A 70-gene tumor expression profile was established as a powerful predictor of disease outcome in young breast cancer patients. This profile, however, was generated on microarrays containing 25,000 60-mer oligonucleotides that are not designed for processing of many samples on a routine basis.To facilitate its use in a diagnostic setting, the 70-gene prognosis profile was translated into a customized microarray (MammaPrint) containing a reduced set of 1,900 probes suitable for high throughput processing. RNA of 162 patient samples from two previous studies was subjected to hybridization to this custom array to validate the prognostic value. Classification results obtained from the original analysis were then compared to those generated using the algorithms based on the custom microarray and showed an extremely high correlation of prognosis prediction between the original data and those generated using the custom mini-array (p < 0.0001).In this report we demonstrate for the first time that microarray technology can be used as a reliable diagnostic tool. The data clearly demonstrate the reproducibility and robustness of the small custom-made microarray. The array is therefore an excellent tool to predict outcome of disease in breast cancer patients. Microarray analysis is a widely used technology for studying gene expression on a global scale. However, the technology is presently not used as a routine diagnostic tool. Various studies have shown that microarray analysis results in improved diagnosis and risk stratification in many cancers -12. MoreOne study involved the discovery of a profile associated with the risk of early development of distant metastasis in young patients with lymph-node negative breast cancer . The devThis profile, however, was generated on oligonucleotide microarrays containing approximately 25,000 60-mer oligonucleotides. Using these arrays for clinical practice would not only be costly, but their one-sample-per-chip design would not allow for high throughput processing of many samples on a routine basis. Recently, an 8-pack format with 8 identical sub-arrays, containing a limited number (1900) of 60-mer oligonucleotides became available. This would allow less sample RNA input for labeling and hybridization and data processing time could be substantially reduced, permitting test results to become available within 5 days.Nonetheless, there are several issues to consider when 'reading' expression profiles from mini-microarrays. Data processing steps, such as normalization to remove systemic variation and background subtraction, may require re-optimization for the smaller number of probes present. Apart from such issues of data processing, the original biological samples used to generate the original profile need to be available for confirmation and validation purposes.In this paper we describe the development of a customized diagnostic breast cancer mini-array, MammaPrint, based on the Amsterdam 70-gene expression profile , and desRecently, using complex microarrays, a 70-gene prognosis profile was identified that is a powerful predictor for the outcome of disease in young breast cancer patients. This profile was generated using 78 tumor samples of patients having lymph node negative disease by hybridization of fluorescent-dye labeled RNA to microarrays containing 25,000 60-mer oligonucleotide probes. To enable the use of this prognostic classifier in a diagnostic setting, custom-made 8-pack mini-arrays were developed (Agilent Technologies). This mini-array is a single 1" × 3" slide containing eight identically printed regions or sub-arrays, each containing 1,900 60-mer oligonucleotide probes, including the 70 prognostic classifier genes [To increase measurement precision, each of the signature genes was spotted in triplicate and an error-weighted average of the intensity ratios was calculated. In the original studies another method was used to decrease uncertainties of the array measurements, i.e., the use of the quantity Xdev ,20, howeTo determine if the customized mini microarray test performs as well as the original 25 k microarrays ,13, RNA The expression intensities of the 70 signature genes for the 78 original samples hybridized to the customized array are shown in Figure To perform a comprehensive evaluation of the mini-array results, we compared in Figure A more detailed evaluation revealed seven discordant cases between MammaPrint risk assessment and the published data. These cases included two patients that did not develop distant metastases, who were classified as being poor prognosis in the published data , but theTo further investigate if the differences seen were due to technical variation of the current test or could be otherwise explained, 49 samples were amplified and hybridized a second time to the 8-pack mini-array , which is in agreement with the expected chance of misclassification (14%) based on the area of the Gaussian that falls on the other side of the decision boundary  and m = 0.430).To more accurately estimate the risk of metastases associated with the 70-gene prognostic profile, a validation study  was perfWe calculated the probability of a patient remaining free of distant metastases and overall survival according to the prognosis profile and compared this to the published data .Once more the data generated using the customized array is highly similar  to the original data.As was seen before, Kaplan-Meier curves showed a significant difference in the probability that patients would remain free of distant metastases when classified in the good or poor prognosis profile group Figure  between When the probability of a patient remaining free of distant metastases was compared between the current result  between duplicate samples was observed, indicating very low measurement variability Figure . The dia between Even though the technical accuracy is extremely high, samples close to the threshold have a higher chance of misclassification than samples further away from the threshold. Based on the known variation in MammaPrint results, a small proportion of MammaPrint samples with indices close to the prediction threshold may have been misclassified. In principle, the chance of a patient with a poor clinical outcome incorrectly being assigned to a good prognosis profile should be minimized. Based on analysis of MammaPrint results generated to date, less than 1.1% of all samples fall into this category. Repeated analysis of borderline samples makes the area of classification uncertainty on either side of the threshold substantially smaller, reducing the proportion of false negative classifications to less than one percent.The reproducibility of the current test and the similarity of its results to those obtained from the original data demonstrate that it is an excellent tool to predict outcome of disease in breast cancer patients and is highly suitable for a clinical diagnostic clinical setting.An external validation series by the Transbig consortium using this same customized mini-array, evaluating outcome prediction of 307 patients from 5 European hospitals who were diagnosed with lymph node negative breast cancer before the age of 60 years and who did not receive adjuvant therapy, showed the independent clinical validation of the 70-gene expression profile.Using the MammaPrint microarray test in the clinical setting will provide more accurate information on recurrence risk as compared to conventional clinical criteria and will thus improve the guidance for the requirement of adjuvant therapy for young women diagnosed with breast cancer. As a direct result, many patients could be potentially spared the side effects and risks of such treatment, improving quality of life and reducing healthcare costs.One hundred and sixty-two patients with lymph node negative (pN0) breast cancer, age of diagnosis before 55 years, not having received adjuvant therapy and that were part of our previous studies ,13 were Aliquots of total RNA of 149 frozen tumor samples was available for this study, for 13 samples  new RNA was isolated from available frozen tumor tissue as described previously ,13,22. TThe customized mini-array contained 1,900 60-mer oligonucleotide probes that comprise the 232 prognosis related genes [10ratios. To establish appropriate relative weights, the Rosetta error model was used, which corrects for the uncertainties in individual probe measurements [Fluorescence intensities on scanned images were quantified, values corrected for background non-specific hybridization, and normalized using Feature Extraction software version 7.5.1 (Agilent Technologies). Data was further analyzed using custom algorithms in Matlab version 7.1 (The Mathworks). To obtain an overall expression value for each of the signature genes on the array, an error-weighted mean value was calculated for the three identical probes belonging to the same gene as logurements ,24,25. POutcome prediction for the 78 tumor samples used in Figure et al. (13). For each of the 84 tumors from patients that were not included in the original Nature study [Outcome prediction for the 145 tumor samples used in Figure re study , a correre study , correlare study . The onlre study . For thiet al. . For eacOdds ratios were calculated based on a two by two contingency table. P-values associated with odds ratios were calculated by Fisher's exact test. Survival periods of patients were analyzed from the calendar date of surgery to the time of the first event or the date on which data were censored, according to the method of Kaplan Meier. The curves were compared using the log rank test.OR: Odds RatioCI Confidence IntervalStdev: standard deviationHR: Hazard ratiopN0 lymphnode negativeAMG and LJV were involved in the setup of experiments, data analysis, writing of the manuscript and the general overview; AF, set up of experiments and general overview; LJMJD, carrying out experiments and data analysis; ATW, RCFP, NB, JSTLD carrying out experiments, TJB and MOW data analysis, RB set up of experiments and scientific input, LFAW setup of experiments and data analysis. All authors have read and approved the final manuscript


