biostatistic equations.

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A Beginner's Guide to Microarrays

Manufacturer: Springer
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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.

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DNA Microarrays: Current Applications
Rinaldis&Lahm
Manufacturer: Taylor & Francis
ProductGroup: Book
Binding: Hardcover

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ASIN: 1904933254

Computational Genome Analysis: An Introduction (Statistics for Biology & Health)

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"Computational genome analysis: An Introduction" Deonier R., Tavare S., Waterman M. Springer-Verlag New York, Inc., Secaucus, NJ

Computational Genome Analysis: An Introduction (Statistics for Biology & Health)
Richard C. Deonier , Simon Tavaré , and Michael S. Waterman
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 0387987851

Book Description

Computational Genome Analysis: An Introduction presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book is appropriate for a one-semester course for advanced undergraduate or beginning graduate students, and it can also introduce computational biology to computer scientists, mathematicians, or biologists who are extending their interests into this exciting field.

This book features:

Topics organized around biological problems, such as sequence alignment and assembly, DNA signals, analysis of gene expression, and human genetic variation

Presentation of fundamentals of probability, statistics, and algorithms

Implementation of computational methods with numerous examples based upon the R statistics package

Extensive descriptions and explanations to complement the analytical development

More than 100 illustrations and diagrams (some in color) to reinforce concepts and present key results from the primary literature

Exercises at the end of chapters

Michael S. Waterman is a University Professor, a USC Associates Chair in Natural Sciences, and Professor of Biological Sciences, Computer Science, and Mathematics at the University of Southern California. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Professor Waterman is Founding Editor and Co-Editor in Chief of the Journal of Computational Biology. His research has focused on computational analysis of molecular sequence data. His best-known work is the co-development of the local alignment Smith-Waterman algorithm, which has become the foundational tool for database search methods. His interests have also encompassed physical mapping, as exemplified by the Lander-Waterman formulas, and genome sequence assembly using an Eulerian path method.

Simon Tavaré holds the George and Louise Kawamoto Chair in Biological Sciences and is a Professor of Biological Sciences, Mathematics, and Preventive Medicine at the University of Southern California. Professor Tavaré's research lies at the interface between statistics and biology, specifically focusing on problems arising in molecular biology, human genetics, population genetics, molecular evolution, and bioinformatics. His statistical interests focus on stochastic computation. Among the applications are linkage disequilibrium mapping, stem cell evolution, and inference in the fossil record. Dr. Tavaré is also a professor in the Department of Oncology at the University of Cambridge, England, where his group concentrates on cancer genomics.

Richard C. Deonier is Professor Emeritus in the Molecular and Computational Biology Section of the Department of Biological Sciences at the University of Southern California. Originally trained as a physical biochemist, His major research has been in areas of molecular genetics, with particular interests in physical methods for gene mapping, bacterial transposable elements, and conjugative plasmids. During 30 years of active teaching, he has taught chemistry, biology, and computational biology at both the undergraduate and graduate levels.

Customer Reviews:

"Computational genome analysis: An Introduction" Deonier R., Tavare S., Waterman M. Springer-Verlag New York, Inc., Secaucus, NJ.......2006-07-08

This textbook was based on the authors' instructional experiences in undergraduate Computational Biology courses for Bachelor seniors, first-year Master's, and Ph.D. students at the University of Southern California. Readers could also include investigators in medical schools, computer scientists, biologists, applied mathematicians, biochemists, and persons working in the biotechnology industry.

This text is based on the classic man-machine-work model in which a human performs laboratory-level work while also interacting with a digital computer. The complete inventory of all DNA that determines the identity of an organism is known as the genome. The computer or 'machine' utilizes the R language and produces statistical solutions dealing with genomes. The objects analyzed fall into these categories: the basic unit of life or the cell; the chemical energy stored in ATP (Adenosine triphosphate), the genetic information encoded by DNA (Deoxyribonucleic Acid) , and that information transcribed into RNA (Ribonucleic Acid). Since all life on the planet is based on cells, except for viruses, one can see why this volume is an important contribution to the scientific knowledge base particularly with reference to the evolution of species.

The R language developed at Bell Laboratories is used throughout the text. R is a probability statistics environment available for free download and can be used with Windows, Macintosh, and Linux operating systems. It functions very much like the S-PLUS statistics package. Since the reader would need to know how to actually implement the concepts in computational biology to fully understand them, the authors include examples of computations using R. This volume is described as a "roll up your sleeves and get dirty" introduction to the computational side of genomics and bioinformatics. It is intended to provide a foundation for an intelligent application of the available computational tools and for intellectual growth as new experimental approaches lead to new computational tools.

One must accept the fact that analyzing cells, DNA, and RNA is based on probability statistics. The text utilizes 1% algebra, 1 % integral calculus and 98% probability statistics --- the 98% being processed in R language. It isn't intended to describe the laboratory processes and protocols used to manipulate the samples but it does directly connect the computer solutions to the laboratory or work activity. Each chapter ends with a number of problems; while this is typical of the classical textbook, it would have been helpful if a teacher's answer book had been appended.

The Chapter headings are: Biology in a Nutshell; Words, Word Distributions and Occurences; Physical Mapping of DNA; Genome Rearrangements; Sequence Alignment; Rapid Alignment Methods: FASTA and BLAST; DNA Sequence Assembly; Signals in DNA; Similarity, Distance, and Clustering; Measuring Expression of Genome Information; Inferring the Past: Phylogenetic Trees; Genetic Variation in Populations; Comparative Geonomics; Glossary; A Brief Introduction to R; Internet Bioinformatics Resources; Miscellaneous Data.

Leonard C. Silvern
Systems Engineering Laboratories
Clarkdale, AZ

DNA Microarrays: A Practical Approach (Practical Approach Series)

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Helpful but could be better
Nice Overview

DNA Microarrays: A Practical Approach (Practical Approach Series)

Manufacturer: Oxford University Press, USA
ProductGroup: Book
Binding: Paperback

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

ASIN: 0199637768

Book Description

DNA microarrays, or biochips, are small glass chips embedded with ordered rows of DNA and by providing a massive parallel platform for data gathering represent a fundamental technical advance in biomedical research. Such biochips enable the use of advanced fabrication, detection, and data mining technologies that allow data gathering at an unprecedented rate. The first chapter is an introduction to the technology of DNA microarrays, emphasizing the methodological fundamentals of biochips. The next two chapters describe the use of confocal scanning in microarray detection and techniques for the efficient cloning and screening of differentially expressed genes. Chapter 4 describes assay optimization for enzymatic assays and chapter 5 antisense scanning arrays. This is followed by a chapter on the manufacture of molecular arrays using ink-jet printing technology. Chapter 7 describes gene expression analysis from cDNA microarrays and then chapter 8 covers the use of expression data in bioinformatics. Chapter 9 describes the use of active microelectronic arrays for DNA hybridization analysis and the last chapter details the use of microarray technology in pharmacogenomics. This Practical Approach book is a comprehensive overview of the new and expanding field of DNA microarray technology and will be invaluable to any researcher interested in the use of biochips.

Customer Reviews:

Helpful but could be better.......2005-09-20

I am basically a newbie in this area and was hoping to learn the basics of using microarrays for sequencing and expression analysis. While it was somewhat helpful for me, I think that it is aimed at a more expert audience already well versed in molecular biology techniques. I found the documentation and other info on the Affymetrix site to be more comprehensive and explanatory.

Nice Overview.......2002-10-16

This book is a good primer on microarrays.

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