Note: If you wish to use Messina to detect differential
expression, and not construct classifiers, you may find the
messinaDE function to be a more convenient
interface.Messina constructs single-feature threshold classifiers
(see below) to separate two sample groups, that are in a
sense the most robust single-gene classifiers that satisfy
user-supplied performance requirements. It accepts as
primary input a matrix or ExpressionSet of feature data x;
a vector of sample class membership y; and minimum
classifier target performance values min_sens, and
min_spec. Messina then examines each feature of x in turn,
and attempts to build a threshold classifier that satisfies
the minimum performance requirements, based on that
feature. The results of this classifier training and
testing are then returned in a MessinaClassResult object.
The features measured in x must be numeric and contain no
missing values, but apart from that are unrestricted --
common use cases are mRNA measurements and protein
abundance estimates. Messina is not sensitive to the data
transformation used, although for mRNA abundance
measurements a log-transform or similar is suggested to aid
interpretability of the results. x containing discrete
values can also be examined by Messina, though if the
number of possible values of the members of x is very low,
the algorithm is unlikely to be very powerful.