The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods.In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85%) were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis.Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and microarray technologies. Predictive models generated by this approach are better validated than those generated on a single data set, while showing high predictive power and improved generalization performance. Gene expression profiling by DNA microarrays has become an important tool for studying the transcriptome of cancer cells, and has been successfully used in many studies of tumour classification and of identification of marker genes associated with cancer e.g. -3]. With. With3]]The goal of this study is to investigate the benefit of performing supervised classification analyses across disparate sources of microarray data. Methods of supervised classification analysis render it possible to automatically build classifiers that distinguish among specimens on the basis of predefined class label information (phenotypes), and in many cancer research studies e.g. -3] the a the a3]]Recently, Wright et al.  have proFLT3 or RAS, thought to belong to an intermediate risk group [We adopt this approach and demonstrate the use of two data integration methods, namely median rank scores, which has already been successfully applied for comparability assessment of five different breast cancer microarray data sets , and quask group -32. Whilsk group ,26,27, eWe investigated six publicly available cancer microarray gene expression data sets to perform cross-platform supervised classification analysis. We selected three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer and acute myeloid leukaemia, respectively. All pairs of studies allowed for either classification of cancer versus normal tissue or cancer subtype differentiation. Each pair was chosen to consist of one study using cDNA arrays and one study based on oligonucleotide arrays. We studied how to combine pre-processed data sets measured with different microarray platforms for an integrated classification analysis. The process can be divided into the following main parts: First, we determined the overlap of genes common to both platforms using the UniGene database. Next, we derived numerically comparable quantities from the expression values of both platforms by application of median rank scores or quantile discretization. Then, the support vector machine algorithm, an approved method for supervised classification analysis, was applied to different classification settings.Figure As the next step, we applied the median rank scores (MRS) method or quantile discretization (QD). In order to check whether the comparability of the data from different platforms is improved after data transformation by these methods, we compared the distribution of gene expression values per microarray between arrays of different studies. We selected one microarray per study and produced a quantile-quantile plot (QQ-plot) for every pair of microarrays from corresponding studies as shown in Figure After data integration by the median rank scores method or quantile discretization, respectively, two different types of cross-platform classification analyses were performed: training of a classifier on only one data set of a pair followed by classifier evaluation on the other data set, and classifier training and testing on data instances randomly chosen by a cross validation from the combined data set.The first type of analysis was performed on non-integrated data and on integrated data, respectively. Evidently, without data integration, a classifier created on one set cannot correctly classify data instances of the other set Figure . This isExcept for the pair of breast cancer microarray data sets, the application of the MRS versus QD showed no significantly different effect on the achieved classification accuracies. For the training on the data set of Gruvberger et al. and classification of the data of West et al., the classification result was significantly better after application of QD in comparison to the result obtained after using the MRS method. In all other cases both methods can be considered equivalent.In addition to the above mentioned separated training and validation, cross-validation analyses were performed on combined data sets. High classification accuracies were achieved with training and testing on data instances randomly chosen from both data sets  . As presRXRA, PBX3, ABL2, SOCS1, and EGR2  analysis for the leukemia studies (see Methods for details). We generated six lists of genes, two lists for an analysis of the combined leukemia studies, integrated by MRS or QD, and two lists for each of the two leukemia studies analysed separately, using only samples of the either the MRS or QD data which belong to one study. A number of 512 elements was selected for each list, which corresponded to minimal cross-validated error rate in the integrated analyses of data from both leukemia studies. Interestingly, the intersection of the lists generated in analyses using only data of one of the two leukemia studies comprises only about 40 UniGene clusters, independently of whether MRS or QD was used  since they are found and validated on a larger number of samples, thus the effect of study-specific biases can be reduced. For all study pairs used here, we achieved high classification accuracies when using data samples randomly chosen from both data sets of a comparison pair for classifier building and testing. Our findings endorse the encouraging results of first attempts of multi-platform microarray classification analysis ,19.For integration of microarray measurements from different platforms, Bloom et al.  used a sEven when a classifier is built on one data set of a pair of compared studies and the samples of the other study are classified, good classification results can be observed for the prostate and breast cancer studies. In this case, the generalization ability of the classifier is sufficient to correctly classify most of the samples of the other study, and thus the classifier obtained on the data of one study can be validated by the data of another study. In contrast, the results for the AML studies indicate that the generated classifiers based on only one of these studies are too specific. This might be due to fact that the sample sets of either study are not representative enough to cover all characteristic transcriptional features observable for the investigated phenotypes. Indeed, the results for the cross-validation analysis using samples from both AML studies show that classifiers with better generalization performance can be obtained underlining the potential of a cross-platform classification analysis.Selection of discriminative gene expression signatures is an important task frequently performed in microarray studies. Here, we applied RFE analysis for selecting subsets of genes with distinctive expression patterns on the data of the leukemia studies of Bullinger et al.  and ValkPBX3 [X [ABL2 tyrosine kinase [BCL2 [ERBB3 [TERT, which has been found by Hahn et al. to be one of three necessary factors for transforming a normal cell into a tumour cell [The analysis of gene lists obtained by RFE indicated that gene signatures can be generated on a combined set that comprise important genes that were not part of gene signatures generated on either set alone. Notably, the intersection of lists from the Bullinger and Valk data sets with the list from the combined set contained only a few genes, none of them to be known of high importance in the context of AML. Similarly, the intersection between the Bullinger and Valk data sets was not large . In contPBX3 , the retPBX3 [X , the ABLe kinase  or earlye kinase . In addise [BCL2  or ERBB32 [ERBB3 . Most noour cell .S and significance values for this test statistic by a permutation approach are calculated . At a sails see , but theConsidering integrated classification analyses in general, a limiting factor for future application is posed by inconsistencies in biological phenotype annotation across studies. In many cases, it is hard to obtain consistent annotation on the samples used that would allow to form comparable groups for classification analysis. This is due to lack of ontologies for description, or the use of categories that are based on subjective evaluation such as histological grading or borderline expression of a molecular marker as determined by immunohistochemistry. In such respect, it would be highly desirable to introduce systems for annotation of samples that are analogous to the MIAME standard for description of technical details of hybridization . Until sMore study results will be needed to validate our findings. Cross-platform analyses have to be conducted considering more than two studies at a time. Here, the problem of having relatively few genes in common between all studies will gain increasing importance. Methods to make use of gene expression values only available on some platform(s) but not on others will be required. For this, the adaption of a recent approach by Guo et al.  could beCross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and microarray technologies. Predictive models generated by this approach are better validated than those generated on a single data set, while showing high predictive power and improved generalization performance. The results presented here for the three sample study pairs indicate that this approach bears the potential to become a widely applicable technique for inter-validation of studies performing classification of microarray gene expression data.All data for this study were downloaded from public web sites Table  and wereThe UniGene database (Build 171)  was usedIn the case of the breast cancer data sets ,25, all For the comparison of the leukemia microarray data sets ,27, we sTo derive numerically comparable measures of gene expression for different microarray platforms we used either median rank scores or quantile discretization. Before either of these methods was applied to the preprocessed data, all expression values of oligonucleotide arrays were divided by the median expression value per array to scale absolute intensity values to relative ratio values.n is then replaced by element n of the sorted median expression vector. Thus, the gene expression values of all microarrays of the non-reference sets are replaced by surrogate values with comparable numerical range relative to the reference data set. Therefore, the study comprising most microarrays should be designated as the reference set. Under certain circumstances it might make sense to chose the reference set according to another criterion than sample size, e. g. when the largest data set shows an inferior expression data quality in comparison to the smaller sets. Note that the only information being preserved for the non-reference set are the relative ranks of gene expression values. To keep our analyses comparable with regard to the selection of the reference set, we always selected the study using a cDNA microarray as reference data set because for two of the three investigated pairs of studies the study using a cDNA microarray contained more samples (microarrays) than the corresponding study that used an oligonucleotide microarray.