The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training. The combination of biomarkers  allows us to represent the information contained in biological samples and fluids, supporting clinical decisions . NumerouHowever, reliable clinical signatures are restricted by dataset availability, which often reduces their statistical power ,6. ArtifMachine learning (ML) approaches have been demonstrated to be useful in medicine. For example, studies report that ML could be used in cancer diagnosis  and progRegularized Generalized Linear Models such as L1/L2 regularized and Elastic net regression address overfitting and aim to balance between accuracy and simplicity of a model ,23. The The Elastic net balances between LASSO (L1-norm) and ridge penalties (L2-norm) shrinking some coefficients close to zero (like ridge) and some exactly to zero (similar to LASSO) . This moSeveral statistical methods have been developed for survival data analysis ,25. The We previously introduced a sample merging approach that is compatible with current Bioconductor releases . It alloWe demonstrate the workflow of our toolbox by analyzing three lung cancer datasets from microarray profiling downloaded from the GEO database. The datasets GSE18842  and GSE19804 (60 tumor/60 non-tumor samples) were downloaded (getGEO) and are already GCRMA normalized deposited in GEO. For the datasets GSE19188 (91 tumor/ 65 non-tumor samples), we downloaded the raw data (CEL files). The files were imported into the R environment and subsequently GCRMA normalized (resulting “ExpressionSet” object) using the gcrma package version 2.56.0   analysis after batch correction resulted in 699 significantly deregulated transcripts (ed genes ). Figureed genes , Solute ed genes , and Coled genes  functionWe further tested the 699 DEGs for enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways  for a diagnostic signature. We divided the merged dataset into a training dataset  and test dataset . We used a L1/L2 regularized logistic regression to fit a Generalized Linear Model in order to perform a feature selection to include only the potentially most predictive variables (here genes) in the model. The 10-fold cross-validation results in a lambda of 0.009260 and 0.059521 . Figure We further applied the Elastic net regression. The 10-fold cross-validation shows a lambda of 0.010288 and 0.063129 . Notably, we manually set alpha = 0.9 as the grid search for lambda  calculates an alpha = 0.1 (lambda = 0.521401), resulting in a signature without an improved predictive performance. The Elastic net regression model identified, for lambda.min, an 80 gene transcript signature (69 unique gene symbols), and for lambda.1se, a 41 transcript signature (36 unique gene symbols) . The calTo address overfitting and reduce model instability, the framework allows to include further datasets for validation. We validated the gene signatures in three independent datasets . The GSE30219 contains <5% non-cancerous samples, whereas the GSE102287 and GSE33356 are more balanced validation datasets. The results of the validation are depicted in p-value < 0.05). We found known lung cancer drivers such as Lipoprotein Lipase (LPL) [After determining the diagnostic signature, we tested for a relevant prognostic signature. For this, we analyzed the significant influence of the 699 DEGs on the patient survival outcome using a Univariate Cox Proportional Hazard Model . The Cox regression analysis revealed 22 DEGs that have a significant influence (effect size) on the patient survival (se (LPL)  and CC Cse (LPL) . Next, we trained the prognostic 22 gene classifier using an algorithm comparing the expression profiles between tumor and healthy samples of the merged datasets GSE18842 and GSE19804 . We additionally validated the identified 22 prognostic gene signature in two independent datasets  to evaluate its impact on the patient outcome. Here, we tested whether the 22 gene signature can classify patients with high and low mortality risk. Therefore, we classified the patient samples into high risk and low risk groups using the trained classifier. p-value = 0.0002166) and GSE50081 . This indicates that the identified 22 gene classifier reflects a common prognostic signature of dominant tumor factors that can differentiate between high and low risk tumor disease.The Kaplan-Meier estimators in Our intention was to develop a general and easy to use toolbox that identifies reliable diagnostic and prognostic signatures including the important steps of data augmentation and validation, especially for users with limited bioinformatics resources. It is therefore a step-by-step protocol rather than an improved algorithm or ML method approach.The tool applies a comparison between the two ML models LASSO and Elastic net, which aim to balance between accuracy and simplicity of a model. LASSO and Elastic net regularization are well-established methods for gene expression analysis, allowing to construct predictive models from datasets with non-linear and large dimensional variable numbers . EspeciaThe use of ML implies the need for a substantial amount of data in order to train the model, in which the integration of different datasets might be required. However, gene expression analysis often suffers from selection bias, poor sample quality, and poor sample size estimation, influencing the statistical power and validity of downstream analysis ,36,37. Cq-value (can be set by the user) to reduce the false positives and find real biologically deregulated genes but also considering sample size and power estimation approaches based on statistical and clinical significance [Several studies calculate signatures using ML approaches, but often fail during independent validation stages . To overificance ,41. ThisIn our example, the identified gene signatures from two different ML models show a high diagnostic power and might be promising for the clinic to classify between lung cancer and non-cancer samples. The confusion matrix for the LASSO and Elastic net regression models are similar. Comparing the calculated signatures shows a common set of 12 transcripts (12 unique gene symbols), and similar accuracy and predictive performance. However, this is of course not always the case. For example, studies in breast cancer reported two independent prognostic signatures identified with similar approaches showing only few common genes, which were experimentally validated . This ilThe common gene set contains known cancer markers. For instance, TMEM106B has been shown to be a valuable marker of lung cancer metastasis , whereassigidentDEG, sigidentEnrichment, sigidentDiagnostic, and sigidentPrognostic—are wrapper functions around all included smaller functions to execute the analysis steps. However, these can also be run separately, depending on the interests of the users.The significance and novelty of the toolbox lies in its functionality as an „all-in-one tool”: it offers an analysis path combining meta-analysis with functional gene expression analysis and robust diagnostic and prognostic signature calculation. The code is implemented in an R package. The four main functions—The toolbox benefits from its generic design and modularity. We designed it for Affymetrix as a widely used microarray profiling platform  and illuWe demonstrated the method’s power to be applied to datasets containing a large number of gene probes using the Affymetrix HG-U133 Plus 2.0 platform. However, the merging algorithm is not restricted to this platform, allowing the potential integration of other popular microarray profiling platforms such as HG-U133A, HG-U133B, and HG-U133A 2.0. Moreover, the modularity of the framework allows the future incorporation of additional platforms, such as Illumina, but also other high-throughput data such as genomic, proteomic, metabolomic, and radiomic data. For instance, the Elastic net model shows applicability to genome-scale data such as the identification of genomic markers of drug sensitivity ,47. IndeExisting tools such as SurvMicro  and SurvThe framework from Hughey et al. 2015 identifies a diagnostic signature combining meta-analysis with an Elastic net regression . This aphttps://gitlab.miracum.org/clearly/sigident), which provides the four main functions—sigidentDEG, sigidentEnrichment, sigidentDiagnostic, and sigidentPrognostic. The whole workflow is documented in detail in the R package vignette.For the meta-analysis  and the functional gene expression analysis , we used our previously published sample merging approach, which is based on a modified code of the inSilicoMerging package combined with the limma package . This apFurthermore, for the DEGs we added a functional gene ontology (GO) and KEGG pathway enrichment analysis using the goana and kegga functions from the limma package (Entrez IDs as input). A further GO and pathway over-representation test is implemented using the clusterProfiler package version 3.12.0  (includiThe calculation of statistically robust multi-gene signature combinations focuses on diagnostic and prognostic signatures. For diagnostic signatures, we used the LASSO and Elastic net penalty as implemented in the R package glmnet version 2.0.18 . The hypFor the prognostic signature detection we applied a survival and risk assessment analysis using a Cox Proportional Hazard Model as implemented in the survival R package version 2.44.1.1 . The CoxWe developed an efficient toolbox for the identification of diagnostic and prognostic gene signatures. It is the first R package tool that combines meta-analysis with gene expression analysis and ML approaches for the systematical calculation of statistically robust gene signatures. This helps to reduce study biases and improves the statistical power for the identification of reliable signatures from large sample cohorts. Importantly, the tool is not restricted to a specific disease. We believe that our toolbox will be useful for the research community and opens new windows for an effective analysis of data and a better clinical management of diseases.


