The advancement of high-throughput RNA sequencing has uncovered the profound truth in biology, ranging from the study of differential expressed genes to the identification of different genomic phenotype across multiple conditions. However, lack of biological replicates and low expressed data are still obstacles to measuring differentially expressed genes effectively. We present an algorithm based on differential entropy-like function (DEF) to test for the differential expression across time-course data or multi-sample data with few biological replicates. Compared with limma, edgeR, DESeq2, and baySeq, DEF maintains equivalent or better performance on the real data of two conditions. Moreover, DEF is well suited for predicting the genes that show the greatest differences across multiple conditions such as time-course data and identifies various biologically relevant genes. Next-generation sequencing (NGS) technology has rapidly become the tool for many genome-wide transcription studies. Production of millions or billions of short sequences from individual RNA molecules together with lower costs and higher budgets have enabled many methodologies, such as RNA sequencing (RNA-Seq) . RNA-SeqThe theoretical methods for the study of RNA-Seq data can be grouped into two major categories. The first representative methods are limma , edgeR ,13,14, DIn this paper, we introduce an approach based on the differential entropy-like function (DEF), an algorithm for discovering the differentially expressed genes across multiple conditions. From the theoretical point of view, the importance of the proposed method derives from its information-theoretic background. On the one hand, DEF allows the recognition of differentially expressed genes on multiple conditions such as time-course data and multiple tissues data. Compared with the popular alternative methods, DEF obtains a wider application. On the other hand, DEF is effective in detecting the differentially expressed genes with the characteristic that it does not rely on the probability distribution. Another characteristic of DEF is its adaptability on zero expressed gene counts, which could give rise to the other troubling aspect that many hypothesis test methods fail. The paper is organized into the following sections. P values , FDR (baySeq) or an entropy-like value (DEF). P Value or FDR less than 0.05 was considered to indicate statistical significance. The cut-off values of DEF were 0.05  samples , and appIn this study, we performed a detailed comparative analysis of DEF method with several other methods for differential expression analysis by RNA-Seq data. For these methods, we focused on the specialty of generalized log-cpm normalization, especially conditions on low expression read counts. Small sample statistical analysis is still a tough task, while our proposed method could avoid the bias to some extent. Low biological or technical replicates in each sample could cause inaccuracy on the distribution-dependent methods. In contrast to other approaches, our model implies that the numbers of read counts are not entirely distinct, but they are connected between samples of replicates. This is a form of information sharing between genes and between samples, which are made possible by calculating the differential entropy-like function of the proposed model. The important contribution of this study is the solution of zero counts when performing differential expression analysis. Zero expressed gene counts give rise to the further troubling aspect that many hypothesis test methods fail. However, our model successfully handles the two dilemmas. We emphasize that the difference does exist when one sample has zero read counts while the other does not. DEF not only shares many differentially expressed genes but also detects additional differentially expressed genes. Additionally, we studied the effect of the standard deviation on the gene-wise mean expression level. DEF is also faster and more convenient, and converts RNA-Seq data into a form that can be analyzed using a single value of a gene. It is demonstrated that combining generalized log-cpm normalization with provided DEF function can lead to a more powerful analysis to other alternatives, such as either parameter-dependent method or some other distribution-dependent method on their own. The last and the most meaningful contribution of our DEF method is its application on variable biological conditions with multi-sample and time-course data. The biological basis for diversity in gene expression between different conditions is likely to be complex. Analysis of multiple samples and time-course data helps to expose the biological bases underlying tissue diversity. The analysis of data from the same or different populations studies that each contained some population replicates showed us that the differentially expressed genes made by our strategy were likely to be biologically meaningful, as their phenotypes do have to distinguish among the gene set we test.DEF performed as well as existing RNA-Seq methods, especially when the gene-wise expression was low. Meanwhile, DEF performed effectively on multiple conditions such as multi-sample and time-course RNA-Seq data. One characteristic of DEF is its independence of the statistical distribution of feature counts. Most methods for gene expression differential analysis are based on the negative binomial model, which seems unreasonable sometimes. Low replicates in each group cause inaccuracy in the distribution-dependent methods and zero expressed gene counts give rise to the other troubling aspect that many hypothesis test methods fail. However, DEF based on information theory successfully deals with the two issues. Following the previous studies, in differential expression analysis, an important task is to identify the genes that are expressed at higher variability across multiple samples without prior information. Accumulations of comparative studies for multi-sample data are desired, which is what we have always been focused on. DEF could work well with multiple conditions even with few replicates. The different degree of genes could be ordered by their relative DEF values. Moreover, we expect our work to inspire and support further theoretical research on modeling gene expression data and we believe that our software, DEF, will prove to be a useful addition to the existing methods for the statistical analysis of RNA-Seq and similar types of data. Some cell types will be more similar to each other, which will pose more challenges for meta-data analysis. Nevertheless, we speculate that the current method can be applied to single-cell data and comprehensive evaluations are the subsequent tasks.http://bowtie-bio.sourceforge.net/recount/. We used the R package “recount” to get each count table combined with sample phenotype data. All datasets were obtained from the “recount” online resource n RNA samples whose sequence reads have been summarized according to the number mapping to each gene. The raw matrix Y with elements j, i in sample j. However, log-cpm values could be negative, which is a problem in some specific conditions. Besides, the normalization size k played an important role to ensure that Normalization of the count data is a crucial step in the analysis of RNA-Seq data, which has a strong impact on the detection of differentially expressed genes. A normalization strategy called generalized log-cpm was used. The log-cpm method is a well-accepted normalization step of gene expression by dividing the corresponding library size (in millions) of each read count shown in voom method . Specifii is expressed across all samples with reads count 1 after normalization, that is i is not differentially expressed by the differential entropy-like function. This agrees with the fact gene i is not differentially expressed across the samples. For another example, gene i is expressed differentially with read counts 1, 2, 3 after normalization across three samples. Then,H is, the greater the expression difference among n samples is. For each gene, we calculated a differential entropy-like function value, which permits the direct estimation of the degree of expression. For each gene, its difference degree across multiple samples can be quantified by DEF value. This gene was defined as a differential expression when H was larger than a reasonable threshold; otherwise, it was judged as non-differential expression gene. We implemented the method presented in this article in the software package DEF, which was written in R language. A software implementation is available from https://github.com/xiaoxiaoxier/DEF. DEF expects a matrix of unnormalized count data as input and the output of the analysis is the IDs of the differentially expressed genes. When using DESeq2, edgeR and bayseq for comparison, all parameters were left at their default values. For baySeq, we took 5000 samples for estimating the priors with the quasi-likelihood approach.Subsequently, a novel method based on the differentially entropy-like function was used for detecting the differentially expressed genes across multiple samples in RNA-Seq data. Internally, DEF uses the normalization method generalized log-counts per million, which is a simple and reasonable scale for normalization by providing scale factors to make counts comparable between different samples. For the normalized read counts matrix


The SLS model could not only distinguish patients with different recurrence and mortality risks through univariate analysis, but also served as an independent factor for relapse‐free and overall survival. Compared with the conventionally used TNM stage system, the SLS model clearly possessed higher predictive accuracy. Moreover, the SLS model also effectively screened chemotherapy‐responsive patients, as only patients in the low‐SLS group could benefit from adjuvant chemotherapy. The following cell infiltration and competing endogenous RNA (ceRNA) network functional analyses further confirmed the association between the SLS model and stromal activation‐related biological processes. Additionally, this study also identified three phenotypically distinct colon cancer subtypes that varied in clinical outcome and chemotherapy benefits. In conclusion, our SLS model may be a significant determinant of survival and chemotherapeutic decision‐making in colon cancer and may have a strong clinical transformation value.The heterogeneity in prognoses and chemotherapeutic responses of colon cancer patients with similar clinical features emphasized the necessity for new biomarkers that help to improve the survival prediction and tailor therapies more rationally and precisely. In the present study, we established a  The surgical adjuvant chemotherapy (ADJC) based on combination of oxaliplatin, fluorouracil and leucovorin is an effective option in clinical practice of patients with stage II‐III colon cancer,Accumulating evidence has suggested that genetic difference of tumours is the major cause of heterogeneous anti‐cancer drug response.http://www.ncbi.nlm.nih.gov/geo/), which consisted of the transcriptome profile data of 988 samples. To establish a lncRNAs‐based model that could be used for improving the relapse risk prediction and tailoring therapies, we specially identified lncRNAs that were significantly associated with both cancer prognosis and biological processes including angiogenesis, hypoxia, TGFβ signalling