High Throughput Biological Data (HTBD) requires detailed analysis methods and from a life science perspective, these analysis results make most sense when interpreted within the context of biological pathways. Bayesian Networks (BNs) capture both linear and nonlinear interactions and handle stochastic events in a probabilistic framework accounting for noise making them viable candidates for HTBD analysis. We have recently proposed an approach, called Bayesian Pathway Analysis (BPA), for analyzing HTBD using BNs in which known biological pathways are modeled as BNs and pathways that best explain the given HTBD are found. BPA uses the fold change information to obtain an input matrix to score each pathway modeled as a BN. Scoring is achieved using the Bayesian-Dirichlet Equivalent method and significance is assessed by randomization via bootstrapping of the columns of the input matrix. In this study, we improve on the BPA system by optimizing the steps involved in “Data Preprocessing and Discretization”, “Scoring”, “Significance Assessment”, and “Software and Web Application”. We tested the improved system on synthetic data sets and achieved over 98% accuracy in identifying the active pathways. The overall approach was applied on real cancer microarray data sets in order to investigate the pathways that are commonly active in different cancer types. We compared our findings on the real data sets with a relevant approach called the Signaling Pathway Impact Analysis (SPIA). Bayesian Network (BN) models have gained popularity for learning biological pathways from microarray gene expression data lists and do not incorporate in their models the topology via which genes in these classes interact with each other. Other popular commercial approaches, such as the Ingenuity Pathway Knowledge Base  or PathwayAssist  also identify known pathways as active based on HTBD simply by considering the number of genes shared by the input list and the target pathway. All aferomentioned methods use some variation of the main idea that a functional class is relevant to the observed HTBD if the class possesses a statistically significant amount of the input gene list.From a life sciences perspective, data analysis results make most sense when interpreted within the context of biological networks and pathways. Previously established individual gene analysis based methods have been extended to network and pathway scale mostly along the lines of gene set analysis (GSA) We have recently proposed an approach, called Bayesian Pathway Analysis (BPA), for analyzing HTBD using BNs In the BPA approach, pathways are retrieved from the KEGG database 1N and 2N samples in the two groups, the observation matrix consists of 1×N2N rows. Each column represents the FC for the corresponding gene in each of the 1×N2N pairwise comparisons. These continuous FC values are discretized using a cut-off of 2. If the FC value is greater than 2 or less than 0.5 (i.e. the gene is deregulated), it is converted into 1, and otherwise it is converted into 2.BPA assumes a two-group (e.g. case vs. control) normalized gene expression data as input. The observation matrix to score each DAG is obtained by generating the fold change (FC) values for each pair of samples in the two groups. In this matrix, columns represent genes in the DAG and rows represent pairwise comparisons. If there are The degree to which a pathway explains given HTBD is measured using the Bayesian Dirichlet equivalent (BDe) score with equivalent sample size method N pathways where a subset, aN, of these pathways is active. A performance criterion is assessed by the accuracy of predicting active and passive pathways. In addition to improving the memory and CPU usage of the algorithm, we also added new organisms for which the BPA system can be used and we provide a web portal at http://bioinfo.unl.edu/bpa/ which hosts the stand-alone version of the optimized software along with a tutorial and example data sets.In this paper, we have two fundamental aims. Our first aim is to improve on the BPA system by using the following strategies. In order to optimize the discretization phase, we tried Equal Width, Equal Frequency, K-means, Column K-means, Bi-directional K-means, and Automatic Threshold Discretization Our second aim in this study is to apply the improved pathway analysis approach on real cancer data sets. For this purpose, we downloaded real microarray data sets from the NCBI's GEO database regarding bladder, brain, breast, colon, liver, lung, ovarian and thyroid cancers. We investigated the pathways that are commonly identified as active in these various cancer microarray data sets.In the original BPA system, the observation data matrix for BN scoring is composed of the 2-level discretized FC levels for the genes in the network to be scored. The degree to which a pathway explains given HTBD is measured using the “Bayesian Dirichlet equivalent” (BDe) score and the statistical significance of this measurement is assessed by randomization via bootstrapping where the observed score is ranked against scores obtained from randomized data sets. Randomized data sets are obtained by changing the structure of the columns of the observation matrix via sampling with replacement of each column separately.In rd, 5th, and 6th normal samples are replaced with 1st, 7th, and 9th cancer samples. The observation matrix is generated by pairwise comparison of the signal values over the new order of two classes followed by discretization. This procedure is repeated B times and pathway scores are calculated using the discretized matrices. As a result, the statistical significance of the observed score can be assessed accurately via ranking against scores obtained from different observation matrices generated by these B randomized data sets. If the score of a given pathway is Sn, its p-value is assessed using I(a) is 1 if a is “true” and 0 otherwise. The significance of each pathway is reported as this nominal p-value and the corresponding false discovery rate (FDR) calculated using the Benjamini-Hochberg procedure In order to overcome this problem, we applied the permutation method previously described to randomize gene expression data sets BPA utilized a discretization method such that the continous FC value is represented as 1 if it's greater than 2 or less than ½ (i.e. a gene is dysregulated), and as 2 otherwise. Another use of the 2-level discretization is choosing a cut-off value of 3, i.e., the FC is represented as 1 if its value is greater than 3 or less than 1/3 and as 2 otherwise. In 3-level discretization with the cutoff value 2, the fold change is represented as 1 if its value is greater than 2, as 2 if less than ½, and as 3 otherwise. In 3-level discretization with the cut-off value of 3, the fold change is represented as 1 if its value is greater than 3, as 2 if less than 1/3, and as 3 otherwise.N-by-M matrix E is used to denote the observed FC matrix, where N is the number of pairwise comparisons and M is the number of genes. E denotes the FC value of comparison n for the gene m. E denotes FC data of comparison n for all genes, and E denotes the FC data of gene m for all the comparisons.In this study, we propose new discretization methods n into k intervals of equal width between minE and maxE. Thus the intervals of comparison n have width w =  max - min)E/k, with boundary points at min +w, Emin +2w, …, Emin + (k - 1)wE where k is a positive integer.EWD divides the observation matrix row E into k intervals so that each interval contains the same number of FC values.EFD divides the sorted E into k intervals by k-means clustering so that similar FC values of comparison n are placed in the same interval.K-means divides E into k intervals by k-means clustering so that similar FC values for the gene m are placed in the same interval.Co-k-means divides k+1, giving every FC value two discretized values. If the product of the two values is equal to or greater than 2x, and less than 2(x+1), the final discretized value of this expression value is x, where x is a positive integer ranging from 1 to k.In the bi-k-means method both k-means and co-k-means are respectively implemented with parameter E is divided into two intervals according to a certain cut-off value in the global option. The local option of this method divides E into two intervals according to the cutoff values defined for each column (gene) separately.There are two options for the automatic threshold discretization, which iteratively determines the cut-off values by minimizing the variance. The whole FC data In addition to the BDe scoring scheme, we propose the following score metrics to be used in the BPA system.M, D is observed data, and p is the number of parameters in the model.AIC is one of the most commonly used information criteria, which selects the model that minimizes the negative likelihood penalized by the number of parameters BIC is another widely used information criteria and unlike AIC, BIC is consistent and improves in performance with large sample sizes N.BIC differs from AIC only in the second term, which depends on the sample size D, the NML model selection criterion chooses the model M for which D′ of the same size as D. After taking the logarithm, the score is in a form of penalized log-likelihood given G =  {1G,…,mG} as the parent set in the DAG (i.e. iG is the parent set of the node iX in the DAG):iD column vectors. Even though the penalty term has an exponential number of terms, it can be evaluated efficiently using a linear-time algorithm introduced in Silander et al. We generated synthetic transcriptional regulatory networks and produced simulated gene expression data with noise using SynTReN v1.12 To test the optimized and improved BPA performance on real data sets, we used 1 bladder, 2 brain, 2 breast, 1 colon, 2 liver, 1 lung, 1 ovarian, and 2 thyroid cancer data sets. In choosing the data sets, we fixed the platform to be Affymetrix to prevent bias and used data sets where tumor and normal samples are clearly defined and the cancer samples are as homogenous as possible. Most of the chip data came from the Affymetrix HG-U133 Plus 2.0 GeneChip, which is composed of more than 54,000 probe sets representing over 47,000 transcripts providing a comprehensive picture of the human transcriptome. Other chip types include HG-U133A and HG-U133A_2, which represent approximately 22,000 probesets. Prior to application of the proposed approach, raw microarray data has been normalized using Affymetrix Microarray Analysis Suite (MAS) 5.0 algorithm For each data set, we applied the proposed analysis method with 1000 permutations and assessed significant pathways with a nominal p-value of 0.05 and an FDR of 0.25.1–D10). According to the simulation results, the best discretization method is the 2-level k-means discretization applied to the rows of the observation matrix. This approach achieves an accuracy of 0.962±0,031. Therefore, 2-level k-means method is used as the discretization method for the experiments to determine the best scoring criterion.In The datasets, which are used for the performance measurement of discretization methods, are also used for the assessment of the scoring methods. The obtained prediction accuracies are listed in In We also investigated the commonality of significant pathways in cancer types represented by two data sets except for the thyroid cancer, which has resulted in very few significant pathways. These results for the BPA analysis are summarized in In Although the improved BPA system outperformed the old BPA system on synthetic data sets (data not shown), we compared the performance of both methods on the real cancer microarray data. The list of pathways deemed significant by the old BPA system is represented in These results on the real cancer data sets  and S5 iwww.broadinstitute.org/gsea/datasets.jsp). We used this data set to identify pathways deregulated following a mutation in the tumor suppressor p53 gene. Out of the 50 samples, 17 are wild type and 33 carry mutations in the p53 gene. The pathways identified as active by BPA due to the mutations in p53 are listed in We also applied the improved BPA method on the NCI-60 cancer cell line microarray data set used in describing the Gene Set Enrichment Analysis (GSEA) method Our synthetic data simulations identified k-means clustering as the best performing discretization method. We find this result reasonable as k-means uses the distribution in the data to minimize the total mean squared error with respect to the discretized values and the real FC occurrences. Also based on the synthetic data results, the scoring method that yielded the highest accuracy was the factorized normalized maximum likelihood (fNML) score In the real microarray data analysis using BPA, the pathway that came out in most of the cancer data sets as significantly active (8/12) is the Cell adhesion molecules (CAMs) pathway. CAMs are located on the cell surface and participate in the activity of a cell binding with other cells. One of