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

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.     

生物信息学基因表达差异分析

基因的差异化表达由多种因素共同导致,并且与许多疾病的发生和发展有密切联系,对差异化表达的基因进行生物信息学以及生物统计学的分析对于研究细胞调节机制和疾病机理有着重要意义。目前,对差异化表达的基因有以下几种主流的研究方法：DNA微阵列（DNA microarray）,抑制性消减杂交（SSH）,基因表达连续性分析（SAGE）,代表性差异分析（RDA）,以及mRNA差异显示PCR（mRNA DDRT-PCR）。目前许多基因差异化表达数据是建立在时段（time series）基础上,因此对基于时间变化的基因差异化表达分析变得尤为重要。本文将对差异化表达基因的几种主流方法进行详细阐述,并介绍一种基于傅里叶函数的时段基因差异化表达分析。     

Outlier sums for differential gene expression analysis

We propose a method for detecting genes that, in a disease group, exhibit unusually high gene expression in some but not all samples. This can be particularly useful in cancer studies, where mutations that can amplify or turn off gene expression often occur in only a minority of samples. In real and simulated examples, the new method often exhibits lower false discovery rates than simple t-statistic thresholding. We also compare our approach to the recent cancer profile outlier analysis proposal of Tomlins and others (2005).     

Multivariate analysis of microarray data: differential expression and differential connection

Background  

Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression.     

Shrunken p-values for assessing differential expression with applications to genomic data analysis

In many scientific problems involving high-throughput technology, inference must be made involving several hundreds or thousands of hypotheses. Recent attention has focused on how to address the multiple testing issue; much focus has been devoted toward the use of the false discovery rate. In this article, we consider an alternative estimation procedure titled shrunken p-values for assessing differential expression (SPADE). The estimators are motivated by risk considerations from decision theory and lead to a completely new method for adjustment in the multiple testing problem. In addition, the decision-theoretic framework can be used to derive a decision rule for controlling the number of false positive results. Some theoretical results are outlined. The proposed methodology is illustrated using simulation studies and with application to data from a prostate cancer gene expression profiling study.     

Global analysis of gene expression by differential display

Differential display (DD) is one of the most commonly used approaches for identifying differentially expressed genes. However, there has been lack of an accurate guidance on how many DD polymerase chain reaction (PCR) primer combinations are needed to display most of the genes expressed in a eukaryotic cell. This study critically evaluated the gene coverage by DD as a function of the number of arbitrary primers, the number of 3′ bases of an arbitrary primer required to completely match an mRNA target sequence, the additional 5′ base match(s) of arbitrary primers in first-strand cDNA recognition, and the length of mRNA tails being analyzed. The resulting new DD mathematical model predicts that 80 to 160 arbitrary 13mers, when used in combinations with 3 one-base anchored oligo-dT primers, would allow any given mRNA within a eukaryotic cell to be detected with a 74% to 93% probability, respectively. The prediction was supported by both computer simulation of the DD process and experimental data from a comprehensive fluorescent DD screening for target genes of tumor-suppressor p53. Thus, this work provides a theoretical foundation upon which global analysis of gene expression by DD can be pursued.     

Proteomic analysis of differential protein expression in atherosclerosis

Although recent studies have shown that several pro-inflammatory proteins can be used as biomarkers for atherosclerosis, the mechanism of atherogenesis is unclear and little information is available regarding proteins involved in development of the disease. Atherosclerotic tissue samples were collected from patients in order to identify the proteins involved in atherogenesis. The protein expression profile of atherosclerosis patients was analysed using two-dimensional electrophoresis-based proteomics. Thirty-nine proteins were detected that were differentially expressed in the atherosclerotic aorta compared with the normal aorta. Twenty-seven of these proteins were identified in the MS-FIT database. They are involved in a number of biological processes, including calcium-mediated processes, migration of vascular smooth muscle cells, matrix metalloproteinase activation and regulation of pro-inflammatory cytokines. Confirmation of differential protein expression was performed by Western blot analysis. Potential applications of the results include the identification and characterization of signalling pathways involved in atherogenesis, and further exploration of the role of selected identified proteins in atherosclerosis.     