As one of the most popular data representation methods, non-negative matrix decomposition (NMF) has been widely concerned in the tasks of clustering and feature selection. However, most of the previously proposed NMF-based methods do not adequately explore the hidden geometrical structure in the data. At the same time, noise and outliers are inevitably present in the data.2,1-norm constraint when estimating the residual. This is because the L2,1-norm is insensitive to noise and outliers.To alleviate these problems, we present a novel NMF framework named robust hypergraph regularized non-negative matrix factorization (RHNMF). In particular, the hypergraph Laplacian regularization is imposed to capture the geometric information of original data. Unlike graph Laplacian regularization which captures the relationship between pairwise sample points, it captures the high-order relationship among more sample points. Moreover, the robustness of the RHNMF is enhanced by using the LClustering and common abnormal expression gene  selection are conducted to test the validity of the RHNMF model. Extensive experimental results on multi-view datasets reveal that our proposed model outperforms other state-of-the-art methods. Due to the development of sequencing technology, more and more gene expression data have been detected. At the same time, there are many meaningful biological information in gene expression data. The effective analysis and research of this information are of great significance to the prevention and treatment of diseases. And multi-view data obtained by integrating data from different sources have gained much attention in the field of machine learning . It is wTo meet the demand for studying explosive gene expression data, modern biologists are increasingly concerned with clustering and feature selection. Clustering is the process of dividing a series of genes or samples into different subsets, and the genes or samples in the same subset are similar . GeneralAs an effective matrix decomposition method, non-negative matrix factorization (NMF)  is widelAs we mentioned above, these methods have enhanced the performance of NMF, but there also exist the following limitations: (1) In fact, there is an intrinsic geometrical information in the high-dimensional data. But these methods ignore the nonlinear low-dimensional geometrical structure in the original data. (2) There are always noise and outliers in real data. Therefore, we need a robust NMF-based approach to effectively suppress noise and outliers.L2, 1-norm when estimating the residual can be effectively alleviated  ∈ Rm × n, the column vector xj is a sample vector. NMF aims at finding two non-negative matrices U =  ∈ Rm × k and V =  ∈ Rk × n whose products are similar to the data matrix X [U represents a basis matrix, and V represents a coefficient matrix. The minimizing objective function of the NMF is as below:F represents the Frobenius norm of matrix. xj can be seen as a linear combination of columns of U, parameterized by each column of V [In the field of bioinformatics, gene expression data are usually expressed in the form of a matrix. The sample is represented by a column of matrices, and the level of gene expression is represented by the rows of the matrix. Given a data matrix matrix X . U repreumn of V .X ∈ Rm × n, the ‖X‖2, 1 is to first calculate L2-norm for rows to form a column matrix, and then calculate L1-norm for column matrix [Given any matrix n matrix , i.e.,2\L2, 1-norm will cause row sparsity [L2, 1-norm is not susceptible to noise and outliers, so the robustness of the algorithm can be improved.As shown above, sparsity . At the Inspired by the simple graph theory, hypergraph came into being . In the G =  represent a hypergraph, where vertex set is represented by V, hyperedge set is E, and W is the diagonal matrix that represents the weights of the hyperedges. As shown in Fig. E = {e1 = {v1, v2, v3}, e2 = {v3, v4, v5}, e3 = {v5, v6, v7,v8}}. In Fig. H ∈ RV| × |E|| represents the hypergraph’s incidence matrix. Then, we can calculate it as below:Let a triple ei, its weight Wi is denoted as follows:k represents the value of k nearest neighbors for each vertex. d(v) represents the degree of vertex v and is expressed as follows:For any hyperedge And the degree of each hyperedge can be denoted as:E = HW(De)−1HT and Dv is a diagonal matrix composed of d(v). W denotes a diagonal matrix composed of w(e). De used to represent the diagonal matrix composed of f(e).The unnormalized hypergraph Laplacian matrix  is definsi and sj are low-dimensional representations of the original data points xi and xj.Hypergraph regularization  can be dTraditional NMF is a good part-based representation algorithm . HoweverL2, 1-norm, and hypergraph. The objective function of RHNMF is defined as follows:Tr(⋅) represents the trace of the matrix and α ≥ 0 denotes the weighting parameter to balance two terms.To overcome the above limitations, we present a new method called RHNMF. It considers the robustness of the algorithm and the high-order relationship between the data. In other words, RHNMF method is the integration of NMF, B‖2, 1 = Tr(BDBT), the objective function in Eq.  and (16), we specifically analyze the arithmetic operations of each iteration of the RHNMF method. Assume that the original data matrix Xm × n, m represents the number of genes, the number of samples is represented by n, k denotes the number of factors, g represents the number of nearest neighbors when constructing hyperedges in our algorithm. Therefore, we need 2mnk + 2(m + n)k2 + n(g + 3)k additions, 2mnk + 2(m + n)k2 + (m + n)k + n(g + 1)k multiplications, and (m + n)k divisions for (15) and (16). The overall costs of RHNMF method are O(mnk).In this subsection, the computational costs of the RHNMF are presented. The general method to describe the computational complexity is to use arithmetic operations. Multiplicative iterative update rules guarantee L2, 1 [In this section, we apply the RHNMF model to cluster samples and select com-abnormal expression genes. To verify the validity of RHNMF, we compare it to other methods on multi-view dataset. These comparison methods include K-means, PCA, NMF , NMFL2, L2, 1 , GNMF [1L2, 1 , HNMF [1L2, 1 , SHNMF [L2, 1 , and RGNL2, 1 .The Cancer Genome Atlas (TCGA) program applies high-throughput sequencing technology to understand the mechanisms of the occurrence and development of cancer cells . In thisα affects the experimental results. Because the value of the regularization parameter represents the degree of consideration of high-order relationship among data points, the value of the appropriate regularization parameter will contribute to the experimental results. So fivefold cross-validation is used to select the optimal parameters. The scope of the selection is {10r : r ∈ {−5, −4, −3, …, 3, 4, 5}}. Figure α are 105, 105, 100, and 104 on PAAD_HNSC_CHOL_GE, PAAD_ESCA_CHOL_GE, PAAD_HNSC_ESCA_GE, and HNSC_ESCA_CHOL_GE, respectively.In our proposed RHNMF method, the balance parameter In the experiment, we perform 50 times for each method. To illustrate the superiority of our algorithm, we compare it with other methods in the clustering of multi-view data. Then, we employ the K-means algorithm on the decomposed coefficient matrix for sample clustering.si denotes the ground truth label and ri represents the cluster label that is obtained in the clustering experiment. map(ri) denotes the mapping function that maps label ri to the label si using the Kuhn–Munkres algorithm [δ denotes a delta function. When x = y, δ is 1; otherwise, δ is 0. In addition, n represents the number of samples.In the experiment, we employ two evaluation metrics to evaluate the clustering results , 22. Thelgorithm . Then, δC and C', NMI is expressed as:H(C) and H(C') represent the entropies of C and C'. MI represents the mutual information between two cluster sets.NMI represents the degree of similarity between two cluster sets and it has been widely used. For two cluster sets To illustrate the effectiveness of RHNMF, we perform the clustering experiment on the multi-view datasets. Then, we use AC and NMI to evaluate the clustering result. Finally, the details of clustering results are summarized in Table (i) On these four multi-view datasets, the HNMF and SHNMF outperform the GNMF method, and the RHNMF also is higher than the RGNMF method. This is because the graph regularization only considers the intrinsic geometric relationships between pairs of samples. Hypergraph regularization and sparse hypergraph regularization, on the other hand, consider the manifold structure among more samples. That is, hypergraph Laplacian is able to find geometric information between multiple samples with similar embedding. This shows that the method of applying the hypergraph regularization term constraint has higher clustering accuracy.L2, 1-norm constraints in the error function, we divide the seven methods based on NMF into three groups for comparison. The NMFL2, 1 is approximately 5% and 10% bigger than the NMF on AC and NMI, respectively. RGNMF exceeds GNMF by 27% and 41% on AC and NMI. RHNMF is higher than HNMF and SHNMF, by about 22% and 30% on the mean of AC and NMI. We can see that the methods with L2, 1-norm have better clustering results. This is because multi-view data has more samples, so there will be more noise and outliers. Fortunately, L2, 1-norm can enhance the robustness of the algorithm.(ii) According to whether there is (iii) The NMF clustering results on the PAAD_ESCA_CHOL_GE and PAAD_HNSC_ESCA_GE datasets are not the worst. The reason may be that the improvement of traditional NMF will cause the loss of useful information and affect the clustering results. The clustering result of K-means is obtained by directly clustering the original data set without dimensionality reduction. From Table L2, 1-norm makes the clustering effect obviously.(iv) In Table The development of single-cell RNA sequencing (scRNA-seq) technology has enabled the measurement of gene expression in individual cells. This gives us an unprecedented opportunity to study biological mechanisms at the cellular level. The main single-cell analysis is to study the heterogeneity of cells, that is, to cluster a large number of cells into different groups. Therefore, in this subsection, we perform clustering experiments on single-cell datasets using the nine methods described above. The single-cell dataset for lung epithelial cells is available in the NCBI’s Gene Expression Omnibus (GEO GSE84147), including 540 cells  . Table 3Cancer is the most common type of modern diseases, and it is a serious threat to human life and health. Changes in the genome often lead to cancer , 26. Thehttp://www.genecards.org/) can analyze the selected genes. GeneCards is a searchable comprehensive database that succinctly provides genomes, proteomics, and all known and predicted human genes. Tables In the experiment, the gene selection method used is introduced in . We seleN is obtained by matching the differential genes selected by each method to the virulence gene pool of every integrated dataset. The RHNMF method gives the largest N. This is because L2, 1-norm is not sensitive to noise and outliers. And the row sparsity produced by the L2, 1-norm constraint will contribute to the selection of com-abnormal genes. Therefore, our method is effective for the selection of com-abnormal genes.In Table The com-abnormal expressed genes selected by RHNMF and not selected by other methods are listed in Table Table L2, 1-norm on the error function to enhance the robustness of the algorithm. Experimental results on the multi-view datasets demonstrate the superiority of the RHNMF in comparison with other representative methods.In this paper, we design a novel non-negative matrix factorization model called RHNMF for sample clustering and the selection of com-abnormal genes. On the one hand, considering the low-dimensional manifold information existing in the high-dimensional data, the hypergraph regularization term is applied to the objective function of RHNMF. On the other hand, we use However, the proposed method inevitably has limitations. For example, our method uses a traditional hypergraph, which does not capture high-order discriminant manifold information. For future work, we will introduce sparse hypergraph to solve the above problem.