(MRS)  The basib (b = 8) for all our analyses. For each experiment, b subsets with equal number of values are determined using the quantiles of the array expression values as cut points, where a cut point is here defined as the expression value separating an ordered set of expression values into two subsets. The two central bins with the median value as cut point are merged into one bin yielding one central interval. Every expression value is replaced by an integer value corresponding to the bin it falls into, where zero is assigned to central bin and all other bins are numbered consecutively beginning with the bins next to the central one, using positive integers for bins containing values above the median and negative integer values for the others. Both methods were implemented using the R software for statistical computing [(QD) This method is based on equal frequency binning . Here, tomputing . Code isn for which one of the two approaches gave a correct classification and the the other approach gave a wrong classification is determined. If both approaches perform equally well, then among these n samples the proportion p of samples for which approach A gave a correct classification should be equal to the proportion q of samples for which approach B gave a correct classification. Therefore, the null hypothesis of equal classification performance of A and B can be tested by a binomial test with null hypothesis p = q = 0.5.For each pair of studies, classification analyses were performed on the UniGene matched gene expression values. We investigated how well a classifier trained on one data set predicts class labels of the other data set after application of MRS and QD, respectively, compared to no application of MRS or QD. For each pairwise combination of these three approaches, a statistical test with the null hypothesis of equal performance in classification of the given test set was realized according to Salzberg : For comIn addition, we examined the class prediction accuracies by 10-times repeated (i.e. 10 resampling replicates) 10-fold cross-validation. Arrays of both studies were chosen randomly for training and testing after data integration by the median rank scores method and by quantile discretization, respectively. Finally, we performed a cross-validated classification analysis on each data set alone using all available pre-processed gene expression values.C and degree were tuned by cross-validating parameter combinations in a grid search over a two-dimensional parameter space with ranges from 2-5 to 210 and 1 to 3, respectively.We used support vector machines (SVM) for supervised classification analysis, applying the libsvm implementation by Chang and Lin with a polynomial kernel function . Hyperpadelta (threshold for centroid shrinkage) was tuned over the default parameter range given in the pamr package.For classification with nearest shrunken centroids (PAM), we used the corresponding R package pamr, available on the Bioconductor website . The hypParameter tuning for both classification methods was done by a three-fold cross-validation and was repeated for cross-validation in each single iteration . No variable pre-selection was performed on the preprocessed data prior to classifier construction. The scheme of our workflow for calculating class prediction accuracies is shown in The whole process of cross-platform classification analysis in comparison to a meta-analysis approach is summarized in Figure Independently of the classification analysis described above, we applied a SVM based Recursive Feature Elimination (RFE) method  for seleMoreover, we visualized results from RFE analysis by performing hierarchical clustering of the leukemia data based on the generated gene lists. For hierarchical clustering, we used the method "hclust" of the R package mva, applying the following parameter settings: Manhattan distance function was performed on data transformed to zero mean and unit variance, and clustering was done using a complete linkage algorithm .AML: acute myeloid leukemiaFEP: functional expression profileGEP: gene expression profileGO: gene ontologyMIAME: minimum information about a microarray experimentMRS: median rank scoresPAM: prediction analysis of microarraysQD: quantile discretizationQQ-plot: quantile-quantile plotRFE: recursive feature eliminationSVM: support vector machinePW conceived of the study, carried out the analyses and drafted the manuscript. RE participated in the design of the study and helped to draft the manuscript. BB coordinated the study, participated in its design, performed the hierarchical cluster analysis and helped to draft the manuscript. All authors read and approved the final manuscript.Barplot of results from a classification analysis where all data of one study is used to built a classifier (training), which is then used to classify all samples of the other study (test), using PAM classifiers. The names below the bars indicate which study was used for classifier training (left name) and testing (right name). The bars represent the achieved classification accuracies, i.e. the fraction of samples correctly classified. The colour of a bar indicates the method used for data integration. P-values are obtained by a statistical test with the null hypothesis that the two marked classification approaches perform equally well on the given test set (see methods for details). The target variable for classification analysis of the prostate cancer data was 'tissue type' , for the breast cancer data the estrogen receptor (ER) status (ER positive vs. ER negative), and for the leukemia data the karyotype of the samples , t, inv(16) or normal karyotype, respectively). Abbreviations: MRS, median rank scores; QD, quantile discretization, PAM, prediction analysis of microarrays.Click here for fileClassification results observed by cross validation using PAM classifiers. Figures represent achieved classification accuracies, i.e. the fraction of samples correctly classified. The upper table shows results for cross validation analysis of both data sets of a pair, where samples for training and testing are selected randomly from both studies. For this, data sets were integrated by either MRS or QD. The bottom table contains the results of a cross-validated classification analysis performed separately for each study, using all available gene expression data after pre-processing (without application of MRS or QD). Abbreviations: MRS, median rank scores; QD, quantile discretization, PAM, prediction analysis of microarrays.Click here for fileThe overlap between lists of genes found by RFE analysis based on single sets  and based on both data sets integrated by MRS or QD. Abbreviations: MRS, median rank scores; QD, quantile discretization, RFE, recursive feature elimination.Click here for fileAll six lists of genes found by RFE analysis (see Methods for details). In every list, the corresponding UniGene identifiers of the genes are ordered according to their rank as determined in the RFE analysis. Abbreviations: RFE, recursive feature elimination.Click here for fileWorkflow for calculation of the presented class prediction accuracies. (a) Classifier performance evaluation on an independent data set as applied for calculation of the results presented in Figure Click here for file


Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list.A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups.This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile. The classification of human tumors using microarray data has been an area of intense research, but it remains a daunting task to validate a new profile and generate a clinically useful test. Many different gene expression-based predictors have been developed for breast cancer -9, and tA major challenge for microarray studies, especially those with clinical implications, is validation ,16. Due Our goals were to 1) create a new breast tumor intrinsic list, (2) validate this list on an independent dataset to show the clinical significance of the "intrinsic" classifications, and 3) to derive an objective "intrinsic subtype" classifier that could be used clinically .-25.23-25STAT1, which is thought to be the transcription factor responsible for mediating IFN-regulation of gene expression [A possible new tumor group (IFN) characterized by the high expression of Interferon (IFN)-regulated genes was observed in the combined test set analysis Figure . Accordipression ,27. Genepression . In summTo determine how many biologically relevant tumor subtypes/groups might be present within the cluster in Figure In the combined test set, the standard clinical parameters of ER status, node status, grade, and tumor size  were significant predictors of RFS using Kaplan-Meier analysis Figure , thus shTo further evaluate the prognostic/predictive value of the intrinsic subtype classification, we performed multivariate Cox proportional hazards analysis of the combined test set using the six intrinsic subtypes/groups defined above and the five standard clinical parameters with RFS, OS, or DSS as the endpoint Table , the BasTo further characterize and better understand the intrinsic subtypes, we determined whether an association existed between intrinsic subtype and grade, node status, ER status, age, and tumor size in the combined test set. Two-way contingency table analysis showed significant association between grade and subtype, with HER2+/ER- and Basal-like tumors more likely to be grade 3  using the combined test set hierarchically clustered using the 306 Intrinsic/UNC genes Figure . For theTo validate the SSP, we tested it on two additional datasets not used previously. The first was the 60-patient Ma et al. dataset, which represents a group of early stage ER+ tamoxifen-treated patients . The tumWe also applied the SSP onto the 105-sample dataset used to derive the Intrinsic/UNC gene list, which is technically not a test set for the SSP because it was used to derive the Intrinsic/UNC gene set. The tumor groups identified by the SSP showed significantly different RFS  the finding that various Gene Ontology terms were significantly over-represented relative to chance in individual gene clusters seen in this analysis and (2) the groupings of the samples showed inter-dataset mixing and were significant predictors of outcome in univariate Kaplan-Meier and multivariate Cox analysis. It is also remarkable that this classification was successful in predicting outcome despite the fact that the Intrinsic/UNC gene set was reduced from 1300 genes to 306 genes in the combined test set; this indicates the robust nature of the intrinsic subtypes as defined by the new Intrinsic/UNC gene list.vs. Poor outcome predictor developed by van't Veer and colleagues[One of the accomplishments of this manuscript was to develop an unchanging and objective intrinsic subtype predictor that could be used routinely in the clinical setting. This was accomplished by first identifying a new intrinsic gene set and then using this set to develop the Single Sample Predictor (SSP) that was shown here to be both prognostic on the local therapy-only patient subset from Chang et al.  and predolleagues,11, (2) olleagues,34, (3) olleagues, and (4)olleagues. The resolleagues, showed i.e. genes) may sometimes show discordance across data sets, the portraits created by the combined patterns of the individual brushstrokes is conserved and recognizable across datasets because of the similarities to the family portrait [The results of this study advance our current knowledge of the intrinsic breast tumor subtypes and provides an objective method (SSP) for prospectively classifying tumors that could be used in the clinical setting. More broadly speaking, our findings show that while the individual brushstrokes , The University of Utah, Thomas Jefferson University, and the University of Chicago. This sample set represents an ethnically and geographically diverse cohort. Total RNA was purified from each sample using the Qiagen RNAeasy Kit. RNA integrity was determined using the RNA 6000 Nano LabChip Kit and Agilent 2100 Bioanalyzer. Total RNA amplification and labeling were done as previously described in . Microar2 ratio of Cy5 to Cy3 intensity values were first filtered to select genes that had a signal intensity of at least 30 units above background in both the Cy5 and Cy3 channels. Only genes that met these criteria in at least 70% of the 146 microarrays were included for subsequent analysis. Next, we performed an "intrinsic" analysis as described previously[et al. for a complete description of this calculation [We derived a new breast tumor intrinsic gene set, referred to as the "Intrinsic/UNC" list, using a training set composed of the 105 tumor samples described above, 9 normal breast samples, and 26 sample pairs . 