Oncogenic FAIME Features of HNSCC”  among three distinct HNSCC datasets  is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets , and stratify recurrence-free survival in patients from two independent studies . Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies .Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These “causality challenges” hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate “personal mechanism signatures” of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of “ deterministic biological mechanisms of single-gene biomarkers and the statistical predictive power of multi-gene signatures that are disconnected from mechanisms, we developed FAIME, a novel method that transforms microarray gene expression data into individualized patient profiles of molecular mechanisms. We have validated its capability for predicting clinical outcomes, including cancer patient samples derived from six different clinical trial cohorts of head and neck cancers. This method provides opportunities to harness an untapped resource for personal genomics: clinical evaluation and testing of individually interpretable mechanistic profiles derived from gene expression arrays.Clinical utilization of multi-gene expression signatures that are predictive of therapeutic response has been steadily increasing, however, interpretation of such results remains challenging because multi-gene signatures, generated from analyzing different patient cohorts, tend to be equally predictive but contain minimal overlap. Whereas pathway-level analyses of expression arrays show promise for generating clinically meaningful mechanistic signatures, current approaches do not permit single-patient based analyses that are independent of cross-group calculations. To bridge the gap between  In cancer, expression signatures of poor prognosis a priori knowledge and experimental genome-wide expression data Aside from differences in computational methods used for deriving gene expression signatures, several hypotheses have been postulated to explain the lack of gene overlap and low reproducibility of the genetic makeup among existing expression signatures. One explanation is that different genes are merely separate aspects of the same groups of molecular pathways or mechanisms dependencies between samples preclude their utility for single-sample prognostication. Furthermore, pathway signatures derived from these state-of-the-art methods have been validated in predicting qualitative clinical outcomes, such as complete remission vs. disease progression. These methods, however, are not designed for making prediction using continuous clinical measures, such as recurrence-free survival timeThe state-of-art data-driven but rate limiting methods for generating pathway signatures focus on the coordinated changes in expression of multiple genes in a pathway experimentally detected in animal models Functional Analysis of Individual Microarray Expression (FAIME) that can translate patient microarray data into pathway and molecular functional profiles on a single-sample level and can be applied to quantitative phenotypes of outcome prediction . FAIME, by computing statistical scores on individual patients, retains sample independence within a cohort and enables subsequent mechanism-level clustering or signature validation. We demonstrate the potential of FAIME in personalized genomics using relatively small-size cohorts of Head and Neck Squamous Cell Cancer (HNSCC) in which FAIME produces single-patient survival prediction.We hypothesized that molecular mechanisms delineated from gene expression deregulation profiles are accessible as genome-wide measurements of pathways at a single-sample level. Here, we present a novel methodology, the Ninety percent of patients with HNSCC will present with disease that is locoregionally confined and will be considered for curative intent therapy s are used as a multi-mechanism outcome predictor in a straightforward unsupervised diagnosis classification task using independent datasets to demonstrate their predictive capabilities at an entry-level task. Next, the HNSCC FAIME Feature features are used for unsupervised prognostic classifiers with the continuous “recurrence-free survival time” variable, which require single sample scoring devoid of class-based assignment to retain independency between classified samples. In order to identify single mechanism outcome predictors, Cox regression analyses are conducted on all individual FAIME mechanism scores in two datasets and two significant prognostic mechanisms are identified by meta-analysis. As a validation, FAIME Scores are compared to the 1st component of a Principal Component Analysis for their prognostication.Methods) as a measure of concordance between FAIME predictions and other methods.The FAIME method provides a translation of each sample's gene expression to molecular mechanisms (FAIME-score). We conduct three analyses to compare the robustness of FAIME to that of previous approaches. We first examine the stability of FAIME Scores within samples, followed by demonstration of the reproducibility of deregulated FAIME-derived mechanisms across three independent HNSCC datasets, and the determination of the precision and recall of FAIME mechanisms using a “proxy gold standard”  are compared to the unsupervised pathway scoring methods Mean-G and Median-G . These analyses are conducted in HNSCC datasets A, B, and C . Since FAIME Scores are based on the rank of gene expression in each sample, they remain stable when using different gene normalization techniques, and are even applicable to minimalist log-2 transformed gene expression data devoid of cross-sample normalization (Methods). In contrast, while MAS5 normalization reduces sample-to-sample variability, the distribution of pathway scores derived from the Mean-G or Median-G methods is highly variable across samples. Additionally, the normality of FAIME Scores within samples, termed FAIME profiles are comparable to those of log-transformed gene expression values .In B, and C Table 1 Text S1Text S1) Text S1) method, which requires supervised group assignment, has been shown more accurate than Mean-G and Median-G in determining deregulated pathways p<0.001; Methods). Among these 72 overlapping GO terms, we define 57 Oncogenic FAIME Features of HNSCC comprising 24 molecular functions and 33 pathways, which are later utilized for FAIME evaluations in classification tasks. In comparison, all three other methods exhibit lower reproducibility of pathway predictions across three data sets  or by two out of the three methods (65%). A more thorough evaluation of FAIME's concordance with alternative state of the art methods is also conducted and discussed as below (In -Panel (iii) uses the KEGG pathway mechanisms predicted by enrichment in dataset C as a proxy gold standard to evaluate the precision and recall of GSEA predictions (blue line) and those of FAIME (red line). Subsequently GSEA serves as a proxy gold standard in -Panel (vi) to evaluate the predictions from enrichment (grey) and those of FAIME (red). Overall, the accuracies of the FAIME derived mechanisms perform at comparable or better levels than those produced by conventional methods in 10 out of the 12 precision-recall curves, confirming its higher reproducibility in comparison to the well validated conventional methods. In Figure S3, we also construct an extensive empirical distribution of KEGG pathways and GO Molecular Functions predicted by FAIME as a control to conservatively confirm the significance of the observed overlap reported in Oncogenic FAIME Features of HNSCC to discriminate HNSCC phenotypes from non-HNSCC, and to assess the clinical relevance of FAIME Scores, we conduct a retrospective analysis of two independent HNSCC datasets. The 57 Oncogenic FAIME Features of HNSCC are able to classify clinical samples in independent datasets D (n = 35), E (n = 91), and GSE9844 (n = 38) with zero, four, and three total misclassifications, respectively . Furthermore, samples from the two validation datasets can be accurately classified by the 24 molecular functions from GO (GO-MF) subset, or by the 33 KEGG pathways subset of the Oncogenic FAIME Features of HNSCC as shown in Oncogenic FAIME Features of HNSCC is robust as it is confirmed by four conventional unsupervised classification methods tested (Methods). When used jointly, the FAIME prioritized GO-MF and KEGG pathways are equally successful in separating HNSCC tumor samples from non-tumor controls in datasets D and E : AUC = 0.99, p = 4.3×10−7; Dataset E: n = 91, AUC = 0.92, p = 1.8×10−12, Table S2).In order to confirm the capability of the Table S1). Cluster α consists of a set of intra- and extra-cellular events such as cancer pathways, p53 signaling, ECM-receptor interaction and focal adhesion. Cluster β consists of nuclear events that control DNA replication, DNA damage repair, and cell cycle progression. Although each individual mechanism of Clusters α and β is known for their involvement in the development of human cancer, the observed coordinated activation of all components within each cluster is novel. It suggests that oncogenesis requires not only one pathway activation or molecular function enhancement, but also the cooperation among a majority or all of the mechanisms we have prioritized. Additionally, we have also identified a third cluster – . There is an increasing appreciation and understanding of the involvement of metabolism in cancer. Our observations of the extensive and coordinated reduction in cellular biosynthesis and metabolism support the notion that cancer is a “metabolic disease” As shown in both panels I and II of In summary, the application of the FAIME algorithm to multiple scales of biological functions  demonstrates a novel quantitative approach that is capable of identifying distinct molecular mechanisms associated with the onset of cancer, and could thus facilitate the elucidation of complex biology.Oncogenic FAIME Features of HNSCC, as a whole, have significantly higher prognostic power. They stratify HNSCC samples in two distinct HNSCC datasets (E and F) into two recurrence-free survival subgroups . We then compare the utility of FAIME-derived scores with other enrichment methods that also do not require phenotypic group assignment. In contrast, Enrichment analysis, Mean-G and GSEA scores fail to provide stratification of patients by RFS time . These results attest to the utility of FAIME for learning predictive mechanism patterns from gene expression in pursuance of quantitative phenotypes such as survival analysis. In order to compare the prognostic