Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software <s> … … … … <s> The comparison of two treatments generally falls into one of the following two categories: (a) we may have a number of replications for each of the two treatments, which are unpaired, or (b) we may have a number of paired comparisons leading to a series of differences, some of which may be positive and some negative. The appropriate methods for testing the significance of the differences of the means in these two cases are described in most of the textbooks on statistical methods. <s> BIB001 </s> Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software <s> … … … … <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> BIB002
, which is the count in tuple t for sample i, m i is the library size. The posterior probability of model given data is: where Suppose that a sample A i is in the set E q where the count of this sample at a particular tuple t is y it , which follows a negative binomial(µ it , ϕ q ) (θ q = (λ q , ϕ q )). The mean count µ it is a product of the library size scaling factor, m i , and the proportion of reads in set E q , λ q . We have: baySeq first estimates the empirical distribution on the set of parameters for null and alternative models through sampling from a negative binomial distribution and a quasilikelihood approach.  Then, it estimates the prior probabilities starting from a prior followed by an iterative process updating the pri ors until convergence. For detailed steps, please refer Hard castle and Kelly. 14 Hypothesis testing can be easily denoted with the tuple system, for instance a twogroup case, and Nonparametric. Overview. In this section, we discuss two nonparametric methods for RNAseq DEA by Li and Tib shirani BIB002 and Tarazona et al.  In SAMseq, Li and Tibshirani 15 calculated a modified twosample Wilcoxon statistic using the ranked counts for twogroup comparison. BIB001 The authors proposed two resampling strategies for producing equal sequencing depths of the samples: downsampling and Pois son sampling, and also suggested that ties can be broken by inserting a small random number in resampling. NOISeq by Tarazona et al.  first used pseudocounts corrected by the library size m k(i) under two conditions (K=2) to calculate logratio (M) and absolute value of difference (D). Then, a test statistic is derived from M and D with a null hypothesis of no differential expression; in other words, M and D are no different than ran dom variables either estimated from the real or simulated data. Modeling. The two nonparametric methods discussed here are explained separately in the test boxes, as they each has a unique model setup.