15, 9, and 2 of the 26 sample pairs were different physical pieces of the same tumor (taken at the same time point), tumor-metastasis pairs and normal sample pairs, respectively. The background subtracted, Lowess normalized logculation ).et al. 2001 and 2003[et al. 2002[et al. 2003[2 transformed and Lowess normalized[The independent test set was a 315-sample "combined test set" consisting of three DNA microarray datasets . To comormalized. Next, mormalized. Gene anormalized, and mulormalized. ClusterThe Single Sample Predictor (SSP) is a Nearest Centroid-based method based upon the work of Hastie and Tibshirani ,45,46. OTo create each intrinsic subtype centroid, we averaged the gene expression profiles for samples clearly assigned to each subtype  using the hierarchical clustering dendrogram as a guide . Two-way contingency table analysis and unpaired Student's t-test were done using WinSTAT. For the "combined test set", multivariate Cox proportional hazards analysis was performed using SAS .C.M.P. was the Principal Investigator and instigated the study, helped with design and wrote the paper, while J.P., O.I.O, and P.S.B. were the Principal Investigators at each of the three other participating institution and were involved in the study design, implementation and manuscript writing. C.F., J.S.M., B.F.Q., A.N., and J.P. were responsible for the statistical analyses and some writing. Z.H and X.H. performed all of the tumor RNA preparation and microarray experiments and were involved in the writing. J.W. and Y.L. were responsible for all data management and some data analysis. C.L. was responsible for the pathological assessment of most tumor samples and was involved in the writing. M.E. and D.O. were involved in data analysis, interpretation and writing. Tumor sample collection, clinical data acquisition and interpretation was accomplished by L.C., M.E., R.N., M.T., A.R.O., D.D., L.P., E.N., M.M., H.H., M.M., J.F.Q., L.R.S., E.R., and L.D., and it should be noted that this was separately accomplished at four institutions.Supplemental Table 1. Clinical and microarray information associated with each patient in the 105-sample training dataset.Click here for fileet al. sample names begin with the letters "BC", Sotiriou et al. sample names begin with "Exp", and van't Veer et al. sample names begin with "sample".Supplemental Figure 1. Complete hierarchical cluster diagram of the 315-sample combined test set analyzed using the Intrinsic/UNC gene set, which was reduced to 306 genes based upon the gene overlap between datasets. Sorlie Click here for file


Gene expression profiling using microarrays has become an important genetic tool. Spotted arrays prepared in academic labs have the advantage of low cost and high design and content flexibility, but are often limited by their susceptibility to quality control (QC) issues. Previously, we have reported a novel 3-color microarray technology that enabled array fabrication QC. In this report we further investigated its advantage in spot-level data QC.2>0.94) were observed, with modest compression in the microarray measurements (correction factor<1.17).We found that inadequate amount of bound probes available for hybridization led to significant, gene-specific compression in ratio measurements, increased data variability, and printing pin dependent heterogeneities. The impact of such problems can be captured through the definition of quality scores, and efficiently controlled through quality-dependent filtering and normalization. We compared gene expression measurements derived using our data processing pipeline with the known input ratios of spiked in control clones, and with the measurements by quantitative real time RT-PCR. In each case, highly linear relationships (ROur microarray analytical and technical advancements enabled a better dissection of the sources of data variability and hence a more efficient QC. With that highly accurate gene expression measurements can be achieved using the cDNA microarray technology. Microarray technology allows a comprehensive examination of gene expression profiles and the regulations of their changes at a whole genome level -3. It haBecause of the lack of consistent quality control (QC) standards, spotted arrays fabricated in academic laboratories are usually more susceptible to QC problems than commercial arrays -8. TheirMatarray, which specializes in quantitative QC of data acquisition [qcom, which gives an overall assessment of the data quality acquired from each spot on the array [qcom plot for data filtering and normalization [We have previously reported a microarray image processing software uisition ,13. Usinhe array . Throughlization ,13. Nevelization -18. Noislization ,16,17. Tlization -17, whiclization . The applization -17.In this report, we further investigate the advantage of TDAV in better dissecting the sources for data variability at the spot level, and develop a data filtering and normalization procedure that incorporates the information from the TD image. We utilize data from four different microarray experiments to validate our procedure. We evaluate the accuracy of our microarray measurements by comparing them with the known input ratios for spiked in control clones, and with the measurements by quantitative real time RT-PCR.Arabidopsis clones from the rat thymus experiment (experiment 1). Details of the design of this experiment are described in the Methods. Spots either saturated or possessing high background were eliminated using Matarray [2~0.82, p < 0.0001). A similar trend exists for other input ratios and the results are summarized in table Compression in microarray measurements  have often been observed ,20. FurtMatarray . After fMatarray ,17. In oMatarray , and fitMatarray . We findWe have also examined the data variability in relation to the TD spot intensity. At each spiked in ratio, we determined the standard deviation (SD) and the coefficient of variation (CV) in ratio measurements for every data point using its 20 nearest neighbors on the ratio-TD intensity plot. We find that above 5,000 RFU/pixel both SD and CV are essentially constants. With deficient TD intensities below 5,000 RFU/pixel SD increases initially followed by a drop at very low TD intensities due to the severe data compression in ratio measurements, whilst CV increases monotonically with decreasing TD intensity. The CV results for the data points corresponding to the spiked in ratio of 30:1 are given in figure Sources of variation often have localized characteristics across the whole slide. One major type of such spatial-dependent bias is the heterogeneity in the printing characteristics among the pins. Its exact cause is not clear and its characteristics not well characterized. Normalization methods have been designed to correct spatial bias. For example, the local mean normalization , and theResults in the preceding sections suggest that the TD intensity is a major factor that causes spot-level data reliability. In addition, other artifacts on the TD image can also influence the accuracy of expression measurements from post-hybridization images, including noise, spot size and shape irregularities ,17. BaseqTD = qint *qcom, TD     (1)qcom, TD is the composite TD image quality score, defined according to signal-to-noise ratio, spot size, and background levels and variation, similarly as given in the equation (7) of [qint is given by:where n (7) of . qint isMatarray the default threshold = 5,000 RFU/pixel.In Arabidopsis clones, with respect to qTD. We found that like qcom  at p = 0.05. We divided them into 50 bins, and for genes in each bin we determined the mean qTD and qcom, and the Pearson correlation in log ratio measurements between the replicates. A typical result is given in figure qTD or qcom leads to significant improvement in replicate consistency. Notice that majority of the spots have high qTD due to the fact that all the slides we use for hybridization have already been pre-selected using TDAV [qTD and qcom (R<0.5), which validates that they are two non-redundant quality measures each capturing a different major source of data stability, and QC by each is necessary.Utilizing data from the four microarray experiments we examined the replicate consistency, and the agreement between microarray measurements and the known input ratios for the spiked in e images ), qTD caing TDAV ,17. We hIncorporating our new quality score definition for the TD image, we have developed the following filtering and normalization procedure:R - qTD plot and decide on a filtering threshold for qTD . Normally this will be the value where there is an abrupt increase in data variability, in the same fashion as shown in figure R - qcom filtering and normalization). The quality score for all spots below this threshold will be reset to qTD = 0.1. Evaluate the log qTD -dependent normalization for all data points with qTD > 0 utilizing the robust scatter plot smoother LOWESS [2. Perform a local r LOWESS ,21,24.R - qcom plot and decide on a filtering threshold for qcom . The quality score for all spots below this threshold will be reset to qcom = 0, as described in figure 3. Evaluate the log qcom -dependent LOWESS normalization for all data points with qcom > 0. The LOWESS fit for SD will also be determined, and the Z-score will be calculated for normalized log R every spot by: 4. Perform a local aaa@4C7D@. Figure After all normalization a final quality score will be definedQf = qTD•qcom     (3)Qf > 0 will be retained for further data mining and modeling.Only spots with Qf = 0 on any replicate slide were dropped . We calculated the correlation coefficient between replicate hybridizations for common DE genes at p= 0.05 according to both normalization procedures. There were totaling 74 pairs of replicate hybridizations and good agreements were observed, with mean replicate correlation r = 0.73 ± 0.21 according to Z-norm. The difference between the two methods are presented in figure r is 0.06) due to the already high data quality.The integration of our technical and analytical developments has resulted in a unique procedure for comprehensive, quantitative microarray data processing and QC. To demonstrate the advantage of our filtering and normalization method, we compared it to a commonly used localized LOWESS normalization method that utilizes the ratio-intensity dependence   Rating TADV ,17 and tQf = 0 on at least one array. Excluding these genes a highly linear relationship  existed for the remaining 15. If we were to