The results indicated that mapping methods have minimal impact on the final DEGs analysis, considering that adopted data have an annotated reference genome. Regarding the adopted experimental model, the DEGs identification methods that have more consistent results were the limma+voom, NOIseq and DESeq2. Additionally, the consensus among five DEGs identification methods guarantees a list of DEGs with great accuracy, indicating that the combination of different methods can produce more suitable results. The consensus option is also included for use in the available software.The correct identification of differentially expressed genes (DEGs) between specific conditions is a key in the understanding phenotypic variation. High-throughput transcriptome sequencing (RNA-Seq) has become the main option for these studies. Thus, the number of methods and softwares for differential expression analysis from RNA-Seq data also increased rapidly. However, there is no consensus about the most appropriate pipeline or protocol for identifying differentially expressed genes from RNA-Seq data. This work presents an extended review on the topic that includes the evaluation of six methods of mapping reads, including pseudo-alignment and quasi-mapping and nine methods of differential expression analysis from RNA-Seq data. The adopted methods were evaluated based on real RNA-Seq data, using qRT-PCR data as reference (gold-standard). As part of the results, we developed a software that performs all the analysis presented in this work, which is freely available at  High-throughput sequencing has become the main choice to measure expression levels, i.e., RNA-Seq . RNA-SeqRNA-seq technology has some advantages over the cDNA microarrays, such as the high level of data reproducibility through lanes and flow-cells, which reduces the number of technical replicates for the experiments. Besides, RNA-seq allows to identify and quantify the expression of isoforms and unknown transcripts . RegardiIn general, the RNA-Seq technology is very useful for differential expression analysis involving some specific conditions , in whicThe methods for differential gene expression analysis from RNA-Seq can be grouped into two main subsets: parametric and non-parametric. Parametric methods capture all information about the data within the parameters. In these cases, it is possible to predict the value of unknown data from observing the adopted model and its parameters. When parametric methods are applied to differential gene expression assume that, usually after a normalization, each expression value for a given gene is mapped into a particular distribution, such as Poisson –11 or neRegarding the RNA-Seq differential expression analysis, some tools such as edgeR  and baySSome research effort were developed in order to evaluate the statistical methods of normalization and detection of DEGs and the influence of the libraries preparation on the results , to evalet al. [et al. [et al. indicated that the ranking between three methods of DEGs identification can generate a more accurate identification [et al. [et al. [et al. [In particular, Rapaport et al.  evaluate [et al.  evaluatefication . Li et a [et al.  evaluate [et al.  presente [et al.  presenteet al. [et. al (2017) can contribute significantly in addition to expression analyses from RNA-Seq data previously produced [Recently, Yu et al.  presenteet al. , i.e., fet al.  presenteet al. . A comprproduced .Differently from these studies, we evaluated the impact of the mapping methodology on the results of differential gene expression analysis. We also assess the methodologies of DEGs analysis through a different perspective, not only indicating the better methodologies. Previous studies and its results were presented, indicating that DEGs analysis are influenced by many factors such as preparation of libraries and the structure of the experiment. In this context, we analyzed the influence of essential steps in DEGs identification with RNA-Seq data and developed a software that allows to obtain the results of the main DEGs identification methodologies. The comparative study among the six mappers, including one pseudo-alignment and one quasi-mapping tool commonly used in differential expression studies led to identify the importance of this step in the analysis and identification of DEGs. A gold standard qRT-PCR data was also adopted in order to evaluate the accuracy of DEG identification tools and indicate those with high reliability on its results. Another contribution of this work was the evaluation of integrated results from DEGs identification methods, our tool allows to perform the consensus of five different methods of differential expression analysis, as a result genes indicated as differentially expressed have more reliability and accuracy.In this study, we present an extended review of the main methodologies for differential gene expression analysis with RNA-Seq data, evaluating the impact of the mapping and quantification methodologies. For this study we adopted the mapping softwares Bowtie2 , TopHat This work adopted a real data set produced for the Microarray Quality Control (MAQC) project , 40. TheGene Expression Omnibus, access: GSE5350, platform GPL4097 [As part of the MAQC project, approximately a thousand genes were analyzed by qRT-PCR . The qRTThe conversion from the annotation for the RNA-Seq data (ENSEMBL) to the qRT-PCR data was performed by the on line tool bioDBnet , which eThe adopted RNA-Seq data set was mapped in the human genome/ transcriptome (hg19), with the annotation file of the same version, both from the GENCODE project . The conThe HTSeq software (v.0.6.0)  was adopThe mapper performance were obtained by considering the following DEG identification methods: edgeR, DESeq, baySeq and NOIseq, to Tophat, Bowtie2 and BWA, to analyze Salmon, STAR and kallisto were performed edgeR and NOISeq. DESeq and baySeq can be run only with count data .In this work we compared eight methods for the DEGs or transcripts identification. When applying each software we focus on the most used approaches. Thus, we follow the guidelines available in the manual, applying the default parameters, including the standardization methodology of each software. All differential expression analysis were performed using the same counting matrix, generated by HTSeq.For each evaluated mapper a counting matrix, or abundance matrix was generated, in this way the mapper tools were evaluated for DEG methods. RNA-Seq data were mapped using BWA, TopHat, Bowtie and STAR mappers. The quantification was obtained from Salmon and kallisto tools. The counting table of each mapper was used as input for the DEG identification methods , thus generating lists of DEGs for each DEG identification method which different mapper. Results of Salmon, STAR and kallisto was used as input for edgeR and NOISeq. The results were compared with qRT-PCR (gold standard), allowing to evaluate if the mapping influences the performance of DEGs detection. The EBSeq, SAMSeq and limma+voom, DESeq2 and sleuth methods were added to the study for individual evaluation of DEG identification tools, using only the mapping results of the TopHat mapper. Exceptionally sleuth received quantification output from the kallisto tool, as indicated in its user guide. baySeq [baySeq : Uses thDESeq [DESeq : Based oEBSeq [EBSeq : DevelopEBSeq , which aedgeR [edgeR : A Poisslimma+voom [mma+voom : Based omma+voom .NOIseq [NOIseq , 39: AdaSAMseq: [SAMseq: : Non-parDESeq2 [DESeq2 : DESeq2 sleuth [sleuth : The sleThe DEGs identified by the adopted methods (with TopHat mapper) are used to evaluate the performance statistics presented in the Results Section.Datasets. To evaluate the impact of the genomic mapping tool on DEG analysis, all the mapping software with default parameters were adopted. The counting matrix of each mapping was generated by the HTSeq package [log2FC ≥ ±2 and P–value ≤ 0.05. The complete list of DEGs is available in In order to evaluate the mapping methods, the human genome described in subsection  package , throughWe compared the identified DEGs in RNA-Seq  against the DEGs from qRT-PCR. It is possible to observe in In this way, we observed that the impact of the mappers on the final results is minimal. In In order to evaluate methods that do not use mapping, but other strategies to quantifying reads, we compared edgeR and NOISeq results using Salmon and STAR to quantification of mapped genes. Differential Expression.As presented in the previous section, the impact of the mappers on expression analyses is minimal. In this way, all following analysis were developed considering only the TopHat mapping results. At this step of the present work, we analyze the results of the following software tools: limma+voom , EBSeq , SAMseq We compared the genes indicated as differentially expressed by the nine tools and the DEGs indicated by qRT-PCR. The softwares were performed as defined in each manual, and the genes listed by the tools were considered as differentially expressed, through the limit indicated by the manual of each tool. The performance of the adopted DEG identification methods were evaluated based on the match between each method results and the qRT-PCR. It is possible to notice that the EBSeq, SAMseq and DESeq methods, although using different approaches for DEG identification have similar behavior, presenting low TPR (True Positive Rate) and low ACC (Accuracy). The performance of the DESeq can be justified by the fact that the tool obtains better results with small samples (two samples per condition), as presented in . The resThe NOIseq, DESeq2 and limma+voom methods performed well, with high TPR and ACC rates. The limma+voom tool had already been pointed out in previous works as one of the better results in the DEG ranking and for analyses with more than two samples . NOIseq The individual evaluation of DEG identification methods indicate clearly that each method led to distinct results. Moreover, some methodologies have better results with a greater amount of samples, while others present variations on its results influenced by other characteristics, such as sequencing depth and outliers with abnormally high counts.x methods, where x is number of methods that have identified each DEG. The results of each combination of DEG identification methods was compared to the gold standard from qRT-PCR.In order to verify the compatibility between the individual results of each DEG identification method and to identify possible improvement in performance, we evaluated the results by integrating the adopted methodologies in this work. We evaluated the performance among the results with the integration of nine to one methods, so that for each gene identified as differentially expressed by In order to evaluate the performance of the DEG integration methods, we verified the combination of methods that performs better. To identify the combination of methods that has a more effective DEGs indication and, with the least amount of errors, we evaluated the DEGs identification performance for each subset of methods: nine, eight, seven, six, five, four, three, two and one. The performance results of each subset is presented in As expected, the performance of each subset indicate that to consider more methods together tends to improve the accuracy and to reduce the error rates. As reported in the context of the inference of gene networks, the collective knowledge or data integration can produce better results than individual results , 49. BasIn order to identify the best combination of DEG identification methods of each cardinality , we adopt the ROC (Receiver operating characteristic) curve , a standThe consensus of five DEGs identification methods presented the best integrated result with higher SPC and TPR values that lead to results with high accuracy. https://github.com/costasilvati/consexpression, in which the user can choose between executing the methodology with the default consensus (five methods), thus obtaining the best balance between SPC and TPR. Another possibility for the application of this methodology is to change the number of methods that define the consensus for the desired quantity of methods, taking into account the cost of the change, for the other performance measures, as well as to use only one of the adopted methods in this work.The inclusion of methods brought considerable gains in specificity (SPC), however from integration of six methods, TPR values goes through a considerable decline. This result indicated the default value for the software available at In order to define which group of methods has the best consensus, it is important to evaluate how each method behaves in the aggregate results, especially in the group of five indications. log2 between two conditions and the value of the difference between the two corresponding conditions are likely to be higher than a noise. The noise distribution is obtained by comparing all pairs of repetitions within the same condition. In the limma+voom method, the read counts are converted into log2 of counts per million (logCPM) and the mean variance ratio is modeled with precision weights.Regarding the consensus of five methods, the baySeq method indicates all DEGs presented in the five consensus results. The DESeq2 indicate 97.6%, limma+voom methods indicate 96.5% of them, and NOISeq indicates 95.9%. For the analysis with baySeq, it is necessary to define a collection of models and each model is a subdivision of the samples into groups, the samples in the same group are assumed to share the same parameters of the underlying distribution. In the DESeq2 method, a model is created to the counts observed, this model is fit using Bayes theorem to guide the movement by each gene. In the NOISeq method, the transcript is differentially expressed if the ratio of In summary, the baySeq method tends to higher FP values, as presented in This work presents an extended review regarding methods for the identification of differentially expressed genes (DEGs) or transcripts. We evaluated the influence of six mapping methods, including one pseudo-alignment and one quasi-mapping, nine main methods for the DEGs identification and the integration of these methods in order to produce a consensus from their results. The evaluation of the adopted methods was performed by comparing the respective results from a reference qRT-PCR for the same tested transcripts.We have identified that the impact of the mapping tool on the final results is minimal, indicating the DEGs identification method is the main choice for differential expression analysis in RNA-Seq data.We did not identify among the evaluated methods a tool that obtained optimum results in all performance measures, for the evaluated experimental conditions. The NOIseq, DESeq2 and limma+vomm methods present the best individual results with 95%, 95% and 93% of Specificity and 80%, 84% and 81% of True Positive Rate, respectively.Regarding the integration of DEG identification methods, we identified that the combination of five methods improves the sensitivity of identification and provides more reliable results. The five methods used integrated produced results with 91% of Specificity and 83% of True Positive Rate, thus indicating the consensus of five methods present better balance than individual solutions.https://github.com/costasilvati/consexpression, which implements the presented analysis and can be easily used in order to replicate this work, as well as to analyze other RNA-Seq data sources.Finally, this study also contribute to present a freely available software at S1 TableDifferentially expressed genes indicated by qRT-PCR.(PDF)Click here for additional data file.S2 TablePerformance of each DEGs identification method with different mappers.(PDF)Click here for additional data file.