power of FAIME to other prognosticators of HNSCC, we note that for dataset E in this study, the authors report a non-statistically significant trend for HPV+ HNC patients to have better survival outcomes when compared to HPV- HNC patients . In contrast, we show that when FAIME is run on this dataset, we obtain a significant prognostic indicator for survival outcome for datasets E, F, and GSE2837  in  and Figure S5.In addition to its ability to discriminate between the non-tumor control and HNSCC tumor tissues, the 57 Figure S6, six of the 57 Oncogenic FAIME Features of HNSCC significantly overlap with the KEGG pathways and GO-MF that can be derived by enrichment of the 31 genes (54 probes) associated with disease-specific survival in a study by Thurlow et al. Oncogenic FAIME Features of HNSCC are enriched in the four original “intrinsic” groups of Perou's molecular classification of HNSCC samples .We next investigate if these FAIME-derived patterns could recapitulate known biological and pathophysiological knowledge. As shown in It has been demonstrated that some multi-gene expression signature classifiers, derived from comparisons made between the cancer and control tissues, can provide both diagnostic stratification of clinical tumor samples as expected, as well as prognostic prediction of clinical outcomes, such as RFS or overall survival across patients within a clinical cohort Methods, receptor signaling complex scaffold activity [GO:0030159] . Since disease recurrence is also a predictor of RFS, FAIME Scores of these pre-treatment samples are also significantly decreased in patients with rapid onset of disease recurrence in both datasets E and F .In order to prioritize other potential RFS mechanisms and to further demonstrate the utility of FAIME Scores for the study of quantitative phenotypes (e.g. RFS), we conduct a Cox regression analysis of recurrence-free survival time for each of the 208 KEGG pathways and 956 GO terms for which a FAIME Score could be computed in datasets E and F with four or more genes per mechanism Methods, Table 1.p = 0.0027 and 0.044, Datasets E and F). Furthermore, this first component and the FAIME Scores of the KEGG apoptosis geneset are correlated . The detailed list of genes of the KEGG canonical apoptosis geneset is listed in Table S4. Additionally, we show that FAIME Scores of the KEGG apoptosis geneset are significantly increased in the HNSCC tissues of patients with no evidence of disease recurrence as compared to those of patients with recurrence within both datasets E and F . Consistent with these results, HNSCC patients are treated with radiotherapy and conventional chemotherapy regimens that should subsequently induce apoptosis through DNA damage cell cycle checkpoints. Therapy-resistant tumors are more likely to recur early, which is consistent with the observed reduction in the FAIME apoptosis geneset score in patient samples with disease recurrence reflecting a reduced capability of checkpoints to elicit an apoptotic response to therapy-induced DNA damage. The correlation of reduced level of expression of the KEGG apoptosis geneset reported by the FAIME Score in patients with more rapid onset of disease recurrence may reflect aberrant cell cycle checkpoint and DNA damage repair regulation leading to an enhanced survival of HNSCC cancer cells in recurrent patients. This computational FAIME method identifies deregulated genesets associated to mechanisms, and after validation of the FAIME Scores in a prospective study, we will also pursue to validate the deregulation of mechanisms associated to this genesets, such as the apoptosis geneset. Since a pathway-level classifier can be considered as a significant predictor based on the ensemble effect of its constituent gene in a patient, these same genes need not be consistently deregulated in each patient. In other words, the effect is measured at the mechanism level.As a proof-of-concept study we focus on the topmost candidate RFS mechanism, and conduct a Principal Component Analysis (PCA) of datasets E and F for the prioritized KEGG apoptosis geneset (KEGG pathway [hsa04210] containing known genes associated with apoptosis pathways annotated in humans) since PCA can provide an unbiased approach to derive a metric representing the highest biological variation across samples that can be associated with the biological mechanism(s) responsible for this variation. As expected, PCA's first component of the KEGG apoptosis geneset gene expression is also a predictor of deregulated RFS , arguably future studies may explore alternate models, particularly for different types of genomic data that have not been modeled.In principle, FAIME Scores are applicable to other scales of individual quantitative genomic data annotated into genesets from a knowledgebase, for example, from single protein activity measurements. For these extensions, alternative decreasing weights and their effects on FAIME Score need to be discussed. In this manuscript, the weighting strategy used by the FAIME Score is a rank based decreasing from the highest expressed genes to non-expressed genes in individual samples. We chose the weights to decay exponentially based on previous modeling of expression data we conducted Oncogenic FAIME Features of HNSCC to predict survival in a cohort of HNSCC patients treated with a) cytotoxic chemotherapy (e.g. induction chemotherapy), b) radiation, and/or c) an EGFR inhibitor. Future studies will test FAIME's ability to distinguish between patient tumor subtypes: 1) HPV positive vs. HPV negative, 2) low risk for failure vs. high risk for failure to therapy, and 3) emerging genetic drivers of HNSCC (ongoing TCGA cancer genome atlas effort in head and neck cancer). Here, we describe the development and evaluation of FAIME, a computational rather than biological approach designed specifically with the intent for enabling clinicians to functionally interpret individual patient samples using quantitative phenotypes. Additional improvements are required to translate this technology into clinical care, such as the ability to directly interpret a single microarray without cross sample normalization. For example, the “Gene Expression Barcode” relies on a reference standard to interpret single array gene expression Currently, the effort required for interpreting the biological significance of individual genes in expression signature classifiers is challenging. As shown by Bild et al, mechanism-based predictors can bring us one step closer to guiding targeted therapies by using oncogenic pathways derived from cellular experiments Table S5). We provide in Figure S4 showing the validation of the Oncogenic FAIME Features of HNSCC in an independent laser microdissected tumor and non-tumor tissue dataset GSE9844 with a positive predictive value = 92% (3 misclassifications) and p = 0.04 . Furthermore, in our comparison of laser microdissected tumor tissue with non-tumor control tissue (GSE9844), we identified 751 out of 2,699 significantly deregulated FAIME Scores that were calculated using genesets of mechanisms . Of the 57 Oncogenic FAIME Features of HNSCC (57 GO and KEGG genesets), 47 were confirmed among these 751 and are deregulated in the same up or down directions originally observed in datasets A, B, and C . Furthermore, we found that Oncogenic FAIME Features of HNSCC were able to accurately classify laser microdissected tumor tissues from non-tumor tissues . We have thus demonstrated that utilizing FAIME Scores to classify laser microdissected tumor tissue is not only feasible, but is also concordant with results from non-laser microdissected tissue datasets in this study.Indeed, we recognized that different extraction methods of the tissue, including laser microdissection, could contribute to generating different classifiers. Conceivably, classifiers built from non-laser microdissected data may reveal important genetic signatures encompassing tumor interactions with surrounding stromal cells, but more precise methods are needed to isolate these cells. We note that all tumor samples in the three training datasets and testing datasets D, GSE2837, and GSE9844 contain at least 70% tumor cells . The utility of gene expression signatures in HNSCC lags behind better suited ones developed in other cancers, such as breast cancer, where expression classifiers are commercially available  samples from an independent, non-smoker individual with no history of HNSCC, (ii) paired samples from a distant uninvolved site in patient with HNC , and (iii) paired samples from the margin of a tumor. For each of these three types of control samples, Table S7 provides the percentage of samples per anatomical location. Several of the non-tumor tissue control samples are enriched specifically for epithelial cells , the cell type of origin for head and neck cancer cells, while other non-tumor controls were extracted from mucosae tissues . Additionally, a description of the TNM stage, P53 status, HPV status, smoking status and alcohol intake status is reported in 8C.Eight of FAIME , while faffy. The normalized gene expression profiles of the larger datasets E and F (Probe-set filtering. Since probe-sets with little variability across samples provide no discriminatory power The gene expression CEL files of the GEO and ArrayExpress datasets A–D of  E and F Table 1 Microarray platform annotation was downloaded from the GEO website (http://www.ncbi.nlm.nih.gov/geo/) for the GSE686 dataset using an Agilent platform. For the other datasets using Affymetrix platforms, the annotations are derived from the Bioconductor package hgu95av2.db_2.3.5, hu6800.db_2.3.5 and org.Hs.ef.db_2.3.6.Gene Ontology (GO) annotations for human genes were downloaded from NCBI (ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz) on December 11, 2009. The GO Molecular Functions (GO-MFs) with more than 3 annotated genes are studied.KEGG pathway annotations are imbedded in Bioconductor database KEGG.db version 2.3.5. This map is based on data provided by: KEGG GENOME (ftp://ftp.genome.jp/pub/kegg/genomes) with a date stamp of September 16, 2009. The KEGG pathways with more than 3 annotated genes are studied.The FAIME profile is designed to utilize expression arrays of individual samples for establishing the basis of generating individualized functional profiles Figure 2G) in each sample are sorted in a descending order according to their expression levels, and then an exponential decreasing weight (w) is assigned to the ordered genes (Equation 1). The resultant weighted expression values are used to prioritize relatively highly expressed genes as in the first step of