Pancreatic cancer is characterized by its unsatisfying early detection rate, rapid disease progression and poor prognosis. Further studies on molecular mechanism and novel predictive biomarkers for pancreatic cancer based on a large sample volume are required.Multiple bioinformatic analysis tools were utilized for identification and characterization of differentially expressed genes (DEGs) from a merged microarray data . Data from the GEO and TCGA database was utilized to validate the diagnostic and prognostic value of the top 5 upregulated/downregulated DEGs. Immunohistochemical assay (46 paired pancreatic and para- cancerous samples) was utilized to validate the expression and prognostic value of COL11A1, GJB2 and CTRL from the identified DEGs.A total number of 300 DEGs were identified from the merged microarray data of 100 pancreatic cancer samples and 62 normal samples. These DEGs were closely correlated with the biological characteristics of pancreatic cancer. The top 5 upregulated/downregulated DEGs showed good individual diagnostic/prognostic value and better combined diagnostic/prognostic value. Validation of COL11A1, GJB2 and CTRL with immunohistochemical assay showed consistent expression level with bioinformatics analysis and promising prognostic value.Merged microarray data with bigger sample volume could reflect the biological characteristics of pancreatic cancer more effectively and accurately. COL11A1, GJB2 and CTRL are novel predictive biomarkers for pancreatic cancer.The online version of this article (10.1186/s12935-018-0669-x) contains supplementary material, which is available to authorized users. Pancreatic cancer is characterized by its unsatisfying early detection rate, rapid disease progression and poor prognosis , 2. Mult2Fold change| > 1.5 and adjusted p < 0.05). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to analyze the functional classification and enriched signaling pathways of the DEGs Affymetrix Human Genome U133 Plus 2.0 Array). Datasets from other platforms were excluded to avoid the potential bias caused by technological difference between different platforms. In accordance with these standards, three qualified datasets  were selected for further analysis.Raw data of the selected microarrays were extracted with the R language package affy. Extracted expression data were normalized and transformed to log2 based logarithm with the rma function of affy package. Batch effect was excluded with the R language package sva before the aforementioned data were merged into one dataset.http://www.broadinstitute.org/gsea/index.jsp) in accordance with the official tutorial.Efficacy and accuracy of the merged data to reflect the biological differences between tumor and normal groups were evaluated with Gene Set Enrichment Analysis . For repeated gene expression data, the one with smaller p value was used for the downstream analysis. The expression level of DEGs was visualized with R language package ggplot2, pheatmap and GraphPad Prism 7 software.The R language package limma was utilized for the calculation of DEGs  was utilized for GO analysis and KEGG analysis of the DEGs. The results were visualized with R language.The R language package clusterProfiler and online database Database for Annotation, Visualization and Integrated Discovery  and visualized with Cytoscape software in accordance with the official tutorial [The PPI network of identified DEGs was calculated with STRING database  utilizing clinical data from TCGA database.The expression level of the top 5 upregulated and downregulated genes from the identified DEGs was validated with microarray data from TCGA database and further visualized with GraphPad Prism 7. Prognostic value of these 10 selected genes was evaluated with survival analysis. Survival analysis was performed with OncoLnc platform , cell junction  and tube formation  and biological process (BP) classification Fig. a and b. We utilized STRING database to analyze the PPI network of the DEGs to identify the key genes and their interactions in pancreatic cancer progression. The visualized results showed that epidermal growth factor (EGF) was located in the core of our PPI network Fig. . COL11A1The top 5 upregulated and downregulated DEGs were selected for further validation of their potential diagnostic value Fig. a. SurvivWe validated the expression level of COL11A1, GJB2 and CTRL with IHC assay of 46 paired pancreatic cancer and para-cancerous tissue sections Table a. RepresIn the present study, we utilized multiple bioinformatic analysis and IHC analysis to investigate two key hypotheses. (1) Merged microarray data with bigger sample volume could reflect the biological characteristics of cancer group and normal group more effectively and accurately. We could obtain a more representative and accurate results from the merged microarray data. (2) DEGs identified from the merged microarray data hold promising potential as key regulators and predictive biomarkers for pancreatic cancer. GSEA analysis of the merged data showed that the cancer group was enriched in genes closely correlated with cell proliferation, migration, invasion and angiogenesis. These results suggested that our merged data was qualified to reflect the characteristic expression profile of cancer and normal group. GO analysis and KEGG pathway analysis of the identified DEGs showed that they were closely correlated with ECM regulation and pancreatic secretion. These results were consistent with the clinical features of pancreatic cancer which is characterized by early local invasion/distant metastasis and loss of normal exocrine function. PPI network analysis showed that EGF was the core in the interaction network of identified DEGs. Multiple clinical trials on investigating the efficacy of combining Cetuximab (a targeted antibody against EGFR) with currently applied therapies have been reported –27. All In this study, we showed that merged microarray data with bigger sample volume could reflect the biological characteristics of pancreatic cancer more effectively and accurately. Results of bioinformatic analysis and IHC analysis suggested that COL11A1, GJB2 and CTRL are novel predictive biomarkers for pancreatic cancer.Additional file 1. Identified differentially expressed genes of the merged data.Additional file 2. Representative images of IHC assay. A. IHC assay of COL11A1 B. IHC assay of GJB2 C. IHC assay of CTRL.


This data article presents complementary results pertaining to the research article entitled “Sub-acute restraint stress progressively increases oxidative/nitrosative stress and inflammatory markers while transiently upregulating antioxidant gene expression in the rat hippocampus” . The present article provides additional gene expression data of selected neuroinflammatory markers and regulatory enzymes involved in oxidation-reduction reactions. Male Wistar rats aged 7–8 weeks were exposed to control, 1, 2, or 3 episodes of 6-h restraint stress in the light cycle after which the whole brain was quickly removed and the hippocampus excised for relative gene expression analysis. Specifically, mRNA levels of inflammatory regulators including allograft inflammatory factor 1, class II major histocompatibility complex, integrin alpha M, interferon gamma, and prostaglandin-endoperoxide synthase 2 were analyzed by real-time PCR. The gene expression of redox regulatory enzymes including glutathione peroxidase 1, glutathione peroxidase 4, superoxide dismutase 1, superoxide dismutase 2, myeloperoxidase, and NADPH oxidase subunit P47phox were also determined. These data provide useful insights in the molecular basis of inflammatory and redox regulation in the hippocampus following a short term to repeated psychological challenge in rats. Specifications tableValue of the data•Allograft inflammatory factor 1 and integrin alpha M mRNA expression data can be used to demonstrate the regulatory hierarchy of microglial activation markers at the transcriptional level in the hippocampus following repeated stress.•MHC class II transactivator, interferon gamma, and myeloperoxidase mRNA expression data can be used to indicate stress induces microglial activation.•These data provide evidence for temporal dynamics of neuroinflammatory and antioxidant regulation following stress.1We have presented genomic data from the hippocampus in support of the research article entitled ‘Sub-acute restraint stress progressively increases oxidative/nitrosative stress and inflammatory markers while transiently upregulating antioxidant gene expression in the rat hippocampus’ published in Free Radical Biology and Medicine 22.1ad libitum access to standard rat chow and water. Prior to experimentation, rats were habituated to human handling for 10 min per day over six days and on each experimental day were transported to an experimental room within the same animal facility for acclimation one hour prior to any experimental procedures.The University of Queensland Animal Ethics Committee approved all experimental procedures under approval number SBS/456/14/URG. Individually housed male Wistar rats (Rattus norvegicus) aged 7–8 weeks were sourced from The University of Queensland Biological Resources and maintained within the Australian Institute of Biotechnology and Nanotechnology animal facility. Rats were housed under standard laboratory conditions  with a 12:12 h light-dark cycle (lights on at 07.00 h) and 2.2n = 8 per group) using restrainers described previously from our laboratory Treatment groups consisted of unstressed, acute  and repeated  restraint from 9.00 to 15.00 h . The primer/probe sets analyzed were FAM-labelled Aif1 (Rn00574125_g1), Ciita (Rn01424725_m1), Gpx1 (Rn00577994_g1), Gpx4 (Rn00820818_g1), Ifng (Rn00594078_m1), Itgam (Rn00709342_m1), Mpo (Rn01460205_m1), Ncf1 (Rn00586945_m1), Ptgs2 (Rn01483828_m1), Sod1 (Rn00566938_m1), and Sod2 (Rn00690588_g1). Each primer/probe was analyzed in reactions multiplexed and normalized with a VIC-labelled primer/probe assay for glyceraldehyde 3-phosphate dehydrogenase  and mRNA levels were determined using the formula 2−ΔCT where ΔCt = (Ct target gene – Ct Gapdh).Total RNA was extracted from hippocampal tissues using the QIAGEN RNeasy mini kit and reverse transcribed into cDNA as described previously 2.4p-values less than 0.05 were considered statistically significant.Data were analyzed using statistical software GraphPad Prism . Data were first analyzed for normality using the Brown-Forsythe test. One-way ANOVA with Fisher׳s least significant difference test were used to compare normally distributed data. A non-parametric Kruskal-Wallis ANOVA with Dunn’s test was used for data with significantly different standard deviations. All comparisons were made against the unstressed group of animals. Results were expressed as mean ± standard error of the mean (± SEM) and