calibrate microarray measurements using the RT-PCR results as a standard, we would have Rcorrected = (Rmeasured)q, with a correction factor q ~1.88 previously reported [R2 ~ 0.90 with a correction factor q ~ 1.27. This result strongly indicates that with stringent, efficient QC protocols, cDNA microarrays are able to generate high quality, quantitative gene expression measurements comparable to that by RT-PCR.In figure reported . If the How much impact on the biological interpretation can such improvement in data quality bring about? To answer this question we have performed ontological analysis for the DE genes in each experiments using OntoExpress  and EASEqTD. In addition, we have developed a comprehensive two-step data filtering and normalization procedure based on the log R - qTD and log R - qcom plots, which was found to be more efficient than the commonly used MA-LOWESS approach. By confirming our microarray data with the known input ratio of spiked in controls clones, and with RT-PCR, we demonstrated that acquiring accurate measurements using cDNA microarrays is achievable with our TDAV technology and our data QC procedure. Furthermore, in a recent study where we compared measurements from our cDNA microarrays with those from Affymetrix and Agilent oligonucleotide array platforms, we observed a high correlation among the three, with no significant differences in terms of data quality. Specifically, using ANOVA we have found that the different platforms did not cause more variation than the replicate hybridizations within each individual platform [In this report we have shown that when the probe amount is inadequate, severe compression in gene expression measurements occur with complex, gene-specific characteristics. Likewise, the normal variation in the amount of probes printed and immobilized is a major source for printing pin-dependent artifacts in the data. Utilizing TDAV, these problems rooted from array fabrication can be effectively dissected from hybridization problems, and efficiently controlled through the definition of a quality score platform . This isplatform -8. Thus Our algorithms, although initially developed for cDNA arrays, are potentially applicable to spotted oligonucleotide arrays as well. Recently we have developed the three-color oligonucleotide array platform by introducing a third-dye labeled universal tracking oligonucleotide into the printing buffer, thus the quality of array fabrication can be quantitatively evaluated through the measurements of the tracking oligonucleotide . A high Arabidopsis clones, as well as negative controls including PolyA and spotting buffer. Specifically, the Arabidopsis clones were printed in a 2 fold serial dilution from 200 ng/ul to 6.25 ng/ul (6 dilutions in each series), 4 replicate series on each slide. To ensure consistent prehybridization TD image collection, we have implemented a confocal laser scanner calibration method utilizing FluorIS , a non-bleaching, reusable, calibration/standardization tool [All the microarrays used in this report were fabricated in house with TDAV technology using an OmniGrid arrayer equipped with a stealth print head with 32 SMP3 MicroQuill pins . The University of Iowa rat library, consisting of 35,040 clones, was used as a source of probe for the cDNA microarrays, and was printed over two poly-L-lysine coated glass slides, each possessing 18,432 features. Control clones were also printed on each array including β-actin, GAPDH, and 9 ion tool . This enion tool ,17.Matarray package [lyp/lyp (referred to as DP in this report) and diabetic resistant DR+/+ (DR) rats at day 40, and between day 65 and day 40 DP rats. This analysis utilized 4 animal pairs for each comparison, and 4 replicate hybridizations were performed for each pair, with 2 hybridizations reverse labeled to control for dye bias, totaling 32 hybridizations. For the 16 hybridizations (8 each way of dye labeling) that compared day 65 and day 40 DP rats, the labeling reactions of total thymus RNA were spiked with 4 Arabidopsis in vitro transcripts  at known input ratios , giving rise to a total of 1216 data points. These clones enabled an evaluation of microarray measurement accuracy through the comparison of measured output ratios to the known RNA input ratios. (2) Gene expression profiling of the kidney from an end-stage renal failure (ESRD) rat model. Three pairs of 22 week-old Fawn Hooded Hypertensive (FHH) rats and control August Copenhagen Irish (ACI) rats were compared. For each animal pair, 2 replicate hybridizations were performed, with 1 reverse labeled, totaling 6 hybridizations. (3) Time course profiling of apoptosis progression in pancreatic islet β cells. Cells from a rat β cell line RIN-m5F were treated with a protein kinase C inhibitor staurosporine [All microarray data analyses were performed using our in-house  package ,13. Infoosporine  at a higosporine . (4) ProIn the last experiment that profiled the BB-DR and WF rat liver, expression of 22 genes that were deemed of biological interest according to our previous experiments had also been measured by quantitative real time RT-PCR. Two sets of primers were designed for each locus with Oligo 6.66  and were blasted against the whole genome to ensure gene specificity. The primers were synthesized by Sigma Genosys . A Rotor-Gene 3000 , was used to conduct monoplex quantitative real-time RT-PCR as previously described . RelativTDAV, third dye array visualization; QC, quality control; TD, third dye; SD, standard deviation; CV coefficient of variation; LOWESS, locally weighted scatter plot smoothing; DE, differentially expressed.XW, MJH, SG, and HJJ designed research; XW, SJ and MJH, performed research; LM, CM, BX performed experiments; CM, LYC, BX and HJJ contributed reagents/analytical tools; SJ, NJ, LYC, XW analyzed data; XW, MJH, SG wrote the paper.


Contrasting biological, chemical and hydrogeological analyses highlights the fundamental processes that shape different environments. Generating and interpreting the biological sequence data was a costly and time-consuming process in defining an environment. Here we have used pyrosequencing, a rapid and relatively inexpensive sequencing technology, to generate environmental genome sequences from two sites in the Soudan Mine, Minnesota, USA. These sites were adjacent to each other, but differed significantly in chemistry and hydrogeology.Comparisons of the microbes and the subsystems identified in the two samples highlighted important differences in metabolic potential in each environment. The microbes were performing distinct biochemistry on the available substrates, and subsystems such as carbon utilization, iron acquisition mechanisms, nitrogen assimilation, and respiratory pathways separated the two communities. Although the correlation between much of the microbial metabolism occurring and the geochemical conditions from which the samples were isolated could be explained, the reason for the presence of many pathways in these environments remains to be determined. Despite being physically close, these two communities were markedly different from each other. In addition, the communities were also completely different from other microbial communities sequenced to date.We anticipate that pyrosequencing will be widely used to sequence environmental samples because of the speed, cost, and technical advantages. Furthermore, subsystem comparisons rapidly identify the important metabolisms employed by the microbes in different environments. Banded iron formations started appearing ~3,700 million years ago when localized sea floor cyanobacterial photosynthesis raised oxygen concentrations high enough that dissolved iron precipitated. That iron powered the industrial revolution. The Soudan Iron Mine in Minnesota, USA was active from 1884 to 1962, and during this period 17.9 million tons of iron ore, primarily hematite, were removed. Nowadays the mine is used as a state park and as a facility for high-energy physics experiments.Metagenomics is a term used to describe "the functional and sequence-based analysis of the collective microbial genomes contained in an environmental sample",2. RandoMost current sequencing is a modification of the classical Sanger method, where extending DNA fragments are stopped by the random incorporation of a fluorescently labeled ddNTP. The different-sized fragments are then separated using capillary gel electrophoresis and detected with a LASER. Pyrosequencing is a fundamentally different methodology because only one dNTP is added into the reaction at a time -14. If tThis report describes the first application of pyrosequencing to environmental samples. From this sequence data, we identify the 16S rDNA sequences present in the sample, and apply new annotation methods to this data using the SEED database. This pa5 microbes per ml. X-ray diffraction analyses of the minerals in this area show that chlorite-serpentine [64O10(OH)8], clinochlore, ferroan [64O10(OH)8], quartz, and silinaite [LiNaSiO5·HCl] are present in the Black sediments. Water slowly flows from the borehole into the stream running down the main mine tunnel. As the water comes in contact with oxygen in the passageway, the pH rapidly decreases to 4.37 and redox potential increases to -8 mV. The Red sample contained 1.2 × 106 microbes per ml, and these sediments include goethite [FeO(OH)], followed by szaibelyite [BO2(OH)], and sussexite [BO2(OH)].The two environments sampled within the Soudan Mine are shown in Figure DNA was purified from the two samples, amplified using the GenomiPhi procedure , and then sequenced by 454 Life Sciences. A summary of the sequence characteristics determined using the pyrosequencing technique is shown in Table The two samples produced more than 70 Mbp of sequence data from over 700,000 sequences, and there was no significant skew in the sequence data (as measured by dinucleotide frequency) when the data generated by pyrosequencing was compared to complete genome sequences.Brevibacterium and Corynebacterium that volatilize sulfur via an organic intermediate and can also break down complex heterocyclic and polycyclic ring structures. In either case, the ferrous iron can not be assimilated biologically, and the microbes are forced to scavenge for the limited ferric iron (Fe3+) available. In contrast, in the Red sample, goethite [FeO(OH)] is present and ferric iron is more readily available for biological utilization. The Black sample is enriched for amino acid degradation pathways and microbes may be assimilating nitrogen or carbon through these pathways. It is not currently apparent from where free amino acids would be supplied.Several subsystems involved in iron uptake and utilization such as siderophores and ABC transporters for ferrichrome and heme are more common in the Black sample. The overall concentration of iron at the two sites was similar Table . However3 first to NO2 and then to N2 gas. The Black sample is enriched for these denitrification genes suggesting that the latter pathway predominates while the Red sample is enriched for components of the aerobic respiratory pathway. Moreover, the Black sample had a lower concentration of free nitrate than the Red