Bioconductor package OrderedListg,sr be the expression rank for each gene g ∈ G in a sample s, let |G| be the total number of distinct genes in the array and the weight assigned to each gene per sample  is calculated as follows:To quantitatively assign a mechanism's “expression deregulation” via its gene members, whose expression is measured in a microarray, all expressed genes (set NC) is defined as the uni-dimensional average of the weighted expression values of a gene-set. Specifically, the sum of the weighted expression of gene element in a gene-set is normalized according to its cardinality. For every GO term, there is a gene-set iGO in which genes satisfy g ∈ iGO and a complement gene-set (G/GOi) comprised of all available genes in the array that are not annotated to this GO term. Thus we calculate the normalized centroid of each gene-set iGO in each sample s and that of its complement gene-set as follow:A Normalized Centroid (F in equations) of each gene-set of a GO term is calculated in every sample as the difference between the normalized centroid of its gene-set and that of its complement gene-set (Equation 3). We define functional scores as functional biological mechanisms of the gene-set associated with a GO term in a given example.Furthermore, the Functional FAIME Score .FAIME scoring and profiling are implemented using open source R software (Eq.3) regardless of the gene expression preprocessing methods, a FAIME Score distribution for each sample is illustrated using boxplots . These boxplots present the median, 25% to 75% interval of distribution (box) and the 1.5 inter-quartile ranges  calculated using R package graphics. Specifically, the FAIME-derived pathway scores are applied to (i) minimalist within-sample log2 transformation, (ii) within- and across-sample normalization using MAS5 method Data preparation and databases). Using the “affy” package as5” function with default parameters, and the within-sample normalization is conducted using the “expresso” function without cross-sample normalization. Similarly under each of above three pre-processing conditions, the arithmetic mean of all the Gene expression values in each pathway (Mean-G) and their median (Median-G) are also applied in each sample according to previously detailed methods Figure S1 for comparison to those of FAIME.To evaluate the stability of FAIME-derived pathway scores . The KEGG pathway and GO-MF overlap is illustrated as Venn diagrams for each approach (Equation 3) of each KEGG or GO gene-set between the tumor and control tissue groups in each of the three HNSCC datasets . To estimate the statistical significance of FAIME-derived mechanisms, a permutation-based test is applied 1000 times for each dataset (resampling sample phenotype assignment), with each application preserves the intra-subcategory relationships of the KEGG and GO terms inherited from the genes. Then, empirical p-values of the observed statistics are calculated from the null distribution using the Bioconductor package TwilightFAIME-derived KEGG and GO-MF terms significantly associated with HNSCC malignant transformation are obtained by comparing the s A,B,C, Table 1 Oncogenic FAIME Features of HNSCC) as illustrated in the Venn diagrams of Oncogenic FAIME Features of HNSCC is estimated empirically by randomly resampling, without replacement, the same number of predicted KEGG/GO terms from each dataset 1,000 times, and at each time of sampling, the potential 3-way overlap is measured. Additionally, hypergeometric enrichment of GSE6631 expression profile is used as a gold standard (data not shown), in which significantly deregulated FAIME-derived molecular functions and pathways in HNSCC are robustly and consistently present, independent of changing parameters used in analyses such as (i) within array normalization vs. the conventional within and across array normalization (ii) with or without gene-filtering, iii) using a parametric Z test or a non-parametric U-test, and (iv) GO gene annotations downloaded from the NCBI vs. those provided by Bioconductor GO2ALL. Furthermore, to demonstrate that mechanism overlap as measured by gene-sets is higher than conventional gene overlap, we compare FAIME-derived GO-MF or KEGG overlap across datasets to the overlap of the original published gene signatures The reproducibility of deregulated FAIME-derived KEGG pathways and GO-MFs between HNSCC tumor samples and non-tumor control tissues is measured by the overlap of the significant mechanisms among the three distinct datasets  the concordance of FAIME-derived molecular mechanisms with results obtained by three conventional methods Figure 4Stability and Robustness of FAIME Scores with and without cross-sample normalization and gene filtering  comprising these 57 Oncogenic FAIME Features of HNSCC  are calculated individually for each sample s in the validation datasets  to cluster samples in the validation datasets. An empirical p-value is accordingly calculated after running 1000 simulations .The 57 proaches Table 2.s are implicated in patient survival. This assumption of the Oncogenic FAIME Features of HNSCC ’ prognostic power is based on the fact that mechanisms involved in oncogenicity are correlated with patient survival Oncogenic FAIME Features of HNSCC  are calculated on each tumor sample that has associated patient survival data in the independent datasets E and F separately . Based on the HNSCC,sFP of each sample in each dataset, patients are divided into two or three groups using the Bioconductor package cluster by the unsupervised partitioning method called “clustering large applications” (CLARA) whose robustness has also been previously shown survival for the predicted clustering of patient samples. The log-rank p-value is used to evaluate the prognosis of this sample clustering. Note that in dataset E, duplicate samples are available for 6 patients leading to two HNSCC,sFP. Thus to avoid duplicate dependent measures for each patient, the average expression of the HNSCC,sFP is used when partitioning with CLARA.An additional experiment is conducted to confirm that FAIME-derived HNSCC featureOncogenic FAIME Features of HNSCC we derived with those of a gold standard: the mechanisms that are significantly enriched among genes associated with disease-specific survival Figure S6). We next use FAIME Scores to recapitulate Perou's molecular classification of four “intrinsic” HNSCC sample groups in dataset F Oncogenic FAIME Features of HNSCC. The R software's Fisher's Exact Test is employed to evaluate the overall enrichment in the resultant 4×4 contingency table consisting of the four FAIME-derived groups of samples and the four HNSCC molecular mechanism groups published by Perou and colleagues.Two validation studies are conducted. First, we investigate if these FAIME-derived patterns can recapitulate known biological and pathophysiological knowledge. This is done by comparing the survival (default parameters for censured data) survival). A meta-analysis of these Cox-regression p-values is then performed using the Stouffer Z-transform method The FAIME-derived mechanism scores of the 208 measurable KEGG pathways and 956 GO-MF terms with more than 3 gene members are calculated for each of the 71 patient samples in dataset E and the 60 patient samples in dataset F. In each dataset, the RFS prognostic power is assessed using a Cox proportional hazards regression with the Bioconductor package st component is compared with FAIME Scores for the RFS prognosis analysis. Bioconductor package amap is employed to run the PCA analysis and the values of the resultant 1st component are compared with FAIME Scores for each patient in dataset E and F. Two types of analyses are conducted: 1) the non-parametric Spearman correlation is calculated between 1st component values and FAIME Scores across all samples, and 2) the Cox regression analyses of RFS are conducted for both individual FAIME Scores and 1st component values for each sample. Note that for the patient with duplicate measured samples in dataset E, we use the mean expression value to represent its expression measurement and genes with non-measurable expression values are assigned to a value of zero in Dataset F.As a validation, the gene members of significant RFS genes are extracted to conduct the principal component analysis. For each mechanism, the resultant 1Figure S1FAIME pathway scores are more stable under three preprocessing conditions than those of conventional pathway scoring .(EPS)Click here for additional data file.Figure S2The normality of FAIME-profiles are comparable to the normality of gene expression.(EPS)Click here for additional data file.Figure S3The accuracy of differentially expressed FAIME profiles using pooled GSEA results as a “proxy gold standard”.(EPS)Click here for additional data file.Figure S4Oncogenic FAIME Features of HNSCC accurately classify samples of an independent dataset of laser microdissected HNSCC tumors (GSE9844).Significantly altered (EPS)Click here for additional data file.Figure S5Oncogenic FAIME Features of HNSCC stratify recurrence-free survival in independent dataset GSE2837.(EPS)Click here for additional data file.Figure S6Oncogenic FAIME Features of HNSCC and FA-gene enriched mechanisms.Enrichment between (EPS)Click here for additional data file.Figure S7FAIME Score of the KEGG apoptosis geneset is significantly increased in patients with no evidence of disease (NED) recurrence.(EPS)Click here for additional data file.Table S1Oncogenic FAIME Features of HNSCC identified between tumor tissue and non-tumor control tissue in all three HNSCC datasets .57 (PDF)Click here for additional data file.Table S2Gene-based vs. FAIME-based classification of HNSCC tumor vs. non-tumor control samples.(PDF)Click here for additional data file.Table S3Two Prioritized Recurrence-Free Survival (RFS) prognostic mechanisms identified by FAIME in two HNSCC datasets .(PDF)Click here for additional data file.Table S4KEGG apoptosis geneset (hsa04210) measurable among 26 laser microdissected head and neck tumor samples (GSE9844).(PDF)Click here for additional data file.Table S5Head and neck tumor samples.(PDF)Click here for additional data file.Table S647 significant Oncogenic FAIME Features of HNSCC confirmed in laser microdissected HNSCC dataset GSE9844.(PDF)Click here for additional data file.Table S7Non-tumor control samples.(PDF)Click here for additional data file.Table S8Clinical features.(PDF)Click here for additional data file.Text S1Implementation of Enrichment, GSEA and CORG.(PDF)Click here for additional data file.Text S2R codes to run FAIME.(PDF)Click here for additional data file.


