Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug’s known mechanism of action. Also, the models predict each drug’s potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets. It is well known that many cancer drugs are effective only in a subset of cancer patients, and selection of drug responsive biomarkers is crucial to find the right patients for the right drug. Also, early identification of biomarkers that can select sensitive and resistant patient subpopulations is particularly important in order to be able to test biomarker hypotheses in early clinical drug development and utilize those biomarkers during later clinical development to stratify patients into appropriate treatment arms. This will facilitate the development of companion diagnostic tests along with novel treatments. Therefore, it is ideal to discover drug response biomarkers prior to clinical trials using pre-clinical data sets, for example, by in vitro cell line screening of drug sensitivity.Numerous studies of in vitro drug sensitivity screens –3 coupleAlthough there are a few successful examples of predictive gene expression signatures to predict disease prognosis, for example the 70-gene MammaPrint signature  and the The poor model performance could be improved if more appropriate modeling approaches for the complex clinical endpoints of interest were used. For instance, poor prediction of overall survival for Multiple Myeloma patients in the MAQCII study could be partly due to applying an arbitrary survival cutoff (24 month) for patients . Both geIn order to generate translational drug-sensitive models, e.g. building predictive models using cancer cell line data that can be used to predict patient response in clinical trials, more detailed considerations are needed along the model building/testing process. For example, as observed from the MAQC II study, a simple application of t-test for feature selection is not sufficient . Insteadhttp://www.Eurofinspanlabs.com). IC50 values were used as dependent variables and gene expression data from untreated cells was used as independent variables to build the predictive models. A Partial Least Squares Regression (PLSR) modeling approach was utilized because it can effectively handle a high number of independent variables with minimal demands on sample size [In the current work, a combination of novel statistical and functional feature selection approaches were used to build in vitro gene transcription based predictive models, for Erlotinib and Sorafenib response using a 240 cancer cell line panel . A 240 tumor cell line panel (Oncopanel) was used to identify cell lines that were sensitive or resistant to Erlotinib treatment . The Oncopanel contains cancer cell lines covering a variety of cancer indications . For eacA PLSR model workflow was developed and an Erlotinib sensitivity model was trained using OncoPanel data . The keyFirst, an RMA normalization of the baseline gene expression of the OncoPanel cell lines was conducted. Next, an intensity cutoff of 40% of the whole genome to remove genes with low intensities was applied. Third, a variance cutoff of 1 to keep genes with the highest variability of expression among the panel cell lines was applied. This data reduction step narrowed down the number of probesets by an order of magnitude from 54,675 to 3,787.2(IC50)) for all cell lines. For each probeset, permutation testing was run by randomly assigning a response sign to the cell lines panel. After Fisher's transformation (f = 0.5 * ln ((1+r) / (1-r), where r is the correlation coefficient on each permutation), the permutation resulted in f-values that follow a normal distribution and a raw p-value of each gene was calculated based on the fitted mean and standard deviation from the permutation data. An adjusted p-value was calculated using Benjamini-Hochberg control of false discovery rate [Feature selection was conducted on the 3787 probesets based on correlation between gene expression level and drug response  and balance validation (30%) subsets: we first equally divided the cell lines into three groups by IC50 values, then conducted random splits on each group to get training and validation subsets. When merging the training and validation subsets from these three groups, both training and validation subsets having some sensitive and some resistant cell lines  were generated. The training subset was then further divided into random training (60% of training) and random validation (40% of the training) subsets. Overall, the training set was divided into three parts: random training (42%), random validation (28%) and balance validation (30%) subsets . HundredPearson correlation between experimental and predicted IC50s. The AUC measure uses a data-driven cutoff to divide cell lines into sensitive- and resistant-groups using experimental IC50s, and use a series of cutoffs on predicted IC50s to define predicted sensitive- and resistant groups, to generate the Receiver Operating Characteristic (ROC) curve and calculate AUC values. In this sense, although a regression-based modeling framework was built, some classification measures to help select top models were still used; 2. Among all splits, a correlated AUC and correlation measures on random validation  were selected based on all five criteria out of 150 initial balance split model sets. Then, one top model with best correlation between predicted and experimental IC50 in balance validation set was selected as a top model in each of the top five model sets, resulting in 5 top models from 150*1000 = 150000 total splits. Pairwise overlapping scores among these models were then calculated (the overlapping score was defined as Jaccard similarity index), on the real random training cell line sets as well as generated an overlapping score distribution on randomly select two sets with the same size of cell lines as in the random training (a permutation test). In order to remove top models that were significantly overlapping to each other, a 90% quantile cutoff on pairwise overlapping scores was used as a significant overlapping cutoff.There are multiple potential ways to identify key biomarker genes from a single training/testing dataset, for example, several sparse PLSR approaches were developed by Chun and Keles , 30, Lê Starting from the five highest weighted genes and adding one gene at a