Differential expression analysis is one of the most common types of analyses performed on various biological data (e.g. RNA-seq or mass spectrometry proteomics). It is the process that detects features, such as genes or proteins, showing statistically significant differences between the sample groups under comparison. A major challenge in the analysis is the choice of an appropriate test statistic, as different statistics have been shown to perform well in different datasets. To this end, the reproducibility-optimized test statistic (ROTS) adjusts a modified t-statistic according to the inherent properties of the data and provides a ranking of the features based on their statistical evidence for differential expression between two groups. ROTS has already been successfully applied in a range of different studies from transcriptomics to proteomics, showing competitive performance against other state-of-the-art methods. To promote its widespread use, we introduce here a Bioconductor R package for performing ROTS analysis conveniently on different types of omics data. To illustrate the benefits of ROTS in various applications, we present three case studies, involving proteomics and RNA-seq data from public repositories, including both bulk and single cell data. The package is freely available from Bioconductor (https://www.bioconductor.org/packages/ROTS).

During the differentiation of naive CD4 + T cells into effector T cells, cell fate decisions into various Th subsets are made, and Th cell lineage-specific gene expression patterns are established and maintained. Epigenetic mechanisms, such as histone and DNA modifications, play a crucial role in these processes. Among these, modification of core histones by reversible lysine acetylation is controlled by histone acetyltransferases (HATs) and histone deacetylases (HDACs), which are "classically" considered as transcriptional coactivators and corepressors, respectively. However, HDACs are also recruited to active gene loci and might, potentially with HATs, act context dependently as modulators of gene transcription. Moreover, many nonhistone targets have been emerging, and HATs/HDACs function beyond the epigenetic control of gene expression (10-12). To date, 18 members of the HDAC family (many of which are expressed in the T cell lineage) that are grouped into 4 classes have been identified (13). We have recently generated mice with a T cell-specific deletion of the class I histone deacetylases HDAC1 and HDAC2, which resulted in MHC class II-restricted CD4 + CD8αβ + T cells that, upon activation, initiate the upregulation of a Runx3/ CBFβ-dependent CD8 effector T cell-like program (14,15). This observation indicates that CD4 lineage insight.jci.org

Forkhead box protein P3 + (FOXP3 + ) regulatory T cells (T reg cells) play a key role in maintaining tolerance and immune homeostasis. Here, we report that a T cell-specific deletion of the transcription factor MAZR (also known as PATZ1) leads to an increased frequency of T reg cells, while enforced MAZR expression impairs T reg cell differentiation. Further, MAZR expression levels are progressively downregulated during thymic T reg cell development and during in-vitro-induced human T reg cell differentiation, suggesting that MAZR protein levels are critical for controlling T reg cell development. However, MAZRdeficient T reg cells show only minor transcriptional changes ex vivo, indicating that MAZR is not essential for establishing the transcriptional program of peripheral T reg cells. Finally, the loss of MAZR reduces the clinical score in dextran-sodium sulfate (DSS)-induced colitis, suggesting that MAZR activity in T cells controls the extent of intestinal inflammation. Together, these data indicate that MAZR is part of a T reg cell-intrinsic transcriptional network that modulates T reg cell development.