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Abstract Two correspondences raised concerns or comments about our analyses regarding exaggerated false positives found by differential expression (DE) methods. Here, we discuss the points they raise and explain why we agree or disagree with these points. We add new analysis to confirm that the Wilcoxon rank-sum test remains the most robust method compared to the other five DE methods (DESeq2, edgeR, limma-voom, dearseq, and NOISeq) in two-condition DE analyses after considering normalization and winsorization, the data preprocessing steps discussed in the two correspondences. 

Abstract Skin epidermis constitutes the outer permeability barrier that protects the body from dehydration, heat loss, and myriad external assaults. Mechanisms that maintain barrier integrity in constantly challenged adult skin and how epidermal dysregulation shapes the local immune microenvironment and whole‐body metabolism remain poorly understood. Here, we demonstrate that inducible and simultaneous ablation of transcription factor‐encodingOvol1andOvol2in adult epidermis results in barrier dysregulation through impacting epithelial‐mesenchymal plasticity and inflammatory gene expression. We find that aberrant skin immune activation then ensues, featuring Langerhans cell mobilization and T cell responses, and leading to elevated levels of secreted inflammatory factors in circulation. Finally, we identify failure to gain body weight and accumulate body fat as long‐term consequences of epidermal‐specificOvol1/2loss and show that these global metabolic changes along with the skin barrier/immune defects are partially rescued by immunosuppressant dexamethasone. Collectively, our study reveals key regulators of adult barrier maintenance and suggests a causal connection between epidermal dysregulation and whole‐body metabolism that is in part mediated through aberrant immune activation. 

Abstract BackgroundEstimating and accounting for hidden variables is widely practiced as an important step in molecular quantitative trait locus (molecular QTL, henceforth “QTL”) analysis for improving the power of QTL identification. However, few benchmark studies have been performed to evaluate the efficacy of the various methods developed for this purpose. ResultsHere we benchmark popular hidden variable inference methods including surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP) against principal component analysis (PCA)—a well-established dimension reduction and factor discovery method—via 362 synthetic and 110 real data sets. We show that PCA not only underlies the statistical methodology behind the popular methods but is also orders of magnitude faster, better-performing, and much easier to interpret and use. ConclusionsTo help researchers use PCA in their QTL analysis, we provide an R package along with a detailed guide, both of which are freely available athttps://github.com/heatherjzhou/PCAForQTL. We believe that using PCA rather than SVA, PEER, or HCP will substantially improve and simplify hidden variable inference in QTL mapping as well as increase the transparency and reproducibility of QTL research. 

Abstract When identifying differentially expressed genes between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. Expanding the analysis to limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test, we found that FDR control is often failed except for the Wilcoxon rank-sum test. Particularly, the actual FDRs of DESeq2 and edgeR sometimes exceed 20% when the target FDR is 5%. Based on these results, for population-level RNA-seq studies with large sample sizes, we recommend the Wilcoxon rank-sum test.