Understanding the root molecular and genetic causes driving complex traits is a fundamental challenge in genomics and genetics. Numerous studies have used variation in gene expression to understand complex traits, but the underlying genomic variation that contributes to these expression changes is not well understood. In this study, we developed a framework to integrate gene expression and genotype data to identify biological differences between samples from opposing complex trait classes that are driven by expression changes and genotypic variation. This framework utilizes pathway analysis and multi-task learning to build a predictive model and discover pathways relevant to the complex trait of interest. We simulated expression and genotype data to test the predictive ability of our framework and to measure how well it uncovered pathways with genes both differentially expressed and genetically associated with a complex trait. We found that the predictive performance of the multi-task model was comparable to other similar methods. Also, methods like multi-task learning that considered enrichment analysis scores from both data sets found pathways with both genetic and expression differences related to the phenotype. We used our framework to analyze differences between estrogen receptor (ER) positive and negative breast cancer samples. An analysis of the top 15 gene sets from the multi-task model showed they were all related to estrogen, steroids, cell signaling, or the cell cycle. Although our study suggests that multi-task learning does not enhance predictive accuracy, the models generated by our framework do provide valuable biological pathway knowledge for complex traits. A fundamental challenge in genomics is discovering and understanding the molecular and genetic basis of complex traits. A deeper understanding of complex traits will potentially lead to a better diagnosis and treatment of complex diseases. A number of studies have used gene expression assays to model, at a molecular level, direct influences driving phenotypic variation. A shortcoming of this approach is that gene expression differences may be driven by many genomic and environmental factors, including underlying genetic variation Several previous efforts have explored integrating different genomic data types ERBB2, an important breast cancer gene, simultaneously showed amplification, loss of heterozygosity, loss of methylation, and a drastic increase in gene expression.Other studies searched for significant differences in individual genes for multiple genomic data Genome-wide association studies generally utilize genotype data by itself, but a few studies have integrated this genotype data with other genomic data. One study integrated genotype data with gene expression data for various cancer types to find genes with expression changes driven by genotype differences Some studies performed a pathway-level integrative analysis Our study provides a new framework for integrating different genomic data types that consists of two key steps, sample-specific pathway analysis and multi-task learning, that individually have proven useful in classification analyses but have never been used together. Most previous integrative approaches performed sequential or independent analyses of each data type. Our method differs from these approaches in that genome-wide expression and genotype data, encoded as pathway enrichment scores, are simultaneously used to build the final predictive model. This eliminates the restriction of using results from one independent analysis to filter results from the other, and instead allows the model to equally and simultaneously consider data from each experiment.Gene set analysis explores biological data in the context of pathways. This approach examines the simultaneous enrichment of multiple genes belonging to particular pathways, in contrast to single-gene analysis, which searches for differences in individual genes. For complex traits, many phenotypic differences are associated with perturbations in specific pathways Although gene set enrichment analysis can improve the interpretability of results Multi-task learning In order to examine the ability of multi-task learning to simultaneously utilize similar and different pathway enrichment properties in our study, we compared the predictive ability of multi-task learning to single-task learning and a concatenated data learning model. Single-task learning independently builds separate models for each task and does not consider whether there is similar or different information between tasks. In this study, we performed single-task learning by independently using a standard SVM to build a predictive model for each data type. A concatenated data model combines all data together by simply concatenating it into a single data set to take advantage of all information together, but it does not distinguish which task the information originated from. In this study, we built concatenated data models by combining all of the enrichment scores for all data types together into a single data set and used a standard SVM to build a single predictive model.Multi-task learning builds a model that attempts to take advantage of the strengths of both single-task learning and concatenated data models. It does this by calculating a common effect shared among all tasks (see Methods), similar to a concatenated data model. At the same time, it determines a task-specific effect that is unique to each task (see Methods), similar to a single-task model. Successful multi-task learning models should show an improvement in predicative performance when compared to a single-task model and a concatenated data model. The framework we describe here can integrate several different types of genomic data with each sample having been assigned to one of two phenotypic classes, along with a collection of gene sets. It uses this to produce a predictive model that can also identify gene sets important in distinguishing phenotype.In this study, we examine the performance of this framework under a variety of conditions, and determine how useful this framework is for genomic data. Although our framework can be used to integrate many different genomic data types, this study focuses on the integration of gene expression and genotype data. Incorporating genotype data required the development of a novel method for obtaining sample-specific enrichment scores for this data. To test the performance of our framework, we generated simulated data and compared the predictive accuracy of multi-task learning to single-task learning and a concatenated data model. Results show that multi-task learning has a similar predictive accuracy as the single-task learning and concatenated data models. We also show that models that consider all tasks, such as multi-task or concatenated data models, are better at discovering gene sets with pathways containing genes that are both differentially expressed and genetically associated with a phenotype. We also used our framework to explore differences between estrogen receptor (ER) positive and negative breast cancer. The top 15 gene sets from the multi-task model were involved with estrogen, steroids, cell signaling, or the cell cycle.An overview of the analysis pipeline for integrating gene expression and genotype data within our framework is presented in In step one, ASSESS takes as input gene-based genomic data for samples belonging to one of two phenotypic classes. It then produces sample-specific enrichment scores for a collection of gene sets. To do this, it first calculates a correlation statistic for each gene in each sample that represents the degree to which the gene-based data matches the summary profile of that gene in samples from one phenotype class compared to the other . Then, iIn step two, we use the enrichment scores from ASSESS that are calculated independently for several different data types as the input tasks to the multi-task model . Multi-tsummed prediction model that sums the predictions from the single-task models for each data type to obtain a single prediction for each sample. Second, we created a summed enrichment score model that sums the ASSESS enrichment scores for each gene set in each sample and uses these summed enrichment scores within a single-task model to obtain a single prediction. Third, we used a merged model that takes enrichment scores from ASSESS for each data type for a given sample and merges them into a single feature vector of enrichment scores for that sample. We used these merged enrichment scores with a single-task model to obtain a single prediction.Since these methods do not treat matched data, meaning expression and genotype data are from the same samples, differently from unmatched data, they may fail utilize important information if the data is matched. To address this, we explored models that specially consider multiple data from matched samples that produce a single prediction for each sample. First, we used a ASSESS was previously developed for use with gene expression data, but not genotype data. Therefore, we first extended ASSESS to obtain sample-specific enrichment scores for genotype data. We next evaluated the performance of our framework by simulating multiple data sets to explore the following questions: 1) does the similarity of tasks influence the predictive performance of a multi-task model; 2) does the number of samples impact the predictive performance; 3) does the number of tasks influence the predictive performance; and 4) does an integrated approach improve our ability to discover pathways that are enriched in several data types. Finally, we applied our framework to a breast cancer data set to analyze differences between ER+ and ER- samples.To facilitate the integration of genotype data with gene expression, we extended the previously developed software package ASSESS to obtain sample-specific gene set enrichment scores for genotype data. A key challenge in using any gene-based method for the analysis of genotype data is mapping the SNP-based data to the gene level. We designed our framework to select a single SNP to represent each gene . For allAfter mapping the genotype data to the gene level, we calculated the ASSESS correlation statistic for each sample and gene  by compaMulti-task learning may offer an advantage when there is a balance of similar and different pathway enrichment among different data types. If there is too much similar enrichment, then multi-task learning may not outperform a concatenated data model. If the enrichment is too different, then a multi-task model may not outperform single-task learning. To test the performance of our multi-task framework, we created a simulation to compare the predictive accuracy of a multi-task model to a single-task and concatenated data model with varying similarity in the tasks.We simulated gene expression and genotype data with gene sets belonging to one of the following gene set types:10 genes that were differentially expressed between the two phenotype classes and genetically associated with phenotype;10 genes that were differentially expressed but not genetically associated;10 genes that were not differentially expressed, but genetically associated;10 genes that were neither differentially expressed nor genetically associated.We generated data for 5 experimental scenarios, each with a varied number of gene sets from each gene set type . Data wiFor each scenario, we simulated matched expression and genotype data for 50 training samples, which were equally split into 2 phenotypes. The data was matched such that the expression level of a gene for a given sample was generated taking into account the genotype of the SNP associated with that gene for that sample (see Methods). We used our multi-task framework to train predictive models with these samples. Then, we used these models to obtain predictions for 50 test samples as to which phenotypic class they belong to. We also used the same data and ASSESS-based enrichment scores to evaluate single-task SVMs and an SVM with the expression and genotype enrichment scores concatenated. In addition, we used the same enrichment scores to evaluate the summed prediction, summed enrichment score, and merged models, which utilize matched data. We repeated this procedure 200 times to obtain 10,000 predictions for each scenario and calculated the percentage of correct predictions for each scenario and SVM model type , and 4.For the expression data, the predictive performance was similar for all scenarios and model types . For theWe next determined the effect that sample size has on our multi-task framework when compared to a single-task or concatenated data model. To do this, we first simulated matched expression and genotype data using gene sets from scenario 3. In the previous analysis, we used 50 samples to train the model. In this analysis, we varied the number of training samples from 10 to 200. As above, we used the training samples to build a multi-task, single-task, and concatenated data model, and we simulated an equal number of test samples to generate predictions. We also evaluated the summed prediction, summed enrichment score, and merged models, which utilize matched data. We repeated to obtain 10,000 predictions for each number of samples and calculated the percentage of correct predictions for each number of samples and each type of model , and 7.For the expression data, the predictive accuracy was similar among all model types . For theWe also evaluated the effect of varying the number of tasks. We generated expression data sets, each corresponding to a task, with 20 training samples evenly divided into 2 phenotypes. We simulated phenotype associated gene sets with 10 genes that were differentially expressed between the phenotypes, and background gene sets with 10 genes that represented a null model of random expression. We generated a task by simulating the first 30 gene sets as phenotype associated gene sets and the next 20 gene sets as background. For the last 50 gene sets, 30 were randomly chosen to be phenotype associated and the other 20 background. We generated additional tasks in the same way. As a result, the first 50 gene sets contained similar enrichment