and epithelial‐mesenchymal transformation (EMT), which have been well‐studied in multiple solid tumours, and defined as important stroma‐related factors mediating tumour metastasis and drug resistance, including in colon cancer.Here, we downloaded five datasets of colon cancer derived from Gene Expression Omnibus , defined as the interval between the date of diagnosis and date of tumour relapse, and overall survival (OS), defined as the interval between the date of diagnosis and death.The workflow chart is shown in Figure 2.2As all samples involved in the downloaded colon cancer datasets were hybridized to Affymetrix HG‐U133 Plus 2.0, the raw microarray data were all renormalized using a robust multiarray averaging method with ‘affy’ and ‘simpleaffy’ packages. The ‘ComBat’ algorithm was applied to reduce the likelihood of batch effects from non‐biological technical biases. The lncRNA annotations were collected from three sources: (a) Based on the ‘Comprehensive gene annotation’ file provided by the GENCODE website, we screened the official gene annotation documents of the Affymetrix HG‐U133 Plus 2.0 platform for the lncRNA gene; (b) all GPL570 platform probes were mapped to the ‘Transcript sequences’ file downloaded from the GENCODE website using SeqMap and re‐annotated with ensembl ID.2.3P value < .001) were considered lncRNA‐related PCGs, and these genes were entered into the Gene Ontology (GO) enrichment analysis to determine the lncRNA function. Finally, we chose 50 lncRNA‐related PCGs with the highest correlation as representatives to draw the triple ceRNA network using Cytoscape software, to show the interaction between genes more clearly.lncRNA function was explored using the triple competing endogenous RNA (lncRNA‐miRNA‐mRNA) network2.4Gene set variation analysis is the most often used method to estimate biological process activity.2.5To quantify the infiltration levels of stromal cells located in tumour microenvironment, the single‐sample gene set enrichment analysis (ssGSEA) method was applied using the ‘GSVA’ package.2.6The least absolute shrinkage and selection operator (LASSO) regression is a commonly used method for feature selection and has been applied to the Cox proportional hazard regression model.2.7t test (two groups) and one‐way ANOVA test (more than two groups) were used for normally distributed data comparison; otherwise, the Mann‐Whitney U test (two groups) and Kruskal‐Wallis test (more than two groups) were performed. Nomogram construction and validation were performed following Iasonos’ guide.P values were two‐tailed. Statistical significance was set at P < .05.The ‘CancerSubtypes’ R package was used for identifying molecular cancer subtypes based on aggregating multiple genomic platform data.33.1P value < .05). Among them, 372 lncRNAs were significantly correlated with RFS via the Cox univariate regression analysis in the entire cohort. Next, we used 1000‐times bootstrapping to test the predictive robustness of the above 372 genes as we mentioned in ‘Methods’ section. Finally, 82 lncRNAs, the expression levels of which were stably and significantly correlated with prognosis, were identified and defined as robust prognostic stroma‐related lncRNAs  and validation cohorts (n = 296) as described in the ‘Methods’ section. Comparison of patient characteristics between the two groups showed no significant differences   cells, Th17 cells and Th2 cells, which acted as protective prognostic factors. Exploring the clinical indications of SLS showed that male, advanced stages, relapse or death, proficient mismatch repair status and chromosome instability‐positive patient characteristics all had significantly raised SLS scores  landscape was evaluated. Using unsupervised clustering, we identified four distinct TME infiltrated cell clusters Figure A, and ths Figure C. In ters Figure D. Finalls Figure E to furts Figure F, includ3.5In a previous study, we identified 100 robust prognostic TME genes and found that a panel consisting of these genes could not only precisely predict the relapse and mortality risks in patients with colon cancer, but also discriminate patients who would potentially benefit from ADJC.3.6http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254 database to verify whether it was also correlated with relapse and ADJC treatment effect in patients with gastric cancer. The clinical characteristics of this cohort were also listed (see Table http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254 cohort (see Table We also analysed the role of the SLS model in the 4The rapid development of high‐throughput technology has enabled researchers to study cancer heterogeneity more deeply and comprehensively, and identify ideal molecular biomarkers that could be used to precisely predict prognosis and guide treatment. Among them, lncRNAs, which possess tissue‐specific and cancer‐specific expression patterns and play essential roles in tumorigenesis and cancer progression,http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39582 dataset, we noticed that only patients in a low‐SLS group could benefit from ADJC, which indicated that the SLS model could effectively screen patients who are chemotherapy‐responsive. We also uncovered a difference in SLS value between FOLFIRI‐sensitive and FOLFIRI‐insensitive groups, which further implied that our SLS model had the potential to differentiate between chemotherapy benefits in either adjuvant or metastatic settings. These findings suggested that the primary chemotherapy resistance caused by high activation levels of stroma‐related biological process could be one reason for the unpromising prognosis of high‐SLS patient; the development of new therapeutic models by a combination of chemotherapy and molecule‐targeted drugs that could inhibit activation of specific stroma‐related pathways might be helpful for improving their curative effect. Moreover, the SLS signature also showed excellent performance in recurrence prediction and the identification patients for whom ADJC may be more beneficial in gastric cancer, indicating a broad applicability of this signature in gastrointestinal cancer. Taken together, our results suggest that the SLS signature may be a significant determinant of survival and chemotherapeutic decision‐making in colon cancer and have a strong clinical transformation value. Of note, due to limitations of the sample size in the datasets, additional prospective studies are required to further verify our findings.To construct an lncRNA‐based model that could provide information on patient prognosis and therapeutic benefit, we first used correlation analysis to obtain 2654 lncRNAs that were significantly associated with the biological processes that trigger tumour metastasis and chemotherapy resistance, including angiogenesis, hypoxia, EMT and TGFβ signalling activation.In this study, we also constructed a new colon cancer molecular subtype by SNF‐CC method that aggregates the expression data of 52 lncRNAs and 100 robust prognostic TME genes identified in our previous study.Among the 52 lncRNAs involved in the SLS signature, 14 have been experimentally demonstrated to be linked with cancer. Therein, four lncRNAs including CYTOR, MEG3, MIR100‐HG and MIR31HG have been previously reported to play an oncogenic role in colon cancer. Wang et al reported that CYTOR could directly interact with NCL and Sam68, and the latter two further activated the NF‐κB pathway and EMT to contribute to colon cancer progression.Limitations to our study include the following: First, as this study was conducted based on publicly available datasets, some important clinicopathological features were not available for analysis and could lead to potential error or bias. Second, all colon cancer transcriptome profiling collected in the present study was produced by the GPL570 platform; the lncRNA candidates identified here may not represent the complete lncRNA populations underlying colon cancer biological behaviour, owing to the fact that the GPL570 platform only contains a small part of all possible lncRNAs. Moreover, the lncRNAs that were identified by re‐annotation of probes should also be validated by further studies. Finally, as mentioned above, the biological function of some lncRNAs involved in SLS signatures still requires experimental verification.5In conclusion, our study developed a novel lncRNA signature that can be used as a reliable tool for personalized prognosis prediction and for treatment decision‐making in patients with colon cancer. Further prospective clinical trials are warranted to validate our findings.The authors declare that they have no competing interests.RZ, HS and WL contributed to the planning of the study and drafted the manuscript. ZS participated in manuscript revision and verified the numerical results by an independent implementation. RZ, HS, JZ, DZ and JW prepared all the figures and tables. JW, ZH, XR, JB, YL and MS contributed to interpretation of data and reviewing of the manuscript. All the authors reviewed and approved the final manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Sarcoidosis is often misdiagnosed as tuberculosis and consequently mistreated owing to inherent limitations in histopathological and radiological presentations. It is known thatthe differential diagnosis of Tuberculosis and Sarcoidosis is often non-trivial and requires expertise and experience from clinicians. Therefore, it is of interest to describe amultilayer neural network model to differentiate pulmonary tuberculosis from Sarcoidosis using signal intensity data from blood transcriptional microarray. Genes that aresignificantly upregulated in Pulmonary Tuberculosis and Sarcoidosis in comparison with healthy controls were used in the model. The model classified Pulmonary Tuberculosis andSarcoidosis with 95.8% accuracy. The model also helps to identify gene markers that are differentially upregulated in the two clinical conditions. Tuberculosis (TB) is among the ten commonest causes of death worldwide-2. DespiSarcoidosis is a chronic, multisystemic granulomatous disease that involves abnormal collection of inflammatory cells forming lumps beginning in the lungs, skin or lymph nodes, whichis of unknown aetiology and a close clinical mimic of TB. Sarcoidosis and PTB are both granulomatous, exhibit clinico-radiological presentations of great similarity, which makes differentialdiagnosis a huge challenge in countries with high prevalence. -10 A DNAAn extensive systematic search of all literature pertaining to gene expression in Mycobacterium tuberculosis was performed by searching the Gene Expression Omnibus (GEO) datasets(as on April 2016) using the algorithm as given below. G1 AND (("M" OR "m" OR "S" AND (H OR h))G2 AND (("M" OR "m" OR "S" AND (H OR h))G3 AND (("M" OR "m" OR "S" AND (H OR h))Where,G1 = Gene expression; G2 = Expression array; G3 = Microarray; M = Mycobacterium tuberculosis; m = TB; S = Sarcoidosis; H = Homo sapiens; h = humanThe query lexicon was restricted to Homo sapiens so that only datasets from human studies would be included in the study. Only those studies in the English language were included forfurther analysis. On the basis of simple scoring algorithm the confidence of Literature mining is tested.Gene expression profiling of a data is the measure of the activity/expression of thousands of genes at once to analyse a wide range of cellular functions taking place in an organism.These profiles can distinguish an actively multiplying cell or to check the expression levels of cells that have reacted to the drug treatment. Many transcriptomics technologies are involvedin measuring the expression levels of the genes, in a cell, which is expressed at a given time frame by quantifying the mRNA levels. This can encompass several thousand genes at the sametime or sometimes even the entire genome. This can yield vital information on the activity, functions of the gene of our interest.  