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A Test for Partial Differential Expression
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DOI：10.1198/016214507000001319.png)
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Abstract
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Even in a single-tissue type cancer is often a collection of different diseases, each with its own genetic mechanism. Consequently, a gene may be expressed in some but not all of the tissues in a sample. Differentially expressed genes are commonly detected by methods that test for a shift in location that ignore the possibility of heterogeneous expression. This article proposes a two-sample test statistic designed to detect shifts that occur in only a part of the sample (partial shifts). The statistic is based on the mixing proportion in a nonparametric mixture and minimizes a weight distance function. The test is shown to be asymptotically distribution free and consistent, and an efficient permutation-based algorithm for estimating the p value is discussed. A simulation study shows that the tests is indeed more powerful than the two-sample t test and the Cramer-von Mises test for detecting partial shifts and is competitive for whole-sample shifts. The use os the test is illustrated on real-life cancer datasets, where the test is able to find genes with clear heterogeneous expression associated with reported subtypes of the cancer.
Keywords:
Asymptotics
Consistency
Differential gene expression
Nonparametric mixture
Permutation test
Weighted minimum distance
Journal
J
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3
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5.2K
Citations:
4.8W
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