Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software <s> Introduction <s> BackgroundHigh-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data.ResultsWe compare statistical methods for detecting genes that are significantly DE between two types of biological samples and find that there are substantial differences in how the test statistics handle low-count genes. We evaluate how DE results are affected by features of the sequencing platform, such as, varying gene lengths, base-calling calibration method (with and without phi X control lane), and flow-cell/library preparation effects. We investigate the impact of the read count normalization method on DE results and show that the standard approach of scaling by total lane counts (e.g., RPKM) can bias estimates of DE. We propose more general quantile-based normalization procedures and demonstrate an improvement in DE detection.ConclusionsOur results have significant practical and methodological implications for the design and analysis of mRNA-Seq experiments. They highlight the importance of appropriate statistical methods for normalization and DE inference, to account for features of the sequencing platform that could impact the accuracy of results. They also reveal the need for further research in the development of statistical and computational methods for mRNA-Seq. <s> BIB001 </s> Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software <s> Introduction <s> Summary: High-throughput RNA sequencing (RNA-seq) is rapidly emerging as a major quantitative transcriptome profiling platform. Here, we present DEGseq, an R package to identify differentially expressed genes or isoforms for RNA-seq data from different samples. In this package, we integrated three existing methods, and introduced two novel methods based on MA-plot to detect and visualize gene expression difference. Availability: The R package and a quick-start vignette is available at http://bioinfo.au.tsinghua.edu.cn/software/degseq Contact: xwwang@tsinghua.edu.cn; zhangxg@tsinghua.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. <s> BIB002 </s> Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software <s> Introduction <s> Background ::: High throughput sequencing has become an important technology for studying expression levels in many types of genomic, and particularly transcriptomic, data. One key way of analysing such data is to look for elements of the data which display particular patterns of differential expression in order to take these forward for further analysis and validation. <s> BIB003 </s> Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software <s> Introduction <s> Microarrays first made the analysis of the transcriptome possible, and have produced much important information. Today, however, researchers are increasingly turning to direct high-throughput sequencing - RNA-Seq - which has considerable advantages for examining transcriptome fine structure - for example in the detection of allele-specific expression and splice junctions. In this article, we discuss the relative merits of the two techniques, the inherent biases in each, and whether all of the vast body of array work needs to be revisited using the newer technology. We conclude that microarrays remain useful and accurate tools for measuring expression levels, and RNA-Seq complements and extends microarray measurements. <s> BIB004 </s> Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software <s> Introduction <s> We discuss the identification of genes that are associated with an outcome in RNA sequencing and other sequence-based comparative genomic experiments. RNA-sequencing data take the form of counts, so models based on the Gaussian distribution are unsuitable. Moreover, normalization is challenging because different sequencing experiments may generate quite different total numbers of reads. To overcome these difficulties, we use a log-linear model with a new approach to normalization. We derive a novel procedure to estimate the false discovery rate (FDR). Our method can be applied to data with quantitative, two-class, or multiple-class outcomes, and the computation is fast even for large data sets. We study the accuracy of our approaches for significance calculation and FDR estimation, and we demonstrate that our method has potential advantages over existing methods that are based on a Poisson or negative binomial model. In summary, this work provides a pipeline for the significance analysis of sequencing data. <s> BIB005 </s> Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software <s> Introduction <s> Motivation: A number of penalization and shrinkage approaches have been proposed for the analysis of microarray gene expression data. Similar techniques are now routinely applied to RNA sequence transcriptional count data, although the value of such shrinkage has not been conclusively established. If penalization is desired, the explicit modeling of mean–variance relationships provides a flexible testing regimen that ‘borrows’ information across genes, while easily incorporating design effects and additional covariates. ::: ::: Results: We describe BBSeq, which incorporates two approaches: (i) a simple beta-binomial generalized linear model, which has not been extensively tested for RNA-Seq data and (ii) an extension of an expression mean–variance modeling approach to RNA-Seq data, involving modeling of the overdispersion as a function of the mean. Our approaches are flexible, allowing for general handling of discrete experimental factors and continuous covariates. We report comparisons with other alternate methods to handle RNA-Seq data. Although penalized methods have advantages for very small sample sizes, the beta-binomial generalized linear model, combined with simple outlier detection and testing approaches, appears to have favorable characteristics in power and flexibility. ::: ::: Availability: An R package containing examples and sample datasets is available at http://www.bios.unc.edu/research/genomic_software/BBSeq ::: ::: Contact:yzhou@bios.unc.edu; fwright@bios.unc.edu ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online. <s> BIB006 </s> Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software <s> Introduction <s> BackgroundFinding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data.ResultsWe conducted an extensive comparison of eleven methods for differential expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data.ConclusionsVery small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the ‘limma’ method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method. <s> BIB007 </s> Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software <s> Introduction <s> RNA-sequencing (RNA-seq) has rapidly become a popular tool to characterize transcriptomes. A fundamental research problem in many RNA-seq studies is the identification of reliable molecular markers that