time, the representative PLSR model was retrained/retested by increasing signature gene size one at a time (a forward selection approach). The core PLSR model was identified as an early plateau point on the model performance curves on both AUC and correlation measures . The resA subset of the core PLSR model as the pathway-based PLSR model was selected using the functional analysis platform MetaCore (Thomson Reuters) . The 191In the 51-gene signature , 22 geneTo test for drug-specificity of the Erlotinib PLSR model, a predictive model to Sorafenib was built using data from Oncopanel cell lines treated with Sorafenib to generate IC50 values as well as the same baseline gene expression data and modeling framework as for Erlotinib. Therefore, the data reduction is the same for both Erlotinib and Sorafenib and the models are directly comparable.Unlike Erlotinib’s IC50 distribution , the SorA network of Sorafenib signature genes was generated using protein-protein interactions and canonical pathway information . ActivatThe BATTLE clinical trial data was used as an independent testing dataset, to evaluate the performance of the OncoPanel cell line data derived drug sensitivity models. In this Phase II clinical trial , subsetsThe baseline tumor gene expression data in patients was generated using the Affymetrix HG Gene 1.0 ST array, which is a different platform than the U133plus2 array used in the OncoPanel experiments. To address the issue of platform incompatibility, the Affymetrix “U133PlusVsHuGene_BestMatch” file was used to identify matching probesets between U133plus2 and HG Gene 1.0 ST arrays, then a quantile normalization of the BATTLE data against OncoPanel gene expression was performed. All 51 genes from the Erlotinib functional pathway-based model were present in the BATTLE dataset, as well as all 113 Sorafenib signature genes.The cutoffs of Erlotinib or Sorafenib predicted scores were data-driven by re-predicting IC50s on the Oncopanel cell lines, using the corresponding drug sensitivity models . The disThe patients’ PFS cutoffs for Erlotinib and Sorafenib were selected at 2.4 and 4.2 months based on their PFS distributions, respectively . We are As shown in Similarly, the Sorafenib model built from OncoPanel Sorafenib screen data (IC50s) can predict drug response for the 39 Sorafenib treated patients . The SorThe Erlotinib model was further tested to predict the response of Sorafenib treated patients. For these heavily pre-treated patients in the BATTLE clinical trial, both the PFS and model-predicted scores suggested that the majority of the patients are not Erlotinib sensitive. The overall accuracy of the Erlotinb model in predicting Sorafenib treatment outcome was 64% . InteresThe Sorafenib model failed to predict the BATTLE Erlotinib treated patients’ response, with a positive predictive value of 14% and an overall accuracy of 48% . It is wPutting together, the Erlotinib or Sorafenib models trained from Oncopanel data can be used to predict patients’ response upon corresponding treatments. However, the models performed poorly in cross-evaluation (i.e. Sorafenib signature for predicting Erlotinib response and vice versa). This suggests that both the Erlotinib and the Sorafenib models are drug specific.Another way to assess cell line data derived drug sensitivity models was to test the models for their ability to predict PFS. Patients in the BATTLE clinical trial were assigned to a marker positive (drug sensitive) or a marker negative (drug resistant) sub-group based on the corresponding PLSR drug predicted sensitivity scores, then marker +/- patient groups were compared based on the clinical output (PFS).When the Erlotinib model is used to stratify BATTLE patients in the Erlotinib arms, the median PFS for the model-predicted Erlotinib-sensitive patient group was 3.84 month while the PFS for model-predicted Erlotinib-resistant patients was 1.84 month, corresponding to a p-value of 0.09 and Hazard ratio of 0.43  over the marker-resistant group , with a Importantly, the signatures were drug-specific and did not predict across arms. The Erlotinib predictive model failed to separate marker-sensitive vs. marker-resistant groups for Sorafenib treatment arm and vice versa, suggesting that the predictive models are drug specific .After the Erlotinib and Sorafenib sensitivity models were tested using the BATTLE clinical trial data, they were further used to predict patients’ tumor samples from gene expression data in the public domain. The percentage of predicted Erlotinib or Sorafenib sensitive samples were generated based on cancer indications, and a higher percentage of predicted drug sensitivity for a cancer type was considered as a potential indication that the drug may work well on treating that type of cancer.An internal data repository was built based on the tranSMART translational medicine platform  using daExtensive manual curation and text mining were performed to standardize metadata on patient and clinic features. For the purpose of applying the drug sensitivity models on patients’ cancer samples, our quality control contains the following aspects: (1). Remove cell line data; (2). Remove normal tissue samples; (3). In some solid tumor studies, the samples were actually collected from blood or saliva—we also removed data from these studies. After these quality control steps, the resulting 11331 samples was categorized into various cancer indications.Each sample’s baseline gene expression was used to predict its potential Erlotinib or Sorafenib response, and percentage of predicted drug sensitive samples were calculated upon each cancer indication. Overall, differential pattern of drug sensitive indications is generally consistent with clinical trials outcomes for Erlotinib and Sorafenib, respectively . Lung cahttp://www.clinicaltrials.gov/). In addition, Sorafenib model predictions suggested there are a high percentage of drug sensitive samples in bone cancer and haematopoietic neoplasms , which creates balance validation subsets as well as random validation subsets. The feature selection was done on the whole dataset, so that the same feature genes could be used to train and compare models among different splits. This also enabled finding the best model that represented consensus information among the whole dataset. By identifying minimally overlapping