sample, presumably because nitrate is being used as an electron acceptor during respiration  of subsystems were present in one or more of the sequenced metagenomes, and over half (52%) of the subsystems are present in every metagenome. A comparison of the subsystem classification reveals trends between the metagenomes Figure . For exaThis is the first metagenome analysis performed using pyrosequencing, which is approximately 10 to 30 times cheaper than current Sanger sequencing. Pyrosequencing also eliminates the need for cloning, thus removing the potential for both aberrant recombinants in the surrogate host and for cloning-related artifacts such as counterselection against potentially toxic genes such as those found on phages. The maiThe systems approach of integrating biology, chemistry, and geology has yielded significant insights into the metabolism of two environments in the Soudan Mine. The oxidized sample is using aerobic respiratory pathways while the reduced sample is using anaerobic pathways. Nitrogen assimilation, iron acquisition, and sulfur metabolism are all differentiated between these two samples from close proximity within the same mine. However, many more significant differences between the samples remain unexplained by our current knowledge of bacterial physiology and metabolism. Explaining these differences will be a grand challenge for the future. By combining pyrosequencing, subsystems analysis, comparative metagenomics, and statistics, Occam has used his razor on metagenomics.Samples were collected from several sites in the Soudan Mine. This analysis concerns the sample collection at two sites on Level 27 .Bacterial-specific primers 27F (5'-AGAGTTTGATCMTGGCTCAG-3') and the universal 1492R primer (5'-TACGGYTACCTTGTTACGACTT-3')  were useh, temperature and conductivity were conducted in situ. The sediment samples were collected as slurries with a pipette resting on and in the sediments. Those slurries were transferred to clean centrifuge tubes, allowed to settle by gravity and then the fluid was decanted.Water samples were collected by filtering the water through 0.2 μm filters into clean bottles. Field measurements of pH, EMajor anions in the water were determined by GC  and major and trace elements by ICP/MS . The mineral identifications are based on XRD  measurements. The X-ray peaks were relatively small. Much of the sediment was apparently not well crystallized.-5. At this cutoff approximately 3 of the observed hits would be expected to occur at random[The unassembled sequences provided by 454 were compared to the SEED database using the BLASTX algorithm on the Teragrid cluster at Argonne National Laboratories,23. All -5 and a minimum sequence match length of 50 nt.The BLASTN algorithm was used to identify 16S genes from release 9 of the RDP database . These BThe statistical analysis of subsystems present in each sample was performed essentially as described elsewhere . The preLW, MB, and FR collected the samples from the mine. DP, MS, SA, and CA performed chemical and hydrogeological analyses. MH extracted the DNA and processed samples for sequencing, BR-B, and RE performed computational and statistical analysis. RE authored the manuscript, and all authors edited and commented on the paper.							A gzip compressed archive of the fasta files (those ending .fa.gz) and quality scores  of sequences from the Red samples as supplied by 454, Inc.Click here for file							A gzip compressed archive of the fasta files (those ending .fa.gz) and quality scores  of sequences from the Black samples as supplied by 454, Inc.Click here for fileTable1SRed. Two lists (one for the Red Sample and one for the Black Sample) describing all the similarities found in the data. The table has the following columns: "Classification I" and "Classification II" are hierarchical classifications of the subsystems. "Subsystem" is the name of the subsystem [ubsystem . "FunctiClick here for fileTable1SBlack. Two lists (one for the Red Sample and one for the Black Sample) describing all the similarities found in the data. The table has the following columns: "Classification I" and "Classification II" are hierarchical classifications of the subsystems. "Subsystem" is the name of the subsystem [ubsystem . "FunctiClick here for fileTable1S. Two lists (one for the Red Sample and one for the Black Sample) describing all the similarities found in the data. The table has the following columns: "Classification I" and "Classification II" are hierarchical classifications of the subsystems. "Subsystem" is the name of the subsystem [ubsystem . "FunctiClick here for fileTable2S. The occurrence of subsystems in either the Red Sample or the Black Sample were compared to the subsystems found in the following metagenomes: AMD [P > 0.95) in either of the samples are shown, along with the sample that the subsystem is more likely to be found in. Subsystem names and classification are as found at [mes: AMD , Farm [6mes: AMD , and themes: AMD . For eacfound at . The texClick here for fileTable2S. The occurrence of subsystems in either the Red Sample or the Black Sample were compared to the subsystems found in the following metagenomes: AMD [P > 0.95) in either of the samples are shown, along with the sample that the subsystem is more likely to be found in. Subsystem names and classification are as found at [mes: AMD , Farm [6mes: AMD , and themes: AMD . For eacfound at . The texClick here for file


DNA microarrays are a powerful technology that can provide a wealth of gene expression data for disease studies, drug development, and a wide scope of other investigations. Because of the large volume and inherent variability of DNA microarray data, many new statistical methods have been developed for evaluating the significance of the observed differences in gene expression. However, until now little attention has been given to the characterization of dispersion of DNA microarray data.® with 22 different types and we demonstrate that the Gaussian  frequency distribution is characteristic for the variability of gene expression values. However, typically 5 to 15% of the samples deviate from normality. Furthermore, it is shown that the frequency distributions of the difference of expression in subsets of ordered, consecutive pairs of genes (consecutive samples) in pair-wise comparisons of replicate experiments are also normal. We describe a consecutive sampling method, which is employed to calculate the characteristic function approximating standard deviation and show that the standard deviation derived from the consecutive samples is equivalent to the standard deviation obtained from individual genes. Finally, we determine the boundaries of probability intervals and demonstrate that the coefficients defining the intervals are independent of sample characteristics, variability of data, laboratory conditions and type of chips. These coefficients are very closely correlated with Student's t-distribution.Here we examine the expression data obtained from 682 Affymetrix GeneChipsKα coefficients defining these intervals are invariant; these coefficients offer a convenient universal measure of dispersion of data. The fact that the Kα distributions are so close to t-distribution and independent of conditions and type of arrays suggests that the quantitative data provided by Affymetrix technology give "true" representation of physical processes, involved in measurement of RNA abundance.In this study we ascertained that the non-systematic variations possess Gaussian distribution, determined the probability intervals and demonstrated that the This article was reviewed by Yoav Gilad (nominated by Doron Lancet), Sach Mukherjee (nominated by Sandrine Dudoit) and Amir Niknejad and Shmuel Friedland . Reviewed by Yoav Gilad (nominated by Doron Lancet), Sach Mukherjee (nominated by Sandrine Dudoit) and Amir Niknejad and Shmuel Friedland . For the full reviews, please go to the Reviewers' comments section.®, we first have to derive a representative value of gene expression from the signals of individual probes . The Affymetrix MAS 5 and GCOS use Tukey's biweight algorithm and yield an absolute expression value for each probe set . The method of low-level analysis, developed by Li and Wong  is de is de®, So far, very little attention has been given to the general properties of the dispersion of gene expression levels. With respect to applicability of various statistical methods it is useful to know how the standard deviation behaves across the expression range and whether this behavior is consistent from one assay to another and among the different types of arrays. Verification of normality of the frequency distribution of random fluctuations is particularly relevant. All parametric methods are based on concordance of the observed frequency distribution with the normal (Gaussian) distribution. Most physical and chemical systems, where random variations result mainly from collective interactions of large ensembles of particles, exhibit frequency distributions close to the Gaussian. The underlying mechanisms of microarray data variability are certainly of the same nature as the collective phenomena in physical systems but the ensemble of the processes involved is so complex that one would expect some compound distribution, far from the simple form expressed by the Gaussian prototype.Kα. It was found that the values of Kα coefficients obtained using various arrays are, at least in the first approximation, invariant. Finally, we compare the probability of coefficients Kα with the corresponding values of inverse t-distribution.The object of the present study is to examine the frequency distributions, general properties of the standard deviations and coefficients of the probability intervals. It was found that the general characteristics of dispersion are useful for quality control, reduction of a system dimension and other purposes. Firstly an overview of the frequency distributions is given for both replicate arrays (five or more replicates) and consecutive sampling of the expression difference in the ordered pairs of genes in two-array comparisons. Subsequently, we describe the consecutive sampling analysis and evaluation of the linear characteristic function, approximating the standard deviation of the data variability across the arrays. The standard deviation function is then employed to define the probability intervals encompassing specific percentages of the observed values. The boundaries of these intervals are defined by probability coefficients In the present investigation we analyzed 682 Affymetrix microarrays of 22 different types. Our main objective was to study the microarray data derived from particular biological investigations, generated in many different microarray core laboratories, rather than the sets of arrays produced in the context of technology development or testing methods of analysis. Only a few "testing" sets were included. We evaluated the CEL files using MAS 4  and employed the "Average Difference" as expression signal value. Because MAS 5 and GCOS distort the frequency distributions in the near-zero region by ignoring the negative values, MAS 5 and GCOS outputs are not suitable. Prior to the analysis, we verified the linearity and quality of the data, in particular, the absence of clusters with significantly different expressions. All data on each array were normalized to 100% of the array mean; all Affymetrix control genes were excluded.