We proposed a non-parametric method, named Non-Parametric Change PointStatistic (NPCPS for short), by using a single equation for detectingdifferential gene expression (DGE) in microarray data. NPCPS is based on thechange point theory to provide effective DGE detecting ability.NPCPS used the data distribution of the normal samples as input, and detectsDGE in the cancer samples by locating the change point of gene expressionprofile. An estimate of the change point position generated by NPCPS enablesthe identification of the samples containing DGE. Monte Carlo simulation andROC study were applied to examine the detecting accuracy of NPCPS, and theexperiment on real microarray data of breast cancer was carried out tocompare NPCPS with other methods.Simulation study indicated that NPCPS was more effective for detecting DGE incancer subset compared with five parametric methods and one non-parametricmethod. When there were more than 8 cancer samples containing DGE, the typeI error of NPCPS was below 0.01. Experiment results showed both goodaccuracy and reliability of NPCPS. Out of the 30 top genes ranked by usingNPCPS, 16 genes were reported as relevant to cancer. Correlations betweenthe detecting result of NPCPS and the compared methods were less than 0.05,while between the other methods the values were from 0.20 to 0.84. Thisindicates that NPCPS is working on different features and thus provides DGEidentification from a distinct perspective comparing with the other mean ormedian based methods. When normal gene expression is exposed to radiation, virus infection, etc., it wouldcause gene mutation or gene abnormal activation, which probably leads to cancerarising Based on microarray gene expression profiling Most of the aforementioned methods attempt to identify the abnormal data points basedon the overall percentile of the gene expression profile. However, it is reasonableto assume that the DGE detection could be achieved by searching for the change pointof the gene expression profile If we consider the single-gene expression profile asa data sequence, for non-DGE sequence, there is no significant change between thedata distributions of normal and cancer samples; for DGE sequence, since the geneexpression is over regulated in cancer group, the data distribution of cancer andnormal samples become distinctly different, which would result in a significantchange point in the sequence of gene expression profiles.Change point problem Since a few of the currently available change-point methods deal explicitly withestimation of the number and location of change points, and moreover these methodsmay be somewhat vulnerable to deviations of model assumptions usually employed r is the change point of r is not the change point,we have Suppose Gene expression profile of a single gene could also be considered as a sequence ofindependent variables as below:y is a variableincreasing with a fixed step that is subject to user's selection (we selected100 in the simulation study and experiment).To detect the change point, in the hypothesis test we used a modified Kolmogorovstatistic (K-statistic), which evaluates the distance between two distributionfunctions:Therefore, the testing procedure is defined asC(0.05) = 1.358 andC(0.05) = 1.628.Null hypothesis To give an estimate of change point, we defineg×n matrix, which containsg rows of genes with their expression levels inn samples, in which normal group hasn1 samples, and disease group hasn2 samples. Let ijxbe the expression intensity of the ith gene in thejth sample of the normal group, whilei = 1, 2, …, g,j = 1, 2, …,n1; let ijy be theexpression intensity of the ith gene in thejth sample of the disease group, whilei = 1, 2, …, g,j = 1, 2, …,n2. The median of the ith geneis defined asixmed, the normal-group median of theith gene, and iymed thecancer-group median asGene expression profile obtained from microarray data is often considered as aThere have been many parametric methods proposed based on the mean, medianand median absolute deviation (MAD) of the gene expression profile, andfollowing are some typical methods.COPArq(i) is therth percentile of the ith geneexpression values, which is subject to the user's selection.OSQ3(i)+IQR(i)and smaller thanQ1(i)−IQR(i)are statistically considered as DGE. The OS statistic is defined asbelow.ORTiC is the cancer group of theith gene. For over expression,MOSTikMOST isconsidered as a differential gene expression. The testing statisticikMOST is definedas:k isunknown, the data are normalized firstly bykμ and2kσ, and theikMOST is definedasPPSTLRSnS is the summation of the expressionintensities of all the samples and the LRS statistic is asfollows,T-statisticiS is the estimate of combined standarddeviation. Differentially expressed genes are recognized when the testingstatistic exceeds a certain threshold.1 normal samplesand n2 cancer samples, of which k(0<k<n2) cancer samplescontained DGE simulated by adding a constant μ to the original normal randomnumbers. Multiple simulations were carried out according to different values ofsample size n, DGE sample size k, andsignificance level α. The FPR . Foreach Monte Carlo simulation, NPCPS was applied to an artificial 7000-genedataset  composed of n The FPR  and aver The FPR  werecomn2 = 25, the FPRn2 = 50,k shouln and k. When k wasgetting greater, all methods produced better ROC  and seven other methods,namely COPA, ORT, OS, MOST, T, LRS, and PPST, on normally distributed datasets with differentμ, tter ROC . For μ =  and other sevenmethods on datasets generated from skew-normal (SN) distribution . For difgood ROC . The othThe AUC of ROC is summarized in The microarray data used in the experiment are provided by West C(0.05) = 1.628 was selected,which yields a detecting result of 1978 DGE genes. nD value). The gene expression values and thechange-point (CP) positions of the cancer-relevant genes are illustrated inFor NPCPS, Moreover, by comparison among the ranking results of all eight methods based onthe test statistic, it was noticed that genes top-ranked by NPCPS were rankedconsiderably lower by other methods, most of which were mean and median basedparametric methods. When inspecting all the 7129 genes, the overall trend inranking difference between NPCPS and other methods became more obvious. As a non-parametric statistics, NPCPS does not rely on assumptions that thedata are drawn from a given probability distribution. It is applicable toinput data derived from various types distributions and doesn't requiredata pre-processing. As such it is the opposite of parametric statistics,which would have inferior performance when the input data are not in theassumed distribution, as in the ROC simulation on normal and skew-normaldatasets .The gene expression profile generated from microarray data usually containssamples of thousands of genes. Genes in the cancer samples might be over orunder expressed. Majority of the DGE detecting methods have differentformulas for under expressed and over expressed genes, respectively. Forexample, OS and ORT use different percentile values for over-expression andunder-expression, respectively, and apply both formulas to the samemicroarray data. If over expression formula is applied to under expresseddata, the DGE can not be correctly recognized. However, the detected resultsmight contains false alarms, since both over-expression and under-expressionformulas are applied to the same gene, and might be detected as DGEsignificant for twice. Unlike the other methods, NPCPS works for both typesof DGE by using the same calculating formula, which would reduce the FDR,and do not require further analysis and computation aiming to clean thefalse alarms. When over expression formula was applied to under-expressedgene data , andvicn = n1+n2samples on the single gene level, we can learn that, for one sample containingthousands of genes, how many genes were over expressed or under expressed. Thisstatistical information can be used to analyze features of each sample, and theresults of which could be applied to the estimation of the differentiationdegree of cancer in different development stages.The biological meaning of nD, while the rest 3521 had positivenD. NPCPS use nDto evaluate the change in distribution between normal and cancer samples, anddirectly measure the DGE type as either over expressed or under expressed. Thisfeature is valid based on the expression value in nD) shows typical under expression and nD) shows typical over expression. nD and DGE in a more intuitivemanner where cumulative data distributions of several typically ranked genes aregiven.NPCPS results showed that, among the 7219 genes, 3608 had negativenD(under-expression), 9C and 9D are corresponding to negativenD (over-expression), respectively. Bycomparing the empirical distribution of cancer and normal samples, in nD value.Genes in Genes in nD.As comparison, In summary, NPCPS is less sensitive to right boundaries and tends to find genesthat have greater cumulative distance between the data distribution of normaland cancer groups. For such genes, the samples in normal and cancer group mayhave the same data range but should have very different distributions.Therefore, the detecting result of NPCPS would be different from other comparedmethods, which are more sensitive to outliers that influence the data range,rather than the cumulative distance between distributions. In other words, NPCPSvalues continuous change in data distribution over the whole data range, whilethe other methods look for a significant change of mean or median. This wouldexplain the low correlation between NPCPS and other methods.A non-parametric statistical method, NPCPS, was proposed for DGE detection basedon change-point theory. NPCPS uses the data distribution of normal and cancersamples as the only input to detect a change point that indicates DGE, in orderto identify potential cancer genes. Distribution-based NPCPS does not requiredata pre-initialization and is computationally efficient compared with othermedian-based parametric methods. Contrast to the compared methods, NPCPS dealswith both over-expression and under-expression by the same equation. Anotherunique feature is that the proposed NPCPS could estimate both the number and thelocation of cancer samples with DGE could be estimated. Simulation study andexperiments showed that, the proposed NPCPS method had better reliability andaccuracy; NPCPS was more effective than the compared parametric methods; similarROC curves was given compared with LRS when sample size was larger; when thesimulated DGE value was smaller, i.e. DGE was less significant, NPCPS had bettersensitivity compared with the other seven methods. Simulations also indicatedthat, for cancer subgroup with size greater than 8, the NPCPS had FPR less than0.01. Besides, the detection results of NPCPS had very low correlation with thecompared methods, both parametric and non-parametric, which indicates that NPCPSprovides meaningful detection results different from other methods. Since cancersamples could be categorized according to different stages in the cancerdevelopment, DGE detection can also be considered also a multi-class problem.Further effort could be focused on the multi-change-point in the distribution ofmicroarray gene expression profile.