high score models and calculating consensus weighting among them, the common features in the training dataset were captured. Besides the Erlotinib and Sorafenib case studies described in the current work, the method was further validated through our participation in the NCI-DREAM challenge. The NCI-DREAM challenge was a community effort comprised of 44 teams to predict drug-sensitivity to 28 drugs .A translational medicine case study consisting of generating predictive models of Erlotinib and Sorafenib sensitivity from a cell line panel dataset, followed by model validation in a clinical setting is presented. The models predict clear clinical benefits: for example, the Erlotinib drug sensitivity model separated marker-sensitive and marker-resistant patient groups with the former demonstrating twice as long survival than the latter  as the clinical end-point. Moreover, the training data (Oncopanel) covers a mixture of cancer indications, while non-small cell lung cancer (NSCLC) was the only cancer indication in the BATTLE trial. Also, the signatures were generated on the Affymetrix U133plus2 platform and tested on the data generated on Affymetrix Human Gene 1.0 ST platform. Despite all these differences, the cell line derived Erlotinib and Sorafenib sensitivity models predicted BATTLE trial PFS outcomes with high accuracy of 84% and 79%, respectively .Given the fact the Erlotinib is an EGFR inhibitor while Sorafenib has multiple tyrosine kinase receptors as targets, one would expect that model specificity between Erlotinib and Sorafenib would be difficult to achieve. Strikingly, the Erlotinib and Sorafenib drug sensitivity models were drug specific, i.e. the Erlotinib model failed to predict Sorafenib patients’ PFS and vice versa Figs  and 4.The drug sensitivity models were used to predict percentage of drug sensitive samples per cancer indication for a large number of cancer expression datasets in various indications. The predicted Erlotinib or Sorafenib sensitive cancer indications were generally consistent with known FDA approvals on Erlotinib or Sorafenib . Therefohttps://www.eurofinspanlabs.com). For testing the Erlotinib predictive model, the BATTLE trial patients’ baseline gene expression [Among the 240 Eurofin cancer cell lines, baseline gene expression data was downloaded from 170 cell lines from ArrayExpress (E-MTAB-37). The remaining 70 cell lines gene expression data was purchased from Eurofins (pression  (GSE3307pression .http://www.affymetrix.com) and was used to identify good matching probesets in Affymetrix U133plus2 array (for training dataset) and in Human Gene 1.0 ST array (for testing dataset). Based on the observations from both MAQC II project [Since gene expression data was generated from different microarray platforms, it is necessary to normalize them before model training/testing. A “U133PlusVsHuGene_BestMatch” file was downloaded from Affymetrix website . There are 213 cell line IC50 data on both Erlotinib and Sorafenib datasets.Erlotinib and Sorafenib sensitivity screen data were purchased from Eurofins (Two-layers of cell line data QC were conducted that reduced the number of cell lines from 213 to 183 before model training and testing. The first layer involved curve-fitting on dose-response data for each compound. The following criteria were used to QC each compound’s dose-response curve: (1). Cell lines were removed if there were three or more missing data points on the dose-response curve; (2). Cell lines were removed if there were four or more data points with CV > 30%; (3). Cell lines where the dose-response curve displays unusual behavior (e.g. has a stronger inhibitory effect at lower concentrations then at higher concentrations) were removed; (4). Cell lines that only demonstrate inhibitory effects at the highest tested concentration were removed. The second layer of QC evaluates whether cell line screen results actually reflect a drug-treatment effect. Clustering analysis was conducted among a group of internal and standard of care compounds. Compounds that have similar mechanism of action were clustered together, suggesting that cell line screen data actually reflects real drug-treatment effect and therefore could be possible to identify drug-sensitive predictive models from cell line data (data not shown).We designed and implemented a PLSR based modeling framework on drug-sensitivity predictive model building. More details on the PLSR method and its biological applications have been previously published –26. We sIn the current work, we use the “pls” package from R (version 3.0.2) for PLS regression. The loading numbers from first and second PLS components were used to calculate gene weights as sqrt(PLS1^2 + PLS2^2). The gene weights were used to compare top splitting cases and forward feature selection steps.To reveal functional connectivity between the genes from Sorafenib and Erlotinib predictive models, we reconstructed the causal molecular network by connecting these genes via canonical molecular pathways. The network was built in several steps.First, in addition to the predictive models’ genes, we identified “topologically significant” genes for the signature genes. The algorithm of selection of topologically-significant direct regulators, or “overconnected” genes, is described in  and remoNext, we selected molecular pathways significantly enriched with both genes of predictive signature and topologically significant genes using enrichment analysis in the Pathway Maps ontology in MetaCore (Thomson Reuters).Finally, the identified enriched molecular pathways were manually combined in the causal molecular network. The resulting network included both sensitivity (negative correlation of expression with IC50) and resistance-specific (positive correlation of expression with IC50) genes. The networks were further populated with direct interactions between proteins encoded by signature genes. Also, we included genes with genetic alterations correlated with the drug response phenotype in OncoPanel cancer cell line panel in the reconstructed causal networks.S1 Fig(TIF)Click here for additional data file.S2 FigPairwise comparison was done among top performing models, as well as with the consensus model. The numbers in the lower left part of the figure are PLSR model derived loading values for individual genes, and the numbers in the top right part of the figure are Pearson correlations between models.