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Bayesian Inference for Gene Expression and Proteomics
Author(s) -
John Lu Z. Q.
Publication year - 2007
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2007.00506_8.x
Subject(s) - inference , bayesian probability , computational biology , proteomics , bayesian inference , gene expression , expression (computer science) , gene , computer science , biology , artificial intelligence , genetics , programming language
are relevant to medical statistics. It describes the statistical theory behind each technique, provides the SAS code that is needed to perform it, shows the SAS output and describes how to interpret it. As a trial statistician, for me the one thing that is missing is much detail of clinical trials—the index has no entries for randomization or crossover trials. However, other books on analysing clinical trials in SAS are available, so maybe I should just buy one of those. This is primarily a book for someone who is fairly new to SAS. It would be an ideal book for students on undergraduate or post-graduate medical statistics courses that involve SAS and would also be a very useful addition to the libraries of statistical teams in pharmaceuticals companies, to give to new members of staff. But, even for experienced readers, there are new hints or tips to pick up. It is a great book to have on your shelf to dip into and to remind yourself about things that you have forgotten, or for a straightforward guide to techniques that you have not previously used. This book inspired me finally to get to grips with the output delivery system, which is something that I have been meaning to do for years but have never quite got round to. Having a friendly step-by-step guide in front of me, rather than a technical SAS bulletin that is full of far too many options, made all the difference.