Sample size calculation is an important issue in the experimental design of biomedical research. For RNA-seq experiments, the sample size calculation method based on the Poisson model has been proposed; however, when there are biological replicates, RNA-seq data could exhibit variation significantly greater than the mean (i.e. over-dispersion). The Poisson model cannot appropriately model the over-dispersion, and in such cases, the negative binomial model has been used as a natural extension of the Poisson model. Because the field currently lacks a sample size calculation method based on the negative binomial model for assessing differential expression analysis of RNA-seq data, we propose a method to calculate the sample size.We propose a sample size calculation method based on the exact test for assessing differential expression analysis of RNA-seq data.The proposed sample size calculation method is straightforward and not computationally intensive. Simulation studies to evaluate the performance of the proposed sample size method are presented; the results indicate our method works well, with achievement of desired power. Next generation sequencing (NGS) technology has revolutionized genetic analysis; RNA-seq is a powerful NGS method that enables researchers to discover, profile, and quantify RNA transcripts across the entire transcriptome. In addition, unlike the microarray chip, which offers only quantification of gene expression level, RNA-seq provides expression level data as well as differentially spliced variants, gene fusion, and mutation profile data. Such advantages have gradually elevated RNA-seq as the technology of choice among researchers. Nevertheless, the advantages of RNA-seq are not without computational cost; as compared to microarray analysis, RNA-seq data analysis is much more complicated and difficult. In the past several years, the published literature has addressed the application of RNA-seq to multiple research questions, including abundance estimation -3, detecOne of the principal questions in designing an RNA-seq experiment is: What is the optimal number of biological replicates to achieve desired statistical power?  Because RNA-seq data are counts, the Poisson distribution has been widely used to model the number of reads obtained for each gene to identify differential gene expression ,13. FurtSample size determination based on the exact test has not yet been studied, however. Therefore, the first goal of this paper is to propose a sample size calculation method based on the exact test.In reality, thousands of genes are examined in an RNA-seq experiment; differential expression among those genes is tested simultaneously, requiring the correction of error rates for multiple comparisons. For the high-dimensional multiple testing problem, several such corrected measures have been proposed, such as family-wise error rate (FWER) and false discovery rate (FDR). In high-dimensional multiple testing circumstances, controlling FDR is preferable  because In sum, in this article, we address the following two questions: (i) For a single-gene comparison, what is the minimum number of biological replicates needed to achieve a specified power for identifying differential gene expression between two groups? (ii) For multiple gene comparisons, what is the suitable sample size while controlling FDR? The article is organized as follows. In the Method section, a sample size calculation method is proposed for a single-gene comparison. We then extend the method to address the multiple comparison test issue. Performance comparisons via numerical studies are described in the Results section. Two real RNA-seq data sets are used to illustrate sample size calculation. Finally, discussion follows in the Conclusions section.Yij be the random variable corresponding to the pseudocount, with yij being the observed value of Yij, of the jth  sample of the ith  group where n0 and n1 are the numbers of samples from the control and treatment group, respectively. Assume pseudocount Yij can be modeled as a negative binomial (NB) distribution, NB. Here, γi represents the normalized gene expression level of group i, dij represents a normalization factor for the total number of reads mapped in the jth sample of the ith group, and ϕ is the dispersion. We use the NB parameterization where the mean is μij = dijγi and variance is In an RNA-seq experiment, the total number of reads, also referred to as library size, mapped to the genome are different among the samples. In such cases, the counts in each group are not identically distributed, and it is difficult to develop an exact test for assessing the differential expression analysis of RNA-seq data. To handle this issue, ,15 propoi. Under the null hypothesis (1), the sum of the total pseudocount, Y1+Y0, follows a negative binomial distribution. In analogy with Fisher’s exact test, [p-value can be easily calculated for conducting the exact test.Because the pseudocounts in each group have an approximately identical negative binomial distribution ,15, the ct test, ,15 propoct test,  developect test, ,15, the In the following simulation and application sections, we used edgeR version 3.0.6 for estimating model parameters and performing the exact test.n0 = n1 = n), which is a special but common case. The following method could be easily extended to the unbalanced case (i.e. let n0 = n and n1 = kn where k is a predetermined ratio of the sample size of the control group to the treatment group). In order to perform sample size calculations, it is necessary to construct a power function for the testing described above. The power of a test is the probability that the null hypothesis is rejected when the alternative hypothesis is true. Since the distribution of the exact test statistic under the alternative hypothesis is unknown, however, it is difficult to derive a closed-form expression of the power function. Instead of deriving the distribution of test statistic under the alternative hypothesis, [p-value. Here, we borrow their concept to calculate power. For a given p-value, p where y0 and y1 are the observed pseudo-sums, described in the previous section, the power can be expressed asIn this section, we focus on sample size calculation based on the exact test for a single-gene comparison as described in the test statistics section. For simplicity, we assume the RNA-seq experiment uses a balanced design  is the probability mass function of the negative binomial distribution with mean μ as well as dispersion ϕ, α is the the level of significance, and I(.) denotes the indicator function. For a given desired power 1-β, the power of the test can be represented as the function of sample size in the formwhere n to attain the given power 1-β at level of significance α can then be calculated by solving (2) through a numerical approach, such as a gradient-search or bisection procedure.Thus, the required sample size In reality, thousands of genes are examined in an RNA-seq experiment, and those genes are tested simultaneously for significance of differential expression. In such cases, the sample size calculation for a single-gene comparison discussed above cannot be applied directly. Jung, 2005  incorporFor the multiple testing problem,  suggesteR0 is the number of false discoveries and R is the number of results declared significant .where To calculate the sample size for microarray data analysis,  proposedm0 is the number of true null hypotheses and E(R1) is the expected number of true rejections. By borrowing their concepts, the expected number of true rejections for RNA-seq data can be calculated as,27, wherρg is the fold change, ϕg is the dispersion, and μg0 is the average read count in the control group for gene g∈M1 (the set of prognostic genes), respectively. Thus, to guarantee an expected number of true rejections, say r1, and control FDR at a specified level f, we havewhere andα, we haveBy solving equation (3) with respect to α∗ is the marginal type I error level for the expected number of true rejections r1 at a given FDR f. Replacing α with α∗ in (4), we have the function with respect to n aswhere g1(n) = 0 via a numerical approach, the required sample size for controlling FDR at level f can be obtained.Then, by solving ρg, dispersions ϕg, and average read counts μg0 of gene g for the set of prognostic genes g∈M1 prior to the RNA-seq experiment. However, we may not have enough information to estimate all of those parameters in practice. To address this issue, we propose the following method to obtain a conservative estimate of the required sample size. Because the power increases as | log2(ρg)| or μg0 increases and ϕg decreases, we suggest using a common ρg, μg0, and ϕg, respectively. In such cases, it gives a more conservative estimate of the required sample size.To calculate the sample size, we have to estimate all of the fold changes ρ∗, ϕ∗ to estimate each ρg, μg0, and ϕg, g∈M1, in the multiple testing context, α∗ and β∗ can be calculated as r1f/(m0(1-f)) and 1-r1/m1, respectively, where m1 is the number of prognostic genes. In other words, the power function (2) can be applied in the case of multiple gene comparison, with the replacement of α and β with α∗ and β∗.When we use The procedures for sample size calculation detailed in this section can be summarized as follows: m : total number genes for testing; m1 : number of prognostic genes; r1 : number of true rejections; f : FDR level; w : ratio of normalization factors between two groups; {μg0,g∈M1} : average read counts for prognostic gene g in control group; {ρg,g∈M1} : fold changes for prognostic genes g in control group; {ϕg,g∈M1} : dispersion for prognostic genes g in control group;1. Specify the following parameters: 2. Calculate sample size: μg0, ρg, and ϕg for each prognostic gene g are known, use a numerical approach to solve the equation below with respect to n.(a) If all the parameters α∗ = r1f/(m0(1-f)) and m0 = m-m1;where (a) Otherwise, ρ∗, a minimum average read count ϕ∗;•••specify a desired minimum fold change ρ = ρ∗, ϕ = ϕ∗, α = r1f/(m0(1-f)), and β = 1-r1/m1 in equation (2) and solve it with respect to n.