Despite initial response in adjuvant chemotherapy, ovarian cancer patients treated with the combination of paclitaxel and carboplatin frequently suffer from recurrence after few cycles of treatment, and the underlying mechanisms causing the chemoresistance remain unclear. Recently, The Cancer Genome Atlas (TCGA) research network concluded an ovarian cancer study and released the dataset to the public. The TCGA dataset possesses large sample size, comprehensive molecular profiles, and clinical outcome information; however, because of the unknown molecular subtypes in ovarian cancer and the great diversity of adjuvant treatments TCGA patients went through, studying chemotherapeutic response using the TCGA data is difficult. Additionally, factors such as sample batches, patient ages, and tumor stages further confound or suppress the identification of relevant genes, and thus the biological functions and disease mechanisms.To address these issues, herein we propose an analysis procedure designed to reduce suppression effect by focusing on a specific chemotherapeutic treatment, and to remove confounding effects such as batch effect, patient's age, and tumor stages. The proposed procedure starts with a batch effect adjustment, followed by a rigorous sample selection process. Then, the gene expression, copy number, and methylation profiles from the TCGA ovarian cancer dataset are analyzed using a semi-supervised clustering method combined with a novel scoring function. As a result, two molecular classifications, one with poor copy number profiles and one with poor methylation profiles, enriched with unfavorable scores are identified. Compared with the samples enriched with favorable scores, these two classifications exhibit poor progression-free survival (PFS) and might be associated with poor chemotherapy response specifically to the combination of paclitaxel and carboplatin. Significant genes and biological processes are detected subsequently using classical statistical approaches and enrichment analysis.The proposed procedure for the reduction of confounding and suppression effects and the semi-supervised clustering method are essential steps to identify genes associated with the chemotherapeutic response. Ovarian cancer is prevalent in women  and is aet al. (2005) detected differentially expressed genes among primary chemosensitive, primary chemoresistant, and postchemotherapy tumors using cDNA-based microarrays [et al. (2005) applied a supervised learning algorithm and selected 14 genes to predict the relapsed outcome of ovarian cancer patients after platinum-paclitaxel chemotherapy [et al. (2009) further considered chromosomal aberrations and proposed that DNA copy number alterations (CNAs) at genes such as CCNE1 and NCOA3 are associated with chemoresistance [Microarrays have been used to study genes and molecular functions associated with chemoresistance. For example, Jazaeri roarrays . Additiootherapy . Etemadmsistance . While mIn this regard, the Cancer Genome Atlas (TCGA) data need to be carefully assessed for eligibility to a chemotherapy study. Recently, the TCGA Research Network concluded an ovarian cancer study with thousands of microarray data including mRNA expression, DNA copy number, miRNA, SNP, and CpG methylation data from more than 500 ovarian tumor samples . While aHerein, a procedure for reducing the confounding and suppression effects is proposed, in which, factors such as experimental batches, clinical treatment, patient ages, tumor stages, and molecular classifications are carefully considered and dealt with. Beginning with a batch effect correction, we chose eligible samples through a rigorous sample selection process. In this paper, we will focus only on patients with paclitaxel and carboplatin treatment in order to remove possible confounding factors due to better drug or treatment combination when examining the survival outcome, and in the meantime, to maximize the ability of discriminating tumor subtypes. After the selection, 85 ovarian cancer samples treated only with the combination of paclitaxel and carboplatin were selected for training, and another independent 83 samples treated mainly with the combination of paclitaxel and carboplatin but including some other drugs were applied for testing. Then, gene expression, copy number, and methylation data were analyzed in a novel semi-supervised clustering method. By performing a series of statistical hypothesis testing and clustering tasks, two molecular classifications with poor progression-free survival (PFS) were identified. Comparing these classifications to other samples with good PFS, genes significantly associated with chemotherapeutic response were detected, and enriched biological processes were further examined using a gene ontology enrichment analysis method.In this paper, the proposed procedure and the semi-supervised clustering method are detailed with flow-charts and mathematical explanations in Methods. In Results, the clustering results and the subsequent differences in chemotherapeutic response are compared via Kaplan-Meier curves. Discussions of analysis results and conclusions are provided in Discussion and conclusions.http://cancergenome.nih.gov/). Level 3 gene expression data derived from Aymetrix U133A platform, level 2 copy number data derived from Agilent CGH-1x1M platform, and level 3 methylation data derived from Illumina HumanMethylation27 platform were chosen and downloaded. The gene expression data are constituted of 12,042 normalized log2 values, and each value represents an expression level of a gene. Copy number data contain 962,434 normalized log2 ratios among which 358,119 ratios with gene annotations were utilized. To match these copy number ratios with gene expression levels, values corresponding to a same gene were averaged. Methylation data are 27,578 beta values, and each value refers to the percentage of methylation for a specific CpG site. Among the 27,578 beta values, 19,448 linked to 10,068 unique genes included in the gene expression data were considered in this study. In total, a cohort of 514 tumor samples comprehensively containing gene expression, copy number, and methylation profiles was obtained.Getting started from collecting data, this section demonstrates the methods and criteria we applied for achieving the results. First of all, we downloaded the ovarian cancer data from the TCGA repository website  and exhibit a slight bimodal distribution [i band ij brepresent two vectors of beta values for gene i across all 514 samples and batch j, respectively. To enforce beta values from different batches to have the same median, the methylation beta value ijk bfor gene i from batch j and sample k is rescaled, and the adjusted value where ribution . Again, i band ijb, respectively.where After correcting the batch effect, a rigorous sample selection process was applied for finding the eligible samples for a targeted chemotherapy response. The ultimate goal of this step is to derive as many eligible samples as possible with the least possible confounding effects for both training and testing. To meet this requirement, clinical data were carefully examined. Since the criterion used for distinguishing a good response from a poor response was the progression free survival (PFS) time, which is defined in the next subsection, factors related to prognosis, such as tumor stages, drugs, and treatment time, were considered. As shown in Figure Semi-supervised clustering has been proposed and applied for identifying molecular subtypes in a variety of studies . It outpTo reduce suppression effects due to the genomic (or epigenomic) differences and maximize the ability of discriminating poor prognosis from good prognosis, a rigorous feature selection process was applied, in which, factors such as patient ages and tumor stages were carefully considered to minimize confounding effects.ik cfor gene i from sample k, copy number statuses were categorized into gain (ik c> 0.4), normal (-0.5 ≤ ik c≤ 0.4), and loss (ik c< -0.5). Like a scanning process, the survival functions of the samples with normal copy number were tested against those with copy number gain  as well as those with copy number loss , feature by feature. To ensure the confidence in hypothesis testing, features leading to a small sample size  in either group were not considered, and features resulting in log-rank p-values less than 0.05 were then selected as candidates. After this, confounding factors such as ages and tumor stages were considered for feature refinement. Specifically, if the median of ages in both groups of samples were significantly different  in a Wilcoxon rank-sum detection test, or, if these two groups of samples' tumor stage assignment are distribute significantly differently , then the selected features were removed from the candidates to eliminate possible detrimental effects from confounding factors. Moreover, the candidates were further refined using gene expression fold-changes. Similar to the scanning process used for survival comparison, the averaged gene expression levels derived from both groups of samples were compared, and the candidates causing expression fold-changes larger than 1.3  were retained while those without significant expression fold-changes were disqualified for further consideration. We applied a relatively loose cutoff here only to preserve the concordance of gene expression alteration to the copy number change.A feature in copy number data refers to a gene in human genome, and the feature selection was done basically by a univariate log-rank test, which is a non-parametric hypothesis test comparing the survival functions of two groups of samples, and a series of feature refinements. First of all, we define PFS as the interval between the date of surgical resection and the date when a failure event occurred . For samples without progression, recurrence, or death, the dates of last follow-up were considered as PFS with censoring. Second, based on the copy number ratio i selected from testing the gains against the normal, the F-score ik Ffor feature i and sample k on copy number data is given byAfter feature selection, a scoring function was applied to the copy number data in the selected genes. Instead of using the original copy number ratios, which may be noisy and contain unrelated information, the hierarchical clustering scheme was applied to the data, discretized using a scoring function, called F-score, which transfers the grouping information and the survival conditions into discrete numbers, 1 (favorable), 0 (unknown), and -1 (unfavorable). Specifically, for a feature i (refer to Subsection Feature selection). Additionally, ik c= NaN refers to missing copy number ratios in feature i of sample k. Similarly, for a feature i selected from testing copy number losses against the normal, the F-score ik Ffor feature i and sample k on copy number data is given bywhere p-value.The scores were assigned on a feature-by-feature basis. If a feature was selected twice in both cases , the F-scores were assigned according to the selection with smaller log-rank kk' dbetween samples k and k' were evaluated using the Jaccard coffiecient, which is given byBased on the selected features and discretized copy number data, hierarchical clustering was applied