Consensus clustering, a new method for analyzing microarray data that takes a consensus set of clusters from various algorithms, is shown to perform better than individual methods alone. Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene-expression profiles to clusters. Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene-expression analysis. Here we introduce consensus clustering, which provides such an advantage. When coupled with a statistically based gene functional analysis, our method allowed the identification of novel genes regulated by NFκB and the unfolded protein response in certain B-cell lymphomas. There are many practical applications that involve the grouping of a set of objects into a number of mutually exclusive subsets. Methods to achieve the partitioning of objects related by correlation or distance metrics are collectively known as clustering algorithms. Any algorithm that applies a global search for optimal clusters in a given dataset will run in exponential time to the size of problem space, and therefore heuristics are normally required to cope with most real-world clustering problems. This is especially true in microarray analysis, where gene-expression data can contain many thousands of variables. The ability to divide data into groups of genes sharing patterns of coexpression allows more detailed biological insights into global regulation of gene expression and cellular function.Many different heuristic algorithms are available for clustering. Representative statistical methods include k-means, hierarchical clustering (HC) and partitioning around medoids (PAM) -3. Most Recently, a number of studies have attempted to compare and validate cluster method consistency. Cluster validation can be split into two main procedures: internal validation, involving the use of information contained within the given dataset to assess the validity of the clusters; or external validation, based on assessing cluster results relative to another data source, for example, gene function annotation. Internal validation methods include comparing a number of clustering algorithms based upon a figure of merit (FOM) metric, which rates the predictive power of a clustering arrangement using a leave-one-out technique . This anweighted-kappa metric  ,30 Bfl-1N-glycan biosynthesis. This functional group was overexpressed in cell lines of plasmablast or plasma-cell tumors, where physiological upregulation of the ER is required for cellular function. This process is controlled by two transcription factors, ATF6 and XBP1 [ATF6 transcript was present as a defining signature gene in the ER/UPR functional group. This suggests that ATF6 and XBP1 may be responsible for upregulation of the calcium-ion binding and N-glycan biosynthetic genes. Two responsive elements have been defined for ATF6 and XBP1 respectively, the ER stress-response element (ESRE), comprising the binding site CCAATN9CCACG and the unfolded protein response element (UPRE), comprising the binding site TGACGTG(G) [N-glycan biosynthetic genes but no ESRE sites were found. This suggests that these two groups of genes are regulated through two distinct mechanisms by transcription factors ATF6 and XBP1 as a result of ER stress.The consensus cluster associated with the ER/UPR functional group contained genes not previously associated with ER stress-induced upregulation. The gene-expression profiles were visualized and annotated as described for the NFκB functional group Figure . Annotatand XBP1 . The ATFACGTG(G) . ATF6 anACGTG(G) . ESRE siweighted-kappa metric. This metric effectively scores how well different cluster method pairs assign the same genes to the same clusters. The weighted-kappa metric readily shows that, even for highly correlated gene-expression profiles present in the ACS dataset, no two clustering algorithms have complete agreement, although the global search methods such as SA seem to produce the most consistent results. Overall this emphasizes that no single analysis method will identify all patterns in the gene-expression data; therefore multiple analyses should be performed and compared [Grouping data into sets based on a consistent property is a common occurrence in biological analysis. This has recently increased in importance with the production of large microarray datasets. Implicit in the experimental rationale is the fact that patterns of coexpressed genes should be identifiable in a gene expression matrix and these can be linked to shared biological processes. However, different clustering algorithms are known to partition data into different groups -13. We acompared .et al. was to use a meta-consensus across different clustering algorithms rather than to re-sample over the same algorithm. Our results represent this extension and confirm the validity of consensus clustering. We have developed both robust and consensus clustering, with these methods offering specific advantages over the use of individual clustering algorithms for microarray analysis. The robust clustering algorithm is useful for creating clusters of genes with high confidence and is extremely effective for reducing the dimensionality of large gene-expression datasets. However, robust clustering can be restrictive in discarding genes that do not have full agreement. Consensus clustering overcomes this problem, requiring a minimum-agreement parameter to generate clusters based on the combined results of a number of existing clustering methods. This strategy enables the effective identification of cluster groups that are of high reliability and cluster method independent.We and others recently described the use of consensus clustering to improve confidence  or as a The choice of clustering algorithms and parameter settings is a major stumbling block for all gene array cluster analysis. The effect of varying the parameters depends on the cluster method used. The performance of cluster methods has been extensively investigated . The autet al. using a database of clusters [et al. used the hypergeometric distribution. This distribution can be formally shown to asymptotically become the binomial distribution when the population size increases. Therefore, when used on large gene-expression datasets our methods are directly analogous to Wu et al. However, consensus clustering has the advantage over a database of clusters by producing low-probability clusters containing a significant percentage of known elements from functional groups.Consensus clusters are likely to contain gene subsets that are co-regulated by common transcriptional control networks or are coexpressed to participate in cellular processes that together manifest a global phenotype of the cell or tissue. In either case, these clusters are of high biological value. To facilitate further analysis it is useful to know which clusters are involved in a given biological process. By supplying a list of genes from a given biological process or network, the use of the normal approximation to the binomial distribution of these genes over all consensus clusters, allows the identification of clusters of high functional significance. Similar statistical assignment of gene function based on cluster analysis was performed by Wu clusters . To assiBfl-2, has been experimentally verified [Two functionally significant clusters, the NFκB-responsive cluster and the ER/UPR cluster, were investigated further here. Within the NFκB-responsive cluster 50% of the putative promoters of genes investigated had canonical NFκB-binding sites within 1,000 base pairs of the transcriptional start site, suggesting that they are NFκB responsive ,30. The verified . AnalysiXBP1 mRNA is cleaved by the ER stress activated protein IRE1 to yield the transcriptionally active form of XBP1, inducing further genes of the UPR [N-glycan biosynthesis genes suggests that these genes are differentially regulated by ATF6/XBP1 and XBP1 respectively.In cells, the presence of unfolded proteins in the ER is associated with induction of the ER/UPR. However, during the maturation of B-cells to antibody-secreting plasma cells, expansion of the ER to accommodate increased secretion of immunoglobulins is thought to be coupled to the final stages of plasma-cell maturation. The induction of the ER/UPR occurs via the coordinated activation of the transcription factors ATF6 and XBP1 ,33. ATF6 the UPR . The actα-mannoside-like protein (EDEM), whose induction depends solely on IRE1/XBP1 activity [N-acetylglucosamine-1 phosphate transferase (DPAGT1) and UDP-GlcNAc:α-6-D-mannoside β-1-2-N-acetylglucosaminyltransferase II (MGAT2), which catalyze essential steps in the biosynthetic pathway of complex N-linked glycans, supporting a clear link between the dolichol pathway and the UPR [DPAGT1 and MGAT2 expression is regulated solely by the IRE1/XBP1 pathway. Altogether, these results show that consensus clustering and gene functional group analysis provide a highly accurate way of mining gene-expression data for novel insights into different genes within the cluster.The only known UPRE target gene is ER degradation-enhancing activity . Inducti the UPR . In addiRobust and consensus clustering provide a platform for more efficient microarray analysis pipelines. There is effectively no limit to the number of different clustering algorithms that can be used to feed into the consensus clusters, and each clustering algorithm could be run under different parameter sets to fully explore a microarray dataset . In addiAll areas of functional genomics that produce high-dimensional datasets with inherent patterns will require data partitioning to allow interpretation. Consensus clustering in the context of statistically defined functional groups could allow a consistent analysis platform for such diverse data types.r, to measure the linear relationship between two variables, x1 and x2, where the variable can be either discrete or continuous [We implemented and compared a representative sample of methods from the statistical, AI and data-mining communities. The methods used were average linkage HC, PAM, SA and CAST. As all the clustering techniques use correlation between variables, we used the Pearson's correlation coefficient, ntinuous . HC and ntinuous , while Cm out of n total objects that are the closest (according to a distance matrix) to the remaining (n - m) objects. The fitness of these medoids is calculated by placing the remaining (n - m) objects in a group according to the nearest medoid and summing the distances of the group members from this medoid. These m selected objects are the initial medoids. A Swapping procedure is then applied to reassort the objects until there is no improvement in the fitness of the medoids [HC is an agglomerative method that produces a hierarchical (binary) tree or dendrogram representing a nested set of data partitions. It has been applied successfully to many gene-expression datasets . Section medoids . As with medoids .