®. Typically, for good-quality data, between 85 and 95 percent of samples passed the test. Moreover, a limited number of tests using the data obtained from fiberoptic bead-based oligonucleotide microarrays by Illumina led to the same conclusion  uses for ANOVA the threshold of 0.01), nonetheless the noted deviations call for caution when using parametric methods, in particular considering that every analysis involves multiple testing. Our conclusion differs from that of Gilles and Kipling , Churchill, BioTechniques (2004), 37:173 [c], Kerr, Churchill. Genet Res. (2001), 77:123 [d], Kerr, Churchill (2001), PNAS, 98:8961 [e], Kerr et al. (200), J. Comp. Biol., 7:519 [f], but we did not find confirmation of normality in dispersion of single-color microarrays. In case of the spotted two-color microarrays the authors detected non-normal distribution of residues [d-f]First we would like to thank Dr. Gilad for his review and for bringing up an interesting issue of the MM signals. Regarding the question of normal distribution, we looked over Dr. Churchill's papers dealing with microarray technology, including Cui et al. Biostatistics (2005) [a], .Regarding the question of usefulness of MM signals: Our main objective is to show that the dispersion across all types of arrays, experimental conditions and organisms exhibits some common basic properties and we did not intend to make a case in favor of the Affymetrix approach. Nonetheless, we feel that the fact that such common description can be found supports reasoning of the Affymetrix.The discussion is still on and various arguments have been brought up both for and against using the MM signals. We fully agree with the reviewer that a major part of the signal of MM probes is due to the "specific" RNA of interest. However, this is to be expected, since among all RNA molecules attaching to the MM probe, the particular RNA of interest is most likely closest to its structure. It is exactly the ability to distinguish between the perfect match and "almost-perfect-match" that makes the measured signal reliable. If a substantial quantity of the specific RNA is present and the signals of both PM and MM are equal or MM exceeds PM, it suggests either saturation or low distinguishing power. Under such circumstance the MM signal provides useful information, indicating that the particular PM signal might not reflect the true RNA concentration. In case of saturation, taking the PM signal only would correctly indicate that the specific RNA is present, however, the relationship abundance-signal would be strongly nonlinear. As we mention in the Discussion, the results of various studies aiming at validation of different approaches are inconclusive. Evidently, more research is needed to establish the optimal technology and corresponding statistical procedures. It is likely that no single methodology could be found universally optimal and different circumstances would call for different approaches.Sach Mukherjee, Department of Statistics, University of California, Berkeley, CA, 94720-3860, USA The authors present an empirical study of the distributional characteristics of data from Affymetrix gene expression microarrays. One of the questions posed at the outset concerns the relationship between the mean and variance of microarray data  and is subsequently answered in the following way: "...the standard deviation is linearly proportional to the mean expression level" . However, this latter finding seems widely recognized already, and has been discussed in some detail in the literature . Yet none of these papers are cited in the article.Author Response: We agree that the fact that the standard deviation in the high region is proportional to the signal and at the low end it does not converge to zero has been generally accepted, but we are not aware of the study that systematically verified the linear relationship. Rocke and coworkers derived a similar model from theoretical considerations and corresponding references were included.The authors also criticize the use of log-transformation  (again without referring to the literature on the topic) but then seem to use just such a transformation as a pre-processing step before regression . Yet under a data model with both multiplicative and additive noise, data are only log-normally distributed at high expression levels . Furthermore, log-transformation may inflate the variance of observations with low expression levels. Indeed, the authors find that "...larger percentages of failures [in passing a K-S test of Normality] occur in the near-zero region." . Might not this effect simply be due to the log-transformation?Author Response: It appears that our procedure was not clearly described and we revised the text accordingly. We actually use the logarithmic transform only in the regression procedure to balance the residuals, i.e. to prevent the residuals of the high-expression genes to outweigh the low-expression range. Thus instead of regressingSD(Ymean) → a1 + a2Ymeanwe fitSD(Ymean)) → log(a1 + a2Ymean)log data and not log-transformed data. This is the only occasion when we use the log-transform, in all other procedures we employ non-transformed normalized data.This reviewer found the approach taken to "consecutive sampling" in studying the mean-variance relationship in paired arrays somewhat ad hoc. For example, the 250 probe sets having highest expression level are excluded from the analysis. What effect does this exclusion have on the analysis? Is it appropriate to leave out data (arguably some of the most interesting data) from an empirical study of this kind? This issue is not really discussed. The authors also state that "we usually require the mean discrepancy of the ten consecutive samples below 1%" . Does this mean the data are ignored if the discrepancy is higher than this threshold?Author Response: The consecutive samples provide reliable representation of the standard deviation only if the within sample differences of means are small, say below 1%. At the maximum of the expression range the density of the points is small and, consequently, differences in the mean values are large. To obtain dependable coefficients of the standard deviation function these data have to be excluded from the regression procedure. Subsequently, we assume that validity of the standard deviation characteristic function can be extrapolated to the maximum expression value. Indeed, in the differential expression analysis all data to the maximum expression are included. The text was revised to avoid misunderstanding.Regarding a discrepancy in identity (4): The consecutive sampling program automatically keeps track on assumptions and signals detected problems. Identity (4) is a convenient check of the variability of means and, generally, of the reliability of characteristic function; it is typically fulfilled within 0.1%. If the difference between the right-hand and left-hand sides of the equation exceeds 1% the program raises a flag, indicating problematic data. In such case the researcher conducting analysis examines the data and determines the reason for discrepancy; if no corrective measure can be found, the sample is excluded from the analysis.The presentation of mathematical details is not always very clear in the paper. Equations (2) and (3) would benefit from either a derivation or a reference. Equally, some of the phrasing is somewhat difficult to interpret, e.g. "...we can calculate an estimator of the standard deviation of gene expressions variability of a population of replicate arrays from two-array comparisons" .Author Response: Derivation of equations (2) and (3) is described in The sentence in question was reformulated.Kα coefficients with t-distribution, §5 onwards] puzzling inasmuch as it did not relate to, or strengthen, any of the main arguments of the paper. The case presented was also far from convincing: given that there are so many existing methods for detecting differential expression, it is surely reasonable to expect any new method to be accompanied by strong empirical evidence and/or theoretical arguments in its favor.Finally, this reviewer found the introduction of a new methodology for finding differentially expressed genes [Results, Probability intervals and correlation of the Author Response: Actually, the application of the probability intervals to the differential expression analysis had not been included in the earlier versions of the manuscript. However, during the internal reviews we frequently encountered a question "how can the dispersion analysis and probability intervals help biologist to analyze data and to detect significant differences in gene expression" . To answer this question we included a brief description of the consecutive sampling and coincidence analysis, which we use as a standard procedure, usually in combination with the RMA and/or other approaches. To provide better description we revised the text and included an additional reference.In conclusion, the basic idea behind the paper, of characterizing microarray data distributions using a large set of real-life experimental data, is a very good one, but the paper is not well tied to the literature and suffers at times from a somewhat ad hoc approach.References:Rocke DM, Durbin B. Approximate variance-stabilizing transformations for gene-expression microarray data. Bioinformatics. 2003 May 22; 19 (8):966-72.Durbin BP, Hardin JS, Hawkins DM, Rocke DM. A variance-stabilizing transformation for gene-expression microarray data. Bioinformatics. 2002; 18 Suppl. 1:S105-10.Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics. 2002; 18 Suppl. 1:S96-104.Amir Niknejad and Shmuel Friedland, Department of Mathematics, Statistics and Computer Science University of Illinois at Chicago 851 S. Morgan Street Chicago, IL 60614 USA The paper addresses issues related to analysing DNA Microarrays data focusing on differences of gene expression. The paper is an extension of previous paper of J.P. Novak (reference# 8) by employing various parametric and nonparametric statistics tools and extensive use of statistical packages for very large data sets. The premise of the paper is that the standard deviation of samples of difference of gene expression in DNA microarrays is a linear function of their mean. The paper is a very good work in the area of quality control of Data in DNA Microarrays and certainly a contribution to the field. There are several points that the authors should clarify:1. The authors mentioned that "majority of microarray samples (85%–95%) conform to a Gaussian distribution". What is the reason for the rest of 5%–15% of microarrays sample which do not conform with normality? Is it a biological reason or just manufacturing technology problem?Author response: We thank Dr. Niknejad and Dr. Friedland for the very helpful review. In response to the question above: According to our extensive experience with the Affymetrix arrays and limited experience with the Illumina fiberoptic bead-based oligonucleotide microarrays, the manufacturing technology is an unlikely reason. The outliers, the most frequent cause of non-normal distribution, are probably caused by random fluctuations in the experimental procedures, such as hybridization or labeling. A discontinuity in the frequency distribution  or its derivative is difficult to explain. 