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.

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

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

A survey is given of differential expression analyses using the linear modeling features of the limma package. The chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments. Experiments with technical as well as biological replication are considered. Empirical Bayes test statistics are explained. The use of quality weights, adaptive background correction and control spots in conjunction with linear modelling is illustrated on the β7 data.

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limma: Linear Models for Microarray Data

Gastric cancer was the world’s third leading cause of cancer mortality in 2012, responsible for 723,000 deaths1. The vast majority of gastric cancers are adenocarcinomas, which can be further subdivided into intestinal and diffuse types according to the Lauren classification2. An alternative system, proposed by the World Health Organization, divides gastric cancer into papillary, tubular, mucinous (colloid) and poorly cohesive carcinomas3. These classification systems have little clinical utility, making the development of robust classifiers that can guide patient therapy an urgent priority.

The majority of gastric cancers are associated with infectious agents, including the bacterium Helicobacter pylori4 and Epstein–Barr virus (EBV). The distribution of histological subtypes of gastric cancer and the frequencies of H. pylori and EBV associated gastric cancer vary across the globe5. A small minority of gastric cancer cases are associated with germline mutation in E-cadherin (CDH1)6 or mismatch repair genes7 (Lynch syndrome), whereas sporadic mismatch repair-deficient gastric cancers have epigenetic silencing of MLH1 in the context of a CpG island methylator phenotype (CIMP)8. Molecular profiling of gastric cancer has been performed using gene expression or DNA sequencing9–12, but has not led to a clear biologic classification scheme. The goals of this study by The Cancer Genome Atlas (TCGA) were to develop a robust molecular classification of gastric cancer and to identify dysregulated pathways and candidate drivers of distinct classes of gastric cancer.

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Comprehensive molecular characterization of gastric adenocarcinoma

New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.

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voom: precision weights unlock linear model analysis tools for RNA-seq read counts

The MexTAg transgenic mouse model of mesothelioma replicates many aspects of human mesothelioma, including induction by asbestos, pathogenicity and response to cytotoxic chemotherapy, despite high levels of the SV40 large T Antigen (TAg) in the mesothelial compartment. This model enables analysis of the molecular events associated with asbestos induced mesothelioma and is utilised here to investigate the molecular dynamics of tumours induced in these mice, using gene expression patterns as a read out. Gene expression of MexTAg mesothelioma cell lines bearing a high or low number of copies of the TAg transgene were compared to wild type mouse mesotheliomas and normal mouse mesothelial cells using Affymetrix microarray. These data were then compared to a similar published human microarray study using the same platform. The main expression differences between transgenic mouse and wild type mouse mesotheliomas occurred for genes involved in cell cycle regulation and DNA replication, as would be expected from overexpression of the TAg oncogene. Quantitative PCR confirmed that E2F and E2F regulated genes were significantly more upregulated in MexTAg mesotheliomas and MexTAg mesothelial cells compared to wild type mesotheliomas. Like human mesothelioma, both MexTAg and wild type mesotheliomas had more genes underexpressed than overexpressed compared to normal mouse mesothelial cells. Most notably, the cdkn2 locus was deleted in the wild type mouse mesotheliomas, consistent with 80 % human mesotheliomas, however, this region was not deleted in MexTAg mesotheliomas. Regardless of the presence of TAg, all mouse mesotheliomas had a highly concordant set of deregulated genes compared to normal mesothelial cells that overlapped with the deregulated genes between human mesotheliomas and mesothelial cells. This investigation demonstrates that the MexTAg mesotheliomas are comparable with wild type mouse mesotheliomas in their representation of human mesothelioma at the molecular level, with some key gene expression differences that are attributable to the TAg transgene expression. Of particular note, MexTAg mesothelioma development was not dependent on cdkn2 deletion.

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Consistent gene expression profiles in MexTAg transgenic mouse and wild type mouse asbestos-induced mesothelioma



The last five years have seen a rapid increase in interest and understanding of signal transduction pathways. While the description of such pathways has become more detailed and complex, a number of consistent findings have emerged. Modular domains, such as SH2 and SH3 domains, are present on a wide variety of proteins and mediate specific protein-protein interactions. By defining the interaction mediated by such domains, a ‘language’ of interaction between proteins in signalling pathways is emerging. As more signalling proteins are identified it has become apparent that most oncogenes and tumour suppressor genes are components of major signalling pathways. Therefore, studies on the basic biology of signal transduction are having a direct impact on our understanding of cell transformation. With the characterisation of signalling pathways in a range of organisms, it has also become obvious that signalling pathways are ancient and have been highly conserved over the last billion years of evolution. A practical result of this finding has been the ability to exploit results obtained in genetically tractable invertebrate species such as C. elegans and Drosophila melanogaster to investigate signal transduction in mammals. This is an approach we have emphasised in our investigation of signal transduction by tyrosine kinase receptors in human and mouse cells. Results obtained in these studies with the Sos and Siah proteins are reviewed.

A combined genetic and biochemical approach to mammalian signal transduction.

Cancer medicine is poised on the cusp of long-awaited change. For example, cancer taxonomy remains based on histopathological and anatomical principles, originating in concepts established prior to 1900. This classification has proven useful, but imperfect. Often, two histologically identical tumours frustratingly behave quite differently. Similarly, our current treatment strategies were established in the 1950s. Although great progress has been made in optimizing the use of cytotoxic therapies, these treatments by and large are nonspecifically targeted to all dividing cells. The toxicities associated with palliative treatments conferring a routine response rate of 25% have generated understandable cynicism, even (or especially) in the medical fraternity. Novel treatment and prognostic paradigms are needed, which we assume will be based on a molecular understanding of cancer. After some years of expectancy, recent proofs-of-principle suggest that molecular biology is generating effective and specific treatments. For example, a monoclonal antibody-based treatment for breast cancer, trastuzumab, was developed because molecular studies had identified a critical oncogenic role for amplified epidermal growth factor signalling 1 . Imatinib mesylate has provided important options for the treatment of chronic myeloid leukaemia 2 and gastrointestinal stromal tumours 3 by targeting specific molecular aberrations. The remarkable aspect of these treatments is not simply that they work, but their relative lack of toxicity. The recent development of robust, affordable and high-throughput methods of molecular analysis in the past decade promise to radically accelerate the pace at which new potential targets are identified. These technical developments have been based on the following achievements (in historical order): 1 Acceptance that cancer is a genetic disease, caused by somatic or germline mutations. This was predicted in 1914 by Theodor Boveri 4 but not proven until the 1980s with cloning of the first human oncogene ( ras ) 5 and tumour suppressor gene (the retinoblastoma tumour suppressor). 6

Molecular medicine: a clinician's primer on microarrays.

This appendix provides further specifics on the models described briefly in the Methods section (2) of the paper A comparison of background correction methods for two-colour microarrays. Kooperberg: Kooperberg et al. (2002) suggest an empirical Bayes model made up of a convolution of normal distributions to background adjust the signals. Observed foreground and background mean intensities (Xf and Xb) and their standard deviations (SDf and SDb), along with the number of foreground and background pixels (nf and nb) for each spot in a given channel are used in the model,

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Andrew J. Holloway

Papers

Consistent gene expression profiles in MexTAg transgenic mouse and wild type mouse asbestos-induced mesothelioma

A combined genetic and biochemical approach to mammalian signal transduction.

Molecular medicine: a clinician's primer on microarrays.

Appendix: Further details on the background correction models and their implementation