However, the increasing need for integration of multiple data sets leads to a requirement for clustering methods applicable to mixed data types, where the straightforward application of standard methods is not necessarily the best approach. A particularly common problem involves clustering entities characterized by a mixture of binary data  and continuous data . Here, we present a generic method based on a probabilistic model for clustering this type of data, and illustrate its application to genetic regulation and the clustering of cancer samples. We show that the resulting clusters lead to useful hypotheses: in the case of genetic regulation these concern regulation of groups of genes by specific sets of transcription factors and in the case of cancer samples combinations of gene mutations are related to patterns of gene expression. The clusters have potential mechanistic significance and in the latter case are significantly linked to survival. The method is available as a stand-alone software package  from  Since clustering was first applied to microarray gene expression data for yeast , the useIncreasingly emphasis in the field is shifting to the integration of multiple genome scale data sets. For example alongside gene expression, studies of cell lines and differentiation ,4 now ofA number of different approaches to clustering entities described by variables of mixed type are now becoming available. For example, Morlini  developeMany problems of current interest in our field involve clustering entities described by a mixture of binary and continuous variables: for example, genetic regulation can be described in terms of the presence or absence of transcription factor binding and histone marks in promoter and enhancer elements and this can determine patterns of gene expression ; equallyWe chose a simple model based framework, using a joint probability distribution of binary and continuous variables that is a mixture over an unknown number of clusters. An attractive feature of this probabilistic approach is that it provides a natural treatment of data sets where some variables may be irrelevant, for example passenger mutations in cancer samples or transcription factor binding to DNA without regulatory significance, and where there may be false positives and negatives, for example in chromatin immunoprecipitation data. In outline the method employs a heuristic search for an approximate optimal model followed by refinement using an expectation-maximization procedure. We investigated model selection with simulated data using a range of criteria related to the well-known Akaike-Information-Criterion (AIC) , the BayN entities (data points) i, representing genes, tumor samples etc., each characterised by rn such variables), and the continuous variables could be gene expression values in en samples, experiments or time points. We assume a probability distribution which is a mixture of mN components (clusters)m = 1; B denotes the Bernoulli distribution with parameter N is a normal distribution with parameters We consider a set of L is the (maximized) log-likelihood from the distribution above, N and k is the number of parameters in the model.Since the number of clusters is unknown and difficult to estimate we adopted an initial heuristic search for an approximately optimal model, followed by refinement of the solution by expectation maximization. The heuristic search employed a Monte-Carlo simulated annealing algorithm  to optimN dependency in the penalty) was that for small samples the standard AIC is an underestimate of the optimal penalty, but a more complex correction is not suitable numerically for our approach.We investigated several different functions d points . The AICFull details of the heuristic search algorithm and equations for expectation maximization are given in Supplementary information, along with details of how the algorithm was parametrised using simulated data. Algorithm parameters in the form of input files for the clustering program for the two biological test cases are also provided in supplementary information.As an initial test of the methodology and objective functions we examined their ability to find correct solutions for the number of mixture components and the assignment of data points to components in data simulated from the probability distribution above. Simulated data was also used to parametrize the algorithms see . For theet al.  from Spellman and co-workers  and 103 et al. . In the gulation –24. Accogulation . This isB, which maximizes L is a matrix of TF binding probabilities and E a matrix of gene expression patterns. As such LeTICE is a method based on a similar premise of integrating TF binding data and expression data to find regulatory relationships, but being based on different underlying methodology it is an ideal comparator, albeit only relevant to the problem of genetic regulation. To provide a direct comparison of algorithms, LeTICE was applied to the dataset described above. Note that LeTICE takes binding p values directly as input and that it has its own TF and gene pre-selection criteria, in this case it selecting 18 TFs and 289 genes. LeTICE was then run with the optimum runtime parameters suggested in the original paper.LeTICE  was alsoAs part of this study we also examined the effect of using normalized  and un-normalised gene expression data. We also compared joint clustering to clustering expression data separately, which can be done by simply omitting binary variables in the input to our program.In evaluation of our method we considered comparison with the known literature on genetic regulation in the yeast cell cycle, as well as measures of the functional coherence of clusters based on Gene Ontology (GO) using the GOSemSim  package To test the application of our methodology to data from cancer samples, we applied it to the Acute Myeloid Leukemia (AML) mutation and gene expression data generated by The Cancer Genome ATLAS (TCGA) Research Network ,30. ThisDatasets from TCGA were retrieved through using cBioPortal for Cancer Genomics tool ,37. In tN dependency, solutions with higher objective function values and too few clusters are found. We note that this seems to be a failure of the optimization method rather than the objective function, and suggest that it reflects optimization on a surface where the likelihood gives limited ‘downhill’ information compared to the strong penalty on parameter numbers. Using data simulated with higher variability and less well defined clusters , to time/cell cycle resolved transcription factor binding data and to other types of continuous data.Supplementary DataClick here for additional data file.