among all tasks, and the last 50 gene sets contained enrichment unique to each task. We used this data to build multi-task models with the number of tasks used to build each model varying from 2 to 100. We also used this data one task at a time to build single-task models for comparison. After using the 20 training samples for each task to train the model, we used 20 test samples for each task to obtain predictions. We repeated to obtain 10,000 predictions for each number of tasks and determined the percentage of correct predictions for each number of tasks . We alsoFor the expression data, the predictive accuracies of all multi-task models and of the single-task model showed no significant difference . For theIn addition to being used for class prediction of unknown samples, we can analyze the trained models to determine whether this integrative approach provides an improved ability to discover gene sets enriched across multiple data types. A predictive weight for each gene set can be derived from the predictive model that results from an SVM analysis (see Methods). Gene sets with higher weights contribute more to prediction, and also may be more important in distinguishing phenotype. After training a multi-task SVM model, a common weight can be derived that is interpreted as a measure of importance for prediction derived from all tasks (see Methods). Gene sets with larger common weights can be viewed as sharing common information important for prediction across all tasks. These gene sets may represent biological pathways with important factors in multiple data types that are influencing phenotype. We designed the following simulation to determine the ability of our framework to discover this type of gene set.We simulated matched expression and genotype data for 400 samples that were evenly divided into 2 phenotypes. This data contained the same gene set types as the first simulation study. We created this data set with 1 of gene set type 1 (the target gene set), 5 of gene set type 2, 5 of gene set type 3, and 89 of gene set type 4. We wanted to test the ability of our framework to extract the one target gene set, which contains genes that are both differentially expressed and genetically associated. We used our framework to train multi-task, single-task, and concatenated data predictive models. For multi-task, we determined the rank of the common weight in the predictive model for the target gene set. For single-task, we calculated the rank of the weight in the predictive model for the target gene set in the expression model and the genotype model separately. For the concatenated data model, we calculated the rank of the target gene set in a single predictive model built by concatenating the enrichment scores of the expression and genotype data. We also took the sum of the weights for both single-task models and determined the rank of the combined weight for the target gene set. In addition, we trained the summed enrichment score and merged predictive models. For the summed enrichment score model, we calculated the rank of the target gene set in a single predictive model built by taking the sum of the enrichment scores from the expression and genotype data. For the merged model, we built a single predictive model by taking the enrichment scores from the expression and genotype data and merging them into a single feature vector for each sample. We then took the sum of the expression and genotype weights for each gene set and determined the rank of the combined weight for the target gene set. We repeated this analysis 1000 times and calculated the average rank for each model type .The average rank for the target gene set was significantly lower in all models that considered both tasks  compared to either of the separate single-task models . This suTo provide support for the usefulness of our framework in identifying phenotypically relevant pathways, we applied it to matched expression and genotype data for breast invasive carcinomas (BRCA) generated through The Cancer Genome Atlas (TCGA) project. This data set contained 61 samples that were classified as estrogen receptor (ER) negative and 203 samples that were ER positive. Our collection of gene sets was compiled from the curated canonical pathways in the Molecular Signatures Database (MSigDB) First, we wanted to determine the ability of the data to predict ER status. We performed leave-one-out (LOO) cross-validation to calculate predictive accuracy for multi-task, single-task, concatenated, summed prediction, summed enrichment score, and merged models , and 11.We next examined gene sets with the highest weights in the predictive models for their biological relevance to ER status. To allow for a direct comparison of the predictive weights among gene sets, we first normalized the enrichment scores from ASSESS (see Methods). We calculated the ranks of all gene sets for multi-task, single-task, concatenated, summed enrichment score, and merged models. A complete list of all ranks and weights for all gene sets and model types is presented in A list of the top 15 gene sets with the highest common weight in the multi-task model is presented in ERBB2, influences the expression and activity of the estrogen receptor ESR1) gene. The “Nuclear Receptor Transcription” gene set also contains the ESR1 gene, and nuclear receptor coactivators are thought to participate with the estrogen receptor pathway Estrogen plays an important role in breast cancer Estrogen is a steroid hormone, and we found that four of the top 15 gene sets were involved with the synthesis or metabolism of steroids: “Steroid Hormone Biosynthesis” (rank 3), “Steroid Biosynthesis” (rank 8), “Cholesterol Biosynthesis” (rank 9), and “Steroid Metabolism” (rank 15). In addition to estrogen, other steroid hormones, such as progesterone, play an important role in breast cancer The estrogen receptor participates in cellular signaling initiated by the binding of estrogen and facilitating the activation of downstream processes. In addition to the estrogen-related pathways, three of the top 15 gene sets were similarly involved with other types of cell signaling: “FRS2-Mediated Cascade” (rank 4), “Neurotransmitter Release Cycle” (rank 6), and “ECM-Receptor Interaction” (rank 12). The FRS2-mediated cascade links Fibroblast Growth Factor Receptor (FGFR) to the eventual activation of several important signaling pathways. One study showed that blocking FGFR inhibited breast cancer proliferation and led to downregulation of the MAPK and PI3K pathways Tumors accumulate genetic damage that results in a perturbed cell cycle which increases the number of tumor cells by stimulating cell birth or inhibiting cell death or cell-cycle arrest Although the simulation study showed that our integrative framework provided an improved ability to discover pathways that are enriched over multiple data types, multi-task learning performed about the same as similar integrated learning methods. Also, the predictive accuracy of multi-task learning was practically the same as similar learning methods. All of these results suggest that while an integrated pathway approach may be useful for discovering relevant pathways, it may not be necessary to use multi-task learning for most studies. Further research should explore alternative prediction methods.This study focused on the integration of gene expression and genotype data. However, our framework may also be suitable for other genomic data types, such as copy number variation and DNA methylation. Also, the sample-specific enrichment scores from multiple data types can be used for many sample-level pathway-based analyses, such as clustering to find subtypes of samples with similar pathway enrichment profiles.Results from this study indicate that a pathway-based integrative analysis is a promising approach to identify pathways that are influenced by both gene expression changes and genotype variation. All of the top 15 pathways from the multi-task model built using breast cancer data have been previously associated with breast cancer. This suggests that an integrative approach may be useful for discovering pathways related to complex diseases, especially diseases that are not as well understood, and for determining the contribution that each data type has for each pathway. The “Phase II Conjugation” gene set is an example that had a strong association in both the expression and genotype data, and this gene set had the second highest multi-task common weight, which was higher than in either of the single-task models alone. This supports the use of an integrative approach in discovering gene sets that may have a direct link between genotype and expression.Our integrative framework contains two keys steps: 1) pathway enrichment analysis using ASSESS and 2) building a predictive model using an SVM. This framework is designed for integrating different genomic data types into a predictive model for samples that have been designated into one of two phenotypic classes.i-th sample and j-th gene, and 1C and 2C are the two phenotypic classes. Next, ASSESS independently uses the correlation statistics for each sample to compute enrichment scores for each gene set using a weighted Kolmogorov-Smirnov statistic. The original implementation of ASSESS includes two metrics for calculating correlation statistics for expression data.To perform the gene set analysis step of our framework, we used a software package called ASSESS To normalize the original enrichment scores, we permuted the class labels and recalculated new background enrichment scores 1000 times. If the original enrichment score for a sample and gene set was positive, this score was divided by the average of the positive background enrichment scores for that sample and gene set. If the original enrichment score was negative, this score was divided by the absolute value of the average of the negative background enrichment scores.i-th sample for the j-th gene in class 1, and To calculate gene set enrichment scores for genotype data, we extended ASSESS. The first step is associating SNP-based genotype data with genes. To do this, we first identify all SNPs that are within a pre-defined distance surrounding and including a given gene. Then, we use Person's chi-square test to determine the extent to which each SNP correlates with phenotype. Finally, we select the SNP that has the maximum correlation with phenotype as the “representative” SNP for that gene. After obtaining gene level data, each correlation statistic is calculated asiα. These sample weights are used to derive predictive weights for each gene set asC is a regularization parameter, iy is the class assignment of the i-th sample and ix is data for the i-th sample.To perform the predictive modeling step of our framework, we used a software package called SVM-Light μ is a positive parameter that controls the relatedness of the models, and stδ = 1 if s and t belong to the same task, stδ = 0 otherwise. The SVM trains a predictive model by calculating nonnegative Lagrange multipliers for each sample and task, C is a regularization parameter, i-th sample in task t, and i-th sample in task t. These weights are used to calculate common weights for each gene set asT is the number of tasks. The common weights are summed with the task-specific effects for each gene set and task to compute the final predictive weights.To utilize a multi-task framework, we used regularized multi-task learning We simulated data similar to a previous study that integrated gene expression and genotype data for pathway analysis N is the number of causal SNPs, i-th sample for the j-th SNP, jβ is the log of the heterozygote odds ratio for the j-th SNP, and ie is an error term for the i-th sample drawn from a standard normal distribution. We randomly assigned each sample to either class 1 or class 2, with the probability of being assigned to class 1 equal to the probability calculated in the model above. We also generated random genotype data using PLINK. For the random genotype data, we drew allele frequencies from Beta and assigned a heterozygote odds ratio of 1.Each genotype data set contained genes that were either genetically associated or had a random genotype. We mapped genes that were genetically associated to a single causal SNP, and we mapped genes that had a random genotype to a single random SNP. We simulated the causal SNPs based on parameters estimated from genotype information for glioblastoma generated through The Cancer Genome Atlas (TCGA) project μ and the covariance matrix Σ of the genes in the P53PATHWAY. We used this to generate baseline expression levels by drawing from a multivariate normal distribution, 0X∼N. We added a disease effect to these genes by linking each gene to a causal SNP and calculated the final expression level asi-th sample for the j-th gene, i-th sample for the j-th SNP, and 0μ and determined the standard deviation of all genes and the average as 0σ. We used these parameters to generate random expression levels by drawing from a normal distribution, X∼N.Each gene expression data set contained genes that were either differentially expressed or had random expression. We simulated the expression data based on the TCGA glioblastoma study. We based genes that were differentially expressed on the P53PATHWAY. We calculated the mean vector http://cancergenome.nih.gov). We selected samples that provided matched gene expression and genotype data. We filtered samples to only include patients who were white, female, 40 to 70 years of age at initial diagnosis, and had a known estrogen receptor (ER) status of positive or negative. We also eliminated the sample with barcode “TCGA-A2-A0CY” because of unreliable genotype data. This resulted in a data set of matched gene expression and genotype data for 61 ER negative samples and 203 ER positive samples.We obtained breast invasive carcinoma (BRCA) data generated through The Cancer Genome Atlas (TCGA) project from their data portal Click here for additional data file.Table S2Predictive weights in breast cancer analysis. The predictive weights for the breast cancer data using multi-task, single-task, concatenated, summed enrichment score, and merged models.(XLSX)Click here for additional data file.