θ0 = 100, c = 0.99994 and number of iterations = 1,000,000.SA  is an itCAST  is a heux1 and x2, to be expressed as binary (b). As Pearson's correlation coefficient is bounded, it provides a good basis for defining a binary relationship between two variables as defined by:Methods such as CAST and SA require the differences/relationships between a pair of observations, α ≤ 0 is a constant and  is a floor function that returns the largest integer less than or equal to the real number y.where 0 <Two datasets were used for evaluating the cluster methods. The first is a set of multiply repeated control element spots relating to the Amersham Score Card (ASC) probe set on the Human Genome Mapping Project Human Gen1 clone set array . The ASCThe second dataset consists of a series of 26 arrays  measuring gene-expression difference across a set of human B-cell lymphomas and leukemias . The datp is a linear multivariate time series defined byTwo synthetic datasets were generated using a vector autoregressive process (VAR) or a multivariate normal distribution (MVN). The two datasets contained 1,830 genes and 20 conditions (arrays). The VAR process of order x(t) is the n-dimensional vector of observations at time t, Ai is the n × n autoregressive coefficient matrix at time lag i, and ε(t) is the zero mean n-dimensional noise vector at time t . Therefore x(t) is a linear combination of the previous observations plus some random noise. For the synthetic dataset, each cluster was generated by a vector autoregressive model of order p = 1 and size n equal to the number of genes in the cluster.where x has a MVN distribution if every linear combination of that vector is also normal. Under such conditions we use the notation x ~ N to denote that x follows the MVN distribution, where μ is the mean vector and Σ is a positive definite matrix of covariance. The probability density function of x is given byFor the MVN dataset, a vector of random variables μ and covariance Σ.where |Σ| = det(Σ). For the synthetic dataset, each cluster was drawn from an MVN distribution with varying mean weighted-kappa was used [N, is defined in the following equation, where Countij is the number of elements in the matrix cell indexed by ,To compare the resultant clusters for each method, a statistic known as was used . This men is the number of variables (genes) in the clusters as this represents the number of unique variable pairings.and weighted-kappa metric is calculated from the contigency table by:The wij is the weights for each category comparison; po(w) and pe(w) represent the observed weighted proportional agreement and the expected weighted proportional agreement; Countij is the ith, jth element of the 2 × 2 contingency table; N is the sum of the elements within this table; Row(i) and Col(i) are the row and column totals for this table respectively and Kw is the weighted-kappa value. The interpretation of weighted-kappa values indicates the strength of agreement between two observers (Table where, rs Table  is used


The objective of this study was to translate and to test the reliability and validity of the 10-item Dermatology Life Quality Index (DLQI) questionnaire in Iranian patients with vitiligo.Using a standard "forward-backward" translation procedure, the English language version of the questionnaire was translated into Persian  by two bilinguals. Seventy patients with vitiligo attending the Department of Dermatology, Saadi Hospital, Shiraz, Iran, were enrolled in this study.The reliability and internal consistency of the questionnaire were assessed by Cronbach's alpha coefficient and Spearman's correlation, respectively. Validity was performed using convergent validity.In all, seventy people entered into the study. The mean age of respondents was 28.3 (SD = 11.09) years. Scores on the DLQI ranged from 0 to 24 . Reliability analysis showed satisfactory result (Cronbach's α coefficient = 0.77). There were no statistically significant differences between daily activity (DA) and personal relationship (PR) scale mean scores in generalized versus focal-segmental involvement in sufferers . There were also strong differences between the mean scores of the PR  scale with the involvement of covered only and covered/uncovered areas (P= 0.016) that was statistically significant in the second group.The study findings showed that the Persian version of the DLQI questionnaire has a good structural characteristic and is a reliable and valid instrument that can be used for measuring the effects of vitiligo on quality of life. Vitiligo is an important skin disease having major impact on the quality of life of patients suffering from vitiligo. Appearance of skin can condition an individual self-image, and any pathological alteration can have psychological consequences ,2. Many The DLQI is calculated by summing the score of each question resulting in a maximum of 30 and a minimum of 0. The higher the score, the more quality of life is impaired. The DLQI can also be expressed as a percentage of the maximum possible score of 30.The scores for each of these sections can also be expressed as a percentage of either 6 or 3.Since the DLQI is a brief, simple, easy to complete, and its application in research settings as a screening tool is well documented, it was decided to translate the DLQI into Persian  and to examine reliability and validity of this questionnaire in an Iranian population with vitiligo.The standard "forward-backward" procedure was applied to translate the questionnaire from English into Persian. Two independent bilinguals translated the items and two others translated the response categories and after cultural adaptation, a provisional version was provided. Subsequently, it was back translated into English and then the final version was provided. The cultural adaptation was done by the translation of the "partner" to the "spouse" in Persian language and adding of it in question 8 and 9, respectively.The final draft of the Persian version was administered to a sample of patients with vitiligo that referred to Department of Dermatology, Saadi Hospital, Shiraz, Iran. There were no restrictions on patient selection with regard to extension of lesions. The patients were introduced to the subject of this study and informed about the personal nature of the questionnaire, and all those who gave consent were given the DLQI questionnaire to complete. The questionnaires were completed by the patients whom were referred to our clinic for psoralen and UVA (PUVA) therapy. The patients were categorized by extension of lesions into covered only (vitiligo patches are covered by cloths) and covered/uncovered involvement and by severity of disease to focal-segmental  , which iValidity was performed using convergent validity to demonstrate the extent to which the DLQI correlated with global quality of life. Construct validity was checked by factor analysis.Seventy patients aged 18 to 68  years completed the questionnaire. We did not have any incomplete questionnaires; therefore we included all questionnaires in our study.Scores on the DLQI ranged from 0 to 24. The reliability of the questionnaire was obtained by Cronbach's alpha coefficient (α = 0.77). There were no statistically significant differences between the sex, item scores and mean DLQI score. Table Cronbach's α coefficient by gender, marital status, severity, and extension of disease are shown in Table There were no statistically significant differences between item and mean DLQI scores of males versus females and married versus single cases.Cronbach's α reliability coefficients ranged from 0.69 to 0.78 for symptoms and feelings (SF) scale, 0.71 to 0.78 for daily activities (DA) scale, 0.67 to 0.88 for leisure (L) scale, and 0.50 to 0.99 for personal relationships (PR) scale. The reliability coefficient for work and school (WS) scale was equal to treatment (T) scale that was 0.100. Table The L scale was found to have significant relationship with the severity of the disease  .The DA and PR scales, also had no statistical association with severity factor .The PR scale had strong correlation with the type of the extension of lesions (covered only versus covered/uncovered type) .There was no association between the numbers of treatment sessions with the type of disease .The number of treatment sessions and mean DLQI score was found to have a positive correlation coefficient  but this correlation was statistically significant in the generalized type only .Spearman's correlation coefficient of severity of the disease with questions 5, 6, and 8 were 0.25, 0.24, and 0.26 respectively. For question 8 and the extension of disease and also for question 1 and the stage of disease it was equal to 0.26.The result in question 4 and gender status was statistically significant (0.008) there was no statistically significance correlation between the age and each of questions.The result of question 9 and marital status was statistically significant (P = 0.002) and it was higher in married patients. The range of the paired correlations between the items was 0.17–0.68.Factor analysis is performed to determine the Persian version is a two-dimensional measure including social and psychological parameters Table .The DLQI questionnaire is a well-known instrument for measuring dermatological distress and has been translated into a variety of languages ,9 and 1.The translation process set by the international quality of life assessment (IQOLA) project was built on lessons from cross-cultural psychology and other health survey projects to develop protocols for translating, validating, and norming health status questionnaires, entails forward translation by at least two translators who were native speakers of the target language, rating of translation equality by two other bilinguals, and back translation by two translators who were native speakers of American-English or British-English . BecauseVitiligo is an acquired depigmentation disorder of great cosmetic importance affecting 1–4% of the world's population. The