  Most of the statistical tests currently used to detect differentially
expressed genes are based on asymptotic results, and perform poorly for low
expression tags. Another problem is the common use of a single canonical cutoff
for the significance level (p-value) of all the tags, without taking into
consideration the type II error and the highly variable character of the sample
size of the tags.
  This work reports the development of two significance tests for the
comparison of digital expression profiles, based on frequentist and Bayesian
points of view, respectively. Both tests are exact, and do not use any
asymptotic considerations, thus producing more correct results for low
frequency tags than the chi-square test. The frequentist test uses a
tag-customized critical level which minimizes a linear combination of type I
and type II errors. A comparison of the Bayesian and the frequentist tests
revealed that they are linked by a Beta distribution function. These tests can
be used alone or in conjunction, and represent an improvement over the
currently available methods for comparing digital profiles.