•••replace In this section, we conducted simulation studies to evaluate the accuracy of the proposed sample size formula. The parameter settings in simulation studies are based on empirical data sets.m = 10000 and the number of statistically significant prognostic genes m1 = 100. We wanted to detect the expected number of true rejections r1 = 80, which corresponds to a power of 80% (i.e. β∗ = 0.2). All parameters μg0, ρg, and ϕg  were assumed to be unknown. Thus, we used a minimum fold change ρ∗ and a minimum average read count ϕ∗ to estimate each ρg, μg0, and ϕg, g = 1…,10000. We varied 2-fold changes log2(ρ∗) = 0.5,1.0,1.5,2.0 or 2.5; and ϕ∗ = 0.1, or 0.5. With these settings, α∗ = 8.162×10-5,4.253×10-4, and 8.979×10-4, which correspond to controlling FDR at level 1%, 5%, and 10%, respectively.We set the total number of genes for testing to be α∗ and β∗ into the formulas (2) and calculated sample size by solving this equation. In addition, for each design setting, we generated 5000 samples from independent negative binomial distributions based on the calculated sample size n; for the control group, the count of each gene is generated by R program from a negative binomial distribution with mean ϕ∗; for the treatment group, the count of each gene is generated from a negative binomial distribution with mean ϕ∗. Then, edgeR is used to estimate model parameters and perform the exact test. The number of true rejections was counted using the q-value procedure proposed by [Then, we substituted posed by . The expw = 1. For a fixed log2-fold change, dispersion, and FDR, sample size increases when μ0 decreases. This result is as expected; a small average read count provides less information, such that a larger sample size is required to detect the difference. For a fixed ϕ∗, and FDR, sample size increases when log2(ρ∗) decreases . This result is as expected; a larger sample size is required for detecting a smaller difference. For a fixed ρ∗), and FDR, sample size increases when ϕ∗ increases. This result, also, is as expected; the variation increases when dispersion increases, such that a larger sample size is required to detect the difference. Note that all r1=80); thus, the proposed method estimated a sample size that achieves correct power at the specified FDR level.In Table ϕ∗ = 0.0029, and the ratio of the geometric mean of normalization factors between the two groups was estimated as w = 0.9 using edgeR. Suppose we want to identify 80% of the prognostic genes (i.e. r1 = 0.8×175 = 140), while controlling FDR at 1% (i.e. f = 0.01). Based on the pilot data, we set m = 17306, m1 = 175, m0 = 17131, r1 = 140, and f = 0.01. In this case, we haveTo identify differentially expressed genes between human liver and kidney RNA samples,  exploredandn, the required sample size can be obtained. In the second column from the left of Table ρ∗ = 0.10,0.25,0.50,0.75,1.25,1.50,2.00,2.50, and 3.0. From Table % if the desired minimum fold change ρ∗ is 3.0.After substituting those parameters into equation 2) and solving it with respect to  and solvnw, score test ns, log transformation of Wald statistic nlw, log transformation of score statistic nls, transformation of Poisson ntp, and likelihood ratio test nlr) with the same settings as the negative binomial method. As we can see, the sample size calculation based on the negative binomial and Poisson methods are similar. This result is as expected since the data set explored by [ρ∗ = 0.10) and very large fold changes (ρ∗ = 3.0). This result is expected since it tends to the same conclusion no matter what statistical model is used when the treatment effect is very large .Li, 2013  proposedlored by  has techϕ∗ = 0.6513, and the ratio of the geometric mean of normalization factors between the two groups was estimated as w = 1.08. Suppose we want to identify 80% of the prognostic genes (i.e. r1 = 0.8×133 = 107), while controlling the FDR at 10%. Based on the pilot data, we set m = 13267, m1 = 133, m0 = 13134, r1 = 107 and f = 0.1. In this case, we have α∗ = 9.0512×10-4 and β∗ = 0.2. In the second column from the left of Table ϕ∗ = 0.6513>0. This indicates that the read counts in this data set exhibit over-dispersion. In such a situation, it is inappropriate to model this data set based on the Poisson, and the sample size calculation based on the Poisson model will be underestimated due to underestimation of variance (i.e. the study based on the corresponding sample size will be underpowered).Blekhman, 2010  used RNAIn this research, we assume independent gene expression levels; however, this assumption may not hold in reality. For correlated RNA-seq gene expression data, evaluation of the accuracy of our method is an important future research question; however, generating a negative binomial distribution for correlated high-dimensional data will be a challenge. Moreover, most of the major R packages dedicated to RNA-seq differential analyses  are now starting to enable multi-group comparisons. However, the proposed method is developed for comparing two-group means. Thus, the sample size calculation for multi-group comparisons would be an interesting research topic for us in the future. In addition, it has already been noted that typical RNA-seq differential analyses have very low power; see for example the simulation studies in , where pIn recent years, RNA-seq technology has emerged as an attractive alternative to microarray studies, due to its ability to produce digital signals (counts) rather than analog signals (intensities), and to produce more highly reproducible results with relatively little technical variation ,33. WithThe authors declare that they have no competing interests.Authors CIL, and PFS were involved in the development of the models. CIL and PFS wrote the manuscript. SY generated the original idea and guided and supervised the research. All authors read and approved the final version of this manuscript.


Analysing gene expression data from microarray technologies is a very important task in biology and medicine, and particularly in cancer diagnosis. Different from most other popular methods in high dimensional bio-medical data analysis, such as microarray gene expression or proteomics mass spectroscopy data analysis, fuzzy rule-based models can not only provide good classification results, but also easily be explained and interpreted in human understandable terms, by using fuzzy rules. However, the advantages offered by fuzzy-based techniques in microarray data analysis have not yet been fully explored in the literature. Although some recently developed fuzzy-based modeling approaches can provide satisfactory classification results, the rule bases generated by most of the reported fuzzy models for gene expression data are still too large to be easily comprehensible.In this paper, we develop some Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy (MOEAIF) methods for analysing high dimensional bio-medical data sets, such as microarray gene expression data and proteomics mass spectroscopy data. We mainly focus on evaluating our proposed models on microarray gene expression cancer data sets, i.e., the lung cancer data set and the colon cancer data set, but we extend our investigations to other type of cancer data set, such as the ovarian cancer data set. The experimental studies have shown that relatively simple and small fuzzy rule bases, with satisfactory classification performance, can be successfully obtained for challenging microarray gene expression datasets.We believe that fuzzy-based techniques, and in particular the methods proposed in this paper, can be very useful tools in dealing with high dimensional cancer data. We also argue that the potential of applying fuzzy-based techniques to microarray data analysis need to be further explored. Microarray techniques allow simultaneous measuring of the expression of thousands of genes under different experimental environments and conditions. They allow us to analyse the gene information very rapidly by managing them at one time. The gene expression profiles from particular microarray experiments have been widely used for cancer classification -3. Howevm microarray experiments can be represented as an m × n gene expression matrix , usually in the range of 2,000-30,000, is much larger than the number of examples . In addition, microarray data often brings in multiple missing gene expression values and noisy signals from the experiments. Therefore, classifying cancer mi-croarray gene expression data can be regarded as a high-dimensional-low-sample data problem with lots of noisy or missing data.The data from a series of Unsupervised methods, such as Clustering , and SelDifferent from black-box methods, fuzzy rule-based models can not only provide good classification results, but also easily be explained and interpreted in human understandable terms by using fuzzy rules. This provides the researchers or clinician an insight into the developed models. At the same time, fuzzy systems adapt numerical data (input/output pairs) onto human linguistic terms and offer very good capabilities of dealing with noisy and missing data. Compared to other popular rule-based models in the area, such as C4.5 Decision Trees (DTs) ,12, the Unfortunately, rule-based methods have suffered some well-known limitations in dealing with high dimensional data. Very high dimensional feature vectors and lack of enough training samples are two major challenges for modeling microarray data in general, hence for the success of applying fuzzy models to this problem too. However, some recent developments in fuzzy systems provide us with some good ways to obtain good diagnosis results. For example, Vinterbo et al.  firstly Normally, the accuracy of each fuzzy rule-based classifier is measured by the number of correctly classified training or testing patterns, while its in-terpretability is measured by the complexity of the model, more specifically, the number of fuzzy rules and the total number of antecedent conditions. Whereas both accuracy and interpretability were considered, multi-objective evolutionary based methods are introduced into our systems, hence, the name of Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy (MOEAIF) models. We evaluated our proposed model on some well-known cancer data sets, i.e., the ovarian cancer data set, the lung cancer data set and the colon cancer data set. Experimental results are listed and discussed in the later section. Compared with most previously reported models, accurate and small fuzzy rule bases were obtained.A major goal for diagnostic research is to develop diagnostic procedures based on inexpensive microarrays that have enough probes to detect certain diseases. This requires the selection of some genes which are highly related to the particular classification problem, i.e., the informative genes. This process is called Gene Selection (GS), which corresponds to feature selection from any machine learning task in general. Two