Diagnostic accuracy of lymphoma, a heterogeneous cancer, is essential for patient management. Several ancillary tests including immunophenotyping, and sometimes cytogenetics and PCR are required to aid histological diagnosis. In this proof of principle study, gene expression microarray was evaluated as a single platform test in the differential diagnosis of common lymphoma subtypes and reactive lymphadenopathy (RL) in lymph node biopsies.116 lymph node biopsies diagnosed as RL, classical Hodgkin lymphoma (cHL), diffuse large B cell lymphoma (DLBCL) or follicular lymphoma (FL) were assayed by mRNA microarray. Three supervised classification strategies  using diagonal linear discriminant analysis was performed on training sets of array data and the classification error rates calculated by leave one out cross-validation. The independent error rate was then evaluated by testing the identified gene classifiers on an independent (test) set of array data.LMO2), Chemokine (C-C motif) ligand 22 (CCL22) and Cyclin-dependent kinase inhibitor-3 (CDK3) specifically for FL, cHL and DLBCL subtypes respectively.The binary classifications provided prediction accuracies, between a subtype of interest and the remaining samples, of 88.5%, 82.8%, 82.8% and 80.0% for FL, cHL, DLBCL, and RL respectively. Identified gene classifiers include LIM domain only-2 (This study highlights the ability of gene expression profiling to distinguish lymphoma from reactive conditions and classify the major subtypes of lymphoma in a diagnostic setting. A cost-effective single platform "mini-chip" assay could, in principle, be developed to aid the quick diagnosis of lymph node biopsies with the potential to incorporate other pathological entities into such an assay. The increasing complexity of lymphoma diagnosis and classification is the result of a vastly improved understanding of its underlying molecular pathogenesis and resultant attempts to group subtypes of lymphoma in a clinically and biologically meaningful manner. From a practical perspective, this increasing complexity places great demands on the pathologist. Accurate diagnosis and classification of lymphoma in lymph node biopsies requires pathological evaluation utilising morphological analysis of an acceptable biopsy specimen, together with a series of supplementary tests including immunophenotyping by immunohistochemistry and flow cytometry, and increasingly cytogenetics, FISH and PCR data . Such anGenome-wide gene expression profiling (GEP) is a novel approach to disease classification based on the molecular biology of the disease. This 'genetic fingerprint' data thus allows the identification and classification of individual tissue samples according to their distinct gene expression profiles. There is a significant body of research employing GEP in lymphoma, having been used for a number of purposes including distinguishing closely related lymphoma phenotypes such as diffuse large B-Cell lymphoma (DLBCL) versus Burkitt lymphoma  and DLBCDistinct from previous microarray studies of lymphoma, this single institute study evaluated whether gene expression microarray as a single platform could be used to distinguish three major subtypes of lymphoma and non-malignant reactive lymphadenopathy (RL) in individual lymph node samples. To our knowledge, this study is a first attempt to apply such a strategy to lymph node specimens across different subtype diagnoses in a diagnostic setting. Our heterogeneous study set enabled the identification of gene signatures that are likely an accurate representation of each diagnostic type, given that this was determined by comparing each diagnostic type against the remaining cases by binary classification approach.3) was collected in RNAlater solution  and then cryopreserved in liquid nitrogen. A further five biopsy samples  were obtained from the Newcastle Mater Misericordiae Hospital. The diagnosis and classification of each specimen was made or reviewed by an expert haematopathologist (JT) practising at our centre [n = 16) of DLBCL were subclassified into germinal centre B (GCB) and non-GCB cell types (n = 5 and n=11 respectively) by immunohistochemistry according to the Hans algorithm [n = 16), reactive hyperplasia with progressive germinal centre transformation (n = 3) or granuloma related to toxoplasmosis (n = 1), dermatopathic lymphadenitis (n = 1) and normal (n = 2).Lymph node specimens from patients undergoing biopsy for suspected lymphoma were identified by the Department of Pathology at St Vincent's Hospital, Sydney. Samples were collected subject to written consent for this human research ethics approved study (H00/028/1). A portion of the fresh biopsy specimen  using a pellet pestle and total RNA isolated using RNeasy micro-column purification . The integrity of total RNA was assessed by denaturing agarose gel electrophoresis . Only samples with distinct 28 S and 18 S RNA bands were assayed by microarray. The total RNA extracted from granulocyte colony-stimulating factor mobilised peripheral blood stem cells (PBSC) samples from 10 healthy individuals (collected with informed consent) were pooled and used as the reference RNA for microarray assays. Two-colour fluorescent probe synthesis was carried out on 2 μg of patient (Cy5 - Red) and reference (Cy3 - Green) RNA and then competitively hybridised to a microarray using the 3DNA 900MPX kit  according to the manufacturer's protocol. A GenePix 4000A scanner and GenePix Pro 3.0 image analysis software  was used to capture microarray images and quantify fluorescent signals from each feature. The microarrays used in this study were printed by the Adelaide Microarray Facility  using the Compugen library of 19000 70-mer oligonucleotides, which covers over 12000 generic human genes.http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO Series accession number GSE23647. This study is comprised from two batches of arrays developed over two years. The batches were analysed and normalised separately to maintain the independence of the two datasets.Data from GenePix result files were pre-processed by within-array print-tip Lowess normalisation. The quality of each array was assessed prior to analysis to ensure only arrays of sufficient quality were retained. A quality score was obtained using the QC CV scoring from arrayQuality . The micThe statistical analysis was performed using the R statistical software version 2.8.1. Microarray gene expression data from 81 patients of batch-1 arrays (training set) were used to develop a diagnostic profile. An independent series consisting of a further 35 patients (batch-2 arrays - test set) were used to independently assess the classification accuracy of the profile. The patient information, diagnosis and number of samples used in the training and test dataset of this study are indicated in Table bss/wss) criteria was used for feature selection performed within each CV fold. As a measure of discriminative power in two-class classification, the selective use of features ranked high in bss/wss enriches for potential biomarkers of interest. The top ranked bss/wss genes ranging from 10-500 (increments of 10) were assessed within the classification development to identify the number of genes required to obtain a minimal  cross-validation error rate  with classification error rates in the training set determined by leave one out cross-validation (LOOCV). The ratio of between sum of squares to within sum of squares  multi-class strategy was performed to classify the four main classes examined in this study  in a single step.2. A series of independent local (selected data) pair-wise (binary-class) comparisons of the four main classes examined was made. Comparisons included lymphoma versus RL, cHL versus NHL (inclusive of 3 and 17 cases respectively of rare T- and B cell lymphoma subtypes), and lastly, FL versus DLBCL, the two most prevalent forms of NHL in Caucasian populations . These w3. A number of 'global binary comparisons' were performed by pair-wise comparisons of samples from an individual subtype versus the remaining data.For all classification strategies, both LOOCV and independent test set accuracy rates were determined as mentioned above.Heat maps of the set of genes (classifiers) yielding the optimal LOOCV accuracy rates were produced. Clustering was performed for both samples and genes utilising hierarchical clustering with Euclidean distance as the dissimilarity function with complete linkage agglomeration. Dendrograms are displayed on the appropriate axis of the heat map.n = 142) suspected of lymphoma were assessed for quality using the arrayQuality package. Arrays with a CV QC score of >1 were identified as sub-optimal in quality and subsequently removed from analysis to result in a final total of 116 arrays analysed. A summary of the diagnosis of each of these biopsies and number of each subtype examined is shown in Table Microarrays quantifying gene expression in lymph node biopsies n = 42 suspecA diagnostic profile for the global multi-class approach consisting of 50 genes was developed, and demonstrated an optimal LOOCV accuracy rate of 83.6% for the diagnosis of biopsies as RL, HL, FL or DLBCL Table . Most caUsing local binary classification  were considered in each analysis, we next investigated if we can diagnose a specific disease type compared to the remainder of all case types examined in this study. The gene classifiers, identified from this type of comparison are likely more specific to the disease type itself, as opposed to signature genes identified purely from a comparison of two subtypes. Samples were classified by comparing each subtype of lymphoma or RL to all remaining cases at the global level and accuracy rates for this type of binary classification determined. Based on varying optimal number of genes, LOOCV accuracy rates of training sets were all above 82%, with test set accuracy rates of 88.5%, 82.8%, 82.8% and 80.0% for FL, cHL, DLBCL and RL respectively.TAF3) and Lim domain binding 2 (LDB2). Molecular classifiers identified for cases of cHL include the expression of a selection of chemokine ligands, and the transcription factor STAT1 (Table LMO2) was highly differentially expressed in FL compared to the remainder of cases examined (Table CDKN3) transcripts was associated with DLBCL . The development of new techniques such as quantitative nuclease protection assays on formalin-fixed, paraffin-embedded tissue blocks would overcome any difficulty in obtaining fresh tissue for microarray gene expression profiling and make GEP much more widely available even in small biopsies .The 18% technical exclusion rate of samples arrayed in this study hampers the diagnostic utility of microarray. However, increased familiarity with the assay will reduce the exclusion rate, and in laboratories with a limited caseload, referral to a centralised service may be preferable. Given the substantial improvement of microarray technology since the initiation of this study, the use of newer genome-wide microarray platforms