limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

One of the most common analysis tasks in genomic research is to identify genes that are differentially expressed (DE) between experimental conditions. Empirical Bayes (EB) statistical tests using moderated genewise variances have been very effective for this purpose, especially when the number of biological replicate samples is small. The EB procedures can however be heavily influenced by a small number of genes with very large or very small variances. This article improves the differential expression tests by robustifying the hyperparameter estimation procedure. The robust procedure has the effect of decreasing the informativeness of the prior distribution for outlier genes while increasing its informativeness for other genes. This effect has the double benefit of reducing the chance that hypervariable genes will be spuriously identified as DE while increasing statistical power for the main body of genes. The robust EB algorithm is fast and numerically stable. The procedure allows exact small-sample null distributions for the test statistics and reduces exactly to the original EB procedure when no outlier genes are present. Simulations show that the robustified tests have similar performance to the original tests in the absence of outlier genes but have greater power and robustness when outliers are present. The article includes case studies for which the robust method correctly identifies and downweights genes associated with hidden covariates and detects more genes likely to be scientifically relevant to the experimental conditions. The new procedure is implemented in the limma software package freely available from the Bioconductor repository.

As single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed, and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available. Here, we present the Splatter Bioconductor package for simple, reproducible, and well-documented simulation of scRNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types, or differentiation paths.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1305-0) contains supplementary material, which is available to authorized users.

OBJECTIVEInsulin resistance and other features of the metabolic syndrome have been causally linked to adipose tissue macrophages (ATMs) in mice with diet-induced obesity. We aimed to characterize macrophage phenotype and function in human subcutaneous and omental adipose tissue in relation to insulin resistance in obesity.RESEARCH DESIGN AND METHODSAdipose tissue was obtained from lean and obese women undergoing bariatric surgery. Metabolic markers were measured in fasting serum and ATMs characterized by immunohistology, flow cytometry, and tissue culture studies.RESULTSATMs comprised CD11c+CD206+ cells in “crown” aggregates and solitary CD11c−CD206+ cells at adipocyte junctions. In obese women, CD11c+ ATM density was greater in subcutaneous than omental adipose tissue and correlated with markers of insulin resistance. CD11c+ ATMs were distinguished by high expression of integrins and antigen presentation molecules; interleukin (IL)-1β, -6, -8, and -10; tumor necrosis factor-α; and CC chemokine ligand-3, indicative of an activated, proinflammatory state. In addition, CD11c+ ATMs were enriched for mitochondria and for RNA transcripts encoding mitochondrial, proteasomal, and lysosomal proteins, fatty acid metabolism enzymes, and T-cell chemoattractants, whereas CD11c− ATMs were enriched for transcripts involved in tissue maintenance and repair. Tissue culture medium conditioned by CD11c+ ATMs, but not CD11c− ATMs or other stromovascular cells, impaired insulin-stimulated glucose uptake by human adipocytes.CONCLUSIONSThese findings identify proinflammatory CD11c+ ATMs as markers of insulin resistance in human obesity. In addition, the machinery of CD11c+ ATMs indicates they metabolize lipid and may initiate adaptive immune responses.

Supplementary data are available at Bioinformatics online.

Permutation tests are amongst the most commonly used statistical tools in modern genomic research, a process by which p-values are attached to a test statistic by randomly permuting the sample or gene labels. Yet permutation p-values published in the genomic literature are often computed incorrectly, understated by about 1/m, where m is the number of permutations. The same is often true in the more general situation when Monte Carlo simulation is used to assign p-values. Although the p-value understatement is usually small in absolute terms, the implications can be serious in a multiple testing context. The understatement arises from the intuitive but mistaken idea of using permutation to estimate the tail probability of the test statistic. We argue instead that permutation should be viewed as generating an exact discrete null distribution. The relevant literature, some of which is likely to have been relatively inaccessible to the genomic community, is reviewed and summarized. A computation strategy is developed for exact p-values when permutations are randomly drawn. The strategy is valid for any number of permutations and samples. Some simple recommendations are made for the implementation of permutation tests in practice.

Three surface molecules of mouse CD8+ dendritic cells (DCs), also found on the equivalent human DC subpopulation, were compared as targets for Ab-mediated delivery of Ags, a developing strategy for vaccination. For the production of cytotoxic T cells, DEC-205 and Clec9A, but not Clec12A, were effective targets, although only in the presence of adjuvants. For Ab production, however, Clec9A excelled as a target, even in the absence of adjuvant. Potent humoral immunity was a result of the highly specific expression of Clec9A on DCs, which allowed longer residence of targeting Abs in the bloodstream, prolonged DC Ag presentation, and extended CD4 T cell proliferation, all of which drove highly efficient development of follicular helper T cells. Because Clec9A shows a similar expression pattern on human DCs, it has particular promise as a target for vaccines of human application.

Abstract