Tumor classification is crucial to the clinical diagnosis and proper treatment of cancers. In recent years, sparse representation-based classifier (SRC) has been proposed for tumor classification. The employed dictionary plays an important role in sparse representation-based or sparse coding-based classification. However, sparse representation-based tumor classification models have not used the employed dictionary, thereby limiting their performance. Furthermore, this sparse representation model assumes that the coding residual follows a Gaussian or Laplacian distribution, which may not effectively describe the coding residual in practical tumor classification. In the present study, we formulated a novel effective cancer classification technique, namely, Fisher discrimination regularized robust coding (FDRRC), by combining the Fisher discrimination dictionary learning method with the regularized robust coding (RRC) model, which searches for a maximum a posteriori solution to coding problems by assuming that the coding residual and representation coefficient are independent and identically distributed. The proposed FDRRC model is extensively evaluated on various tumor datasets and shows superior performance compared with various state-of-the-art tumor classification methods in a variety of classification tasks. Thus, we should develop novel analysis methods that can deeply mine and interpret these data to obtain insight into the mechanisms of tumor development. To date, a number of methods have been proposed for classifying cancer types or subtypes9. These common methods, including support vector machine10, linear discriminant analysis11, partial least squares (PLS)12, and artificial neural networks13, have been used to mine gene expression data.Microarray techniques have been used to delineate cancer groups or to identify candidate genes for cancer prognosis. The accurate classification of tumors is important for cancer treatment. With the advancement of DNA microarray and next-generation sequencing technology15 and further extended to cancer classification18 and miRNA-disease association prediction20. For example, Hang et al. proposed a SRC-based method to classify six tumor gene expression datasets and obtained excellent performance18. Zheng et al. further combined the idea of metasample and proposed a new SRC-based method for tumor classification called metasample-based sparse representation-based classifier (MSRC)16. These experiments showed that MSRC is efficient for tumor classification and can achieve high accuracy. Li et al. proposed a new classifier called the maxdenominator reweighted sparse representation-based classifier (MRSRC) for cancer classification5. These experiments showed the efficiency and robustness of MRSRC. All SRC-based methods model a classification problem to identify a sparse representation of test samples, whereas the L1 sparsity constraint represents a test sample as the linear combination of these training samples.Machine learning-based methods have been widely used in tumor classification. However, these methods require a predictive model to predict the labels of test samples. Predictive model selection is a complex training procedure that easily leads to overfitting and decreased prediction performance. Recently, given the non-requirement for model selection and robustness to noise, outliers, and incomplete measurements, sparse representation-based classifier (SRC) was proposed for face recognitionm is used to represent a dictionary D = {D1, D2, … Dc} ∈ Rm×n, that is, y ≈ Dα where the sparse representation vector α ∈ Rn only shows several large entries. Then, the test samples are classified based on the solved vector αand the dictionary D. The selection of vector α and the dictionary D is crucial to the success of the sparse representation model. The previously described SRC-based methods directly regarded the training samples of all classes as the dictionary to represent the test sample and classified the test sample by evaluating which class leads to minimal reconstruction error. Although these methods showed interesting results, noise, outliers, incomplete measurements, and trivial information in the raw training data made this classification less effective. These naive methods also do not make maximize the discriminative information in the training samples. These problems can be addressed by properly learning a discriminative dictionary.In the sparse representation model, the test sample y ∈ Ret al. proposed that samples of the same class possesses similar sparse representation coefficients21. Mairal et al. proposed a task-driven dictionary learning framework that minimizes the different risk functions of the representation coefficients for different tasks22. In general, these of methods aims to learn a shared dictionary by all classes and classify test samples with representation coefficients. However, the shared dictionary loses the class labels of the dictionary atoms. Thus, classifying the test samples based on the class-specific representation residuals is not feasible.In general, discriminative dictionary learning methods can be divided into two categories. In the first category, a dictionary shared by all classes is learned, whereas the representation coefficients are discriminative. Jiang et al. learned a dictionary for each class, classified the test samples by using the representation residual, and applied dictionary learning methods to face recognition and signal clustering23. Wang et al. proposed a class-specific dictionary learning method for sparse modeling in action recognition24. In the previously mentioned methods, test samples are classified by using the representation residual associated with each class, but the representation coefficients are not used and are not enforced to be discriminative in the final classification.In the second category, discriminative dictionary learning methods learn a dictionary class by class, and atoms of the dictionary correspond to the subject class labels. Yang et al. proposed a Fisher discrimination dictionary learning framework to learn a structured dictionary25. In discrimination dictionary learning, the sparse representation coefficients present large between-class scatter and small within-class scatter. Each class-specific sub-dictionary presents good reconstruction of the training samples from that class and poor reconstruction of the other classes. By Fisher discrimination dictionary learning, the representation residual associated with each class can effectively be used for classification and the discrimination of representation coefficients can be exploited.To solve the previously discussed problems, Yang et al. proposed a regularized robust coding (RRC) method for face recognition26. The RRC model searches for a maximum a posteriori (MAP) solution of the coding problem by assuming that the coding residual and representation coefficient are independent and identically distributed. However, either SRC-based or RRC methods or both do not take full advantage of discriminative information in representation coefficients. In the present study, we present RRC based on the Fisher discrimination dictionary learning method, a novel and effective cancer classification technique combining RRC methods and the concept of Fisher discrimination dictionary learning, which can maximize the use of discriminative information in representation coefficients and representation residuals. The proposed Fisher discrimination regularized robust coding (FDRRC) model extensively applies to various tumor GEP datasets and shows superior performance to different state-of-the-art SRC-based and machine learning-based methods in a variety of classification tasks.All SRC-based methods assume that the coding residual follows a Gaussian or Laplacian distribution, which may not be effective for describing the coding residual in practical GEP datasets. To address this problem, Yang The remainder of the paper is organized as follows: Section 2 mainly describes the experimental process and presents the experimental results obtained from eight tumor datasets. Section 3 discusses the proposed method, concludes the paper and outlines future studies. Section 4 describes the fundamentals of FDRRC.18, SVD + MSRC27 and MRSRC5. SRC, MSRC, and MRSRC are SRC-based methods that have been widely used in tumor classification in recent years. All experiments are implemented in the Matlab environment and conducted on a personal computer .In present study, eight publicly available tumor data sets are used to evaluate the performance of FDRRC. The experiment is divided into four sections. In the first section, cancer datasets and dataset preprocessing are introduced. In the second section, parameter selection is discussed. In the third section, describes the various samples used in the experiment with 400 top genes on eight datasets. In the fourth section, to make a fair performance comparison, cross-validation (CV) is presented. The proposed method is compared with several representative methods, such as SRCFor a more comprehensive comparison of the performance of these methods, eight tumor GEP datasets are used to evaluate the proposed method. These datasets include five two-class datasets and three multi-class datasets. The summarized descriptions of the eight GEP datasets are provided in Table 28, colon cancer dataset29, gliomas dataset30, diffuse large B-cell lymphoma (DLBCL) dataset31 and Prostate dataset32. The acute leukemia set contains 72 samples from two subclass. The colon cancer data set includes 62 samples, with gene expression data for 40 tumor and 22 normal colon tissue samples. The gliomas data set consists of 50 samples from two subclasses (glioblastomas and anaplastic oligodendrogliomas), and each sample contains 12,625 genes. For the DLBCL data set, RNA was hybridized to high-density oligonucleotide microarrays to measure the gene expression. The target dataset contains 77 samples of 7,129 genes. The target class has 2 states, including 58 diffuse large b-cell lymphoma samples and 19 follicular lymphoma samples. For the prostate tumor data set, the gene expression profiles were derived from tumors and non-tumor samples from prostate cancer patients, including 59 normal and 75 tumor samples. The number of genes is 12,600. Table The five two-class tumor datasets are acute leukemia dataset33, MLLLeukemia34, and LukemiaGloub28. The ALL data set total contains 248 samples and 12,626 genes from six subclasses. The MLLLeukemia data set contains 72 samples and 12,582 genes per sample with three subclasses. The LukemiaGloub data set contains 72 samples with three subclasses. Each sample contains 7,129 genes. Table For multi-class datasets, the data sets include the small round blue cell tumors (ALL)35. Then, the top 400 genes are selected from each dataset, thereby presenting a good trade-off between computational complexity and biological significance.GEP data offer high dimensionality and a small sample size. Redundant and irrelevant data significantly affects classification. To compare the performance of FDRRC and SRC-based methods in the gene selection, the ReliefF algorithm is applied to the training setλ1 and λ2, which are both presented in Eq. outputs the largest integer smaller than τm. According to the experiments7, τ = 0.9 can be set in the classification of tumors. Parameter w can balance the contributions of the representation residual and representation vector to the classification. We search for w from a small set {0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1} by five-fold VC.Five parameters should be set in the FDRRC model. The dictionary learning phase employs two parameters: ed in Eq.. In genetion Eq.  and w frtion Eq. . Parametci|) denotes the minimum number of subclass set of samples in the training data. Suggesting that when Qis 5, then 5 samples per-subclass are randomly selected and used as the training set and the rest are assigned to the test set. In this experiment, the training/testing is performed 10 times, and the average classification accuracies are presented.Different divisions of the training set and test set can greatly affect the classification performance. To avoid the effects of an imbalanced training set, the balance division method (BDM) is designed to divide each original data set into a balanced training set and test set. For this BDM, Q samples from each subclass are randomly selected for use in the training set, and the remaining samples are used in the test set. Here, Q is an integer number. In the present study, we set Q = 5to The average prediction accuracies that vary with different values of Q are shown in