the primary features of cancer cells is uncontrolled growth where the cells are immune to density-dependent inhibition. Cancer cells keep on growing, forming multiple levels, even when the cell density is increased. This is mainly due to the malfunctioning in CAMs, which has been shown to play an important role in cancer progression Other pathways that need to be emphasized are “Citrate (TCA/tricarboxylic acid) cycle”, “Complement and coagulation cascade” and “Adipocytokine signaling” pathways that are found to be significantly active in 7 cancer data sets out of 12. Citrate cycle, also known as the tricarboxylic acid cycle (TCA cycle) or the Krebs cycle, is part of cellular respiration. It is a series of chemical reactions used by all aerobic organisms to generate energy. Its central importance to many biochemical pathways suggests that it was one of the earliest parts of cellular metabolism to evolve Our synthetic data results show that the improved BPA system identifies the activity of a pathway with over 98% accuracy. Although there is no gold standart in assessing the active pathways regarding the real microarray data of a certain phenotype, BPA's reproducibility in the same cancer types has been over 50% in average. When the pathway database is limited to the one used by SPIA, this reproducibility exceeds 70%. Finally, when all the cancer datasets are considered, the agreement between the two methods is around 60%. Given the technical and biological variation, such a high degree of overlap between different pathway analysis schemes is very promising.In an attempt to identify patways specific to particular cancer types, we investigated pathways that are consistently found to be active for the same cancer types (and non-active for the other cancer types) by the current BPA system. For brain cancer, “Parkinson's disease pathway (hsa05012)” was found active in both brain cancer data sets and only in one of the remaining 10 cancer data sets. Parkinson's disease (PD) is one of the most common neurodegeneretive disorders associated with cell loss in the substantia nigra region of the midbrain Comparison of the old BPA system with the improved one proposed in this paper on the real cancer microarray data sets revealed that the old BPA system fails to identify pathways for some data sets and provides less biologically meaningful pathways showing insignificant agreement with the improved BPA and SPIA methods. Moreover, we see a less degree of agreement for the same cancer data sets in the old BPA system. For example, the old system identified only 3 pathways in one of the “brain” data sets compared to 122 pathways identified for another data set for the same cancer type. These differences are very likely due to the improved randomization, discretization, and scoring strategies introduced in the current BPA system.Pathways that have been identified as significantly activated by BPA due to a p53 mutation are listed in Table S1Prediction accuracy of different discretization methods on synthetic datasets. The superscripts denote the number of levels used in the discretization method.(DOCX)Click here for additional data file.Table S2Lists of active pathways identified by BPA on real cancer microarray data sets.(DOCX)Click here for additional data file.Table S3Lists of active pathways identified by SPIA on real cancer microarray data sets.(DOCX)Click here for additional data file.Table S4Lists of active pathways identified by the old BPA on real cancer microarray data sets.(DOCX)Click here for additional data file.Table S5Overlap of active pathways identified by the new and old BPA systemson real cancer microarray data sets.(DOCX)Click here for additional data file.


Glioblastoma multiforme (GBM) is the most common and aggressive adult primary brain cancer, with <10% of patients surviving for more than 3 years. Demographic and clinical factors (e.g. age) and individual molecular biomarkers have been associated with prolonged survival in GBM patients. However, comprehensive systems-level analyses of molecular profiles associated with long-term survival (LTS) in GBM patients are still lacking. We present an integrative study of molecular data and clinical variables in these long-term survivors  to identify biomarkers associated with prolonged survival, and to assess the possible similarity of molecular characteristics between LGG and LTS GBM. We analyzed the relationship between multivariable molecular data and LTS in GBM patients from the Cancer Genome Atlas (TCGA), including germline and somatic point mutation, gene expression, DNA methylation, copy number variation (CNV) and microRNA (miRNA) expression using logistic regression models. The molecular relationship between GBM LTS and LGG tumors was examined through cluster analysis. We identified 13, 94, 43, 29, and 1 significant predictors of LTS using Lasso logistic regression from the somatic point mutation, gene expression, DNA methylation, CNV, and miRNA expression data sets, respectively. Individually, DNA methylation provided the best prediction performance (AUC = 0.84). Combining multiple classes of molecular data into joint regression models did not improve prediction accuracy, but did identify additional genes that were not significantly predictive in individual models. PCA and clustering analyses showed that GBM LTS typically had gene expression profiles similar to non-LTS GBM. Furthermore, cluster analysis did not identify a close affinity between LTS GBM and LGG, nor did we find a significant association between LTS and secondary GBM. The absence of unique LTS profiles and the lack of similarity between LTS GBM and LGG, indicates that there are multiple genetic and epigenetic pathways to LTS in GBM patients. A small proportion of patients survive for exceptionally long periods of time; for example, fewer than 10% of GBM patients survive more than 3 years [Glioblastoma multiforme (GBM) is the most frequent malignant form of primary brain cancer in adults. The median survival time for GBM patients is approximately 14 months with intensive multimodal therapy that includes surgical resection, chemotherapy, and radiotherapy [ 3 years –4. ThereMGMT hypermethylation and mutations in isocitrate dehydrogenase (IDH1) have been the most frequently identified genomic marker of improved patient response to chemotherapy and therefore longer patient survival [Several patient characteristics like age, performance status and tumor localization have been identified as predictors of survival time –7. Howevsurvival , 22, 23.survival , the trasurvival  was qualsurvival ) have idIDH1 and methylation of the MGMT promoter, which are themselves frequently associated with secondary GBM  chemotherapy are mutations in novo GBM ). These In this study, we leverage 6 types of molecular data from The Cancer Genome Atlas (TCGA) GBM samples, including germline and somatic point mutation, gene expression, DNA methylation, copy number variation (CNV) and microRNA (miRNA) expression data. We use machine learning to identify molecular markers characteristic of LTS in GBM, and construct integrative models that incorporate multiple sets of molecular profiles that are jointly predictive of LTS when combined with clinical and demographic data. We also explore whether LTS cases of GBM have molecular characteristics typical of LGG by comparing the similarity of LTS tumor genetic profiles to LGG. The results of our analyses have important implications for our understanding of the molecular pathology of GBM, as well as providing insight into the design of novel prognostic and therapeutics indicators.Clinical and demographic data describing patient age at initial diagnosis, gender, ethnicity/race, treatment history, vital status and follow-up/survival times were collected from TCGA  Table 1Table 1. All genomic/molecular data, including exome sequences, germline mutations, probes for microarray gene expression, DNA methylation, CNV and miRNA expression were retrieved from TCGA for both GBM and LGG patients. The platforms and levels of data are summarized in Level 1 gene expression data were collected for GBM, LGG, and normal tissue samples from TCGA. The expression data was RMA adjusted  and tranPrediction of LTS is a statistical binary classification problem. Models were individually constructed for each molecular data type using both False Discovery Rate (FDR)—adjusted univariate logistic regression (ULR) and Lasso logistic regression (LLR). Integrative models that combine clinical variables with one or more types of molecular profiles were constructed using LLR. The individual and integrative model construction procedures are schematically represented in p-values adjusted using a Bonferroni correction for each class of data . Significantly LTS-associated features were selected with Bonferroni-adjusted q < 0.05. On tests performed on individual traits, the unadjusted p < 0.05 was used for feature selection.Univariate logistic regression (ULR). A univariate logistic regression model was fitted for each gene or probe in every class of genomic data, with the genotype at each variant site as a predictor Y = 0,1 (non-LTS vs. LTS) with β =  by restricting the sum of the absolute values (L1 norm) of the coefficients βj to values no greater than the shrinkage parameter λ. By selecting an appropriate λ, a Lasso model can be tuned to include any number of variables in the final regression model; smaller values of λ will set more coefficients βj to zero, effectively removing them from the model. We implemented Lasso logistic regression using the ‘glmnet’ library in R with the binomial distribution option, reflecting the binary response variable Y. λ was selected using 10-fold cross validation so that the model minimizes cross-validation error. Prediction performance was evaluated with Area Under the Receiver Operating Characteristic Curve (AUC) estimation, a commonly used evaluation metric for binary classification. A perfect model will score an AUC of 1, while at the other extreme an AUC near 0.5 reflects models with no predictive power that essentially select Y = 0,1 by a random guess.Lasso logistic regression (LLR). Least absolute shrinkage and selection operator (Lasso) is a penalized multivariable regression model whereby parameter shrinkage and feature selection are done simultaneously . Lasso ip < 0.05 in the ULR models; only those genes/probes above the threshold were pooled for multiple regression analysis. We identified a core set of n = 212 samples (including 23 LTSs) with all molecular data types represented except for point mutations. Both somatic and germline point mutations were excluded from the combined data sets because no point mutations were significant predictors of LTS in adjusted ULR models and their inclusion would have produced a much smaller sample set since so few tumors contained any particular somatic mutation. Consequently, separate regression analyses were performed with clinical information and point mutation genotypes as predictors of LTS.Integrative Models. Integrative LASSO logistic regression models were constructed by using 4 classes of molecular data in combination as predictors of LTS, i.e. clinical information, gene expression levels, DNA methylation scores, CNV counts, and miRNA expression levels were used in combination to identify subsets of molecular markers that were jointly predictive of LTS. To prevent overfitting, initial feature selection was performed for each class of data by selecting all variables with unadjusted A total of 69213 features representing the 4 classes of molecular data from 212 patients were jointly modeled using LASSO logistic regression. Prior to model fitting, each variable was z-transformed to zero mean and unit variance so that variables across different classes of data would be on the same scale. We also constructed regression models using specific combinations of data classes and excluding others (e.g. gene expression + methylation data used to predict LTS while excluding CNV and miRNA data etc), as this allows us to compare predictive performance of models and to determine the marginal effects of incorporating additional data classes on LTS prediction. To evaluate the prediction performance for individual models, we performed tenfold cross-validation and computed the mean AUC over 100 iterations. In every iteration, the data set was divided into 10 subsets, and the LLR was repeated 10 times. One of the 10 subsets was used as the test set and the other 9 subsets were pooled to form a training set in order to compute the average AUC across all 100 iterations. The advantage of this method is that it minimizes the bias from the division of data into training and test sets.Because LTS account for <10% of GBM samples, logistic and LASSO regression analyses of the entire data set by necessity use imbalanced data, which can potentially bias estimation and prediction in logistic regression and machine-learning models . To detep-values was performed for each probe to compare mean expression levels between the sample sets. DEGs were identified as those in which the FDR adjusted q < 0.01 and the median log fold-change across probes was at least two fold (|log2FC| ≥ 2).To explore the relationship between LTS GBM and LGG tumors, we represent each sample in a coordinate space defined by the principal components of gene expression and methylation measures. Gene expression data from the AgilentGA4502A microarrays for both GBM and LGG samples was analyzed following Loess normalization and quantile normalization to correct for within and between-array bias, respectively. Significantly differentially expressed genes (DEGs) were identified as follows: a Student’s t-test with Benjamini-Hochberg FDR correction of the I, a measure of autocorrelation, was used to measure the extent to which samples from a defined subset (e.g. LTS patients) cluster together due to similar expression values [I value near 0 indicates that LTS expression levels are randomly dispersed among the GBM samples, a value near 1 indicates that the LTS samples are closest to one another in PC space, while a value of -1 indicates a perfectly uniform spacing of LTS and non-LTS samples in PC space . The same approach was used to study DNA methylation patterns between GBM and LGG with three different gene sets: 1. All genes with methylation probes; 2. ULR selected genes; 3. LLR selected genes.Principal Component Analysis (PCA) was performed on four sets of genes: 1. DEGs between LGG and GBM tumors. 