for identifying the molecular classifications associated with chemotherapy response. The distances k Findicates the vector of F-scores in sample k, k' Frefers to the vector of F-scores in sample k', and the length of these vectors are equal to the number of selected features. With all pairs of sample-sample distance being evaluated using Eq. (7), complete linkage was applied, and subsequent clusters derived. We used Kaplan-Meier analysis to check if these clusters exhibited significantly different survival distributions, and proposed molecular classifications if the log-rank p-value was less than 0.05.where K-Nearest Neighbor algorithm (weighted KNN) was applied to the testing datasets to justify the proposed classifications with the selected features. First of all, the testing samples were discretized in the F-scores using the same features and criterion mentioned in Subsection Data discretization. Then, the cluster of samples in the training datasets enriched with unfavorable scores were labeled as poor profiles (l = -1) while the other cluster of samples were labeled as good profiles (l = 1). Using the Jaccard coffiecients as sample-sample distances, the discriminant function f Pfor an independent sample f in the testing dataset was consideredThe weighted h lstands for the corresponding label of the sample h, and fh ddenotes the distance between the sample f in the testing dataset and its h-th nearest sample in the training dataset, which is given bywhere f Findicates the array of F-scores in sample f , and h Frefers to the array of F-scores in sample h. The maximum function used in Eq. (9) is to ensure no zero in denominator in Eq. (8). The sample f in the testing dataset was classified as a good profile if f P> 0 or as a poor profile otherwise. In this study, K was chosen to be 3. Samples classified as good profiles as well as those samples classified as poor profiles were compared using the Kaplan-Meier survival analysis.where i from sample k were categorized into three statuses: hypermethylated p-values less than 0.05 upon hypermethylated versus non-hypermethylated, or hypomethylated versus non-hypomethylated, were selected as candidates. Because of the continuous nature of beta values, the candidate refinement was moderately adjusted: if the beta values for a candidate feature exhibit a large correlation with the sample ages (Spearman correlation > 0.5), or if the beta values for the candidate feature derived from the stage III samples and the stage IV samples were significantly different in median by Wilcoxon rank-sum test, this candidate was removed from further consideration. Except for the aforementioned differences, other steps for identifying molecular classifications such as the candidate refinement using expression fold-change , F-scoring, and the hierarchical clustering (with the Jaccard coffiecient and complete linkage) remain the same. To illustrate the F-score i and sample k on methylation data more clearly, the following relation is provided. For a feature i, found PFS-related after feature refinement, the F-score k is given byThe proposed semi-supervised clustering was also applied to the methylation data. In order to fit the method, the batch-adjusted beta values for gene i in the feature selection for methylation data. Also, i of sample k. After data discretization, hierarchical clustering was applied to identify molecular classifications associated with chemotherapy response. Moreover, the testing datasets were also classified using the weighted KNN algorithm for classification justification.where t-test p-values less than 0.01. After deriving the significant gene set, enriched gene ontology terms  were identified using the Gene Ontology Enrichment Analysis Software Toolkit (GOEAST) [http://omicslab.genetics.ac.cn/GOEAST/.Once the molecular classifications associated with chemotherapy response were identified, differentially expressed genes in comparing the poor copy number profiles and the good profiles, or in comparing the poor methylation profiles and the good profiles, were detected using classical statistical approaches and methods for gene expression analysis. The good profiles mentioned herein study refer to the samples neither classified as poor copy number profiles nor classified as poor methylation profiles. Since confounding and suppression effects were reduced by the proposed procedure, more strict thresholds were simultaneously applied: fold change larger than 1.5 and (GOEAST)  availablTo exemplify how the L/S adjustment modified the microarray data for batch effect correction, the gene expression values and methylation data of POLR2L, a gene encoding a subunit of RNA polymerase II, before and after the correction were compared in this section. POLR2L is one of the human housekeeping genes steadily expressed in cells. If the samples had been well assigned to random batches during the experimental design step, the medians and variances of the measures from different batches would approach the same values. To justify this, the PFS among all pairs of batches were compared and tested using the log-rank test; as a result, the PFS among most pairs of batches were found not significantly different , and the improvement might be attributed to the utilization of additional drugs other than paclitaxel and carboplatin. Considering treatments may affect PFS, especially with additional drugs that may prolong patients survival time, we set aside 83 samples only for testing purpose in order to avoid possible confounding effects, while maintaining 85 samples with paclitaxel and carboplatin treatment only to minimize the suppression effect at the same time during the training stage. By doing so, we optimized the trade-off between the sample size and analysis confidence, assuming that the 83 testing samples involving additional treatments preserve similar molecular signatures observed from the 85 training sample set.The clinical characteristics of the 85 samples for training and the 83 samples for testing are generally identical except for the survival outcome. As shown in Table As shown in Figure Figure The clustered results on methylation profiles are shown in Figure p-value of 4.0 × 10-5, indicating the potential existence of molecular subtypes with distinct chemotherapy response.The training results derived from both the copy number data and the methylation data are integrated and shown in Figure KNN algorithm detailed previously was applied to 83 independent testing samples, and the classified results based on both the copy number profiles and the methylation profiles were integrated and shown in Figure p-value comparing the light gray (good) to the black (poor) is 0.021, and the Kaplan-Meier curves in testing reveal similar patterns to those in the training. These results validate the finding we derived from the training dataset.To justify the selected features and identified molecular classifications derived from the training samples, the weighted t-test and expression fold-change, 107 differentially expressed genes between these two groups of samples were detected, and the top 15 differentially expressed genes ranked by t-test p-values are shown in Additional file p-values are listed in Additional file p-value less than 1.8 × 10-5. The other 30 PPTs derived from methylation profiles were also compared to the 45 GPTs, and 34 differentially expressed genes were detected using t-test and expression fold-change. Similarly, the top 15 differentially expressed genes ranked by t-test p-values are shown in Additional file Significant genes and enriched ontologies were identified using classical statistical approaches and an enrichment analysis. Based on the training results, as shown in the Venn Diagram and the Kaplan-Meier curves in Figure As high-throughput technologies such as microarray and short-read sequencing become more and more popular in biological studies, the complexity and dimensions these datasets possess make the statistical analysis more difficult. Additionally, extraneous variables resulting from sample variability may hinder analysts from avoiding false discovery and subsequently achieving reliable results. Dealing with these extraneous variables, whether confounding or suppression, is challenging. To address these issues, we proposed a procedure for the reduction of confounding and suppression effects, in which a batch effect correction, a sample selection process, and a semi-supervised clustering method were considered. The batch effect correction used an L/S adjustment to eliminate confounding effects due to experimental differences, and the sample selection was designed to focus on single chemotherapy, reducing variability due to different treatments. After batch effect correction and sample selection, a novel semi-supervised clustering method that further addressed the confounding from ages and tumor stages was applied, and gene expression values, DNA copy number ratios, methylation data, and clinical information were analyzed for unraveling molecular classifications associated with chemotherapeutic response. As a result, significant genes and enriched ontologies were identified.The batch effect due to experimental processing is a known issue in microarray studies, and there has been several attempts to correct the batch effect for gene expression profiling experiments. For example, the software "COMBAT"  utilizedThe molecular classifications were identified from copy number and methylation profiles. CNAs are common genomic aberrations shown to associate with mRNA expression level changes, gene function modifications, and significant differences in prognosis of a variety of tumors . Also, met al. [in vitro study [Differentially expressed genes detected by comparing the tumors with poor prognosis to the samples with good prognosis provide us candidate genes for studying the functions or mechanisms differentiating chemotherapy response. Comparing with the 14 genes selected by Hartmann et al.  for predro study . ObviousThe proposed procedure can help reduce confounding for mining useful information; it is powerful but not omnipotent. In fact, the best way to achieve a confident conclusion may rely on rigorous experimental designs, not simply relying on state-of-the-art statistical analysis techniques. In other words, the less confounding factors an experiment has, the more reliable results an experimentalist can generally achieve through a case-control study.The authors declare that they have no competing interests.FH, TH, and YC designed the methods for data analysis. ES, AB and ED participated in the formulation of research topic. All authors collectively wrote the paper, read and approved the final manuscript.Supplementary figures and tables. This additional file contains supplementary figures and tables mentioned in the study, including Figures S1-S3 and Tables S1-S6.Click here for file


