ABSTRACT. Deregulation of cardiac miRNA gene-regulatory networks is a feature of different heart diseases, including ischemic (ICM) and nonischemic (NICM) cardiomyopathy. Here, based on the paired miRNA and mRNA expression profiles in ICM and NICM, we identified the differentially expressed miRNAs and mRNAs and the expression signatures distinguishing ICM/NICM from control samples. Furthermore, we constructed a functional miRNA network for each disease. Analysis of the topological features of these networks revealed that the Wnt signaling pathway and cell cycle (de)regulation play critical roles in the development of ICM and NICM. In addition, comparison of the miRNA and mRNA functional profiles revealed that their expression patterns in ICM and NICM differ. These findings revealed hundreds of novel heart-failurerelated miRNAs with important regulatory functions. In summary, RNA- seq-based transcriptome profiling in the failing human heart revealed a complex transcriptional regulation associated with the disease. The newly uncovered importance of miRNAs in disease pathogenesis highlights their value as potential diagnostic and therapeutic targets.

ABSTRACT. Control of the false discovery rate is a statistical method that is widely used when identifying differentially expressed genes in highthroughput sequencing assays. It is often calculated using an adaptive linear step-up procedure in which the number of non-differentially expressed genes should be estimated accurately. In this paper, we discuss the estimation of this parameter and point out defects in the original estimation method. We also propose a new estimation method and provide the error estimation. We compared the estimation results from the two methods in a simulation study that produced a mean, standard deviation, range, and root mean square error. The results revealed that there was little difference in the mean between the two methods, but the standard deviation, range, and root mean square error obtained using the new method were much smaller than those produced by the original method, which indicates that the new method is more accurate and robust. Furthermore, we used real microarray data to verify the conclusion. Finally we provide a suggestion when analyzing differentially expressed genes using statistical methods.