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Algorithms form a bridge between mathematical expression and computational evaluation. Statistical algorithms and insights are particularly relevant in high-throughput analysis: the large volume of data masks small sample size; technical artifacts abound; and nuanced questions require innovative rather than cookie-cutter approaches. The R / Bioconductor project represents an effective way to bring useful computation on high-throughput data to a broad scientific research community. These points are explored through Bioconductor packages and work flows, and by articulation of Bioconductor principles associated with reproducible research conducted in an open environment.
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