Chronic lymphocytic leukemia (CLL) is typically regarded as an indolent B-cell malignancy. However, there is wide variability with regards to need for therapy, time to progressive disease, and treatment response. This clinical variability is due, in part, to biological heterogeneity between individual patients’ leukemias. While much has been learned about this biological variation using genomic approaches, it is unclear whether such efforts have sufficiently evaluated biological and clinical heterogeneity in CLL.To study the extent of genomic variability in CLL and the biological and clinical attributes of genomic classification in CLL, we evaluated 893 unique CLL samples from fifteen publicly available gene expression profiling datasets. We used unsupervised approaches to divide the data into subgroups, evaluated the biological pathways and genetic aberrations that were associated with the subgroups, and compared prognostic and clinical outcome data between the subgroups.Using an unsupervised approach, we determined that approximately 600 CLL samples are needed to define the spectrum of diversity in CLL genomic expression. We identified seven genomically-defined CLL subgroups that have distinct biological properties, are associated with specific chromosomal deletions and amplifications, and have marked differences in molecular prognostic markers and clinical outcomes.Our results indicate that investigations focusing on small numbers of patient samples likely provide a biased outlook on CLL biology. These findings may have important implications in identifying patients who should be treated with specific targeted therapies, which could have efficacy against CLL cells that rely on specific biological pathways. Chronic lymphocytic leukemia (CLL) is a generally indolent B-cell malignancy. However, since the time that clinical staging systems were developed The use of gene expression profiling of CLL cells as an experimental approach has informed the understanding of CLL biology dramatically. Comparisons of genomic expression of CLL cells from patients grouped by clinical outcomes or by prognostic markers have led to the identification of numerous genes or gene signatures associated with these phenotypes Despite these contributions, it is unclear whether the gene expression profiling approach used to date is sufficient. The marked clinical and biological heterogeneity of CLL and limited numbers of subjects in individual studies may make it difficult to fully and accurately assess genomic heterogeneity in CLL. To address this methodically and objectively, we evaluated publicly available CLL gene expression datasets in concert in order to assess the number of samples required to capture genomic heterogeneity in CLL and to determine if CLL subgroups defined by gene expression profiling have biological and clinical relevance.Homo sapiens and Affymetrix U133 generation gene expression arrays. Thereafter, datasets were manually sorted to remove duplicate data files (between datasets) or data files that represented culture experiments. Of the fifteen identified datasets, one publicly available dataset was from our previous research at the Duke University and Durham VA Medical Centers The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) website was queried using terms including lymphoma, B-cell lymphoma, CLL, chronic lymphocytic leukemia, small lymphocytic lymphoma, and SLL. Datasets were then filtered to include only Gene expression data (CEL) files from selected datasets were downloaded from the GEO website. CEL files were normalized using RMA and MAS5 normalization methods using the affy package in Bioconductor We identified subgroups of the combined RMA normalized dataset by performing Consensus Clustering The combined dataset was reordered randomly. Subsequently, sub-datasets of the combined dataset  were defined. The probes in these sub-datasets were filtered to approximately the top 10% with the highest standard deviation (2222 probes). Thereafter, Consensus Clustering was performed on the sub-datasets using the settings outlined above. This process was iterated twenty-five times. CDF plots for the sub-datasets were compared to CDF plot for the entire dataset and scored for similarity in terms of area under the curve and slope of the curves. Statistical difference in score of the sub-datasets compared to the entire dataset was assessed using the Fisher’s exact test.Assessment of gene and genomic pathway annotation was performed using Gene Set Enrichment Analysis (GSEA) GSE16746 contained 60 samples that had been arrayed with Affymetrix U133A gene expression array and with Affymetrix 250K Nsp SNP Array The samples in GSE16746 were assigned into subgroups based on their branch membership in unsupervised hierarchical clustering. The frequency of each copy number variation within each subgroup was compared to the expected frequency using the Pearson’s Chi-squared test, and regions of copy number variation with a p-value of less than 0.05 was considered significant. Insufficient total number of samples per group precluded performing multiple testing corrections.Molecular prognostic marker and outcome data were gathered for each sample included in the analysis, if available. In most cases, the data were available on GEO or in supplemental tables in the publications, but in some cases we retrieved the information from the corresponding author directly.Samples in the combined dataset were separated based on subgroup. Significant differences in available molecular prognostic markers between subgroups were assessed using Pearson’s Chi-squared test. For datasets with clinical treatment response data (GSE15490 and GSE10137) https://genepattern.genome.duke.edu/gp/pages/login.jsf) Statistical analyses, raw gene expression normalization, Consensus Clustering, and circular binary segmentation were performed using R. Genomic pathway analyses were performed using Gene Set Enrichment Analysis . We used the Duke University GenePattern server (H mutation status) were available for many, but not all, data files.From a query of the GEO database for CLL-containing datasets, we identified fifteen datasets that contained 893 unique CLL sample data files . The numAfter downloading datasets from GEO and normalizing the files from individual datasets, we combined the datasets, filtering the data to include common probes. The combined dataset was further normalized with the Bayesian Factor Regression Method (BFRM) to reduce batch effect. We evaluated the efficacy of this normalization process using principal component analysis of the combined dataset prior to and after normalization. Prior to BFRM normalization, samples were grouped with other samples from the same dataset . HoweverTo assess the extent of genomic heterogeneity in CLL, we evaluated the combined dataset with unsupervised hierarchical clustering using the Consensus Clustering algorithm. As seen in Before assessing the biological and clinical relevance of genomically-defined CLL subgroups, it was important to determine if the number of samples in the combined dataset were sufficient to evaluate genomic heterogeneity in CLL. Assuming there is no bias in the availability of genomic data, we would expect that increasing the number of samples in the combined dataset would cease to increase the number of subgroups once maximum genomic heterogeneity has been reached. Therefore, we evaluated the combined dataset in an iterative fashion to determine if a smaller number of CLL samples could be used to obtain the same subgroups as the entire combined dataset. To do so, we used the Consensus Clustering algorithm to evaluate the CDF of two to eight subgroups on increasing numbers of randomly selecting samples from within the entire dataset. This process was repeated 25 times. CDF plots of sub-datasets were compared to the CDF plot of the entire dataset. The CDF plots for sub-datasets of 50 to 550 samples were different than the CDF plot for the entire dataset , whereas 600 to 850 samples were not statistically different than the CDF plot for the entire dataset . Representative plots are displayed in As described above, we identified seven CLL subgroups by performing unsupervised hierarchical clustering on the entire combined dataset. We sought to determine if these genomically-defined CLL subgroups have biological relevance, using gene and genomic pathway annotation tools such as Gene Set Enrichment Analysis (GSEA) and genomic signatures of oncogenic pathway activation. As seen in The annotations revealed by GSEA were consistent with our analysis using gene expression signatures that measure oncogenic and cell signaling pathways. These signatures were developed from experimental perturbations of pathways and provide a quantitative estimate of the state of the cellular pathway in a given sample in situ hybridization (FISH) were not compared in this analysis since this information was captured by otherwise obtained FISH data. Since copy number variations (CNV) in the CLL leukemia cells could contribute to alterations in gene expression, we assessed the extent to which amplifications and deletions were associated with specific genomically-defined CLL subgroups. We could assess this using one dataset (GSE16746) in which sixty CLL samples were simultaneously evaluated using gene expression profiling and single nucleotide polymorphism (SNP) arrays. The sixty samples were subdivided based on their genomic subgroup, and CNVs were evaluated for statistically significant enrichment within subgroups. CNVs that represented commonly tested cytogenetic aberrations from fluorescent H mutation status, ZAP70 and CD38 expression, and interphase cytogenetic aberrations, identify subgroups of CLL patients with varying clinical outcomes. We hypothesized that genomically-defined CLL subgroups would be significantly associated with these prognostic markers and would have different clinical outcomes. We assessed the extent to which high-risk CLL prognostic markers are associated with CLL subgroups, and found a significantly different distribution of these markers in different subgroups . Comparing the different subgroups, the percent of samples that were IgVH unmutated ranged between 25% and 82%, the percentage of ZAP70 positive samples ranged between 0% and 83%, the percentage of CD38 positive samples ranged between 18% and 75%, and the percentage of 17p or 11q deletion samples ranged between 7% and 100%. It is notable that CLL samples with particular prognostic markers are not exclusively found within specific genomically-defined CLL subgroups. This likely reflects a level of heterogeneity not fully captured by these commonly used markers. Since the molecular prognostic markers are associated with clinical outcome endpoints, differences in frequency of these markers within genomically-defined CLL subgroups could indicate that the differences between these subgroups underlie an important part of the observed clinical variation observed in CLL.Molecular prognostic markers, including IgVubgroups . Each prH and ZAP70 status were not. We then assessed the extent to which subgroups with particular gene and pathway annotations had disparate clinical outcomes. As seen in To assess the relationship between the CLL subgroups and clinical outcomes further, we evaluated overall survival in 68 CLL samples from our institution evaluated previously (GSE10138) We assessed the extent to which the genomically-defined subgroups could be used not only as prognostic markers, but as predictive markers of therapy response. When we evaluated two datasets that included response to treatment data (GSE10137 and GSE15490), we found no significant correlation between CLL subgroup and