disease has a major impact on quality of life of patients, many of whom feel stigmatized by their condition .Porter et al. studied the effect of vitiligo on sexual relationships and found that embarrassment during sexual relationships was especially frequent for men with vitiligo . Salzer Weiss et al compared the difficulties faced by patients with vitiligo with those with leprosy in India . There mIn general, the finding of this study indicated that mental health in vitiligo patients is poor and it is strongly associated with their quality of life. Since the patients with higher DLQI scores responded less favorably to a given therapeutic modality , improviThis study reports data from a validation study of the 10-item DLQI questionnaire in Iran. In general the findings showed promising results and were comparable with other research finding throughout the world . The twoThe mean DLQI score in this study was 7.05 that is lower than that obtained by Finlay and Khan (mean 7.3)  and ParsReliability was associated by internal consistency of the questionnaire reporting Cronbach's alpha coefficient and validity was examined by convergent validity showed satisfactory results (α = 0.77). Cronbach's α was < 0.7 for males, focal/segmental, and covered vitiligo that may be related to small sample size and cultural differences.The Persian version of the DLQI questionnaire proved to be acceptable to patients and it is worth nothing that occasionally the questionnaire was administered by a trained nurse in face-to-face interviews. However this was done in illiterate patients and some ones indicated that some questions were difficult to answer, especially question 8. Perhaps this was the reason why a weaker correlation was found for this item with its corresponding subscale.The study finding showed that the Persian version of the DLQI questionnaire has a good structured characteristic and is a reliable and valid instrument that can be used for measuring the effects of the vitiligo on quality of life. Especially, the reliability of this questionnaire was high in females and patients with generalized involvement, because of the great cosmetic importance in these groups.None declared.DLQI: Dermatology Life Quality IndexSD: standard deviationIQOLA: international quality of life assessmentThe pre-publication history for this paper can be accessed here:


Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to estimate a protein's concentration in a sample. Deploying ELISA in a microarray format permits simultaneous estimation of the concentrations of numerous proteins in a small sample. These estimates, however, are uncertain due to processing error and biological variability. Evaluating estimation error is critical to interpreting biological significance and improving the ELISA microarray process. Estimation error evaluation must be automated to realize a reliable high-throughput ELISA microarray system.In this paper, we present a statistical method based on propagation of error to evaluate concentration estimation errors in the ELISA microarray process. Although propagation of error is central to this method and the focus of this paper, it is most effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization, and statistical diagnostics when evaluating ELISA microarray concentration estimation errors.We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of concentration estimation errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error.We summarize the results with a simple, three-panel diagnostic visualization featuring a scatterplot of the standard data with logistic standard curve and 95% confidence intervals, an annotated histogram of sample measurements, and a plot of the 95% concentration coefficient of variation, or relative error, as a function of concentration.This statistical method should be of value in the rapid evaluation and quality control of high-throughput ELISA microarray analyses. Applying propagation of error to a variety of ELISA microarray concentration estimation models is straightforward. Displaying the results in the three-panel layout succinctly summarizes both the standard and sample data while providing an informative critique of applicability of the fitted model, the uncertainty in concentration estimates, and the quality of both the experiment and the ELISA microarray process. Proteomic approaches are resulting in the identification of large numbers of proteins that can potentially be used as disease markers or drug targets. Unfortunately, proteomic approaches currently lack the throughput or quality metrics necessary to evaluate hundreds or thousands of samples that may be required to determine clinical usefulness of a biomarker . TraditiProcessing a ELISA microarray experiment produces large volumes of data of wide variety and high complexity. Similar to traditional 96-well ELISA data, ELISA microarray data often are perturbed by processing error -7. ProceStatistically evaluating ELISA microarray concentration estimation errors depends upon both the availability of the appropriate set of comparable measurements and the choice of data analysis methods. Sufficient replication within and across arrays is key to making precise estimates of both concentrations and errors . Hence, In this paper, we describe and illustrate a methodology for calculating the concentration estimation error of each assay in an ELISA microarray experiment based on a statistical analysis of the most likely sources of error. We expect the resulting data analysis algorithms to be a key component in a bioinformatics package for evaluating ELISA microarray data.Making concentration estimates and estimating their errors in our ELISA microarray studies involve a sequence of steps beginning with the layout of the ELISA microarray and design of the experiment. Following execution of the analytical components of the experiment, the statistical analysis proceeds with data screening, normalization, and model identification. Estimation and evaluation of the standard curves and error estimation functions come next. Finally, the standard curves and error estimation functions are applied and then evaluated using a modeling diagnostic.To estimate errors in concentration estimates, it is necessary to carefully lay out the microarray and design the experiment. Our layout features several distally separate replicates of each assay spot on each microarray to evaluate local processing effects. Our design addresses selection and application of treatments – in particular, replicate treatments – to a collection of arrays. This replication facilitates adjustments for the sources of variability that lead to ambiguous concentration estimates ,17. In aTo illustrate our technique for evaluating estimation errors in an ELISA microarray experiment, we used a subset of data from an ELISA microarray investigation of breast cancer biomarkers. The ELISA microarray experiments were performed as previously described ,3. BriefData screening, an exploratory data analysis, serves several purposes – identifying outliers, anomalous values, and experimental design shortcomings; identifying data transforms to improve curve-fitting and application; identifying measurement trends and other processing effects; and suggesting an appropriate functional form for the standard curve ,18-21. TBecause our protein arrays feature fewer spots per array than do typical gene expression microarrays, a different approach to normalization, suitable for low spot-frequency arrays, is required. This normalization is critical, given that array-to-array processing error is common and that standard curves are estimated from reference spot intensities calculated from one set of arrays and then applied to sample spot intensities estimated from a separate set of arrays.e transformations of both concentrations and spot intensities.A scatterplot of intensity estimates of standard spots versus concentration is particularly useful. First, outliers and anomalies may be readily apparent. Second, the spacing between concentration values may be assessed. If standard concentrations follow from a dilution series, then the separation between concentrations decreases significantly with the decrease in concentration. This results in spot intensities measured at higher concentrations having much more leverage on the fit of the model than may be desirable. It should also be apparent whether the variability in spot intensity is increasing with mean spot intensity. Both increasing spacing in the concentrations and heteroskedasticity in the measured intensities affect the model fit and follow-on statistical inferences . These mA scatterplot of raw or transformed standard spot intensity versus concentration also provides an indication of the appropriate model for the data. In particular, data following a sigmoid curve favor the logistic curves while data apparently lacking the horizontal asymptotes of a sigmoid curve favor a linear or power law model. Although several models may be fit and one selected based on a goodness-of-fit statistic (see next section), the scatterplot is a useful visual check on this selection.Several plots provide useful information about the quality of the fitted model. Of special importance are the scatterplot of residuals versus concentration and the scatterplot of residuals versus estimated intensity. In both cases, the variability of the residuals should be centered about zero and constant across concentration or intensity. Model bias is indicated by a systematic drift of residuals to one side of the zero line. Heteroskedasticity is indicated by a systematic change in the variation of the residuals. Both may indicate that a better model is necessary before proceeding to estimation of sample concentrations and estimation of concentration errors.An ELISA standard curve expresses protein concentration as a function of spot intensity. One standard curve is required for each assay. In an ELISA microarray experiment, the standard data are collected by fixing a set of concentrations and measuring spot intensities via imagery of the treated arrays. A standard curve is estimated by fitting an appropriate function to the set of  measurement pairs . This eqCommon parametric choices for standard curve models are multiparameter logistic functions and power law functions. For an ELISA microarray, a strictly monotone model is consistent with the belief that a monotone change in concentration should result in a monotone