  The process of biomarker discovery is typically lengthy and costly, involving
the phases of discovery, qualification, verification, and validation before
clinical evaluation. Being able to efficiently identify the truly relevant
markers in discovery studies can significantly simplify the process. However,
in discovery studies the sample size is typically small while the number of
markers being explored is much larger. Hence discovery studies suffer from
sparsity and high dimensionality issues. Currently the state-of-the-art methods
either find too many false positives or fail to identify many truly relevant
markers. In this paper we develop a likelihood ratio-based approach and aim for
accurately finding the truly relevant protein markers in discovery studies. Our
method fits especially well with discovery studies because they are mostly
balanced design due to the fact that experiments are limited and controlled.
Our approach is based on the observation that the underlying distributions of
expression profiles are unimodal for those irrelevant plain markers. Our method
has asymptotic chi-square null distribution which facilitates the efficient
control of false discovery rate. We then evaluate our method using both
simulated and real experimental data. In all the experiments our method is
highly effective to discover the set of truly relevant markers, leading to
accurate biomarker identifications with high sensitivity and low empirical
false discovery rate.