In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data.To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods  and conducts experiments on 11 gene expression cancer datasets.Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification. Researchers have tried to analyze thousands of genes simultaneously by microarray technology to obtain important information about specific cellular functions of gene(s) which can be used in cancer diagnosis and prognosis . The genThe gene selection method can be divided into three classes, the wrapper, the filter, and the embedded approaches. Wrappers utilize learning machine and search for the best features in the space of all feature subsets. Despite their simplicity and often having the best performance results, wrappers highly depend on the inductive principle of the learning model and may suffer from excessive computational complexity because the learning machine has to be retrained for each feature subset considered . The wraParticle Swarm Optimization (PSO), developed by Kennedy and Eberhart , is a po2 DB in Taiwan  interval. The process was repeated until either the fitness of the given particle was 1.0 or the number of the iterations was achieved by the default value of T = 100. Table The PSO parameters are chosen by a survey on several related research articles concerning the utilization of PSO. Such parameter setting was optimized by literatures (refer to -37). MorK-fold cross-validation is used to evaluate algorithms. In this study we set K = 5, and the details are stated as follows: in each iteration, the algorithms apply K folds of data to earn one or more models, and subsequently the learned models are asked to predict the data in the validation fold. The performance of the algorithm on each fold is tracked by its accuracy. Upon completion, the K samples of the accuracy is available for validation.To guarantee the impartial comparison of the classification results and avoid generating random results, this study adopted a five-fold cross-validation strategy. Cross-validation is a statistical method by dividing data into two segments for evaluating and comparing learning algorithms. One part used to learn or train a model and the other used to validate the model. Stone  and GeisFigure To confirm effectiveness of our proposed PSODT, this study compares its accuracy with the other seven popular classification algorithms . Table In summary, SVM classification method which is generally considered as one of the most powerful machine learning classifiers is based on the statistical learning theory . However2 data bank in Taiwan [This study investigated a set of clinical practice data including 13 actual cancer cases from the Mn Taiwan . The rawAll raw data of microarray  were pre-processed using three different algorithms: Affymetrix Microarray Suite 5 (MAS5), robust multi-chip average (RMA), and GC-robust multi-chip average (GCRMA) as implemented in the Bioconductor packages. RMA and GCRMA processed data on a multi-array basis. All of the arrays of the same platform were uniformly pre-processed to reduce variance. The cancer microarray consisted of 13 cancer types, namely, bladder, blood, bone marrow, brain, breast, cervix uterus, colon, kidney, liver, lung, lymph node, ovary, and prostate. The information of each cancer is shown in Table Table To perform a five-fold cross-validation, we selected five independent sets of cDNA clones (refer to supplementary Tables one to five of Appendix three at ). A totaWe proposed a novel method to identify tissue-specific genes as well as housekeeping genes with altered expression patterns that provide a high discrimination power on cancer classification. These genes may play as an important role in diagnosis and/or pathogenesis of various types of tumors. Eleven cancer datasets were used to test the performance of the proposed method, and a five-fold cross-validation method was used to justify the performance of our proposed method. Our proposed approach achieved a higher accuracy as compared with all the other methods.This proposed method has integrated with the nonlinear search capability of PSO and linearly separable advantage of DT to apply to microarray cancer datasets for gene selection. Hawse have identified representative cancer genes (453 genes) from numerous microarray data  that can reduce costs. In addition, we compared our proposed method with four well-known algorithms using a variety of datasets (diverse sizes and numbers of classes and features). Consequently, our proposed method outperformed all the other benchmark methods and is compatible to SVM for certain specific datasets.Further studies to be further conducted are suggested as follows. First, PSO may result in better solutions by optimizing parameter settings; therefore, self-adaptation parameters of particle size, number of iterations, and constant weight factors are worth developing. Second, adding hybrid search algorithms in PSO algorithm may improve its performance; for example, swarms with mixed particles may further enhance the effectiveness. Third, the improvement in the execution time for large-sized data sets could be treated as a research subject in the future.The authors declare that they have no competing interests.KHC, KJW and AMA designed all the experiments and wrote most of this paper. KSC discussed and refined the paper. MLT, WCC, KPT and KMW interpreted the results. TSY and NCT guided the whole project. All authors read and approved the final manuscript.


