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Statisticians have met the need to test hundreds or thousands of
genomics hypotheses simultaneously with novel empirical Bayes
methods that combine advantages of traditional Bayesian and
frequentist statistics. Techniques for estimating the local false
discovery rate assign probabilities of differential gene
expression, genetic association, etc. without requiring subjective
prior distributions. This book brings these methods to scientists
while keeping the mathematics at an elementary level. Readers will
learn the fundamental concepts behind local false discovery rates,
preparing them to analyze their own genomics data and to critically
evaluate published genomics research. Key Features: * dice games
and exercises, including one using interactive software, for
teaching the concepts in the classroom * examples focusing on gene
expression and on genetic association data and briefly covering
metabolomics data and proteomics data * gradual introduction to the
mathematical equations needed * how to choose between different
methods of multiple hypothesis testing * how to convert the output
of genomics hypothesis testing software to estimates of local false
discovery rates * guidance through the minefield of current
criticisms of p values * material on non-Bayesian prior p values
and posterior p values not previously published
This book serves as a brief introduction to phylogenetic trees and
molecular evolution for biologists and biology students. It does so
by presenting the main concepts in a variety of ways: first
visually, then in a history, next in a dice game, and finally in
simple equations. The content is primarily designed to introduce
upper-level undergraduate and graduate students of biology to
phylogenetic tree reconstruction and the underlying models of
molecular evolution. A unique feature also of interest to
experienced researchers is the emphasis on simple ways to quantify
the uncertainty in the results more fully than is possible with
standard methods.
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