(TIF)Click here for additional data file.S3 FigThe network was reconstructed from canonical signaling pathways regulated by signature genes and signature specific direct interaction network. Sensitivity-specific signature genes are highlighted with blue thermometers, resistance-specific genes are red thermometers; topologically significant genes are highlighted with yellow thermometers. White starts in object images mark groups of proteins.(TIF)Click here for additional data file.S4 FigThe network was reconstructed from canonical signaling pathways regulated by signature genes and signature specific direct interaction network. Sensitivity-specific signature genes are highlighted with blue thermometers, resistance-specific genes are highlighted with red thermometers; topologically significant genes are highlighted with yellow thermometers. White starts in object images mark groups of proteins.(TIF)Click here for additional data file.S5 Fig(A) Erlotinib IC50 distribution on Oncopanel cancer cell line panel; (B). Sorafenib IC50 distribution on Oncopanel cancer cell line panel. The top figure was for the whole Ricerca cell line panel and the bottom figure was after removing data for middle one-third IC50s.(TIF)Click here for additional data file.S6 FigRed points were top performing models on 1000 random splits on this balanced split, based on both AUC and correlation measures.(TIF)Click here for additional data file.S7 FigRed vertical line was cutoffs selected to separate drug sensitive vs resistant cases for each drug.(TIF)Click here for additional data file.S8 FigRed vertical line was cutoffs selected to separate patients into responder and non-responder sub-groups for each drug.(TIF)Click here for additional data file.S1 TableA raw p-value of each gene was calculated during feature selection step based on the fitted mean and standard deviation from the permutation data (see (DOCX)Click here for additional data file.S2 TableA raw p-value of each gene was calculated during feature selection step based on the fitted mean and standard deviation from the permutation data (see (DOCX)Click here for additional data file.S3 TablePearson’s correlation coefficient was calculated for a vector of log2(IC50) and a vector of genetic events. Positive value of correlation for a given gene was interpreted as correlation with resistance. Negative value can was interpreted as an association with sensitivity. Correlation P-values were calculated by a permutation test with 1000 shuffles. Fisher’s exact test was used for association analysis between resistance/sensitivity classes and genetic events. “R” stands for resistance, “S” stands for sensitivity.(DOCX)Click here for additional data file.S1 TextDescription and discussion of reconstructed causal models omitted from main manuscript and shown in (DOCX)Click here for additional data file.S2 TextAdditional results and methods omitted from the main manuscript. We provide results of association analysis between genetic events and drug response supporting major observations in the manuscript.(DOCX)Click here for additional data file.


In microarray studies, the number of samples is relatively small compared to the number of genes per sample. An important aspect of microarray studies is the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure together with the survival prediction model. In this study, a new method based on combining wavelet approximation coefficients and Cox regression was presented. The proposed method was compared with supervised principal component and supervised partial least squares methods. The different fitted Cox models based on supervised wavelet approximation coefficients, the top number of supervised principal components, and partial least squares components were applied to the data. The results showed that the prediction performance of the Cox model based on supervised wavelet feature extraction was superior to the supervised principal components and partial least squares components. The results suggested the possibility of developing new tools based on wavelets for the dimensionally reduction of microarray data sets in the context of survival analysis. Microarray studies are widely used in biological and medical studies because they allow researchers to monitor tens of thousands of gene expression profiles simultaneously. Much of the interest in microarray data analysis derives from the potential of identifying the genes that relate to biological processes, the classification of tumor types, the stages based on gene expression patterns, and the study of gene interactions , 2. Howep is usually much larger than the number of subjects n . Existing statistical methods such as CPH model require fewer predictors than cases [Survival analysis is a statistical method that especially deals with the modeling and analysis time from a well-defined time origin until the occurrence of some event or end point of interest. A major complexity of analyzing such data is right censoring, where the event of interest is known to occur only after a certain time point. One popular regression model that takes into account the censored response is the Cox Proportional Hazards (CPH) regression model . A substan cases . Thus, aan cases .L1/2 shooting regularization method, which is used for variable selection in the CPH model [Rosenwald et al. described a feature selection approach for identifying genes related to survival time that fits CPH models to each gene and selected those that pass a threshold for significance . Liu et PH model . AlizadePH model , 10. Li PH model .Several studies have compared dimension reduction methods in survival prediction based on microarray data. Bøvelstad et al. applied seven dimension reduction methods in order to predict survival in patients with diffuse large B-cell lymphoma (DLBCL) using gene expression dataset. Totally, their results showed that the ridge regression had best performance .One of the methods used for feature extraction from the high dimensional data is wavelet transform. Normally, one dimensional discrete wavelet transform (DWT) is used to reduce dimensionality in the analysis of high dimensional biomedical data . The priHowever, few studies have used wavelet transform in the area of survival analysis. For