Figs 2 to 302 in increments of 5. For these experiments, the number of samples per subclass of the training set, was selected from {5, 6, 7, 8, 9, 10} by five-fold VC. The results are shown in Fig. To compare the performance of the four models with different feature dimensions on eight tumor data sets, we run experiments using the ReliefF algorithm to select genes from 10Figure To evaluate the classification performance on imbalanced split training/testing sets, we perform a 10-fold stratified CV experiment to evaluate the classification performance between FDRRC and SRC-based methods. All samples are randomly divided into 10 subsets and nine subsets are used for training, the remaining samples are used for testing.The 10-fold CV results are summarized in Tables The results of the present study, show that FDRRC outperforms the sparse representation-based methods  in most experiments. FDRRC outperforms the sparse representation-based methods probably because the representation residual associated with each class can be effectively used for classification, the discrimination of representation coefficients has been exploited, the coding residual is independent and identically distributed and the local center can help to distinguish outliers.In the present, we proposed a new method, called FDRRC for classifying tumors. This method adopts the Fisher discrimination dictionary learning method and the concept of the local center with the RRC model. The FDRRC model learns a discriminative dictionary and seeks a MAP solution to the coding problem. Classification is achieved by a local center classifier, which takes full discriminative information in representation coefficients. We also compare the performance of FDRRC with those of three sparse representation-based methods by using eight tumor expression datasets. The results demonstrate the superiority of FDRRC and validate the effectiveness and efficiency of FDRRC in tumor classification.36 and lncRNA-disease association37 in future studies.Compared with the other methods, FDRRC exhibits a stable performance with respect to various datasets. The properties of this FDRRC algorithm should be further investigated. Thus, we will extend the algorithm with a superior discriminative dictionary and consider the driver genes to tailor the algorithm in our future studies. In addition, FDRRC will be used to predict miRNAX1, X2, …, Xc} ∈ Rm×n is a training sample set, where c corresponds to the number of subclasses, and m, n are dimensionality and the number of samples, respectively. The jth class training samples Xj can be presented as columns of a matrix xj,i is a sample of jth class, and nj refers to the number of jth class training samples. Let L = {l1, l2, … lc} denote the label set, whereas y ∈ Rm is a test sample. Then, the SRC-based problem can be represented as follows:α∧, SRC-based method assigns a label to test sample y according to the following equation:Xi. The classification rule is set as identity(y) = argmini{ei}.Assuming that X = {X1, X2, …, Xc}, the Fisher discrimination dictionary learning model not only requires that D should be highly capable of representing X  but also that D can strongly distinguish the samples in X. The Fisher discrimination dictionary learning model can be expressed as follows:f(α) is a discrimination term imposed on the coefficient matrix α, ris the discriminative data fidelity term, and λ1 and λ2 are scalar parameters.Given the training samples X = {αi as Xi over Di. For the discriminative data fidelity term r, Xi could be well represented by Di but not by Dj,j ≠ i. This relationship indicates that We can write f(α), the Fisher discrimination criterion38 is expected to minimize the within-class scatter of α, denoted by SW(α), and maximize the between-class scatter of α, denoted by SB(α). SW(α) and SB(α) are defined as follows:mi and m are the mean vectors of αi and α, respectively, and ni is the number of samples in class Xi. Thus, the criminative coefficient term can be defined as follows:tr(⋅) means the trace of a matrix, η is a parameter, and For the discriminative coefficient term Finally, the Fisher discrimination dictionary learning model can be expressed as follows:αi class by class. When computing αi, all αj, j ≠ i are fixed. The objective function expressed in Eq.  = r + λ2 fi(αi) and τ = λ1/2. The method of FISTA39 can be employed to solve Eq.  fθ(ei), then we can obtain the equation below:αi of sparse representation coefficient α =  are independent and identically distributed and contain the PDF fσ(αi), then we can acquire the following formula:In the SRC-based method, coding residual e = y − Dα follows Gaussian distributionρσ(α) = −lnfσ(α), then, the above equation can be converted into the following:ρθ(e) and ρσ(α); and minimizing energy function.Finally, MAP estimation of α can be expressed as follows:ρθ(e), given diversity in gene variations, predefining distribution presents difficulty. In RRC model, unknown PDF ρθ(e) is assumed symmetric, differentiable, and monotonic. Therefore, ρθ(e) features the following properties: (1) ρθ(0) is global minimal of ρθ(Z); (2) ρθ(Z) = ρθ(−Z); (3) if |Z1| < |Z2|, then ρθ(Z1) < ρθ(Z2). Without loss of generality, we let ρθ(0) = 0. Meanwhile, ρθ(e) is allowed to feature a more flexible shape, which adapts to input testing sample y, to make the system more robust to outliers. Then, by Taylor expansion, Equation  is symmetric, differentiable, and monotonic, ωθ(ei) can be assumed as continuous and symmetric while being inversely proportional to ei. With these considerations, the logistic function which features the same properties is a good choice for ωθ(ei)41. Thus, we can obtain the following:ρθ(0) = 0, we can formulate Equation (For Equation  can be aρσ(α), we can assume that sparse representation coefficient αi follows a generalized Gaussian distribution as only the representation coefficients associated with training samples from the target class can feature high absolute values. As we do not know beforehand the class of the test sample, a reasonable prior can be that only a small percent of representation coefficients contains significant values. Then, we can used the following equation:For ρθ(e) and ρσ(α), minimized energy function can be used in the iteratively reweighted RRC (IR3C) algorithm, which was designed by Yang et al., to solve the RRC model efficiently26. The RRC (IR3C) algorithm is described in Table After determining distributions Equation  is the cmi is the mean sparse representation coefficient vector of class Xi, mean vector mi can be viewed as the center of class Xi in the transformed space comprising D. Thus, we label mi as the local center. For classification of tumor, when y originates from class i, residual ∧ should be close to mi but far from mean vectors of other classes. Considering the above factors, we define the following classifier:w is a parameter for balancing contribution of the two terms to classification. Finally, we can obtain the label of y according to the following formula:Assuming that 26 and Fisher discrimination dictionary learning model, we can obtain the algorithm of FDRRC. Table By combining the IR3C algorithm


Transcriptomic profiles can improve our understanding of the phenotypic molecular basis of biological research, and many statistical methods have been proposed to identify differentially expressed genes (DEGs) under two or more conditions with RNA-seq data. However, statistical analyses with RNA-seq data are often limited by small sample sizes, and global variance estimates of RNA expression levels have been utilized as prior distributions for gene-specific variance estimates, making it difficult to generalize the methods to more complicated settings. We herein proposed a Bartlett-Adjusted Likelihood-based LInear mixed model approach (BALLI) to analyze more complicated RNA-seq data. The proposed method estimates the technical and biological variances with a linear mixed-effects model, with and without adjusting small sample bias using Bartlkett’s corrections.We conducted extensive simulations to compare the performance of BALLI with those of existing approaches . Results from the simulation studies showed that BALLI correctly controlled the type-1 error rates at various nominal significance levels and produced better statistical power and precision estimates than those of other competing methods in various scenarios. Furthermore, BALLI was robust to variation of library size. It was also successfully applied to Holstein milk yield data, illustrating its practical value.BALLI is statistically more efficient and valid than existing methods, and we conclude that it is useful for identifying DEGs in RNA-seq analysis.The online version of this article (10.1186/s12864-019-5851-6) contains supplementary material, which is available to authorized users. Transcriptomic profiles can improve our understanding of the phenotypic molecular basis of biological research, and many attempts have been made to identify differentially expressed genes (DEGs) by microarray analysis. However, microarray analysis often suffers from many systematic errors, such as hybridization and dye-based detection bias, hampering the detection of true DEGs , 2. ReceRNA-seq quantifies the numbers of reads aligned to particular transcripts or genes, and various approaches have been proposed to manage RNA-seq data . There aNegative-binomial distributions for read counts and normal distributions for log-transformation of counts per million (CPM) successfully describe distributions of RNA-seq data. However, RNA-seq is relatively expensive compared with microarray, and thus, further adjustment has been made to handle the problem of small sample size. If the sample size is small, the estimated variance can have large standard errors, and thus, multiple methods that incorporate prior knowledge have been proposed. For example, variances of read counts are assumed to be positively related to their means, and their relationships can be estimated by comparing the means and variances of read counts for all genes. This can often be utilized to shrink variance parameters , 12. edgExisting methods shrink the variance estimate of each gene toward global variance estimates or use variance estimates based on the relationships between means and variances. Such assumptions are often very useful if the sample sizes are small. However, there are multiple factors that can distort these relationships, and if they are violated, the performance of existing approaches can be affected. For example, the quality of data is highly dependent on the preparation steps, and unexpected noise, such as noise from different storage periods or sequencing organization of samples, can occur during preparation steps. Moreover, read counts of cancer tissues are more heterogeneous than those of normal tissues, and biological variances can be affected by disease status . Thus, thttp://cran.r-project.org) or http://healthstat.snu.ac.kr /software/balli/.In this article, we present new methods for identifying DEGs with RNA-seq data, namely, BALLI and LLI. Statistical analyses with log-transformed read counts are often more powerful than other existing methods and are relatively insensitive to various errors , 17. ThuM different groups, and the averages of the expressed read counts for each gene were compared among these groups. For case-control studies, M = 2. We assumed that there were nm subjects in group m and denoted the total sample size by N; thus, we got N = n1 +  …  + nM. We defined dummy variables for subject i in group m byWe assumed that there were A design matrix for group variables is defined byn1 subjects were in group 1; the second n2 subjects were in group 2; and so on. The effect of continuous variables