basic approaches for feature selection used in machine learning and information theory literature are the filter methods and the wrapper methods ,20,20,21An ideal design of fuzzy rule-based models for microarray data analysis is when we find fuzzy rule-based models with good interpretability but with acceptable testing accuracy too. Compared to most popular methods in cancer microarray gene expression data analysis area, rule-based fuzzy models usually have relative high computational complexity. In order to obtain good fuzzy rule-based models, we adopted the following recent techniques to reduce the complexity of fuzzy rule-based models.qR used in our models are in the form of:The fuzzy rules qR: If x1 is qA1 and … and nx is qnA, then Class qC with q.CF• x =  is the n-dimensional input vector, qiA is an antecedent fuzzy set for the i – th input variable, qC is a consequent class, and qCF is a certainty degree . The rule weight is a real number in the unit interval .In the above rule, n antecedent conditions , and others may have only a few antecedent conditions .For a high-dimensional problem like microarray data analysis, the antecedent conditions of the generated rules are normally very numerous. Short fuzzy if-then rules with only a few antecedent conditions are obviously easy to understand for human users, and therefore a novel technique has been applied, as explained below. We simultaneously generated 14 fuzzy sets from multiple fuzzy partitions, as shown in Figure n. Each combination is used as the antecedent part qA of the fuzzy rule q.R Its consequent class qC and rule weight qCFare specified from compatible training patterns with qA in the following heuristic manner.Since we have 15 antecedent fuzzy sets for each attribute of our n-dimensional pattern classification problem, the total number of combinations of the antecedent fuzzy sets is 15px with the antecedent part qA of the rule qR via a product operation like:First, we calculate the compatibility degree of each pattern qAµ(px) = µq1A(px1) ⋅…⋅qnAµ(pnx) (1)qAµ(•) is the membership function of qi.A Then, the confidence of the fuzzy rule qA ⇒ Class h is calculated for each class h as follows:where m denotes the number of training patterns.where qC is specified by identifying the class with the maximum confidence:The consequent class M is the number of classes.where qA, we do not generate any fuzzy rules with qA in the antecedent part. This specification method of the consequent class of fuzzy rules has been used in a few studies since early 1990s [qA ⇒ Class h instead of the confidence degree. The support of the fuzzy rule is calculated as follows:When there is no pattern in the fuzzy sub-space defined by ly 1990s . It shouqCF have been proposed and examined. In this paper, we only consider binary classification problem, we can use the following specification because good results were previously reported for that in these papers [Different specifications for the rule weight e papers ,28:(5)S be a subset of candidate fuzzy rules, i.e., a fuzzy rule-based classifier. Each pattern px is classified by a single winner rule wR, which is chosen from the rule set S as follows:Let We only generate short fuzzy rules with a few antecedent conditions, and it should be noted that the DC conditions can be omitted from fuzzy rules. This restriction is used in order to find a compact set of fuzzy rules with high interpretability. As for short fuzzy rules, we only use fuzzy rules that satisfy both the minimum confidence and support as candidate rules for the multi-objective genetic fuzzy rule selection mechanism.T groups according to their consequent classes, where T is a user-defined parameter. Fuzzy if-then rules in each group are sorted in the descending order of a pre-screening criterion . For selecting Q candidate rules, the first Q/T rules are chosen from each of the T groups, and in this manner, we can choose an arbitrarily specified number of candidate fuzzy if-then rules . It should be noted that the aim of the candidate rule pre-screening is not to construct a fuzzy rule-based system, but to find candidate rules, from which a small number of fuzzy if-then rules are later selected. For using a variety of candidate rules in rule selection, we choose the same number of fuzzy if-then rules  for each class. By applying these new techniques, the models complexity and computational cost is significantly decreased.Rule confidence and support can be used as pre-screening criteria for finding a tractable number of candidate fuzzy rules. The generated fuzzy if-then rules are then divided into In this section, we describe how to apply multi-objective evolutionary algorithms (MOEA) to extract fuzzy rule sets considering the balance between model accuracy and model interpretability.S  in the current population is defined by the three objectives using the following fitness function:Our task is to select a smaller number of simple fuzzy if-then rules with high classification performance, and this is performed by maximizing the classification accuracy, minimizing the number of selected rules, and minimizing the total rule length at the same time. Therefore, the fitness value of each string w = , NCCP(S), NOR(S), and NOA(S) are the weight vector, the number of correctly classified training patterns, the number of selected fuzzy rules in S, and the total number of antecedent conditions in S, respectively. The weights w1,w2,w3 must satisfy the following conditions:where R consisting of the Q candidate rules can be represented by a binary string as:As suggested by ,30, a ruqr = 0 means that the q-th candidate rule qr is not included in the rule set R, while q'r = 1 means that q'r is included in R.where To keep the model complexity low, a simple two-points crossover strategy is applied to each pair of parent strings to generate a new string. Biased mutation is applied to the generated string to efficiently decrease the number of fuzzy if-then rules included in each string, that is, different mutation probabilities are used for the mutation from 1 to 0 and the one from 0 to 1. For example, the mutation probability from 1 to 0, w1,w2,w3 are the user defined weights of the three search objectives in the fitness function, and PA10 and PA10 are two initial fixed parameters. A larger probability is normally assigned to the mutation from 1 to 0 than to that from 0 to 1, in order to efficiently decrease the number of fuzzy if-then rules  included in each string. By applying the Equations 11 and 12, the mutation rate can be automatically adjusted according to user different purposes. Our MOEAIF can be summarized as follows:where Step 1: Randomly generate popN  binary strings of length Q as an initial population. Specify the crossover probability cp, two mutation probabilities, PA10 and PA10, and the stopping condition;1. Step 2: Generate popN children strings by applying crossover and mutation to the current population;2. Step 3: Calculate the three-objectives fitness value for each string; unnecessary rules are removed from each string;3. Step 4: Update the next population by selecting top ranked individuals;4. Step 5: Stop, if the stopping condition is satisfied or the maximum number of training epochs is reached, otherwise return to Step 2.5. We evaluated our proposed MOEAIF models on three cancer data sets, namely the ovarian cancer data set, the lung cancer data set and the colon cancer data set.Lung Cancer Data Set• http://cilab.ujn.edu.cn/datasets.htm.Lung Cancer Classification differentiates between malignant pleural mesothe-lioma (MPM) and adenocarcinoma (ADCA) of the lung. There are 181 reported samples in total, where 31 samples belong to MPM and 150 samples belong to ADCA. The training set contains 32 samples, 16 MPM and 16 ADCA, and the remaining 149 samples are used for testing. The expression levels of 12,533 features were report in each sample. Each feature represents one probe, for example, the feature 1018.at represents the probe 1018 at. The data is available at Ovarian Cancer Data Set• http://clinicalproteomics.steem.com. There are 253 reported samples in this data set, where 91 samples belong to normal and 162 samples belong to ovarian cancers. The normalization is done over all the 253 samples for all 15154 M/Z identities. After the normalization, each intensity value is to fall within the range of 0 to 1. The data is available at http://cilab.ujn.edu.cn/datasets.htm.The ovarian cancer data set was first reported in . The aimColon Cancer Data Set• http://sdmc.i2r.astar.edu.sg/rp/ColonTumor/ColonTumor.html.The data set used here was firstly reported in . This daw1, w2 and w3 are used to simulate different users’ requirements. From Table Different sets of w1=0.5, w2=0.3, and w3=0.2.We further explore the rule base obtained by our MOEAIF approach for lung cancer data set, where The whole training process is given in Figure Compared to the lung cancer data set, it is more difficult to find an efficient small rule subset for the ovarian cancer data, see Figure We can easily extract fuzzy rules from the lung cancer data set by using a trained MOEAIF model. This fuzzy rule base can classify the testing data with the accuracy of 0.8993 by using only three rules  method. The classification performance obtained by our models is also competitive. We also point out that an ideal design of fuzzy rule-based models for microarray gene expression data analysis includes two important tasks: designing low-complexity fuzzy models, and finding trade-off points between classification accuracy and model interpretability. We believe that fuzzy techniques and, in particular, the methods proposed in this paper can be very useful tools in dealing with microarray data. There also are some important issues that need to be addressed in the future. For example, some microarray gene expression data sets were generated directly from the probes set, and in some cases several probes may correspond to the same gene, or several different genes may hybridise to the same probes . If some of the input features (or probes) in a single rule are specific to the same gene(s), then this rule need to be deleted. Due to lack of enough training examples, satisfactory classification results were not always guaranteed in some small data sets, for example, the colon cancer data set in this paper.The authors declare that they have no competing interests.Zhenyu Wang has done the experiments and drafted the text, while Vasile Palade has contributed in guiding the experiments and checking and improving the text of the paper.


