  We propose a new statistics for the detection of differentially expressed
genes, when the genes are activated only in a subset of the samples. Statistics
designed for this unconventional circumstance has proved to be valuable for
most cancer studies, where oncogenes are activated for a small number of
disease samples. Previous efforts made in this direction include COPA, OS and
ORT. We propose a new statistics called maximum ordered subset t-statistics
(MOST) which seems to be natural when the number of activated samples is
unknown. We compare MOST to other statistics and find the proposed method often
has more power then its competitors.
