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\iteman{io-port 05346565}
\itemau{Lian, Heng}
\itemti{MOST: Detecting cancer differential gene expression.}
\itemso{Biostatistics 9, No. 3, 411-418 (2008).}
\itemab
Summary: We propose a new statistic 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 have 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 cancer outlier profile analysis [{\it S. A.. Tomlins} et al., Science 310, 644--648 (2005)], outlier sum [{\it R. Tibshirani} and {\it T. Hastie}, Biostatistics 8, No.~1, 2--8 (2007; Zbl 1121.62102)], and outlier robust $t$-statistics [{\it B. Wu}, ibid. 8, No.~3, 566--575 (2007; Zbl 1121.62105)]. We propose a new statistic called maximum ordered subset $t$-statistic (MOST) which seems to be natural when the number of activated samples is unknown. We compare MOST to other statistics and find that the proposed method often has more power then its competitors.
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\itemut{COPA; microarray}
\itemli{doi:10.1093/biostatistics/kxm042}
\end