Genome scale expression profiling of human tumor samples is likely to yield improved cancer treatment decisions. However, identification of clinically predictive or prognostic classifiers can be challenging when a large number of genes are measured in a small number of tumors.We describe an unsupervised method to extract robust, consistent metagenes from multiple analogous data sets. We applied this method to expression profiles from five "double negative breast cancer" (DNBC) (not expressing ESR1 or HER2) cohorts and derived four metagenes. We assessed these metagenes in four similar but independent cohorts and found strong associations between three of the metagenes and agent-specific response to neoadjuvant therapy. Furthermore, we applied the method to ovarian and early stage lung cancer, two tumor types that lack reliable predictors of outcome, and found that the metagenes yield predictors of survival for both.These results suggest that the use of multiple data sets to derive potential biomarkers can filter out data set-specific noise and can increase the efficiency in identifying clinically accurate biomarkers. Microarray gene expression profiling provides an unbiased, comprehensive view of an entire molecular system, and is well suited to identify the relevant factors that define the cancer phenotype. However, the success of this method can be impeded by problems arising from the parallel measurements of tens of thousands of gene expression levels sampled in a far lower number of tumor specimens, typically a few hundred at most. Two specific problems have impacted cancer research: First, overfitting has produced several seemingly promising diagnostic patterns that have not been verifiable in independent studies ,2. SeconSeveral gene combinations have been used to characterize the cancer phenotype -7. For eHere we describe an unsupervised method to derive metagenes by leveraging the consistent expression patterns found in multiple gene expression data sets of the same cancer subtype. Our approach is based on the postulate that analogous microarray data sets, such as those from patient cohorts selected under similar criteria, are representative collections from a larger population "expression space". In this expression space, individual samples are robustly separated by a set of metagenes, some of which may be clinically relevant. However, each individual data set may be adulterated by sampling artifacts and with data set specific noise. Therefore, our approach is to derive metagenes that are consistently observed in several cohorts and are likely representative of the entire population. By first identifying metagenes in an unsupervised fashion, and then evaluating association between the metagenes and clinical outcome, we reduce the risk of overfitting.Using this method we derived metagenes from expression profiles of DNBC, stage III ovarian cancer and early stage lung cancer, respectively. Then we verified the association of these metagenes with clinical outcome in independent validation cohorts of the three cancer types.We created a reference data set of DNBC from five previously published breast cancer cohorts that were all profiled on the same microarray platform HG-U133A) and were without neoadjuvant drug response data 3A and we-21 , with at least two members. We identified 108 genes with a substantial contribution to at least two PCs in any of these clusters, hypothesizing that these genes are likely to capture consistent biologically-relevant information about DNBC (CPC genes)  as an unsupervised method to identify a subset of genes representing highly variable patterns in DNBC expression profiles. In PCA, each principal component (PC) is defined by a vector of gene expression weights. We hypothesize that the between-sample variability of tumor is driven by a finite number of biological effects, which are summarized into the principal components. Hence a finite number of components will explain the majority of the variation of the data matrices. Therefore, we define the likelihood as the fraction of total variance that is explained by the given number of principal components. For each individual data set, we performed PCA and used the Bayesian information criterion (BIC) to select a set of 3-6 PCs that best represent the predominant variation in the data without including components that are likely to represent noise  Figure .To validate the consistency of these CPC genes, we collected four independent DNBC data sets and subjected them to PCA using only the 108 CPC genes ,25-27. Aconsistent expression indices (CEIs) based on the sign of the non-trivial gene weights from each factor; thus each CEI comprises between 23 and 80 of the CPC genes  to distill the information in the CPC genes into six biologically relevant metagenes Figure . FA can We hypothesized that the six CEIs, which account for highly conserved biological variation among DNBC cases in the five reference data sets, are also associated with certain clinical phenotypes of the tumors. We investigated whether the CEIs were predictive of response to specific treatment regimens in four independent test cohorts in which expression profiles were obtained from DNBC samples prior to neoadjuvant therapy Table . Two of P = 0.005 for CEI1, AUC = 0.77, P = 0.009 for CEI3, Table P = 0.02 for CEI1, AUC = 0.78, P = 0.001 for CEI3, Table We evaluated the association between pathologic complete response (pCR) and each of the six CEIs using area under the receiver operating characteristic (ROC) curves (AUC). In the MDA1 data set we observed a strong positive association between CEI1, CEI3 and pCR . On the other hand, there is no detectable association between CEI3 and response to either FEC or epirubicin treatment , a typical poor pathological response   or sparsHR = 3.20, P = 0.016) as well as in patients not given tamoxifen treatment   clinical outcome data.P = 0.011, Additional file P = 0.02) but with poor response (RD) to carboplatin  annotations for biological processes in the CPC genes. For the CPC genes of the DNBC derived CEIs, the most enriched GO categories included immune and inflammatory response. For the lung cancer derived CEIs, the top categories included digestion, response to external stimulus, and oxidation/reduction , we clustered each data set based on the probe levels of ESR1 and HER2 using the Partitioning Around Medoids (PAM) algorithm. The DNBC is determined by the cluster with consistent low expression of both genes.k of top PCs by the minimum of the BIC:For each of the reference data sets independently, we computed the coefficient of variation (CV) based on the anti-logarithm of RMA probe levels and kept probe sets with a CV greater than one and less than 1000; thus we selected 614 to 1714 probe sets from each data set. Next we performed PCA on these highly variable probe sets in each data set, and selected an optimal number n is the number of samples, k is the number of components selected, and ν is the unexplained variance which equals the residual sum of squares, given by:Here, σi is the standard deviation of probe set i, p is the number of probe sets, and ωj is the standard deviation explained by PC j . For each PC, we calculated the Pearson correlation coefficient (PCC) between its component scores and the expression level of each probe set and the significance of the correlation is assessed by Student's t-test. Probe sets with a P < 0.01 for PCC were selected to represent the PC. After the selection, each PC contains 42 to 211 representative probe sets.Here, i and j:To compare PCs derived from various data sets, we defined the following measure of the dissimilarity between PCs Jij is the Jaccard index (the ratio between size of the intersection and the size of the union of the representative probe sets of component i and j) and Cij is the cosine correlation coefficient between the weights of the common representative probe sets of component i and j.Where We used this distance function to perform average linkage hierarchical clustering on the selected PCs from all reference data sets. For each distinct cluster, we selected the set of genes found in at least two members.We retrieved the RMA expression profile of the CPC genes from the reference data sets. When a gene was represented by multiple probe sets, we selected the probe set with largest standard deviation to represent that gene. For each of the expression matrices retrieved, we computed the standard z-scores for each gene and merged the matrices into one.We performed factor analysis of the merged z-scores using the "varimax" rotation and with the number of factors set to six . For eacThe ROC curves were based on individual CEI scores and treatment response. We calculated the area under the curve (AUC) using the trapezoidal rule  and estiFurther details are available in Additional file The authors declare that they have no competing interests.QL conceived the study, analyzed the data and helped draft the manuscript; ACE, NJB participated in the data analysis and helped draft the manuscript; CD, BH and CS contributed data and participated in the data analysis; WFS, LP contributed data; SB helped draft the manuscript; ALR contributed data and helped draft the manuscript; ZS conceived the study and drafted the manuscript. All authors read and approved the final manuscript.Summary of the tumor expression data sets used in this study. (a) Summary of all data sets used in this manuscript; (b) The number of DNBC samples from each data set used in each figure; (c) The number of ER-positive/Her2-negative breast cancer samples from each data set used in each figure; (d) The number of ovarian cancer samples from each data set used in each figure; (e) The number of lung cancer samples from each data set used in each figure.Click here for fileSupplementary methods. Supplementary methods.Click here for fileCEIs derived from three tumor types. CEIs derived from DNBC, Stage III ovarian cancer and early-stage lung cancer by consistent principal component analysis.Click here for fileAUCs and P values for prediction of TFAC response. Summary of AUCs and P values for prediction of TFAC response in DNBC using published metagenes and signatures derived using various supervised methods.Click here for fileCorrelation between DNBC-derived CEIs and known metagenes. Colorgram showing the pooled Pearson correlation coefficients between DNBC-derived CEIs and known metagenes.Click here for fileAUCs for prediction of pathological response in five DNBC cohorts which received neoadjuvant chemotherapy of different regimens using various unsupervised methods. (a) CEIs derived from consistent principal components; (b) Components derived using independent component analysis; (c) Components derived using sparse principal component analysis. The pooled correlation coefficients were estimated from seven breast cancer data sets based on a meta-analysis.Click here for fileDNBC-derived CEI3 predict clinical outcome of in ER-positive HER2-negative breast cancer. (a) ER-positive HER2-negative samples which received endocrine or radio-therapy from the EMC, JBI1, GIS, KUH, UCSF and NKI cohorts; (b) ER-positive HER2-negative samples which received no systematic therapy.Click here for fileValidation of the association between CEIs and clinical outcomes in ovarian cancers and lung cancers. (a) Hazard ratios based on 5-year follow-up of three ovarian cancer-derived CEIs in the validation cohort (DU) based on univariate and multivariate Cox regression; (b) Summary cross-validation of CEIs derived from three early-stage lung cancer data sets and validated in the fourth for the association to clinical outcomes; (c) hazard ratios based on 5-year follow-up of seven lung cancer-derived CEIs in the validation cohort (DU) based on univariate and multivariate Cox regression.Click here for fileGene Ontology (GO) annotation analysis. Gene Ontology of CEIs derived from (a) DNBC, from (b) stage III ovarian cancer, and from (c) early stage lung cancer.Click here for file