example, Liu et al. used continuous wavelet transform combined with a genetic algorithm to select genes related to survival in colon cancer . This stWe performed simulation study to evaluate and compare the performance of the proposed supervised wavelet method with supervised PCA and supervised PLS. The simulated data set was first presented by Bair and Tibshirani, for evaluation purposes . FollowiThe survival times of samples 1–50 were generated as normal random numbers with a mean of 10.0 and a standard deviation of 2.0, and the survival times of samples 51–100 were generated as normal random numbers with a mean of 8.0 and a standard deviation of 3.0. For each sample, a censoring time was generated as a normal random number with a mean of 10.0 and a standard deviation of 3.0. If the censoring time turned out to be less than the survival time, the observation was considered to be censored .We applied the supervised wavelet transform method to a set of gene expression data with survival information on two real datasets. The first dataset was related to the diffuse large B-cell lymphoma (DLBCL) dataset of Rosenwald et al. and the second dataset was related to the lung cancer dataset of Beer et al. , 18.http://llmpp.nih.gov/lymphoma/data.shtml.The DLBCL dataset included expression measurements of 7,399 genes on 240 patients, together with their survival times. A total of 138 deaths were observed during the study with the median death time of 2.8 years. The dataset is available at The lung cancer dataset also included expression measurements of 7,129 genes on 86 lung adenocarcinoma patients, together with their survival times. The survival times were observed in 24 patients and the censored times in 62 patients. A detailed description of lung cancer dataset can be found in the original publication . We usedh represents the hazard function at time t for a subject with covariates x. For different covariates, CPH regression models the hazard as a proportional factor applied to time-dependent baseline hazard that corresponds to a reference population for which the covariate values are all zero. This baseline hazard function is h0(t) and the effect of the covariates x is modeled linearly using βTx, which is known as the risk score. The coefficient vector β is estimated by maximizing the partial likelihood:Rj represents all patients at risk at the jth failure time and k is the number of distinct failure times. The hazard ratio between different observations i and j by  and a mother wavelet (ψ).A wavelet is a “small wave,” which has its energy concentrated in time. In signal processing, a transformation technique is used to transfer a data in another domain where hidden information can be extracted. Wavelets have a nice feature of local description and separation of signal characteristics and give a tool for the analysis of transient or time-varying signal . A wavelf(t) ∈ L2(R) in terms of φ(t) and ψ(t) as follows:φ(t), ψ(t), c0, and dj represent the scaling function, mother wavelet function, scaling coefficients (approximation coefficients) at scale 0, and detail coefficients at scale j, respectively. The variable k is the translation coefficient for the localization of gene expression data. The scales denote the different (low to high) scale bands. The variable symbol j0 is scale (level) number selected.Wavelet transforms are classified into two different categories: the continuous wavelet transforms (CWT) and the discrete wavelet transforms (DWT). DWT is a linear operation that operates on a data vector, transforming it into a wavelet's coefficient. The idea underlying DWT is to express any function h(m − 2k) and h1(m − 2k) are the low-pass filters and high-pass filters.One-dimensional discrete wavelet transform decomposes a signal as a sum of wavelets at different time shifts and scales (frequencies) using DWT. For this purpose, the signal is passed through series of high pass and low pass filters in order to analyze low as well as high frequencies in the signal as follows:f(t) using the pyramid algorithm is shown in The whole process of obtaining the wavelet transform of d1, while the low pass filter associated with scaling function produces approximation coefficient  c1. Then the approximation coefficients c1 are split into two parts by using the same algorithm and are replaced by c2 and d2, and so on. This decomposition process is repeated until the required level is reached. The coefficient vectors are produced by down sampling and are only half the length of the signal or the coefficient vector at the previous level.At each level, the high pass filter produces detail coefficients (wavelet coefficients) The main advantage of the wavelet transform is that each basis function is localized jointly in both the time and frequency domains. From a viewpoint of time-frequency, the approximation coefficients are corresponding to the larger-scale low-frequency components and the detail coefficients are corresponding to the small-scale high-frequency components. Generally, the former can be used to approximate the original signal and the latter represents some local details of the original signal –14, 20.There are different families of wavelets symlet, coiflet, daubechies, and biorthogonal wavelets. They vary in various basic properties of wavelets, like compactness. Among them, Haar wavelets belonging to Daubechies wavelet family are the most commonly used wavelets in database literature because they are easy to comprehend and fast to compute.g = 1,2,…, 7399.The proposed method starts by adopting a univariate Cox model for each gene:β/se(β), which are measures of the correlation between the gene expression level and patient survival. Then, in each step, we pick out the top number of genes included with higher Wald's statistic. Then, this reduced set of genes is modeled by the one-dimensional discrete wavelet transform to extract the relevant information and finally, the wavelet approximation coefficients in the first levels of decomposition are used in a multiple Cox regression model  method is a supervised dimension reduction technique that is usually employed to correlate a response variable to the explanatory variables. PLS components are linear combinations of the predictor variables, constructed to maximize an objective criterion based on the sample covariance