show differential expression between distinct sample groups. Together with the growing popularity of RNA-seq, a number of data analysis methods and pipelines have already been developed for this task. Currently, however, there is no clear consensus about the best practices yet, which makes the choice of an appropriate method a daunting task especially for a basic user without a strong statistical or computational background. To assist the choice, we perform here a systematic comparison of eight widely used software packages and pipelines for detecting differential expression between sample groups in a practical research setting and provide general guidelines for choosing a robust pipeline. In general, our results demonstrate how the data analysis tool utilized can markedly affect the outcome of the data analysis, highlighting the importance of this choice. <s> BIB008 </s> Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software <s> Introduction <s> A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth. <s> BIB009 </s> Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software <s> Introduction <s> Next generation sequencing is quickly replacing microarrays as a technique to probe different molecular levels of the cell, such as DNA or RNA. The technology provides higher resolution, while reducing bias. RNA sequencing results in counts of RNA strands. This type of data imposes new statistical challenges. We present a novel, generic approach to model and analyze such data. Our approach aims at large flexibility of the likelihood (count) model and the regression model alike. Hence, a variety of count models is supported, such as the popular NB model, which accounts for overdispersion. In addition, complex, non-balanced designs and random effects are accommodated. Like some other methods, our method provides shrinkage of dispersion-related parameters. However, we extend it by enabling joint shrinkage of parameters, including those for which inference is desired. We argue that this is essential for Bayesian multiplicity correction. Shrinkage is effectuated by empirically estimating priors. We discuss several parametric (mixture) and non-parametric priors and develop procedures to estimate (parameters of) those. Inference is provided by means of local and Bayesian false discovery rates. We illustrate our method on several simulations and two data sets, also to compare it with other methods. Model- and data-based simulations show substantial improvements in the sensitivity at the given specificity. The data motivate the use of the ZI-NB as a powerful alternative to the NB, which results in higher detection rates for low-count data. Finally, compared with other methods, the results on small sample subsets are more reproducible when validated on their large sample complements, illustrating the importance of the type of shrinkage. <s> BIB010 </s> Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software <s> Introduction <s> We discuss the identification of features that are associated with an outcome in RNA-Sequencing (RNA-Seq) and other sequencing-based comparative genomic experiments. RNA-Seq data takes the form of counts, so models based on the normal distribution are generally unsuitable. The problem is especially challenging because different sequencing experiments may generate quite different total numbers of reads, or ‘sequencing depths’. Existing methods for this problem are based on Poisson or negative binomial models: they are useful but can be heavily influenced by ‘outliers’ in the data. We introduce a simple, nonparametric method with resampling to account for the different sequencing depths. The new method is more robust than parametric methods. It can be applied to data with quantitative, survival, two-class or multiple-class outcomes. We compare our proposed method to Poisson and negative binomial-based methods in simulated and real data sets, and find that our method discovers more consistent patterns than competing methods. <s> BIB011
In the past decade, deep sequencing has emerged as a power ful alternative to microarrays for the highthroughput profil ing of gene expression. Comparing with microarrays, RNA sequencing (RNAseq) possesses a number of technological advantages such as a wider dynamic range and the freedom from predesigned probes.  BIB004  It also comes with a unique data feature as discrete sequencing reads. In order to account for this unique data feature, statistical methodologies and computational algorithms have been developed based on various data distributional assumptions such as Poisson, nega tive binomial, beta binomial, (full or empirical) Bayesian, and nonparametric. 4-16, For researchers who are new to the analysis of RNAseq data, in this paper we provide an introductory overview of the methods and software available for the differential expres sion analysis (DEA) of RNAseq data when the analysis goal is to identify genes that are relevant to a disease such as cancer.   BIB001 In addition, for those who are interested in the statistical aspects of these methods, we also provide an overview of their parameter estimation algorithms and hypothesis testing strategies. The overview of these statistical aspects in our paper provides a unique contribution to the review literature on RNAseq DEA methods.  BIB001   BIB007 BIB008 BIB009 For read ers who are interested in a performance comparison of RNA seq DEA methods, they can refer to a large body of such papers in the literature.  BIB007 BIB008 BIB009 The rest of the paper is organized as follows. In the Notation and Normalization Methods section, we intro duce the unified notations used for the methods reviewed in our paper and touch on the normalization methods typically used to preprocess RNAseq data before DEA. In the Statistical Modeling of RNAseq Data section, we review the statistical modeling RNAseq DEA catego rized by the distributional assumptions such as Poisson, BIB002  BIB005 negative binomial, 7-10 beta binomial,  BIB006 Bayesian, BIB010 BIB003 and nonparametric. BIB011  All reviewed methods directly work with genelevel count data for DEA and have available R packages. For interested readers with advanced statistical knowledge, the parameter estimation algorithm for each method is presented separately in a text box, and the typi cal statistical testing frameworks that have been proposed for RNAseq DEA are reviewed in the Statistical Testing section. Finally, computational tools implemented for the reviewed methods are summarized in Table 2 . We note that the methods reviewed in this paper are not an exhaustive collection of available methods in the literature. Rather, we reviewed a list of most commonly used categories of model ing assumptions and included a few representative methods for each category, to help researchers who are new to the field orientated and started in the still evolving literature on this topic.