response to chemo-immunotherapy regimens . In sum, these results indicate that CLL subgroups, defined in an unsupervised manner with raw gene expression data, are based on biological processes and have prognostic relevance in terms of overall survival.Clinical variability in CLL is widely appreciated but poorly understood. By pooling and evaluating publicly available gene expression profiling data using unsupervised methods, we defined subgroups of CLL that have unique biological and clinical differences. This evaluation of genomic subgroups is not meant to replace currently used clinical prognostic markers. However, it is meant to demonstrate that CLL is much more heterogeneous  than can be accounted for using current prognostic markers.Our results could have been affected by potential confounders such as bias in publicly available datasets and the lack of associated prognostic and clinical outcome data for all samples. The agreement in our evaluation of the data using two methods of assessing biological significance suggests that our approach and methods are valid. Additionally, the concordant results in two datasets with regards to response to therapy stratified by genomic subgroup also suggests that these confounders are likely not influencing our analysis. At the most, including additional data might increase the identified genomic heterogeneity in CLL as a whole.Our evaluation of the extent of genomic heterogeneity in CLL demonstrates that approximately 600 or more unique samples are required to divide CLL into subgroups with the identified clinical and biological relevance. Because this number of total samples may be too great for typical clinical or translational research, a focus on specific subgroups would enrich for genetic or biologic backgrounds that could have particular relevance.Genomic research in CLL is moving towards next-generation whole exome and whole genome sequencing approaches. Several studies using next-generation sequencing approaches identified SNPs in CLL cells with clinical relevance in independent large cohorts of CLL patients. For example, NOTCH1 mutation was found in 4–12% of CLL patients Research using gene expression profiling has informed laboratory-based and clinical investigations and clinical practice in CLL. In part because of the resulting improved understanding of CLL biology, therapies targeting important pathways in CLL, such as B-cell receptor signaling and downstream second messenger systems, are being developed and clinically evaluated. Within the context of our results, these targeted therapies may have selective efficacy in certain genomically-defined subgroups. The assessment of patient responses to such targeted therapies, stratified by gene expression data, would validate the notion of using targeted therapies for subsets of CLL patients defined by leukemia biology.Our work also identified CLL subgroups that are associated with biological processes that have not been extensively studied in the context of CLL biology, for example RNA processing and interferon pathways/inflammation. Importantly, we found that CLL samples, grouped by biological annotation, are associated with differences in overall survival in CLL patients. These results may lead to laboratory-based investigation to understand the functions of these pathways in CLL, validation within other patient cohorts, and potentially the development of additional novel targeted therapies.In conclusion, we have found that the evaluation of genomic data from a large number of CLL patients allows us to identify heterogeneity within CLL and to learn more about the genomic, biologic, and clinical differences that span this malignancy. Dividing patients into distinct genomic groups could have implications for future research, for CLL prognosis, and for developing targeted therapeutics and associated biomarkers.Table S1Full list of genes contained in the amplified region on chromosome two, enriched in subgroups 1, 2, and 5.(PDF)Click here for additional data file.Text S1Code used to perform the analyses in the manuscript, using the statistical environment R.(PDF)Click here for additional data file.


Inflammatory bowel diseases, ulcerative colitis and Crohn’s disease are considered to be of autoimmune origin, but the etiology of irritable bowel syndrome remains elusive. Furthermore, classifying patients into irritable bowel syndrome and inflammatory bowel diseases can be difficult without invasive testing and holds important treatment implications. Our aim was to assess the ability of gene expression profiling in blood to differentiate among these subject groups.Transcript levels of a total of 45 genes in blood were determined by quantitative real-time polymerase chain reaction (RT-PCR). We applied three separate analytic approaches; one utilized a scoring system derived from combinations of ratios of expression levels of two genes and two different support vector machines.All methods discriminated different subject cohorts, irritable bowel syndrome from control, inflammatory bowel disease from control, irritable bowel syndrome from inflammatory bowel disease, and ulcerative colitis from Crohn’s disease, with high degrees of sensitivity and specificity.These results suggest these approaches may provide clinically useful prediction of the presence of these gastro-intestinal diseases and syndromes. CD is me colitis. When the colitis.(p-ANCA) and anti-Saccharomyces cerevisiae antibody (ASCA) have been reported to be markers for UC and CD, respectively. However, p-ANCA is also detected in 10–40% of patients with CD and ASCA is detected in 6–14% of patients with UC[a) Escherichia coli outer membrane porin C (Omp-C), (b) protein from Pseudomonas fluorescens[c) flagellin c-BIR1 (anti-CBIR1)[ANCA, ASCA, I2 (antibody to Pseudomonas fluorescens), and Omp-C, have significantly higher post-operative complications[Developing biomarkers that can be easily obtained and allow for the correct diagnosis early into evaluation can avoid costly interventions that expose patients to multiple unnecessary procedures. Blood markers for both IBD and IBS have been sought for decades. For IBD, perinuclear antineutrophil cytoplasmic antibody uorescens and (c) ications. Other iications, but tesications.e.g., heirarchical clustering, K-means clustering, which depend upon the general ability to find common features across a sample population or forms of linear discriminate analysis, which depend upon the ability to find linear combinations of features that have the ability to separate two or more classes. The former method is a common method to analyze large numbers of features, such as microarray data whereas the latter is a more common method for analysis of smaller numbers of features. Both methods are suitable for further analyses using machine learning methods such as support vector machines, logistic regression, principal components analysis or prediction analysis for microarrays. Using a form of linear discriminant analysis, we have attempted to employ mRNA transcript profiles to distinguish between subjects with multiple sclerosis and other comparator groups[Additional biomarker candidates include DNA variants, differences in RNA transcript abundances, including mRNAs, microRNAs, long intergenic non-coding RNAs, proteins, or metabolites. A general view is that different profiles of biomarkers could provide useful information to guide clinical decision-making; from diagnosis to choice of optimal therapies and in some cases these biomarker profiles are being implemented in clinical practice,12-24. Sr groups,26. Our Blood samples collected in PAXgene tubes were obtained from CTRL, IBS, CeD, CD or UC subjects. Diagnosis of IBD, both CD and UC, was made by colonoscopy or sigmoidoscopy and tissue biopsy to localize inflammation to all layers of the intestinal wall (CD) or only the inner lining layer (UC). Diagnosis of IBS was made by the absence of pathologic damage in the colon after examination by colonoscopy or sigmoidoscopy. Inclusion criteria were diagnosis by a gastro-intestinal specialist using these methods. Age, race and gender were not statistically different among the different study groups. Time of blood draw, for example, morning/afternoon clinics, was also not statistically significant among the different study groups. Relevant institutional review board  approval was obtained from all participating sites. Informed consent was obtained from all subjects.housekeeping genes in 300 ng cDNA. The gene probes on the TLDA plate were: ABR, ACTB, ACTR1A, ADAMTSL4, ANAPC1, APOBEC3F, ASL, B2M, BRCA1, CD55, CDH1, CDKN1B, CHEK2, CSF3R, CTSS, EPHX2, EXT2, FOS, FOSL1, GAPDH, GATA3, GNB5-1, GNB5-1, GSTM4, HLA-DRA, HRAS, IFI27, IL11RA, JUN, KRAS, LEPREL4, LLGL2, NRAS, OAS1, ORC1L, PGK1, PMAIP1, POU6F1, RANGAP1, SC65, SPIB, TAF11, TBP, TGFBR2, TP53-1 TP53-2, TXK. GNB5-1 and −2 and TP53-1 and −2 interrogate different exon-intron junctions[a) previous studies demonstrating differential expression among control and multiple autoimmune diseases, (b) protein products possess known inflammatory functions, (c) expression levels change in response to pro-inflammatory stimuli (cytokines), and/or (d) protein products have known roles in cell cycle progression and/or apoptosis. Patient diagnosis was blinded for all experimental procedures. Relative expression levels were determined directly from the observed threshold cycle (CT).Total RNA was purified using Qiagen’s RNA isolation kits using standard protocols and was reverse-transcribed using poly-A primers uisng Superscript III . A TaqMan Low Density Array (TLDA) was designed to analyze expression levels of 44 target genes and of four unctions. InclusiBioinformatics Toolkit  and other techniques to identify a lower dimensional space of gene expressions that could be used to classify controls from cases. The results were disappointing and we concluded that looking at ratios of the gene expression data may be a more productive approach. The computational algorithm and permutation testing strategy employed to identify discriminatory combinations of ratios to create the ratioscore (our terminology) have been previously described[D denote the set of gene-expression levels associated with the disease group and C denote the set of gene-expression levels associated with the control group. The algorithm searches for the “best” set of gene ratios that partitions D and C:Principal Component Analysis (PCA) was applied directly to the normalized gene expression data using MATLAB’s escribed. For comD and C. For each ratio, the number of elements in the disease group that are larger than the largest ratio in the control group is computed. The top 500 ratios that separate elements in D and C are saved. This calculation is repeated 200 times resulting in a set of 200 subsets of ratios (each subset having 500 ratios).• 80% of the control and disease groups are randomly selected. Gene-expression level ratios are formed for elements in R={r1, r2, …, rn}, that produce the maximum of separation of D and C. Associate with each of the ratios in R, there are threshold values, T={t1, t2, …, tn}, which correspond to the highest value in the control group for each of the ratios in R.