The high per-sample cost of RNA-Seq often precludes the large number of experiments needed to partition observed variance into these categories as per standard ANOVA models. We show that the partitioning of within-condition to between-condition variation cannot reasonably be ignored, whether in single-organism RNA-Seq or in Meta-RNA-Seq experiments, and further find that commonly-used RNA-Seq analysis tools, as described in the literature, do not enforce the constraint that the sum of relative expression levels must be one, and thus report expression levels that are systematically distorted. These two factors lead to misleading inferences if not properly accommodated. As it is usually only the biological between-condition and within-condition differences that are of interest, we developed ALDEx, an ANOVA-like differential expression procedure, to identify genes with greater between- to within-condition differences. We show that the presence of differential expression and the magnitude of these comparative differences can be reasonably estimated with even very small sample sizes.Experimental variance is a major challenge when dealing with high-throughput sequencing data. This variance has several sources: sampling replication, technical replication, variability  The potential advantages of RNA-Seq as a gene-expression profiling method are indisputableOne expected outcome from an RNA-Seq experiment is a list of genes for which there is evidence of differential expression between two experimental conditions. A large number of tools have been developed to infer such a gene list, and these tools have evolved as researchers have used increasingly sophisticated statistical methods to estimate and compare relative transcript abundances from sequencing read countswithin an experimental condition is assumed to be either negligible compared-to or simply considered part-of the overall error. However, Meta-RNA-Seq involves changes in both organism abundance and transcript abundance, and these can be confounded using existing analysis tools.Existing RNA-seq analysis tools, reviewed in Pachtercannot reasonably be ignored, especially in Meta-RNA-Seq experiments, and if such variation is not correctly accommodated for then misleading inference will occur. We show that single organism RNA-Seq and Meta-RNA-Seq datasets can be examined by robust statistical methods similar to traditional random-effect ANOVA models that decompose sample-to-sample variation into four parts: within-condition variation, between-condition variation, sampling variation, and general (unexplained) error. We propose that evidence for differential expression in these datasets can be accurately evaluated using both ''statistical significance'' and ''effect-size'' estimates derived from such models because these two values respectively describe the confidence we have that expression in the conditions are different, and the magnitude by which they differ. Others have argued that characterizing biological data in this way is more informative than decisions based upon p-value thresholds because p-values encourage acceptance or rejection of a null hypothesis rather than an explicit assessment of the evidenceThe main purpose of this work is to show that within-condition variation Most RNA-seq datasets contain many genes with zero read counts e.g.  due to sMost statistical analyses of RNA-Seq data model the read counts obtained from the sequencer as having come from a Poisson-like process such that for gene The total number of reads formal equivalence between Poisson and multinomial processes does not imply inferential equivalence. Specifically, rather than using Unlike previous methods, however, we exploit the fact that et alInstead of maximum-likelihood, we use standard Bayesian techniquesfull posterior distribution of probabilities drawn from the Dirichlet distribution such that Due to the large variance and extreme non-normality of the marginal distributions This multivariate distribution ensures that none of the inferred proportions are ever exactly zero even if the associated count is zero, and that probability is conserved  version 1.0 package for the R statistical software system. The ALDEx package takes as input a table containing per-gene sequencing counts for each replicate and a list indicating which replicate should be grouped in what condition. Each sample is a column, and per-gene counts are in rows. It currently requires two or more samples in two conditions. It computes one table for all genes containing the following: individual median expression data for each gene in each sample, the median expression for each gene in each condition, the median expression level across conditions (A), the median absolute fold difference  women and one each from a women with intermediate- or full-grade BVThe three tools behaved very differently in this dataset as shown in L. iners exemplifies the first pattern, where the organism is found in either 3 or 4 samples. Here, the transcript abundances and within- and between-condition differences are distributed widely throughout the plots. The second pattern is similar to that for Gardnerella vaginalis and is typical for organisms that are abundant in both samples of one condition and absent from both samples of the other condition. Here the transcripts are abundant in one condition only, exhibit a wide range of expression values and tend to the lower end of the within-group difference. The final pattern is similar to that for Megasphera, which was abundant in only one sample of one condition, and rare or absent in the others. Here the average expression values tend to the lower average expression range and the within-condition difference is at the upper range of values. Note the difference between the number of differential transcripts for Gardnerella vs. for Megasphaera which reflects the typically lower within-condition transcript variation. Taken together, these plots show that the expression levels of a transcript in a Meta-RNA-Seq experiment, and the variation of those levels within- and between-conditions are driven by two factors. The first, is the abundance of an organism across samples, and the second is the transcript abundance within an organism. Transcripts derived from organisms that are found in all samples form a subset that have distributions similar to those for single-organism RNA-Seq, and therefore in isolation might be amenable to analysis with existing tools. However, transcripts derived from organisms that are found in only one condition, or only one sample, clearly deviate from this ideal and identifying differential transcripts using ALDEx provides an approach that can find those genes that are consistently different between conditions benefits from this approach.It is likely that both edgeR and DESeq performed poorly because the underlying assumptions of their statistical models was a poor fit for the data. In particular, the points in While the cost of high-throughput sequencing continues to fall, conducting an RNA-Seq experiment is still a relatively expensive undertaking. Extracting and analyzing the results adds an under-appreciated layer of complexity and costOne current approach to examining metatranscriptomics has been championed by the marine metagenomics community that uses pyrosequencing and a non-parametric bootstrap testOthers have used the Illumina platform to generate Meta-RNA-Seq data composed of millions of reads and have used more diverse statistical tools drawn from the metagenomic armamentarium that are largely concerned with identifying gene presence or absence as opposed to gene abundance over a large range. Statistical analyses in these studies have ranged from simple, such as listing those genes with two parameters from one value by assuming that the data follows idealized behaviour. This approach works acceptably in datasets with high intra-condition reproducibility, but is prone to error if the dataset deviates from that ideal, as is likely true for the majority of Meta-RNA-Seq data. For example, the B. cereus dataset, while having an extremely high read density, does not display a high concordance with either of these fixed-effect-model methods. As outlined below, this is caused, in part, by the failure of existing tools to centre the data in such a way that proportionality is preserved.To our knowledge, ALDEx is the only method capable of producing an estimate of the ratio of between-condition difference to variability seen within the different conditions for RNA-Seq data since it assumes a random-effects model. We examined the behaviour of two widely-used tools based on fixed-effect models: edgeR and DESeq, both based on negative binomial models. It is important to note that, in essence, both tools effectively estimate ALDEx uses a Dirichlet-multinomial model to infer transcript-abundance from read counts, followed by mixture-modelling to explicitly account for within- and between-condition variation among experimental samples. These data are examined in an ANOVA-like framework with conservative cutoff values that identify differentially-expressed genes where the within-condition expression variance is much smaller than the between-condition variance. A further innovation is that the data is transformed by removing the scaling-dependent subspace inherent when proportional data is log-transformed, ultimately removing a potential large source of multivariate statistical bias  isolated from or detected in the vagina were included in a reference sequence set for mapping. These 234,991 sequences  were clustered by sequence identity (95% nucleotide identity over 90% sequence length) using CD-HIThttp://code.google.com/p/aldex/. DESeq version 1.6.1 was used for these analyses using the per-gene dispersion estimates. The edgeR version 2.4.6 package was used. A false discovery rate of 0.1 was used to identify putative differentially-expressed transcripts as recommended by the documentation. Cuffdiff version 1.3.0 was used with a mean fragment length of 200 bp and the default false discovery rate of 0.05.ALDEX version 1.0.3 was used. It can be accessed at: File S1Supporting information.(ZIP)Click here for additional data file.