With the developments of DNA sequencing technology, large amounts of sequencing data have become available in recent years and provide unprecedented opportunities for advanced association studies between somatic point mutations and cancer types/subtypes, which may contribute to more accurate somatic point mutation based cancer classification (SMCC). However in existing SMCC methods, issues like high data sparsity, small volume of sample size, and the application of simple linear classifiers, are major obstacles in improving the classification performance.To address the obstacles in existing SMCC studies, we propose DeepGene, an advanced deep neural network (DNN) based classifier, that consists of three steps: firstly, the clustered gene filtering (CGF) concentrates the gene data by mutation occurrence frequency, filtering out the majority of irrelevant genes; secondly, the indexed sparsity reduction (ISR) converts the gene data into indexes of its non-zero elements, thereby significantly suppressing the impact of data sparsity; finally, the data after CGF and ISR is fed into a DNN classifier, which extracts high-level features for accurate classification. Experimental results on our curated TCGA-DeepGene dataset, which is a reformulated subset of the TCGA dataset containing 12 selected types of cancer, show that CGF, ISR and DNN all contribute in improving the overall classification performance. We further compare DeepGene with three widely adopted classifiers and demonstrate that DeepGene has at least 24% performance improvement in terms of testing accuracy.Based on deep learning and somatic point mutation data, we devise DeepGene, an advanced cancer type classifier, which addresses the obstacles in existing SMCC studies. Experiments indicate that DeepGene outperforms three widely adopted existing classifiers, which is mainly attributed to its deep learning module that is able to extract the high level features between combinatorial somatic point mutations and cancer types. Cancer is known as a category of disease causing abnormal cell growths or tumors that potentially invade or metastasize to other parts of human body . It has To alleviate the impact of cancer to human health, considerable research endeavors have been devoted to the related diagnosis and therapy techniques, among which somatic point mutation based cancer classification (SMCC) is an important perspective. The purpose of SMCC is to detect the cancer types or subtypes based on somatic gene mutations from the patient, so that the cancer condition of the patient can be specified. Due to the drop in the cost of DNA sequencing in recent years, the availability of DNA sequencing data has increased dramatically, which greatly promotes the developments of SMCC . CompareClinically, SMCC may significantly facilitate cancer-related diagnoses and treatments, such as personalized tumor medicine , targetek-means clustering and picks up the top genes in each group that are closest to the centroid locations. These methods are simple and effective in some cases, but their heuristics are designed for continuous gene expression data, and are not directly applicable to discrete, and especially binary point mutation data.Normal sequencing results involve extremely large number of genes, usually in tens of thousands, but only a small discriminatory subset of genes is related to the cancer classification task. The other genes are largely irrelevant genes whose existence will only obstruct the cancer classification. Many recent works have been conducted in identifying the discriminatory subset of genes. For example, Cho et al.  apply thEven within the discriminatory subset, the majority of genes are not guaranteed to contain informative point mutations and often remain normal  , which rDifferent genes related to specific types of cancer are generally correlated and have complex interactions which may impede the application of conventional simple linear classifiers such as linear kernel support vector machine (SVM) . TherefoIn recent years, the drastic developments of machine learning methods have greatly facilitated the researches in bioinformatics, including SMCC. In order to predict the cancer types/subtypes more effectively, many machine learning approaches have been applied in existing cancer type prediction works, which have shown promising results –18. CurrIn recent years, the developments of deep neural network (DNN)  have equThe proposed CGF procedure locates the discriminatory gene subset based on mutation occurrence frequency. CGF utilizes features from the whole dataset instead of the current sample alone (e.g. mean and standard deviation), and thus more objectively reflects the correlations among the genes which can more effectively summarize the discriminatory subset. In addition, CGF does not require any prior knowledge from the original data and therefore functions well on both discrete and binary point mutation data.The proposed ISR procedure converts the sparse gene data into indexes of its non-zero elements. ISR eliminates the vast majority of zero gene elements, and significantly reduces the complexity of the gene data during such process.We establish a fully connected DNN classifier that uses the gene data after CGF and ISR for cancer classification. With the capacity of high-level feature extraction, our classifier is able to effectively extract deep features from the complexly correlated gene data, and significantly improve the classification accuracy compared with conventional simple linear classifiers such as SVM.We compile and release the TCGA-DeepGene dataset, which is a reformulated subset of the widely applied TCGA dataset  in genomIn this paper, we propose a novel SMCC method, named DeepGene, designed to simultaneously address the three identified issues. DeepGene is a DNN-based classification model composed of three steps. It first conducts two pre-processing techniques, including the clustered gene filtering (CGF) based on mutation occurrence frequency, and the indexed sparsity reduction (ISR) based on indexes of non-zero elements; the gene data is then classified by a fully-connected DNN classifier into a specific cancer type. The proposed DeepGene model has four distinct contributions:The flowchart of DeepGene is shown in Fig. DeepGene has three major steps, namely clustered gene filtering (CGF), indexed sparsity reduction (ISR), and DNN-based classification. The CGF and ISR are two independent pre-processing modules, the results of which are then concatenated in the final DNN classifier.A ∈ {0, 1}m × n be the matrix of raw data with binary value, where the n columns correspond to the n samples (cases) in the dataset, and the m rows correspond to the m genes per sample. The binary value indicates whether a mutation is observed:The CGF step is based on the mutation occurrence frequency of the gene data, and its workflow is summarized in Table A by row, and concatenate the result with the row indexes for later reference :The next step is to group where “&” and “|” stand for logical AND and OR.Asum*(1), which stands for the index of the gene with the highest occurrence frequency, we calculate its similarity with each of the following genes. If their similarity is larger than a predefined threshold dCGF, the latter gene is merged into the group of Asum*(1). After the loop for Asum*(1), we conduct the loop for the next ungrouped element in Asum*, until all the genes are grouped with a unique group ID.Starting from nCGF genes in each group with the highest mutation occurrence frequency, where nCGF is another predefined threshold. Groups that have fewer than nCGF elements are discarded. All of the selected genes are then united as the result of CGF  procedure, which minifies the sparsity by converting the gene data into the indexes of its non-zero genes. For a 1 × p, ISR filters out the majority of its zero elements and leaves most (if nNZ ≥ nISR) or all (if nNZ < nISR) of its non-zero elements. Since nISR ≪ length(p), the percentage of zero elements will drop dramatically after ISR, which means the impact of data sparsity will be significantly suppressed.The significance of ISR is apparent. For each gene sample As introduced in the previous two sections, both CGF and ISR have their own advantages when conducted alone. However, the performance can be even higher if they are combined together  is computed as:As shown in Fig. f is the activation function, zl is the total weighted sum of the input:where Wl and bl are the weight matrix and bias vector of layer l (to be learned in training). In our case, we adopt the ReLU [f, and x1 is the input gene data after pre-processing. The size of the last layer L’s output xL equals to the number of cancer types ncancer (ncancer = 12 in our case). xL is then processed by a softmax layer [J is computed by the logarithm loss function:where the ReLU  functionax layer , and theyi ∈ {0, 1} is the ground truth label of cancer type i, andwhere i.is the softmax probability of cancer type J is transferred from the last layer to the former layers via back-propagation [W and b of each layer are updated. The training then enters the next epoch, and the feed-forwarding and back-propagation are conducted again. The training stops when a pre-defined epoch number is reached. In testing, only the feed-forwarding is conducted (for once) for a testing sample, and the type of cancer i corresponding to the largest softmax probability of Pi is adopted as the classification result. The workflow of the DNN classifier is summarized in Table In training, the loss pagation , by whicOur experiments are all conducted on the newly proposed TCGA-DeepGene dataset, which is a re-formulated subset of The Cancer Genome Atlas (TCGA) dataset  that is A). This data matrix is the product of our proposed TCGA-DeepGene subset, where each sample (column) is assigned one of the labels {1, 2, …, 12} meaning the 12 types of cancer above.The TCGA-DeepGene subset is formulated by assembling the genes that contain somatic point mutation on each of the 12 selected types of cancer. Detailed sample and point mutation statistics for each cancer type can be found in Table To facilitate the 10-fold cross validation in the following experiments, we randomly divide the samples in each of the 12 cancer categories into 10 subgroups, and each time we union one subgroup from each cancer category as the validation set, while all the other subgroups are combined as the training set. This formulates 10 training/validation configurations with fair distributions of the 12 types of cancer, and will be used for the 10-fold cross validation in our following experiments.Emax is set to 50; the learning rate is set to 50-point logarithm space between 10− 1 and 10− 4; the weight decay is set to 0.0005; and the training batch size (i.e. the number of samples per training batch) is set to 256.For the proposed DNN classifier, the output size is set to 12 (i.e. the 12 types of cancer to be classified); the total training epoch nCGF is set to 5; the non-zero element threshold nISR is set to 800; the hidden layer number and parameters per layer of the DNN classifier are set to 4 and 8192, respectively.Additionally, in order to facilitate the evaluation of variable parameters, we set each parameter a default value: the distance threshold is set to 0.7; the group element threshold For all the evaluations in our experiments, we randomly select 90% (2810) samples for training, and the rest 10% (312) samples for testing. In parameter optimization steps for DeepGene, we adopt the 10-fold cross validation accuracy on the training set as the evaluation metric; on the other hand, in the comparison with widely adopted models, we adopt the testing accuracy as the evaluation metric.The CGF and ISR steps are implemented by original coding in MATLAB, while the DNN classifier is implemented on the MatConvNet toolbox , which idCGF and the group element threshold nCGF. To determine the two variables, we change them in 2-dimensional manner, while keeping all the other variables the default values as described in the “dCGF = 0.7 and nCGF = 5 for the following experiments based on the observed experimental results, since they contribute to the optimal performance.There are two variables that need to be experimentally determined for the CGF step, namely the distance threshold nISR needs to be experimentally determined for the ISR step. We monitor the number of non-zero elements for each sample in the dataset, and plot the corresponding histogram in Fig. nISR = 800, which not only concentrates the data to the non-zero elements, but also greatly shrinks the data length.The non-zero element threshold We also need to determine the network architecture for the DNN classifier, which involves two variables: the hidden layer number (#layer) and the parameter number per layer (#param). Enlightened by , we moniAfter determining the related parameters for the three steps of DeepGene, we evaluate the impact of our two major innovations, i.e. CGF and ISR. It is mentionable that we conduct CGF and ISR separately and concatenate their results ;Only CGF .Based on Fig. The 10-fold cross validation results are shown in Fig. k-Nearest Neighbors (KNN) [We then select three most representative data classifiers that are commonly used in SMCC as comparison methods, namely Support Vector Machine (SVM) , k-Nearers (KNN)  and Naïvrs (KNN) . In ordeSVM: we use the LIBSVM toolbox [2 = 4 and 2‐ 5 = 0.0313 for -c and -g, respectively, which lead to the best results in Table  toolbox  in imple toolbox , the ker toolbox ; this suKNN: we compare the performances of Euclidean distance and Pearson correlation coefficient, which are the two most commonly used similarity measures in gene data analysis [analysis . The 10-NB: following [ollowing , the aveIn the performance comparison between different models, the testing accuracy is adopted as the evaluation metric see the “” SectionThe main purpose of the CGF step is to filter out irrelevant genes in the samples and locate the candidate discriminatory gene subset. It first groups the genes based on popularity (mutation occurrence frequency) and inter-sample similarity, and then selects the top genes in each group, and finally unites all the genes selected as the output.dCGF and nCGF, are experimentally determined  and large-scale data to conventional classifiers, which will be discussed in our future works.Currently DeepGene is only tested on datasets of somatic point mutations with known cancer types, i.e., the histological biopsy sites are already known. Therefore, in this study, DeepGene only demonstrates the power of capturing complex association between somatic point mutation and cancer types, and more of its application potentials will be evidenced by tumor samples with completely unknown cancer type information (such as CTC or ctDNA data) in our future works. The association between point mutation and other genetic aberrations such as copy number variance, translocation, and small insertion and deletion will also be covered in our future works. It will be proved that to a large extent, adopting point mutation alone is good enough for cancer type or subtype classification.In this paper, we propose the DeepGene method for somatic point mutation based cancer type classification. DeepGene consists of three major steps. The CGF step concentrates the gene data with mutation occurrence frequency; the ISR step reduces the gene data sparsity with the indexes of non-zero elements; and in the final step, the DNN-based classifier takes the processed data and generates the classification result with high-level data feature learning.We conduct experiments on the compiled TCGA-DeepGene dataset, which is a reformulated subset of the TCGA dataset with mutations on 12 types of cancer. Controlled variable experiments indicate that CGF, ISR and DNN classifier all have significant contribution in improving the classification accuracy. We then compare DeepGene with three widely adopted data classifiers, the results of which exhibit the remarkable advantages of DeepGene, which has achieved > 24% of performance improvement in terms of testing accuracy against the comparison classification methods.We demonstrated the advantages and potentials of the DeepGene model for somatic point mutation based gene data processing, and we suggest that the model can be extended and transferred to other complex genotype-phenotype association studies, which we believe will benefit many related areas. As for future studies, we will refine our model for other complex and large-scale data, as well as broadening our training dataset, so that the classification result can be further improved.