• The 500 subsets are then processed looking for the smallest number of ratios, D, the ratios in R are computed, {α1, α2, …, αn}. If αi ≥ ti, then we assign the ratio a 1; otherwise, it is assigned a 0. In this way, we generate an n-tuple of 1’s and 0’s for each member of D. For example, if n = 6, then a typical 6-tuple would be {1,1,0,0,1,0}. This would mean that this individual in the disease group would have 3 ratios that exceed the corresponding ratios in the control group.• For each member of the disease group n-tuples is calculated. The larger the percentage, the better the separation of D and C.• Lastly, the percentage of members in the disease group that have nonzero The algorithm alllows one to identify the smallest number of ratios that partitions the case and control groups.Mathematica  and the second SVM employed LS-SVMLab software (http://www.esat.kuleuven.be/sista/lssvmab). We decided to use the two independently developed SVM since the choice of kernels, optimization algorithms, and the training algorithms can produce differing results. There was little difference in the performance of the two machines when classifying the different case–control combinations. To confirm the results of the Ratioscore Method and the SVM approaches, logistic regression was employed to separate to the case and control sets using the gene ratios. Its performance was in line with the other two approaches and hence, we have chosen not to report these results.Two support vector machines (SVM) were independently created and trained using ratios identified by the Ratioscore Method. The first SVM was coded in The Welch’s-corrected T-test not assuming equal variances was employed to calculate p-values in two-way comparisons. Fisher’s exact test was employed to calculate p-values in 2 by 2 comparisons. The Bonferroni’s method was employed to correct for multiple testing.GAPDH transcript levels. We employed a heatmap to depict those genes differentially expressed in individual subject cohorts relative to the CTRL cohort, p-value < 0.05 , IBS subjects were recruited from multiple sites within the United States. Demographic characteristics of the different gastrointestinal disease cohorts were not statistically different from the CTRL cohort Table. We measGAPDH, gene expression data from IBD samples or IBS samples and CTRL samples were analyzed using unsupervised and supervised heirarchical clustering using all genes or only those genes whose expression was statistically significant using the supervised T-test. We found that unsupervised heirarchical clustering segregated 72% of IBD samples in one major branch and 28% of IBD samples in the second major branch. Similarly, 36% of CTRL samples were segregated into the branch with most of the IBD samples while 64% of CTRL samples were segregated into the alternate branch. Comparison of IBS and CTRL using unsupervised heirarchical clustering also did not produce the desired level of discrimination between case and control cohorts. Supervised heirarchical clustering and principal components analysis produced a similar low level of overall accuracy.Initially, we employed standard methods of microarray analyses including unsupervised heirarchical clustering, supervised heirarchical clustering, and principal components analysis using the TIGR microarray software Multiexperiment Viewer to segregate patient groups. After normalization to housekeeping genes in the analysis and the assumption that expression levels of housekeeping genes did not vary among different subject populations. Third, comparisons of ratios or combinations of ratios may more accurately identify cellular phenotypes that may contribute to disease. For example, a ratio containing one gene in the numerator that is over-expressed in the case cohort relative to the control cohort and one gene in the denominator that is under-expressed in the case cohort relative to the control cohort should produce a greater ratio value difference between individuals in the two cohorts than a single expression value. Fourth, ANAPC1, RANGAP1, and LEPREL4 genes encode unique proteins and each participates in mitosis[ANAPC1 while a second individual with the same disease may exhibit a deficiency in expression of RANGAP1 and a third with the same disease may exhibit a defect in LEPREL4 expression levels. Any of these defects has the potential to produce a common cellular phenotype. Our approach makes it possible to capture each subject as positive for a given disease. We refer to this as the Ratioscore Method.For these reasons, we turned to a type of linear discriminant analysis classifier (Ratioscore Method) that we employed previously to discriminate subjects with multiple sclerosis from different control cohorts. We employed a search algorithm to identify those ratios of gene-expression levels in which the greatest number of subjects in the test group possessed a ratio value greater than the highest ratio value in the comparator group. We employed a second algorithm to perform permutation testing of one subject group to identify the optimum set of discriminatory ratios. CeD was excluded from this analysis due to the low number of cases in this cohort. Examination of expression levels of ratios of genes rather than individual genes offered the following advantages. First, ratios normalized for differences in mRNA or cDNA template quantity and quality among different samples. Second, ratios obviated the need for inclusion of a  mitosis-33. ThusPGK1/POU6F1 . This introduced a second fitting parameter C. Programs written in Mathematica  were created and random training subsets of the two groups were chosen to find the parameters, β and C. Each training subset consisted of 60% of the total dataset. The values of the two fitting parameters that produced the smallest number of incorrect cases and controls were used to define the SVM. This SVM analysis also accurately discriminated the different subject groups: (i) IBD and CTRL, (ii) IBS and CTRL, (iii) IBD and IBS, and (iv) CD and UC (TableSupport Vector Machines (SVM) were also employed to classify the data into two distinct groups. The inputs for the SVM were the same ratios used to calculate the ratioscores. For example, when separating IBS patients from CTRL subjects, the same 19 ratios of normalized gene-expression ratios employed to compute the ratioscore were used as input to the SVM. In the SVM calculations, we chose the radial basis kernel (RBK) to perform the UC Table.LS-SVMLab software (http://www.esat.kuleuven.ac.be/sista/lssvmlab) to validate the SVM created with Mathematica. The procedure for training the SVM followed the following algorithm:A second SVM was also employed using X  was randomly selected from the total set of data and used to train the SVM.• L-fold cross-validation was performed. In this type of training a certain fraction of the training set was omitted from training and the remaining portion of the partial training set was used to estimate the parameters of the SVM. This was repeated L times. We used L = 10. At the completion of the training, a composite estimate for the parameters was obtained.• On the selected training set, X% of the total data, the SVM was applied to the total data set.• Once the SVM was trained on Numbers of correct and incorrect classifications were tabulated for total sets , training sets and validation sets Table. OverallThis second SVM was used to discriminate between the different subject groups, IBD and CTRL, IBS and CTRL, IBD and IBS, and CD and UC producing levels of sensitivity and specificity comparable to the Ratioscore Method or the first SVM method Table. We detee.g., the union of the set from CTRL vs. CD, CTRL vs. UC, and CD vs. UC. In this case, the SVM identified 99.1% of CTRL, 100% of UC, and 84.8% of CD. One factor that may contribute to this increased accuracy is that the number of gene ratios used in the training of the SVM was increased from 23 ratios to 49 thus introducing additional parameters into the SVM structure.In the above discussion, two support vector machines were independently created and trained using the ratios identified by the Ratioscore Method. There was little difference in the performance of the two machines when used to classify the different case–control combinations. One advantage of the SVM-based approach is that it can be used to classify more than two groups. As an example of classification into three groups, we considered data for UC (N = 40), CD (N = 46), and CTRL (N = 113). Using gene ratios determined by comparing CTRL (controls) to UC+CD (cases), the SVM identified 99.8% of CTRL, 72.5% of UC, and 56.5% of the CD. Hence, the performance of the tertiary classification was not as accurate as the binary classifications. However, the tertiary classification was improved by using a different set of gene ratios, ASCA and p-ANCA have clinical utility in diagnosing IBD. ASCA IgA is found in 35-50% of patients with CD but < 1% of patients with UC. ASCA IgG is found in 50-80% of patients with CD but only 20% of patients with UC. In contrast, atypical p-ANCA is found in 70% of UC patients but only 20% of CD patients[a) IBS and IBD, and (b) the two forms of IBD, UC and CD, using three independent methods based upon transcript levels in blood of a discrete set of genes. Each method employs the same input, which are multiple ratios of expression levels of two genes. The analytic methods, ratioscore, two SVM methods, and logistic regression, produce similar levels of overall accuracy determined by ROC curves which exceed 95%. We have summarized the overall process of going from the raw samples to classification in FigureIBS and IBD can exhibit overlapping clinical symptoms making diagnosis difficult without invasive procedures,12,34. Tpatients. Here, wIn contrast, biomarkers for IBS are non-existent and diagnosis largely depends upon the absence of pathological findings in the colon. Previously identified experimental biomarkers to distinguish UC and CD clearly do not perform with the same degree of accuracy as experimental approaches described here. Thus, we propose these gene expression ratio tests using the Ratioscore Method, SVM, or logistic regression for analysis represent simple non-invasive tests that could accurately classify patients to IBS or IBD categories and IBD patients to UC or CD categories even without colonoscopy or sigmoidoscopy and tissue biopsy.PGK1 is associated with IBS, CeD, CD, and UC and PGK1 is known to be induced by hypoxia and may be induced by other forms of stress, inflammation or generalized mucosal irritation[ABR, ACTR1A, EXT2, HRAS, and KRAS are over-expressed in both IBS and CeD but not CD and UC. In contrast, APOBEC3F, ASL and SPIB are under-expressed in CD and UC, but not IBS and CeD. Thus, the IBS gene-expression signature is more similar to the CeD gene-expression signature and the UC signature is more similar to the CD signature. It is uncertain if this suggests that IBS may bear additional relationships to CeD. An improved understanding of mechanisms producing differences in levels of specific gene transcripts in IBS may further our understanding of the pathogenesis of IBS.UC and CD are chronic inflammatory autoimmune diseases. Using various strategies, numerous studies have identified unique gene-expression signatures in blood or peripheral blood mononuclear cells (PBMC) associated with different