MicroRNA (miRNA) expression profiling data has recently been found to be particularly important in cancer research and can be used as a diagnostic and prognostic tool. Current approaches of tumor classification using miRNA expression data do not integrate the experimental knowledge available in the literature. A judicious integration of such knowledge with effective miRNA and sample selection through a biclustering approach could be an important step in improving the accuracy of tumor classification.SFSSClass is developed that judiciously integrates a biclustering technique SAMBA for simultaneous feature (miRNA) and sample (tissue) selection (SFSS), a cancer-miRNA network that we have developed by mining the literature of experimentally verified cancer-miRNA relationships and a classifier uncorrelated shrunken centroid (USC). SFSSClass is used for classifying multiple classes of tumors and cancer cell lines. In a part of the investigation, poorly differentiated tumors (PDT) having non diagnostic histological appearance are classified while training on more differentiated tumor (MDT) samples. The proposed method is found to outperform the best known accuracy in the literature on the experimental data sets. For example, while the best accuracy reported in the literature for classifying PDT samples is ~76.5%, the accuracy of SFSSClass is found to be ~82.3%. The advantage of incorporating biclustering integrated with the cancer-miRNA network is evident from the consistently better performance of SFSSClass  over USC .In this article, a novel classification technique called SFSSClass algorithm which judiciously integrates a biclustering technique for simultaneous feature (miRNA) and sample (tissue) selection, the cancer-miRNA network and a classifier. The novel integration of experimental knowledge with computational tools efficiently selects relevant features that have high intra-class and low inter-class similarity. The performance of the SFSSClass is found to be significantly improved with respect to the other existing approaches.In this article, we develop the  C. elegans, Drosophila, Zebrafish or Human 18]. In aIn  a bead bDs1, Ds2 and Ds3) are considered for the experiments. For Ds1, training and test data are generated from miGCM_218.gct [Ds2 training and test data are generated from [Ds3 training data is generated from miGCM_218.gct and test data is generated from PDT_miRNA.gct [Ds1, 66 tumor samples are chosen from 9 MDT types among which 17 randomly chosen samples are considered for test data and the remaining are considered as training data. For Ds2, we have considered a total of 43 human cancer cell lines comprising central nervous system (CNS), colon, leukemia, melanoma, ovarian and renal tissue types. Another three tissue types such as breast, lung and prostate are excluded from the analysis as mentioned in [2 of raw expression value, in at least 10% of the cell lines. A total of 278 probes (miRNAs) have been selected. From 43 selected cell lines we have randomly chosen 12 cell lines as test set. For Ds3, 77 MDT samples from 11 distinct tumor types are chosen for training set and 17 PDT samples are chosen for the test set. The data is preprocessed, as suggested in [Three data sets  where U is the set of cancer types, V is the set of miRNAs and  ∈ E iff v is differentially expressed or dysregulated in cancer type u. In other words, a bipartite graph based network model is constructed consisting of two disjoint sets of nodes where edges only exist between nodes from different sets. Here U is a set of 31 cancer types and V is a set of 192 cancer associated miRNAs. In order to develop the network, the differential expression patterns of experimentally verified human miRNAs in different cancer and normal tissue types obtained from extensive literature search are taken into account. Other relevant parameters that have been considered are location of the miRNAs at fragile sites and cancer associated genomic regions, epigenetic alteration of miRNA expression and abnormalities in miRNA processing target genes and proteins. The complete network is provided in a tabular form in Table S1 of Additional file In order to globally observe and identify the miRNAs and associated cancer modules, generation of a approach . We formdik). The class centroid of an inatance i in class k is defined as the average expression level of that instance over all the samples in class k. Similarly, the overall centroid of an instance i is defined as the average expression level of that instance over all the experiments.Uncorrelated shrunken centroid (USC) algorithm  is the rxij = Expression level for instance i = 1, 2, ..., p and samples j = 1, 2, ..., n. Let number of classes = K and Ck = Set of all nk samples in class k.Let ith instance overall centroid is,For k and instance i is,and the class centroid of class dik is standardized by the within class standard deviation of instance i(si),S0= median value of SiS over all instance i.where dik| > Δ then the instance with the corresponding class centroid is selected as relevant feature and used for classification. This can be stated as,The term 'significant' can be measured by shrinkage threshold Δ. If |k) are selected as relevant features. Based on the where x* and class k can be defined asNow, the discriminant score for a new sample x* to the shrunken class centroid and the second term represents a correction for the class prior probability.where x* is assigned to the class k.Based on the minimum discriminant score sample SΔ is produced for a given shrinkage threshold Δ. As Δ increases, the number of relevant instances decrease since in this case the difference between the class centroid and the overall centroid of an instance needs to be larger for it to be considered as relevant. In USC, a set of redundant, correlated instances are further removed by computing the pairwise correlation for each pair of instances. If the pairwise correlation is greater than a correlation threshold ρ , the instance with the smaller relative difference is removed from the set of relevant instances. This way a set of relevant instances is generated for each shrinkage threshold Δ and correlation threshold ρ. This relevant instance set is then used for the classification. The USC algorithm is equivalent to the SC algorithm when ρ = 1 i.e. no correlated instances are removed from the list.In SC, a set of instances, SFSSClass that uses biclustering for simultaneous feature and sample selection (SFSS). A flow chart of SFSS technique is provided in Figure Prediction accuracy of a classifier can be improved through the selection of relevant features and samples. The features are called relevant if these have high intra-class compactness and low inter-class similarity. In this regard we note that although expression data is available for a large number of miRNAs, only a small subset actually shows a similar expression pattern in a subset of tumor types due to their tissue specific regulatory nature. Thus, in this article we propose a technique called cancer-miRNA network is used in SFSSClass for selecting the relevant biclusters. We have used biclustering algorithm SAMBA [cancer-miRNA network. From a potential bicluster we choose only the relevant samples appearing in the cancer-miRNA network, but all the miRNAs are considered. The reason for considering all the miRNAs in a bicluster is that biological investigation has already revealed that genes belonging to the same cluster  are likely to be co-regulated. Selected relevant miRNAs and samples are then used as the training set for the purpose of classification.The hm SAMBA  (a briefSΔ) is chosen based on shrinkage threshold Δ , where Δ and SΔ are inversely proportional. Again, a pairwise correlation for each pair of miRNAs  in SΔ is then computed for each Δ and it is determined whether this correlation is greater than a correlation threshold ρ. If so then the miRNA with smaller relative difference is removed from the set of relevant miRNAs. The optimal parameters (Δ and ρ) are determined from the results of the ten random fourfold cross validation. Based on the selected criteria the classification of the test set has been performed. We used publicly available tool EXPANDER version 3.2 for SAMBA http://acgt.cs.tau.ac.il/expander/expander.html and TIGR MeV version 3.1 [A set of relevant miRNAs  compared to the case without biclustering (~41.2%). This clearly shows the efficiency of the proposed method for extracting the relevant data through which more improved classification is possible.In a part of the investigation we have classified the PDT samples based on a set of MDT training set. After performing simultaneous feature and sample selection from the training set, 91 miRNAs and 37 samples are selected from 9 tumor types, viz., colon, pancreas, kidney, bladder, prostate, ovary, uterus, lung and breast. In our biclustering experiment the miRNAs that are significantly dysregulated in mesothelioma or melanoma, did not appear in association with these two tissue types in any of the obtained biclusters. We have compared the prediction accuracies obtained by the proposed method with those reported previously in several literature including USC. The detailed results are shown in Table cancer-miRNA network proposed in our previous work [SFSSClass algorithm which judiciously integrates a biclustering technique for simultaneous feature (miRNA) and sample (tissue) selection, the cancer-miRNA network and a classifier. The performance of the SFSSClass is found to be significantly improved with respect to the other existing approaches. For example, while the best accuracy of classifying PDT samples obtained from [SFSSClass is found to be ~82.3%. The advantage of incorporating biclustering integrated with the cancer-miRNA network is evident from the consistently better performance of SFSSClass over USC .Recent evidences indicate that miRNAs have important roles in human malignancies and act as onco/tumor suppressor miRNAs. The cancer associated genomic regions, putative and experimentally verified target onco/tumor suppressor genes, significant over or under expression of the miRNAs in specific cancer cell lines are a few potential evidences of the involvement of miRNA in cancers. Limited work has been done towards revealing the fact that a number of miRNAs can control commonly altered regulatory pathways. However, this becomes immediately evident in the global graphical interface provided by the ous work . In thisned from  is ~76.5cancer-miRNA network, the concept of integrating domain knowledge (obtained through literature mining) based feature selection with classification may be useful in other Bioinformatics domains. For example, a very low prediction accuracy is obtained when classifying the PDT samples based on mRNA expression profiling data, ~ 5.9% in [cancer-gene network, biclustering and the classifier may improve the prediction accuracy. In future, specific information extracted from the cancer-miRNA network such as cancer specificity of miRNAs, hub miRNAs, over/under expressibility of miRNAs, etc., will be integrated with SFSSClass for more accurate prediction of tumor tissue origin.Although the proposed approach is applicable to  5.9% in . In thisThe authors declare that they have no competing interests.RM and SB performed all analysis and wrote the manuscript. UM and MQZ provided critical insights into the article. All authors read and approved the final manuscript.Appendix for "SFSSClass: An integrated approach for miRNA based tumor classification". The detailed information about cross validation result and chosen optimal parameters for both the USC and the proposed method are given in the figures s1 to S6. A complete list of all the miRNAs involved in different cancer types is provided in Table S1. The differential expression patterns of miRNAs in different tumor tissues along with a list of references (PubMed-indexed for MEDLINE or PMID) are also present in this table. The information is obtained by extensive literature search. Other relevant parameters that have been considered are location of the miRNAs at fragile sites and cancer associated genomic regions, epigenetic alteration of miRNA expression and abnormalities in miRNA processing target genes and proteins.Click here for fileA brief description on various classifiers that have been used for classifying tumor samples.Click here for file