Compared to previous results obtained using CRC resection material, we demonstrate that CMS classification in biopsy tissue is significantly less capable of reliably classifying patient subtype . In contrast, there was no significant difference in classification rate between biopsies and resections when using the CRIS classifier. Additionally, we demonstrated that CRIS provides significantly better spatially‐ and temporally‐ robust classification of molecular subtypes in CRC primary tumour tissue compared to CMS . These findings have potential to inform ongoing biopsy‐based patient stratification in CRC, enabling robust and stable assignment of patients into clinically‐informative arms of prospective multi‐arm, multi‐stage clinical trials. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.Colorectal cancer (CRC) biopsies underpin accurate diagnosis, but are also relevant for patient stratification in molecularly‐guided clinical trials. The consensus molecular subtypes (CMSs) and colorectal cancer intrinsic subtypes (CRISs) transcriptional signatures have potential clinical utility for improving prognostic/predictive patient assignment. However, their ability to provide robust classification, particularly in pretreatment biopsies from multiple regions or at different time points, remains untested. In this study, we undertook a comprehensive assessment of the robustness of CRC transcriptional signatures, including CRIS and CMS, using a range of tumour sampling methodologies currently employed in clinical and translational research. These include analyses using (i) laser‐capture microdissected CRC tissue, (ii) eight publically available rectal cancer biopsy data sets ( Recent studies have defined the molecular taxonomy of colorectal cancer (CRC) by transcriptional, methylation, and mutational profiling n = 24; samples, n = 72), we previously demonstrated the potential for discordant assignment of these patient‐of‐origin matched samples when using transcriptional classifiers, as different CMS classifications were mapped to different regions of the same tumour, due to stromal‐derived intratumoural heterogeneity (ITH) Using macrodissected tissue from the central tumour (CT), invasive front (IF), and lymph node (LN) from individual patients  laser capture‐microdissected (LCM) CRC cohort to examine if stromal ITH occurs with this more precise specimen‐preparation methodology. Additionally, we assessed subtyping robustness in a meta‐analysis of publicly available rectal cancer biopsy datasets. We also performed temporal/spatial assessment of the stability of these classifiers, using both patient‐matched serial biopsies collected over a 3‐week period from the phase II AXEBeam study The study design is summarised in the supplementary material, Figure https://www.ncbi.nlm.nih.gov/geo). Rectal cancer biopsy datasets are detailed in Table n > 20) were curated. When possible, raw unprocessed data were downloaded and expression profiles underwent standard Robust Multi‐array Average (RMA) normalisation prior to molecular subtyping. When only post‐processed data were available, we downloaded series matrices to perform molecular subtyping. All probes were used and no variance filtering was performed on any data prior to molecular subtyping, to ensure the presence of all 273 CMS genes and 565 CRIS genes from the published classification models.All public datasets were downloaded from the gene expression omnibus (GEO)  was profiled using the Affymetrix Primeview array. This trial investigated the efficacy of bevacizumab/chemo‐radiation combination in rectal cancer. Biopsies were taken before therapy and 3 weeks into the first cycle of bevacizumab, but before chemo‐radiation.COPERNICUS is a phase II study of neoadjuvant oxaliplatin‐based chemotherapy, followed by short‐course radiotherapy and surgical resection in patients with rectal cancer. Within S:CORT, we generated transcriptional profiles using the Affymetrix Almac Xcel array from the COPERNICUS (NCT01263171) trial cohort (52 biopsy samples); 50 samples (96.2%) generated suitable quantities of RNA for analysis, while 44 (84.6%) yielded robust transcriptional profiles.n = 24). This cohort is available from the NCBI GEO repository under accession number GSE95109. et al signature et al signature et al signature et al signature were used as our stem‐like (CMS4) signature. The Kennedy et al signature et al signature BRAF‐mutant tumours. The colorectal intrinsic signature (CRIS) We previously evaluated eight CRC gene expression signatures for variation in their ability to robustly cluster matched multi‐region‐of‐origin CRC gene expression profiles We previously indicated that the 30‐gene, stem‐like (CMS4) Jorissen and Eschrich gene signatures contain genes highly expressed in fibroblasts; the Sadanandam (CMS) and Kennedy signatures have a more balanced expression across cell types, whilst the Popovici and CRIS gene signatures contain predominantly epithelial‐specific gene signatures , developed using tissue microarray analysis in combination with MSI genotyping, to classify CMS1 (based solely on MSI) and combined CMS2/3 and CMS4 subtypes. In this publication, CDX2 is used as a marker for epithelial‐like tumours (CMS2/3), whereas FRMD6, ZEB1, and HTR2B have higher expression in mesenchymal‐like tumours (CMS4).In addition to the previous eight gene expression signatures, we assessed the clustering capabilities of a recently published refined CMS protein expression classifier To validate the improved ability of CRIS gene signatures to classify by patient‐of‐origin rather than region‐of‐origin, we utilised divisive analysis clustering (DIANA) and normalised Pearson similarity scoring. This Pearson score was used to define the ratio between the covariance and the standard deviation of the multi‐region CRC samples, where higher ratios (up to 1) indicate increased similarity. These two methodologies, as previously published, assess variation in clustering between gene signatures t‐tests, were performed using GraphPad Prism 6 . Plots for integrative visualisation purposes were generated using StratomeX tool within Caleydo software version 3.1.5 downloaded from http://caleydo.org/tools/stratomex.Other statistical analyses, including Fisher's exact and unpaired A visual assessment of neoplastic cell content, performed only within the macrodissected area of tissue used for molecular profiling, was made at 4× magnification. This value was estimated by a pathologist blinded to CMS and molecular data.Samples from a cohort of CRC tumour resection tissue samples that had been dissected into CT and IF regions using LCM were evaluated using a series of transcriptional profiling approaches . Importantly, as this dataset has been generated using LCM epithelial tissue, it more closely resembles an epithelial‐enriched CRC biopsy sample, rather than the macrodissected resection tissue used in our previous studies n = 20; and supplementary material, Figure t‐test, p = 0.0001; and supplementary material, Figure First, we employed the published CMS classifier [which uses a random forest (RF) posterior probability score] to evaluate each matched CT and IF sample. Using this method, each tissue sample is assigned a score for each individual CMS class  before a final classification is made. Using these individual CMS scores, we created a CMS ratio based on the change in RF score, from CT and IF regions, for each patient‐matched sample . We observed that 40% of all LCM cohort samples profiled could not be confidently assigned to a CMS group (termed UNK), particularly in IF samples; only 5% CRIS‐UNKs are observed in the same sample series .Additionally, when re‐employing the CMS RF classifier, alongside the nearest template predictor (NTP) CRIS classification method, we observed increased concordance in spatial stability (correct identification of patient‐of‐origin) in multi‐regional samples when employing CRIS as opposed to CMS classification  to curate a meta‐dataset containing 543 treatment‐naïve rectal cancer biopsy gene expression profiles from eight independent datasets  of patient samples  n = 37) of patients were UNK . Direct comparison of CMS and CRIS classifications for each of the 543 rectal cancer biopsies revealed that 94% of CMS‐UNK patients could subsequently be assigned a CRIS subclass .Serial biopsies can provide information on treatment response and clinically‐relevant changes in tumour biology; therefore, evaluating the temporal stability of molecular subtypes in repeat CRC biopsies is highly relevant. We analysed the transcriptional profiles of ten patient‐matched serial biopsy samples (taken both before and following 3 weeks of bevacizumab treatment) from the AXEBeam phase II trial . Again, we confirmed a high number of UNK samples by CMS analysis , with only 30% (3/10) of patients displaying a concordant CMS classification; lack of classification does not appear to be due to treatment‐induced transcriptional changes, as six of the ten UNK samples were obtained before treatment. Conversely, all samples were classified by CRIS, with 90% (9/10) temporal concordance across matched serial biopsies taken during this clinical trial  , generated within S:CORT p = 0.013, Fisher's exact). A detailed pathological review of haematoxylin and eosin (H&E) specimens was performed to test the ability of histological feature assessment to predict CMS subtypes, particularly for the CMS1/CMS4 stromal‐dependent subtypes. In a masked pathological analysis, we observed that a lower tumour and higher stromal percentage correlated with increased CMS1/CMS4 classification scores , again emphasising the histopathological features underlying this classification system. This is depicted in Figure Using transcriptional profiles from COPERNICUS (KRAS mutations and activation of metabolic pathways). In contrast, the CRIS classifier identifies five tumour subtypes based on cancer cell intrinsic biology from within the TME. In this study, we assessed for the first time the ability of the CMS and CRIS molecular subtypes to robustly classify tumour samples, with particular emphasis on prospective pretreatment biopsy tissue, when confronted with potential spatial and/or temporal confounders. Initially, using a cohort of patient‐matched LCM invasive front and central tumour regions from CRC resections, we demonstrated that the epithelial enrichment achieved by LCM is not sufficient to overcome the confounding effect of stromal intratumour heterogeneity. These results validate our previous findings that CRIS is a more robust patient stratifier than CMS, while also indicating that epithelial enrichment using the precise but time‐consuming LCM method cannot eliminate the potential for stromal‐derived ITH to undermine patient stratification. Our assessment of 543 rectal cancer biopsies  also revealed a significantly larger proportion of unclassified biopsies than has previously been reported for resection samples when using the CMS classifier. In contrast, the CRIS classifier assigned the same biopsies into proportions consistent with those observed in resection material. This observation indicates that while CMS classification provides important prognostic information in CRC resection samples, it may not be suited to classification in FFPE biopsy material.Transcriptomic dissection of CRC tumours has identified two molecular classifiers with potential clinical relevance. The CMS classifier identifies two histological subtypes – CMS1 (immune‐rich) and CMS4  – and two epithelial‐rich subtypes – CMS2 (upregulated for WNT and MYC pathways) and CMS3  molecular subtypes has, to date, been investigated using large retrospective collections of resected CRC tissue et al indicate via their analysis that setting a threshold for CMS4 detection across multiple biopsies may help to ensure a more robust classification CRC biopsies are currently used for both cancer diagnosis and patient stratification, employing small panels of clinically important biomarkers, such as Biomarker‐informed clinical trials such as FOxTROT (ISRCTN 87163246) and FOCUS4 (ISRCTN90061546) have involved the application of multiple molecular tests on biopsy material, which may be limited in quantity , following diagnostic assessment. While these studies have employed mutational status for patient stratification, evaluation of transcriptional‐based signatures in collaborative programmes such as S:CORT aims to provide a clinical rationale for such stratification in clinical practice. There is no doubt as to the potential clinical importance of the TME and CMS classification system, with numerous studies highlighting its prognostic value. However, given the nature of stromal ITH and the current lack of a standardised method for the collection of biopsy material, even within ongoing clinical trials, this method can easily be confounded by sampling bias. The implementation of a standardised biopsy collection method may remove this confounding issue, but until such a reproducible biopsy protocol is developed, our data support the use of CRIS stratification as the molecular pathology methodology of choice underpinning reproducible prospective patient stratification from current routine biopsy tissue.In addition to a robust subtype assignment and clear prognostic value, the clinical relevance of defining CRIS lies in its potential predictive value, which gives insights into the biology underlying the epithelial component of the tumour, which may in turn guide an informed (targeted) therapy approach. We have previously shown that CRIS‐C patient‐derived xenografts (PDX) respond to EGFR inhibition (cetuximab) In conclusion, we highlight the robust nature of the CRIS transcriptional classifier in diagnostic endoscopic biopsy material, which is the relevant entry point to ongoing and forthcoming CRC clinical trials. The limitations of CMS identified previously by our group are still evident when using LCM processing of samples, suggesting that this time‐consuming method does not eliminate the potential for ITH to confound patient classification, as previously identified in macrodissected samples. Given the limited control over the spatial region‐of‐origin of biopsy tissue available for analysis, our current data support patient stratification using CRIS transcriptional subtypes, which minimise potentially confounding ITH. This work provides a strong rationale to investigate the prognostic/predictive value of CRIS subtypes in biopsy‐led and statistically‐powered prospective CRC trials.All authors wrote and critiqued the manuscript. MA, SDR, PS, AMBM, AR, CI, EM, DGM, ML, and PDD generated, analysed and interpreted data. SG, CH, RA, DV, TM, ML, and PDD were the clinical trial and SCORT Management Group; MA, ML, and PDD designed the study.Supplementary figure legendsFigure S1. Study designFigure S2. Comparison of normalised random forest scores between stromal subtypes (CMS1 and 4) and epithelial subtypes (CMS2 and 3)Figure S3. Assessment of divisive clustering capabilities in matched CRC CT and IF regions using eight previously published CRC gene expression signaturesFigure S4. Assessment of the clustering capabilities of the refined CMS classifier published by Trinh et alSupplementary figure legendsClick here for additional data file.Figure S1. Study design.Click here for additional data file.Figure S2. Comparison of normalised random forest scores between stromal subtypes (CMS1 and 4) and epithelial subtypes (CMS2 and 3). (A) Line plots showing changes in normalised random forest score between CT and IF for CMS1–4 (B) Left: line plot of normalised stromal CMS random forest scores between CT and IF. Right: line plot of normalised epithelial CMS random forest scores between CT and IF.Click here for additional data file.Figure S3. Assessment of divisive clustering capabilities in matched CRC CT and IF regions using eight previously published CRC gene expression signatures. (A) 30 gene; (B) Sadanandam Click here for additional data file.Figure S4. Assessment of the clustering capabilities of the refined CMS classifier published by Trinh et al.Click here for additional data file.


Pancreatic cancer is a complex and heterogeneous disease with the etiology largely unknown. The deadly nature of pancreatic cancer, with an extremely low 5-year survival rate, renders urgent a better understanding of the molecular events underlying it. The aim of this study is to investigate the gene expression module of pancreatic adenocarcinoma and to identify differentially expressed genes (DEGs) with prognostic potentials. Transcriptome microarray data of five GEO datasets , including 117 primary tumor samples and 73 normal pancreatic tissue samples, were utilized to identify DEGs. The five sets of DEGs had an overlapping subset consisting of 98 genes (90 up-regulated and 8 down-regulated), which were probably common to pancreatic cancer. Gene ontology (GO) analysis of the 98 DEGs showed that cell cycle and cell adhesion were the major enriched processes, and extracellular matrix (ECM)-receptor interaction and p53 signaling pathway were the most enriched pathways according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Elevated expression of gap junction protein beta 2 (GJB2) and reduced endoplasmic reticulum oxidoreductase 1-like beta (ERO1LB) expression were validated in an independent cohort. Kaplan-Meier survival analysis revealed that GJB2 and ERO1LB levels were significantly associated with the overall survival of pancreatic cancer patients. GJB2 and ERO1LB are implicated in pancreatic cancer progression and can be used to predict patient survival. Therapeutic strategies targeting GJB2 and facilitating ERO1LB expression may deserve evaluation to improve prognosis of pancreatic cancer patients. Pancreatic adenocarcinoma is the most common type of pancreatic cancer, accounting for 85% of cases, and is a leading cause of cancer associated death worldwide . The incKRAS mutations and more than 50% of cases have inactivation mutations of tumor suppressor genes including CDKN2A, TP53 and SMAD4  Affymetrix Human Genome U133 Plus 2.0 Array. GSE15471 and GSE18670 datasets were composed of matched tumor samples and adjacent non-tumor samples. Data of circulation tumor samples and haematological cells in GSE18670 were not included in this study. GSE71729 dataset including 145 primary pancreatic ductal adenocarcinomas and 46 normal pancreatic samples was used as a validation dataset, the platform of which was Agilent-014850 Whole Human Genome Microarray 4×44K G4112F. TCGA pancreatic adenocarcinoma gene expression data (IlluminaHiSeq) and the associated clinical data was downloaded from the UCSC Cancer Browser (https://genome-cancer.ucsc.edu/) for survival analysis. Data from normal samples and data without follow-up information was removed and finally 165 samples were eligible for survival analysis.The discovery and validation datasets were obtained from the Gene Expression Omnibus  was processed through the robust multiarray average algorithm RMA in the Affy package of R . Express2 fold change (FC)| > 1 and P < 0.05 were considered to be differentially expressed between tumors and normal tissues. Hierarchical clustering of the DEGs was based on the Euclidean method which calculates the distance and on complete linkage which is for the tree construction. Visualization of the identified DEGs including volcano plot, venn diagram and heat map was achieved by using ggplot2, VennDiagram and gplots packages of R, respectively.The limma package was used for identification of DEGs . Genes whttps://david.ncifcrf.gov/) was utilized to perform GO analysis and KEGG pathway analysis [P < 0.05 were considered as significantly enriched.During functional enrichment analysis of the 98 common DEGs, the online software Database for Annotation, Visualization, and Integrated Discovery  and GraphPad Prism  were used for statistical analysis. Student's


The sequencing of the transcriptomes of single-cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types and for the study of stochastic gene expression. In recent years, various tools for analyzing single-cell RNA-sequencing data have been proposed, many of them with the purpose of performing differentially expression analysis. In this work, we compare four different tools for single-cell RNA-sequencing differential expression, together with two popular methods originally developed for the analysis of bulk RNA-sequencing data, but largely applied to single-cell data. We discuss results obtained on two real and one synthetic dataset, along with considerations about the perspectives of single-cell differential expression analysis. In particular, we explore the methods performance in four different scenarios, mimicking different unimodal or bimodal distributions of the data, as characteristic of single-cell transcriptomics. We observed marked differences between the selected methods in terms of precision and recall, the number of detected differentially expressed genes and the overall performance. Globally, the results obtained in our study suggest that is difficult to identify a best performing tool and that efforts are needed to improve the methodologies for single-cell RNA-sequencing data analysis and gain better accuracy of results. Single-cell RNA-sequencing (scRNA-seq) has emerged a decade ago as a powerful technology for identifying and monitoring cells with distinct expression signatures in a population, and for studying the stochastic nature of gene expression; a task, this latter, possible only at single-cell level. Compared to bulk RNA-seq, scRNA-seq data are affected by higher noise deriving from both technical and biological factors. Technical variability mostly originates from the low amount of available mRNAs that need to be amplified in order to get the quantity suitable for sequencing. This process may lead to amplification biases or “dropout events,” when the amplification or the capture are not successful ;Bimodal distribution with different proportions of cells in the two components and equal component means across conditions (DP);Unimodal distribution for one condition and bimodal distribution for the other, with one overlapping component and with equal component means across conditions (DM);Unimodal distribution for one condition and bimodal distribution for the other, with different component means across conditions (DB).In addition to real scRNA-seq datasets   cut-off of 0.05. Precision and Recall metrics were calculated as, respectively, the number of true positives among all positive calls and the number of true positives among the true number of DEGs.We tested four methods developed for differential expression analysis of genes between single-cell populations: MAST (version 1.0.5) . A logistic regression model for the discrete variable Z and a Gaussian linear model for the continuous variable (Y | Z = 1) are considered:MAST employs a generalized linear hurdle model to account simultaneously for stochastic dropouts and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. The rate of expression Xi is the