Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

/pdf/gene-set-enrichment-analysis-a-knowledge-based-approach-for-mbpti6qljw.pdf

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

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.

/pdf/limma-powers-differential-expression-analyses-for-rna-4yl7ae3arh.pdf

limma powers differential expression analyses for RNA-sequencing and microarray studies

Metabolic and immune systems are among the most fundamental requirements for survival. Many metabolic and immune response pathways or nutrient- and pathogen-sensing systems have been evolutionarily conserved throughout species. As a result, immune response and metabolic regulation are highly integrated and the proper function of each is dependent on the other. This interface can be viewed as a central homeostatic mechanism, dysfunction of which can lead to a cluster of chronic metabolic disorders, particularly obesity, type 2 diabetes and cardiovascular disease. Collectively, these diseases constitute the greatest current threat to global human health and welfare.

/pdf/inflammation-and-metabolic-disorders-1u8s009p99.pdf

Inflammation and metabolic disorders

Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org
 .

/pdf/gsva-gene-set-variation-analysis-for-microarray-and-rna-seq-1bwfkmry7e.pdf

GSVA: gene set variation analysis for microarray and RNA-seq data.

The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of “hallmark” gene sets as part of MSigDB. Each hallmark in this collection consists of a “refined” gene set, derived from multiple “founder” sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.

https://www.cell.com/cell-systems/pdf/S2405-4712(15)00218-5.pdf

The Molecular Signatures Database Hallmark Gene Set Collection

DNA microarrays can be used to identify gene expression changes characteristic of human disease. This is challenging, however, when relevant differences are subtle at the level of individual genes. We introduce an analytical strategy, Gene Set Enrichment Analysis, designed to detect modest but coordinate changes in the expression of groups of functionally related genes. Using this approach, we identify a set of genes involved in oxidative phosphorylation whose expression is coordinately decreased in human diabetic muscle. Expression of these genes is high at sites of insulin-mediated glucose disposal, activated by PGC-1α and correlated with total-body aerobic capacity. Our results associate this gene set with clinically important variation in human metabolism and illustrate the value of pathway relationships in the analysis of genomic profiling experiments.

/pdf/pgc-1alpha-responsive-genes-involved-in-oxidative-1t3qxw5cbf.pdf

PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes

IN the double quasar 0957 + 0561, the first detected example of a gravitational lens1, a quasar at redshift z = 1.41 appears as two images separated by ˜6 arcsec and accompanied by an extended radio source2–4. The Hubbie constant, H0, can be estimated from the angular separation and the time delay between the appearance of the same brightness variations in the two images—a method that avoids all the usual uncertainties that attend the estimation of distance by the use of standard candles. From 11 years of Very Large Array (VLA) observations we have found the time delay to be 1.40 ± 0.10 yr, a significantly larger and more robust value than has been obtained from optical measurements5,6. Using a standard lens model7 and taking the observed velocity dispersion of luminous matter in the lensing galaxy8 to measure the true gravitational potential, we find HQ = 46 ± 14 (42 ± 14) km s-1 Mpc-1 for cosmic density parameter ω0 = 0 (1), in approximate agreement with Rhee's recent estimate8. But if, as is likely, there is dark matter in the lensing galaxy, H0 could be as high as 69 ± 21 (63 ± 21) km s-1 Mpc-1. The agreement of several completely independent estimates of H0 gives some confidence that the scale of the Universe is indeed known to about a factor of two.

The Hubble constant from VLA measurement of the time delay in the double quasar 0957+561

Einstein rings and related phenomena

ELLIPTICAL galaxies acting as gravitational lenses occasionally produce spectacular images-Einstein rings-of distant objects. Giant arcs(1) and radio rings(2) have been observed. A wide variety of image morphologies is possible, the generation of which is qualitatively understood in terms of large magnifications at caustic and critical lines in the lensing geometry(3-5). From the angular size of the image, and with knowledge of the distances of the lensed and lensing object, rough estimates of the mass of the lensing galaxy can be obtained. We have made high-resolution radio-interferometric observations of MG1654 + 1346 (ref. 6) a radio quasar in near-perfect alignment with an elliptical galaxy, and show that radio emission is distorted into a narrow Einstein ring that lies within a diamond-shaped region bounded by caustic lines. From the details of the ring structure, a new model for the lensing geometry is deduced which leads to a more accurate estimate of the mass of the lensing galaxy.

Galaxy mass deduced from the structure of Einstein ring MG1654+1346.

Time variations in compact extragalactic objects can be used to constrain both their emission mechanisms and models for their geometry. In particular, periodic variations constrain the popular black hole model for these sources1,2. There have been many reports of the presence (and absence) of both periodic3–8 and non-periodic9 variability in the BL Lacertae object OJ 287 = 0851 + 202, from the optical and infrared through centimetre radio wavelengths (Table 1). The apparent consistency of the reported periodicity near P =15.7 min and the absence of variations in other sources observed in the same way lends some credence to these variations, even though they are only a few per cent of the mean intensity. We observed OJ 287 with the Very Large Array (VLA) at 5, 15 and 22 GHz several times in early 1983. The 5-GHz data, taken coincident with the 22-GHz observations of Valtaoja et al.8, show OJ 287 to have decreased in flux by about 1.8% in an irregular way over a span of about 7 h, and to have fluctuated by about 0.5% on a timescale of about 15 min. Brightness temperatures derived from causality constraints range between 1016 and 1020 K. Fourier spectra of the time series were analysed using a novel one-dimensional complex CLEAN algorithm to remove the effects of the data sampling. At 5 GHz there was no harmonic component to the intensity of OJ 287 which exceeded 0.1% of the mean for any period between 80 and 5,000 s. The limits at 15 and 22 GHz were 2% and 0.8%, respectively.

J. Lehar

Papers

PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes

The Hubble constant from VLA measurement of the time delay in the double quasar 0957+561

Einstein rings and related phenomena

Galaxy mass deduced from the structure of Einstein ring MG1654+1346.

Very Large Array observations of rapid non-periodic variations in OJ 287