Pairing of samples arises naturally in many genomic experiments; for example, gene expression in tumour and normal tissue from the same patients. Methods for analysing high-throughput sequencing data from such experiments are required to identify differential expression, both within paired samples and between pairs under different experimental conditions.baySeq package (versions 1.11.6 and greater) available from Bioconductor (http://www.bioconductor.org).We develop an empirical Bayesian method based on the beta-binomial distribution to model paired data from high-throughput sequencing experiments. We examine the performance of this method on simulated and real data in a variety of scenarios. Our methods are implemented as part of the RWe compare our approach to alternatives based on generalised linear modelling approaches and show that our method offers significant gains in performance on simulated data. In testing on real data from oral squamous cell carcinoma patients, we discover greater enrichment of previously identified head and neck squamous cell carcinoma associated gene sets than has previously been achieved through a generalised linear modelling approach, suggesting that similar gains in performance may be found in real data. Our methods thus show real and substantial improvements in analyses of high-throughput sequencing data from paired samples. A number of methods have been developed in recent years for the detection of differential expression in high-throughput sequencing data. The data are generally modelled using an over-dispersed Poisson distribution , althribution  has alsoribution ,7 or a mribution .Analysis methods for an important class of experimental design, that involving paired data, are less well developed. In a paired experimental design, we are generally interested in examining how the ratio of expression between paired counts varies, a scenario that arises naturally in a number of important settings. For example, in oncological studies we may take normal and tumour tissue from the same patient and wish to determine whether the ratio of gene expression differs from a one-to-one ratio between patients within a treatment group, or whether this ratio varies between treatment groups. Similarly, we may wish to compare individuals pre- and post-infection to establish how different strains of a species respond to infection. Paired samples provide a useful approach to such problems as even when the expression of particular genes varies substantially between individuals, the effect of treatment may be relatively consistent. By using paired samples, we can account for individual-specific effects and consequently better detect treatment effects.within each pair. That is, we are interested in distinguishing those data which show an approximately one-to-one ratio of expression  for each pair of counts, and those which show a consistent change between each pair. In the examples above, this is equivalent to discovering differential expression between normal and tumour tissue, or between pre- and post-infection cases, taking into account individual-specific effects. In the second case, we are interested in discovering differential expression between groups of paired samples. In our examples, this would correspond to changes in relative expression as a result of treatment. Depending on the nature of the experiment and the data produced, either or both of these forms of differential expression may be of interest.Two key questions arise in analyses of paired data. Firstly, we can examine differential expression We present here an empirical Bayesian method based on an over-dispersed binomial distribution, the beta-binomial, for addressing the problem of detecting both types of differential expression in paired sequencing data. The beta-binomial distribution has previously been suggested as a suitable model for the analysis of unpaired high-throughput sequencing data , in whicedgeR Bioconductor package and described in McCarthy et al is a measure of the over-dispersion of the data, where ϕ = 0 makes the model equivalent to the binomial distribution.where ϕ for fixed p.The variance of the binomial distribution under this parametrisation is M by sampling from the dataset. For each set of samples Eq, we would like to find an estimate of the mean and dispersion of the distribution underlying the data from a single tuple pair; Dc. By finding estimates of the mean and dispersion for a large number of tuple pairs, we create the sampling Θq. The chief difficulty here lies in properly estimating the dispersion. Suppose that the data from a given tuple pair shows genuine differential expression. If the model that we are testing assumes that there is no differential expression, then the dispersion will be substantially over-estimated for this tuple pair. Since we do not know in advance which tuple pairs are genuinely differentially expressed and which are not, we need to consider the replicate structure of the data in order to properly estimate the dispersions. We define the replicate structure by considering the sets {F1, ⋯Fs} where i, j ∈ Fr if and only if sample pair We can derive an empirical distribution for the parameters of a model Dc by maximum-likelihood methods. We consider the likelihood of the tuple pair Dc under the replicate structure to be Given this structure for the data, we can estimate the dispersion of the data in a tuple pair Πrc and ϕc to maximise this likelihood. This gives us a value for ϕc, the dispersion of the cth tuple pair.and choose Πqc as the constant proportion of expression to be examined for all c.In analysis of paired data, one question of interest may be to identify tuple pairs which show a particular ratio of expression between the sample pairs. The most usual case will be a one-to-one ratio , indicating that there is no differential expression of the tuple pair between the sample pairs. To model this, we simply set the Eq. To approximate a distribution on the Θq for such a model, we can estimate the proportion Πqc of reads in the first count of each pair of samples for the tuple pair c. We achieve this by using the value previously acquired for ϕc and estimating Πqc by maximum likelihood methods. For notational simplicity, we define the data associated with the set Eq as Dqc to be Alternatively, we may wish to consider a model in which we are not interested primarily in the value of the ratios of expression between sample pairs, but only on whether these ratios are similar or different amongst various experimental groups defined by the sets Πqc to maximise this likelihood for each q. We can then form the set Θq = {} by repeating one of these processes for multiple sampled tuple pairs. We can then calculate  We then choose Dqc when estimating the dispersion in Eqn 4.This method of estimating the dispersion assumes that the dispersion of a tuple pair is constant across experimental groups. Where the number of samples is small, this is likely to be the best approach. Where there is an expectation that the dispersion will be substantially different between experimental groups, and there are adequate numbers of replicates, there may be advantages to estimating the dispersions individually for each of the different sets of samples in each model, while still considering the replicate structure within these sets. This is easily done by restricting the data (and corresponding replicate structure) to Dc we apply the BIC to select the most likely model based on the calculated likelihoods P(Dc|M) for each model M. This allows us to estimate the proportion of data that are best modelled by each of the models, which can be used as an estimate of the prior probablities of each model when calculating P(M|Dc) for any individual tuple pair Dc.A number of options are available when considering the prior probabilities of each model ous work . An alteM. In practice, the number of models may be further reduced by considering only those that are biologically plausible.Finally, we need to consider the scaling factor M given the observed data for tuple pair c is Hm is the set of the top m tuple pairs for the given model M, the false discovery rate is thus estimable as False discovery rates can be estimated directly from the posterior likelihoods estimated for each model. If the likelihood of a model We use both simulated and real data to compare the beta-binomial method described to the edgeR-GLM  and DESen sample pairs . We begin by simulating differential expression within pairings only, that is, some of the tuples are simulated so that the relationship between the paired counts is not one-to-one. A more complex experimental design is then simulated by the inclusion of simulated data in which the ratio of expression between the paired counts also differs between experimental groups.We base our simulations on those described by Robinson & Smyth , simulatWe assess the performance of the methods by ranking the tuple pairs by their strength of association with each type of differential expression and computing the true and false positive rates using these ranked lists. For increased robustness, we estimate the mean of these rates over one hundred simulations under each set of conditions.ith sample of a non-differentially expressed cth tuple pair, the paired counts uic and λcLiQicMic and λc, which define a baseline of expression for the tuple pair when scaled by the library size, are sampled randomly from a set of values empirically estimated by the edgeR method [Li and For the R method  from a SR method . The Li Qic to vary for each sample pair i as well as for each tuple pair c. We simulate this variation by setting ν is sampled from a uniform distribution between -2 and 2, allowing for up to sixteen-fold variation in expression between sample pairs due to individual effects.We simulate individual effects in the data by allowing Mic allow us to introduce differences between experimental groups of sample pairs and between the members of a pair, while allowing for variation between biological replicates. They are sampled from a beta distribution with shape parameters c, i. For differentially expressed tuple pairs, we select the values μic and ϕc, which indicates the level of dispersion  are drawn from a beta distribution with shape parameters 1, 10.The within paired counts, that is, we search for tuple pairs which show evidence for a deviation from a one-to-one expression ratio between the paired counts. We simulate one thousand differentially expressed tuple pairs. For a differentially expressed tuple pair c, i, where fc is drawn from a uniform distribution between -b and b, where b is allowed to vary.We begin by simulating the simplest case of a paired analysis. In this scenario we are interested only in the differences b = 1, 2 and 4 for n = 4 and 10.We examined the performance of the methods on the basis of ROC curves. Figure For low false positive rates, the performance of the methods is approximately equal as each identify the ‘low-hanging fruit’, those tuple pairs showing high differential expression with relatively low biological variation. However, for higher false positive rates the beta-binomial method shows a clear and consistent gain in performance over the generalised linear modelling approaches. The DESeq-GLM in general performs better than edgeR-GLM, especially for higher numbers of sequenced libraries. For high library numbers, the performance of DESeq-GLM approaches that of our beta-binomial approach.A, A1, …, An paired with samples B, B1, …, Bn paired with A and B.We next consider the more complex case where differential expression exists both within paired counts, and between experimental groups. This is equivalent to an experimental set-up in which we