can also be tested, and then some or all of the columns of the design matrix, X, become their realization. We assumed that expressed read counts were observed for G genes and were denoted by rgi for gene g of subject i . Then, the library size for subject i, Ri, was equivalent to cpm function in the edgeR R package. If we denoted the normalized Ri with the trimmed mean of the M-value X and Here, Dandg, Mg, Pg, νg, and τg depend on the structure of Vg, and counterparts to general Vg s are shown in Additional file The forms of DThe log-likelihood function for our final model is given byP = Vg−1 − Vg−1tVg−1)tVg−1, the profile log-likelihood of Vg is a function of Here, optimize function in R [Σg, b. Then, , where ion in R . It alwa, which maximizes the likelihood under the null hypothesis. Under the null hypothesis, if we let P0 = Vg−1 − Vg−1Z(ZtVg−1Z)ZtVg−1, the profile log-likelihood of The Bartlett-adjusted LRT requires Vg0 and denoted it as We considered two real datasets consisting of samples from unrelated Nigerian people and Holstein cattle, respectively. Nigerian subjects participated in the International HapMap Project and comprised 29 male and 40 female participants . The reaψg = 1 and N was greater than or equal to 12. LLI also successfully controlled the nominal type-1 error rates if N was greater than or equal to 20. However, if N was less than 20, p values by LLI were inflated. edgeR showed the least performance, and the estimated type-1 error rates were always inflated at 0.05, 0.01, and 0.005 nominal significance levels. Interestingly, DESeq2 tended to be conservative at 0.1 and 0.05 but liberal at 0.01 and 0.005 nominal significance levels. Thus, we could conclude that the proposed linear mixed model with Bartlett’s correction reasonably controlled the type-1 error, and Bartlett’s correction was required if the sample size was less than 20.We applied the proposed linear mixed models to the simulated data based on Nigerian individuals’ RNA-seq data and calculated empirical type-1 error rates and statistical powers with these models. The data were then compared with DESeq2 (v1.20.0), edgeR v3.22.3), and voom (v3.36.5). Table .22.3, anN = 12, 16, 20, 24, 28, 40, 64, or 68. Figure p values are less than 0.1. According to Fig. N was less than 40  and DESeq2 (0.399). The estimated precision of BALLI was 0.940, and those of voom and DESeq2 were 0.930 and 0.907, respectively. If N = 40 and δ = 0.5σ, the estimated power and precision of BALLI were 0.342 and 0.943, which were higher than those of DESeq2  and voom .Figure Our method was also applied to simulation data based on RNA-seq data from Holstein cows. The results were similar to those of the simulation data based on Nigerian people’s data. Additional file P values obtained from LLI and edgeR tended to be inflated. DESeq2 generally showed deflation of type-1 error rates at a 0.1 nominal significance level. Second, we considered the effects of library size variance on statistical analyses. Data with unequal library sizes among subjects were generated by negative binomial distribution whose mean parameters (agi) were the product of mean estimates, under the equal library size assumption, and random numbers from U, where u = 0.2, 0.4, 0.6,  0.8, or 1, and dispersion parameters were estimated from u became smaller, the library size had larger variances. Figure u. Figure u. The estimated type-1 error rates of LLI were affected by sample size. If N was larger than or equal to 40, LLI controlled the type-1 error rates the most correctly and was not affected by the library size variation. BALLI was slightly conservative, but the amount remained constant. Results at the 0.005 significance level are provided in Additional file RNA-seq data are generally known to follow negative binomial distribution, and we conducted simulation studies with RNA-seq data generated from negative binomial distributions. First, we assumed that library sizes were the same among subjects. The overall trend of the estimated type-1 error rate was similar to that of simulation studies based on real RNA-seq data. Estimated type-1 error rates by voom and BALLI usually maintained the nominal significance levels Table . P valueu. BALLI usually had the best estimated power and precision, as was observed in simulation studies based on real RNA-seq data. For example, when u = 0.2, N = 28, and δ = 0.5σ, the estimated power by BALLI was 0.438, whereas those for DESeq2 and voom were 0.317 and 0.352, respectively  were significantly validated. According to Seo et al. (2016), however, no DEGs, including the nine genes, were found at an FDR 0.1 significance level by DESeq2, voom, and their methods owing to the lack of statistical power [TOX4 gene between the high and low milk yield groups at the FDR-adjusted 0.1 significance level, but other methods did not detect any significant genes. The FDR-adjusted p value of TOX4 by BALLI was 0.1267, which was much smaller than those of DESeq2, edgeR, and voom. Table p values for the nine genes, including TOX4. P values for most of the nine genes obtained by LLI and BALLI were smaller than those obtained by other methods. The nine genes were not significantly affected by parity or lactation period , DESeq2 (27.21 s) and edgeR (33.25 s) ; otherwise, it is better to use BALLI.In this article, we suggested new methods, designated BALLI and LLI, for identifying DEGs with RNA-seq data. We assumed that log-cpm values of read counts asymptotically followed normal distributions, and the linear mixed-effects model with Bartlett’s correction was proposed. The proposed methods were compared with existing methods, such as DESeq2, edgeR, and voom, with extensive simulation studies. According to our results, negative-binomial-based approaches often failed to preserve the nominal type-1 error rates. For example, Furthermore, we evaluated the effects of library size variations on statistical analyses. We found that library size variance could affect the estimated type-1 error rates, and the effect was the largest for voom. Library sizes are affected by multiple factors, such as the amount of mRNA and the sequencing instrument, which can generate substantial variation among library sizes for subjects. Our simulation studies showed that BALLI was robust with regard to library size variation in samples of various sizes and was a reasonable choice if large library size variance was observed.μ + ϕ with first order approximation. In addition, voom considered log-cpm value as a response and assumed that they were normally distributed. However, our simulation studies revealed the superiority of the proposed methods compared with voom, which was found to be attributable to their different variance structures. For the proposed methods, 1/μ + ϕ was derived from the first order approximation of the negative binomial distribution and thus may be a natural assumption for RNA-seq data. Furthermore, for 1/μ + ϕ, ϕ obviously indicates the overdispersion parameter, and biological and technical variances can be estimated with BALLI. However, voom assumes ϕ/μ, and the amount attributable to biological or technical variances cannot be clearly defined.The proposed methods assumed that log-cpm values of read counts asymptotically followed a normal distribution and that their variances were approximately equal to 1/μ + ϕ and had the most generalized variance parameter space. Incorporation of φ = 1 yielded BALLI and LLI, and ϕ = 0 yielded voom. We found that BALLI was the most efficient in the considered scenarios; however, in real data analyses, various factors affected variance structure. For example, subjects with different ethnicities can cause φ to be larger than 1, and thus, a better model may differ according to RNA-seq data. φ and ϕ can be estimated with the proposed linear mixed model by implementing only a simple modification, and thus, we can choose the best model using AIC or LRTs. The selected models can then be utilized to identify DEGs. This model was implemented as an R package and can be downloaded from CRAN (http://cran.r-project.org) or http://healthstat.snu.ac.kr/software/balli/. Furthermore, in contrast to methods based on a generalized linear mixed model such as MACAU [We also suggested the most flexible and general linear mixed model for log-cpm. The proposed model assumed that the variance of log-cpm was φ/as MACAU , the proWith simulation studies for various scenarios, we showed that the proposed methods were usually the most efficient. However, Bartlett’s correction seems inappropriate when N ≤ 6 The forms of CAdditional file 2:Steps of generating simulation data. (DOCX 18 kb)Additional file 3:N = 4, 6, 8, 28, 40, 64, and 68 based on Nigerian people’s data. (DOCX 21 kb)Estimated type-1 error rates with simulation data for Additional file 4:N = 4, 6, 8, 12, 16 and 20 based on Holstein cow’s data. (DOCX 20 kb)Estimated type-1 error rates with simulation data for Additional file 5:N = 12, 16 and 20 based on Holstein cow’s data. (DOCX 197 kb)Estimated powers and precisions with simulation data when δ = 0.5σ or 1σ and Additional file 6:N = 4, 6, 8, 28, 40, 64, and 68 based on simulated data from negative binomial distribution. (DOCX 21 kb)Estimated type-1 error rates with simulation data for Additional file 7:N = 12, 16, 20, 24, 28, 40, 64, or 68 at the 0.005 nominal significance level. (DOCX 371 kb)Effect of varying library sizes on the type-1 error rates when u = 0.2, 0.4, 0.6, 0.8, and 1 and Additional file 8:N = 12, 16, 20, 24, 28, 40, 64, or 68. (DOCX 593 kb)Effect of varying library sizes on the statistical power and precision when u = 1, 0.8, 0.6, or 0.4, δ = 1σ and Additional file 9:Analysis of covariables, parity and lactation period, for true nine DEGs of Holstein cow's data. (DOCX 18 kb)Additional file 10:Computation time for each method when analyzing Holstein cow’s data. (DOCX 14 kb)


RNA sequencing (RNA‐seq) for generating transcriptomic data is the most popular. By analysing global patterns of gene expression in organs/tissues/cells of interest or in response to chemical or environmental perturbations, researchers can better understand an organism's biology. Tools designed to work with large RNA‐seq data sets enable analyses and visualizations to help generate hypotheses about a gene's function. We present here a user‐friendly RNA‐seq data exploration tool, called the ‘eFP‐Seq Browser’, that shows the read map coverage of a gene of interest in each of the samples along with ‘electronic fluorescent pictographic’ (eFP) images that serve as visual representations of expression levels. The tool also summarizes the details of each RNA‐seq experiment, providing links to archival databases and publications. It automatically computes the reads per kilobase per million reads mapped expression‐level summaries and point biserial correlation scores to sort the samples based on a gene's expression level or by how dissimilar the read map profile is from a gene splice variant, to quickly identify samples with the strongest expression level or where alternative splicing might be occurring. Links to the Integrated Genome Browser desktop visualization tool allow researchers to visualize and explore the details of RNA‐seq alignments summarized in eFP‐Seq Browser as coverage graphs. We present four cases of use of the eFP‐Seq Browser for ABI3,SR34,SR45a and AF65BU2, where we examine expression levels and identify alternative splicing. The URL for the browser is https://bar.utoronto.ca/eFP-Seq_Browser/.Improvements in next‐generation sequencing technologies have resulted in dramatically reduced sequencing costs. This has led to an explosion of ‘‐seq’‐based methods, of which http://sps:urlprefix::https; RNA‐seq data at http://sps:urlprefix::https and http://sps:urlprefix::https. Code