2. DEGs between GBM and normal brain tissues. 3. genes whose expression levels were significant predictors of LTS in the ULR models. 4. genes selected for inclusion the LLR model. For each set of genes, the expression values were projected onto principal components 1 and 2, representing each sample’s coordinates in this PC space. Moran's n values . This meAdditionally, we used unsupervised hierarchical clustering of gene expression values to compare LTS with non-LTS GBM and LGG expression profiles. In this cluster analysis, each sample is represented as a vector of expression values and classified by pairwise Pearson Correlation Coefficient distance.p = 0.9877), a distribution-free test for bimodality, which suggests that survival times follow a homogeneous, unimodal distribution.For the 591 patients in this study, the median age at initial diagnosis is 59 years; the patient cohort is 61.4% male . We consAmong the 591 patients with known vital status and survival times, 44 7.4%) survived longer than three years—a commonly-used survival milestone for GBM patients .4% survi–and wereIn a logistic regression model that included only clinical and demographical variables, younger age at initial diagnosis, Karnofsky performance score (KPS) and presence/absence of chemotherapy were the three significant predictive factors of LTS . While pr = -0.323, p = 1.93E-9) correlation between KPS and age, as well as the small fraction of patients (13.5%) who did not receive chemotherapy. Moreover, an identical set of predictor biomarkers and nearly identical coefficients were obtained from LLR that initially included KPS and chemotherapy as from models where only age is initially included on the same set of samples. Because of this and the reduction of sample size, age is the only clinical variable included in subsequent regression analyses.We evaluated the regression models with or without the inclusion of KPS and chemotherapy, and found that they converged to a set of predictors that included age but not KPS or chemotherapy , presumaIDH1 and PRSS1  gene and the TGS1 (trimethyguanosine synthase), with β = -0.28, -0.18, respectively, indicating that the wild type genotypes at these loci are weakly predictive of LTS , among these are mutations in genes whose somatic variants are well-known to correlate with GBM survival time such as nd PRSS1 . LLR idend PRSS1 . Most ofq < 0.05) found a significant enrichment in phosphoproteins  and genes in acetylation pathways   [NEUROG1, a transcription factor regulating growth of neurons [For the gene expression data, there are 38 significant LTS predictors with FDR-adjusted ULR  vs 94 wi.39E-06) . In contLETMD1, a known oncogene [CDKN1B [RSPO3 [TNS4, whose oncogenic role has been documented for colorectal and other cancers[MGMT promoter region methylation with LTS in LLR models, nor is methylation of this region a significant LTS predictor in a ULR model following FDR correction. However, the association between MGMT hypermethylation and LTS is significant in a ULR model (p = 0.036) without adjustment. There was no overlap between the set of genes that were differentially expressed between LTS and non-LTS and those that were differentially methylated.Among the 4 classes of genomic data, DNA methylation is the strongest predictor of LTS with the highest mean AUC  in LLR models , which woncogene , the kno [CDKN1B , as wellB [RSPO3 . All of r cancers, has thehsa-miR-222. The regression coefficient of hsa-miR-222 expression levels on LTS is -0.169, indicating that downregulation of this miRNA is associated with LTS. There are 11 and 29 significant CNV probes prediction LTS with ULR and LLR, respectively (DUSP28 [HPR CNVs (i.e. duplication at this locus is correlated with LTS). Mutations in HPR have been documented in the literature as predictors of recurrent breast cancer [STAM, encoding a signal transduction adapter molecule, was found to be a significant predictor in both gene expression and CNV analyses. Higher expression and amplification of this gene was associated with LTS . The same is true when multiple classes of genomic data are combined in a single regression model, e.g. expression+methylation+microRNA data combined with age give virtually identical AUC values to age alone, indicating that pooled biomarker data does not outperform individual classes of biomarkers in LTS prediction, perhaps as a consequence of increased number of features. Applying a more stringent cutoff (4.5 years) for LTS classification does not change the results qualitatively in terms of either the significant predictors or the magnitude of AUC . This indicates that the results of the regression analyses are not strongly predicated on the choice of cutoff time used to define LTS.In terms of AUC, combining one or more classes of genomic data with age in an LLR does not strongly enhance prediction of LTS when compared to age alone. It can be seen in BRAF does not appear as a significant predictor, whereas in the integrative model it has β = 4.62. While CAV1  appears in the LLR model, its β = 11.34 value is much larger in the joint regression model than in LLR on methylation data alone. Downregulation or loss of function in CAV1 has previously been documented as a determinant of aggressiveness in GBM tumor growth [The principal value of constructing integrative LLR models lies in the fact they identify molecular markers that are jointly significant predictors of LTS which are not individually predictive in ULR, nor predictive in LLR when applied to a single class of data see . For exar growth .DCT, dopachrome tautomerase). This result is consistent with methylation being the strongest individual predictor of LTS in both integrative models and in models that only incorporate a single class of data.All but one of the gene expression probes that appear in the integrative LLR model are not significant LTS predictors for LLR on expression data alone. We remark, however, that most of these expression probes are only weakly predictive of LTS in the joint model, with β≤0.28 , and all LGG tumors share common nodes in the hierarchical clustering analysis discussed below . However, most of the neighbors of LTS in the dendrogram are non-LTS GBMs, even for those in the subtree that contains LGG (as in the scatterplot). The same is true of GBMs in patients who have a prior history of LGG, that is, known secondary GBMs occur throughout the dendrogram and show no specific association with LGG expression profiles. In fact, only a single LTS has a documented prior history of LGG (corresponding to an insignificant OR = 1.04 for LTS association with LGG). From both the hierarchical clustering and PCA analyses, we conclude there is no significant association between progression from LGG and subsequent LTS in GBM.In the hierarchical clustering analysis, selecting a twofold partition generates one subtree that consists solely of GBMs and another that contains both GBM and all LGG samples  cluster with LGGs when the dendrogram was partitioned into two clusters  samples in the data set. On the other hand, given the abundance of high frequency variant germline genotypes, the small number of germline mutation predictive of LTS is somewhat surprising, since genome wide association studies (GWAS) , 49 haveAlthough mutational genotypic markers for LTS are limited, we did identify gene expression phenotypes, epigenetic markers, and copy number variant genotypes that are significantly predictive of LTS, with the exception of DNA methylation. The fact that DNA methylation has comparable or better predictive power than age is probably related to the coordinated regulation of aging and DNA methylation patterns ; the samApart from this comparatively small set of genomic markers, there is no strong genomic “signature” that unambiguously distinguishes LTSs from other GBM. No molecular markers unique to LTS were identified, and there wasn’t the large-scale statistical difference in either gene expression or methylation patterns such as those observed between GBM and LGG. This result is consistent with the observed unimodality of the survival time distribution for the GBM patients . If LTS Furthermore, while there are biomarkers that significantly predict LTS in logistic regression models, there are no molecular profiles that strongly define LTS as a distinct subclass in the way that LGGs are molecularly distinct from GBM. This is evident from the occurrence of LTS samples throughout PCA scatterplots, and the lack of a single node or subtree subtending most or all LTSs in dendrograms. Such results suggest that there are many patterns of gene expression and methylation that lead to LTS phenotypes. Similarly, the lack of a consistent molecular signature among secondary GBMs, or a shared signature between secondary GBM and LTS implies that secondary GBMs are not associated with improved patient outcomes in comparison to primary GBM.IDH1+ GBMs have expression profiles resembling LGG may indicate that IDH1 mutated GBMs are either misidentified LGGs or represent a unique, LGG-like pathology among high-grade gliomas, this observation does not account the majority of LTS cases. The fact that the overall gene expression and methylation profiles of LTS tumors lack a unique molecular signature and don’t show a significant similarity to LGGs simply indicates that there are multiple genomic paths to similar clinical phenotypes and patient outcomes, just as there are multiple genetic and epigenetic paths to cancer. We anticipate that the molecular correlates of different categories of LTS will be further elucidated once larger data sets become available.Finally, this study found no tendency among LTS GBM cases to resemble the molecular profiles of LGG, which argues strongly against LTS cases being misdiagnosed LGGs. This observation, together with a lack of an association between LTS and secondary GBM, also suggests that LTS in GBM is not a consequence of the retention of LGG-like biological features in high-grade glioma tumor cells. Our finding that only S1 FigIDH status and LGG history, red indicates IDH1+ or history of LGG diagnosis; grey indicates IDH1- or no history of LGG diagnosis; white indicates no data available for the sample.Hierarchical clustering (HC) on the expression levels of ULR predictor genes (N = 38) was used to classify the samples. The row dendrogram, showing the relationship among samples, was based on Pearson correlation coefficients, thecolumn dendrogram on Spearman correlation coefficients. In the row-side color bars of (TIF)Click here for additional data file.S2 FigIDH status and LGG history, red indicates IDH1+ and a history of LGG diagnosis, respectively; grey indicates IDH1- or no history of LGG diagnosis, respectively; white indicates that no data is available for the sample.Hierarchical clustering (HC) on the expression levels of LLR predictor genes (n = 94) was used to classify the samples. The row dendrogram (clustering of samples) is based on Pearson correlation coefficient and column dendrogram on Spearman correlation coefficient. In the row-side color bars of (TIF)Click here for additional data file.S1 Tableβ) in Lasso regression model are listed in the table.The genes with non-zero penalized regression coefficients ((XLSX)Click here for additional data file.S2 Tablep < 0.05 are shown. For the other classes of data, genes with adjusted q < 0.05  were shown. “Estimate” is the coefficient associated with the variable; “Std.Error” is the standard error associated with these estimates; “Pr (>|z|)” is the p-value associated with the z-value.For somatic point mutation, genes with unadjusted (XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Tablep-value (Benjamini-Hochberg) q < 0.05 for related annotation terms are shown. Fold enrichment is the ratio of annotation terms of LTS predictors to those of all genes.Annotation clusters with adjusted (XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