change in spot intensity.I as a function of concentration C and parameters P1, P2, P3 and P4, is defined asWe estimate standard curves with both logistic and power law parametric models. The four-parameter logistic model , expressI as a function of concentration C and parameters P1 and P2, in loge terms, isThe two-parameter power law model  expressie (I) = P1 + P2 loge (C) + εlogε, are independent and normally distributed with mean 0 and variance σ2. With either of these parametric models, concentration estimation errors may be estimated using propagation of error, also known as the delta method.We assume the errors, denoted by the term PRESS statistic, a direct measure of the predictive capability of each candidate model and the augmented covariance matrix isCS isHence, the formula for estimated standard error of IS and standard error , the estimated concentration and approximate 95% confidence interval  areFor a given intensity estimate CS = C(IS)CL 95%= CS - 2SE[CS]     (2)andCU 95%= CS + 2SE[CS]     (3)C in terms of I and the four parametersFor example, consider the four parameter logistic model, Eqn. 1. The concentration estimation equation is obtained by solving this equation for C (Eqn. 4) with respect to I and the parameters P1, P2, P3 and P4The Jacobian matrix is obtained by taking the partial derivatives of the inverted four-parameter logistic function of A three-panel display combining a histogram of normalized sample spot intensities for a given antigen, its corresponding standard curve, and the graph of the concentration coefficient of variation, or relative error, versus concentration provides pertinent information about the conduct of the current experiment as well as information to improve future experiments. The standard curve panel presents a scatterplot of normalized standard spot intensities versus standard concentrations. The scatterplot is overlain with the estimated standard curve expressing concentration as a function of spot intensity. This panel also includes approximate 95% confidence intervals. These intervals summarize the uncertainty in concentration estimates due to both the uncertainty in estimating the standard curve and the uncertainty in the sample spot intensity estimate. Finally, a highlighted region helps distinguish concentration estimates s with acceptable errors from concentration estimates with possibly less than acceptable errors.IL, CL) and , are the two points such that the confidence intervals begin to increase significantly in length. This segment generally corresponds to the linear segment of a standard curve. We identify the intensity IL of the lower pair as the smallest intensity such that 95% UB(IL) is less than 95% UB(I) for intensity values I less than IL. Similarly, we identify IU as the largest intensity such that 95% LB(IU) is greater than 95% LB(I) for intensity values I greater than IU. We define CL and CU to be CL = C(IL) and CU = C(IU), respectively. We believe that this is a conservative approach to identifying intensities that generate concentration estimates with acceptable errors.The segment of the standard curve corresponding to acceptable concentration errors may be determined using the 95% confidence intervals. The lower and upper endpoints of this segment,  and . Consider the union of the two rectangular regions defined by the two sets of vertices , and . This union defines an L-shaped region covering the standard curve segment and bound at its extremes by the intensity and concentration segments. From this visualization, one can quickly grasp the dynamic range of acceptable intensities and the potential range of acceptable concentration estimates.An informative visualization of acceptable concentration estimates may be generated using the points /C(I), or relative error of a concentration estimate – as a function of concentration. This provides an alternative view of the error in concentration estimation over the concentration range covered by the concentration estimation equation. A standard curve modeled with a four-parameter logistic function generally will have a bathtub shape due to the increasing uncertainty in concentration estimates at the two ends of the concentration range where the curve approaches horizontal asymptotes.The second panel in this display shows the concentration coefficient of variation – that is, %The third panel in this display features an annotated histogram of sample spot intensity estimates on the intensity axis opposite the scatterplot. In this representation, it is easy to see the extent of overlap between the distribution of sample intensity estimates and the range of intensities that result in concentration s estimates with acceptable errors.e(fluorescent intensity) the difference between the mean of its slide's control spot loge (intensities) and the corresponding loess estimate.To evaluate concentration estimation errors in the example analysis, we attempted to quantify or understand those errors that we can and minimize those errors that we cannot. We began with data screening. The most significant anomaly uncovered during this exploratory analysis of the cancer biomarker data was a decreasing trend in control spot intensity as a function of array print order Figure . The treOur evaluation addressed the selection and fitting of an acceptable concentration estimation model. To that end, we examined two plots. The first displays the fluorescent intensities of the standards as a function of concentration Figure . Two chae transformation of the intensity estimates stabilizes the variability across concentrations , each must be addressed to achieve the best fit of the standard curve and resulting concentration estimation inferences. A logs Figure .With the heteroskedasticity and undue leverage addressed, we estimated a standard curve by selecting one of two models: a four-parameter logistic model (Eqn. 1) and a power curve model (Eqn. ??). We chose the logistic model as the model that fits the data best visually and in terms of the PRESS statistic Figure . The logFigure In the standard curve panel Figure , we see The histogram Figure  shows thThe concentration coefficient of variation, or the ratio of the concentration estimation error to the corresponding estimated concentration, offers an alternative expression to a confidence interval as a means to evaluate concentration estimation error. A graph of this estimation error as a function of concentration offers a comprehensive summary of the variation in the concentration coefficient of variation over the concentration range Figure .Presenting the results in this type of plot allows us to immediately look for several potential problems. First, does the fitted curve seem reasonable, given the data points to which we are fitting? We also can determine whether most of the unknown sample data fall within the acceptable range of the curve. The usable concentration range is made clear and, if it is too limited in range, it is immediately apparent. If problems are identified, several fixes are available, including changing the settings on the imager or using a different concentration range to create the standard curves.Evaluation of errors in estimating concentrations is important to establishing confidence in protein concentration estimates. Propagation of error provides a straightforward approach to estimating concentration estimation errors in ELISA microarray experiments. When presented in a simple multi-panel visualization, the propagated errors provide valuable information about individual concentration estimates, the applicability of the estimated standard curve, quality of the experiment, and the conduct of the ELISA microarray processing. The visualization provides a rapid assessment of the quality of the data, particularly in regard to the goodness of fit of the estimated standard curve and its capability to estimate concentrations over the observed range of intensities of biological samples.DSD framed evaluation of ELISA microarray concentration estimation errors as a statistical problem. KKA suggested the use of propagation of error and outlined the initial derivation. DSD and AMW derived the appropriate propagation of error equations, developed the algorithms, and designed the diagnostic visualizations. AMW encoded the algorithms, analyzed the microarray imagery, and produced the statistical results. SMV designed and printed the arrays and then carried out the ELISA microarray experiments. RCZ conceived the study, designed it with SMV, and then coordinated all our efforts. All authors submitted comments on drafts, then read and approved the final manuscript.


Given that gene duplication is a major driving force of evolutionary change and the key mechanism underlying the emergence of new genes and biological processes, this study sought to use a novel genome-wide approach to identify genes that have undergone lineage-specific duplications or contractions among several hominoid lineages. Interspecies cDNA array-based comparative genomic hybridization was used to individually compare copy number variation for 39,711 cDNAs, representing 29,619 human genes, across five hominoid species, including human. We identified 1,005 genes, either as isolated genes or in clusters positionally biased toward rearrangement-prone genomic regions, that produced relative hybridization signals unique to one or more of the hominoid lineages. Measured as a function of the evolutionary age of each lineage, genes showing copy number expansions were most pronounced in human (134) and include a number of genes thought to be involved in the structure and function of the brain. This work represents, to our knowledge, the first genome-wide gene-based survey of gene duplication across hominoid species. The genes identified here likely represent a significant majority of the major gene copy number changes that have occurred over the past 15 million years of human and great ape evolution and are likely to underlie some of the key phenotypic characteristics that distinguish these species. This genome-wide analysis reports the major lineage-specific gene copy number changes that have occurred over the past 15 million years of human and great ape evolution The evolution of genomes has been primarily driven by single basepair mutation, chromosomal rearrangement, and gene duplication , with thOver the past few years, as the human genome sequence has become available, it has become apparent that recent segmental duplications