2. The authors mentioned that "filtering 15% of genes would enhance the performance for clustering methods". The question is how is this filtering being done, and what is its effect on the data set as a whole and the biological ramification of it.Author response: Generally, all clustering methods are sensitive to noise, however, the problem is more difficult in unsupervised clustering, where members of presumed classes are unknown. Hierarchical clustering and principal component analysis appear to be among the most sensitive, while self organized maps are more robust. Approach to the problem and optimal percentage of the probe sets filtered out depends on a given set of data. Actually, we did not specify percentage in the text – fifteen percent is relatively low and should refer to the set used for analysis, usually reduced by eliminating probes with overall near-zero values. For the clustering procedures we raise the required threshold and try to identify "informative" probe sets, i.e. probe sets likely to be characteristic for presumed classes. Very small groups are virtually impossible to discover in noisy data, so we assume some minimum number k of samples in any particular group – say k ~ 5. Then we select only the probe sets, with k or more expression at least r-fold larger or s-fold smaller than the total median; typically 2 < r, s < 5. It is important to repeat the clustering procedure for several sets of parameters to ensure that the identified classes are independent of filtering constanst.We are also concerned with mostly focusing on Affymetrix technology for coming up for means of quality control receipt. It will be a good idea to see how their model fares for other brands of microarrays.Author response: Our experience is limited to the Affymetrix and Illumina microarrays. However, in our opinion, it is likely that dispersion characteristics of all single-color arrays are similar.It would be helpful if the authors mention how their findings can help molecular biologists to make inferences about gene expression data of various microarray data sets and their biological implications.Author response: Beside filtering of the randomly variable probe sets in noisy data the most practical application is combination of the consecutive sampling analysis and coincidence testing applied to evaluation of the observed differences between experiment and control arrays. The unique advantage of this approach is that it can be applied to assays with small number of replicates (two or more). It is a nonparametric method, theoretically equivalent to repeated random selection, and it is easy to estimate the probability of false positives. Moreover, it is based on pair-wise comparisons and it enables automatic detection of problematic arrays. We extended the discussion of this application in the section Results and added two references.Derivation of equalities (2) and (3).Click here for file


The prevalence of high resolution profiling of genomes has created a need for the integrative analysis of information generated from multiple methodologies and platforms. Although the majority of data in the public domain are gene expression profiles, and expression analysis software are available, the increase of array CGH studies has enabled integration of high throughput genomic and gene expression datasets. However, tools for direct mining and analysis of array CGH data are limited. Hence, there is a great need for analytical and display software tailored to cross platform integrative analysis of cancer genomes.We have created a user-friendly java application to facilitate sophisticated visualization and analysis such as cross-tumor and cross-platform comparisons. To demonstrate the utility of this software, we assembled array CGH data representing Affymetrix SNP chip, Stanford cDNA arrays and whole genome tiling path array platforms for cross comparison. This cancer genome database contains 267 profiles from commonly used cancer cell lines representing 14 different tissue types.System for Integrative Genomic Microarray Analysis (SIGMA) of cancer genomes, can be accessed at .In this study we have developed an application for the visualization and analysis of data from high resolution array CGH platforms that can be adapted for analysis of multiple types of high throughput genomic datasets. Furthermore, we invite researchers using array CGH technology to deposit both their raw and processed data, as this will be a continually expanding database of cancer genomes. This publicly available resource, the  Array comparative genomic hybridization (CGH) is a method used to detect segmental DNA copy number alterations and is widely used to discover chromosomal aberrations in cancer and other genetic diseases ,2. In thNational Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) repository [European Bioinformatics Institute (EBI) ArrayExpress [With the increasing utilization of array CGH, it has not only become important to establish standards for data deposition, but to develop tools to facilitate public access and to ease mining of available data. Currently, the pository  and EuroyExpress  provide yExpress . HoweverSIGMA) for comparative analysis of multiple genomes.In this study we have created a database consisting of high-resolution, whole-genome array CGH profiles for nearly 200 commonly used cancer cell lines profiled on four different array platforms, which have been instrumental in biochemical and pharmacogenetic studies. Moreover, we have developed a user friendly, web-based java application called the System for Integrative Genomic Microarray Analysis  interrogation of a single sample, (2) visualization and analysis of a single group of samples, (3) comparative analysis of two groups of samples, and (4) integration of data from multiple platforms  locus is amplified only in a subset of samples  with cervical SqCC see . The strSIGMA to allow users to view multiple platforms of data simultaneously. We use the breast cancer cell line, MCF7, to illustrate this functionality. Data from four different array CGH platforms were available publicly: SMRT array, Stanford cDNA microarray, Affymetrix 10K SNP array and the Affymetrix 100K SNP array. Figure Cross platform comparison is essential to the multi-dimensional descriptions of a genome. Here, we have included a feature in et al (2006) compared methylation patterns with copy number profiles in lung cancer cells. Utilizing genomic and epigenetic data from this study for the H1395 lung cancer cell line, we illustrate a parallel display in SIGMA. Figure S100 calcium binding protein A10 (S100A10) gene within this region has been previously shown to be over-expressed in lung cancer [One of the novel features we have provided is the integrative visualization of copy number alterations with DNA methylation status. The major premise in studying copy number alterations at the DNA level is that these are the primary changes involved in driving changes in gene expression. Though gene dosage variation may be responsible for expression changes, alteration in DNA methylation pattern also contributes significantly to regulating gene expression. Recently, methods for global methylation analysis to measure aberrant DNA methylation status across tumor genomes have been developed -12. Wilsg cancer . It is rSIGMA application facilitates consolidation and structuring of diverse sources of array CGH data into a repository that is accessible with a new easy-to-use built-in web-based analytical application. The launch version contains data for 267 array CGH profiles, representing cancer cell lines of over 14 different types of tissue. The ability of SIGMA to incorporate multiple array CGH platforms facilitates the archiving of array CGH data from future publications, regardless of current or future array platform used. Though currently SIGMA's architecture facilitates the direct mining of genomic and epigenomic data, this can be easily adapted, and not limited to, high resolution genetic and gene expression surveys.We have developed an application for the integrative cross platform analysis of array CGH data. The NCBI GEO [Stanford Microarray Database [The raw data for 267 CGH profiles in the database was obtained from a variety of sources. They include both published and unpublished data ,15-22. PNCBI GEO , StanforDatabase  or websiDatabase , the earDatabase ,20, the Database ,13. For Java WebStart application interface allowing users to formulate queries and perform visualization, an Apache Web Service which facilitates the connection of the user application to the database and a relational database which is implemented using MySQL  identification (if applicable), array platform used and the description of the cancer type as part of the schema. For two channel array-based profiles, the dye which was used to label the sample is also recorded. Lastly, mapping information pertaining to a clone or probe and its position in the genome is kept in file with a fixed format, such that subsequent improvements and updates to the genomic positioning of the array elements can be easily incorporated. Moreover, since individual microarray software platforms use their own map version, map information for all platforms were compiled based on data from the UCSC Genome Browser [ Browser . CurrentdChip [et al. (2004) were referenced against 12 normal individuals and the NCI-60 profiles were referenced against 6 normal diploid controls. The gender of the profiles were not specified, hence data from the X chromosome may not be an accurate reflection. Segmentation of all data was performed using aCGH-Smooth [Data for each platform were processed as similarly as possible. SMRT array data were normalized using the stepwise framework for normalization with default parameters . SimilardChip  with defH-Smooth , with daSIGMA: System for Integrative Genomic Microarray AnalysisCGH: Comparative Genomic HybridizationSNP: Single Nucleotide PolymorphismProject name: SIGMA Project home page: Operating system(s): Platform independentProgramming language: JavaOther requirements: Java version 1.6+License: Free for academic and research use, commercial users please request special permissionRC designed and developed SIGMA and wrote the manuscript. WWL and BPC were involved in the design and development. AC, DM and AT were the programmers. JJD was involved with initial design and CM provided manuscript editing and program feedback. WLL is the principal investigator of the lab.List of cell lines in database. Detailed description of all the cell lines.Click here for fileSingle group visualization. Whole genome frequency plot of lung adenocarcinoma.Click here for fileTwo group whole genome comparison. Whole genome frequency plot comparison of squamous cancer of the lung and cervix.Click here for file