Chromosomal aberrations such as deletions, duplications and translocations of DNA segments can lead to highly significant positive correlations of gene expression levels of neighboring genes. This should be utilized to improve the analysis of tumor expression profiles. Here, we develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in tumor. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the simultaneous usage of higher-order state-transitions and autoregressive emissions as novel model features. We apply autoregressive higher-order HMMs to the analysis of breast cancer and glioma gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other related methods. The performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions. This benefit could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumors independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas. An implementation is available under  Copy number changes of genes are frequently found in different types of cancer Over the last years, several approaches have been developed for the analysis of tumor expression profiles in the context of chromosomal locations of genes. Methods like CGMA  Generally, HMMs provide a sound mathematical grounding for the analysis of biological sequences However, this only addresses the first limitation by overcoming the restrictive first-order state-transition process. Independent from that, extensions of the standard emission process have been initially realized in speech recognition by the integration of autoregressive emission distributions enabling the modeling of state-specific direct local dependencies within a range of successive measurements To overcome this, we develop a novel model class of autoregressive higher-order HMMs enabling an improved modeling of local dependencies between successive measurements. Autoregressive higher-order HMMs simultaneously utilize higher-order state-transitions in combination with autoregressive emissions as novel model features. Globally, this model class has very general modeling capabilities including mixture models, standard first-order HMMs and higher-order HMMs as special cases. We motivate the development of autoregressive higher-order HMMs by considering the analysis of individual tumor expression profiles in which local dependencies of gene expression levels are frequently caused by deletions and duplications of underlying chromosomal regions. The existence of such local chromosomal dependencies between expression levels of genes in close chromosomal proximity is clearly shown for three different types of cancer in We apply our autoregressive higher-order HMMs to the analysis of publicly available breast cancer and glioma gene expression data. We first systematically evaluate the performance of autoregressive HMMs to predict overexpressed genes in breast cancer with known underlying increased gene copy numbers. Then, we analyze to which extent these results are transferable to other similar breast cancer expression profiles. We further complement this by a sensitivity analysis evaluating the robustness of autoregressive HMM predictions. Next, we perform an in-depth comparison study to related existing methods on breast cancer gene expression profiles, where we first investigate general characteristics of predicted differentially expressed genes followed by the identification of overexpressed genes with known increased copy numbers. Finally, we consider different types of gliomas and demonstrate that autoregressive HMMs are useful tools to reveal systematic differences in expression levels of genes in known cancer signaling pathways.The materials part provides a brief overview of the considered data sets. The methods part gives a detailed description of the model class of autoregressive higher-order HMMs.This section shortly introduces the considered breast cancer and glioma data sets.Pollack et al. A breast cancer data set by We created a glioma gene expression data set based on data from the Repository for Molecular Brain Neoplasia Data  This section develops the basics of the model class of autoregressive higher-order HMMs in the context of the analysis of tumor expression profiles. This includes a detailed model description, a general characterization of the model class, the integration of prior knowledge into the training of the model, basics of the model training and the model initialization.S :  = {‘ = ’,‘−’,‘+’}. Considering the usually observed distribution of log-ratios  algorithms Since we propose autoregressive Gaussian emissions as a novel model feature, we briefly summarize important steps of the estimation of the corresponding autoregressive means. The maximization step of the emission parameter estimation part of the log-posterior leads to a state-specific system of linear equations that enables to update the basic mean and the corresponding coefficients for each autoregressive mean. Standard solvers for systems of linear equations can be used to compute the solution of the given system. We utilize the publicly available Jama package Specific details of the estimation of initial state, transition and emission parameters of the autoregressive higher-order HMM and a computational scheme of the training algorithm are given in Appendix B in For the identification of differentially expressed genes in tumors, an initial autoregressive HMM must be specified to enable a good adaptation of the model to the data during the training. We transfer initial model and prior settings described in This basic initialization strategy has led to robust results in With the goal of performing exhaustive comparisons of different autoregressive HMMs, we trained each HMM with an autoregressive emission process of order In this section, we first systematically compare different autoregressive HMMs for their performance to identify overexpressed genes with underlying increased copy numbers in breast cancer. Next, we analyze to which extent the obtained results can be transferred to other similar breast cancer gene expression profiles. This is complemented by performing a sensitivity analysis to study the robustness of predictions obtained by autoregressive HMMs. After that, we compare the performance of autoregressive HMMs to related existing methods. Here, we first investigate the different methods for general characteristics of their predictions of differentially expressed genes. We further compare the different methods based on their identification of overexpressed genes with underlying increased copy numbers in breast cancer. Finally, we transfer autoregressive higher-order HMMs to different types of gliomas and perform an in-depth systematic analysis of differentially expressed genes with respect to known cancer signaling pathways.Pollack et al. We utilize breast cancer gene expression and corresponding gene copy number data by Seifert et al. Here, we do this in analogy to To analyze the performance of the identification of overexpressed genes with underlying increased copy numbers by different autoregressive HMMs, we used fifty percent of the breast cancer gene expression data set. We trained each initial AR. This is also reflected in closely related array comparative genomic hybridization (aCGH) data analysis pipelines, where models are initially adapted and later used for the analysis of a single individual data set, but where transfers of trained models to other data sets are typically not done. However, a successful transfer of trained HMMs from one data set to a new data set can especially save time for the analysis of large data sets by avoiding time-consuming training steps. Thus, we transferred all previously trained autoregressive HMMs to other similar breast cancer gene expression profiles to evaluate the reached model performances after this transfer in analogy to our previous study.The results are summarized in Thus, this study indicates that a careful transfer of previously trained autoregressive HMMs to other similar tumor expression profiles can be realized, but one should keep in mind that a faster analysis of large data sets may result in reduced accuracy of predictions.We additionally performed a sensitivity analysis by systematically varying initial model parameters and prior parameter settings to analyze the robustness of the predictions made by autoregressive HMMs. Since this is generally very time-consuming, we focused on those parameters that are expected to have the greatest influence on the prediction of differentially expressed genes. We first tested twelve different model initializations and trained corresponding autoregressive HMMs on fifty percent of the breast cancer gene expression data set using identical prior hyperparameter settings. Specific details are given in Table S1 in y stable  and widey stable . Similary stable .We next tested twelve different prior parameter settings and trained the corresponding autoregressive HMMs utilizing our generally considered fixed initial model parameterization described in the methods section. Details of the tested prior parameter settings are provided in Table S1 in In summary, this study indicates that changes of the initial model parameter settings and of the prior parameter settings do not substantially affect the predictions of autoregressive HMMs. Thus, the predictions of autoregressive HMMs are robust with respect to reasonable changes of initial parameter settings.To compare the identification of differentially expressed genes by autoregressive HMMs to related methods, we analyzed the breast cancer gene expression data set We applied all methods with their proposed initial standard settings to all breast cancer gene expression profiles. We note that experts of specific methods might be able to further improve the predictions by fine-tuning of specific model parameters. The different methods implement specific algorithms to adapt their initial parameters or test statistics to the data followed by the prediction step. All methods except ChARM, DSHMM, AR. The classification of gliomas according to these subtypes has important implications for the prognosis and the treatment of glioma patients A representative global overview of how strongly oligodendrogliomas, astrocytomas and glioblastomas are affected by under- and overexpression in specific cancer signaling pathways with respect to random expectations is shown for the top 300 genes in Figure S8 in In more detail, considering underexpressed genes, we observe that the same cancer signaling pathways are always affected by nearly the same number of genes independent of the type of glioma  only higher-order state-transitions or (ii) only autoregressive emissions did not lead to performances reached by the best-performing autoregressive HMMs that combine these two features simultaneously. The best-performing models had state-transitions of order two to three in combination with autoregressive emissions of order two to four. For the analysis of other data sets, we suggest to consider the most parsimonious model of the best-performing models. That is, one may use the HMM with second-order state-transitions and second-order autoregressive emissions. Since this only involves second-order state-transitions, such a model can also be trained within reasonable time on large data sets. Altogether, our results clearly indicate that autoregressive higher-order HMMs are valuable tools for the analysis of individual tumor expression profiles.Currently, autoregressive higher-order HMMs enable to analyze expression levels of genes in tumor with respect to their linear chromosomal order. This linear processing of gene expression levels allows to improve the predictions via the modeling of existing local chromosomal dependencies. This provides a solid basis that could be further extended by taking prior knowledge about underlying transcriptional networks e.g.  or spatiText S1Mathematical basics of the prior distributions for initial state, state-transition and emission parameters and details of the chosen prior hyperparameters are given in the section 'Appendix A: Prior distributions’. A detailed derivation of the Bayesian Baum-Welch algorithm for autoregressive higher-order HMMs is given in the section ‘Appendix B: Bayesian Baum-Welch algorithm’. An in-depth analysis of identified hotspots of differential expression in gliomas is given in the section ‘Appendix C: Application of Autoregressive Higher-Order Hidden Markov Models to glioma data’. The supporting Figures S1–S8 are provided in the section ‘Supporting Figures’. The supporting Tables S1–S4 are given in the section ‘Supporting Tables’.(PDF)Click here for additional data file.