such as Illumina bead arrays would also improve the utility of this technology and contribute to reducing the technical exclusion rate seen in this study. Incorporation of microRNA array data  may alsoIn our study, 13 of the 40 classifier genes identified from a specific  comparison of cHL with NHL were also strong classifiers when cHL was globally compared to both NHL and reactive samples. This indicates that our classification strategy encompasses unique gene sets that can classify across more than two types of pathological conditions. Although some gene classifiers identified in our study were common to other reported GEP studies, the absence of some previously identified key classifiers may be due to variable probe make-up across different microarray platforms or resulting from differences in the type of diagnostic classes used in our classification compared to most published GEP studies . Our disCD7, CCL17 and STAT1 has been reported to be associated with cHL supports the reliability of our microarray data presented in this study [CD163, a macrophage marker, may reflect a low number of macrophages present in the node microenvironment in many cases of FL. The importance of this information is not diminished as increased reactive macrophages in a rare subset of FL have been reported to be associated with poorer survival [LMO2, another strong molecular classifier identified for FL, has been reported to be expressed in approximately 50% of FL [LMO2 expression in DLBCL, its absence in our list of top 20 classifying genes of DLBCL may be due to the fact that only 5 cases examined (26%) are of GCB cell origin by immunohistochemistry. Instead, we have identified the gene cyclin D kinase inhibitor 3 (CDKN3), a known marker of the ABC-like DLBCL [The fact that high expression of is study -24. As His study . Similarsurvival . LMO2, a0% of FL . However0% of FL . Given tke DLBCL , to be eTAF3, a negative regulator of the tumour suppressor p53 [The lower expression of several immunoglobulin genes in reactive node tissue may reflect the differences in the cellular makeup of the microenvironment of normal lymph node tissue compared to those diseased with lymphoma. Consistent with the phenotype of non-cancerous tissue, we detected reduced expressions of a potentially cancerous gene ssor p53 .This proof of principle study has shown that microarray as a single platform assay can achieve reasonable diagnostic accuracy with the ability to differentiate lymphoma from non-cancerous reactive lymphadenopathy, and also classify three common subtypes of lymphoma. Molecular classifiers identified to be characteristic of each subtype examined could be combined onto a cost effective custom 'mini' microarray to screen lymph node biopsies for expression profiles to assist the diagnosis of four common outcomes: reactive, cHL, FL or DLBCL in a relatively quick and inexpensive manner. Indeed the use of GEP as a diagnostic and prediction tool for other diseases is currently commercially available. One example includes the CupPrint and MammaPrint array service provided by Agendia for patients with breast cancer. The increased sensitivity in RNA extraction techniques has made GEP much more widely available even for paraffin sections, small fine needle aspiration and core biopsies, which are routinely used in a pathology lab. The application of GEP may streamline current diagnostic tests by allowing more selective use of costly and time consuming ancillary tests such as immunohistochemistry, immunophenotyping by flow cytometry, cytogenetics, FISH and PCR in every case of lymphoma. Its use may provide an objective diagnostic test that could be standardised across pathology laboratories.The authors declare that they have no competing interests.THL, TJM, and ML carried out the microarray experiments and interpretation of results. AC and JYHY carried out the computational and statistical analysis. AB participated in the histopathology review and interpretation of data. JT provided expert histopathology review and interpretation of data. DDFM participated in study design, guidance in analysis and interpretation of data. All authors have contributed to and approved the manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/4/27/prepubDetermination of optimal error rates. a pdf file describing how the optimal error rates for each classification was obtained.Click here for fileClassifier genes that distinguish cHL from NHL. a pdf file containing one tableClick here for fileClassifier genes that distinguish FL from DLBCL. a pdf file containing one table.Click here for fileComplete list of all classifier genes. an excel file containing 7 spreadsheets.Click here for fileDendrogram analysis of clinical covariates. A pdf file showing that the classifiers are not biased towards or against clinical covariates using dendrogram analysis.Click here for file


Relating expression signatures from different sources such as cell lines, in vitro cultures from primary cells and biopsy material is an important task in drug development and translational medicine as well as for tracking of cell fate and disease progression. Especially the comparison of large scale gene expression changes to tissue or cell type specific signatures is of high interest for the tracking of cell fate in (trans-) differentiation experiments and for cancer research, which increasingly focuses on shared processes and the involvement of the microenvironment. These signature relation approaches require robust statistical methods to account for the high biological heterogeneity in clinical data and must cope with small sample sizes in lab experiments and common patterns of co-expression in ubiquitous cellular processes. We describe a novel method, called PhysioSpace, to position dynamics of time series data derived from cellular differentiation and disease progression in a genome-wide expression space. The PhysioSpace is defined by a compendium of publicly available gene expression signatures representing a large set of biological phenotypes. The mapping of gene expression changes onto the PhysioSpace leads to a robust ranking of physiologically relevant signatures, as rigorously evaluated via sample-label permutations. A spherical transformation of the data improves the performance, leading to stable results even in case of small sample sizes. Using PhysioSpace with clinical cancer datasets reveals that such data exhibits large heterogeneity in the number of significant signature associations. This behavior was closely associated with the classification endpoint and cancer type under consideration, indicating shared biological functionalities in disease associated processes. Even though the time series data of cell line differentiation exhibited responses in larger clusters covering several biologically related patterns, top scoring patterns were highly consistent with a priory known biological information and separated from the rest of response patterns.  In many biological and medical research fields, such as stem cell research, drug development or analysis of disease status, it is important to integrate data from different sources, such as cell lines, in vitro cultures from primary cells or clinical biopsies. Data integration has the possibility to combine the knowledge derived from different experiments, providing a bigger picture surrounding the new data and improving the interpretation of results . HoweverData integration approaches have been implemented on different levels using gene expression data. The classical analyses started with the integration on a single gene level, e.g. by interpreting differential gene expression in newly performed experiments using knowledge from gene annotation databases. These analyses were then extended to sets of genes, corresponding to specific biological functionalities, pathways or genomic locations -4. The gThis last step has been implemented by extension of gene set enrichment analyses to include signatures derived from high-throughput experiments , expliciThe present article, in contrast, focuses on the relation of gene expression changes to various tissue or cell type specific expression patterns. This specific focus becomes increasingly relevant as outlined by the following two examples. First, differentiation of pluripotent stem cells towards neural cells or cardiomyocytes, for instance, is anticipated to bear enormous potential for drug screening and regenerative medicine . In ordeGlobal analyses of gene expression patterns across diverse tissues and cell lines are typically performed in an unsupervised way, e.g. based on principal components analysis (PCA) ,20. PCA The presented PhysioSpace method serves as an exploratory research tool that allows getting a large scale overview of the data in terms of defined physiological coordinates. PhysioSpace complements single gene based analyses, gene set and pathway methods and unsupervised global methods like PCA.The PhysioSpace algorithm defines directions (signatures) in a supervised way based on retrospective microarray data. These directions are directly associated with specific phenotypes defined by data postprocessing.The directions are derived by comparing samples of a specific tissue with a reference via a t-test  with different tissues. We then apply the method to analyze tumor development comparing different breast cancer grades and prostate Gleason scores, as well as to investigate the effect of smoking on gene expression of lung cancer tissues. These examples show three principally different outcomes of the PhysioSpace method that are used to exemplify possible interpretations. Furthermore, the cancer data are utilized to investigate the relationship between PhysioScores and permutation p-values, providing useful information for the applicability of PhysioSpace in the case of low numbers of replicates. The PhysioSpace method is then applied on tracking of induced pluripotent stem cell (iPSC) differentiation experiments towards neural cells, cardiomyocytes and trophoblast lineages in a physiological context. It detects the direction and dynamics of differentiation, uncovering interesting information from data with very small numbers of replicates, and matches well to biological expectations. The comparison to a classical forward enrichment algorithm is performed on the cancer, differentiation, and simulated data, with overall slightly better results for the implemented algorithm. The robustness of the proposed method is demonstrated by the use of different PhysioSpaces  and dataClinical datasets often suffer from large, non-phenotype associated variation, affecting the determination of fold change vectors. This can lead to spurious associations, especially in the case of relatively small sample sizes and large heterogeneities in the data. The spherical transformation  and cancerous and normal lung tissues (GSE19804) were downloaded, normalized and merged . Datasetλ=0,0.01,…,0.05 . For each value ofλ, the mixed samples were then compared to the remaining 