High-throughput DNA methylation datasets for evaluating false discovery rate methodologies

Publication year: 2011 Source: Computational Statistics & Data Analysis, Available online 29 October 2011 N. Asomaning, K.J. Archer When analyzing high-throughput genomic data, the multiple comparison problem is most often addressed through estimation of the false discovery rate (FDR), using methods such as the Benjamini & Hochberg, Benjamini & Yekutieli, the q-value method, or in controlling the family-wise error rate (FWER) using Holm’s step down method. To date, research studies that have compared various FDR/FWER methodologies have made use of limited simulation studies and/or have applied the methods to one or more microarray gene expression dataset(s).

Publication year: 2011 Source: Computational Statistics & Data Analysis, Available online 29 October 2011 N. Asomaning, K.J. Archer When analyzing high-throughput genomic data, the multiple comparison problem is most often addressed through estimation of the false discovery rate (FDR), using methods such as the Benjamini & Hochberg, Benjamini & Yekutieli, the q-value method, or in controlling the family-wise error rate (FWER) using Holm’s step down method. To date, research studies that have compared various FDR/FWER methodologies have made use of limited simulation studies and/or have applied the methods to one or more microarray gene expression dataset(s).

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High-throughput DNA methylation datasets for evaluating false discovery rate methodologies

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