is available at http://sps:urlprefix::httpsThis article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. Tool is at  We present a tool, the eFP‐Seq Browser, for rapidly identifying RNA sequencing samples with strong expression levels of a given gene, or where the read maps for a given gene/sample best match a particular gene model. Details can be called up with convenient links to the Integrated Genome Browser. Well‐known examples include published visualization tools such as the Integrated Genome Browser (IGB)  experiment are mapped to a Arabidopsis thaliana, focusing on two large publicly available RNA‐seq compendia and an up‐to‐date release of Arabidopsis gene model annotations designated ‘Araport11’. The two compendia include 113 RNA‐seq experiments used by Cheng et al.  genes, as their mRNAs are alternatively spliced depending on developmental stage or tissue. We then compared the visual results from the eFP‐Seq Browser with previously published SR mRNA splice variant quantification  was visualized in the eFP‐Seq Browser using the Araport11 data set with the RPKM value set to the absolute scale. With splice variant 1 selected, we organized samples from top to bottom by decreasing correlation values  to confirm existing transcript assemblies or identify new ones. These gene isoforms are the ones visible in the isoform dropdown menu of the eFP‐Seq Browser. The utility of the eFP‐Seq Browser lies in being able to rapidly sort or filter RNA‐seq coverage profiles to identify samples where the greatest abundance of a given isoform occurs.Switching to splice variant 2 in the dropdown menu and then arranging by the highest correlation values strikingly changed the top displayed hits  of 4‐week‐old short‐day‐grown plants and 5‐day‐old etiolated seedlings. In IGB, the samples are colour‐coded as they are in the eFP‐Seq Browser. Examining the two samples in IGB, the 5‐day‐old etiolated seedling appears to have many more reads supporting the splice variant 2 alternative acceptor site in SR34 . We previously found SR45a to have different ratios of transcripts supporting variant 1 and 2 when exposed to heat stress  is one of two genes encoding U2AF65, which helps to define 3′ splice sites. U2AF65B has a single splice variant in Araport. However, in our previous RNA‐seq study in 3‐week‐old leaves we found support for an intron retention event, indicating the presence of an additional splice variant  files on Amazon's S3 data servers to offer public access. Each BCFtools mpileup call can produce an output of approximately 1–3 MB in size depending on the size of the locus. Transmitting outputs for all samples in a given expression compendium to the researcher's web browser would take a considerable amount of time because of network speed limitations and because the researcher's computer must store and work with data >100 MB in size. To mitigate this, the eFP‐Seq Browser engine performs most analyses on the BAR server and transmits only the results (mostly as compact images) to the researcher's computer.samtools. To calculate the RPKM value for each locus in each BAM file, the number of reads mapped to the entire genomic region of the locus is required. However, this information is not part of the mpileup output even though it is used to create the mpileup. Therefore, the eFP‐Seq Browser determines the number of reads mapped to the specified genomic region separately by means of the samtools view command. This leads to the same information being extracted from the BAM files twice.Performing most of the analysis on the server side introduces another computational challenge because of specific shortcomings of the current version of Additional coding‐related challenges include the limitations imposed by modern web browsers on the number of simultaneous HTTP requests. Currently, Google Chrome and Mozilla Firefox restrict this number to six simultaneous requests – if the server capacity allows and if this limit can be increased, it could potentially lead to a significant improvement in load time because more than six RNA‐seq read map images could be created simultaneously.The design of the tool is such that it can be extended to include more RNA‐seq data sets by simply amending the XML configuration file with another entry. The eFP‐Seq Browser engine will automatically analyse the data set and present the new information to the researcher in the front‐end tool. Researchers wishing to view and explore their own RNA‐seq data with the eFP‐Seq Browser tool can easily do so. Researchers would make the RNA‐seq data (as BAM files) accessible on their Google Drive or Amazon S3 for instance, and then fill out a short form describing the samples, including choosing the most appropriate image to describe the sample from a library of plant part images . This information is used to generate a custom configuration file . The eFP‐Seq Browser engine is able to access the BAM files and display data for a given gene based on the custom configuration file. Researchers can choose to share their data with others if desired. Furthermore, as the BAM file format is organism and sequence independent, the eFP‐Seq Browser tool can easily be extended to other organisms with minimal effort, with just two small web services based on standard GFF and Fasta files of genomic sequences needing to be modified. The eFP‐Seq Browser code is openly available on Github under a GPL v.2 licence.rpb), and the RPKM value. The correlation value is used to determine the similarity of the selected gene splice variant to the sample read coverage. A correlation rpb value close to 1 would indicate support for a splice variant in the specific tissue/condition of the sample. The rpb  is useful to quickly identify the sample for which the RNA‐seq coverage is closest to the selected gene model, as in Figure Two primary statistical calculations are performed by the eFP‐Seq Browser: the point biserial correlation coefficient . The relative value represents the log2 of the fold‐change in expression (increase or decrease) of the gene of interest in the sample relative to the sample controls (sample controls are identified in the Details element of the configuration file). A value of −2 would thus represent a four‐fold decrease relative to the control sample(s). In the relative mode, the eFP‐Seq Browser displays the expression pictographs coloured with a blue–yellow–red scale. We have curated the example data sets such that each RNA‐seq experiment has an associated control experiment or experiments used to make the relative comparison . Based on the curated ‘control’ samples the average of each experiment's controls is computed, and then the log2 of the ratio of a given sample's RPKM value to the average of the RPKM value of the controls  is taken to be able to generate a blue–yellow–red colour scale. While statistical methods that take into account actual read abundance, such as DESeq2  and Araport's ‘gene_structure_by_locus’ API .The eFP‐Seq Browser depends on two data sources: a custom RNA‐seq API based on bcftools and view commands of samtools to extract data from the RNA‐seq data sets stored in the data layer. The mpileup command of bcftools , 5′ untranslated region (UTR) and 3′ UTR. The UTR regions are depicted with a light green colour, while the exon/CDS features are shown by a dark green colour – to be consistent with the NCBI's colour scheme for gene structures  and reads below 36 bp. Sequences from each library were aligned to the TAIR10 genome with tophat v.2.1.1  file must be present for the data API to quickly extract a genomic region. The file names for each BAM repository file are stored in an extensible markup language (XML)‐based configuration file that also contains their respective titles, descriptions, SRA record numbers, total reads mapped, read map method, Arabidopsis thaliana plant part [scalable vector graphics (SVG) image file name and subpart if applicable], publication link, controls and replicate controls.The eFP‐Seq Browser was developed as a three‐tier architecture comprising a presentation layer (front‐end web application), a logic layer (two APIs described above) and a data layer  , each of the BAM files listed in the XML file is queried to generate mapping coverage images and number of reads across the locus. This information is returned in Javascript Object Notation (JSON) format by a Python‐based API program running on the BAR server. As RNA‐seq mapping coverage information is returned, the table is updated on‐the‐fly via CSS and jQuery to show the splice variants, mapping coverage and SVG‐based eFP images for visualization of the gene's expression level in each sample. Extensive user testing was conducted to ensure the eFP‐Seq Browser was researcher‐friendly.et al., The XML file described in the previous section that is used to store sample metadata is compatible with and is based on the IGB Quickload ‘annots.xml’ configuration file format  endpoint within the IGB, running as a stand‐alone application on the researcher's desktop.  The IGB then displays the requested gene and loads the requested data set into a track, labelled with the same text shown in the eFP‐Seq Browser and using the same colour and labelling as used for the read map image. In addition, the Available Data section of IGB lists the eFP‐Seq Browser data sets inside a folder named Bio‐Analytic Resource bearing the BAR logo. Within that folder, data sets a researcher has loaded from the eFP‐Seq Browser are checked, and these are also listed in the Data Management Table in IGB. This consistency helps maintain context across the two different applications that present the same data but at different levels of detail.To view alignments in IGB, researchers can click links labeled ‘Show: Alignments in IGB’ in the ‘Details’ column of the eFP‐Seq Browser's results table. When a researcher clicks such a link, a new web page opens at The eFP‐Seq Browser can access BAM and BAI files stored in Google Drive folders. The share link option in Google Drive can be generated and copied into eFP‐Seq Browser's ‘BAM file Repository Link’ in the ‘Generate Data’ form. A researcher would also enter other metadata to generate and download an XML file describing the researcher's own data. When this XML file is uploaded into the eFP‐Seq Browser, the Google Drive folder is mounted on the BAR server using ‘Filesystem in Userspace’ (FUSE). The Google Drive API v.3 is used to access folders and files stored in Google Drive. Custom Perl scripts build the filesystem on the BAR.samtools to access data stored on the Google Drive. Data requested by samtools is transferred using HTTP ranged requests and processed by the eFP‐Seq Browser engine. An advantage of using this approach is that large BAM files do not have to be uploaded to the BAR server. Since those files are never uploaded, the researcher's data remain confidential and the BAR server never runs out of storage. A limitation of this system is the upper limit of Google Drive API calls per minute. If the limit is reached and read map graphs cannot be generated, retrying after a few minutes should solve the issue. Integration with CyVerse's Data Store is planned for the near future (Merchant et al., Google has a limit on the number of Google Drive API calls per minute. To overcome this, we have been granted an increase in the number of API calls per minutes after communication with Google staff. This allows us to use AS, PP, AP, EE, JW, AW, MC and RS built the eFP‐Seq Browser or provided resources for its construction. NHF and SRW provided examples shown in the paper. AP, MWV, CYC, APC and VK enabled AWS connectivity and provided data sets. CT, AEL and NJP wrote the manuscript with input from all authors.The authors declare no conflict of interest.


