MOST: detecting cancer differential gene expression

TL;DR

This paper introduces MOST, a new statistical method for detecting differentially expressed genes in cancer studies, especially when gene activation occurs only in a subset of samples, outperforming existing methods.

Contribution

The paper presents MOST, a novel statistic tailored for identifying subset-specific gene activation, improving detection power over previous methods like COPA, OS, and ORT.

Findings

MOST often has higher power than competitors

Effective in detecting genes activated in small sample subsets

Applicable to cancer gene expression analysis

Abstract

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.