autoimmune diseases. Some arritation. FurtherLimitations to our study include selection of patients with pre-existing diagnoses of IBS and IBD, as this may not completely represent patients in the general population in whom these tests may be performed. However, in other studies we have shown that subjects with clinically isolated syndrome, a precursor of multiple sclerosis, who progress to a diagnosis of multiple sclerosis score positive in ratioscore- or SVM-based analyses, similar to those described here. This may suggest that subjects with initial clinical symptoms associated with IBD or IBS, CD or UC, may be discriminated by this approach. Future longitudinal approaches are planned to evaluate utility of these tests. Additional methods, such as analysis of gene-expression ratios in multi-dimensional space rather than binary space may improve the diagnostic capabilities of these tests. We employed three independent approaches to evaluate the ability of gene-expression ratios to discriminate subjects with gastro-intestinal diseases with overlapping clinical symptoms and each produced high degrees of specificity and sensitivity. Thus, these minimally invasive tests may assist in excluding or establishing a diagnosis of IBS or IBD, CD or UC.IBD: Inflammatory bowl disease; IBS: Irritable bowel syndrome; CD: Crohn’s disease; UC: Ulcerative colitis; CeD: Celiac’s disease; CTRL: ConTRoL ; PBMC: Peripheral blood mononuclear cell; SVM: Support vector machine; RBF: Radial basis function; RBK: Radial basis kernel.NJO and TMA are co-owners of ArthroChip, LLC. No conflicts exist for other authors.SNH, DAS and DBB were responsible for the collection and cataloging of the patient data. JTT and MAH performed the processing of the patient data. PSC, JLT, NJO and TMA developed the modeling and computational processing of the laboratory data.


The diagnosis and prognosis of several diseases can be shortened through the use of different large-scale genome experiments. In this context, microarrays can generate expression data for a huge set of genes. However, to obtain solid statistical evidence from the resulting data, it is necessary to train and to validate many classification techniques in order to find the best discriminative method. This is a time-consuming process that normally depends on intricate statistical tools.geneCommittee is a web-based interactive tool for routinely evaluating the discriminative classification power of custom hypothesis in the form of biologically relevant gene sets. While the user can work with different gene set collections and several microarray data files to configure specific classification experiments, the tool is able to run several tests in parallel. Provided with a straightforward and intuitive interface, geneCommittee is able to render valuable information for diagnostic analyses and clinical management decisions based on systematically evaluating custom hypothesis over different data sets using complementary classifiers, a key aspect in clinical research.http://sing.ei.uvigo.es/GC/.geneCommittee allows the enrichment of microarrays raw data with gene functional annotations, producing integrated datasets that simplify the construction of better discriminative hypothesis, and allows the creation of a set of complementary classifiers. The trained committees can then be used for clinical research and diagnosis. Full documentation including common use cases and guided analysis workflows is freely available at  In thIn this scenario, the M@CBETH  web servFrom a different perspective, the Signature Evaluation Tool (SET)  providesMoreover, several ensemble strategies have also been proposed for microarray data classification -20 as a Each of these tools presents unique characteristics supporting specific tasks comprising the whole process of constructing and validating prediction rules based on genes. However, an easy-to-use tool assisting systematic evaluation and interpretation of the discriminant ability of biologically relevant gene sets  is still in high demand by clinical researchers. In this context, we present geneCommittee, an on-line web-based tool giving specific support for extensively testing the discriminatory power of biologically relevant genes in microarray data classification.The main objective of our geneCommittee server is two-fold: to contribute to the systematic analysis of the discrimination power of genes that are mainly used by wet-lab researchers and practitioners; using available knowledge regarding genes and related biomedical concepts  to enrich microarrays’ data features for more powerful discrimination of sets of genes. In order to accomplish these goals, custom experiments can be defined using several successful classifiers over different microarray data sets following several validation schemas. To our knowledge, there is no equivalent tool able to facilitate the systematic evaluation of biologically relevant gene sets following a straightforward classification perspective.geneCommittee is implemented as an AJAX-enabled web application programmed in J2SE 1.6 Java language and it is designed to run on a standard Tomcat 6 Web application server. Its source code is publicly available in Github repository  being dii) web interface, (ii) controllers, (iii) execution engine, and (iv) data management. Figure geneCommittee is based on a layered design in which the system was structured in four main tiers:  committee controllers, which manage the committee creation workflow, (ii) the diagnostic controller, which is responsible for the classification of new patients, and (iii) the data management controller, which is in charge of data set handling. Additionally, although not represented in Figure In the web interface, the application logic is managed by a set of specific controllers. The design of this layer is strongly based on the Model-View-ViewModel (MVVM) architectural pattern supported by the ZK framework, which reduces the coupling between the user interface and the controller classes. Depending on their primary role, geneCommittee defines three different types of controllers:  contains an ID and a name. Each cell in this sample/gene matrix specifies the expression value of a given gene corresponding to a specific sample. More detailed information about the format can be obtained in the user manual  files where samples are represented in columns and genes are placed in rows. Therefore, each sample (column) contains a Datasets uploaded to geneCommittee should be previously normalized, as no pre-processing utilities are included. We recommend using the Robust Multichip Average (RMA) normalization technique .i) the initial enrichment of raw datasets and (ii) the later construction, training and validation of interpretable classifiers to finally build an accurate committee of experts. Figure The Committee training area is a key piece in the overall architecture of our geneCommittee server. It implements an easy-to-use and straightforward 6-step wizard for giving specific support to both  specify and configure the classifiers that will be used and (ii) the desired global evaluation strategy. The global evaluation system implemented in geneCommittee is very flexible, allowing both the use of a single classification method or a specific combination of multiple classifiers. Since each classification algorithm has its own advantages and drawbacks, and taking also in consideration that its performance directly depends on the data, our proposal of having a committee of experts allows choosing the best combination of methods for a specific dataset. Regarding this feature, at the moment our geneCommittee server supports five different types of standard classifiers: (i) k-nearest neighbours, (ii) decision trees, (iii) support vector machines, (iv) naïve Bayes, and (v) random forest. According to the needs, the user can select any number of classifiers (belonging to the same type or not) for which each configuration can be individually established.After the gene set enrichment process, it is necessary to  to build a new committee . The whole process carried out by geneCommittee working in Diagnostic mode is showed in Figure Once the selected committee concludes the processing of the new dataset, geneCommittee presents the result of the classification process as showed in Figure The information displayed in Figure •Committee: each row contains the diagnostics of one member (expert) of the committee, that is to say, a classifier trained using only the biological information of its associated gene set. Committee members will select one single condition for each patient.•By Gene Set: this section summarizes the committee member’s diagnostics by grouping the outputs of those experts that share the same gene set. Only the conditions with the highest number of votes are shown.•By Classifier: in the same way as the previous area, this section groups the committee member’s diagnostics taking into consideration the type of the classifier.•Voting: this section summarizes the whole diagnostic process carried out by showing the votes that each condition has received, along with a final row evidencing the condition or conditions with the higher number of votes.The diagnostic view showed in Figure The workflow implemented in our geneCommittee server is based on previous works ,22 in whIn order to easily discover the main advantages of geneCommittee, a guest account was created with a publicly available dataset taken from previous work of Gutiérrez et al. . MicroarBy following the pipeline previously commented and implemented in our geneCommittee server, the first action to be done after the selection of the dataset . These datasets are publicly available in the help menu of our geneCommittee server.i) electing the 300 best genes according to the Chi-square test, (ii) selecting those sets obtained in the enrichment step for guaranteeing a p-value < 0.0001 and a minimum coverage of 150 and (iii) choosing those classifiers (trained with default parameters) with a kappa value > = 0.95 in the evaluation step. This resulted in a final committee composed of 34 experts.Using our geneCommittee server, a diagnostic committee was created with the first dataset using the following straightforward configuration:  integrates a set of well-known classification methods in an ensemble framework; b) enables the enrichment of original dataset variables with biologically relevant features; c) provides training classification solutions that allow to select classifiers based on the biological relevance and/or discriminative power; and d) permits applying the experts, a mix of classifier/variable, to diagnose new patients.In our work, geneCommittee was tested using two different gene expression datasets (i.e.: DNA microarray and RNA-Seq). In both cases, our server has demonstrated a good performance which encourages its suitability for analysing data coming from different gene expression data sources.i) a solid understanding of the relative strengths and weaknesses of underlying classification methods and (ii) a biologically plausible and understandable behaviour of such models from a biological point of view.The main innovation of geneCommittee is related with its ability to combine the advantages of using multiple complementary classifications algorithms with the benefits obtained from the true integration of biological knowledge in the classification process. From this perspective, our geneCommittee server deals with two main prerequisites needed for the development of valuable translational microarray-based diagnostic tools: (Project name: geneCommitteeProject home page: http://sing.ei.uvigo.es/GC/Project source page: https://github.com/michada/GeneCommitteeOperating system: Platform independentProgramming language: JavaOther requirements: Internet browser: Explorer 8, Firefox 3, Opera 9.62 and Safari 3License: GNU GPLThe authors declare that they have no competing interests.All authors contributed equally to conception of geneCommittee. MR-J and JA programmed the final solution. JLO provided use cases and tested the usability of the software. FFR generated geneCommittee documentation. JLO and FFR wrote the paper while MR-J and JPA provided comments and discussion. All authors read and approved the final manuscript.Expert manual. This document will guide the user through a step-by-step tutorial showing the capabilities of geneCommittee for extensively testing the discriminatory power of biologically relevant gene sets in microarray data classification.Click here for file