have sample pairs from condition gc to one of the two experimental conditions. This is applied such that for a differentially expressed tuple pair c, the data for half the sample pairs, representing the first experimental condition, are simulated using the values fc is simulated as before and gc is drawn from a uniform distribution between -d and d. Iic is an indicator variable randomly sampled from {0,1} for each tuple pair c indicating whether the effect is assigned to the first or second experimental condition.We again simulate ten thousand tuple pairs. For one thousand of these, we simulate differential expression within paired counts as before. For a second group of one thousand tuple pairs, we also simulate differential expression between experimental conditions. We simulate differential expression between experimental conditions by applying a scaling factor Both the beta-binomial approach and the generalised linear modelling approaches are capable of simultaneously detecting both types of differential expression, however, the form of results acquired by these two approaches differs. For the beta-binomial approach, posterior likelihoods are calculated for each available model, and hence only one model for each tuple pair can be assigned a high posterior likelihood. If the true differential expression of a tuple pair involves changes in expression ratios between experimental groups, the model for consistent change from a one-to-one ratio between paired counts will have a low posterior likelihood as the change will not be consistent across the tuple pair. For the generalised linear modelling approaches, both a pair effect and an experimental group effect, and the significance with which these differ from zero, are calculated for each tuple pair. Consequently, both effects can be present with high significance even when changes in expression are driven primarily by a change in expression ratios between experimental groups.If those tuple pairs simulated as showing differential expression ratios between experimental groups are treated as false positives when considering differences from a one-to-one ratio between paired counts, this heavily penalises the generalised linear model methods. If they are treated as true positives, the generalised linear modelling approaches are evaluated on the basis of two thousand true positives where the beta-binomial method is evaluated on the basis of one thousand true positives, making performance comparisons difficult. To allow fair comparisons between the methods, we therefore exclude the thousand tuple pairs simulated as showing differential expression ratios between experimental groups when calculating the true and false positive rates for detection of differences from a one-to-one ratio within paired counts.b, d and n.Figure In this more complex case, the difference between the performance of the methods is considerably more pronounced. Particularly in identifying differential expression between experimental groups, the beta-binomial method shows considerably better performance than that of both generalised linear modelling approaches. In identifying differential expression from a one-to-one ratio within paired counts, the performance of the beta binomial method is similar to that shown in Figure Mic. The minimax distribution is also a two-parameter distribution on  with density The simulated data described above are drawn from sets of Poisson distributions whose parameters are a multiple of a random variable drawn from a beta distribution. Therefore, the simulated data have a beta-binomial distribution, the model proposed for the analysis. We can examine the robustness of the model by considering an alternative distribution for the simulations. Since the Poisson distribution is a well established model for the technical effects observed in high-throughput sequencing data  we test  The moments of this distribution are given in terms of the beta function such that Mic, nor is it possible to define a dispersion parameter for this distribution. In order to select parameters for the minimax distributions used to simulate the data, we therefore calculate the parameters for the beta distribution as described above. We then  calculate the parameters of the minimax distribution such that the mean and variance of each random variable are identical to those which would be used in the case of the beta distribution. This approach has the advantage that, for given parameters of simulation, the results are directly comparable between those data simulated using a beta distribution and those simulated using a minimax distribution. Consequently, it is not possible to establish closed-form expressions for the parameters Results for the application of the three methods to data simulated using the minimax distribution are shown in Additional file et al[et al[org.Hs.eg.db (version 2.7.1). We then discard data associated with a RefSeq identifier whose gene symbol is not identified and all but one of any duplicated gene symbol, keeping the data with the greatest number of exons. This results in paired count data for 10529 genes.We examine a set of paired data from a recent study of oral squamous cell carcinoma . The stuet al using th al[et al. We map We analyse these data to find both genes displaying a consistent fold-change between tumour and normal tissue, and those genes which show heterogeneity in fold-change between the paired counts belonging to the individual patients. The patients are treated as biological replicates for the purposes of dispersion estimation (Eqn. 4) despite the presence of some genes displaying patient-specific effects. In the absence of true biological replicates, this is required in order to carry out a meaningful analysis. We construct a set of models testing both for consistent differential expression between tumour and normal tissue and for differing ratios of expression between individuals. We acquire posterior likelihoods for each of these models of differential expression and hence can evaluate either the likelihood that each gene displays consistent differential expression between normal and tumour samples, or the likelihood that a gene displays differential expression of any kind .et al[et al[et al[We identify 29 genes displaying a consistent ratio of differential expression between tumour and normal samples at a false discovery rate (FDR) of 0.05 . The methods are computationally intensive but readily parallelisable, so that a full analysis of the Tuch et al[Our method is implemented as part of the software package uch et al data canAs with the most successful approaches to analysis of unpaired sequencing data ,11, our A key assumption made in developing this method concerns the nature of the over-dispersion between samples caused by biological variation. In the absence of available data from which to infer the precise nature of the over-dispersion, we have assumed for computational convenience that the beta distribution is a suitable model for the biological variation in ratios of expression between sample pairs and hence that the distribution of the count data may be modelled with the beta-binomial. The beta distribution is remarkably flexible and is thus likely to be capable of accounting for the behaviour of most paired data, although in certain circumstances this assumption may fail. We note, however, that the principles of the empirical Bayesian approach may be applied for any underlying distribution, and so might be adapted to meet this circumstance.We demonstrate the performance of our methods on both simulated and real data. In analyses of simulated data using a range of parameters, we show considerable gains in performance compared with two implementations of a generalised linear modelling approach, especially when more complex patterns of differential expression are present in the data. The gain in performance using our methods is particularly marked for larger numbers of samples, a result that is likely to be increasingly important as the cost of sequencing experiments declines, allowing larger studies. This gain in performance is also found when the minimax distribution  is used et al[et al[The analysis of the biological data from Tuch et al demonstret al, particu al[et al, which, The comparison of paired mRNA-Seq samples is a major application for our method. However, there are other key applications. In particular, paired data arise naturally in studies of epigenetic markers, such as chromatin and methylation marks, where the prevalance of a particular marker is compared to a baseline measurement for each marker. Our method is, therefore, likely to have wide applicability not only in cancer and other areas of medicine but also in fundamental life science research.The authors declare that they have no competing interests.TJH designed and implemented the methods described and drafted the manuscript. KAK helped to draft the manuscript. Both authors read and approved the final manuscript.Supplementary Figures and Tables. Figure S1: ROC curves showing the performance of the beta-binomial, edgeR-GLM and DESeq-GLM methods in identifying differential expression within paired counts in data simulated using the minimax distribution to simulate biological variation. Simulations are carried out for various combinations of b, a measure of the level of differential expression, and n, the number of paired libraries. Figure S2: ROC curves showing the performance of the beta-binomial, edgeR-GLM and DESeq-GLM methods in simultaneously identifying differential expression from a one-to-one ratio within paired counts (solid lines) and differential expression between experimental groups (dashed lines) in data simulated using the minimax distribution to simulate biological variation. Simulations are carried out for various combinations of b, a measure of the level of differential expression within paired counts, d, the level of differential expression between experimental groups, and n, the number of paired libraries. Table S1: The top twenty-nine genes (FDR < 0.05) showing consistent ratios within patients of differential expression between normal and tumour samples. Table S2: The twenty-five genes identified by Yu et al (2008) as being of interest in a systematic review of head and neck squamous cell carcinoma transcripts, as ranked by their likelihood of showing differential expression of any kind in the Tuch et al (2010) data. Table S3: The nine genes identified by Tuch et al (2010) as being of particular interest, as ranked by their likelihood of showing differential expression of any kind. Table S4: Gene sets showing enrichment (p < 0.01) in the eight up-regulated in tumour genes showing consistent differential expression at FDR > 0.05. Head and neck squamous cell carcinoma gene sets are highlighted. Table S5: Gene sets showing enrichment (p < 0.01) in the twenty-one down-regulated in tumour genes showing consistent differential expression at FDR > 0.05. Head and neck squamous cell carcinoma gene sets are highlighted. Table S6: Gene sets showing enrichment (top fifty) in the 2033 down-regulated in tumour genes showing any differential expression at FDR > 0.05. Head and neck squamous cell carcinoma gene sets are highlighted. Table S7: Gene sets showing enrichment (top fifty) in the 572 up-regulated in tumour genes showing any differential expression at FDR > 0.05. Head and neck squamous cell carcinoma gene sets are highlighted.Click here for file