20 lung samples via the PhysioSpace method with and without spherical transformation. The analysis results with spherical transformation show less confounding effects, i.e. a more specific increase of the ESC score  and compared to unmodified ESC samples. In order to simulate a rather strong signal, the fraction of lung tissues in the mixture was set to 0.1, 0.2,…, 1. For the analysis without spherical transformation, the negative effect of increasing ellipticity in the data can be observed from In order to evaluate the ability of the PhysioSpace method to detect changes in tissue composition, the analysis of simulated mixtures was extended to several different tissues. For this purpose, the dataset GSE18676 was considered, consisting of 22 different tissues and 2 cell lines. Each tissue or cell line is represented by a single sample only. The 24 samples were computationally mixed with one ESC sample from dataset GSE33789 with a mixture proportion of 97% ESC and 3% of the tissue sample and compared to the remaining 9 ESC samples . This anOverall, these results reflect the simulated changes very well, indicating high robustness of the PhysioSpace method even in cases where data from different studies were combined as well as high sensitivity and specificity for detecting mixtures with mixture fractions of only 3%. Following an advice of a referee, we set out to test the dependence of the matching on the mixing fraction lambda and compared the results to those using a typical gene set enrichment algorithm as implemented in the geneSetTest method of the limma package  in R. ThFor the performance assessment, two matching scores were calculated, a tissue matching score and an ESC matching score. They are defined as the ratio of the expected PhysioScore, i.e. of the matching tissue (or the ESCs), and the highest (or lowest) PhysioScore. For example, for the mixing of the bone marrow sample with the ESC sample, the tissue matching score is the ratio between the bone marrow PhysioScore and the highest PhysioScore. This matching score is 1 in case of a perfect match, e.g. if the bone marrow score has the highest value itself, and gradually decreases with the distance of the expected score to the actual highest (lowest) score. We truncated the score at zero to avoid negative values. In Cancer is a highly heterogeneous disease consisting of different subtypes traditionally defined by specific histological markers like grade in breast cancer or Gleason score in prostate cancer . In receIn this context, the PhysioSpace method was applied to investigate differences in global gene expression between breast cancer grades. A dataset with 189 breast cancer samples GSE2990) was analyzed, incorporating 64 breast cancers of grade 1, 48 of grade 2, 55 of grade 3, and 22 with missing information on breast cancer grade. The vectors of differential expression of grade 1 to that of grade 2 and grade 3 cancers were compared to the signatures in the PhysioSpace, resulting in a large number of significantly associated reference signatures . The dom90 was anIn contrast to the breast cancer results, the influence of smoking on gene expression seems to be more phenotype specific. A comparison of the gene expression of  lung tissues from 31 never to 36 former, and 40 current smokers . The embedding of the three differentiation time series and three cancer datasets into these three separate PhysioSpaces are depicted in Figures S5, S6, and S7 in For future extensions of the PhysioSpace the possibility to combine signatures from different microarray platforms is going to increase the universal applicability and thus utility of the PhysioSpace approach. In order to be comparable, the absolute values of the PhysioScores must be in a similar range. We tested the principal possibility of a combination by comparing the PhysioScores from the three different PhysioSpace constructions. A linear adjustment according to the number of matching probes between the signature and the dataset was applied to approximately account for the resulting different gene-set sizes . A selection of the most prominent signatures from all three PhysioSpace constructions is depicted in In order to additionally test the robustness and platform independence of the PhysioSpace method, we analyzed an additional trophoblast differentiation time series . The differentiation was performed in two settings with either 4% or 20% oxygen. In both settings, the Placenta-score and ESC-score dominate  needed for the sample permutation approach can become very cost intensive. Therefore, many studies rely on gene label permutation, which is not sufficient for rigorous assessment of statistical significance, bearing the risk of “heading down the wrong pathway” . The reaThree different types of cancer data were analyzed in order to show the performance of the PhysioSpace method. The results show different kinds of outcome with many, few, or no significant signature associations for breast, lung and prostate cancer datasets. It is already known that determination of biomarkers depends not only on the size of studies but primary on the clinical phenotype ,39. A biThe detailed characterization of human pluripotent stem cells as well as their differentiation dynamics is important for quality control and understanding of the mechanisms and dynamics of cell fate changes. Besides the analysis of single marker genes and proteins, a whole genome based characterization can provide more robust information and outcome measures ,7. LargeThe PhysioSpace compendium is derived from publicly available gene expression data in a straightforward and resource effective manner. More specific compendia for specific applications can be derived easily from the large amount of available datasets.The robustness of the PhysioSpace method allows using very small number of replicates, reducing experimental efforts at the same precision. The high biological relevance of the results confirm its usefulness for wide ranges of applications in drug discovery and (trans-) differentiation approaches. It allows to utilize the tremendously growing repositories of existing data for interpretation of specific wet-lab experiments on the background of physiology, possibly establishing a quantitative link between lab experiments and clinical applications. RNA-seq and related next-generation technologies have an enormous potential for high resolution measurements of small cell populations. Properties of specific measurement techniques must be considered, when applying the PhysioSpace method to datasets created by different measurement techniques. Processing pipelines and normalization for next-generation sequencing data is an active field of development and all count based methods introduce bias through low counts and different transcript lengths  that canAll datasets were downloaded from Gene Expression Omnibus   or ArrayProbe identifiers of different microarray platforms were matched using the getBM method of the biomaRt R package .The breast cancer analyses were performed on the GSE2990 dataset using all data that have information on breast cancer grade. No distinction according to estrogen receptor status was made. Analyses were performed for comparison of breast cancer grade 1 vs. 2 and grade 1 vs. 3.Dataset GSE10072 was used for the analysis of lung gene expression of never vs. former and never vs. current smokers using all samples from cancerous and adjacent tissues in a single analysis. For the investigation of prostate cancer all primary tumors with Gleason-score 6, 7, 8, or 9 from dataset GSE21034 (transcript version) were used. Comparisons were made between samples of Gleason score 6 vs. 7, 6 vs. 8, and 6 vs. 9. In a separate analysis, primary tumors were compared to metastases. Additionally, all data from dataset GSE16560 were used to analyze differences associated with Gleason scores.For the neural, trophoblast, and cardiomyocyte differentiation analyses, datasets GSE9940, GSE30915, and GSE28191 were used, respectively. Samples were grouped according to time of differentiation. No distinction according to treatment was made for the neural differentiation. An additional trophoblast differentiation dataset (GSE10469) was analyzed with separate analyses for the normoxic (20% oxygen) and the hypoxic (4% oxygen) conditions. In all comparisons, differentiating cells were compared to the starting pluripotent stem cell samples.λThe computational mixing was achieved by a linear combination of tissue and ESC samples with mixing factor 2-transformed data. For the first simulation, xESCrepresents a dataset with 20 ESC samples, obtained from GSE33789 by taking each ESC sample twice, and xLungthe first 20 of the 40 randomly drawn lung samples, obtained from GSE19804. Hence, the data simulate an infiltration of up to 5% ESCs  into the rather heterogeneous samples from lung cancer and adjacent lung tissue. In the second simulation, the first 5 ESC samples from GSE33789, each taken twice, were mixed with 10 samples of adjacent normal lung tissue (GSE19804) and compared to the remaining 5 ESC samples. The tissue fraction in the mixture ranges from 0.1 to 1 in steps of 0.1, i.e. the fraction of ESCs wasλ=0.9,0.8,...,0. For the third simulation, a single ESC sample from dataset GSE33789 was mixed with each of the 22 tissues and 2 cell lines from dataset GSE18676 and compared to the remaining 9 ESC samples. The mixing factor was set toλ=0.97.The mixing was performed on non-transformed data, in contrast to all other calculations that were performed on log10-p-values of the t-tests are used as PhysioSpace signatures.The PhysioSpace consists of a compendium of gene expression signatures, representing vectors of differential expression. Differential expression is calculated using a Student’s t-test between samples from a specific tissue or cell line and a computationally built reference. The reference is chosen as the vector of mean expression values of all samples in the dataset, in order to simulate a common reference showing no tissue-specific expression. The standard error of the mean expression is used as standard deviation of the reference for calculation of t-tests. The signed logThree different PhysioSpaces were built for the analyses to show the robustness of the presented results . The firThe second PhysioSpace was built based on the GSE2361 dataset using all available probes. This dataset consists of 36 samples, each representing a different human tissue. Thus, the second PhysioSpace consists of 36 signatures, each representing the differential expression of a single sample to the computationally built common reference.The 369 signatures of the third PhysioSpace were built according to the 369 Groups annotated in dataset E-MTAB-62. Again, all probes of the Affymetrix GeneChip Human Genome U133A array were used to calculate the signatures.For visualization purposes the signatures were hierarchically clustered based on a Pearson-correlation distance using average-linkage, indicating some common gene expression