between response and covariates.PLS finds components that are dependent on both the variance of the gene expressions and the covariance between the gene expressions and the survival, whereas the components in PCA only depend on the variance of the gene expressions . Many meIn order to evaluate the proposed method, in all experiments , data set was randomly divided into training (2/3 of the data) and test (1/3 of the data) sets for 50 times. The methods  were applied to the training set and the test set was used to calculate the evaluation measures. These data sets included 66 samples from 100 samples for simulated data, 160 samples from 240 patients for DLBCL data, and 60 samples from 86 patients for lung cancer data.R statistical package were used for data analysis.For predicting the survival of patients based on gene expression, we applied the proposed dimension reduction method, supervised PCA, and supervised PLS in stage 1 in each data set and then used the data in the reduced subspace to apply in the multiple CPH model in stage 2. In fact, following the evaluation scheme proposed by Bøvelstad et al. in each experiment, the parameters were estimated , Likelihood ratio test statistic, Integrated Brier Score, and C index.The predictive performance of a fitted Cox model based on supervised wavelet coefficients, supervised principal components, and supervised partial least squares components was evaluated using Moreover, in order to evaluate the effect of adding clinical information to genomic data on the performance of model for a lung cancer data set, clinical information was added to genomic data. The clinical features for each patient were included: age, sex, stage, tumor size, and nodal status.R2 statistic measures the proportion of variation in survival data that may be explained by the predictor. A predictor with good predictive performance can explain a high proportion of variation in the survival data. On the other hand, a poor predictor may explain only a little variation in the data. Accordingly, when comparing models, the model with the larger R2 statistic is usually preferred [R2 statistic that may be employed for Cox proportional hazard model as follows:l(·) indicates the log-likelihood function [R2 values are those which were provided by the coxph R function.referred . Nagelkefunction . In the The discriminatory power of a statistical model is assessed by concordance probability estimate (CPE). This estimator is merely a function of the regression parameters and the covariate distribution without using the observed event and censoring times. A value of one for CPE denotes the perfect discrimination .Concordance, or C-statistic, is a valuable measure of model discrimination in analyses involving survival time data. In general, consider selecting random pairs of patients and for each pair note, whether the model correctly predicts an order, for example, a higher model score for the better result. Concordance is then the fraction of pairs for which the model is correct. A completely random prediction would have a concordance of 0.5 and a perfect rule a concordance of one .R denotes the reduced (PH) model obtained when all β's are 0 and F denotes the full model. Thus, the performance is good when LR is large [The likelihood ratio test is a global goodness-of-fit test statistic for a Cox regression model. The test statistic for the likelihood ratio test is given as follows:is large .t, the Brier score for a single subject is defined as the squared difference between observed survival status  and a model based prediction of surviving time t. The Brier score is given byN is the sample size, oi(t) is the observed survival at time t, and pi(t) is the predicted probability at time t. The weight W is used to remove a large censoring bias. The Integrated Brier Score (IBS) is a summary of the prediction error over event time by integrating the formula (At a given time point given byBSt=1N∑i=The results of the predictive performance of the fitted Cox models based on approximation wavelet coefficients, the top number of principal components, and partial least squares components for simulated, DLBCL, and lung datasets are shown in Tables The results showed that the spread of mean values of five evaluation measures over the 50 data sets is fairly large. These variations are caused by selecting the data at random into 50 data sets as well as the variations of the prediction methods performance for the given datasets. In order to determine how much of the variation was due to the prediction methods, we used the supervised wavelet method as a benchmark and, for each of the two other methods, computed the difference between the evaluation criteria in each of the conditions.R2, C index, CPE, and LR were positive, which showed that supervised wavelet method performed better than other methods. In addition, the median values for the Integrated Brier Score criterion in the different conditions were negative. Totally, simulation results and real data analysis confirmed the suitable performance of the supervised wavelet method.Figures The results of the predictive performance of the fitted Cox models based on combination of clinical and genomic information for a lung data set are shown in This study employed the supervised dimension reduction method based on wavelet transform and modeled survival times in the presence of right censoring, taking into account the microarray data information. The proposed method was evaluated by simulations and applied to the Rosenwald et al.'s DLBCL dataset and Beer et al.'s Lung cancer dataset , 21.Considering the fact that most genes are irrelevant to patients' survival, we analyzed the reduced dataset given by selecting genes that were significantly related to survival time based on the Wald's statistic. If the wavelet transform is performed directly by using all of the genes in a data set, there is no guarantee that the resulting wavelet coefficients will be related to survival , 22. ThuThe main purpose of the feature extraction method using wavelet transform is that the approximation coefficients usually comprise the majority of the important information . In addiThe wavelet feature extraction method does not depend on the training dataset to obtain the basis of feature space compared to PCA and PLS methods. Therefore, the wavelet feature