Advances in DNA microarray technologies have made gene expression profiles a significant candidate in identifying different types of cancers. Traditional learning-based cancer identification methods utilize labeled samples to train a classifier, but they are inconvenient for practical application because labels are quite expensive in the clinical cancer research community. This paper proposes a semi-supervised projective non-negative matrix factorization method (Semi-PNMF) to learn an effective classifier from both labeled and unlabeled samples, thus boosting subsequent cancer classification performance. In particular, Semi-PNMF jointly learns a non-negative subspace from concatenated labeled and unlabeled samples and indicates classes by the positions of the maximum entries of their coefficients. Because Semi-PNMF incorporates statistical information from the large volume of unlabeled samples in the learned subspace, it can learn more representative subspaces and boost classification performance. We developed a multiplicative update rule (MUR) to optimize Semi-PNMF and proved its convergence. The experimental results of cancer classification for two multiclass cancer gene expression profile datasets show that Semi-PNMF outperforms the representative methods. In cancer prognosis and treatment, it is crucial to identify different cancer types and subtypes. Traditional methods often rely on similar morphological appearances but easily induce different clinical courses and responses to therapy because of subjective interpretations and personal experience. This usually results in diagnostic confusion. Fortunately, the emergence of the DNA microarray technique removes this barrier in an objective and systematic manner and has showed great potential in outcome prediction of cancer types in genome-wide scales –11.et al. [et al. [et al. [et al. [et al. [Numerous learning methods have been developed for cancer classification based on gene expression profiles –3. For iet al.  used a w [et al.  used the [et al.  utilized [et al. ) to clas [et al.  investig [et al. ) in cancet al. [et al. [et al. proposed sparse NMF  bec bec18]) et al.  originalet al.  show tha T ∈ Rn × m, where n denotes the number of samples and m their dimensionality, PNMF learns the coefficients H ∈ Rn × r to represent original samples, i.e.,F denotes the matrix Frobenius norm and r the number of clusters.Projective non-negative matrix factorization (PNMF) learns a non-negative projection matrix to project high-dimensional data into the lower-dimensional subspace. Because it can learn parts-based representation, PNMF has been widely applied in pattern recognition , 35, 37.As in objective α ≥ 0 is a regularization constant and W denotes the non-negative cluster centroid. Model The objective is very similar to BPNMF , but we β trades off the local coordinate regularization and Hij denotes the i-the row and j-th column element of coefficients H, Wj and Vi, signifying the i-th and j-th row vector of W and V, respectively.According to d examples labeled and the rest unlabeled; the data matrix V can be divided into two parts, i.e., Q denotes the partial label matrix wherein Qij = 1 if vi belongs to the j-th class; otherwise, Qij = 0. Both HU and nU denote the coefficients and number of the unlabeled samples, respectively.To make full use of partial labeled samples, we propagate the labels of labeled samples to unlabeled ones by minimizing the distance between their coefficients and the corresponding class indicator. Particularly, we require the coefficients of labeled samples to be equivalent with the corresponding class indicator. Consider the first Interestingly, Semi-PNMF has two distinct aspects. First, it replaces the learned coefficients of the labeled samples with the corresponding class indicator. The constraint is so strong that the learned basis completely biases the labeled samples. This might induce the trivial solution to the coefficients of the unlabeled samples. Second, Semi-PNMF completely ignores the representation contribution of the labeled samples. It is so unintelligible that the learned basis only favors the unlabeled samples. It appeared that both aspects contradict each other, but intrinsically, they mutually complement each other in our Semi-PNMF. In essence, the first aspect corresponds to supervised learning, which generates the reasonable solution yet does not ensure it is consistent with the underlying data distribution, while the second one considers data distribution but cannot yield the reasonable solution. Thus, the combination of both aspects can mutually complement each other. Semi-PNMF learns the shared basis by the labeled and unlabeled instances, meanwhile inducing similar instances to have a similar representation, i.e., the coefficients. Because we impose the restriction that coefficients of the labeled samples be their labels as well as the local coordinate constraint over the basis and coefficients, the unlabeled sample coefficients are implicitly as sparse as the label vectors. In this way, Semi-PNMF effectively propagates the labels of labeled samples to the unlabeled ones. Consequently, in cancer classification, it is reasonable that, for each unlabeled sample, we choose the index of the largest entry of its coefficient to predict the classes of this sample once objective W and H. Fortunately, it is convex with respect to W and H, respectively. Thus, we can establish the following theorem:It is difficult to optimize Theorem 1: The objective function FU = diag(sum(HU)), A =  wherein B = , wherein b = diag(WWT).Proof. According to W as follows:i-th row vector values of VU.J(W) asBy Obviously, objective We can obtain the derivative of J(HU) as follows:By simple algebra, the formula Setting HU.Thus, according to Moreover, according to Eqs , 12) an an12) anBased on Algorithm 1.According to the above theorem, we summarize the multiplicative update rule (MUR) for Semi-PNMF in Algorithm 1 MUR for Semi-PNMFInput: Examples V ∈ Rm × n, penalty parameter α, partial label matrix Q.Output: HU.W0 and l = 0. 1: Randomly initialize repeat 2: Wl+1 according to  3:  Update  4:  Calculate l ← l+1. 5:  until {Stopping criterion  6:  7: Algorithm 1 utilizes the objective relative error as the stopping criterion; in addition, set ɛ to 10−7 in our experiments. The main time cost of Algorithm 1 lies in line 3 and line 4. Their time complexities are O(r2n+mrn+r2m+rm) and O(mr(n − d)+r2m+rm+r2+r2(n − d)), respectively. Thus, the total time complexity of Algorithm 1 is O(r2n+mrn+mr(n − d)+mrd+r2m+rm+r2+r2(n − d)).To reduce the time overhead, This section conducts a series of experiments on both synthetic and real-world datasets to verify the method proposed in this paper.x ∈ R3, and each of its entry is sampled from the standard uniform distribution U. For each category, we randomly generated 10 samples, within which three samples were selected as labeled samples and the rest as unlabeled ones. Therefore, the synthetic dataset contains 30 samples in total. For clear illustration, three categories are marked as three different colors, and the labeled and unlabeled samples are distinguished by two shapes.This section generates a small synthetic dataset to clarify the mechanism of Semi-PNMF. The synthetic dataset consists of three categories constructed by the following random samples:http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi, and can also be downloaded from the website: https://zenodo.org/record/21712. According to [α = 2, and β = 0.0001, respectively. Because these representative methods enable convergence within 1,500 iteration rounds, we set the maximum number of loops to 1,500. For LDS and TSVM, we adopt the parameter settings provided in the source code to obtain the classification results.This experiment merely compares traditional semi-supervised learning methods including low density separation , transdrding to , we combWe evaluate the cancer classification by the cross-validation over the whole dataset. This process selects one sample as the unlabeled sample and, meanwhile, learns the prediction model on all the samples for cancer diagnosis. For the unlabeled sample, we choose the index of the largest value of the resultant consensus matrix to predict the classes of this sample. As shown in Figs TP, TN, FP, and FN denote the number of true positive, true negative, false positive and false negative samples, respectively.Figs The number of labeled examples is an important factor affecting the performance of semi-supervised learning methods. Hence, it is very necessary to observe the classification accuracy of Semi-PNMF under different numbers (1–6) of labeled samples in each class. Here, we randomly select different numbers of examples from each class as labeled examples and regard the rest as unlabeled. For fair comparison, we independently conduct 100 individual experiment trails to remove the effect of randomness.http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13159), and can also be downloaded from the website: https://zenodo.org/record/21712. We replace the unavailable entries of this dataset with the average values of their k-nearest neighbor elements. This dataset consists of 2,096 samples along with 54,675 probes in total. This dataset contains different cancer subtypes of the acute leukemia and thus is not suited for cancer classification in contrast with the GCM dataset. We also conduct a cancer classification experiment to verify the classification performance of Semi-PNMF compared with low density separation , transdα = 0.2, and β = 0.01. For the traditional semi-supervised learning methods, we adopt the same configurations as the above subsection. The cross-validation process of the above subsection is repeatedly conducted to evaluate the compared methods on this dataset. As shown in Figs For Semi-PNMF, we set two parameters Figs The number of the labeled samples is an important factor affecting the performance of semi-supervised learning methods. Hence, it is very necessary to observe the classification accuracy of Semi-PNMF under different numbers (1–6) of labeled samples in each class. Here, we randomly select different numbers of examples from each class as labeled examples and regard the rest as unlabeled. Then, we independently conduct 10 individual experiment trails to remove the effect of randomness.This paper proposes the semi-supervised PNMF method (Semi-PNMF), which incorporates two types of constraints as well as the auxiliary basis to boost PNMF. Particularly, Semi-PNMF utilizes the linear combination of examples to approximate the cluster centroids such that the cluster centroids have more powerful representative ability. To effectively indicate the classes of unlabeled samples, Semi-PNMF enforces the coefficients of labeled samples to approach their labels, meanwhile representing the unlabeled samples using the identical cluster centroid. To optimize Semi-PNMF, we devised the multiplicative update rule (MUR) to establish the convergence guarantee. Experiments of cancer classification on two real-world datasets show that Semi-PNMF outperforms the representative methods in terms of quantity.Recently, Bayesian methods that incorporate both sparsity and a large number of covariates in the model have been extensively used for parameter estimation and classification in data sets compared to small sample sizes such as gene expression data –41. They


