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Bioinformatics and Computational Biology Solutions Using R and Bioconductor
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There are 1 items available. Please enter a number less than or equal to 1. Select a valid country. Please enter 5 or 9 numbers for the ZIP Code. Handling time. Will usually ship within 3 business days of receiving cleared payment - opens in a new window or tab. In the same vein, it would have been useful to provide the reader with some introduction to the syntax and semantics of R itself and the coding conventions used in the examples; but unfortunately the book simply writes down R code examples without a guide and even without any obvious systematic or self-explanatory coding style.
The somewhat idiosyncratic short-naming convention, although apparently common-place among R users, does not help to make the code examples more transparent either. So, the reader who chooses this book as a holiday reading on a remote island should be advised to take along a reference book on R, in order to make sense of the many R-code samples provided. The problem is not restricted to the R-code examples; even the Bioconductor itself is not introduced in enough detail to convey to the reader a systematic understanding of the design and roadmap of this powerful evolving bioinformatics toolkit.
This is a critical loss to those readers who are hoping to engage in the community effort and who need to learn about fundamental data structures and design conventions of the Bioconductor. For instance, the importance of the exprSet data structure is announced early in the book, and this structure seems to be used in many of the code examples, yet there is not even a tabular or schematic overview provided about it.
It is still possible for the reader who has seen many programming languages to follow the discussion, but those readers who lack such experience may be lost. The strength of the book seems to lie more on a practical application-oriented discussion of the various data analysis approaches with a solid body of explanation, references, and comparison of alternative methods.
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It is also helpful that even the practically oriented reader with a more casual background in mathematics will have a chance of following the discussion. However, the book is again not self-consistent even in the methodological discussion, because most methods are being discussed by reference only and their essentials are not actually described either. The application of these methods is demonstrated using realistic data and there is plenty of example output and diagrams shown that the reader can still follow the point of the discussion albeit somewhat unsure about the specific detail.
This is followed by a series of short case studies which illustrate the application of the sum of material discussed in the four main parts to specific example projects. Each part will be useful to the reader as each is an essential component of working with high-throughput data. Particularly, the parts i, iii, and iv are inherently mathematical, and hence clearly the domain of R-packages and their discussion is most gratifying.
It is good to see R-examples for both statistical applications parts i and iii as well as for discrete mathematics algorithms used in graph theory part iv.
Bioinformatics And Computational Biology Solutions Using R And Bioconductor - Robert Gentleman
Conversely, regarding the treatment of annotation metadata in R part ii , it is not obvious why one would rely solely on R-packages to access such annotations which mostly reside in relational database management systems, XML or web resources, integrating these resources does not seem to be such a unique strength of R. It would have been helpful if the book had discussed how the user can make their own annotation database resources accessible to analysis algorithms executed in R rather than simply showing some subset of R-modules designed to interact with the world outside.
Likewise the short discussion on workflow integration of R-analyses has too narrow a horizon, is caught within the perimeter of R, not discussing any other alternatives such as use of R as part of a larger analysis platform. Second, color figures are dispersed throughout the text rather than being relegated to a central section of color plates. Third, the index indicates whether a term references a package, function or class.
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This book is an excellent resource In summary, this book is a must have for any Bioconductor user. Organized into separate chapters of shared authorship, the book provides a valuable overview of the impact that the authors and their colleagues have had on the analysis of genomic data. Doerge, Biostatistics, December The range of material covered by the book is diverse and well structured. An abundance of fully worked case studies illustrate the methods in practice.