Outlier sums for differential gene expression analysis.

Robert Tibshirani; Trevor Hastie

doi:10.1093/biostatistics/kxl005

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Outlier sums for differential gene expression analysis.

Robert Tibshirani·Trevor Hastie

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Abstract

We propose a method for detecting genes that, in a disease group, exhibit unusually high gene expression in some but not all samples. This can be particularly useful in cancer studies, where mutations that can amplify or turn off gene expression often occur in only a minority of samples. In real and simulated examples, the new method often exhibits lower false discovery rates than simple t-statistic thresholding. We also compare our approach to the recent cancer profile outlier analysis proposal of Tomlins and others (2005).

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Computer Simulation DNA, Neoplasm Data Interpretation, Statistical Gene Expression Profiling Humans Male Oligonucleotide Array Sequence Analysis Prostatic Neoplasms Comparative Study Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

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Robert Tibshirani, Trevor Hastie. 2006-05-15. Outlier sums for differential gene expression analysis.. https://doi.org/10.1093/biostatistics%2Fkxl005

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