design matrix. The fraction of genes that are expressed and detectable in each cell, called cellular detection rate (CDR), can be explicitly modeled as a covariate (a column in the design matrix Xi), allowing a joint estimate of nuisance and treatment effects. In order to improve the inference for genes with sparse expression, the model parameters are fitted using an empirical Bayesian framework. Finally, differential expression is determined using the likelihood ratio test.where In our assessment, MAST with both the adjustment for CDR and the omission of this covariate (MASTNotCDR) were included.p-value to test for significance of expression difference is determined by normalizing to unity the posterior distributions.SCDE models the read counts computed for each gene using a mixture of a NB distribution and a Poisson distribution. The NB distribution models the transcripts that are amplified and detected, whereas the low-magnitude Poisson distribution models the unobserved or background-level signal of transcripts that are not amplified . Although, the dropout component could be modeled as a constant zero  the use of a low-magnitude Poisson process allows accounting for both the dropouts and some background signals that are typical of transcriptionally silent genes. A subset of robust genes  is used to fit, using an EM algorithm, the parameters of the mixture models. For the differential expression analysis, the posterior probability that the gene shows a fold expression difference between two conditions is computed using a Bayesian approach. An empirical Monocle is a tool originally designed for single-cell RNA-seq data analysis for ordering cells by progress through differentiation stages (pseudo-time). The tool is able to identify genes that change significantly over the time and that are differentially expressed across different cell types or conditions. The mean expression level of each gene is modeled with a GAM which relates one or more predictor variables to a response variable asY is a specific gene expression level, and the xi's are predictor variables. The function g is a link function, typically the log function, while the fi's are non-parametric functions, such as cubic splines or some other smoothing functions. The observable (log-transformed) expression level Y is modeled using a Tobit model censored below a user defined expression detection threshold. Monocle's GAM is thuswhere t is the assigned pseudo-time of a cell and s is a cubic smoothing function with (by default) three degrees of freedom. The error term ε is normally distributed with a mean of zero. The tool also supports testing for differential expression between groups. In these tests, the GAM employs the class labels as predictor variables, with no smoothing. Finally, the test for differential expression is performed using an approximate χ2 likelihood ratio test.where φnewCellDataSet at the beginning of the analysis we used the parameter expressionFamily = negbinomial for each dataset. We were not able to estimate the data dispersion since the function performing the parametric fit failed both on simulated and real data and it was not possible to modify it for a local fit and/or a pooled estimation of dispersion.Since we are interested only in the comparison of genes among different conditions, the temporal ordering feature was not used in our study. When creating 3E consists of two separate modules: a module for comparing expression profiles using the Cramér-von Mises, the likelihood ratio test, the Kolmogorov-Smirnov test or the Anderson-Darling test and a module for fitting the transcriptional bursting model  and the Kolmogorov-Smirnov test.In our study, Dj that are assigned to gene i can be modeled by a negative binomial distribution with mean and variance estimated from the data. For each gene, the expectation value of the observed counts for gene i in sample j, i.e., the mean μij of the NB distribution, is modeled as the product of the (unknown) expectation value of the true concentration of reads and a size factor sj accounting for the sequencing depth. The variance of the NB distribution shot noise terms (μij) and a raw variance term:DESeq assumes that the number of reads in a bulk RNA-seq sample sj and to the expected true concentration of reads vi, ρ(j). For each gene, the statistical test is performed defining, for each gene i, the total read counts for each of the two conditions  and computing, under the null hypothesis, the p-value as the probability of the events KiA = a and KiB = b for any pair of numbers a and b, given that a + b equals the observed sum of counts.The raw variance term is proportional to the square of the scaling factor local” option.Since DESeq is able to manage only non-zero data, in the specific cases of Grün and Islam datasets a pseudo-count of +1 was added to zero counts. Estimation of dispersion was performed using the “2 is related to the mean by σ2 = μ + αμ2, requiring the estimation of the over-dispersion parameter α. The method estimates the gene-wise dispersions using a conditional maximum likelihood procedure, conditioning on the total read count of each gene  relative abundance of that gene in the current experimental condition. The variance σTagwise dispersion, using the glmFit function to fit the data and glmLRT to compare the two conditions.In our tests, edgeR was run estimating the The number of selected DEGs resulting from the analysis of simulated data ranged, on average, from 1,021 to 1,741 with a number of true positives from 1,018 to 1,534  curves of the different methods are shown in Figure All methods performed similarly in ranking DEGs, with the exception of Monocle (dark green line), which showed very low precision values for the first genes selected at differentially expressed and high variability between the ten different performed tests. When looking separately at precision and recall values Figures , MAST, Sp-value equal to 1.46e-12, with Wilcoxon p-value always lower than 3.7e-02 for the comparison of MAST and MASTNotCDR with any other method. For precision, we obtained a Kruskal-Wallis p-value equal to 1.22e-12, with Wilcoxon p-value always lower than 3.90e-03 for the comparison of MAST, MASTNotCDR, SCDE, and DESeq with any other method. For recall, we obtained a Kruskal-Wallis p-value equal to 1.75e-13 with Wilcoxon p-value always lower than 0.58e-03 for the comparison of Monocle, D3E and edgeR with any other method.The significant difference among tools' performance scores were assessed by a Kruskal-Wallis test . edgeR and Monocle had the highest recall values for DE, DM, and DB classes , whereas D3E performed better than other in recall for the DP class .In order to understand the ability to detect DEGs in the four different scenarios DE, DP, DM, and DB, we evaluated precision and recall separately on the four classes of DEGs defined in Section Materials and Methods. In general, all tools performed better for the DE and the DM classes, which had the highest precision and recall values with respect to the other two classes Figure . For the3E with CvM test (hereafter D3E_CvM) and MAST without CDR covariate (MASTNotCDR) detected the highest number of DEGs compared to other tools. The intersection of DEGs with Moliner's reference list of the top 1,000 ranking genes accordingly to qRT-PCR . Tools supporting parallel execution in general achieved a considerable speed up, especially Monocle. The remaining bulk methods were generally fast, as they did not include any heavily time-consuming steps.We performed all the analyses on a HPC cluster consisting of 6 octa-core IBM Power7 processors, 640 Gb of RAM and running SUSE Linux Enterprise 11. All the analyses were carried out using R version 3.3.2 and, for DIn this work, we evaluated the performance of six differential expression analysis methods on two published scRNA-seq datasets  across the different tools used, with MASTNotCDR and DWe tested the ability of each tool in detecting true DEGs or experimentally validated DEGs, in terms of precision, both on simulated and Islam real dataset  and 0.22 (for MAST).Among the assessed tools, SCDE outperformed the other methods in terms of precision but, consistently, had a drop in performance in terms of recall, both on real and simulated datasets. In particular, on simulated data, the average observed precision was above the 95% required as input, based on a FDR threshold of 5%, highlighting a good but slightly conservative control of false positive, with a consequent loss in recall.MAST had a contradictory behavior on simulated with respect to real dataset. As SCDE, on simulated data the precision for MAST was above the required cut-off while the recall dropped to lower values with respect to SCDE. In case of the real dataset, the inclusion of the CDR covariate highly affected the results, with a lower number of called DEGs with respect to all the other tools when including it, and a higher number of detections when excluding this covariate. In both cases, however, the intersection size with Moliner's reference gene list  had an average trade-off between precision and recall but did not reach the desired cut-offs for any of these measures. All tools performed well with Grün datasets, regarding the ability in detecting true negatives, with the exception of D3E, which reported a number of DEGs. Finally, bulk methods showed comparable performance with respect to single-cell tools, also in handling the multimodality of simulated data.Globally, considering our test design, none tool emerged as the best one. Some of the scRNA-seq tools (MAST and SCDE) performed best in terms of precision but had a drop in performance in terms of recall. Others  and PRAT 2010 CPDA101217 (“Models of RNA sequencing data variability for quantitative transcriptomics”) grants.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Normalization is an essential step with considerable impact on high-throughput RNA sequencing (RNA-seq) data analysis. Although there are numerous methods for read count normalization, it remains a challenge to choose an optimal method due to multiple factors contributing to read count variability that affects the overall sensitivity and specificity. In order to properly determine the most appropriate normalization methods, it is critical to compare the performance and shortcomings of a representative set of normalization routines based on different dataset characteristics. Therefore, we set out to evaluate the performance of the commonly used methods  and two new methods we propose: Med-pgQ2 and UQ-pgQ2 . Our per-gene normalization approach allows for comparisons between conditions based on similar count levels. Using the benchmark Microarray Quality Control Project (MAQC) and simulated datasets, we performed differential gene expression analysis to evaluate these methods. When evaluating MAQC2 with two replicates, we observed that Med-pgQ2 and UQ-pgQ2 achieved a slightly higher area under the Receiver Operating Characteristic Curve (AUC), a specificity rate > 85%, the detection power > 92% and an actual false discovery rate (FDR) under 0.06 given the nominal FDR (≤0.05). Although the top commonly used methods (DESeq and TMM-edgeR) yield a higher power (>93%) for MAQC2 data, they trade off with a reduced specificity (<70%) and a slightly higher actual FDR than our proposed methods. In addition, the results from an analysis based on the qualitative characteristics of sample distribution for MAQC2 and human breast cancer datasets show that only our gene-wise