The GEOThe dataset comprised of 61 healthy human controls, 47 humans with EPTB, 45 human with PTB and 49 humans with Sarcoid included. The present study EPTB was excluded as it was notcomplex in diagnosis and was not much significant and viable. The gene expression profiling values were log(base2) transformed and percentage shift normalization was performed. Thefold change differences in gene expression between normal and disease samples were calculated for each gene separately. The mean differences between the samples were tested using ANOVAand the significance level is fixed at 4% level. A cut off value of 1.5 fold change was used to classify upregulated genes. -19TablMultilayer Perceptron to classify PTB, sarcoidosis, and Healthy controls were built by taking top upregulated genes from gene expression profiling of GSE83456 as covariates (inputlayer) and classifying into three groups The Receiver Operating Curve (ROC) was employed for evaluating the goodness of the values predicted by the developed MLP models. In a ROC curve true positive rate (sensitivity) isplotted in function of the false positive rate (100-specificity) for different cut-off points of a parameter. Each point on the ROC curve represents a sensitivity/ specificity paircorresponding to a particular decision threshold. The area under the ROC curve (AUC) is a measure of how well a parameter can distinguish between two diagnostic groups .In the current study the diagnostic groups were classified as Healthy, PTB, and Sarcoidosis. One of the reliable ways of evaluating the performance of the classifiers is through accuracy.The accuracy of a test is its ability to differentiate the diseased and healthy samples correctly. To estimate the accuracy of a test, we should calculate the proportion of true positiveand true negative in all evaluated samples.(i)True positives (TP)-correctly classified positive samples.(ii)True Negative (TN)-correctly classified negative samples.(iii)False positives (FP)-Misclassified negative samples.(iv)False Negative (FN)-Misclassified positive samples.ROC curves describe the relation between two indices namely (i) True Positive fraction (TPF) (ii) False Positive fraction (FPF).ROC curves plot TPF (sensitivity) vs FPF (1-specificity) for every possible decision threshold imposed on the decision variable. Generally the Area under the ROC curve (AUC) is usedas a measure of performance. One may be interested to compare TPF and FPF to learn the performance of two clinical diagnostic models where two ROC curves cross. In statiIn binary classification of statistical analysis, the F1 score is the harmonic mean of precision and sensitivity  is a measure of a test's accuracy. P is ratio of the numberof correct positive results and the number of all positive results returned by the classifier, and r is ratio of the number of correct positive results and the number of all relevantsamples where all the samples are identified as positive. The F1 score is considered to be at its best when its value measures 1  and worst at 0. -22Tuberculosis is classified into Pulmonary Tuberculosis, which affects the lungs, and Extra pulmonary, which affects the other parts of the body except for the lungs. Sarcoidosis isvery similar to TB with common symptoms and clinical manifestations; it is often misdiagnosed and mistreated. The clinicoradiological evidence supported by laboratory finding andclinical acumen of the physician can help in the precise diagnosis of the two. It is coTo compare different classification models, Receiver Operating Characteristic (ROC) curves were constructed and the Area under Curve (AUC) was calculated for the two models. The high AUC value is related to high accuracy rate. In ROC space X-axis is Specificity and Y-axis is sensitivity. At the standardized specific threshold, the modeloutputs specificity (100%) and sensitivity (95.8%), to draw a point in ROC space. All the points' Healthy control, PTB and Sarcoidosis converge into ROC curve. The ROC curve revealedhighly significant classifying ability among the disease diagnosis.  ThereforThe aim of the experiment was to (i) distinguish Sarcoidosis from PTB and healthy patients using machine-learning algorithm to classify the disease based on the signal intensity;(ii) possible gene signature were found to check for the overlapped genes whose expression level determines the severity of the disease to establish a easy diagnosis and provides anless opportunity to make erroneous diagnosis. Koth et al. analysed blood transcript using machine learning to look for the overlapping genes in sarcoidosis and PTB. By reapplicationspecific genes for sarcoidosis and PTB were identified. They analysed using three independent machine-learning algorithms:random forests, shrunken centroids ,and elaThe samples collected from the diseased and/or control subjects were processed to obtain expression data at different times. The supervised machine learning classification methodsare employed to discriminate the expression data of the patients having the disease or not having the disease. The testing training data may be obtained by any of the suitable machinelearning classification methods which is typically used to determine the sensitivity, specificity and/or accuracy of the multilayer perceptron model which is capable of determiningwhether said data is indicative of pulmonary tuberculosis or Sarcoidosis. In our study the MLP model showed the generalised classification with an accuracy of 98.6%, specificity of100%, and sensitivity of 95.83% when optimised for accuracy. There were several other methods as per the literature are compared further.Yuanli Wu et al. attempteThis study provides a quick diagnosis and accurate differential classification of Tuberculosis vs Sarcoidosis versus healthy tissue. Our model provides considerable scope to targettherapy appropriately to patients with Sarcoidosis misdiagnosed as tuberculosis, and thus avoids mistreatment. Further studies are needed to validate the efficacy of the machine learningmodels in different populations. We describe a multilayer neural network model to differentiate pulmonary tuberculosis from Sarcoidosis using signal intensity data from blood transcriptionalmicroarray.


NUDT21 knockdown Poly(A)-ClickSeq (PAC-seq) data, and our own mouse hippocampal and human stem cell-derived neuron PAC-seq data—strongly supports the value and protocol-independent applicability of PolyA-miner. Strikingly, in the glioblastoma cell line data, PolyA-miner identified more than twice the number of genes with APA changes than initially reported. With the emerging importance of APA in human development and disease, PolyA-miner can significantly improve data analysis and help decode the underlying APA dynamics.Almost 70% of human genes undergo alternative polyadenylation (APA) and generate mRNA transcripts with varying lengths, typically of the 3′ untranslated regions (UTR). APA plays an important role in development and cellular differentiation, and its dysregulation can cause neuropsychiatric diseases and increase cancer severity. Increasing awareness of APA’s role in human health and disease has propelled the development of several 3′ sequencing (3′Seq) techniques that allow for precise identification of APA sites. However, despite the recent data explosion, there are no robust computational tools that are precisely designed to analyze 3′Seq data. Analytical approaches that have been used to analyze these data predominantly use proximal to distal usage. With about 50% of human genes having more than two APA isoforms, current methods fail to capture the entirety of APA changes and do not account for non-proximal to non-distal changes. Addressing these key challenges, this study demonstrates PolyA-miner, an algorithm to accurately detect and assess differential alternative polyadenylation specifically from 3′Seq data. Genes are abstracted as APA matrices, and differential APA usage is inferred using iterative consensus non-negative matrix factorization (NMF) based clustering. PolyA-miner accounts for all non-proximal to non-distal APA switches using vector projections and reflects precise gene-level 3′UTR changes. It can also effectively identify novel APA sites that are otherwise undetected when using reference-based approaches. Evaluation on multiple datasets—first-generation MicroArray Quality Control (MAQC) brain and Universal Human Reference (UHR) PolyA-seq data, recent glioblastoma cell line  Advances in sequencing techniques have improved our understanding of the transcriptome and unraveled new mechanisms of complex diseases. However, several critical aspects of transcriptome diversity are underexplored. In the mRNA maturation process, the 3′end of precursor mRNA (pre-mRNA) is cleaved and a poly(A) sequence is added. In eukaryotes, all pre-mRNA molecules except histones undergo polyadenylation . In humaExpressed sequence tags (EST)  were iniWith the explosion of emerging technologies, we need new analytical methods. In spite of increasing significance of APA and 3′ sequencing (3′Seq) techniques, there are no computational tools that are designed precisely for 3′Seq data. Several studies have analyzed conventional RNA-seq data to infer alternative polyadenylation changes. DaPars  uses a fde novo differential alternative polyadenylation detection algorithm based on non-negative matrix factorization (NMF)  data   and vecton (NMF) . It is aon (NMF) . It provon (NMF) . The curon (NMF) . This iseq) data , and our‘sam’ format are converted to ‘bam’, sorted and indexed using samtools (Irrespective of sequencing protocol nucleotide composition is biased at the beginning of reads due to random hexamer priming while amplifying cDNA . To imprsamtools .md are merged (md can be adjusted based on polyA resolution supported by the respective sequencing protocol used. Since 3′ sequencing methods use poly(dT) primers—to bind the mRNA poly(A) tail—they can also bind stretches of adenines within the body of the mRNA, resulting in sequencing reads that do not align with the cleavage site  sites are extracted from alignment files as per base coverage features/peaks using the genomecov module in bedtools Figure . A compre merged . Parametage site . We callage site . Typicalage site . HoweverM reads per site in at least one test group  , APA sitnditions . To consered out .a priori information of sample clustering, i.e., a specified set of control and treated samples. The key here is to test the agreement between the clustering consensus of an APA matrix and the ground truth of a priori sample labels, which is essentially a factorization problem. Non-negative matrix factorization (NMF) is an unsupervised clustering paradigm that has previously been demonstrated for multivariate decomposition  is given by:α > 0, β > 0 and Co-clustering frequencies from iterative NMF are modeled using a beta distribution . Beta diα,β are estimated using the moments