extraction method reduces the computation load compared to PCA and PLS .The flexible characteristic of our proposed method makes it appropriate not only for correlating censored patient survival data with microarray gene expression data but also with large-scale biological data stemming from other high-throughput technologies such as DNA copy number analysis and proteomics.Although the proposed method was better than supervised principal components and supervised partial least squares components based on two popular data sets and brief simulation, it is suggested that comprehensive simulation is used in future studies in order to evaluate this method compared with the other dimension reduction methods.The future investigations can focus on different ways of preselecting gene in the first stage of the proposed method. For example, rather than ranking genes based on their Wald's statistic, one would use a different metric to measure the association between a given gene and survival time. On the other hand, another mother wavelet and different level of decomposition can be studied.This study showed the Cox model based on supervised wavelet feature extraction method which had superior predictive performance over the supervised principal components and supervised partial least squares components based on top selected genes. These results exhibit the possibility of developing more advanced tools using wavelets for the dimension reduction of microarray data sets in the context of survival analysis.


A growing number of statistical methods and associated software for platform integration are available to the user, however an understanding of their comparative performance and potential pitfalls is critical for best implementation. In this review we provide evidence-based, practical guidance to researchers performing cross-platform integration, particularly with an objective to discover biomarkers.The diagnostic and prognostic potential of the vast quantity of publicly-available microarray data has driven the development of methods for integrating the data from different microarray platforms. Cross-platform integration, when appropriately implemented, has been shown to improve reproducibility and robustness of gene signature biomarkers. Microarray platform integration can be conceptually divided into approaches that perform early stage integration   Potential roles for biomarkers include early detection of disease in healthy individuals, disease classification, prognosis, prediction of response to therapy, and as surrogate outcomes in clinical trials . The ided arrays . While od arrays ,7, this d arrays , howeverWhile microarrays measure the expression of thousands of genes simultaneously, it is expected that only a small subset of the genes will be associated with the clinical or biological outcome of interest. This subset of genes, often termed a “gene signature” or “prognostic signature”, has a collective expression pattern that is unique to the outcome of interest and thus has potential to function as a biomarker . The genBiomarkers for the monitoring of disease activity of POC are currently lacking. A number of published gene signatures validated using independent samples have been shown to serve as significant predictors of clinical outcome ,13,14,15et al. [.Published prognostic gene signatures derived from internal validation often show little overlap with genes identified by other study groups . Potentiet al.  re-analyi.e., gene signatures providing improved discrimination between two or more patient groups) [Integration of multiple microarray data sets has been advocated to improve gene signature selection . Increas groups) . Classif groups) .et al. [Two fundamental approaches to combine the information of multiple independent microarray studies from different platforms  are metet al.  classifiet al. ,30,31. Wet al. . Early oet al. [ad hoc and remains an open question in the field [Ramasamy et al.  identifihe field . Two crihe field ,33. Therhe field  and affyhe field  for Affyhe field ,37.versus decreasing the number of genes analyzed [Another important pre-processing step is ascertaining which probes represent a given gene within and across the different microarray platforms. The relationship between probes and genes may be determined by mapping probes to the gene using sequence-matched datasets or using gene-level identifiers such as Entrez Gene ID available in the annotations packages in R/Bioconductor  to unifyanalyzed . Co-ineranalyzed . Imputatanalyzed .et al. [p-values estimated from Affymetrix arrays into the study-specific test statistics within a meta-analysis of two Affymetrix array studies using an effect sized model produced more biologically meaningful results than an unweighted model [If multiple probes match a single gene, selecting the probe with the highest interquartile range (IQR) has been recommended . Genes wet al.  applied et al. ,44. Furted model ,45.p-values or effect sizes) require the use of raw gene expression data whereas secondary statistics rely only on ranked lists of genes. Popular methods for meta-analysis mainly combine one of three types of statistics: p-value [p-value for DE of each gene) have been aggregated into a single gene ranking (“consensus”) using a number of methods including the rank product method [In the meta-analysis approach, each experiment is first analyzed separately and the results of each study are then combined. Meta-analysis methods that combine primary statistics , “in the majority of studies” (HSr), or in “one or more studies” (HSB) [p-value in one study likely meets criteria for statistical significance; thus, it detects DE in “in one or more studies” (HSB), whereas the maxP or rank product method tends to detect gene biomarkers DE in “all studies” (HSA).Meta-analysis methods have been categorized based on the hypothesis settings that gene biomarkers are differentially expressed “in all studies” (HSs” (HSB) ,50. In Fversus healthy non-smokers) [r) are generally recommended as they provide robustness and detection of relevant signals across the majority of samples [p-values in at least a given percentage of studies. This method detects biomarkers DE in the majority of studies  based on a user-specific threshold of studies.The choice of the statistical meta-analysis method is selected based on the biological