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| By: Sorin Draghici ISBN: 1584883154 Publisher: Chapman & Hall/CRC Release Date: 04 June, 2003 Bioscience book rank: 373613 | 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. 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. <br /> <br />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. <br /> <br />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). <br /> <br />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. <br />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. <br /> <br />Overall, I find that the value you get per dollar spent when buying this book is high, and thereby I would strongly recommend it. <br /> <br />Dr. Adi L. Tarca, Windsor (CANADA) This book is a must to understand fundamental statistical analysis of microarray data. Must have it. | |
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| By: Helen Causton, John Quackenbush, Alvis Brazma ISBN: 1405106824 Publisher: Wiley-Blackwell Release Date: 25 April, 2003 Bioscience book rank: 335934 | 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.
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<br />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.
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<br />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. 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. <br /> <br />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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| By: Aidong Zhang ISBN: 9812566457 Publisher: World Scientific Publishing Company Release Date: 27 June, 2006 Bioscience book rank: 621850 | ||
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| By: Richard M. Simon, Edward L. Korn, Lisa M. McShane, Michael D. Radmacher, George W. Wright, Yingdong Zhao ISBN: 0387001352 Publisher: Springer Release Date: 08 January, 2004 Bioscience book rank: 315213 | ||
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| By: Ernst Wit, John McClure ISBN: 0470849932 Publisher: Wiley Release Date: 13 August, 2004 Bioscience book rank: 730158 | 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. <br /> <br /> 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. <br /> <br /> 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. <br /> <br /> 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. <br /> <br />//wiredweird | |
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| By: Dov Stekel ISBN: 052152587X Publisher: Cambridge University Press Release Date: 08 September, 2003 Bioscience book rank: 573193 | 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.
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<br />Highly recommended. 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. <br /> <br />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. <br /> <br />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. <br /> <br />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. 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.... | |
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| By: David Bowtell, Joseph Sambrook ISBN: 0879696257 Publisher: Cold Spring Harbor Laboratory Press Release Date: 15 September, 2002 Bioscience book rank: 566171 | This book is very much like its well known predecessor, the currently 3 volume Molecular Cloning Manual with which it shares an editor and a similar title (in fact, it could pass as volume 4). <br>Overall it gives a good coverage of spotted microarray technology starting from the preparation of probes and slides to sample preparation and hybridization. In addition to expression profiling areas covered include uses of microarrays for analysis of chromatin immunoprecipitation samples, DNA copy number determination, and detection of genetic polymorphisms (oligo arrays). Tissue-arrays and micro-dissection techniques for sample preparation are also described. The bioinformatics section is less extensive than the experimental parts, but the chapters on clustering, self organizing maps and databases serve as good introductions to these areas. I especially liked the image acquisition, normalization and quality control sections. These reviews presented their material in a very clear and sensible way. <br>Each chapter starts with a short summary of the relevant background. These introductions are very concise, straightforward, jargon-free and up to date in their references. They are written by leading experts of their fields. In addition, there are several "Information Panels" with similar qualities, covering somewhat more general background material.<br>This is a very nice looking book; it is a pleasure to look at and to hold. I am sure every lab that uses microarrays could benefit from it. | |
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| By: Dhammika Amaratunga, Javier Cabrera ISBN: 0471273988 Publisher: Wiley-Interscience Release Date: 21 October, 2003 Bioscience book rank: 715548 | This book provides an excellent overview of various methods in DNA microarray analysis. It explains most of the theories behind the algorithms, so that you know why the analyses are done in certain way. In fact, I find I get more insights from the book as compare to the research papers which tend to be brief. | |
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| By: Daniel P. Berrar, Werner Dubitzky, Martin Granzow ISBN: 1402072600 Publisher: Springer Release Date: 31 December, 2002 Bioscience book rank: 1064269 | ||
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| By: Mark Schena ISBN: 0471414433 Publisher: Wiley-Liss Release Date: 11 November, 2002 Bioscience book rank: 438395 | Despite the fact that this book has very little about microarray data analysis, it is still a very good book about microarray technology. The author even introduces the reader to the basic organic chemistry and the physics necessary for fully understanding what is a microarray and how it works. The book has many interesting information which I did not see in other similar books and which can help you create the necessary microarray culture you will need. On the other hand, the book does not provide protocols or detailed methods for your experiments (if you are looking for something like this you will need the "DNA MICROARRAYS: A Molecular cloning manual" which is some kind of Maniatis of the microarrays).
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<br />In relation to data analysis this book does not provide you with even the basics for analysing your data, which is a fairly complex task. If you need help with that (and you are a biologist) I would suggest "Data analysis tools for DNA Microarrays" by Sorin Draghici, which is a very good intro book to data analysis and is not too hard on the math side (actually it is as simple as possible but without oversimplifying it). If you are a math person than you might prefer Terry Speed's "Statistical Analysis of Gene Expression Microarray Data" or Cabrera's "Exploration and Analysis of DNA Microarray and Protein Array Data". "...the book is highly recommended as an excellent one stop resource on microarray technology" (Proteomics) "...a very personal, encyclopedic, diffuse compendium of everything known about microarrays...fun to read...full of information that a working biologist...might be quite interested to learn." (ASM News, Vol. 69, No. 7, 2003) | |
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methods in molecular biology, PCR, RT-PCR and real-time quantitative PCR, Differential Display, recombinant DNA, gene therapy, virus protocols, lentivirus methods, gene targeting, mouse knock-out and knock-in, transgenic technology, phenotyping, gene delivery and transfer, transcriptional regulation, RNA methods, RNA Polymerase, gene expression, protein translation regulation, protein kinase, protein phosphorylation, genomics, genomics methods, epigenetics, DNA methylation, DNA sequencing, RNA interference, microarray
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