Semi-parametric differential expression analysis via partial mixture estimation

We develop an approach for microarray differential expression analysis, i.e. identifying genes whose expression levels differ between two or more groups. Current approaches to inference rely either on full parametric assumptions or on permutation-based techniques for sampling under the null distribution. In some situations, however, a full parametric model cannot be justified, or the sample size per group is too small for permutation methods to be valid. We propose a semi-parametric framework based on partial mixture estimation which only requires a parametric assumption for the null (equally expressed) distribution and can handle small sample sizes where permutation methods break down. We develop two novel improvements of Scott's minimum integrated square error criterion for partial mixture estimation [Scott, 2004a,b]. As a side benefit, we obtain interpretable and closed-form estimates for the proportion of EE genes. Pseudo-Bayesian and frequentist procedures for controlling the false discovery rate are given. Results from simulations and real datasets indicate that our approach can provide substantial advantages for small sample sizes over the SAM method of Tusher et al. [2001], the empirical Bayes procedure of Efron and Tibshirani [2002], the mixture of normals of Pan et al. [2003] and a t-test with p-value adjustment [Dudoit et al., 2003] to control the FDR [Benjamini and Hochberg, 1995].     

Proteomic analysis of differential protein expression in atherosclerosis

Although recent studies have shown that several pro-inflammatory proteins can be used as biomarkers for atherosclerosis, the mechanism of atherogenesis is unclear and little information is available regarding proteins involved in development of the disease. Atherosclerotic tissue samples were collected from patients in order to identify the proteins involved in atherogenesis. The protein expression profile of atherosclerosis patients was analysed using two-dimensional electrophoresis-based proteomics. Thirty-nine proteins were detected that were differentially expressed in the atherosclerotic aorta compared with the normal aorta. Twenty-seven of these proteins were identified in the MS-FIT database. They are involved in a number of biological processes, including calcium-mediated processes, migration of vascular smooth muscle cells, matrix metalloproteinase activation and regulation of pro-inflammatory cytokines. Confirmation of differential protein expression was performed by Western blot analysis. Potential applications of the results include the identification and characterization of signalling pathways involved in atherogenesis, and further exploration of the role of selected identified proteins in atherosclerosis.     

Exploratory differential gene expression analysis in microarray experiments with no or limited replication

We describe an exploratory, data-oriented approach for identifying candidates for differential gene expression in cDNA microarray experiments in terms of α-outliers and outlier regions, using simultaneous tolerance intervals relative to the line of equivalence (Cy5 = Cy3). We demonstrate the improved performance of our approach over existing single-slide methods using public datasets and simulation studies.     

DGEclust: differential expression analysis of clustered count data

We present a statistical methodology, DGEclust, for differential expression analysis of digital expression data. Our method treats differential expression as a form of clustering, thus unifying these two concepts. Furthermore, it simultaneously addresses the problem of how many clusters are supported by the data and uncertainty in parameter estimation. DGEclust successfully identifies differentially expressed genes under a number of different scenarios, maintaining a low error rate and an excellent control of its false discovery rate with reasonable computational requirements. It is formulated to perform particularly well on low-replicated data and be applicable to multi-group data. DGEclust is available at http://dvav.github.io/dgeclust/.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0604-6) contains supplementary material, which is available to authorized users.     

EDGE: extraction and analysis of differential gene expression

The authors would like to apologise for the     

EDGE: extraction and analysis of differential gene expression

EDGE (Extraction of Differential Gene Expression) is an open source, point-and-click software program for the significance analysis of DNA microarray experiments. EDGE can perform both standard and time course differential expression analysis. The functions are based on newly developed statistical theory and methods. This document introduces the EDGE software package.