in the human genome are far more frequent than originally believed, comprising approximately 5% of the available sequence . DuplicaThe assessment of DNA copy number changes between different human genomes has been aided by the development of comparative genomic hybridization (CGH), which originally involved cohybridizing differentially labeled test and reference genomic DNAs to normal metaphase chromosomes . A cytogWhile cDNA microarrays, containing sequences derived from tens of thousands of genes, have been used extensively to profile mRNA expression levels , their uUntil now, cDNA aCGH studies have been limited to only within-species comparisons, partly due to concerns related to the increased sequence divergence that would come into play with interspecies applications. Such sequence divergence may produce differential hybridization signals that would be difficult to distinguish from those that arose from copy number changes. Fortunately, despite their significant anatomical and physical differences, hominoid species show a strikingly high degree of similarity at the genome sequence level, with the average sequence divergence values estimated as 1.24%, 1.62%, and 1.63% for human–chimp, human–gorilla, and chimp–gorilla, respectively, and orangutan showing approximately 3.1% sequence divergence when compared to human, chimp, or gorilla .Because of this close sequence conservation, we reasoned that it may be possible to use cDNA aCGH to directly compare the cross-species hybridization signatures of human genes to those of the great apes and to identify genes that have alterations in copy number and/or significant changes in exonic sequence between human and other hominoid species. After we initiated such a cDNA aCGH study, two interhominoid aCGH reports appeared that used arrays containing either cloned or amplified genomic DNAs . While thttp://rana.lbl.gov/EisenSoftware.htm) was used for visualization of aCGH data for each gene as it occurred in the genome, permitting a “gene-by-gene” survey of the data and allowing for easy detection of interspecies copy number variations, whether they occur as single isolated genes or as multigene blocks.Interhominoid cDNA aCGH was carried out in a series of pairwise comparisons using microarrays containing 39,711 human cDNAs, representing the majority of all human genes . The paiResults of the distribution of lineage-specific (LS) aCGH signatures for different individual hominoid species are presented in Homo/Pan split, showed fewer LS signatures (92) than did human (140), even though they represent the same evolutionary age. As mentioned below, this is due in large part to the significant number of LS gene copy number increases found in human.Several interesting features were evident from these data. First, when increases and decreases were scored separately or combined, the number of LS signatures was generally in proportion to the evolutionary age of that lineage, although not in all cases. Bonobo and chimpanzee, from the time since the Second, while all lineages showed more gene copy number increases than decreases, this was most pronounced in humans, with 134 cDNAs representing increases and only six representing decreases. This increase-to-decrease ratio (22.3:1) was significantly greater than that of any of the great apes, which showed ratios ranging from 2.75:1 (chimpanzee) to 1.18:1 (orangutan). It is worth noting that only genes found in the human genome are represented on the cDNA arrays, and if there are genes that are absent in human but present in the great apes, e.g., genes that were lost as the human lineage emerged, those genes would not be part of this analysis. So, while it is likely that the complete loss of both copies of a gene in an LS manner is a rare event, the number of genes identified here as having a reduced copy number specifically in the human lineage may be an underestimate of the true total.Third, as mentioned above, for all lineages tested, the number of genes showing LS increases was greater than those showing LS decreases. Determination as to whether this is due to some, as yet unknown, ascertainment bias of the method or whether this is a real evolutionary tendency favoring gene duplication over gene loss will require further investigation. The favoring of gains over losses is even more striking when two additional factors are considered. (1) The fact that the cDNAs were only from human, while likely to be important to the low number of genes showing human lineage-specific (HLS) losses previously mentioned, does not help explain why, for all lineages tested, the number of LS genes showing increases was greater than the number showing decreases. To the contrary, if there were genes not on the microarray because they were only found in one or more of the great ape lineages, inclusion of such genes would be expected to add to the total number of LS increases, making the disparity between increased and decreased LS genes even greater. (2) If human/great ape sequence divergence was responsible for some of the LS aCGH signals that were obtained, it would, if anything, produce a falsely elevated number of LS decreases.Homo/Pan split, 12; gorilla, 17.3; and orangutan, 17.1.Fourth, while only orangutan had more LS gene copy number increases (222) than did human (134), when the number of genes showing copy number increases was measured as a function of the evolutionary age of the lineage, human showed the greatest number of expansions of any hominoid. When measured as copy number increases per million years of age, the following values were obtained: human, 26.8; bonobo and chimpanzee since the Pan/gorilla, 16 genes; Pan/orangutan, 27, and gorilla/orangutan, 45. For gene increases, this trend continued, with gorilla/orangutan (17) showing more changes than Pan/orangutan (nine). Interestingly, Pan/gorilla showed a departure from this trend with 28 increased genes, suggesting that gene expansion may have been particularly active in the African great apes as a group. There were also a number of more complex gene copy number changes in the five hominoid lineages, with some species showing an increase relative to human for a particular gene and others showing a decrease. These changes are likely due to more than one event, which may be indicative of a genomic region that is relatively unstable and/or of genes whose copy numbers have been influenced by different selection pressures. We identified 190 genes that showed copy number changes in multiple lineages, bringing the total number of LS genes identified to 1,005, which represents 3.4% of the total number of genes tested on the microarrays. Given the relatively conservative selection criteria used  genome assembly lists multiple Chromosome 5 locations for some of the HLS cDNAs contained on the positive BAC  and therefore it is likely that the multiple, closely spaced signals seen in some of the human interphase spreads , as well as a set of flanking genes not shown to be increased in human (control probe), were evaluated by interphase and metaphase fluorescence in situ hybridization (FISH) using bacterial artificial chromosome (BAC) probes see . The FIS spreads A reflectMetaphase FISH showed both the test probe and the flanking probe to be located in the human 5q13 band. Both probes were located in the proximal q arm of the orangutan (PPY) Chromosome 4 and in the p arms of the bonobo (PPA) and chimpanzee (PTR) Chromosomes 4. In the gorilla (GGO), both probes were located on the gorilla Chromosome 19. All of these primate locations are consistent with described evolutionary chromosomal rearrangements, with the orangutan Chromosome 4 considered to be the ancestral Chromosome V . These rIt is of interest that, considering the orangutan Chromosome 4 as the ancestral Chromosome V, rearrangements at this site have occurred in all of the other three great ape species  and in the human (gene duplication). This region is also involved in spinal muscular atrophy (SMA), which is characterized by deletions of one or more genes in this region . Taken tFGF7-like genes. Some genes we identified as having LS aCGH signatures have been previously studied by others using different methods, which provides a means of independently checking the accuracy of the cDNA aCGH data presented here. One such gene, the FGF7 gene on Chromosome 15, was studied by morpheus on Chromosome 16 was reported and shown to exhibit gene copy number variation between several hominoid species  with the copy number estimates reported independently by Recently the identification of a multimember gene family named  species . Using aominoids B. The avCXYorf1 gene family. They found values of seven, two, three, and one for human, chimpanzee, gorilla, and orangutan, respectively. These values closely mirrored the aCGH values that were obtained (obtained C (correlFLJ22004 gene shows the greatest gorilla-specific copy number increase (average log2 ratio = 3.94). This gene resides near the fusion region on Chromosome 2q14.1 (see below) and is contained within BAC RP11-432G15. Consistent with the aCGH data, two independent interhominoid FISH studies, by our lab  and therefore should represent a subset of the segmental duplications found in the WSSD dataset. Results of this analysis confirmed this expectation . The regions near the C-band regions on 16 (15 cDNAs) and Y (14 cDNAs) also showed an enrichment of LS genes, although to a lesser extent. These regions, as well as the pericentromeric regions of the acrocentric chromosomes, which showed enrichment for LS genes, are known to contain highly repetitive DNA, which may make them especially prone to recombination and duplication.Genes identified as having a variation in copy number specific for one or more hominoid lineages occurred either as single isolated genes or as clusters of genes. This latter category likely reflects LS copy number changes that involved blocks of contiguous genes. In addition, certain specific regions of the genome, while not necessarily composed of contiguously positioned LS genes, showed a marked enrichment for LS genes. Surveying the genome for regions containing contiguous gene clusters of LS genes or for regions highly enriched in LS genes  identified 23 prominent sites ; Table 2Previous reports have shown that recent (less than 40 MYA) segmental duplications in the human genome are positionally biased and found more frequently in pericentromeric and subtelomeric regions . ConsistBIRC1 gene, which