method.We model co-clustering frequencies from CM matrix as a beta distribution. The parameters The log likelihood function of a beta model measure how well they fit the underlying data and is given by:L and inter-group L and null L. Intra-group likelihood is modelled on the co-clustering frequencies of samples with the same a priori condition (control or treated), the inter-group likelihood is modeled on the co-clustering frequencies of samples from different conditions (control and treated), and a null distribution is modelled on the whole CM matrix. Differential polyadenylation is tested by evaluating the goodness-of-fit of the respective intra-group and inter-group co-clustering frequencies over the null model. A likelihood ratio test statistic is computed as the ratio of a simpler null model s to a complex alternative model g.2 distribution with 2 degrees of freedom and the P value is computed accordingly  PAC-Seq data (SRP172550). This dataset is first reported in Chu et al. (2019) and was generated to help elucidate the contribution of NUDT21 dependent APA regulation in GBM progression. We examined several genes that showed high levels of mispriming. Seven alternative polyadenylation (APA) sites are annotated for the gene ATRX in PolyA_DB, a database of APA sites backed by sequencing evidence  internal priming of polyadenine stretches within the body of mRNA rather than the poly(A) tail. To evaluate the ability of PolyA-miner to filter misprimed sequencing reads, we applied our method to the Glioblastoma (GBM) LN229 cell line evidence . PolyA-mX Figure  includinX Figure . Similar and IDS . In addi and IDS , a remarNUDT21 knock down PAC-seq data . Both the mouse and human data had significantly high levels of mispriming and noise , similara Figure . These oNUDT21 KD samples than controls, suggesting global 3′UTR shortening . Among them, 844 (92%) and 14 were also detected by PolyA-miner with a total of 1504 3′UTR shortening and 58 3′UTR elongation changes  in g Figure . This obg Figure . Howeverg Figure  and B. Ts Figure , includiNUDT21 KD samples respectively. This high proximal and low distal pattern in NUDT21 KD samples illustrate 3′UTR shortening. Genes with three APA sites also show similar apparent differential usage patterns at the proximal (blue: low to high) and distal (green: high to low) sites  sites . TogetheNUDT21 loss  of the discordant predictions, i.e. the hits that are only predicted by either PolyA-miner or DPU. High clustering stability is observed for PolyA-miner only predictions Figure  with a mTUSC2  makes it less likely to be true. One of the NUDT21 KD sample is as high as controls at the most distal site and is significantly distant from the other two replicates  is high in MAPK1. Shorter the 3′UTR a transcript is more likely to lose miRNA binding sites and less likely to be down regulated. With the shortest dominant isoform MAPK1  over EGFR (−0.11). This ranking is critical for any downstream analysis that takes rank as their input, such as Gene Set Enrichment Analysis (GSEA).Typical differential analyses rank respective hits by mere fold change, but this is not appropriate for APA changes. For example, the gene s Figure . The proe Figure . Just by1 Figure  is more R Figure  with secNUDT21 biology  and human brain PolyA-seq datasets . PolyA-ss Figure . This reon sites . Predomion sites . This obPolyA-miner identified 2441 3′UTR elongated and 366 shortened genes . The DPUANKH  data strongly supports the value and protocol independent applicability of PolyA-miner. We demonstrated a substantial increase in both the number of dynamic APA events detected and novel APA sites using PolyA-miner. With the emerging importance of alternative polyadenylation in understanding development and diseases, PolyA-miner can significantly improve data analysis and help decode the missing pieces of underlying alternative polyadenylation dynamics.http://www.liuzlab.org/PolyA-miner/. The PAC-seq data are available in the NCBI Gene Expression Omnibus (GEO), accession number: GSE147661.PolyA-miner is implemented in Python and the source code is freely available at gkaa398_Supplemental_FilesClick here for additional data file.


Integrating phenotypic and genotypic information to improve prognostic prediction is under active investigation for lung adenocarcinoma (LUAD). In this study, we developed a new prognostic model for event-free survival (EFS) and recurrence-free survival (RFS) based on the combination of clinicopathologic variables, gene expression, and mutation data.We enrolled a total of 408 patients from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project for the study. We pre-selected gene expression or mutation features and constructed 14 different input feature sets for predictive model development. We assessed model performance with multiple evaluation metrics including the distribution of C-index on testing dataset, risk score significance, and time-dependent AUC under competing risks scenario. We stratified patients into higher- and lower-risk subgroups by the final risk score and further investigated underlying immune phenotyping variations associated with the differential risk.p value < 0.001). The higher-risk patients were characterized with transcriptional aberrations of multiple immune-related genes, and a significant depletion of mast cells and natural killer cells.The model integrating all three types of data achieved the best prediction performance. The resultant risk score provided a higher-resolution risk stratification than other models within pathologically defined subgroups. The score could account for extra EFS-related variations that were not captured by clinicopathologic scores. Being validated for RFS prediction under a competing risks modeling framework, the score achieved a significantly higher time-dependent AUC as compared to that of the conventional clinicopathologic variables-based model (0.772 vs. 0.646, We developed a novel prognostic risk score with improved prediction accuracy, using clinicopathologic variables, gene expression and mutation profiles as input, for LUAD. Such score was a significant predictor of both EFS and RFS.This study was based on public open data from TCGA and hence the study objects were retrospectively registered. Lung cancer is the most frequently diagnosed cancer and the leading cause of cancer death, with a total of 2,093,876 new cases  and 1,761,007 deaths  reported worldwide in 2018 [The high-throughput sequencing technology has made it possible a comprehensive interrogation of whole transcriptome and genome of tumor tissues at an increasingly reasonable cost , 5. PrevIn this study, we aimed to improve prognostic prediction for LUAD by extensively integrating clinicopathologic variables, gene expression and mutation profiles as the input. We focused on the analysis of recurrence and death events as there exists minimal ambiguity in the database about the derivation of these outcomes . We beliThe study enrolled from the Cancer Genome Atlas lung adenocarcinoma (TCGA-LUAD) project  a total As shown in Fig. We downloaded the gene expression and somatic mutation detection data generated by TCGA group  for thisp value. We set the p value (unadjusted for multiple testing) cutoff as 3e−04 and 0.08 for gene expression and mutation, respectively. We then used lasso Cox regression [We applied univariate Cox regression model for feature pre-selection of the gene expression or mutation data. The model was fitted between each feature and EFS, and the importance of feature was determined by their ward test gression  to develFor model development, we randomly split the study cohort into training (285 patients) and testing (123 patients) sets. The input data for prediction model were prepared in following ways  to calculating the cumulative probability of each cause. The computational formula of CIF is given byWe also used competing risks modeling to evaluate the significance of the risk score as a univariate predictor for RFS events. As sketched in Fig. λk(t) is the hazard of cause k at time t, where y method  to imposβ is a vector of coefficients and Z is a matrix of covariates. Individuals who fail from another cause are remained in the risk set for where timation . The timtimation  was comptimation . All anaFor differential gene expression analysis, we used the negative binomial generalized linear model with tag-wise dispersion in R package edgeR . The rawp values. All p values were corrected by Benjamini-Hochberg procedure to control the false discovery rate (FDR) and to obtain the adjusted p values [We used CIBERSORT software to deconvolve the relative fractions of different immune cell types from the RNA sequencing data . To infep values .The study workflow was sketched in Fig. For model input data integration, we prepared 5 sets of features selected from single type of data (single type features), 4 sets of features combined from different types of data (combined features), and 5 sets of features incorporated with interaction terms generated within (intra-type) each type of data or between (inter-type) different types of data (combined and interaction features). The size of each feature space was summarized in Table Sp value < 0.001), while for mutation profiles, the mean of prediction accuracy increased by 0.013 . These indicated the benefit of feature pre-selection step.We first compared the prediction accuracy of models developed based on single type of input features. The performance of models based on clinicopathologic variables was the best with a C-index of 0.624 ± 0.028 on the testing set Figure S. To assep value < 0.001; Figure SWe next compared prognostic models that integrate different types of input data. The best prediction model was the model combining three types of input data, which achieved a significantly higher mean C-index 0.639 ± 0.033) on the testing data as compared to the clinicopathologic model (9 ± 0.033β). We named this score as the mul-score  close to the mean of final data integration as described above, and calculated the linear combination of coefficients and features from the model as the event-risk score , we fitted Cox proportional hazard models by setting the score as the single covariate on the testing set. For a fair comparison, we computed the cln-score from a model with a C-index (0.622) also close to the mean of clinicopathologic variables-based data integration. The g Table S. When a g Table S. This sup values. We set the score median within each group as the threshold to stratify for the higher- and lower-risk subgroups. The mul-score generated a more striking stratification within each set of patients as compared to the cln-score .We next assessed the significance of proposed risk score as a predictor for RFS. We used competing risks models for the assessment because a total of 45 death events without recurrence observed in the cohort can act as the competing event for recurrence risk analysis Fig. a. Ignorin = 204) and lower-risk (n = 204) patients as determined by mul-score. A total of 7250 genes was differentially expressed, and 2 GOs were identified as significantly enriched: extracellular matrix organization  and extracellular structure organization .To explore the variations of immune phenotyping associated with differential prognostic risk, we performed differential gene expression analysis between the higher-risk  . We furtK1, JAG1 –36 were elopment , was