Histologically similar tumors even from the same anatomical position may still show high variability at molecular level hindering analysis of genome-wide data. Leveling the analysis to a gene regulatory network instead of focusing on single genes has been suggested to overcome the heterogeneity issue although the majority of the network methods require large datasets. Network methods that are able to function at a single sample level are needed to overcome the heterogeneity and sample size issues.We present a novel network method, Differentially Expressed Regulation Analysis (DERA) that integrates expression data to biological network information at a single sample level. The sample-specific networks are subsequently used to discover samples with similar molecular functions by identification of regulations that are shared between samples or are specific for a subgroup.We applied DERA to identify key regulations in triple negative breast cancer (TNBC), which is characterized by lack of estrogen receptor, progesterone receptor and HER2 expression and has poorer prognosis than the other breast cancer subtypes. DERA identified 110 core regulations consisting of 28 disconnected subnetworks for TNBC. These subnetworks are related to oncogenic activity, proliferation, cancer survival, invasiveness and metastasis. Our analysis further revealed 31 regulations specific for TNBC as compared to the other breast cancer subtypes and thus form a basis for understanding TNBC. We also applied DERA to high-grade serous ovarian cancer (HGS-OvCa) data and identified several common regulations between HGS-OvCa and TNBC. The performance of DERA was compared to two pathway analysis methods GSEA and SPIA and our results shows better reproducibility and higher sensitivity in a small sample set.http://csbi.ltdk.helsinki.fi/pub/czliu/DERA/.We present a novel method called DERA to identify subnetworks that are similarly active for a group of samples. DERA was applied to breast cancer and ovarian cancer data showing our method is able to identify reliable and potentially important regulations with high reproducibility. R package is available at The online version of this article (doi:10.1186/s12885-015-1265-2) contains supplementary material, which is available to authorized users. Novel measurement technologies, such as microarrays and deep sequencing, provide quantitative genome-scale data from diseases, such as cancers, in an unprecedented resolution and speed. Computational methods to analyze and interpret large-scale biological data have become an integral part of medical research to gain knowledge that leads to personalized disease prevention, prognosis and treatment.Particularly in cancers, genome-scale studies have revealed large molecular heterogeneity between patients and even different samples from the very same tumor . AlthougThe breast cancer subtypes have different standard drug treatments based on marker protein expression: HER2 breast cancers are treated with HER2 inhibitors, such as trastuzumab, whereas luminal breast cancers are treated with adjuvant endocrine therapy, such as aromatase inhibitors. TNBC has contrasting features as there is no beneficial standard therapy for majority of patients, probably reflecting the heterogeneity of this subtype .To gain a more comprehensive view to fundamental molecular level processes altered in cancer and suggest effective treatment options, several network level approaches have been suggested -7, such To address these two challenges, we present a novel approach called Differentially Expressed Regulation Analysis (DERA). DERA elevates the analysis of expression data to a network level instead of focusing on single genes. DERA integrates expression data with biological network instead of individual canonical pathways and identifies subnetworks that are similar active for a group of samples. These advantages of DERA are particularly useful to identify subnetworks across interconnected pathways. DERA is suitable to analyze data from small or medium size cohorts, which are challenging to analyze with statistical methods. To show the utility of our approach we applied DERA to TNBC ,14 and hA schematic illustration of the DERA approach is shown in Figure To construct sample-specific networks we take advantage of publicly available databases: Pathway Commons , WikiPataccession number GSE7904 [We analyzed level 1 Agilent two-color gene-expression microarray data for 522 primary breast tumors and 59 controls from The Cancer Genome Atlas (TCGA) repository  as disco GSE7904 ) for theWe also analyzed level 1 Agilent two-color gene-expression microarray data for 572 primary ovarian cancers and 8 controls from TCGA. Out of 572 primary ovarian cancer, we selected 448 high-grade serous ovarian cancer (HGS-OvCa) according to Federation of Gynecology and Obstetrics standards.For TCGA Agilent array breast and ovarian cancer data, expression intensities for tumors and controls were log2 transformed. This was followed by mean-centering across genes. We removed probes that a) mapped to multiple genes or b) did not map to any gene before identifying differentially expressed genes. For GEO data, gene level normalization was performed by using Robust Multi-array Average (RMA) .Differentially expressed genes for each sample are identified as follows  and heterogeneous samples in TNBC [A core regulation is defined as a regulation network which is identical within a subgroup of samples and represents at least  in TNBC . We usedWe have applied DERA into breast cancer and ovarian cancer data sets. In the breast cancer study, our aim was to identify regulations that were unique to TNBC in comparison to other breast cancer subtypes. The aims of the ovarian cancer case study were to test robustness of DERA and compare regulations identified in ovarian cancer to TNBC as they are recently suggested to share similar molecular characteristics .DERA was applied to breast cancer gene expression data to characterize gene regulations that occurred uniquely in TNBC in comparison to other breast cancer subtypes. We analyzed gene expression and clinical data from 366 treatment-naive breast cancer tumors from TCGA_Array data that had ER, PR and HER2 status available. From these samples, 55 samples were categorized as TNBCs . Additionally, we used expression data from 59 samples of normal breast tissue to identify differentially expressed genes for each individual sample.For validation of the results emerging from discovery cohort (TCGA_Array) we used data from two publications. First, we used data (GEO cohort) from David M. Livingston and colleagues who published gene expression cohort for 17 TNBC, 26 non-TNBC and seven normal breast tissue samples . Second,DERA identified 256 core regulations that occurred in at least half of the TNBC samples in the discovery data (TCGA_Array). Reproducibility of the results was tested in two independent cohorts resulting in verification of 110 core regulations  was the most significantly enriched pathway. This is consistent with high proliferative nature of TNBC and with previous findings [We then used DAVID  to identfindings ,26. TogeFOS is present in four different pathways   were over-expressed and promotes cell proliferation [TFDP1, which activates CDKN2A, RRM2, CDC6, TK1 and TYMS, implicates oncogenic activity [FOS, EDN1 and/or JUN, that regulate MMP1, and under-expression of FOSB, that regulates MMP9, are interesting findings because activation of MMP1 and MMP9 has been known to be involved in breast cancer initiation, invasion and metastasis [The 110 core regulations consisted of 28 distinct subnetworks  were unferation -33. Anotactivity , prolifeactivity , and invactivity . Under-etastasis ,37.n=366), TCGA_Seq (n=319) and GEO (n=43) cohorts show that these differentially expressed genes are associated with TNBC phenotype and can distinguish TNBC samples from the other subtypes , Luminal 2 (n=69) and Non-luminal HER2+ (n=23) breast cancer subtypes as for TNBC (n=55) using the TCGA_Array data. The number of differentially expressed genes (n=189) composing the core regulations in TNBC was much higher than that in Non-luminal HER2+ (n=150), Luminal 1 (n=109) and Luminal 2 (n=150), which reflects the fact that in general TNBCs are more aggressive, larger in size and higher grade than the other breast cancers [We performed identical DERA analysis for Luminal 1  and highly correlated . Importantly, regulation pattern of XBP1 by FOXA1 was associated with breast cancer survival  and visible in another cohort (GSE3494)   and validation set (n = 246), and identical DERA analysis with the TNBC analysis, i.e., cut-off T was 0.5 for the discovery set and 0.4 for the validation set (detailed description in Methods), was performed to the HGS-OvCa data.Expression data from 448 HGS-OvCa samples were randomly divided into discovery set , out of 95, 87 differentially expressed regulations were validated . We found that cell cycle related regulation and genes  were up-regulated and PDGFRA regulation was down-regulated in both TNBC and HGS-OvCa.Similarity between HGS-OvCa and TNBC has been seen at molecular level . TherefoFOXM1 activates proliferation related genes , and DNA repair gene BRCA2, was up-regulated in the HGS-OvCa. It has been reported that FOXM1 correlates with poor patient survival and paclitaxel resistance in ovarian cancer [DERA identified a large subnetwork component where transcription factor n cancer . This ren=55) and a small cohort GEO (n=17). In the comparison with GSEA, we created customized gene sets using pathways from WikiPathways to identify the enriched pathways. GSEA was applied to both TCGA_Array and GEO cohorts. There were no pathways which were significantly enriched in both cohorts at false discovery rate (FDR) < 5%   for the TCGA study used here is phs000569.v1.p7.All results in this study are based on existing data, no new experimental material was used. The results published here are in part based upon data generated by The Cancer Genome Atlas pilot project established by the NCI and NHGRI. Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at