We have recently demonstrated that expression profiling is a more accurate and objective method to classify gliomas than histology. Similar to most expression profiling studies, our experiments were performed using fresh frozen (FF) glioma samples whereas most archival samples are fixed in formalin and embedded in paraffin (FFPE). Identification of the same, expression-based intrinsic subtypes in FFPE-stored samples would enable validation of the prognostic value of these subtypes on these archival samples. In this study, we have therefore determined whether the intrinsic subtypes identified using FF material can be reproduced in FFPE-stored samples.We have performed expression profiling on 55 paired FF-FFPE glioma samples using HU133 plus 2.0 arrays (FF) and Exon 1.0 ST arrays (FFPE). The median time in paraffin of the FFPE samples was 14.1 years (range 6.6–26.4 years).n=17 583), and of these, the 5000 most variable probe sets on FFPE expression profiles. This unsupervised selection resulted in a better concordance (R2=0.54) between expression of FF and FFPE samples. Importantly, this probe set selection resulted in a correct assignment of 87% of FFPE samples into one of seven intrinsic subtypes identified using FF samples. Assignment to the same molecular cluster as the paired FF tissue was not correlated to time in paraffin.In general, the correlation between FF and FFPE expression in a single sample was poor. We then selected the most variable probe sets per gene (We are the first to examine a large cohort of paired FF and FFPE samples. We show that expression data from FFPE material can be used to assign samples to intrinsic molecular subtypes identified using FF material. This assignment allows the use of archival material, including material derived from large-randomised clinical trials, to determine the predictive and/or prognostic value of ‘intrinsic glioma subtypes’ on Exon arrays. This would enable clinicians to provide patients with an objective and accurate diagnosis and prognosis, and a personalised treatment strategy. The classification of tumour subtypes influences treatment decisions for many types of cancer. Accuracy in classifying cancer subtypes is therefore necessary to provide patients with correct diagnosis, prognosis and an optimal treatment strategy. As histological classification is often difficult in poorly differentiated tumours, this classification method urgently needs improvement. Gene expression profiling of cancer offers an accurate and objective method for classifying cancer subtypes . Expression profiling using such a cohort has thus far not been performed. Most expression profiling studies using FF tissues have been performed on HU133-type arrays , whereas best results using FFPE samples have been obtained using the Exon 1.0 ST arrays. In this study, we therefore compare the expression of FF samples on HU133 plus 2.0 arrays with FFPE samples on Exon 1.0 ST arrays. Previous studies have demonstrated good overall correlation of HU133 Plus 2.0 with Exon 1.0 ST arrays  was used to isolate the RNA from the paraffin. After isolation the RNA was purified by ethanol precipitation (μl−1 and stored at −80°C until use.Five sections of 10 pitation . The quaCluster 9 (n=5) and Cluster 18 (n=6); t-test in combination with fold change. ACTB, GAPDH were used as control. All reactions were performed in duplicate. Primers and conditions are described in We randomly selected 11 samples that were assigned to two molecular clusters based on the FF expression data (n=55). Expression arrays (HU 133 plus 2.0 (Affymetrix) using FF material was reported previously  was used for the Exon 1.0 ST arrays (Affymetrix). Sample labelling and array hybridisation were performed by AROS Applied Biotechnology AS  according to the standard Affymetrix protocols in combination with Nugen WT-Ovation technology  expression levels of 22 011 genes and 287 329 exons were extracted from Affymetrix Exon 1.0 ST arrays using Expression Console (Affymetrix). ClusterRepro (an R package) was used to assign a sample to a defined molecular subtype .t-test and a Mann–Whitney test. Significance of correlation coefficients was calculated using the P-value calculator for correlation coefficients .Differences between Kaplan–Meier survival curves were calculated by the log-rank (Mantel–Cox) test. Differences in age and RIN-scores of the tissue blocks were calculated using a At total of 55 FFPE samples were included in the study. These samples included 29 glioblastomas, 5 astrocytomas grade III, 5 as grade II, 4 mixed oligoastrocytoma grade III (OA II), 2 OAs grade II, 8 oligodendrogliomas grade III and 2 pilocytic astrocytoma. The median time in paraffin of the FFPE samples was 14.1 years (range 6.6–26.4 years). The median RIN score of the RNA was 2.4 (range 1.1–2.7). Sample characteristics are listed in Cluster 9 (n=5) and Cluster 18 (n=6); Cluster 9 shows a favourable prognosis compared with the other clusters, and is specific for loss of heterozygosity of 1p and 19q, as well as a high frequency of IDH1 mutations. Cluster 18 has poor prognosis and is characterised by EGFR amplifications and CDKN2A deletions. The RT–qPCR results showed that both direction and fold change of all four genes in all samples could be recapitulated on RNA isolated from FFPE samples. The overall correlation was relatively strong r2=0.61 (P<0.001). Correlations (r2) and P-values for individual genes EMP3, SUSD5, CSMD3 and SLC2A10 were 0.34 (P=0.024), 0.840 (P<0.001), 0.849 (P<0.001) and 0.255 (P=0.047), respectively. These results demonstrate that differences in gene expression are retained in RNA isolated from FFPE samples , see also After the hybridisation of the exon 1.0 ST arrays, we compared the RMA normalised expression data of the exon arrays (FFPE) with exon 1.0 ST arrays that were analysed in earlier studies ; log2 normalised data). Selecting the most variable probe set on exon arrays does not always identify those with highest correlation to expression on HU133plus2.0 arrays. However, selection based on variance approaches both data sets independently and avoids potential circular arguments. All further analysis was therefore done using the selection of exon array probe sets based on variance as starting data set.Exon arrays contain one or more probe sets per exon for each gene (287 329 core probe sets for 22 011 genes), whereas only one data point per gene is generated on HU133plus2 arrays . The overlap between the most variable probe sets in FF tissue and the most variable probe sets in FFPE tissue (FF: 5000/FFPE: 17 853) consisted of 4620 matching probe sets .The set of 17 853 probe sets assumes that all genes have at least one informative probe set per gene. It is however possible all probe sets that belong to the same gene perform poorly. We therefore also performed a further selection of the 17 583 exon array probe sets, by selecting the 5000 most variable probe sets of these 17 583 , and a good correlation for CSMD3 . There were no data available of SUSD5 as there were no probe sets of this gene on exon 1.0 ST arrays. These results highlight that exon arrays can show a concordance in gene expression compared with FF tissue, but that a selection of the biologically most informative probe sets is required.We first compared the gene expression data (FF) of the genes used in the qPCR analysis  with the expression data of the FFPE material. For this analysis we used the expression data of the same 11 samples that were also used in the qPCR analysis. r2=0.24, P<0.001). It should be noted that part of the between FF and FFPE sample variability is biological: The snap frozen and FFPE tissues are not taken from exactly the same location within a tumour. For this analysis we compared differential gene expression between samples of cluster 9 and 18 (separately for FF and FFPE). The correlation (r2) in differential gene expression between FF and FFPE was 0.38.We next compared the normalised expression data of the 5000 most variable genes as used in our previous study, with the expression of the most variable exons . Differential gene expression between samples of  P<0.001 . Our resr2=0.315±0.093 range 0.210–0.450, P<0.001). This correlation was much better than the overall correlation  between FF on HU133 Plus 2.0 arrays and FFPE on HuEx 1.0 st arrays (0.034±0.023). The better correlation between FF and FFPE samples on the same platform therefore indicates that differential gene expression is better retained when using the same platform.Expression data of the FF samples is performed HU133 Plus 2.0 arrays, a platform that is different from the platform used for the FFPE samples (HuEx 1.0 st arrays). We have therefore compared expression of all probes (287 329) between FF and FFPE on exon arrays of eight matched samples . In general, the correlation between FF and FFPE samples on the same platform was reasonable (n=42) of the samples (FF: 5000/FFPE: 17 583). However, part of the variability between FF and FFPE samples is biological and may represent tumour heterogeneity. This heterogeneity is specifically notable for assignment to Cluster 0, as assignment to this cluster depends on the relative amount of non-neoplastic tissue present. Indeed, the overlap between FF and FFPE cluster assignment when excluding samples that are assigned to Cluster 0 is 86% (n=42/49). A total of 13 samples were assigned to a different molecular cluster, 7 without Cluster 0.Recently, we described the identification of seven molecular glioma subtypes based on gene expression profiling, which are better predictors of survival than histology (n=41) of the samples, 87% without Cluster 0 (n=41/47).Similar performance in cluster assignment was observed when using the 5000 most variable exon probe sets . The average RIN score of the incorrectly assigned samples was 2.18±0.40, and was not significantly different from the RIN scores of the correctly assigned samples 2.24±0.34, P=0.71).Assignment to the same molecular cluster as the paired FF tissue did not have a significant correlation with the time in paraffin. The ‘wrongly’ assigned blocks even showed a slightly shorter median time in paraffin than the ‘correctly’ assigned samples (11.9 Cluster 0.The high degree of overlap between FF and FFPE sample assignment (both FF: 5000/FFPE: 17 583 and FF: 5000/FFPE: 5000) is reflected in a highly similar patient survival curves . HoweverIn this study, we describe a method that allows analysis of gene expression profiling of FFPE cancer tissue using HuEx 1.0 ST arrays. Our data show that expression data of RNA isolated from FFPE and FF tissues are comparable. However, a selection on the most informative probe sets (based on highest variance for each probe set/gene and highest variance between genes) is required. RIN score and the age of the FFPE tissue blocks do not influence the gene expression results. The average RIN score and the average time in paraffin of the incorrectly assigned samples were not significantly different from the RIN scores and time in paraffin of the correctly assigned samples. The probe sets identified in this study can be used in other profiling studies that lack paired FF samples.Differential gene expression between samples is well retained . This isPrevious studies demonstrated that FFPE samples can be used for gene expression profiling either using the Affymetrix (Exon 1.0 and HU133 plus 2.0) or using the Illumina (DASL) platforms . The platform chosen for this study was based on reports from literature , and it