normalization methods corrected data skewed towards lower read counts. However, when we evaluated MAQC3 with less variation in five replicates, all methods performed similarly. Thus, our proposed Med-pgQ2 and UQ-pgQ2 methods perform slightly better for differential gene analysis of RNA-seq data skewed towards lowly expressed read counts with high variation by improving specificity while maintaining a good detection power with a control of the nominal FDR level. These improvements have enabled studies with a large range of applications including identification of alternative splicing isoforms –3, de noisoforms –6, detecisoforms ,8 and noisoforms , and chaisoforms . In addiisoforms . Howeverisoforms ,13.et al. [Currently, several sequencing platforms exist, which require similar sample pre-processing and subsequent analytical steps, as summarized by Zhang et al. . Brieflyet al.  are furtet al. –19.EDASeq [edgeR, Cufflink-Cuffdiff2 and EDASeq [EDASeq [DESeq and DESeq2 [edgeR [Aroma.light [Cufflinks-CuffDiff2 [In RNA-seq, the expression level of each mRNA transcript is measured by the total number of mapped fragmented transcripts, which is expected to directly correlate with its abundance level. The expected expression level of each transcript is limited by the sequencing depth or total number of reads, which is pre-determined by the experimental design and budget before sequencing. Since the expression level of the transcripts within the sample is dependent upon the other transcripts present , given aEDASeq ,24, per-d EDASeq ,24–26, p [EDASeq ,24, DESed DESeq2 ,28, Trim2 [edgeR , Full Quma.light ,30, Readma.light  and FraguffDiff2 ,32, normuffDiff2 ,33 and nDESeq and TMM-edgeR packages based on the false positive rate and detection power [DESeq and TMM-edgeR were reported to have overall better performance, these studies also report the false discovery rate (FDR) was higher than the nominal FDR, leading to an inflated type I error rate. Therefore, in this study, we explore new normalization methods and find a slight improvement over the existing methods with the dual goals of maintaining a nominal FDR level and a good specificity rate.Given the variety of read count normalization methods for RNA-seq analysis, it can be challenging for scientists to determine which method is optimal with regards to sensitivity and specificity due to a variety of factors such as read depth, biological variation and the number of biological replicates in the RNA-seq data. Previous studies comparing these methods for DEG analysis suggested the use of on power ,23,34–36RNA-seq data are obtained from complex experiments with a variety of technical variations across different conditions and adjustments made for read depth and other variation . For exaedgeR to identify DEGs for the normalization methods including TC, Med, UQ, FQ and our two proposed methods. We used DESeq2 for DESeq normalization and Cufflinks-Cuffdiff2 for FPKM normalization to test DEGs. The benchmark Microarray Quality Control Project (MAQC2 and MAQC3) datasets, simulated data and real human breast cancer RNA-seq data with a variety of properties were used to compare these methods.In this study, we evaluated our methods (Med-pgQ2 and UQ-pgQ2) along with the public available methods. We used the exact test with a negative binomial distribution from Within-sample normalization enables the correction of expression level in each gene associated with other genes in the same sample. Since a long gene or transcript has more reads mapping to it compared to a short gene or transcript with a similar expression, length normalization is taken into consideration in some normalization methods. Currently, the most widely used methods, including both within-sample and between-sample normalization, are RPKM  and FPKMedgeR, DESeq and FQ normalization methods are much better than TC [Since the prominent variation of read counts for a gene between samples is due to differences in library size or sequencing depth, within-sample normalization of raw reads is critical for the comparison of these gene expression measures across experimental conditions. The simplest normalization method is TC normalization, which adjusts the raw reads of each transcript by the total library size per sample. However, the comparison of RNA-seq normalization methods shows that Med, UQ, TMM from  than TC . One rea than TC . To acco than TC ,23.Since the variation among genes within a sample and the variation per-gene across samples due to the systematical bias need to be corrected in order to accurately identify DEGs between conditions, we propose two-step per-gene normalization methods called Med-pgQ2 and UQ-pgQ2.g  in sample j  where G is the total number of genes and n is the total number of samples. Let Ygj be the number of observed reads mapped to a gene g for sample j, Nj be the total number of mapped reads for all genes in sample j, N be the total number of mapped reads across all samples, ugj be the true and unknown expression level and Lg be the length of the specific gene g.In the following, we define statistical notations for characterizing different normalization techniques. For simplicity, we only consider the gene Nj, N and The above In this study, we examine eight existing and two proposed normalization methods with detailed statistical notations as described in Like microarray data analysis, the raw read counts of RNA-seq data are first preprocessed to remove all zero read counts across conditions before the normalization procedure. Thus, in the case of a balanced sample size design, genes with total raw read counts across conditions less than the number of sample replicates are filtered out. For data with an unbalanced sample size design, the gene with an average number of raw reads across conditions less than one are filtered out. In addition, a value of 0.1 is added to the raw counts for those genes to avoid zero read counts that are used for the following normalization as well as the other normalization methods. Our proposed methods include two steps described as follows:a. Median (Med) . Let g in sample j. Median normalization is based on all constitutive gene reads with positive counts for all samples. For each sample j, the normalization factor th percentile or 2nd quartile) of the mapped reads of the genes in each sample after filtering out the genes with zero read counts across samples [Ygj is scaled by  samples . The obsb. Upper Quartile (UQ) . If the majority of genes have very low read counts in a RNA-seq experiment, upper-quartile normalization is preferred to median normalization (50th percentile) [g in sample j. Upper-quartile normalization is based on all constitutive gene reads with positive counts for all samples. For each sample j, the normalization factor th percentile) of the mapped reads of the genes in the sample after filtering out the genes with zero read counts across samples. The observed Ygj is scaled by centile) . Let YgjA study in evaluation of statistical methods for normalization in RNA-seq experiments  demonstra. Med-pgQ2: let g in sample j scaled by the median (Med) in Eq  in Eq ; let Q2gb. UQ-pgQ2: let g in sample j and normalized by UQ (75%) in Eq  is a random variable to model the observed read counts in RNA-seq data, where Y has mean u and dispersion ф. Its probability mass function (pmf), the expected value and the variance of Y are correspondingly:Briefly, Y that a Poisson (u) distribution fails to describe. As ф goes to zero (ф→0), the variance of Y converges to u in probability and the distribution of f(y) in ((u) distribution which was shown by Cameron and Trivedi [The above NB model utilizes the conventional parameterization called “NB2” . The dis f(y) in  converge Trivedi .MAQC2 contains two RNA-seq datasets from the Microarray Quality Control Project (MAQC)  with twoGSE49712_HTSeq.txt.gz for MAQC3 raw read counts with five technical replicates in two biological conditions (UHR and HBR) was downloaded from GEO (GSE49712) . Four reet al.’s study [xgik of each gene g in replicate i and condition k is obtained via log2(ΔCt)/log2(e). The log2 fold change is defined as the mean difference of each gene between the hbr and uhr conditions  genes and 178 genes with 151 unique gene names fall into the true negative (TN) genes. The remaining set of genes lie in a region set to be indeterminate as far as DEG is concerned.PCR validation of the uhr sample from GSM12638 to GSM129641 and the hbr sample from GSM129642 to GSM129645 were downloaded from GEO (series GSE5350). These MAQC data (uhr and hbr) contain a total of 1044 genes assayed and validated using TaqMan qRT-PCR with 4 technical replicates ,41. Thir’s study . BrieflyDataset one is used for simulation containing twenty-four normal tissues and 25 early breast neoplasia (EN) on formalin-fixed paraffin-embedded tissue were sequenced at 3’-end enriched RNA-seq libraries .The mappG be the total number of genes , n = 20 be the total number of samples in two conditions , let yigk be the count for gene g in sample i and condition k with a Poisson distribution: yigk ∽ Pois(λgk). The parameter λgk is estimated from the mean reads per gene across samples from this human breast cancer RNA-seq dataset. Under this model, the null hypothesis H0 (λgA = λgB) means the expression values of gene g between conditions A and B are not significantly different, and the alternative hypothesis H1 (λgA ≠ λgB) means the gene expression values are significantly different between the two conditions. Let p0 and p1 be the proportion of genes generated under H0 and H1 among the G genes, respectively. The data is simulated with 15,000 genes and p1 is 10% corresponding to 1,500 genes. Under H0, the parameter λgA in the gene g of condition A and the parameter λgB in the gene g of condition B were estimated from the breast cancer raw counts corresponding to the mean raw counts of each gene (λgA = λgB); while under H1 the parameters λgA and λgB in the gene g and condition A and B were equal to (1 + α)λgA for 750 downregulated genes and (1 + α)λgB for 750 upregulated genes, respectively, where α is defined as 0.5 and 1. To assess the impact of non-equivalent library sizes, we multiplied yigk by a size factor Si per sample of the condition, which is equal to |εi|, where εi ∼ N. The number of simulation was chosen as 13 due to the small variation of the AUC values from all the normalization methods per simulation.Simulated data was based on the human breast cancer RNA-seq dataset one with two conditions: 24 normal tissues and 25 early neoplasia tissues. The simulation model is similar to the one described in Dillies’s study . Let G btophat2 (v2.0.13) with Bowtie version (2.2.3.0) and the parameter: ‘no-coverage-search’ [Cufflinks-Cuffnorm (v2.2.1). For the other normalization methods, the aligned RNA-seq reads were sorted by samtools (v0.1.19) and the read count matrix for each replicate of the condition was generated using HTSeq-scripts-count (version 2.7) and provided in The MAQC2 RNA-seq libraries with two technical replicates of each sample (uhr and hbr) and the human ER+ breast cancer dataset two were mapped to the human hg19 reference genome using -search’ ,31. For edgeR (v3.8.6) [DESeq2 [DESeq method [Cufflinks-Cuffnorm (v2.2.1) with a default parameter setting was used to perform FPKM normalization. Cufflinks-Cuffdiff2 was used to perform DEGs analysis at both the transcript and gene level using a beta negative binomial model and the t-test for the fragment counts [The normalization methods and the software packages