The Illumina HumanMethylation450 BeadChip has been used extensively as a cost-effective way to profile nearly half a million CpG sites across the human genome. Here we present DiffVar, a novel method to test for differential variability between sample groups. DiffVar employs an empirical Bayes model framework that can take into account any experimental design and is robust to outliers. We applied DiffVar to several datasets from The Cancer Genome Atlas, as well as an aging dataset. DiffVar is available in the The online version of this article (doi:10.1186/s13059-014-0465-4) contains supplementary material, which is available to authorized users. Howeveret al.  showed tet al. . Howeveret al. , making et al. [et al. [Cancer is a heterogeneous disease. Within each type of cancer there is the potential for tumour growth to be driven by perturbations in many different molecular pathways, and these perturbations will vary from individual to individual. Epigenetic instability or the loss of epigenetic control of important genomic domains can lead to increased methylation variability in cancer, which may contribute to tumour heterogeneity . In a laet al.  observed [et al.  proposedAs mentioned, it has been shown that DNA methylation patterns are associated with an individual’s age . InteresSeveral technologies are available for profiling DNA methylation, based on both arrays and sequencing. While the cost of next-generation sequencing has dramatically decreased, it is still too expensive for many large studies to profile widespread methylation. The introduction of the Illumina Infinium HumanMethylation450 BeadChip is a more affordable alternative for measuring genome-wide DNA methylation at 482,421 CpG sites. Consequently, thousands of tumour and normal tissues have been profiled using these arrays, with numerous cancer data sets now publicly available through The Cancer Genome Atlas (TCGA) ,14. Similimma [minfi [edgeR [DESeq [To date, the main focus when analysing DNA methylation data has been detecting CpG sites that are differentially methylated between groups. Methods to detect differences in means for high-dimensional biological data are well established and include approaches such as those taken in the Bioconductor  softwarelimma , minfi [a [minfi , edgeR [i [edgeR  and DESeR [DESeq . A CpG sR [DESeq -25. In oet al. [et al. [et al. [et al. [Methods for detecting differential variability in high-dimensional ’omics data have not been well addressed in the literature to date. Jaffe et al.  have a set al. . Bar et  [et al. ,28 propo [et al. ,29 and t [et al. . Unfortu [et al. . While H [et al.  implemen [et al.  and outl [et al. . Mislabe [et al. . Hence,  [et al. .DiffVar, is available as a function in the missMethyl Bioconductor R package, and depends on the limma framework. We applied DiffVar to several publicly available cancer data sets from TCGA, as well as a publicly available aging data set [Here we present a new method for detecting differential variability for individual CpG sites in methylation data. Our approach is inspired by Levene’s z-test . It is adata set . When wedata set . Applyinβ values, which capture the proportion of methylation at each CpG site, or M values, defined as the log2 ratio of the methylated to unmethylated intensity. Since M values do not display the severe heteroscedasticity that occurs with β values [M values. If only β values are available they can be transformed into M values using a logit transformation, M = logit(β). Similarly M values can be converted back to β values for interpretation and visualisation. For more details on the array and raw data see The focus of this paper is on methylation data from Illumina’s Infinium HumanMethylation450 BeadChip, although our method can be applied to any high-dimensional data such as gene expression data. The output from the array consists of two measurements for each CpG site, the methylated and unmethylated intensity signal. These two measurements can be used to calculate either β values , DiffVart-test on the absolute or squared deviations of the M values from the group mean. This tests the null hypothesis that the group variances are equal. CpG sites that have deviations consistently larger in one group compared to another group will be identified as significantly differentially variable. Figure M values between the two groups, the variations about the mean are similar in both groups; hence this CpG site will not be significantly differentially variable.As mentioned, our method is inspired by Levene’s z-test . Intuitit-test can result in many false discoveries, hence we employ an empirical Bayes modelling framework to stabilise the t-statistics [nk/(nk+1), where nk is the sample size for group k. Moderated t-statistics [In high-dimensional data, it is well known that a simple atistics . Additioatistics  are compatistics  are repos [nk/nk+, where nM values under a hierarchical model while the second strategy involved resampling data from the kidney cancer data set, using only the non-diseased samples. Details of the simulations are provided in M values were generated for 10,000 CpG sites and with the same variance for each group. CpG sites were tested for differential variability using the F test, Bartlett’s test, DiffVar with absolute deviations, and DiffVar with squared deviations. The numbers of significant raw P values at cut-offs of 0.001, 0.01, 0.05 and 0.1 were counted for each of 1000 simulations. Table P value for all tests, showing that all tests have good type I error rate control in this scenario, although DiffVar with squared deviations appears conservative at 0.001. For the resampled data, the Bartlett test and the F test lose type I error rate control for all nominal P values , the F test and the Bartlett test when analysing the full data set. We randomly sampled 5, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 samples for each group and re-analysed the subsetted data to see how many true differentially variable features were recovered at 5% FDR. To estimate the FDR, we classed any significantly differentially variable CpG not in the list of true differentially variable CpGs as a false discovery. We repeated the sampling procedure 100 times for each distinct sample size.n =20 show more than 80% power to detect differential variability , annotation Figure B, with aTo gain additional insight into the differentially variable and differentially methylated CpG sites that are in open seas, we tested whether ENCODE-defined regulatory regions  were oveM values of the CpG sites in a CpG island are highly correlated . We included an extra data set from TCGA  to get a better understanding of differentially variable CpGs that have the tumour methylation signal explained by tumour purity in low (lung), medium (kidney) and high (uterine) tumour purity contexts Figure A. A diffet al. [Carter et al.  have devR-squared values, which can be interpreted as the proportion of the variation in the methylation signal that is explained by tumour purity, were obtained for each CpG in each cancer data set. We classed CpGs that had an adjusted R-squared value greater than 10% to be the CpGs that had the tumour methylation signal best explained by the purity of each cancer sample. For example, for the top differentially variable CpG in the kidney cancer data set, Figure R-squared =0.0267). By contrast, Figure -41, Δβ =0.3), and has a high adjusted R-squared value (0.568) in the uterine cancer data set. For the CpG in Figure To assess the effect of tumour purity on methylation levels, we fitted a linear model with methylation level as the response variable and tumour purity as the explanatory variable for the cancer samples only. The adjusted  value 0.68 in theThe numbers of CpGs on the array that had more that 10% of variability explained by purity were 29,424 for kidney cancer, 47,353 for lung cancer and 25,832 for uterine cancer. However, most of these were not detected as differentially variable between cancer and normal tissue. Of the significantly differentially variable CpGs, 3635 CpGs (3.3%) had at least 10% of the variation explained by tumour purity in kidney, 14,986 (10.2%) in lung and 2745 (2.7%) in uterine cancer. Figure et al. [To study differential variability in aging, we used data from Heyn et al.  to compaComparing the 10,000 top-ranked significantly differentially methylated and top-ranked 10,000 differentially variable CpGs, we observed that there was less overlap than expected by chance Figure B. Of theA closer look at the genomic composition of the significantly differentially methylated and differentially variable CpGs revealed that there were more islands and shores represented in the top-ranked 10,000 differentially variable CpGs compared to differentially methylated CpGs . Far fewer GO terms were significant when testing the genes associated with CpGs that were more variable in newborns compared to centenarians; however, several GO terms related to the major histocompatibility protein complex were significant, as well as a leukocyte-mediated immunity GO term . We performed a analysis , testingmissMethyl Bioconductor package. Our test is based on Levene’s z-test for variances and incorporates an empirical Bayes modelling framework to deal appropriately with high-dimensional data issues. We find the test holds its size, is robust to outliers and outperforms the F and Bartlett tests in terms of controlling the FDR. For smaller sample sizes, the F and Bartlett tests are more powerful; however, this comes at a price of higher FDRs. DiffVar with absolute deviations represents the best compromise between controlling the FDR and having the power to detect differential variability.In this paper we present DiffVar, a new method to detect differential variability between groups of samples. DiffVar is freely available as an R function in the To estimate the variance of a group reliably, more observations are needed than for estimating a mean. We found that for group sizes as low as ten in the kidney cancer data set, there was adequate power to detect differential variability, with the power increasing dramatically up to group sizes of 50 arrays. However, detection of differential variability is also dependent on how large the variability in one group is relative to the other. In addition to a FDR cut-off, one can specify a threshold on the variability ratio, which is the ratio of the variance in one group compared to the variance in the second group. For example, when analysing the cancer and aging data sets, in addition to the FDR cut-off of 5%, we required a variability ratio of at least five between the two groups for a CpG to be called significantly differentially variable.We applied our differential variability testing method to several cancer data sets from TCGA as well as a publicly available aging data set. For the cancer data sets, we found that a large proportion of sites showed differential variability and, as expected, were more variable in cancer compared with normal tissue. For the aging