Whole genome microarray gene expression profiling is the ‘gold standard’ for the discovery of prognostic and predictive genetic markers for human cancers. However, suitable research material is lacking as most diagnostic samples are preserved as formalin-fixed, paraffin-embedded tissue (FFPET). We tested a new workflow and data analysis method optimized for use with FFPET samples.in silico to exclude misdesigned and misannotated probes.Sixteen breast tumor samples were split into matched pairs and preserved as FFPET or fresh-frozen (FF). Total RNA was extracted and tested for yield and purity. RNA from FFPET samples was amplified using three different commercially available kits in parallel, and hybridized to Affymetrix GeneChip® Human Genome U133 Plus 2.0 Arrays. The array probe set was optimized in silico probe set redesign improved sequence detection sensitivity and, thus, may rescue potentially significant small-magnitude gene expression changes that could otherwise be diluted by the overall probe set background.FFPET samples processed using the WT-Ovation™ FFPE System V2 (NuGEN) provided 80% specificity and 97% sensitivity compared with FF samples . In addition, In conclusion, our FFPET-optimized workflow enables the detection of more genes than previous, nonoptimized approaches, opening new possibilities for the discovery, validation, and clinical application of mRNA biomarkers in human diseases. Cancer is a broad label, covering a variety of tumor subtypes that are associated with different prognoses and different responses to the various treatment options available. Traditionally, solid tumors are studied by histopathologic assessment. However, as cancer is a heterogeneous molecular disease, classification of tumor types should ideally be accompanied by molecular biology techniques.Gene expression profiling is a technology for identifying the genes that are active in a given sample of cells or tissue. This technique enables profiling of genes which are differentially expressed in tumors, thereby providing diagnostic and prognostic information for molecular subclassification of tumors -4. Gene The application of retrospective microarray analyses to long-term archived tissues – as well as to fresh biopsy samples that reflect the present disease state – could provide assistance in a number of areas. It may help in the elucidation of disease mechanisms, in detection of novel therapeutic targets, and in identification of prognostic markers. Analysis of samples collected during randomized trials can also be used for identifying predictive markers . QuantitWhole genome microarray gene expression profiling is the ‘gold standard’ for the discovery of prognostic and predictive genetic markers for human cancers . Most geIt is well documented that formalin fixation and paraffin embedding degrades RNA in tissue samples ,12,13. RThere are also differences between the commercially available kits used for RNA extraction and processing, and so the use of different kits may lead to differences in the results obtained. The results of RNA profiling are expected to be platform-specific. Proper annotation of probes and tailored data analysis also help in controlling bias and reducing the variability of the results.Archived specimens have no corresponding FF samples to act as comparator. Therefore, it is important to ascertain the validity of any results obtained from using FFPET samples. Research comparing gene expression profiling in FF and FFPET samples has already been done using various extraction and amplification techniques and a variety of tissues, and it has been concluded that valid data can be obtained from FFPET samples -22, althThe aim of this study is to determine whether FFPET samples are suitable for significant profiling analysis using a new, optimized workflow and data analysis method. The Affymetrix GeneChip® Human Genome U133 Plus 2.0 Array platform was used and the results from three different commercially available RNA amplification kits were compared. The three RNA amplification kits were assessed in parallel and the expression signatures of matched FFPET material and native FF tissue from the same tumor were compared. If it can be shown that gene expression profiling is possible from FFPET samples, and that the profiling can detect stable genes that display good signals, then using this optimized system could reveal many more genes than previously reported from FFPET samples; this would be an important step in disease biomarker discovery.Sixteen breast tumor samples (>12 months old) were purchased from Asterand, Inc. (see asterand.com) for information concerning ethical conduct, eight were estrogen receptor-positive (ER+) and eight were estrogen receptor-negative (ER–). The ER status was confirmed by immunohistochemistry and all samples consisted of more than 70% of tumor cells.Each tumor sample was divided into mirror sections, half being FF and half being formalin-fixed and paraffin-embedded using standard protocols. Total RNA was extracted from 10 consecutive sections cut in each direction from the mirrored area. FFPET sections measured 10 μm and FF sections measured 15 μm. The purified RNA from each fixation type was pooled. The first and the last section for both FF and FFPET samples were hematoxylin and eosin stained to exclude any significant variations in the investigated tumor area.RNA extractions were performed according to the manufacturers’ instructions as follows:. For FFPET sections, the High Pure FFPET RNA Isolation Kit® from Roche Diagnostics was used.For FF cryosections, the RNeasy® Mini Kit and DNA digestion step (QIAGEN AG) were usedThe yield and purity of RNA was determined using a NanoDrop 1000 Spectrophotometer (Thermo Scientific). RNA integrity was measured using a Bioanalyzer (Agilent Technologies) with either the RNA Nano or Pico Kit, depending on sample concentration. The RNA Integrity Number (RIN) and average length of the RNA were used for sample characterization.mRNA integrity in FFPET samples was measured using a semiquantitative reverse transcription-polymerase chain reaction assay (rtPCR). This assay is based on quantitative amplification of 5′- and 3′-end sequences of the housekeeping gene β-actin. Calculating the ratio of 3′ to 5′ amplicons allows for assessment of mRNA integrity: intact RNA transcripts exhibit a ratio of 1, while degraded RNA increases the ratio. A Transcriptor First Strand cDNA Synthesis Kit (Roche) was used to prepare cDNA from 100 ng of total RNA, using an anchored oligo(dT)-primer followed by a subsequent amplification step with the SYBR Green I Master on a LightCycler® 480 instrument (Roche Applied Science). Genomic DNA residues were determined via rtPCR using a LightCycler® 480, with 100 ng purified RNA as template material.RNA samples from FF tissue were amplified using the 3′ IVT Express Kit (Affymetrix). Fluorescence labeling and fragmentation were carried out according to the manufacturer’s protocol.FFPET-derived RNA was amplified with either the GeneChip® Expression 3′-Amplification Two-Cycle Kit (Affymetrix), the WT-Ovation™ FFPE System V2 (NuGEN), or the TransPlex® Complete Whole Transcriptome Amplification Kit (Sigma/Rubicon Genomics).Biotin labeling of the cDNA generated using the Affymetrix technique was performed using the 3’ IVT Express Kit (Affymetrix), while cDNA generated with the NuGEN technique was performed using the FL-Ovation cDNA Biotin Module V2 (NuGEN). Biotin labeling of cDNA generated with the Sigma/Rubicon Genomics technique was performed using the ULS™ aRNA Labeling Kit with Biotin-ULS for Affymetrix Genechips (Kreatech Diagnostics).Hybridization of labeled probes on the Affymetrix GeneChip Human Genome U133 Plus 2.0 Array were conducted using the GeneChip® Hybridization, Wash, and Stain Kit (Affymetrix).Target preparation, hybridization of samples to microarrays, and washing of microarrays were conducted according to the respective manufacturers’ manuals. For this workflow a GeneChip Fluidics Station 450 and a GeneChip Scanner 3000 7G were used.All analyses were performed using C and R statistical data analysis developed at Roche. Extensive quality control of the wet-lab performance, such as background signal and signal gradients, masking of spots (outliers), and estimation of optical noise, was followed by gene expression index estimation. This was performed for every amplification method separately with a modified version of the Robust Multichip Average (RMA) renormalized matched log algorithm .in silico array design was created that was as close to the initial Affymetrix design as possible, but which excluded misdesigned or misannotated probes. Consequently, the original probe set was divided into three subsets: a perfectly matching and unique part ; a subset consisting of probes that appear to match transcripts of multiple genes ; and the remaining probe sets that failed to match against either DNA or any known transcript . An example of a poor-quality original probe set is shown in Figure in silico revision is supported by the results of a t test to determine the likelihood of an association between ER gene expression patterns and ER status: whereas no significant association was detected using the original Affymetrix probe set design (P = 0.35), a strong association was detected using the optimized probe set (P = 0.008). On close examination, the original ER probe set can be seen to contain four nonmatching probes and three nonunique probes.To minimize annotation errors, the original Affymetrix probe set design of the array was not taken into account. Instead, every probe on the array  was first mapped against the latest human genome build and transcriptome databases: UniGene, RefSeq, and EMBL ,25. The Genes controlling the ER signaling pathway were identified in FF and FFPET samples using Ingenuity Pathways Analysis (Ingenuity Systems).All samples yielded sufficient RNA to proceed to microarray processing, and most RNA samples were sufficiently concentrated to enable an accurate assessment of integrity using the RIN tool (lower limit = 25 ng/μl). RIN values were obtained for all but five samples . As expected, RIN values indicated more degradation of RNA in FFPET samples  than in FF samples . This observation was supported by 3′/5′ ratios: ratios ranged from 4.2 to 151.3 in FFPET samples and from 1.0 to 1.5 in FF samples [Table The microarray plots also demonstrate that total RNA from the FFPET samples showed more evidence of degradation compared with total RNA from the FF samples Figure . Of the