1 Original paper A General Method for Accurate Estimation of False Discovery Rates in Identifica- tion of Differentially Expressed Genes

The “omic ” data such as genomic data, transcriptomic data, prote-omic data and SNP data have been rapidly growing. The omic data are large-scale and high-throughput data. Such data challenge tra-ditional statistical methodologies and require multiple tests. Sev-eral multiple-testing procedures such as Bonferroni procedure, Benjamini-Hochberg procedure, Westfall-Young procedure have been developed, among which some control family-wise error rate and the others control false discovery rate (FDR). These proce-dures are valid in some cases and cannot been applied to all types of large-scale data. To address this statistically challenging prob-lem in the analysis of the omic data, we propose a general method for generating a set of multiple-testing procedures. This method is based on the Benjamini-Hochberg theorems. By choosing a C-value, one can realize a specific multiple-testing procedure. For example, by setting C=1.22, our method produces the Benjamini-Hochberg procedure. With C<1.22 our method generates proce-dures of weakly controlling FDR and with C>1.22 the procedures strongly control FDR. Those with C = G (number of genes or tests) and C=0 are respectively the Bonferroni procedure and single-test procedure. These are the two extreme procedures in this family. To let one choose an appropriate multiple-testing procedure in prac-tice, we develop an algorithm by which FDR can be correctly and reliably estimated. Simulated results show that our method works well for accurate estimation of FDR in various scenarios and we illustrate the applications of our method with three real data sets.