data set, we found that a large proportion of CpG sites were more variable in centenarians than newborns. Interestingly, for the top-ranked CpGs in both the cancer and aging data sets, the overlap between differentially variable and differentially methylated CpGs was less than expected by chance.In all cancer data sets we showed that CpG islands were enriched for differential variability. This is important because methylation of CpG islands in cancer cells is known to contribute to gene silencing . CpG sitβ or M values from CpG sites or regions. The DiffVar function will transform a matrix of β values into M values by applying a logit transformation. In general, the DiffVar method for testing differential variability can be applied to any ’omics data that use the limma pipeline for analysis, such as microarray expression data or RNA-sequencing data. For RNA-sequencing data, DiffVar will perform a voom transformation [limma package, for example, testing relative to a threshold  =0.29 towards the prior variance. In other words, more weight is placed on the observed variance during the empirical Bayes shrinkage step. We chose s02 = 0.64, which is slightly more variable than the hyper-parameter estimates obtained when analysing the larger TCGA cancer data sets . The M values were generated to mimic methylation data by randomly sampling half the CpGs from a normal distribution with mean equal to -2 and variance equal to the sampled variance for the unmethylated CpGs; the remainder, representing methylated CpGs, had M values sampled from a normal distribution with mean equal to 2 and variance equal to the sampled variance. The range of M values this simulation scenario produced is shown in Additional file To check the performance of our method, we generated data sets where the truth is known to gain insight into the type I error rate and FDR control. Data were generated under a hierarchical model whereby the variance for each CpG was first sampled from an inverse chi-square distribution and the M values of the non-diseased samples from the kidney cancer data set. First 1000 data sets were generated by randomly sampling 10,000 of the 445,378 CpGs, followed by randomly selecting 100 normal samples, of which 50 were allocated to the normal group and 50 were allocated to the cancer group. Theoretically, for each CpG, the two groups should have equal variance, although additional factors such inter-individual heterogeneity and outliers will make the data more variable. The sampling strategy should preserve any correlation structure between the CpG sites. The range of M values this produced for one such data set is shown in Additional file Type I error rates were also assessed using the M value for one simulated sample was replaced with the maximum simulated M value over all 10,000 CpGs. We introduced differential variability for another 1000 CpGs by simulating larger variances for one of the groups by sampling from a scaled inverse chi-square distribution with scaling factor d0s0 2 and degrees of freedom d0, where d0 = 20 and s0 2 = 1.5. On average, the variability of the 1000 CpGs in the second group is 1.5/0.64 = 2.34 times larger than the variability in the first group.To assess the impact of outliers, we modified the simulations to include outliers for 200 CpG sites. For each of these 200 CpGs, the P values computed. Type I error rates and FDRs were calculated and averaged over the 1000 simulations in each simulation scenario.Differential variability was assessed using our method DiffVar, the F test for equality of variances and the Bartlett test. All three tests had two-sided n =10, 20, 30, 40, 50, 60, 70, 80, 90 and 100) was simulated with 1000 data sets generated for each distinct sample size. The numbers of truly differentially variable CpGs recovered at a 5% FDR and the numbers of false discoveries were recorded for each sample size. Additional file The simulations to assess the effect of sample size were modified slightly by increasing the numbers of simulated CpGs to 50,000 and allowing 5000 CpGs to be differentially variable. Three different levels of variability were considered: Group 2 was roughly twice, five times and ten times more variable than Group 1. A two-group problem with varying sample size (et al. [P values were adjusted for multiple testing using Holm’s method [Ernst et al.  used a het al. . The onls method .R-squared values were used to determine the proportion of variation in the tumour methylation signal explained by the tumour sample purity. Adjusted R-squared values were extracted from a linear model fit, regressing the methylation signal on the ABSOLUTE tumour sample purity estimates of the cancer samples for all CpG sites. This was performed in R using the lm function. CpG sites that had adjusted R-squared values of more than 10% were classed as CpGs that had methylation signal explained by tumour purity.ABSOLUTE tumour purity estimates for the kidney, lung and uterine cancer data sets were downloaded from the Synapse website . AdjustemissMethyl package [The DiffVar method is available from Bioconductor in the  package .


Nasopharyngeal carcinoma (NPC) is a non-lymphomatous, squamous-cell carcinoma that occurs in the epithelial lining of the nasopharynx. Nasopharyngeal carcinoma has a geographically well-defined distribution worldwide, with the highest prevalence in China, Southeast Asia, and Northern Africa. Symptoms of nascent NPC may be unapparent or trivial, with diagnosis based on the histopathology of biopsied tissue following endoscopy of the nasopharynx. The tumor node metastasis (TNM) staging system is the benchmark for the prognosis of NPC and guides treatment strategy. However, there is a consensus that the TNM system is not sufficiently specific for the prognosis of NPC, as it does not reflect the biological heterogeneity of this tumor, making another biomarker for the detection of NPC a priority. We have previously reported on different approaches for microRNA (miRNA) biomarker discovery for Formalin Fixed Paraffin Embedded (FFPE) NPC tissue samples by both a targeted (microarray) and an untargeted  discovery platform. Both miRNA discovery platforms produced similar results, narrowing the miRNA signature to 1–5% of the known mature human miRNAs, with untargeted  having the advantage of indicating “unknown” miRNAs associated with NPC. Both miRNA profiles strongly associated with NPC, providing two potential discovery platforms for biomarker signatures for NPC. Herein, we provide a detailed description of the methods that we used to interrogate FFPE samples to discover biomarkers for NPC. Specificationshttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE46172Microarray deposited data can be found here: http://www.ncbi.nlm.nih.gov/sra/?term=SRP029599RNA-Seq deposited data can be found here: SRX345915, SRX345913, SRX345913 and SRX345909). These samples reference a survey of serum pools from NPC positive and control individuals discussed in Case and control tissue including sample characteristics are presented in Total RNA was isolated from 2 × 10 μm sections from each FFPE case using the miRNeasy FFPE kit (Qiagen) Yields of total RNA derived from FFPE were approximately 100 ng/μm with 260/280 and 260/230 ratios of ~ 2.0 and ~ 1.9, respectively. Analysis on the Agilent Bioanalyzer indicated that the samples were enriched for small RNA species with integrity (RNA Integrity Number or RIN) values of two to three. Though typically indicative of RNA degradation, the robustness of miRNAs in these FFPE tissue 2 transformed using Agilent Feature Extraction (V.10.7). Raw data files in text (.txt) format were analyzed with Agilent GeneSpring software (GX 12.6) All eight samples underwent analysis via microarray . Total Rt-test with a p-value cut-off of 0.05  and a fold-change cut-off of 2.0. Hierarchical clustering was then performed To analyze the differentially expressed miRNAs, quantile normalization was performed to standardize these data across the samples. Raw data (thresholded and log base 2 transformed) were filtered by expression values (20.0–336133.0) with at least two out of the eight samples having values within the cut-off range to remove very low signal values and background influence. The four tumor samples were grouped and analyzed against the four control samples by unpaired Student's 1)project was created, followed by a new experiment, and miRNA was selected for analysis type, followed by the data import wizard for workflow type.A new 2)New miRNA Experiment Steps, the raw intensity files were uploaded. The selected technology was set to 31181_v16_0 and no baseline transformation was performed. The threshold raw signals were set to 1.0 and quantile was chosen as the normalization algorithm along.In 3)Experiment Setup, the samples were grouped into four tumor and four control cases under the Experiment Grouping option. While further interpretations may be created depending on analysis requirements, in this case experimental parameters “tumor/control”  were set up.In the and Non-Averaged for the Average Over Replicates in Conditions. Detected and Not Detected were selected and Compromised in Use Measurements Flagged.The condition tumor and control were selected 4)Filter by Expression, the right entity and interpretation were selected and filtered by raw data value. The lower cut-off value of the interest range was set to 20 and at least two out of eight samples had values within this range.Quality control: The correlation coefficient value of all samples was > 0.7 and therefore all the samples were used in further analysis. Further, 3D Principle Components Analysis (PCA) scores and plotting were used to determine any association among the samples . It was 5)Analysis, the condition was set as tumor versus control, tested by t-test unpaired, and an asymptotic p-value was computed without correction. The fold change cut-off was > 2.0 and analyzed under pairs of conditions with tumor compared to control. Hierarchical clustering analysis of differentially expressed genes from all samples was conducted on both entities and the conditions by normalized intensity values using Euclidean distance metric and Centroid linkage rule.In Significance analysis was completed using GeneSpring Small RNA sequencing was performed on five of the same samples used in microarray analysis  of the thttp://code.google.com/p/ea-utils/wiki/FastqMcf and http://www.bioinformatics.babraham.ac.uk/projects/fastqc.Initial processing was performed using both FastqMcf and FastQC both of which can be accessed at NC_007605.1) and miRNA counts generated for each sample After adaptor removal and quality filtering, ~ 28 million reads were aligned to the human (UCSC hg19) and Human herpes virus 4 (Epstein–Barr virus or EBV) genome  and untargeted  approaches were extensively compared in our previously published manuscript