High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets. Microarrays, as efficient tools to simultaneously monitor the expression of tens of thousands of genes, have been widely applied in both mechanistic and decision-making research during the past decade The extent to which different microarray technologies influence the identification of differential gene expression has been addressed by a large number of studies and is the subject of a review paper The clinical use of array-based diagnostics is relatively late in coming; this is partially due to the demand of a substantial number of patient samples to be used for training, since estimates of a predictor's error rate during model construction are more prone to be biased for small datasetsThe primary issue we addressed is the comparability of models constructed from different platforms. We then further evaluated cross-platform consistency with regard to whether predictive signature features selected on one platform could be directly used to train a model on the other platform and whether predictive models trained using one platform could predict datasets from the other platform with comparable performance. In this study, three commonly-used multi-class machine learning algorithms were applied: fuzzy k-nearest neighbors (FKNN)http://www.fda.gov/nctr/science/centers/toxicoinformatics/ArrayTrack/). Rather than a binary score, the outcome variable selected was the RHI (Response to Hepatocellular Injury) score, which ranges from 0 to 2 that are associated with the severity of chemically-induced hepatotoxicity The same datasets and the way in which they were divided into training and test sets have been previously described A prerequisite for platform comparison is that all datasets are represented by a common set of probes. Three different approaches were used to identify probes associated with the same transcript: SeqMap, RefSeq, and Unigene, resulting in 4860, 6312, and 9954 common transcriptsFigure S1) were designed to ensure that both datasets matched in comparison. AC 1 utilized the original datasets , while AFX datasets in AC 2 were converted to ratio and compared with the AGL ratio data, and AGL datasets in AC3 were converted to intensity and compared with the AFX intensity data. Briefly, the Affymetrix ratio data was calculated using its intensity data in a way similar to Agilent platform, i.e., treated samples were compared to an average of the corresponding samples. In AC3, the intensity data in AGL is the average value of Cy3 and Cy5 corresponding only to the treated samples. Note that the 318 arrays profiled from the same samples using both platforms were used in AC 2 and AC 3, while AFX in AC 1 retained the original 418 arrays. Combined with the three classification algorithms , a total of 27 comparisons were carried out, corresponding to 3 ACs, each with 3 classifiers, and each classifier having 3 probe-mapping methods. Detailed information on the datasets, probe mapping procedures, and ACs has been published previously Due to the technological difference in experimental design between Affymetrix (intensity) and Agilent 2-color (ratio) platforms, three analysis configurations  were transferred to the training set of the other platform  to train another classifier. This procedure was repeated 500 times, and the overall prediction accuracy as well as the prediction accuracy for each subclass was calculated and recorded.Next, signature genes selected in the best classifier) developed on the training set of one platform  was transferred to predict the test set of the other platform . The obtained prediction performance of test sets from both platforms for the 500 repetitions of the procedure were recorded and compared.Lastly, in order to evaluate whether classifiers developed from one platform could perform well on the other platform, the whole classifier , A and B obtained from 500 iterations, respectively, and SD is the standard deviation of . This has the potential to improve the diagnostic use of array-based predictive models by avoiding the additional work and complexity of selecting different predictive signature genes for each platform, and allowing the combination of smaller datasets from multiple platforms that are not large enough on their own to obtain a highly informative gene set.Table S3), the results suggest that the transferability of predictive classifiers with respect to the overall performance was acceptable, except for the transfer of AFX to AGL using AC 1. Based on the previous observation of the effect of the additional 100 control samples on the transferability between AFX and AGL, we decided to conduct another analysis using the 318 common samples. The resulting decreased difference between the predictive accuracy for the AFX and AGL test sets  further confirmed the acceptable transferability of predictive classifiers. Moreover, further analysis combining data sets from AFX and AGL platforms confirmed that the classifiers trained by the combined data sets performed well for independent data sets from both AFX and AGL platforms (Table S4). Generally, probe-mapping methods and classification algorithms did not evidently impact on either the overall model performance or the transferability between different platforms.Figure S6 in which different classification algorithms were utilized, predictive models trained with AGL data show similar performance when applied to the AGL or AFX test set. Models built with the AFX data show greatly reduced predictive accuracy in the AGL test set as compared to the AFX test set, particularly for samples with scores of 1 or 2. This finding was further verified by Table S5. This performance deficit appears to be consistent across both probe-mapping methods and classification algorithms.The predictive performance for the individual sub-classes, however, shows a much different pattern. As shown in We found that predictive classifiers trained on one platform could predict datasets profiled using another platform with acceptable overall predictive performance, despite slight differences between different directionality of transfer and analysis configurations (ACs). However, when the transferability was considered for each subclass, the performance of the test set that corresponded to the data used to train the model was noticeably better. Generally, the transferability of AFX to AGL was relatively poor , while the transferability of AGL to AFX was much better. As was observed consistently in this study, probe-mapping methods and classification algorithms did not impact significantly on either model performance or the overall transferability.The diversity of microarray platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments to construct array-based diagnostic models. Thus, in this study, we investigated the consistency of multiple-class prediction models generated using datasets from different platforms in three aspects: the comparability of model performance from different platforms, whether predictive signature genes selected from one platform could be directly utilized to train another model on the other platform, and whether classifiers trained from one platform could predict datasets profiled from the other platform with comparable performance. The results supported the potential applications in biological and medical decision-making for cross-platform analyses of both new and existing microarray datasets. Moreover, probe-mapping methods and classification algorithms did not exert an apparent affect on either model performance or consistency between microarray platforms. However, the relatively high concordance achieved in this benchmark investigation is only the beginning of exploring cross-platform consistency because it is based on two microarray datasets generated on identical biological samples using different platforms, i.e., this investigation is mainly focused on a technical platform comparison. Undoubtedly, further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets.Figure S1Three analysis configurations (ACs 1-3) used in this study.(TIF)Click here for additional data file.Figure S2Model performance for AFX after removing the additional 100 control samples in platform comparison using AC 1.(TIF)Click here for additional data file.Figure S3Transferability of predictive signature genes. (a) Prediction accuracy for samples in each subclass using LDA in the transfer of AFX to AGL. (b) Prediction accuracy for samples in each subclass using LDA in the transfer of AGL to AFX. (c) Prediction accuracy for samples in each subclass using SVM in the transfer of AFX to AGL. (d) Prediction accuracy for samples in each subclass using SVM in the transfer of AGL to AFX.(TIF)Click here for additional data file.Figure S4Model performance in transferability analysis of predictive signature genes using AC 1 after removing the additional 100 control samples in AFX.(TIF)Click here for additional data file.Figure S5Overall model performance for AC 1 in transferability analysis of predictive classifiers after removing the additional 100 control samples in AFX.(TIF)Click here for additional data file.Figure S6Transferability of predictive classifiers. (a) Prediction accuracy for samples in each subclass using LDA in the transfer of AFX to AGL. (b) Prediction accuracy for samples in each subclass using LDA in the transfer of AGL to AFX. (c) Prediction accuracy for samples in each subclass using SVM in the transfer of AFX to AGL. (d) Prediction accuracy for samples in each subclass using SVM in the transfer of AGL to AFX.(TIF)Click here for additional data file.Table S1Overall prediction accuracy and corresponding (DOC)Click here for additional data file.Table S2(DOC)Click here for additional data file.Table S3Overall prediction accuracy and corresponding (DOC)Click here for additional data file.Table S4Prediction accuracy for models generated from the combined data.(DOC)Click here for additional data file.Table S5(DOC)Click here for additional data file.Methods S1(DOC)Click here for additional data file.


