You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software 
Chapter Title:  Notation and Normalization Methods 
1. Abstract of Cited Paper (BIB001):  The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression, we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  High-throughput technologies are widely used, for example to assay genetic variants, gene and protein expression, and epigenetic modifications. One often overlooked complication with such studies is batch effects, which occur because measurements are affected by laboratory conditions, reagent lots and personnel differences. This becomes a major problem when batch effects are correlated with an outcome of interest and lead to incorrect conclusions. Using both published studies and our own analyses, we argue that batch effects (as well as other technical and biological artefacts) are widespread and critical to address. We review experimental and computational approaches for doing so. 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  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. 
6. Abstract of Cited Paper (BIB006):  RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e.g., tissues, perturbations), while optionally adjusting for other systematic factors that affect the data collection process. There are a number of subtle yet critical aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a"state-of-the-art"computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and in particular, two widely-used tools DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10 samples) can be<1 hour, with computation time<1 day using a standard desktop PC. 
7. Abstract of Cited Paper (BIB007):  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.

Literature Review:
Notation. RNAseq data for G genes and N samples can be described by a G × N matrix y. Each entry y gi (g = 1, …, G, i = 1, …, N) represents the count of sequencing reads for gene g in sample i. For a given g and i, y gi is a nonnega tive integer representing the number of reads mapped to gene g in sample i. For succinctness, we also use notations "⋅" for summations, eg, y y y y and . We use X to represent an N × P design matrix, where P is the number of covariates. For instance, x ip can be an indicator variable of disease status, taking a value of 0 for a normal sample and a value of 1 for a tumor sample. When comparing K groups of samples, C k represents the collec tion of indices of the samples in group k (k = 1, …, K), that is, C k = {i: x i = k}. Each sample can only belong to one group. Normalization methods. Similar to microarray data, RNAseq data are also prone to nonbiological effects due to the experimental process. Consequently, these effects need to be adjusted before any further data analysis. BIB004 One major source of nonbiological effects is sequencing depth, which can be adjusted by rescaling the sequenc ing counts with factors that mimic sequencing depth.  Reads per kilobase per million reads (RPKM) is a simple adjustment that considers gene counts standardized by the gene length and the total number of reads in each library as expression values.   More sophisticated adjustment factors, including trimmed mean of Mvalues (TMM), BIB002 DESeq size factor, BIB007 and quantilebased normalizations such as upper quartile normalization, BIB003 are given in Table 1 BIB005 developed an R package, DEGseq, to identify differentially expressed (DE) genes with an MAplotbased approach. Langmead et al.  incorporated cloud computing in their method called Myrna. Modeling. In a Poisson model, one assumes that Y gi , the number of reads mapped to gene g in sample i, follows a Poisson distribution, y gi ∼ Poisson(µ gi ). µ gi is the rate para meter for gene g in sample i, which equals both the mean and the variance of the read counts. The probability mass function is: and E(Y gi ) = µ gi and Var(Y gi ) = µ gi . The association of µ gi with the same sample group can be described by a loglinear model as follows: where d i represents the sequencing depth of sample i and is assumed for generality. Let β g be the expres sion level of gene g and γ g be the association of gene g with the covariate. For hypothesis testing, γ g1 = … = γ gK = 0 indi cates that the expression of gene g is not associated with the sample group. In the case of two sample group com parison, if γ g = 0, then gene g is not DE between the two sample groups. . However, instead of using the maximum likelihood estimate of the sequencing depth in sample i, the authors sought for a set of genes, denoted by S, that are not DE to estimate sequencing depth in sample i: S is set to be the genes whose GOF g values are in the (ε, 1 -ε) quantile of all GOF g values. Li and others used ε = 0.25 in their study. BIB006 The objective is to test H 0 : and score statistics were proposed to perform the testing. For a twogroup or multiplegroup covariate, the score sta tistic for gene g is With accumulating empirical data (especially with the data available for groups of multiple biological samples), researchers began to observe that in a group, the betweensample variation of sequencing reads for a gene often exceeds the mean.  BIB008 BIB001 This excessive variation that cannot be explained by the Poisson model is called overdispersion. Extensions of the classic Pois son model have been proposed in order to accommodate such overdispersion, including the twostage Poisson models  and the generalized Poisson model.