dowWe then used computational deconvolution methods to investigate the variations of immune cell compositions between the two risk subgroups. As shown in Figure Sp values of the score indicated that the one generated from the best combinatorial model captured a wider spectrum of EFS-related variations. Moreover, the proposed score provided a higher-resolution EFS stratification for pathologically defined subgroup of patients and showed superior time-varying RFS prediction power than conventional clinicopathologic methods . The higher-risk subgroup determined by the score was characterized with RNA expression aberration of multiple immune-associated genes and depletion of activated natural killer cells and mast cells.Our study developed and validated a new prognostic model integrating clinicopathologic variables, gene expression, and mutation data as the input. We used testing datasets to show that the model achieved a higher level of accuracy of EFS prediction than models based on any other input data integrations. Adding interaction covariates to prognostic models showed limited benefits on improving prediction power. We further compared at the level of risk score computed from these models. The univariate model fitting Integrating different types of data is an effective way to improve prognostic prediction. In this study, the model integrating clinicopathologic variables, gene expression, and mutation achieved the best performance in multiple evaluation metrics. Our conclusions were consistent with previous studies. For example, Chen et al.  integratOur study also suggested new therapeutic opportunities for the higher-risk patients. For example, we discovered that the exhaustion of innate immunity components was correlated with prognostic risk. The natural killer cells are lymphocytes that can recognize transformed cells via surface receptor interaction. It has been shown in a recent publication  that theOur proposed score included the expression profile of 13 genes and somatic mutation profile of 10 genes. We recognized that this is a relatively large panel of testing which involves both expression and mutation measurements. However, there already exists multi-panel testing technologies that can be readily translated for the score. For example, a 21-gene expression panel (Oncotype DX) based on qRT-PCR platform has already been made for clinical use to inform breast cancer treatment . For mutOur study has limitations. First, the combinatorial models developed in this study were based on features already selected by models based on individual type of input data, using the same training dataset. This introduced more overfitting and could possibly cause the failure of selecting truly important combinatorial models. Second, the feature pre-selection method remains to be improved. We performed univariate Cox analysis to pre-select important features, and this method only provided a slight improvement for models based on single-type variables. Third, the EFS outcome we defined in this study included death from any causes. We recognized that including death not related to lung cancer could bias EFS estimates, but such detail information was not available from TCGA clinical dataset. We mitigated this by further evaluating the score on RFS analysis. Fourth, all analyses were performed on TCGA-LUAD dataset. More external validations should be made before considering clinical translation of the score.In summary, our study proposed a novel prognostic risk score integrating clinicopathologic variables, gene expression, and mutation data for LUAD. The score was useful for both EFS and RFS analyses.Additional file 1: Table S1: Summary of distribution and EFS association of clinicopathologic variables and genes with top 10 mutation frequencies used in the study. Table S2: Description of input feature sets and the size of feature space. Table S3: The summary of C-index, related to Figure S2. Table S4: The name and coefficient of features of the selected combinatorial model. Table S5: Summary of p values of the coefficients in models fitted with single or multiple risk scores on the testing datasets. Table S6: Summary of model fitting results (likelihood ratio test) and median survival time (in days) of higher- and lower-risk subgroups within each sets of patients. Table S7: Summary of 5 immune-related genes commonly identified as differentially expressed within all patients, stage IIB and IIIA subgroups. Table S8: List of the TCGA ID for the higher or lower-risk subgroup as stratified by our proposed score. Figure S1: The Kaplan-Meier curve for EFS of 408 patients enrolled in this study. Figure S2: The C-index of models developed based on single type of data (A) combined feature sets (B) and models with interaction covariates (C). The white box summarized C-index for models selected from cross-validation (1 standard error rule), while the gray box for those from testing. Abbreviations can be referred to Table S2. Figure S3: Kaplan-Meier curve of higher- and lower-risk subgroups stratified by cln-score within different sets of patients. The sets from A to F are: A, all patients; B, testing set; C, patients in AJCC pathologic tumor stage IA; D, patients in AJCC pathologic tumor stage IB; E, patients in AJCC pathologic tumor stage IIB; F, patients in AJCC pathologic tumor stage IIIA. Figure S4: Barplot summary of inferred relative fractions of cell types (A) and volcano plot summary for the significance of difference in immune cellular compositions between the higher and lower-risk subgroup patients (B).


Stratification of breast cancer (BC) into molecular subtypes by multigene expression assays is of demonstrated clinical utility. In principle, global RNA-sequencing (RNA-seq) should enable reconstructing existing transcriptional classifications of BC samples. Yet, it is not clear whether adaptation to RNA-seq of classifiers originally developed using PCR or microarrays, or reconstruction through machine learning (ML) is preferable. Hence, we focused on robustness and portability of PAM50, a nearest-centroid classifier developed on microarray data to identify five BC “intrinsic subtypes”. We found that standard PAM50 is profoundly affected by the composition of the sample cohort used for reference construction, and we propose a strategy, named AWCA, to mitigate this issue, improving classification robustness, with over 90% of concordance, and prognostic ability; we also show that AWCA-based PAM50 can even be applied as single-sample method. Furthermore, we explored five supervised learners to build robust, single-sample intrinsic subtype callers via RNA-seq. From our ML-based survey, regularized multiclass logistic regression (mLR) displayed the best performance, further increased by ad-hoc gene selection on the global transcriptome. On external test sets, mLR classifications reached 90% concordance with PAM50-based calls, without need of reference sample; mLR proven robustness and prognostic ability make it an equally valuable single-sample method to strengthen BC subtyping. The advent of technological platforms for global gene expression profiling has shown more clearly that BC classification and prognosis is not only determined by the commonly used clinical-pathological variables, but also by intrinsic molecular characteristics, which can be probed using molecular methods and genomic profile investigation. The acquired knowledge has improved BC patient management, providing more accurate prognostic stratification.Breast cancer (BC) is the most common cancer in women worldwide, and in about 80% of cases is invasive, i.e. it breaks through the walls of the glands or ducts where it originated and grows into surrounding breast tissue. Although it is generally referred to as a single disease, BC is heterogeneous in terms of histological composition, molecular features, risk factors, response to treatment, aggressiveness and clinical outcomes8. However, currently adopted prognostic tests consider a limited number of classifying genes; consequently, the effort to measure gene expression in a tumour sample does not exploit the wider information potentially available through Next-Generation Sequencing (NGS)-based global profiling of RNA expression (RNA-seq). Indeed, some recent works performed BC classification on RNA-seq data11, but they mostly considered only known marker genes and applied methods developed for previous technologies, like microarrays or PCR, without substantial modifications or adaptations. Conversely, Paquet et al.12 developed the Absolute Intrinsic Molecular Subtyping (AIMS), a bioinformatics approach to allow a reproducible single-sample classification of BC profiles, while, Raj-Kumar et al.13 proposed a Principal Component Analysis-based approach to improve consistency of subtyping, facing the issue of the non-complete coherence between IHC (immunohistochemistry) and gene expression defined estrogen receptor status; finally, Chen et al.14 implemented a deep-learning approach, called DeepType, to learn and cluster a BC gene expression data representation integrating supervised knowledge about subtypes.Based on gene expression quantification, various tests have been introduced in BC clinical practice over the last 10 years to provide molecular stratification and estimate the risk of relapse after surgery, as to avoid adjuvant treatment in low-risk cases8. To explore in detail the potential of RNA-seq in reconstructing a BC classification system originally developed with a different technology, we considered the so-called “intrinsic molecular subtypes” , which have become part of the common knowledge on the disease and are recognized as prognostically and therapeutically relevant7. Even if these groups firstly emerged by unsupervised hierarchical clustering on global microarray gene expression profiles1, BC classification into intrinsic subtypes is primarily achieved by measuring the expression of a set of only 50 genes, the so-called “PAM50 panel”15. PAM50 classification is obtained by comparing, for a given BC sample, relative expression of these 50 genes versus a reference sample, and by assigning the subtype based on the highest correlation with the five subtype centroids. Distances from all these centroids can be also used to compute a “Risk of Recurrence” (ROR) score, a prognostic indicator whose low value indicates unlikely relapse after surgery and the possibility to avoid post-operative chemotherapy15. The PAM50 assay has been extensively investigated by microarrays and quantitative PCR, and even converted into a Food and Drug Administration (FDA) approved predictive test called Prosigna, working on the Nanostring nCounter platform16. The prognostic value of the PAM50 method and its derivatives has been confirmed by independent studies19. Lately, also RNA-seq profiles have been used for PAM50 classification, mainly based on the algorithm15 developed by Parker et al.13. However, applying a microarray-based classifier to RNA-seq data may provide suboptimal results.Hence, huge dimensionality of exploitable data at reasonable and progressively lower costs, higher accuracy of the expression values, and the chance of multiple surveys on the same data are all crucial reasons of interest for the development of RNA-seq based BC stratification methods for clinical outcome prediction. Combining on a single RNA-seq profile more classifiers, based on different relevant gene signatures