features of signatures clustering closely together.For a comparison of two phenotypes, e.g. different times of differentiation, or cancer stages, the mapping of the differences in gene expression onto the PhysioSpace is done by the following three-step procedure consisting of a spherical transformation, a data-based definition of gene sets and an enrichment calculation via a Wilcoxon rank-sum test.The spherical transformation of the data is an essential step for the statistical validation. It allows the meaningful calculation of a null-distribution of the enrichment score via sample label permutation . D a singular value decomposition is calculated (D=UΣVT). The entries of the diagonal matrix Σ are then truncated to a maximum of t  data matrix In the second step of the mapping procedure two sets of up- and down-regulated genes are determined, each containing the top 5% of up- and down-regulated genes, respectively. Genes are ranked corresponding to their mean fold changes between the two phenotypes. The cutoff of 5% up- or down-regulated genes was chosen, on one hand, to focus on large scale patterns rather than a few genes and, on the other hand, to exclude genes that are only driven by noise. In the application examples it turned out that the results were quite robust with respect to a variation of the cutoff parameter between 1% and 10% (data not shown).10 p-value of the Wilcoxon-test. In the third step, a gene set enrichment score is calculated on the PhysioSpace signatures using the gene-sets of the previous step. The wilcox.test procedure of the R stats-package  is used A sample-label permutation approach with B=1000 permutations is used to rigorously determine statistically significant signatures. The permutation p-value is defined as s0 is the absolute value of the observed PhysioScore, sb,b=1,…,B, are the absolute values of the permutation PhysioScores, and I is the indicator function, being 0 if sb is smaller than s0 and 1 otherwise. All sample permutation p-values were adjusted for multiple testing using the Benjamini-Hochberg correction [where rrection .10 p-value is approximately linear in the relevant range (data not shown). Therefore, the PhysioScore values were linearly transformed to simulate same number of genes. In All analyses were conducted in the R programming language , R versiFile S1Combined supporting information file of additional figures. Figure S1. Self-similarity heatmap of PhysioSpace 1. Figure S2. PhysioScores for the first simulation scenario. Figure S3. PhysioScores of additional datasets. Figure S4. Comparison of PhysioScores and permutation p-values. Figure S5. Extension of Figure 5 showing the entire PhysioSpace 1. Figure S6. Extension of Figure 5 showing the entire PhysioSpace 2. Figure S7. Extension of Figure 5 showing the entire PhysioSpace 3. Figure S8. Single gene analysis of Trophoblast differentiation. Figure S9. Heatmap comparing the differentiation time series results from the presented method to the GSEA-based implementation.(PDF)Click here for additional data file.File S2Excel file of PhysioScores and permutation p-values of all datasets from the main analyses.(XLSX)Click here for additional data file.File S3R-script for the calculation of PhysioScores and permutation p-values.(TXT)Click here for additional data file.


Cross-platform analysis of gene express data requires multiple, intricate processes at different layers with various platforms. However, existing tools handle only a single platform and are not flexible enough to support custom changes, which arise from the new statistical methods, updated versions of reference data, and better platforms released every month or year. Current tools are so tightly coupled with reference information, such as reference genome, transcriptome database, and SNP, which are often erroneous or outdated, that the output results are incorrect and misleading.We developed AnyExpress, a software package that combines cross-platform gene expression data using a fast interval-matching algorithm. Supported platforms include next-generation-sequencing technology, microarray, SAGE, MPSS, and more. Users can define custom target transcriptome database references for probe/read mapping in any species, as well as criteria to remove undesirable probes/reads.AnyExpress offers scalable processing features such as binding, normalization, and summarization that are not present in existing software tools.As a case study, we applied AnyExpress to published Affymetrix microarray and Illumina NGS RNA-Seq data from human kidney and liver. The mean of within-platform correlation coefficient was 0.98 for within-platform samples in kidney and liver, respectively. The mean of cross-platform correlation coefficients was 0.73. These results confirmed those of the original and secondary studies. Applying filtering produced higher agreement between microarray and NGS, according to an agreement index calculated from differentially expressed genes.http://anyexpress.sourceforge.net.AnyExpress can combine cross-platform gene expression data, process data from both open- and closed-platforms, select a custom target reference, filter out undesirable probes or reads based on custom-defined biological features, and perform quantile-normalization with a large number of microarray samples. AnyExpress is fast, comprehensive, flexible, and freely available at  Warnat' studies . Both stDespite the well-known benefits, conducting a cross-platform analysis of gene expression data involves many intricate issues at different layers. A recent guideline discussed several key issues when conducting an integrated microarray data analysis: annotating probes of the individual dataset, resolving the many-to-many relationship between probes and genes, aggregating multiple measurements into a single gene-level value, and combining study-specific estimates . Some auhttp://www.crosschip.org provides comparative analysis between different generations of Affymetrix arrays [http://seq.mc.vanderbilt.edu/exalt, allows the user to upload his/her data and searches for homologous data sets obtained from public repositories. However, the user is still responsible for ascertaining the quality of the probe and its impact at the gene expression level [http://compgen.bio.unipd.it/bioinfo/amadman, performs integration of cross-platform microarray data obtained from GEO [http://people.tamu.edu/~syuan/cptra/cptra.html, another tool for cross-platform analysis of gene expression data [Several tools were developed to resolve the aforementioned hurdles for cross-platform analysis of gene expression data. CrossChip x arrays . It utilon level . Furtheron level . Anotherfrom GEO . Howeverfrom GEO ,7. CPTRAion data , allows ion data . Hence, fastq (Roche 454 or Illumina GA), .csfastq (ABI SOLiD color-space), or .fasta  to remove undesirable probes before pre-processing. For example, we summarize multiple probe level measurements into a single target-level value, where the target is a user-defined expression unit . None of the existing tools can handle integration of NGS and microarray data from different platforms. Thus, we developed a practical, integrated toolkit for cross-platform analysis of gene expression data serving all NGS and microarray platforms for any species. Previously, our group demonstrated that sequence-based probe matching improved the consistency of measurements across different platforms, compared to the widely-used identity-based matching method at that time[Our approach is to start the analysis from raw files, such as .that time-17. We athat time. We now that time-20 and dclosed-platform [open-platform [cel files of the same platform, an Affymetrix U133A microarray), the open-platform has a 1-to-1 relationship with the sample . AnyExpress is capable of dealing with gene expression data from all platforms in contrast to the existing tools that focus on a single platform. A schematic workflow of AnyExpress is displayed in Figure AnyExpress is a software suite for cross-platform analysis of gene expression data. It allows two sources of inputs: (i) genomic position files, obtained from the external alignment software and (ii) probe-level sample measurements files. AnyExpress returns a target-by-sample text file as an output. We define 'tag' as a string of nucleotide sequences used for measuring gene expression abundance. This string is commonly called 'probe' or 'read' for microarray or NGS platform, respectively. Throughout this article, we use tag, probe, and read interchangeably. Next we define 'platform' as a set of tags. Then, we classify platforms into one of two classes, based on the availability of knowledge in a tag's sequence. When the tag sequence was predetermined, as in a microarray or catalysed reporter deposition (CARD) FISH, the platform was considered to be platform ,22. If tplatform ,22. Whiltarget as a biologically meaningful expression unit against which tag will be matched using genomic positions. Each target is a collection of five attributes: chromosome, strand, start position, end position, and identifier. AnyExpress accepts the target as a .BED file where the five fields are separated by tabs. In most cases a single target has multiple associated tags; hence, multiple measurement values will be summarized into a single aggregated value. The target identifier must consist of two substrings concatenated by '@', i.e., targetID = 'superID' + '@' + 'subID'. For 'BRCA1' gene, its identifier (with the corresponding target) could be represented as 'BRCA1@Exon2' , 'NC_007294@Exon2' (RefSeq), or 'ENSG00000012048@Exon2' (Ensembl). The target information will be updated as knowledge of the genome and genes evolve. Species, source database, and build-date are three factors defining a target. Example of .BED format files are as following: 'Human_Ensembl_Feb2009.BED,' 'Human_UCSCKnownGene_Feb2009.BED,' 'Human_RefSeq_Feb2009,' and 'Human_RefSeq_Mar2006.BED'. Unlike existing custom reannotation data approaches in which the user's analysis is limited by a particular type of target that the annotator has predefined, AnyExpress allows users to define their own reference target for any species. In Figure Anyexpress Build before running Anyexpress Combine all in a command-line.We refer to a Exclusion features allow users to apply a biological filter applied against the tags to filter out undesirable ones. Previous studies have shown the negative effect of low quality microarray probes on measurement of gene expression abundance and consequently on the interpretation of the results ,9,24,25.O(2n), AnyExpress adopts a fast interval matching algorithm called PositionMatcher, developed by our group [O(nlogn + n). In a previous study [et al.