The use of DNA microarrays and oligonucleotide chips of high density in modern biomedical research provides complex, high dimensional data which have been proven to convey crucial information about gene expression levels and to play an important role in disease diagnosis. Therefore, there is a need for developing new, robust statistical techniques to analyze these data.depthTools is an R package for a robust statistical analysis of gene expression data, based on an efficient implementation of a feasible notion of depth, the Modified Band Depth. This software includes several visualization and inference tools successfully applied to high dimensional gene expression data. A user-friendly interface is also provided via an R-commander plugin.depthTools package, that could be used, for instance, to achieve a better understanding of genome-level variation between tumors and to facilitate the development of personalized treatments.We illustrate the utility of the  The DNA microarrays and the oligonucleotide chips of high density are broadly used in modern biomedical research and in the study of numerous diseases like cancer or diabetes; also, in the last few years, the validity of this technology has become common in differentiation and development studies and in prenatal diagnostic testing for syndromes that involve small changes on chromosomes which are not seen through a microscope-6. MicroMicroarray gene expression data are complex and high dimensional  and suggest numerous statistical problems; to fully take advantage of the information conveyed by this technology and its impact in the understanding of living processes sound analyses of these data are needed. In this direction, a cohort of techniques, like for instance classifier algorithms, have been developed for gene expression data. A particularly intuitive, computationally inexpensive, and effective collection of methods suitable for the analysis of microarray data is the one proposed by. This coG given groups (multi-class classification). This can translate into a more reliable diagnosis, based on sample profiles. These methods have been successfully applied to real microarray data and have been proven to be robust, efficient, and competitive with other procedures proposed in the literature, outperforming them in several situations, the band containing the proportion p of most central samples. The area of this band for each p defines the scale curve of the collection of samples, and the slope of the scale curve shows how the dispersion varies in the data.Given the ordering defined by the function scalecurve computes the scale curve of the n × d data matrix x and draws the corresponding plot by typing scalecurve(x). To allow the comparison of scale curves from different collections of samples  there is an optional argument y, which takes as value a vector defining the class of each observation in x.The function An application to the normal and tumor prostate samples is as follows:As it can be seen in FigureR.test:Function scalecurve, it is also possible to decide whether two sets of samples come from the same population using the statistical rank test .As we can see, classDS and classTAD:Functions xl, in which the class of each sample, yl, is known, and a test set, xt, whose samples are to be classified into one of G known classes. In the DS procedure, which computes the trimmed mean of each group and classifies a new observation y from the test set in the class having the trimmed mean which is closest to y, we also need to specify the proportion alpha of observations that are trimmed out when computing this mean. In the TAD procedure, to determine the class of the new sample y, a weighted average distance from y to the most central points in each class is computed. The proportion alpha of most external points that are not considered in this weighted distance has to be specified as well. The output in both cases is a vector containing the class predicted for each observation in the test set.These two functions implement the depth-based classification techniques DS and TAD, described in. As any As an example, we use the prostate data, considering the 20 first samples from each class as the learning set and the remaining samples as the test set:Both methods classify correctly 9 out of 10 prostate samples. Notice that these techniques have been previously validated, and have been proven to be robust and very appropriate to analyze the complicated structures of gene expression data.This is a normalized subset of the real data published by. The rawd-dimensional point yi =  in the sample y1,…,yn is computed asThe MBD for a yi inside the bands defined by every two different multidimensional points from the sample[for loops, which are known to be very inefficient in the R environment. Thus, to improve the computational cost of the MBD we used an alternative expression, developed in[n × d matrix Y, we can calculate the multiplicity of each value yi,k in the corresponding k-th column of Y, rather than exhaustively search for all pairs of samples, and use this multiplicity to obtain the MBD of yi. It can be easily shown that this efficient expression can be computed by rewriting the MBD as follows. Given a data set Y = {y1,...,yn} and a point yi ∈ Y, consider matricesand represents the average proportion of coordinates of e sample. The imploped in. If we sY has been increasingly ordered. Let lk be the smallest index in the k-th column ofηk be the multiplicity of yi,k within the same column. Then, the MBD of yi is given bywhere each column from ηk = 1.assuming thatTo see this, note thatfor loops, and reduces the computational time, for n data points in d dimension, from O(d × n2)  to O(n3/4) (to compute the expression above), plus O(d) to reorder the data, using a variant of that of[This is an alternative to the computation recently published by, in whic that of. This isx = , x ∉ Y, that is, the depth of sample x with respect to a data set which does not contain it. This is given byAn analogous expression can be obtained to compute the MBD of new observations lk1, = |{yi),k,k, and of the ordering from center outwards that is derived from this depth. The computational cost of the MBD is drastically improved if we use an implementation based on reordering the columns of the data matrix rather than on exhaustively searching for all possible pairs of samples. An additional plugin for the R-commander has been designed to provide a friendly interface to users who are not familiar with the command line code. These methods can be easily applied to achieve a better understanding of genome-level variation between tumors and to facilitate the development of personalized treatments.We have developed depthTools package implements the statistical tools based on the MBD in a very efficient way, greatly improving the computational cost of the original definition. It allows users to order from center outwards the samples in a high dimensional dataset like gene expression data, and to visualize the change in the dispersion through the scale curve or the central regions defined by different percentages of the most central samples. It is also possible to use the rank test to decide whether two sets of biological samples have the same disease status, and to identify the status of a new sample by means of the classification techniques DS and TAD. These tools will ultimately be useful for rapid diagnosis and efficient treatments. The R-commander plugin facilitates the use of the package avoiding the command line, and allows a wider use of these statistical methods.The http://www.R-project.org) and are freely available athttp://cran.r-project.org/. The packages are accompanied by documentation files to facilitate their use.The package and the R-commander plugin have been developed for the statistical R environment (Project name: depthToolsProject home page:http://cran.r-project.org/web/packages/depthTools/index.html andhttp://cran.r-project.org/web/packages/RcmdrPlugin.depthTools/index.htmlOperating system(s): Platform independent.Programming language: R platform.Other requirements: No.License: GPL (≥ 2)Any restrictions to use: It is available for free download.The authors declare that they have no competing interests.depthTools and Rcmdr.depthTools packages. SLP and JR designed the statistical methods. All authors have read and approved the final version of the manuscript.SLP and AT developed the algorithms and prepared the implementation of the