and algorithms, could strengthen the results; whereas, existing prognostic tests, examining distinct genes on different platforms, show limited concordance in identifying subgroups and good prognosis patients20.Therefore, here we analyzed the possible limits of the standard PAM50 algorithm when applied to RNA-seq profiles, and explored alternative robust strategies to assign intrinsic subtypes to BC samples, also based on supervised learners and feature selection methods starting from global RNA-seq expression data Fig. . Indeed,TCGA and PanCA expression profiles were subject to RSEM21 summarization and upper quartile normalization, while for both GEO datasets only FPKM22 expression values were available. From Principal Component Analysis (PCA), performed independently for RSEM and FPKM data, we noticed that subtype distributions were not overlapping on the two cases  of Estrogen Receptor-positive (ER+) and -negative (ER−) cases, as done for the original PAM50 training15. Therefore, calculating the reference from different subsets having the same ER+/ER− composition could yield discordant classification for some samples. To verify this hypothesis, we constructed ten alternative references for the TCGA dataset, from ten randomly chosen subsets of 400 samples matching the 60/40 ratio of ER+/ER− cases. Each reference was then used to compute Log2ratio values for all TCGA dataset samples, resulting in ten Log2ratio data matrices. As a technical control, one additional Log2ratio matrix was generated against a reference constructed from a subset of 262 samples of the TCGA dataset that were originally employed for the same task by Ciriello et al.9, and that had a 50/50 ratio of ER+/ER− cases. Then, ten other references and Log2ratio matrices were computed using random subsets having same dimensionality (262 samples) and ER+/ER− proportion (50/50) of the technical control subset. Eventually, PAM50 classification was applied to each Log2ratio matrix using the centroids disclosed by Parker et al.15, to assess the concordances of our classifications with the subtype calls published by Ciriello et al.9.For all datasets, results of the PAM50 classification performed by the original authors according to the standard method9 was almost completely concordant (99.27%). The minimal discordance can be attributed to the fact that 52 samples of the original set were not included within the available 817 sample TCGA dataset under study; therefore, our technical control was not completely identical to the reference of Ciriello et al.9. These results confirmed that the choice of the samples used to build the reference significantly affects subsequent subtyping, as much as the adopted reference is different from the one used for the disclosed PAM50 classification. Discordant classifications typically involved samples having comparable correlations with more than one subtype , which is independent of the numerosity of samples in each subtype. An AWCA can be built without the need of matching exactly a given proportion of cases; double averaging, indeed, equates all class contributions avoiding reference estimation to be corrupted by imbalance distribution of subtypes.Having proved that the reference building step affects PAM50 subtyping, we explored an alternative strategy for robust construction of the reference, to improve consistency and reproducibility of PAM50-based subtyping. This iterative strategy starts with a preliminary standard PAM50 classification, and then for each gene computes the average expression within each subtype, taking all the samples of the dataset classified as belonging to that subtype class. The so-obtained within-class mean values are then further averaged for each gene, to obtain a final reference expression value named “average of within-class averages”  and their resulting lack within several of the previously computed random subsets, we decided to exclude the within-Normal-like class averaging calculation from the reference computation. We obtained effectively 10 subtyping instances significantly more concordant with the already published calls  with the published one , and to build AWCA references on any expression data, even from other platforms, as we successfully experienced with microarray data from Affymetrix  in discriminating good and poor prognosis cases emerged from 10-year overall survival analysis. For further details, please refer to Supplementary Material Section Here, we report a comparative analysis of the Risk of Recurrence (ROR) scores computed for the TCGA and GSE96058 datasets, well representative of BC heterogeneity, to provide valuable classifiers while handling independently RSEM and FPKM mRNA-seq data  and Normal-like (25) classes, we built the training set including 50 samples for each Luminal A, Luminal B, Basal and Her2-Enriched class, plus 20 Normal-like samples, as to ensure a good balance of BC subtypes in the learning phase, while keeping out a reasonable number of samples for testing. Normal-like class should incorporate only samples from grossly uninvolved tissue and is not widely recognized as prognostically relevant6 nor used in ROR models16; thus, adding 20 of 25 samples in the training set was not aimed at recognizing this class, but rather at trying to strengthen the ability of the trained classifiers to distinguish other BC intrinsic subtypes from it. Moreover, comparing our classifiers based on whether and which and how many samples were classified as Normal-like, allowed us to better assess their subtyping capabilities after proper training. The GSE96058 dataset was split in a training set of 1,639 samples and a test set with the 1,634 remaining samples. The training set in this case respected the same subtype proportion of the entire dataset  supervised approaches were separately applied to the wide Decision Forest; (2) Decision Jungle; (3) Logistic Regression (LR); (4) Feed-Forward Neural Network (FFNN); (5) Support Vector Machines (SVMs). All the mentioned learners were trained in Azure Machine Learning Studio with known TCGA dataset. Indeed, estimating their performances with this huge and noisy feature space offered useful insights about the suitability of each learner in achieving the subtyping task, provided that too strong incidences of overfitting and curse of dimensionality were mitigated by the embedded feature selection approaches, already owned by, or added to, each classifier under evaluation.The following multiclass classifiers were assessed: (1) TCGA data indicated a simple regularized multiclass Logistic Regression (mLR) as the most promising method to distinguish intrinsic subtypes. It can set differentially the parameters that directly weigh each gene in each subtype class, while providing at the same time parameter shrinkage to deal with the high dimensionality of RNA-seq data and prevent overfitting. Hence, it has also some points of contact with the nearest shrunken centroid technique, already successfully adopted for the intrinsic subtyping task. Its generalization accuracy of 88%, estimated with cross-validation, was the highest one among all the best-trained learners, even if also the Decision Jungle behaved well, with cross-validation accuracies of 86%.Training was done with the adoption of tenfold stratified cross-validation and hyperparameter grid search to perform model selection, i.e., to set properly, with respect to the final task, all the tunable hyperparameters of each supervised model. Thus, for all ML methods under study, we found best-trained models, i.e., models whose hyperparameter setting and learned parameters led to the best generalization accuracy, estimated through cross-validation. As we can see in Fig. TCGA test set, where its accuracy (85%) overcame the ones of all the other best-trained models  reached even higher accuracies both on cross-validation and on the unseen samples of the GSE96058 test set (around 89%), confirming the suitability of this regularized classifier also for the BC subtyping of FPKM expression data  analysis.Regularized mLRs trained on the whole gene sets assigned non-null weights to nearly 1,000 genes, both in TCGA test set they reached slightly increased accuracies (86–87%) with respect to those of the mLR (85%) having a 19,737-gene space , compared to mLRs using the other filters  Fisher scores; (2) Mutual Information; (3) Chi-squared scores; 4) Spearman Correlations. Implementation details are in Supplementary Material Section  Spearmansee Fig. .25, an R package for the analysis of gene expression data whose core capability is the use of linear models to assess differential expression in multifactor designed experiments. We made differential analyses on the 10 pairwise contrasts between the 5 subtypes; we obtained 10 gene lists, each one including all the genes differentially expressed in a pairwise contrast. Then, given an integer value N, through the union of the top N genes  from each of the 10 lists, we obtained a set called limmaN, whose genes are all relevant to distinguish at least a couple of subtypes. We tuned and trained again regularized mLRs on limmaN feature spaces obtained for 11 different N values, ranging from 10 to 1,000. The so-trained models reached cross-validation accuracies within 89–95% and accuracies within 84–88% on the TCGA test set; best ones are more accurate in cross-validation, and in line in testing, compared to the models trained on the filter-based feature spaces and on the PAM50 panel  and precision  . For each starting feature space, we carried out in parallel ten independent runs of backward elimination, performing each run with randomized feature order as to mitigate the bias introduced by the sequential gene scrolling, solvable only through an unfeasible exhaustive search. The algorithm iteratively discards one gene at a time until no more feature elimination improves the accuracy of the regularized mLR model beyond a fixed threshold of gain. Since kept genes in each run were not robust, also due to the needed feature shuffling, ultimately we combined all the genes kept in at least one run, with a downstream preservation strategy. The three gene signatures  preserved from the three corresponding starting feature spaces were then used as feature spaces for training and testing other regularized mLRs on the TCGA cross-validation and testing results in line with our benchmark, i.e., the mLR trained on PAM50 genes . Both best-trained models overcame the performances of the model having all the 30865 sequenced genes as feature space, reaching 90% of cross-validation accuracies and 91% of accuracy over the GSE96058 test set , while the macro-averaged recalls (m-aR) were 0.90 for both limma-based models and 0.88 for the PAM50-based mLR, which showed a lower balanced accuracy on PanCA . The same analysis was performed for the models trained on the GSE96058 training set with PAM50, limma50 and limma50_BWE as feature spaces, and then applied to the GSE81538 dataset (excluding unavailable genes). For both latter cases, accuracies were just below 90% and all performance metrics  reached slightly lower values than the ones found with corresponding intra-dataset testing on the GSE96058 test set.Specifically, the models trained on the PanCA dataset , including genes meaningfully involved in discriminative patterns between classes and only to a limited extent overlapping with the PAM50 panel  overall performances of any approach are