we show is amplified uniquely in humans, are sometimes found in SMA patients. This region and another at 5p14.3–5p13.3 that also contains a cluster of LS genes are near the breakpoint sites of a pericentric inversion that occurred during hominoid speciation  maps to Chromosome 5q13 and was elevated specifically in the human lineage. NAIP has been implicated in delaying neuronal programmed cell death , a leucine zipper-containing gene highly expressed in brain (KIAAA0738), α7 cholinergic receptor/Fam7 fusion gene (CHRFAM7A), a p21-activated kinase (PAK2), a Rho GTPase-activating protein (SRGAP2), a Rho guanine nucleotide exchange factor (ARHGEF5) that is a member of the rhodopsin-like G protein-coupled receptor family, and Rho-dependent protein kinase (ROCK1). Inhibition of ROCK1 has been shown to prevent long-term memory, and ROCK1, together with a RhoGEF and RhoGAP, have been recently implicated in a model of long-term memory based on fear conditioning   at Chromosome 19q13 that is significantly elevated in human relative to most hominoids map to the two chromosomal regions giving the highest LOD scores in a recent genome-wide scan related to specific language impairment , which is thought to be involved in water transport across membranes, shows an HLS increase in copy number, while the genes immediately flanking it (NFX1 and AQP3) do not show HLS aCGH signals. Similarly, AQP7 gene has been recently (less than 40 MYA) duplicated several times while flanking regions show no recent duplication. These data suggest that a series of HLS segmental duplications occurred that focused primarily on the AQP7 gene, which spans 17 kb of the 22 kb duplication. This observation, together with the fact that several of the additional AQP7 copies appear to be potentially functional (see below), raises the possibility that significant selection pressure may have been exerted on AQP7-like genes specifically in the human lineage.The aquaporin 7 gene  and, in bonobos and chimps, the FCER2 gene, encoding a lymphocyte IgE receptor, and the IL1RL1 gene encoding an interleukin receptor 1-like protein. Also, it has been shown that chimpanzees can synthesize a form of sialic acid while humans cannot, owing to the loss of function in humans of a specific sialic acid hydroxylase  encodes the fibroblast growth factor receptor 3 (FGFR3), which when disrupted in humans causes achondroplasia, the most frequent form of short-limb dwarfism. The SET8 gene is also significantly elevated in copy number only in gorilla (average log2 ratio = 2.65) and also related to development. The gene encodes a transcription factor and appears to be homologous (protein similarity of 43% over 110 amino acids) to the Drosophila trithorax gene, which functions in segmentation determination through interaction with bithorax and antennapedia complex genes, suggesting that it may serve a role in gorilla-specific development. There were a significant number of genes (28) showing increased copy numbers specifically in the African great apes . Among these were the MSTP028 gene, encoding a voltage gated potassium channel; the PLA2G4B gene, encoding phospholipase A2β, which shows high brain and (in particular) cerebellar expression; and the SPTBN5 gene, which encodes a nonerythroid spectrin. SPTBN5 is immediately adjacent to PLA2G4B at Chromosome 15q15.1 in the genome and, like PLA2G4B, shows high cerebellar expression, raising the possibility that their function(s) in the African great apes may be linked. Finally, while the HLS and LS genes mentioned above have interesting biological implications related to human and great ape differences, each should be viewed as tentatively HLS or LS until the interhominoid copy number differences for these genes are confirmed by independent methods.Another gene specifically increased in gorilla (average logClassification of HLS and LS genes according to predicted molecular function was carried out by Gene Ontology (GO) analysis. For the great majority of functional categories, both HLS and LS gene groups gave GO distributions similar to that found with all known genes (UniGene collapsed set), with ligand binding, catalytic activity, signal transducer activity, and transporter activity being the four most highly represented functional categories . This anAQP7 gene predicts that of seven closely related (greater than 90%) copies in the genome, at least four appear to be potentially functional. In contrast, the FLJ13263 gene had four closely related sequences, and these all appear to be pseudogene-like. Finally, the fact that it has been shown that pseudogenes can play important functional roles . Using this comparison, we have determined that genes that gave aCGH signatures indicative of copy number increase specifically in the human lineage, showed a 7-fold increase in the frequency of gaps and absent sequence homology in the chimpanzee draft compared to a randomly selected gene (EST) set  database's best reciprocal alignment of the July 2003 human genome and the November 2003 Arachne 4X chimpanzee draft genome (EST) set . Such a However, a limitation of only comparing the human and chimpanzee genomes is that no out-group analysis is provided, preventing discrimination of ancestral and derived forms and limiting the ability to identify gene copy number changes unique to a specific hominoid lineage. In contrast, the interhominoid aCGH studies described here provide reliable genome-wide data for out-group analysis across five primate species, allowing easy identification of LS copy number differences.each of the four great apes studied). Of the 353 genes that were “human > chimp,” 200 were not found in the HLS set, indicating that over half (57%) of the “human > chimp” genes were not HLS.In order to provide some perspective on the importance of out-group data when trying to identify LS gene changes, a comparison was carried out between two aCGH clone sets. One set contained 153 genes we identified by cDNA aCGH that were specifically increased in copy number in the human lineage when compared to each of the four great ape lineages . The other clone set, while derived from the same aCGH experiments using the same cutoff values, contained 353 genes that showed aCGH signals in which the human copy number was greater than the chimpanzee . Comparison of these two datasets allows one to determine how frequently a “human > chimp” gene is also HLS  specifically elevated in the African great apes, showed that, in chimpanzee, signals were widely distributed among many chromosomes, while in gorilla the signals were restricted to two sites, one single copy and the other multicopy  and cDNAs that mapped to multiple genomic locations (multiple locations). In this latter category, we also included a minority of cDNAs that had no assignable location in the genome assembly. We identified HLS cDNAs that showed stronger hybridization with human DNA  and determined how many of these occurred in each of the two mapping categories. HLS signatures were found for 0.185% of unique location cDNAs  and 2.88% of multiple location cDNAs , a frequency difference of more than an order of magnitude (approximately 1:16).Such a strong enrichment, in the multiple location category, of genes showing increased human aCGH signals specific to the human lineage would be expected if such genes were present as multiple closely related copies with distinct genome locations and, as a result, were placed in the multiple location group. No such gene distribution bias would be expected if the LS signatures were mainly due to sequence divergence.Additionally, we estimated what fraction of LS cDNAs in each species were cDNAs with multiple human map positions. Values of 59%, 10%, 13%, 14%, 10%, and 20% were obtained for human, bonobo, chimp, bonobo/chimp total, gorilla, and orangutan, respectively, providing further support that the increased  HLS aCGH signatures that were obtained are likely due to gene copy number increases specific to the human lineage.We also carried out interhominoid FISH using a BAC probe (RP11-93K3) containing a gene (IMAGE 1882505) that gave a reduced signal specifically in the orangutan lineage, which is the lineage where sequence divergence might have its greatest artifactual contribution. Resulting FISH data see  showed 1Cot-1 DNA  indicated that there was no correspondence between genes hybridizing more strongly to Cot-1 and genes that are LS.Lastly, to address the possibility that such signals might be due to highly repetitive sequences associated with LS genes that were not effectively blocked during hybridization, we examined the cDNA sequences of five cDNAs that showed stronger hybridization with human DNA. In all cases no repeats were found that would account for the HLS aCGH data. In addition, hybridization using labeled DNAs that were used for this study were derived from human , bonobo , chimpanzee , gorilla , and orangutan . Human and chimpanzee genomic DNA samples were isolated from blood cells using Super Quick-Gene kits from the Analytical Genetic Testing Center . One gorilla and two bonobo samples were isolated from cell lines using DNeasy Tissue kits from Qiagen . An orangutan sample and a gorilla sample were isolated from blood by other laboratories. Remaining DNAs  were obtained from the Coriell Institute  and originally derived from primary fibroblast cell lines.http://image.llnl.gov) and spotting them onto Corning GAPSII aminosilane slides using a custom-built robotic arrayer (http://cmgm.stanford.edu/pbrown/mguide/index.html). The labeling of genomic DNA and hybridization to cDNA microarrays were performed as previously described (Cot-1 DNA (Invitrogen), 100 μg of yeast tRNA (Invitrogen), and 20 μg of poly(dA-dT)  to block hybridization to nonspecific and repetitive elements in genomic DNA. We adjusted the final hybridization volume (40 μl) to contain 3.5× SSC and 0.3% SDS. Following sample denaturation (2 min at 100 °C) and a Cot-1 preannealing step (20 min at 37 °C), we cohybridized test and reference samples to a cDNA microarray containing 39,711 nonredundant cDNA clones, representing 29,619 different human genes. Samples were hybridized at 65 °C for 16 h. Following hybridization, arrays were washed in 2× SSC, 0.03% SDS for 5 min at 65 °C, followed by successive washes in 1× and 0.2× SSC for 5 min each at room temperature.DNA microarrays used in this study were fabricated by PCR-amplifying IMAGE clones (escribed . In briehttp://genome-www5.stanford.edu) for