for detecting DEGs between conditions using MAQC datasets and the human ER+ breast cancer dataset are summarized in (v3.8.6)  was used [DESeq2 , a succeq method , shows ht counts . In thiset al. method (1982) [A be the area under ROC curve; sd be the estimated standard error and standard deviation, respectively; na and nn be the total number of true positive genes and false positive genes, respectively. Then, d1 = A × (1 − A) + (na − 1) × (Q1 − A2) + (nn − 1)(Q2 − A2), Z statistic was computed as: p.value = 1 − Prob(Z < z). This p-value was used to compare the AUC values between two normalization methods.The area under the ROC curve (AUC) was calculated using Algorithm 2 by Fawcett (2006) . The estd (1982) . Brieflyn = 13 is the number of simulations, sd are the mean and standard deviation of AUC from 13 simulations, respectively.The 95% CI  for the simulated data was computed based on the normal approximation, which is defined as In this study, seven different normalization methods were compared to our proposed methods (Med-pgQ2 and UQ-pgQ2) via the qualitative characteristics of data distributions, intra-condition variation, ROC curve and AUC value as well as PPV, the actual FDR, sensitivity and specificity given the nominal FDR (≤ 0.05).2 expression of raw read counts in In DEGs analysis, one important assumption of null hypothesis about normalized RNA-seq data is that the majority of genes are not differentially expressed between conditions. Therefore, the overall distributions across genes are expected to be similar. Boxplots of non-normalized logIt is important to compare the intra-condition variation among different normalization methods to prevent over correction. We further analyzed the human ER+ breast cancer RNA-seq dataset with 40 ER+ breast cancer samples and 29 controls and MAQC3 with five technical replicates. For the human breast cancer datasets, similar patterns for most of the normalization methods from the boxplots  Figs areFor MAQC3 data, the boxplots  show tha2 fold changes generated from statistical tests for each normalization method .To evaluate the accuracy of normalization methods, we used the MAQC2 and qRT-PCR data to calculate RMSD (root-mean-square-deviation) correlation between the logn method  and the et al. in 1982 [The ROC curve in  in 1982 .z-test. In addition, we further compared the AUC value from one of our methods (Med-pgQ2) to the others using a two-sample one-sided In order to identify the major difference among all the normalization methods for detection of DEGs in MAQC2 and MAQC3 data, we calculated the number of true positive (TP) genes and false positive (FP) genes given the nominal FDR ≤ 0.05. We also calculated the positive predictive value (PPV), the actual false discovery rate (FDR), sensitivity and specificity for both datasets . The resWe further analyzed the DEGs detected only by the top performers such as DESeq, TMM and our methods using different quartile cutoff of mean expression of raw read counts from all genes given the nominal FDR ≤ 0.05. The results for the actual FDR, sensitivity and specificity are listed in edgeR  improve specificity while maintaining good detection power for highly expressed genes, we further analyzed gene-wise dispersion estimated after UQ and UQ-pgQ2 normalization with the aid of The simulated data with 10 replicates and two conditions with different fold changes were used to compare our methods (Med-pgQ2 and UQ-pgQ2) based on the ROC curves. A total of 1,500 genes with a fold change (FC) of 1.5 and 2 are considered as true positive genes and the remaining genes  are considered as true negative genes. We investigated the impact of biological replicates on the performance of normalization methods. We randomly sampled four and six replicates from each of 13 simulated datasets with 10 replicates used in As expected, increasing the number of biological replicates yields a higher statistical power for detection of DEGs . Under tLike RPKM and FPKM, we chose to use the small multiplication factor of 100 for our proposed per gene and per 100 normalization for this study. We also chose different multiplication factors such as 50, 200, 500, 1000 and 1 million to perform per gene normalization. We performed DEGs analysis using Med-pgQ2 and UQ-pgQ2 with these multiplication factors. The comparison results based on DEGs analysis are shown in Our study has some limitations. First, the data normalized by med-pgQ2 and UQ-pgQ2 is restricted for DEGs analysis between groups and not for other purpose such as identifying highly or lowly expressed genes as well as comparing gene A to gene B expression levels within a sample due to the potential change of gene order in a sample after normalization. Second, a simulation data using a Poisson distribution based on real RNA-seq data with additional variation generated from a normal distribution was used for the DEG analysis. We do acknowledge that the lack of simulated data based on the NB distribution is a limitation to the study. However, inclusion of two real data sets (MAQC2 and MAQC3) offsets this limitation to an extent, and the combination of the simulated and real data provides fairly comprehensive and consistent answers. Finally, on one hand, the exact test was used to identify DEGs implemented by edgeR. Although it is recommended for DEG analysis of RNA-seq data in two groups with a small sample size, we think that evaluating the effect of normalization on more complicated study designs beyond two-group comparisons is a worthwhile and interesting endeavor, and we may consider this as potential future work. On the other hand, although a t-test is not commonly used for testing hypothesis in RNA-seq data, it is used for testing DEGs with small sample size in the cDNA Microarray data. Therefore, we need to mention here that a t-test is invariant to linear transformations and thus would be unaffected by the per-gene normalization outlined here.Several studies have previously compared normalization methods . TC, FPKM, RPKM and FQ are not suggested for use in DEG analysis due to multiple issues such as lowly expressed gene issue for TC, length correction bias for FPKM and RPKM, and potentially increasing the intra-condition variation by forcing all the samples to have identical distributions for FQ ,20,22,23et al. in 2012. In their study they compared DESeq, edgeR, baySeq and TSPM (two-stage Poisson model) methods via a simulated data and reported the FDR in these methods are not controlled well and the actual FDR is larger than the observed FDR [In this study, we compared two new normalization methods for RNA-seq data analysis (Med-pgQ2 and UQ-pgQ2) to the seven existing methods  based on DEG analysis. The purpose of using per-gene normalization approach is to remove technical variations using different chips and allow for comparison between conditions based on similar count levels ,48. The rved FDR . MoreoveedgeR is needed to make definitive conclusions. Second, our methods are more universally applicable than using control-gene normalization in removing technical variations since it is hard to identify control genes such as housekeeping genes that remain at the same expression level regardless of the experimental conditions [There are several specific potential applications of our normalization methods worth mentioning. First, our methods may be useful for analyzing microRNA sequencing (miRNA-seq) data. Since miRNA expression is usually low compared to the mRNA with a ratio range 0.1~1.3% of total RNA in rat and mouse species, and 0.5~9.2% of total RNA in human samples, the data might be skewed to the low read counts. Therefore, per gene normalization may increase the sensitivity with a relative better specificity for detection of differentially expressed miRNAs ,50. Howenditions . Third, Finally, the simulated data results show that increasing the number of the biological replicates results in higher ROC curves and AUC values corresponding to higher detection power and lower false positive rate. However, due to the cost of RNA-seq data, the sample size of biological replicates was not considered by some of the earlier researchers using NGS technologies. One study by Hansen et al. in 2011 summarized a large number of published RNA-seq studies with a table showing that most of them had only one or a few biological replicates . The thond step of our methods we chose to scale the median across samples to be per 100 reads instead of per kilobase or per million reads which was used by RPKM or FPKM. Our justification for this choice of a scaling constant in Like many normalization and pre-processing procedures, our methods involve several choices of constants which we evaluated empirically. Primarily, in the 2Taken together, with the regards to all the discussed limitations, we think our proposed gene-wise normalization methods (Med-pgQ2 and UQ-pgQ2) might be a good choice for the skewed RNA-seq data with high variation via improving the false positive rate and maintaining a good detection power for DEGs analysis of RNA-seq data compared to the other normalization methods.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 FigedgeR. The top graph displays mean versus gene dispersion for genes with a quantile cutoff value of mean read count after UQ normalization of ≤ 90%, while the bottom graph displays mean versus gene dispersion for genes with a quantile cutoff value of mean read count after UQ.pgQ2 normalization of ≤ 90%.Gene-wise dispersion was estimated after UQ and UQ-pgQ2 normalization with the aid of (PDF)Click here for additional data file.S5 FigedgeR and assuming a negative binomial distribution. The top graph displays the CV for genes with mean read count after UQ normalization of ≥ 100, while the bottom graph displays the CV for genes with mean read count <100.The calculated coefficient of variation (CV) after UQ normalization and per-gene (UQ.pgQ2) normalization, based on the estimated dispersion parameter from (PDF)Click here for additional data file.S1 TableThe number of true positive (TP) and false positive (FP) genes, positive predictive value (PPV), the actual false discovery rate (FDR), sensitivity and specificity for Med-pgQ2 and UQ-pgQ2 methods are computed with a constant multiplication value  using MAQC2 data. In addition, we also reported the results from DESeq and TMM methods.(DOCX)Click here for additional data file.S2 TableThe number of true positive (TP) and false positive (FP) genes, positive predictive value (PPV), the actual false discovery rate (FDR), sensitivity and specificity for Med-pgQ2 and UQ-pgQ2 methods are computed from the MAQC2 data. The results are reported for the choice of the small positive values added to the read counts .(DOCX)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.S1 File(R)Click here for additional data file.S1 DatasetsRaw read count maqc2 data with 2 replicates in 2 conditions extracted from mapping files using Python command line This data contain 36451 genes and the genes with zero counts in both conditions are filtered out.(ZIP)Click here for additional data file.S2 DatasetsMed-pgQ2-normalized MAQC2 data using our R codes are included.(ZIP)Click here for additional data file.S3 DatasetsUQ-pgQ2-normalized MAQC2 data using our R codes are included.(ZIP)Click here for additional data file.S4 DatasetsDESeq package are included. DESeq-normalized MAQC2 data using (ZIP)Click here for additional data file.S5 DatasetsedgeR package are included.TMM-normalized MAQC2 data using (ZIP)Click here for additional data file.