early on, describes the 11 data sets used
throughout the book to illustrate analytic
techniques, with background information
so as to acquaint the users with the domain
knowledge necessary to adequately interpret
the analytic results. When we do get into the
technical areas, most are examples of clarity
and brevity in scientific writing. In this, as in
most areas of science, it would be ridicu-
lously easy to write at the level of doctoral
candidates, but the authors make the math-
ematics accessible to the biologists and the
biology accessible to the computer types.
While we are on the topic of mathematical genomics, the level of the math used is
mostly high school algebra with minimal
calculus and, as the data comes from fixed
slides, no need of differential equations at
this introductory level. As an added bonus,
there are none of the dreaded “The proof is
left to the student” nor the much dreaded
“It can be shown that…”. (Sure it can,
after 3 pages of calculations).
The output graphics are the simple types
that come out of R, and the mathematical
terms all defined and described, usually at
first appearance. The uses of flowcharts
to describe the processes such as normal-ization of data are an added plus, as is the
extremely logical flow of chapter materials.
The reader is led from the introduction
through the transformations and normal-
izations that precede data analysis, always
being careful to justify these mathematical
acrobatics with necessary domain knowl-
edge. The importance of both biological
and technical replicates is stressed in one
entire subsection, and this is of primary
concern, as estimating variation without
true replicates is a prime statistical felony,
one in which the chip developers circum-
vent in a variety of creative ways.
In summary, this is an excellent text for
both life scientist and computer/mathema-ticians. Highly recommended.
• Exploration and Analysis of DNA
Microarray and Other High-Dimensional
Data. D. Amaratunga, J. Cabrera, and
Ziv Shkedy. John Wiley & Sons.
Hoboken, NJ pp. 317 + xiii. (2014). $120
John Wass is a statistician based in
Chicago, IL. He may be reached at
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