analysis. For each experiment, fluorescence ratios were normalized by setting the average log2 fluorescence ratio for all array elements equal to 0. We included for analysis only those genes that were reliably measured  in greater than or equal to 50% of samples. Genes not meeting these criteria were viewed as absent. Map positions for cDNA clones on the array were assigned using the UCSC GoldenPath assembly (http://genome.ucsc.edu/), November 2002 freeze. Gene copy number ratios were visualized in log2 colorimetric scale with the genes ordered by chromosomal position using TreeView version 1.6 (http://rana.lbl.gov/EisenSoftware.htm). To provide the most accurate depiction of chromosomal gene distribution, cDNAs with multiple genome map positions (more than 1 Mb apart) were represented in TreeView at each assigned map location.Individual microarrays were imaged with a GenePix 4000B scanner  and fluorescence intensities were extracted using GenePix Pro 3.0 software and uploaded into the Stanford Microarray Database (SMD) . Second, for a gene copy number change to be considered unique to a particular species, at least half of the absolute values of comparisons within that species had to meet or exceed a threshold of 0.5 with all such values in the same direction, i.e., either all positive or all negative, and at least half of the absolute values of comparisons within each of the remaining species had to be below a threshold of 0.5. For example, for a gorilla LS gene, at least half of the gorilla comparisons had to meet or exceed the 0.5 threshold, while at least half of the comparisons within each of the remaining species had to be below the threshold. Third, in order to compensate for missing  values for a given cDNA of all “present” values within each species, no more than one could fall below the threshold (0.5) for each species. Fourth, to ensure sufficiently high signal-to-noise in the identification of altered ratios, for a given cDNA and given great ape species, each absolute value of the average of the species versus human comparison for that species had to be at least 2.5-fold greater than the absolute value of each remaining species average, including human versus human comparisons. For HLS genes, the absolute value of each species average of the great ape versus human comparisons had to be at least 2.5-fold greater than the average of the absolute value of the human versus human comparisons.For selection of LS cDNAs, the values considered were the logFor cDNAs in which the copy number was either increased or decreased in two or more hominoids relative to all the other hominoids, the same criteria were used as before, except the cDNA would have to meet or exceed the 0.5 threshold selection criteria for more than one species.2 value = 0.39). In a similar manner, we took advantage of the fact the one of the human-to-human comparisons used in our experiments was between a male and female. In this context, X chromosome genes in the female should be present as two copies while in the male will exist as one copy. Calculation of the average aCGH ratios of 957 such genes in the male/female comparison yielded a log2 value of 0.21. The different values obtained in these two tests may reflect the fact that in the male/female comparison a Y chromosome was present, while this was not true in the other study, which used XO cell lines. The presence of sequences on the Y that are shared with the X could have produced a compression of aCGH fluorescence ratio values, accounting for the difference in X chromosome-related log2 ratios described above. Similar compression effects on X chromosome ratios have recently been reported  and from great ape fibroblast cell lines, obtained from Coriell. The four great ape species studied were bonobo , chimpanzee , lowland gorilla , and Sumatran orangutan .Using standard procedures, metaphase spreads and interphase nuclei were prepared from human lymphocytes  flanking the region amplified in human and containing portions of the RAD17 gene was selected as a control probe. BAC clones were obtained from BACPAC Resources at the Children's Hospital Oakland Research Institute and from Research Genetics. Whole-cell PCR was done to verify that the OCLN, GTF2H2, and BIRC1 genes were on BAC CTD-2288G5 and that the RAD17 gene was on BAC RP11-1077O1. BAC DNAs were prepared using Large Construct Kits (Qiagen). BAC probes were directly labeled with Spectrum Green  and Spectrum Orange (Vysis) using the Vysis Nick Translation Kit and protocol.One BAC clone (CTD-2288G6) containing all or portions of the coding regions for Cot-1 DNA was used to block cross-hybridization of high-copy repeat sequences. In each experiment, dual-color hybridization was performed using a probe carrying genes with a predicted increase in copy number specifically in the human lineage (CTD-2288G6 or CTC-790E5) and a flanking probe (RP11-1077O1 or RP11-1113N2) containing a gene not predicted to show an HLS increase in copy number. For each species, two separate hybridizations were performed: one with the probe containing the genes showing increased human copy number labeled with Spectrum Green and the flanking probe with Spectrum Orange, and the other in which the dyes were reversed. For each probe combination for each species, a minimum of 200 interphase nuclei and ten metaphase spreads were examined. A whole chromosome painting probe for human Chromosome 5  was used to confirm the gorilla Chromosome 19 to be syntenic with the human Chromosome 5 for the region of interest.FISH analyses with the BAC probes were performed using standard techniques. 0. Nuclei that were in S/G2 demonstrated four copies of the control probe and the test probes were proportionately in multiple copies of the number established in the nonproliferating cells. Similar experimental conditions were used for the additional BAC FISH analyses described.The hominoid cell lines used for FISH analysis were grown asynchronously in monolayer culture. Metaphase spreads and nuclei were obtained from a shake-off preparation and thus were somewhat selected for proliferative activity. Similarly, human lymphocyte cultures stimulated with the mitogen phytohemagglutinin contain cells in various stages of the cell cycle. In order to judge the replication state of the nuclei scored, dual-color FISH assays included probes both for DNA sequences that by aCGH showed copy number difference between test and reference DNA and for sequences on the same human chromosome that had the same (diploid) number of copies. Nuclei that showed diploid copy number of this control probe were assessed to be in Ghttp://www.ncbi.nlm.nih.gov/Entrez) sequence retrieval tool and saved locally in FASTA format. Likewise, the random IMAGE clone sequences were obtained by first downloading GI numbers for all human IMAGE clones and then using a random number generator to pick approximately 200 random IMAGE clones from the list of GI numbers. These random IMAGE clone sequences were then downloaded from Entrez in a similar fashion. The April 2002 WSSD dataset was downloaded from the Segmental Duplication Database website . The two IMAGE clone sequence datasets were formatted and “BLASTed” against the WSSD sequences locally using NCBI's stand-alone BLAST executables for Windows. BLASTs were limited to an expect value of e−20 and then the best match was reported by a Perl (http://activestate.com/) script for each query. No restrictions on percent identity of the match or match length were imposed.Sequences of IMAGE clones for each HLS gene were obtained using NCBI's Entrez (http://genome.ucsc.edu/cgi-bin/hgGateway). The BLAT hits were parsed such that only hits with a percent identity greater than or equal to 90% were reported. Furthermore, only hits with a match coverage (match length/query length) greater than or equal to 50% were reported.The HLS gene IMAGE clone sequences see  were comhttp://genome.ucsc.edu/goldenPath/14nov2002/database/). Using the position data obtained from the BLAT alignments along with a 50 kb buffer on both sides of the alignments, the relative repeat content was determined for each HLS gene region using a Perl script. As a comparison, the relative repeat content was determined for the entire genome. Annotated gaps within the regions and the human genome were subtracted from the percent content calculation so that these content values were not skewed by gaps. Only long interspersed nuclear element (LINE), long terminal repeat (LTR), short interspersed nuclear element (SINE), Simple, and Satellite classes of repeats were included in the analysis.Repeat annotation was downloaded from UCSC . Likewise, the Satellite repeat content for the LS genes within the 23 clusters was also determined in a similar fashion.http://apps1.niaid.nih.gov/david/upload.asp)  (oad.asp) . Analysehttp://apps1.niaid.nih.gov/david/)  , a progrGenBank accession numbers were used for the HLS and LS analysis due to nearly half of the genes lacking UniGene numbers, thus making GenBank accession numbers more inclusive of the entire HLS and LS dataset analysis. Alternatively, UniGene numbers were used for the the genome-wide analysis because they provide a nonredundant dataset which is a much closer estimate to the number of genes  in the human genome versus the human RefSeq accession numbers. When subtracting all computer-based models from human RefSeq, only 20,850 RefSeq accession numbers were available for analysis.http://genome.ucsc.edu/goldenPath/gbdDescriptions.html). From the all_est file, 200 random IMAGE clones were picked to ensure that at least one EST per IMAGE clone would map to the human genome. The EST sequences for both the HLS and random datasets were downloaded from GenBank and compared to the July 2003 human genome via a locally installed version of BLAT. BLAT output was parsed so that hits with a score greater than 200 and percent identity greater than 90% were examined for chimpanzee homology. The score and percent identity calculations mimic the calculations performed with the Web-based version of BLAT (http://genome.ucsc.edu/cgi-bin/hgBlat); the formula for these calculations was provided by Donna Karolchik.The HLS dataset is identical to that previously described. The random dataset chosen for this analysis was determined from UCSC's all_est annotation (http://genome.ucsc.edu/goldenPath/hg16/versusPt0/). For each BLAT hit, each block of alignment was compared to the chimpanzee versus human best chain annotation and was