also influenced by the mentioned ambiguity in subtype calling that possibly affects samples with mixed traits; (3) degradation is caused also by the Normal-like class, whose clinical significance remains undetermined6, and that could be excluded from a refined version of the intrinsic subtyping approach, as in PAM50-based Prosigna test16; (4) the few amounts of training samples and the vast number of features influence subtyping capabilities, mainly for the cases most difficult to recognize. Nonetheless, mLR classifications resulted robust also when varying the feature space under consideration, showing high agreement of the classifications compared both with each others , used within the work of Ciriello et al.9 and includes 817 mRNA-seq Version2 RSEM21 profiles (http://cbio.mskcc.org/cancergenomics/TCGA/brca_TCGA/). The second dataset, collected under GEO dataset accession number GSE96058, includes 3,273 BC RNA-seq FPKM22profiles from the Multicenter Sweden Cancerome Analysis Network-Breast Initiative10 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE96058). Eventually, we used two additional public datasets to yield a final evaluation of the studied approaches on external data. The first one, called PanCA dataset, includes 236 BC samples selected from Pan Cancer Atlas and treated with RSEM pipeline . The second one is a GEO dataset, indicated as GSE81538 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE81538), and contains 405 BC samples, subject to FPKM normalization. All used expression data were log2-transformed. Further details are in Supplementary Section Despite BC is one of the cancers with more genomic data available, only a small fraction of public BC RNA-seq data are annotated with PAM50 labels; we used the four of such RNA-seq datasets we could find. The first dataset is part of the Breast Invasive Carcinoma project of The Cancer Genome Atlas , focused on 50 genes, known as PAM50 panel. During its training, class centroids were differentially shrunken, identifying subsets of genes that best characterize and contribute to recognizing each intrinsic subtype. We used these centroids, built by Parker et al.15 .The original PAM50 method16. It focuses on a gene subset of the PAM50 panel called NANO46, and provides both the BC intrinsic subtype and the category of risk of a patient, derived from the estimated risk of recurrence (ROR) score and differentiated also based on lymph node involvement. Both classification and ROR models were independently trained and tested over NanoString profiles, obtained from the proprietary NanoString nCounter platform. Hence, the prognostic assay uses: (1) a normalization pipeline specifically designed for its proprietary technology and a reference included in the Prosigna kit, and consisting of in-vitro transcribed RNA-targets, to be processed together with the sample under study; (2) a Pearson correlation-based Nearest Shrunken Centroid classifier, which excludes the Normal-like class; (3) a proprietary model for ROR score estimation. Here, we used Prosigna centroids and algorithms to assign each sample under study with a corresponding subtype call and ROR score, after normalization against a calculated reference sample.The Prosigna test is a PAM50-based genetic assay used to define a category of metastatic risk at 10 years in hormone receptor-positive women undergoing surgery for invasive BRCA32 for cancer prediction and BC stratification. Accordingly, we assessed several classifiers and embedded regularizations, up to find the most promising approach for the BC intrinsic subtyping task. Specifically, we considered the following techniques, briefly described in Supplementary Material Section Decision Forest as ensemble method with bagging; (2) Multiclass Decision Jungle as alternative ensemble method, using directed acyclic graphs (DAGs) instead of trees; (3) Multiclass Logistic Regression with both Lasso and Ridge regularizations; (4) Fully connected Feed-Forward Neural Network with Ridge regularization, min-max input scaling and sigmoidal outputs; and (5) Support Vector Machines with Lasso regularization and One-versus-All approach.We performed a machine learning survey, tracing some previous studiesIn addition to the mentioned regularizers, we explored further feature selection techniques to handle the high-dimensionality of RNA-seq profiles, facing the curse of dimensionality and the overfitting risk without losing the gene expression interpretation of the maintained features. We evaluated alternatively some filter methods, a differentially expressed genes (DEG)-based approach, and a combined strategy involving a wrapper method. The aim of all these feature selection techniques was distinguishing the informative genes from the non-relevant ones, which behave as noise affecting data and whose removal usually increases or strengthens the predictive power of a classifier. In supervised tasks, filter methods are effective in computation time and robust to overfitting. They score and rank features with respect to the target to be predicted using a statistical measure; accordingly, each feature is either removed or kept in the feature space. The implemented DEG-based approach considers the statistical significance of gene expression variances within or between classes to trace relevant features according to discriminative patterns. Eventually, the applied combined strategy is focused on a wrapper method with sequential backward elimination as heuristic approach. Wrapper methods consider feature selection as a search problem: during the learning phase, different combinations of features are compared based on the cross-validation performances of the chosen model, up to finding a reduced set of relevant features. However, due to the prohibitive computational cost for high-dimensional spaces, we applied this strategy on some promising already reduced gene sets, rather than on all the profiled genes.http://www.rstudio.com/) and Azure Machine Learning Studio (https://studio.azureml.net/), an integrated development environment working on the Azure cloud service platform. For further implementation details, please refer to Section To asses alternative classifiers and feature selection techniques, we worked jointly on RStudio (Supplementary Information.


We show that dearseq controls the FDR while maintaining strong statistical power compared to the most popular methods. We demonstrate this behavior with mathematical proofs, simulations and a real data set from a study of tuberculosis, where our method produces fewer apparent false positives.RNA-seq studies are growing in size and popularity. We provide evidence that the most commonly used methods for differential expression analysis (DEA) may yield too many false positive results in some situations. We present  While numerous approaches have been proposed to perform DEA of RNA-seq data, there is no clear consensus on which method is the most efficient. Three methods stand out as the most commonly used in practice: edgeR , DESeq2 mma-voom   when the opposite is true in reality (i.e. type I error) at a prespecified level . In a high-dimensional context such as gene expression data, the false positive rate or false discovery rate (FDR)  has beenThis control is usually taken for granted and often left out from the benchmarks of DEA methods, while in fact an excessive FDR can be quite problematic. Not controlling the FDR means getting more false positives than expected, which limits the reproducibility of study results. Whole genome DEAs are usually exploratory steps prior to subsequent studies to confirm a gene signature is associated with a particular biological condition. If a majority of the selected genes turn out to be false positives, results may fail to replicate and any downstream health benefits may remain elusive, not to mention the waste of research resources.When comparing DEA methods, the evaluation of their empirical FDR with respect to the targeted  level is often overlooked . NonetheedgeR, DESeq2 and limma-voom make potentially strong distributional assumptions on RNA-seq data. This type of model-based inference may be required when RNA-seq studies include only a small number of observations. However, these methods’ parametric assumptions are not typically verifiable in practice. Any deviation from the hypothesized distribution of test statistics will translate into ill-behaved P-values and therefore uncontrolled FDR. FDR control rests upon the entire distribution of P-values being uniform under the null hypothesis H0 (i.e. for genes that are truly not DE).Inflation of the empirical FDR in DEA can have numerous causes, from inadequate preprocessing of the data to violations of the DEA method’s underlying assumptions. In particular, P-value distribution under the null hypothesis could lead to failure to control the type I error at lower levels (such as 1% or lower) and/or failure to control the FDR. Larger sample sizes do not always solve issues with P-value distributions and FDR control arising from violation of modeling assumptions, and can sometimes even exacerbate the problem of misspecification and its consequences. As sequencing costs keep falling and study sample sizes are increasing, this issue needs to be addressed in large sample sizes as well.So even a slight deviation from strict type I error control can have dramatic consequences on the empirical FDR. In addition, even if type I error were controlled at say 5%, nonuniformity in the dearseq, a new method to perform DEA that effectively controls the FDR, regardless of the distribution of the underlying data. dearseq is a robust approach that uses a variance component score test and relies on nonparametric regression to account for the intrinsic heteroscedasticity of RNA-seq data.Here, we propose dearseq to the three most popular state-of-the-art methods for DEA: edgeR, DESeq2 and limma-voom. We demonstrate that dearseq enforces strict control of type I error and FDR while maintaining good statistical power in a realistic and extensive simulation study where knowing the ground truth facilitates benchmarking the properties of the different methods. We also present a comparative re-analysis of a real-world tuberculosis (TB) data set from Singhania et al.  from RNA-seq data while providing robust control of FDR. dearseq is freely available as an R package on the Bioconductor library.In the ‘Results’ section, we compare the performance of a et al.  studyingdearseq is a new DEA framework based on a variance component score test :The  normalize gene expression across samples;estimate the mean–variance relationship through a local linear regression borrowing information across all genes;test each gene;apply a multiple-testing correction controlling the FDR, such as the Benjamini–Hochberg procedureG be the total number of observed genes. Let gth gene for the ith sample  . Consider now g:Xi is a vector of covariates to adjust for and Φi contains the variables for DEA, such as disease status, treatment arm or other clinical characteristics that are to be associated with gene expression. The parameter of interest is i to model the heteroscedasticity inherent to RNA-seq data.Let Note that the model presented above is very flexible, and can be easily extended to grouped  data to take into account heterogeneity between individuals by adding random effects , it is absolutely necessary to correct for multiple-testing correction, for