An alternative and more direct way of estimating the percentage of genes expressed in a sample is by counting the number of probe sets whose expression values exceed the limit of blank (LOB). LOB marks the lowest level of expression that can definitely be attributed to sample-derived human mRNAs. We estimated the LOB value to be the 95% percentile of the expression of the ‘complete mismatch’ probe sets. The ‘complete mismatch’ probe sets were composed of the probes that failed to match both genomic sequence and known mRNA and EST transcripts, even when up to three mismatches were tolerated. Table The FF sample results, as the ‘gold standard’, were assumed to have 100% sensitivity and specificity for gene detection. The sensitivity of a particular FFPET profiling workflow was defined as the percentage of the probe sets found ‘relevant’ by both the FF and the FFPET method used. The threshold for relevance in the FF samples was taken as a twofold increase or decrease in gene expression of ER+ and ER–. Once again, the best overall results were obtained using the NuGEN kit. Using the NuGEN method, sensitivity, at 80%, was increased by 47 percentage points over the Affymetrix Two-Cycle kit, and 65 percentage points over the Sigma/Rubicon Genomics kit. Specificity, defined as the percentage of the probe sets that showed relevant differential expression in FFPET samples but not in FF material, was slightly lower using the NuGEN kit, at 97%, compared with 99% for the Affymetrix Two-Cycle kit and 100% for Sigma/Rubicon Genomics Figure . SimilarDDX5 was undertaken as an internal control for validating the results of the microarray data. DDX5 has been proposed to be a very stable housekeeping gene, showing greater correlation between quantitative rtPCR and microarray data than GAPDH[DDX5 (data not shown).Independent analysis of the expression of the gene han GAPDH. This anSignal detection sensitivity and specificity generally depends on effect size. For this experiment, the sensitivity and specificity were estimated independently for the couple of subsets of genes that have effect size in a predefined range. The results show that the larger the expected effect is in FF samples, the easier it is to see the same effect in FFPET samples. Dynamic sensitivity and specificity estimations show the correlation of fold changes, such as a greater than twofold increase between ER+ and ER–, in the FF samples with the sensitivity of the corresponding FFPET sample.Principal component analysis is a useful dimensionality reduction technique that has been used to visualize patterns in multivariate gene expression data . SamplesComparison of the principal component analysis plots Figure  for matcThe canonical ER pathway was extracted from the Ingenuity Knowledge Base and the fold changes in ER pathway genes were mapped against those pathways [see Additional file Gene expression profiling classifies individual tumors by their gene expression patterns and may also describe and predict therapeutic resistance and sensitivity patterns. Profiling in several cancers, such as breast cancer, colon cancer, lymphoma, leukemia, and melanoma , has alrThe potential of routine gene expression profiling to predict clinical outcomes for cancer patients has yet to be determined. The Evaluation of Genomic Applications in Practice and Prevention Working Group stated in 2009 that there was “insufficient evidence to make a recommendation for or against the use of tumor gene expression profiles to improve outcomes in defined populations of women with breast cancer” . ClearlyComparison of FFPET sample-derived gene expression profiles with associated clinical outcome data could provide an invaluable resource for the investigation of mechanisms of response and resistance to cancer therapeutics. This will, however, require a suitably reliable method for extracting RNA from these samples and an FFPET-optimized workflow for use in gene expression profiling studies. Two recent studies performed using archived FFPET core biopsies from the NOAH trial   have demFFPET samples could also be used prospectively to identify biomarkers in clinical trials. Biomarkers discovered via microarray gene expression profiling could then be applied clinically using more widely available technologies, such as immunohistochemistry or quantitative rtPCR. Recently, researchers conducting a multicenter randomized phase III trial in colon cancer have suggested that translational studies based on FFPET samples should be included in prospective clinical trials .Methods and protocols for RNA isolation from formalin-fixed tissues have been available for over 20 years  but resuin silico probe design protocols are optimized towards FFPET samples (which were >12 months old). Specificity of gene detection was 99% using the Affymetrix method, 97% using the NuGEN method, and 100% using the Sigma/Rubicon Genomics method. The gene detection sensitivity results obtained from FFPET samples varied depending on which of the three different amplification kits was used. The combination of the NuGEN RNA purification/amplification method with the Affymetrix Human Genome U133 Plus 2.0 Array resulted in 80% sensitivity with 97% specificity. This agrees with Linton et al. [The work reported here shows that there is no loss of data if RNA amplification and n et al.  who alson et al.  have alsIt is also important to show that the genes identified in this new workflow are an accurate reflection of the biology of the sample. The Ingenuity Pathways Analysis shows that the same ER signaling pathway is identified regardless of whether the sample was FF or FFPET, and that therefore the FFPET data obtained are a true reflection of the biology of the tissue sample.The correlation between gene expression profiling results obtained from FF and FFPET samples is greatly improved using the new array design and data processing methods reported here. A comprehensive gene expression profile is necessary to both the discovery of novel biomarkers and the development of new disease subclassifications. Our optimized sample processing workflow and data analysis method enables the detection of genes that cannot be detected using a nonoptimized approach. The gene detection rate reported here for FFPET-derived samples is similar to that obtained using FF samples, demonstrating the importance of optimal RNA amplification and raw data processing with refined probe set alignments, which could facilitate the discovery of biomarkers from FFPET samples. In addition, other commercial microarrays are now available which may overcome the current limitations associated with the Affymetrix GeneChip.The ‘percentage of genes present’ call is a useful tool to evaluate the overall quality of gene expression data. However, if the percentage present call is to be used as a quality control measure, then it may be advisable to set a precise threshold for discarding poor-quality samples so that potentially useful data – such as single gene profiles obtained from individual samples – are not unnecessarily lost. Consequently, the homogenous distribution of percentage present calls in the sample population may be a more useful quality control measure than percentage present call alone. Using this method, a sample with a markedly low percentage present call could be identified as an outlier even in a genome-wide analysis.The different techniques and kits used in gene expression profiling render direct comparisons with previously reported data problematic. Although the redesigned probe set used here is as close to the original Affymetrix design as possible, comparisons with previously reported data sets using the Affymetrix array may also be problematic for several reasons. For example, differences in FFPET techniques between laboratories result in variable degradation of sample RNA. In addition, the age of the sample can impact results, although Tanney and Kennedy have reported that it is possible to extract good-quality transcriptional data from samples as old as 17 years using the Roche High Pure FFPE extraction kit and NuGEN WT-Ovation FFPE RNA amplification system . The quaHandling and fixation protocols vary between centers and evolve over time, so the genetic variation identified between tumors could be an artifact of tissue processing rather than a reflection of tumor biology. To overcome this, it may be important to pool samples from several centers to ensure that they represent the population under consideration as a whole . This woIt is also important to ensure that the sample size selected is sufficient for the study to be adequately powered to detect differences in gene expression. Most tumor studies are aimed at finding genes with at least twofold higher or lower expression than found in normal tissues. Power analysis for this study (data not shown) demonstrated that a protocol with 10 samples per category was 40% powered to detect a twofold change in expression, but could reliably detect a threefold change.in silico probe set redesign may enable the detection of potentially significant but smaller-magnitude changes in gene expression that may be diluted by the standard probe set background. The methods presented here have the potential to enable a vast expansion in the quantity of gene expression data available for analysis alongside other data, such as immunohistochemistry results. These findings suggest that it is crucial to apply the most optimal sample processing approach concerning RNA amplification and subsequent raw data processing, using the refined probe set alignment. Finally, this workflow could be beneficial for identifying potentially novel disease markers, which have been missed in previous data sets due to sensitivity or specificity issues. It is important to further invest in optimizing the gene expression profiling work flow to make sure that RNA information is interpreted correctly for the discovery, validation, and clinical application of mRNA biomarkers in human diseases.In conclusion, optimizing processing methods for use with FFPET samples, together with optimized array design and data processing, should provide a clinically useful data set from the large archive of FFPET samples already available, and from FFPET samples routinely collected in clinical trials. In addition, All authors are employed by Roche Diagnostics GmbH, Germany.MT and MW contributed to the scientific background, study design, planning of experiments and manuscript writing. AB contributed statistical support, data analysis, Affymetrix chip re-design and manuscript writing. SW carried out sample preparation, RNA extraction, sample analysis and QC and contributed to manuscript writing. MPH contributed to study design, planning of experiments, sample analysis and QC and manuscript writing. All authors read and approved the final manuscript.Estrogen receptor signaling comparison of FF and FFPET NuGEN results: FF samples.Click here for fileEstrogen receptor signaling comparison of FF and FFPET NuGEN results: FFPET samples processed using NuGEN workflow.Click here for fileEstrogen receptor signaling comparison of FF and FFPET NuGEN results: FFPET samples processed using Sigma/Rubicon workflow.Click here for fileEstrogen receptor signaling comparison of FF and FFPET NuGEN results: FFPET samples processed using Affymetrix Two-Cycle workflow.Click here for file