MOTIVATION: Differential co-expression analysis (DCA) aims to identify genes in a pathway whose shared expression depends on a risk factor. While DCA provides insights into the biological activity of diseases, existing methods are limited to categorical risk factors and/or suffer from bias due to batch and variance-specific effects. We propose a new framework, Kernel-based DCA (KDCA), that harnesses correlation patterns between genes in a pathway to detect differential co-expression arising from general (i.e. continuous, discrete, or categorical) risk factors. RESULTS: Using various simulated pathway architectures, we find that KDCA accounts for common sources of bias to control the type I error rate while substantially increasing the power compared to the standard eigengene approach. We then applied KDCA to The Cancer Genome Atlas thyroid data set and found several differentially co-expressed pathways by age of diagnosis and BRAF mutation status that were undetected by the eigengene method. Collectively, our results demonstrate that KDCA is a powerful testing framework that expands DCA applications in expression studies. AVAILABILITY AND IMPLEMENTATION: KDCA is publicly available in the R package kdca. The package can be downloaded at https://github.com/ajbass/kdca.

MOTIVATION: Analysis of biological data often involves the simultaneous testing of thousands of genes. This requires two key steps: the ranking of genes and the selection of important genes based on a significance threshold. One such testing procedure, called the optimal discovery procedure (ODP), leverages information across different tests to provide an optimal ranking of genes. This approach can lead to substantial improvements in statistical power compared to other methods. However, current applications of the ODP have only been established for simple study designs using microarray technology. Here, we extend this work to the analysis of complex study designs and RNA-sequencing studies. RESULTS: We apply our extended framework to a static RNA-sequencing study, a longitudinal study, an independent sampling time-series study,and an independent sampling dose-response study. Our method shows improved performance compared to other testing procedures, finding more differentially expressed genes and increasing power for enrichment analysis. Thus, the extended ODP enables a favorable significance analysis of genome-wide gene expression studies. AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented in our freely available R package called edge and can be downloaded at https://www.bioconductor.org/packages/release/bioc/html/edge.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.