[AnyExpress takes the outputs of external alignment software as inputs , which consist of a list of attributes of genomic position . Probes and reads are matched against targets. Matching two entities based their genomic positions is a core part of data integration process in AnyExpress. While naïve comparison of all intervals of target and tag  is a computationally-intensive task with time complexity ur group . Positious study , we showy [et al. as an exFigure As illustrated in Figure .cel files were used in a lung cancer classification study [.cel files were used in a breast cancer study [.cel files using conventional computers fails even before normalization or summarization. Solutions using parallel computing are being proposed, but these are useful only to users with advanced skills and access to high performance computing resources [In early microarray studies, the number of samples for Affymetrix was small (less than 20), so it was easy to create a single column-bound file. But recent studies involve a large number of samples, often exceeding 200, which makes data loading impossible using R, Matlab, or stand-alone software due to limited memory size. Although the number of tags is relatively lower in microarrays than in NGS (1 million versus tens of millions), currently, the number of samples is larger due to the maturity and inexpensive cost of this technology. For example, 186 Affymetrix microarray on study  and 286 er study . These iesources . Thus, wcel file and needs to get column-bound. AnyExpress provides a scalable binding tool, anyexpress BindAffyCel (in a command-line), to create this single column-bound file from a large number of Affymetrix .cel files. We tested the capability of AnyExpress in binding a different number of .cel files downloaded from GEO. For binding, AnyExpress extracts probe identifier as probeID = 'x-coordinate' + ':' + 'y-coordinate' from the .cel file. The user is required to place .cel files of the same platform in the same directory. Only the probe identifiers that are common across all samples will be represented in an output file. In Figure cel files are bound to a single text file '4samples.txt'.We defined the input format of closed-platform samples for AnyExpress as a single column-bound, tab-delimited text file where the first column is a probe identifier followed by measurement values of the samples in the second column. This is a common data format for microarrays in non-Affymetrix platforms. However, in Affymetrix, each sample is a .anyexpress Combine, but it is highly recommended to perform between-sample normalization of this data to remove systematic bias to enable fair comparison among samples [cel file contains over 500,000 probe-level measurement values. When hundreds of samples need to be combined, existing tools can hardly perform quantile-normalization. AnyExpress solves this issue with a highly scalable tool, anyexpress NormalizeColumnBoundSamples.Quantile-normalization  is a wid samples ,33. Of t samples ,33. QuanbedGraph text file. The user needs to upload this file onto the UCSC Genome Browser http://genome.ucsc.edu through his/her own web-browser. The user can draw a plot by typing in five parameters: a directory of user, Project; chromosome; strand ('forward' or 'backward'); start position; and end position. Each platform, closed or open, in the user's Project is drawn as a track in the .bedGraph file. In each track, vertical bars are drawn along the genomic region of interest. The height of the vertical bar is either the number of the reads covering each base in open-platforms or the average signal intensity of the probe covering each base. As a default reference track, the RefSeq gene model is displayed at the bottom of the plot. The user can freely add, hide, or modify the plot through the UCSC Genome Browser, e.g., adjust scales, change color, or add biological reference tracks.Visualization plays a critical role in data validation, interpretation, and hypothesis generation during analysis . Softwarexe file), a collection of Java classes and pre-processed data (reference targets and exclusion features). Once an archived file (.zip) is extracted to the user's local machine, AnyExpress is ready to execute after ENVIRONMENT and PATH variables are set, as in any other command-line software for a Unix-like environment or Windows. Tools available in AnyExpress are summarized in Table http://anyexpress.sourceforge.net. Among seven tools, COMBINE is the main process that performs data integration. Figure anyexpress Combine in a command-line, to combine closed-platform data (microarray: Affymetrix U133A) and two open-platform data sets (NGS: Illumina GA and ABI SOLiD). Tags from these three platforms were matched against 'RefGene2010' using the PositionMatcher algorithm. The tags were also matched against targets 'multiTarget' and 'dbsnp131' for filtering. The exclusion feature 'multiTarget' is automatically generated during the ANNOTATE process. For example, in Figure AnyExpress is composed of an executable wrapper  a 64-bit Linux server with a 2.13 GHz Intel Core™ 2 Duo CPU and 16 GB memory, (ii) a 32-bit iMac with a 2.66 GHz Dual-core Intel Zion and 4 GB memory, and (iii) a 32-bit Windows 7 with a 1.8 GHz Intel Core CPU and 4 GB memory. The executables, the source code, the example data, and the manual are available at et al. [cel files from the NCBI GEO database (accession number: GSE11045) and raw NGS .fastq files from NCBI Sequence Read Archive (submission number: SRP000225). The six samples, all human tissue, were obtained from kidney (3 samples) and liver (3 samples). For pre-processing, six microarray .cel files were bound into a single column-bound file after running AnyExpress:We applied AnyExpress to human gene expression data from Marioni et al. . It cons$anyexpress BindAffyCel ~/celfiles 6sample.txtBED file, we downloaded a probe sequence file (.fasta) of Affymetrix U133 Plus2 from the Affymetrix Support webpage http://www.affymetrix.com and processed it to have a probe identifier as in 'x coordinate' + ':' + 'y-coordinate', used by Thompson et al. [To create a closed-platform .n et al. . Then wen et al.  and AWK n et al. :$ bowtie ~/indexes/hg19 -t -n 0 -B 1 hg19 -f U133PLUS2.fasta U133PL2.bowtie$ awk '{ FS="\t"; OFS="\t"; print $3, $4, $4+length($5)-1,\ $1, $2 }' U133PLUS2.bowtie > U133PLUS2.BEDfasta files to obtain the .BED format file. For Windows users, we provide an AWK-equivalent tool, awk.exe, through the AnyExpress webpage. Open-platform files were aligned and processed in the same way as closed-platform files. The only difference was to replace the Bowtie option from '-f' to '-q' because NGS data used the .fastq format. The following Bowtie-awk running was repeated for all six NGS files (SRR002320.fastq through SRR002325.fastq):Bowtie is a fast and memory-efficient algorithm and tool for short sequence alignment  and AWK $ bowtie -t -n 0 ~/indexes/hg19 -q SRR002320.fastq SRR002320.bowtie$ awk 'BEGIN {FS = "\t"; OFS="\t"} {print $3, $4, $4+length($5)-1,$1, $2 }' SRR002320.bowtie > SRR002320.BEDWe built target and an exclusion features into the system using AnyExpress:$ anyexpress BuildTarget RefSeq_Gene.BED$ anyexpress BuildExclusionFeature dbsnp131.BED'$ANYEXPRESS_HOME/sys/target' and '$ANYEXPRESS_HOME/sys/exclusionFeature' directories.The resulting files were all created in the We combined all 7 platforms (1 closed-platform + 6 open-platforms) of the Marioni data with AnyExpress, using multiTarget and dbsnp131 as exclusion features:$anyexpress Combine -c U133PLUS2.BED 6samples.txt -o SRR002320.BED SRR002321.BED SRR002322.BED SRR002323.BED SRR002324.BED SRR002325.BED -t RefSeq_Gene -e multiTarget dbsnp131 -p /user/jkim/myProjectRun-time messages during the AnyExpress execution are shown in Figure We calculated Spearman correlation coefficients (CC) for the combined data to assess reproducibility. The within-platform CCs were very high in both kidney and liver , while cross-platform CCs were moderate . These results confirmed the results of the original study . The croFor visualization, we drew a coverage plot in the genome regions of gene GPX3 :$anyexpress Plot/user/jkim/myProject chr5 forward 150395999 150410551.bedGraph file was uploaded to a custom track of the UCSC Genome Browser for visualization. We selected four representative tracks out of the original twelve due to page limitations and adjusted the browser setting for clearer viewing. Figure This gene is shown to have tissue-specific expression, higher in kidney but lower in liver . The resWe ran AnyExpress on the Marioni data with four different exclusion feature settings: 'none' = apply no exclusion feature, 'snp'= remove SNP containing tags, 'multiTarget' = remove tags matched to more than one target, and 'both' = apply both 'snp' and 'multiTarget'. We assessed the effect of exclusion features on gene coverage and correspondence of highly expressed genes across the platforms. Figure et al. [n, sorted by fold-change in decreasing order, varying n from 50 to 2000 by 50. For each top-n genes from NGS, we counted the number of genes that were in common with the top-n genes from microarray and divided this number by n . As expected, the proportion that was in common between the two platforms increased with an increase in n. The agreement proportions in 'none' and 'snp' were similar when the list size was smaller than 900, but the proportion was higher in 'snp' than in 'none' when the list size was above 900. 'multiTarget' and 'both' outperformed 'none' for all list sizes. Overall, the CAT plot demonstrated that filtering by exclusion features produced higher agreement between the two platforms. We also assessed the cross-platform correspondence with a modified CAT plot where genes were ranked by a false discovery rate (FDR) adjusted q-value [In Figure et al. . Correlaet al. . Hence, et al. ,37. Prevet al. . The CATet al. ,40. We c q-value , insteadcel files under different memory sizes . Pre-processing processes (BIND or NORMALIZE) took longer than the actual COMBINE process. We found that AnyExpress can manage up to 500 .cel files with 8 GB memory. The user needs to have a memory size larger than 8 GB to process more than 500 .cel files. At the time of writing this manuscript, the price of a 4 GB memory was around 100 US dollars. Considering the cost of high-performance computing, running AnyExpress with additional memory on an average PC or laptop computer is cost-effective for a large-scale cross-platform analysis of gene expression data.We performed stress testing of AnyExpress with a different number of .es Table . The numAnyExpress currently has some limitations as it is based on position matching between tag and reference. Hence it misses exon-spanning tags during the COMBINE process. In the Marioni data, about 4%  of transcript-matched reads were exon-spanning tags. Although these were not counted in the current version of AnyExpress because of their relatively small representation, we are currently working on developing post-processing modules to rescue these tags.cel files and six NGS data. Currently, we are extending AnyExpress in a distributed computing environment to accommodate a larger study.AnyExpress performs within-platform normalization and quantile normalization  for closcel files into a single text file, and (vii) perform quantile-normalization with a large number of microarray samples.We developed AnyExpress, a toolkit that combines and filters cross-platform gene expression data. With sequence-oriented tag mapping and a fast interval algorithm, AnyExpress uniquely offers all of the following features: (i) combine cross-platform gene expression data at a user-defined gene expression unit level , (ii) process gene expression data from both open- and closed-platforms, (iii) select a preferred custom target reference, (iv) exclude undesirable tags based on custom-defined biological features, (v) create a coverage plot along the genomic regions of interest, (vi) bind a large number of Affymetrix .