Large-scale collaborative precision medicine initiatives ) are yielding rich multi-omics data. Integrative analyses of the resulting multi-omics data, such as somatic mutation, copy number alteration (CNA), DNA methylation, miRNA, gene expression, and protein expression, offer tantalizing possibilities for realizing the promise and potential of precision medicine in cancer prevention, diagnosis, and treatment by substantially improving our understanding of underlying mechanisms as well as the discovery of novel biomarkers for different types of cancers. However, such analyses present a number of challenges, including heterogeneity, and high-dimensionality of omics data.We propose a novel framework for multi-omics data integration using multi-view feature selection. We introduce a novel multi-view feature selection algorithm, MRMR-mv, an adaptation of the well-known Min-Redundancy and Maximum-Relevance (MRMR) single-view feature selection algorithm to the multi-view setting.We report results of experiments using an ovarian cancer multi-omics dataset derived from the TCGA database on the task of predicting ovarian cancer survival. Our results suggest that multi-view models outperform both view-specific models  and models based on two baseline data fusion methods.Our results demonstrate the potential of multi-view feature selection in integrative analyses and predictive modeling from multi-omics data.The online version of this article (10.1186/s12920-018-0388-0) contains supplementary material, which is available to authorized users. The advent of “big data” offers enormous potential for understanding and predicting health risks and intervention outcomes, as well as personalizing treatments, through integrative analysis of clinical, biomedical, behavioral, environmental, and even socio-demographic data. For example, recent efforts in cancer genomics under the Precision Health Initiative offer promising ways to diagnose, prevent, and treat many cancers . Recent Computational analyses of multi-omics data offer an unprecedented opportunity to deepen our understanding of complex underlying mechanisms of cancer that is essential for advancing precision oncology . Becap is very large compared to the number of samples n); ii) the differences in scales as well as sampling/collection bias and noise present in different omics data sets; iii) extracting and optimally combining, for the prediction task at hand, features that provide complementary information across different data sources. Unfortunately, baseline methods that simply concatenate the features extracted from the different data sources or analyze each data from each source separately and combine the predictions fail to satisfactorily address these challenges. Therefore, there is an urgent need for more sophisticated methods for integrative analysis and predictive modeling from multi-omics data  (lines 7–10). Finally, the algorithm returns selected view-specific features S1, ⋯Sv.Here, we propose a novel multi-view feature selection algorithm, MRMR-mv, that adapts the MRMR algorithm to the multi-view setting. MRMR-mv (shown in Algorithm 2) accepts as input: a labeled multi-view dataset,  library , is invoWe note that MRMR-mv applies the MRMR criteria across all of the views, as opposed to the baseline methods that apply the criteria to each view separately or to the concatenation of all views. Thus MRMR-mv can select complementary features from within as well as across views. It can also assign different degrees of importance to the views to reflect any available information about their relative utility in the context of a given prediction task.Di =  < Xi, y>. Stage I includes view-specific filters that can be used to encapsulate any traditional single-view feature selection method , then the multi-view feature selection method will be applied to the multi-view input of Stage II. The framework supports two modes of operations: i) training mode, where each enabled filter will be trained using the input so as to produce the filtered version of the input; ii) test (or operation) mode, where test multi-view dataset is provided as input and the trained filters will output the selected features of the input data.Figure ., Lasso  or MRMR)ith view, we: set Ei to 1 and disable all other filters; pass Lasso feature selection method to the ith filter; use AllFilter as Stage II filter. Similarly, to apply MRMR to concatenated views, we: enable Stage I filters and use either AllFilter (to pass the input as is) or any single-view filter; and deploy MRMR as the Stage II filter.The framework can be easily customized so as to allow evaluation of different approaches of predictive modeling from multi-omics data. For example, to build a single-view model by applying the Lasso method to the We implemented Algorithms 1 and 2 and the two-stage feature selection framework in Python using the scikit-learn machine learning library . We willWe report results of experiments on the task of building a predictive model of cancer survival from an ovarian cancer multi-omics dataset derived from the TCGA database. The resulting data set is comprised of three views, namely, CNA, methylation, and gene expression RNA-Seq for each patient along with the corresponding clinical outcomes . Our first set of experiments consider single-view classifiers based on each of the 3 views to obtain view-specific models for comparison with the proposed multi-view models; The second set of experiments compare some of the representative instantiations of the two-stage multi-view feature selection framework in combination with some representative choices of (single-view) supervised algorithms for training the classifiers. In both cases, we experimented with three widely used machine learning algorithms for developing cancer survival predictors: i) Random Forest (RF)  with 500For Stage I feature selection, we experimented with several feature selection methods implemented in Scikit-learn including: RF feature importance ; Lasso ; Elasticg. For the relevance function, f, we experimented with three functions Chi2, F-Statistic (F-Stat), and Mutual Information (MI). All functions are implemented in Scikit-learn.For both MRMR and MRMR-mv feature selection, we used the absolute value of Pearson’s correlation coefficient as the redundancy function, We estimated the performance of the resulting classifiers on the task of predicting cancer survival using the 5-fold cross-validation (CV) procedure. Briefly, the dataset is randomly partitioned into five equal subsets. Four of the five subsets are collectively used to select the features and train the classifier and the remaining subset is held out for estimating the performance of the trained classifier. This procedure is repeated 5 times, by setting aside a different subset of the data for estimating model performance. The 5 results from all the folds are then averaged to report a single performance estimate. In our experiments we used the area under ROC curve (AUC)  to assesk features selected using Lasso feature selection algorithm for choices of k = 10,20,30, …, 100.Tables We evaluated RF, XGB, and LR classifiers trained using each of the individual views with the top We observed that models built using only the methylation view performed marginally better than random guessing  be the probability distribution where p1 and p2 denotes the sampling probability for CNA and RNA-Seq, respectively. In this experiment, we considered 11 different probability distributions obtained using p1 = {0,0.1, 0.2, …, 1}. Then, for each choice of the number of selected features, k, we evaluated 11 MV models using RF algorithm and the same MRMR-mv settings described in the preceding subsection and the 11 different probability distributions for P. We used the percent relative range in the recorded AUC to assess the sensitivity of MV models to changes in P. Figure k, and the sensitivity of MV models to changes in P. Interestingly, our results suggest that as the number of selected MV features increases, the resulting MV models become less sensitive to the selection probability distribution parameter P.The selection probability parameter, concatenation of the two views, CNA and RNA-Seq; iv) SV_S, single-view models obtained by applying MRMR separately to CNA and RNA-Seq views, respectively. In addition, Fig. Figure k = 10, 20, and 80. Baseline single-view models outperformed SV_CNA and SV_RNA-Seq for k≤ 40. The highest observed AUC was 0.7 and was obtained using the MV model and k=100. In the case of XGB based models, SV_S, MV, and Ensemble models outperformed the remaining single-view models. Ensemble models outperformed MV models for 8 out of 10 choices of k. Finally, for models developed using LR algorithm, SV_S, MV, and Ensemble models outperformed the other three single-view models. Regardless of which machine learning algorithm was used, SV_RNA-Seq and SV_C models had the lowest AUC in most of the cases reported in Fig. We noted some interesting observations from our experiments with each of the machine learning algorithms considered in our experiments. In the case of models developed using RF algorithm, MV and Ensemble models outperformed the four single-view models for all choices of the number of selected features, k. Ensemble models outperformed MV models for In order to get insights into the most discriminative features selected by our framework, we considered the top 100 features selected using MRMR-mv jointly from CNA and RNA-Seq views. To determine which features (genes) could serve as potential biomarkers for ovarian cancer survival, at each of the 50 iterations (resulting from running 5-fold procedure for 10 times), we scored each per-view input feature (input to our framework) by how many time it appears in the top 100 features. Table RPS19, PNOC, SFRP1 and KCNJ16 are connected to other frequently altered genes, including MYC or EIF3E as oncogenes, from TCGA ovarian cancer dataset. In particular, ribosomal protein S19 (RPS19), which is known to be up-regulated in human ovarian and breast cancer cells and released from apoptotic tumor cells, was found to be associated with a novel immunosuppressive property [HTR3A is targeted by several FDA approved cancer drugs retrieved from PiHelper [To examine the interplay between the top selected features from each view, we constructed an integrated network of interactions among the features using the cBio portal by integrating the biological interactions from public databases including NCI-Nature Pathway Interaction Database, Reactome, HPRD, Pathway Commons, and MSKCC Cancer Call Map . Examinaproperty . FurtherPiHelper , an openFinally, we performed a gene-set enrichment analysis to identify overrepresented GO terms in the two sets of top 20 features from CNA and RNA-Seq views. Specifically, we used the gene-batch tool in GOEAST   with defWe presented a two-stage feature selection framework for multi-omics data integration. The proposed framework can be customized in different ways to implement a variety of data integration methods. We described a novel instantiation of the proposed framework using multi-view feature selection. We introduced MRMR-mv, which extends MRMR, one of the state-of-the-art single-view feature selection methods, to the multi-view setting. We used the proposed two-stage framework to conduct a set of experiments to compare the performance of single-view and multi-view methods for predicting ovarian cancer survival from multi-omics data. The results of our experiments demonstrate the potential of the two-stage feature selection framework in general, and the MRMR-mv multi-view feature selection method in particular, in integrative analyses of and predictive modeling from multi-omics data.Evaluation of single-view models for predicting ovarian cancer survival using methylation data alone showed very poor predictive performance where as those trained using CNA or RNA-Seq data showed substantially better predictive performance (with AUC between 0.64 and 0.66). Multi-view models that integrate mult-omics data using MRMR-mv, a multi-view feature selection method, were able to outperform single-view models. For example, multi-view models using the top 100 features selected by MRMR-mv from CNA and RNA-Seq data were able to achieve an AUC of 0.7. With the anticipated rapid increase in the size of multi-omics data, we can expect the predictive performance of such models to show corresponding improvements.Further improvements can be expected from better techniques for coping with the ultra high-dimensionality and sparsity of multi-omics data. Of particular interest in this context are methods for pan-cancer analysis , multi-tMRMR-mv jointly selects (from multiple views) a compact yet most relevant subset of non-redundant features across multiple views for the prediction task at hand. Interestingly, the gene-set enrichment analysis of the top 20 genes selected by MRMR-mv from the CNA and RNA-Seq data shows that these genes are also non-redundant with respect to the GO terms that are significantly overrepresented in the CNA and RNA-Seq gene sets. If this observation is validated using other multi-omics datasets, MRMR-mv could be used to uncover, from multi-omics data, the underlying functional sub-networks that collectively orchestrate the biological processes that drive the onset and progression of diseases such as cancer. Ultimately, accurate and personalized prediction of clinical outcomes of different interventions and promising therapeutic targets for different cancer types will require advances in multi-view and multi-scale modeling that bring together information from different complementary data sources into cohesive explanatory, predictive, and causal models .Developing multi-omics data-driven machine learning models for predicting clinical outcome, including cancer survival, is a promising cost-effective computational approach. However, the heterogeneity and extreme high-dimensionality of omics data present significant methodological challenges in applying the state-of-the art machine learning algorithms to training such models from multi-omics data. In this paper, we have described, to the best of our knowledge, the first attempt at applying multi-view feature selection to address these challenges. We have introduced a two-stage feature selection framework that can be easily customized to instantiate a variety of approaches to integrative analyses and predictive modeling from multi-omics data. We have proposed MRMR-mv, a novel maximum relevance and minimum redundancy based multi-view feature selection algorithm. We have applied the resulting framework and algorithm to build predictive models for ovarian cancer survival using multi-omics data derived from the Cancer Genome Atlas (TCGA). We have demonstrated the potential of integrative analysis and predictive modeling of multi-view data in ovarian cancer survival prediction. Work in progress is aimed at further developing effective computational and statistical methods and tools for integrative analyses and modeling of multi-omics data, with particular emphasis on precision health applications.Additional file 1:GOEAST gene-batch output of enriched GO terms in the Biological Processes, Cellular Components and Molecular Function categories for CNA top selected genes. (TXT 45 kb)Additional file 2:GOEAST gene-batch output of enriched GO terms in the Biological Processes, Cellular Components and Molecular Function categories for RNA-Seq top selected genes. (TXT 8 kb)Additional file 3:Graphical output of Multi-GOEAST analysis results of Biological Processes GO terms in the top selected genes in CNA and RNA-Seq. (PDF 110 kb)Additional file 4:Graphical output of Multi-GOEAST analysis results of Cellular Components GO terms in the top selected genes in CNA and RNA-Seq. (PDF 56 kb)Additional file 5:Graphical output of Multi-GOEAST analysis results of Molecular Function GO terms in the top selected genes in CNA and RNA-Seq. (PDF 58 kb)