scored as follows: “chimp positive” indicates the block is entirely homologous to chimp; “chimp partial” indicates the block is partially homologous to chimp but there are gaps in the homology; “chimp gap” indicates the block is within a gap of the chimp homology; “chimp negative” indicates the block has no homology to chimpanzee. The summary numbers are based on all of the blocks of alignments and how they are scored in reference to chimpanzee homology.The BLAT hits, as defined as one or more blocks of alignment within score and percent identity cutoffs, were compared to the chimpanzee versus human reciprocal best chain alignment annotation . The following primer/probe sequences were used: PLA2G4B F 5′-GCAGGTCTGGGTGAGGGTT-3′, PLA2G4B R 5′-GCTGCACCTGATCCCCACT-3′, and the probe 5′-VIC-CAGGAAGTTGCCACACAGGTGAGCA-TAMRA-3′; FLJ31659 F 5′-GCTCAGACATCCAGGGACGA-3′, FLJ31659 R 5′-CGCTTCTCCCAGGATTGGT-3′, and the probe 5′-VIC-CACATTCGTCCAACAGCGGTCGC-TAMRA-3′; BC040199 F 5′-GAGGAAGGTTGGGTGTGGAG-3′, BC040199 R 5′-ACTGGGTGTCCTGCTGGCT-3′, and the probe 5′-VIC-TTGCTTGCTGTGGCCCCAAGCT-TAMRA-3′; CFTR F 5′-CGCGATTTATCTAGGCATAGGC-3′, CFTR R 5′-TGTGATGAAGGCCAAAAATGG-3′, and the probe 5′-6FAM-TGCCTTCTCTTTATTGTGAGGACACTGCTCC-TAMRA-3′.RT-PCR analysis of interhominoid DNA copy number variation was carried out using an ABI Prism 7700 sequence detector  . Exon-sp2, 900 nM forward primer, 300 nM reverse primer, 200 nM probe, 1.25 U AmpliTaq Gold DNA polymerase (PE/ABI), and the template genomic DNA. Thermal cycling conditions were as follows: activation of TaqGold at 95 °C for 10 min followed by 40 cycles of amplification at 95 °C for 15 s and 60 °C for 1 min.Amplification reactions were performed in MicroAmp optical tubes (PE/ABI) in a 50 μl mix containing 8% glycerol, 1× TaqMan buffer A , 300 μM each of dATP, dGTP, and dCTP and 600 μM dUTP, 5.5 mM MgClt), which represents the PCR cycle at which an increase in reporter fluorescence above a baseline signal can first be detected. The starting copy number of each test gene was quantified in the ape samples by determining the Ct of the test gene and using a standard curve for copy number. The standard curve for each gene was generated using the fluorescent data from five serial dilutions of human genomic DNA and calculating a single copy of each gene per haploid human genome, as annotated in the current genome build. Copy numbers of the test genes in ape samples were normalized to the copy number of the CFTR gene, which serves as a control representative of a single gene per haploid genome  Normal human control, PHA-stimulated lymphocytes. Multiple (10–12) signals present: 9p12, 9q12, 4q arm, and two acrocentric p arm regions. The Chromosome 9 signals appear to flank the 9q heterochromatin and centromere regions, with the p arm signal a double signal.(B) Bonobo fibroblast culture. Multiple (10–12) signals.(C) Chimpanzee fibroblast culture. Multiple (10–12) signals.(D) Gorilla fibroblast culture. Multiple (12–15) signals.(E) Orangutan fibroblast culture. Two signals present on a pair of homologues . Also shown is a TreeView image  for IMAGE cDNA clone 1882505.(3.29 MB EPS).Click here for additional data file.Figure S2Figure shows a TreeView image of blocks of HLS genes selected using increasingly relaxed selection criteria. The top-most group represents HLS genes selected using the standard 0.5 cutoff value described in (1.9 MB EPS).Click here for additional data file.Figure S3(A) Normal human control, PHA-stimulated lymphocytes. Signal at Chromosome 2q13 and 22qtel.(B) Bonobo fibroblast culture. Four signals.(C) Chimpanzee fibroblast culture. Four signals.(D) Gorilla fibroblast culture. More than 30 signals. Hybridization to most subtelomeric regions.FLJ22004 cDNAs.(E) Orangutan fibroblast culture. No apparent red signal. Probe BAC RP11-1007701 in green included as internal hybridization control. Also shown are aCGH TreeView images  for three (6.94 MB EPS).Click here for additional data file.Figure S4Pie graphs showing the distribution of GO molecular function categories within HLS, LS, and whole genome gene lists. The top 22 categories are named in the legend in descending order of representation for all three groups. Categories were ranked by normalizing each category value for HLS and LS lists to the genome-wide list and then ranking the sum of these values for each category. Less well-represented categories were omitted from the graphs in order to enhance legibility, and zero values are not listed.(1.1 MB EPS).Click here for additional data file.Figure S52 ratios for each cDNA clone and the copy number ratio of the test gene to the CFTR (control) gene, as determined by RT-PCR, are shown for the indicated species. Also shown are TreeView images of interhominoid aCGH results for the indicated cDNAs and a graphical depiction of the correlation between aCGH signal and copy number ratio to CFTR (RT-PCR).RT-PCR was used to provide an independent confirmation of interspecies cDNA aCGH data for three genes in which aCGH signals were different in the African great apes compared to human and orangutan. The chromosomal location, IMAGE clone ID, and GenBank accession numbers are provided for each cDNA. The species average logPLA2G4B cDNA clone located on human Chromosome 15 using the UCSC November 2002 human genome assembly. The correlation between RT-PCR and aCGH-based copy number estimates is 0.94.(A) FLJ31659 cDNA clone located on human Chromosome 4 using the UCSC November 2002 human genome assembly. As in (A), the correlation between RT-PCR and aCGH data is 0.97.(B) BC040199 transcript located on human Chromosome 7 using the UCSC November 2002 human genome assembly. As in (A), the correlation between RT-PCR and aCGH data is 0.97.(C) (1.29 MB EPS).Click here for additional data file.Figure S6(A) Normal human control, PHA-stimulated lymphocytes. Two signals localized to Chromosome 15q15.1.(B) Chimpanzee fibroblast culture. Two signals on the chromosome syntenic to human Chromosome 15 (at arrows). Multiple additional signals in the subtelomeric regions.(C) Gorilla fibroblast culture. Two signals on the chromosome syntenic to Chromosome 15 (at arrows). Two additional signals on a large metacentric chromosome, which in interphase appear as amplified signals.(D) Orangutan fibroblast culture. Two signals on the chromosome syntenic to human Chromosome 15.(4.39 MB EPS).Click here for additional data file.Protocol S1(2 KB TXT).Click here for additional data file.Table S12 aCGH values are listed for the human versus human samples (n = 5), human versus bonobo samples (n = 3), human versus chimpanzee (n = 4), human versus gorilla (n = 3), and human versus orangutan (n = 3). This table is tab-delimited and can be opened in Microsoft Excel to view the raw numbers or can be browsed using TreeView (see Protocol S1). Column B provides information regarding IMAGE clone number, chromosome, and nucleotide position (UCSC November 2002 freeze), Unigene ID, EST accession numbers, and known gene information.Values are provided for genes (cDNAs) queried by interhominoid aCGH. For each IMAGE clone queried, log(12.84 MB TXT).Click here for additional data file.Table S2–20 was used and the best hit is reported in the table. Query refers to the HLS gene EST sequences; subject refers to the WSSD sequences. Score, expect value, and percent identity (ID) are reported for the best BLAST hit, while the start and stop positions and length for both query and subject are also reported.For each IMAGE clone of the HLS genes, one or more EST sequences were used as a query for a BLAST search against the WSSD dataset. An expect value cutoff of e(434 KB DOC).Click here for additional data file.Table S3For each of the 23 LS gene clusters, Satellite repeat subclass analysis was performed. The table lists the cluster's cytogenetic region, the chromosome and start and stop positions, and the adjusted length after accounting for gaps in the genomic sequence. The percent content for 24 subclasses of Satellite repeats is listed for each of the 23 gene clusters. Summary information includes the average content of the 24 subclasses of Satellite repeats for all of the clusters as well as the average for the entire human genome. The difference and fold change are calculated based on comparing the cluster averages to the entire human genome averages.(111 KB DOC).Click here for additional data file.Table S4Similar to Table S1, but only IMAGE clones with LS characteristics are listed, and each is ranked based on average fluorescence signal (highest to lowest) within each lineage.(269 KB TXT).Click here for additional data file.Table S5(52 KB DOC).Click here for additional data file.Table S6Presented are pertinent data from GO analysis with DAVID, including numbers of classified and unclassified genes in each gene list, as well as the data returned for each of the 22 most represented molecular function categories. Listed are GO identification numbers (GOIDs) and names for each of the top 22 categories, as well as raw values and relative percent values for HLS, LS, and genome classifications. Relative percent columns are taken as the ratio of the number of classifications in each category to the number of genes classified in the list. The average percent is also provided as the average of these relative percent values across the three groups. This is intended as a metric to help gauge deviations in group relative percent values from the combined average value.(81 KB DOC).Click here for additional data file.Table S7The table has three sections: a summary showing the percentages of blocks in each respective chimpanzee homology scoring class; a table with the HLS versus chimpanzee data; and a table with the random versus chimpanzee data. The HLS versus chimpanzee and random versus chimpanzee tables have columns derived from both parsing the BLAT PSL data and from the chimpanzee homology comparison. The table lists the IMAGE clone and the EST accession number used as a query, the hit number, the score and percent identities, the start and stop positions in the query, the chromosome and chromosome start and stop positions, the number of blocks of alignment for the hit, the numbers of blocks that fall into each chimpanzee homology scoring class, and finally the respective chimpanzee scaffold(s) for each hit, if available.(3.58 MB DOC).Click here for additional data file.http://www.ncbi.nlm.nih.gov/Genbank/) for FGF7 is NM_002009 and for morpheus is AF132984.The GenBank accession number (