instance, by using the Benjamini–Hochberg procedure.Because this is a score test, we only need to estimate Q can be shown to be a mixture of al depend on the covariance of dearseq is particularly robust to misspecification: the distribution of Q is the same whether model (n is large enough).The asymptotic distribution of the test statistic mentclass2pt{minimOne advantage of using a variance component score test over a regular score test is the gain in statistical power that comes from exploiting the correlation among Q. Since we are in a multiple-testing setting, it is of the utmost importance to carefully compute the associated P-values .DESeq2 package v1.25.11, edgeR package v3.27.13, limma package v3.41.16 and dearseq package v0.99.8.All computations were run under R v3.6.1 using et al.  may be unfavorable for all three compared methods  since it features a high degree of nonlinearity, deviating from any assumed model. Scenario (iii) is likely the most realistic of the three because it relies only on resampling real RNA-seq samples from the SEQC study  and (ii), 0.5% of genes were generated as truly DE, while the remaining 99.5% were not DE, among 10 000 genes. For scenario (iii), since it is based on resampling from homogeneous samples, it was impossible to induce truly DE genes without making further parametric assumptions . For this reason, in scenario (iii), FDR corresponded to the probability of finding any genes to be DE. In scenario (iv), 5% of the genes were generated as truly DE, while the remaining 95% were not DE, among 10 000 genes. Details of the data-generating mechanisms are provided in dearseq) in terms of type I error control and statistical power, as well as in terms of FDR and true discovery rate (TDR) after Benjamini–Hochberg . To accommodate small sample sizes, we have also developed a permutation-based version of dearseq, which always controlled type I error and FDR, regardless of the sample size. edgeR appeared to control the type I error in scenarios (i), (iii) and (iv) onward, but exhibits slightly inflated type I error for large sample sizes in scenario (i) (from 100 samples). This was much more visible for the FDR, which edgeR failed to control as soon as the sample size rose above 50. In the nonlinear model (ii), neither the type I error nor the FDR is controlled by edgeR. limma-voom exhibited good control of both the type I error and FDR as long as its linear hypothesis was not violated [i.e. not scenario (ii)] and the sample size was large enough (between 8 and 50 samples depending on the scenario). Finally, DESeq2 failed to control either the type I error or the FDR in scenarios (i), (ii) and (iii), with its problems worsening as the sample size increased. In scenario (iv), DESeq2, limma-voom and edgeR exhibited a high FDR at small sample sizes, while the permutation-based version of dearseq controlled the FDR for both small and large sample sizes.In Figure dearseq does not come at a price of reduced statistical power . Interestingly, the permutation approach also exhibits good statistical power .Figure et al. identified a 373-gene signature of active TB from RNA-seq data , where the patient is infected but has no clinical, bacteriological or radiological signs of the disease. Participants to this study were recruited from several medical institutes in London, UK [see  together with a positive result using an M. tuberculosis antigen-specific IFN-γ release assay (IGRA). Healthy control participants were recruited from volunteers at the National Institute for Medical Research  and were negative to both TST and IGRA. In total, 54 participants were included, of whom 21 were active TB patients, 21 were LTBI patients and 12 were healthy controls.In a recent paper, Singhania seq data . TB is a UK [see  for a deedgeR to their Berry London RNA-seq data, which included 14 150 normalized gene counts measured across 54 samples after preprocessing  or those with a latent infection (LTBI) on the other hand  an absolute log2(fold change) > 1 and (ii) an FDR adjusted P-value <0.05 (after correction for multiple testing with the Benjamini–Hochberg procedure). To ensure reproducibility of the numerical values from Singhania et al., the log2 fold changes were calculated using edgeR. The signature was then evaluated by its capacity to distinguish between active TB versus all others. In order to quantify the relevance of each selected gene for distinguishing active TB from control and LTBI, we computed two measures of association, the leave-one-out cross-validated Brier score  to the three comparisons originally performed in Singhania et al.  yields a global signature of 272 DE genes  are due to edgeR-private genes  although the interferon signaling pathway was represented by two genes and some upstream regulators. On the contrary, among the 41 dearseq-private genes, HERC5 is upregulated by regulators belonging to IFN signaling pathways including IFNαβ and IFNε (known to be regulated by M. tuberculosis). Those results emphasize the better predictive ability of the genes identified by dearseq and highlight the potential false positives arising from edgeR.Applying the limma-voom and DESeq2 to further benchmark the performance of dearseq. For DESeq2 and limma-voom, we used the default values following the guidelines from their respective online user guides. For edgeR, we rely on the results directly provided by Singhania et al. , illustrating that dearseq is less prone to generate false positives. DESeq2 identifies the largest signature comprising 471 genes, including all of the 272 genes identified by dearseq and 360 out of the 373 originally identified by edgeR, while limma-voom identifies 402 genes, among which 267 are in common with dearseq and 314 are in common with edgeR. As can be seen in Figure dearseq signature has the lowest average Brier score, meaning that most of the additional genes identified by the three competing methods are less predictive of active TB status. Figure limma-voom-private genes are largely over-represented among the highest Brier scores and the highest marginal P-values. The same conclusion can be drawn for the DESeq2-private genes.In addition, we performed the same analysis using a et al.   likely include numerous false positives. While Agniel et al.  group(s) comparison.The proposed method l et al.  focused edgeR, DESeq2 or limma-voom will not systematically have inflated FDR. As illustrated by our simulation studies, there are some scenarios in which, for some given sample sizes, they control the FDR adequately. However, we have shown here that this is no guarantee, and in practice it is very difficult to know under which circumstances a data analysis is taking place.It is important to note that dearseq solely relies on the central limit theorem convergence for its asymptotic test to work, it guarantees a control of the FDR without needing any model to hold as long as the sample size is large enough. This contrasts with limma-voom that hinges on weighted least squares and can yield incorrect inference if the heteroskedasticity in the variances is not modeled correctly  and the associated computational time. We have undertaken substantial efforts to speed up the implementation of the dearseq R package on Bioconductor, which allows for parallel computing. This leads to reasonable computation times (from a few seconds to no more than a couple of minutes on a laptop) depending on the sample size and the computing power available. If more permutations were deemed essential, one option would be to selectively increase the number of permutations only for the genes where numerical precision is not sufficient to confidently estimate their adjusted P-value, thus limiting the computational burden.Because orrectly . For loworrectly  correctiDESeq2 seems to fail to control the FDR most often. In particular, even under its model assumption of a negative binomial distribution for the data, it can suffer from inflated FDR. This seems counterintuitive as our synthetic data were generated under the negative binomial distribution, and this should benefit DESeq2 and edgeR—since both methods assume this model. As has been noted previously, this behavior can be caused by nonuniformity in the distribution of the P-values arising from DESeq2 or edgeR   ,30–32. IedgeR analysis, we used the edgeRpreprocessed data as input to dearseq . For DESeq2 and limma-voom, we used the raw counts. Indeed, both edgeR and DESeq2 assume the input data to be strictly counts (i.e. integers), due to their negative binomial distribution assumption, though edgeR also has some support for so-called non-integer counts. While this seems sensible given the nature of RNA-seq data, recent innovations in RNA-seq alignment methods such as salmon . RNA-seq is subject to various technical biases, and in particular the library size has an important and well-known impact on downstream analysis if ignored. Therefore, it is important to account for library size one way or another, and several RNA-seq data normalizations have been proposed to do so [e.g. TMM, RPKM, FPKM and CPM  conditions , and are not specifically oriented toward grouped or longitudinal data. Therefore, there is a need in the broader DEA community for a more flexible method.  the FDR .dearseq, a new DEA method that uses a variance component score test to provide a robust, powerful and versatile approach to DEA while avoiding the pitfall of FDR inflation exhibited by the current state-of-the-art methods in certain situations. We also benchmarked this new method alongside the three established methods on both the simulations and the real data analysis to illustrate its excellent performance, in terms of both FDR control and statistical power.We have demonstrated that the three most popular RNA-seq DEA methods may not guarantee control of the number of false positive in their results, even when the sample size increases. To exemplify this problematic behavior, we present extensive simulation studies ranging from realistic resampling of real data to synthetic data generation under the models’ assumptions, as well as a re-analysis of a real-world data set. To offer an alternative solution to DEA practitioners, we have developed DESeq2, edgeR and limma-voom can underestimate the number of false positives in realistic settings. Users should be aware of the possibility of inflated FDR when using these procedures and should consider the use of dearseq that gives theoretical and empirical control of the FDR without sacrificing its statistical power. Given the results of both our simulations and our real-world data re-analysis, we thus formulate the following recommendations: (i) do not rely on a single DEA method and compare the results across several tools, as this strategy may likely eliminate the majority of false positives; and (ii) for your main analysis, we recommend using dearseq or limma-voom over DESeq2 or edgeR. Indeed, limma-voom appears to control the FDR adequately as long as your sample size is large enough and the model assumptions  are reasonable. On the other hand, dearseq ensures an effective control of the FDR regardless of the sample size  and demonstrates good statistical power.These results have important implications for the field, as DEA of RNA-seq data has become widespread. The distributional assumptions and model-based inference inherent to lqaa093_Supplemental_FileClick here for additional data file.