• Project name: AnyExpresshttp://anyexpress.sourceforge.net• Project home page: • Operating system: Linux, Unix, Mac OS X, or Windows• Programming language: Java, shell script, and Python• License: Apache License version 2.0BED: Browser Extensible Data; CARD: Catalysed Reporter Deposition; CAT: Correspondence At the Top; CC: Correlation coefficient; DD: Differential Display; DWD: Distance Weighted Discrimination; FDR: False Discovery Rate; FISH: Fluorescent In Situ Hybridization; GEO: Gene Expression Omnibus; ID: Identifier; MAQC: Microarray Quality Control; MPSS: Massively Parallel Signature Sequencing; NGS: Next-Generation Sequencing; RMA: Robust Multi-array Averaging; RPKM: Reads Per Kilobase exon model per Million mapped reads; SAGE: Serial Analysis of Gene Expression; SD: Standard Deviation; SNP: Single Nucleotide Polymorphism; SVD: Singular Value DecompositionJK initiated the project, designed and implemented the software, and drafted the paper. KP architected the software and implemented core modules. HJ processed data, performed testing, and generated figures/tables. WPK and LOM conceived the study and designed and directed the project. All co-authors contributed to manuscript preparation.AT plot based on statistical significance. This CAT plot depicts the agreement between NGS and microarray data on the detection of differently expressed genes. The X-axis is the number of top genes in NGS data, ranked by the statistical significance  of kidney vs. liver. The Y-axis is the proportion of genes from the microarray that is in common with top-ranked genes from NGS. Four CAT plots are drawn using none, snp, multiTarget, and both (snp and multiTarget simultaneously) exclusion features.Click here for file


Combining multiple microarray datasets increases sample size and leads to improved reproducibility in identification of informative genes and subsequent clinical prediction. Although microarrays have increased the rate of genomic data collection, sample size is still a major issue when identifying informative genetic biomarkers. Because of this, feature selection methods often suffer from false discoveries, resulting in poorly performing predictive models. We develop a simple meta-analysis-based feature selection method that captures the knowledge in each individual dataset and combines the results using a simple rank average. In a comprehensive study that measures robustness in terms of clinical application , microarray platform heterogeneity, and classifier , we compare the rank average meta-analysis method to five other meta-analysis methods. Results indicate that rank average meta-analysis consistently performs well compared to five other meta-analysis methods. We develop a simple, yet robust meta-analysis-based feature selection (FS) method for microarrays that ranks genes by differential expression within several independent datasets,then combines the ranks using a simple average to produce a final list of rank-ordered genes. Such meta-analysis methods can increase the power of microarray data analysis by increasing sample size . The subExisting microarray meta-analysis methods either combine separate statistics for each gene expression dataset or aggregate samples into a single large dataset to estimate global gene expression. The study by Park et al. used analysis of variance to identify unwanted effects  and modeled these effects to detect DEGs . Choi etWe develop the rank average method, a simple meta-analysis-based FS method, for identifying DEGs from multiple microarray datasets and design a study  to compaWe use six breast cancer, five renal cancer, and five pancreatic cancer gene expression datasets  to compat-test) and we assume that this rank order is invariant to batch effects. Using the average rank of a gene across several datasets to obtain the final multidataset rank order, we can infer (1) the relative strength of that gene in differentiating the patient samples of interest and (2) the consistency of the gene's differential expression across multiple studies.The meta-analysis-based FS method proposed in this paper ranks genes individually in each dataset and computes the average rank of each gene. Gene rank order is determined by a measure of differential expression , we denote a gene i across all datasets withri,k,ϑ, of a gene, ϑ. A smaller rank indicates a greater degree of differential expression. In the case of sample aggregation, the ranking function takes the form i across all datasets, weighted by number of samples in each dataset, Nk, isThe remainder of this section uses the following mathematical notation. t-test (T), significance analysis of microarrays (SAM) [kth dataset asϑk, with highest estimated classification performance for each dataset. Because each basic FS method makes different assumptions about DEGs and the correctness of these assumptions varies from dataset to dataset, allowing a different basic FS method for each dataset can improve performance.We consider several basic FS, or gene ranking, methods as follows: fold change (FC), ys (SAM) , rank-suys (SAM) . We explWe use classification performance to assess meta-analysis-based FS methods with the assumption that improved FS leads to higher prediction performance when classifying samples from an independent dataset. We assess prediction performance using independent training and testing datasets because of the small sample size of some of the datasets and because we want to reflect clinical scenarios in which predictive models would likely be derived from data collected from a separate batch of patients. We compare our proposed rank average meta-analysis method to other meta-analysis methods including: (1) the rank products method , (2) theClassification performance depends on both feature selection and number of samples available for training. We are interested in performance gains due to meta-analysis-based FS alone. We isolate this performance gain by training classifiers with samples from a single dataset only, while allowing the features used for training to come from multiple datasets. Thus, any improvement (or degradation) in classification performance of a meta-analysis-based FS method in comparison to the baseline single-dataset FS is due to features selected rather than to increases in training sample size. We assess classification performance using a separate validation dataset and permute the datasets such that each individual dataset in each dataset group—renal, breast, and pancreatic cancer—is used at least once for validation. Moreover, for each permutation, we use 100 iterations of bootstrap sampling from the training datasets to estimate classification performance. The procedure for measuring predictive performance of heterogeneous-dataset combination is slightly different. Each dataset group contains several one-channel Affymetrix datasets and one two-channel dataset (either cDNA or Agilent). Gene expression values of the two-channel datasets are computed as log ratios, resulting in different dynamic ranges compared to the one-channel datasets. We assess the robustness of each meta-analysis-based FS method to heterogeneous data platforms by first determining the performance of the method when combining only Affymetrix data  to identify DEGs. The top gene we identify is LOX, which is an oncogene implicated in clear cell renal cancer . The ADFThe rank average meta-analysis method identifies S100P as the top pancreatic cancer gene, which has been implicated in several studies , 40. TheThe degree of differential expression  of a gene can vary significantly from dataset to dataset. Combining DEG detection results by averaging ranks across datasets reduces variability and improves statistical confidence. Analysis of a single microarray dataset may result in errors during DEG detection—for example, false positives and false negatives . In general, these errors can be reduced by increasing sample size. Combining microarray datasets by averaging ranks effectively increases sample size while enabling robust analysis of heterogeneous data.In order to understand the differences in performance among the six meta-analysis-based FS methods, we identify and list the differences and similarities in t-statistic, SAM, and rank sum—for detecting DEGs  rank average and mDEDS are the only methods that consider multiple basic FS methods—for example, fold change, ing DEGs , row 1. ing DEGs , row 2. ing DEGs , row 3.t-statistic with gene expression data that is not normally distributed). Rank average meta-analysis further benefits from selecting a single basic FS that optimizes prediction performance. On the other hand, the performance of mDEDS meta-analysis can degrade if it includes a basic FS method that is incompatible with the data. Likewise, the performance of rank products can degrade when the fold change FS method is not appropriate for the data. The Choi and Wang methods may also suffer from this problem. However, they seem to perform fairly well when applied to the datasets in this study , microarray data platform heterogeneity, and classifier model . Rank average meta-analysis, performs well because it selects dataset-specific basic FS methods and then averages the ranks across all individual datasets to produce a final robust gene ranking. In comparison to five other meta-analysis methods the rank average method is not always the best method for some factor combinations. However, it is consistently among the best performing in terms of its ability to identify predictive genes. Although we presented results from analysis of microarray gene expression data, the proposed methods may be generalized for other bioinformatics problems that require feature selection.Supplemental Table S1 – Rating meta-analysis methods by prediction performance when combining all available datasets. This table lists the predictive performance  for each clinical application , data platform heterogeneity , and classifier . The numbers in parentheses indicate the performance rating relative to other meta-analysis methods . A mean rating is computed for each clinical application and each meta-analysis method across all combinations of data platform heterogeneity and classifier. An overall mean rating is computed for each meta-analysis method. Ratings are proportional to bar lengths in Figure 3.Click here for additional data file.