purpose of the analysis. A gene serving as a biomarker from a meta-analysis is expected to show concordant biological effects across all or most experiments for a given condition derived from relatively homogenous sources  . While dsmokers) . Meta-an samples . Song an samples  proposedet al. [p-value combination methods , two combined effect size methods (fixed effects and random effects) and four combined ranks methods . The 12 meta-analysis methods were categorized into three hypothesis settings  based on their strengths for detecting DE genes. They then applied four statistical criteria to the assessment of each meta-analysis method: (1) detection capability (the number of DE genes detected); (2) biological association (degree of association between DE list with predefined genes from pathways related to the disease), stability  and robustness .Several comparative studies systematically comparing meta-analysis methods for microarray data have been previously published ,53,54. Cet al.  benchmarA setting, the maxP performed the worst based on their four criteria and the investigators recommend that it be avoided. Rank product method had improved performance but weaker detection capability. The two methods that tended to detect DE in the majority of samples were the Random Effect Model (REM) and the rth order p-value (rOP). rOP outperformed REM based on stronger biological association and detection capabilities, but this was achieved at the expense of diminished stability and robustness.Among the methods based on HSp-values or ranks obtained by two-sided hypothesis testing may result in genes with discordant DE across two-class outcomes which can be difficult to interpret [et al. [p-values to guarantee identification of DE genes with concordant DE direction.It is important to note that differentially-expressed genes determined by combing nterpret . Wang et [et al.  have prot-statistic, F-statistic, log-rank statistic) and the meta-analysis method . Methods combing effect sizes (standardized mean differences or odds ratios) are appropriate for combining two-class outcomes. Meta-analysis of expression studies with continuous outcomes  and survival outcomes (based on log-rank statistics) have typically been performed using combined p-values [et al. [F-statistic has been found to frequently fail to capture concordant patterns of gene expression.The objective and type of outcome types   will govp-values ,55 and cp-values . To capt [et al.  have appCross-platform normalization  consideCross-platform transformation and normalization methods have been developed with an aim to remove the artifactual differences between data from different microarray platforms while preserving the underlying biological differences between conditions. This step is essential, as non-biological differences (“batch effects”) in the gene signature discovery data can obscure real biological differences found between clinical groups.Early attempts at cross-platform merging applied straightforward transformation methods of location and scale (mean and variance) to process the gene expression data from different studies. Batch mean centering  is a simet al. [Over the past decade, a number of more complex cross-platform normalization methods have been published and their performance has been compared in several studies ,32. Fouri.e., each systematic bias) to maximize the minimum distance of all the data on the hyperplane (the margin). However SVM has data pile-up problems along the margin which have been improved by modifying the margin to maximize the sum of the inverse distance in DWD [The Distance Weighted Discrimination method, like Support Vector Machines (SVM), is a margin-based classification method that was developed to improve performance over the latter method. Essentially, SVM finds a hyperplane that separates the two classes  method developed by Shablin et al.  seeks toet al. [A comparative analysis of cross-platform normalization methods by Rudy and Valafar  found thet al.  also fouet al. . Althouget al. .One limitation of some existing cross-platform normalizing methods is that they can only be applied to two batches at a time. While cross-platform normalization steps can be chained together, the effect of these multiple normalization steps or which chaining method is still unclear .Software, including packages in R/Bioconductor and websites allowing users to implement microarray meta-analysis and cross-platform merging and normalization methods are listed in et al. [Directly-merged microarray data  has been argued to have better performance than meta-analysis for the identification of robust biomarkers on the premise that “deriving separate statistics and then averaging is often less powerful than directly computing statistics from aggregated data” . In a coet al.  found siet al. . While cet al. ,73,74, iet al. . A recenet al. .One major limitation of existing cross-platform normalization is that they require that every treatment group or sample type be represented on each platform to allow differentiation of treatment effects from platform effects. Furthermore, cross-platform normalization methods do not guarantee elimination of laboratory or batch effects across experiments and Rung and Brazma  have arget al. [p-values of differential gene expression. The investigators identified 82 genes differentially expressed between sepsis and inflammation and then performed a greedy forward search to determine which combination of these 82 genes produced the best improvement of area under the curve (AUC) in their discovery datasets. This resulted in an 11-gene transcriptional signature that was applied to 15 independent validation cohorts and was found to improve discrimination of patients with infection from those with sterile inflammation compared to use of clinical data alone. This gene signature requires further validation using prospective cohorts, however its excellent discriminatory power in both the discovery and validation cohorts suggests that it is likely to become a useful clinical assay in the future.Sweeney et al.  recentlyHNF4A) and polypyrimidine tract binding protein 1 (PTBP1), as the most significant up- and down-regulated genes in blood samples from PD patients. The relative abundance of HFN4A mRNA was found to correlate with disease severity in PD and the results were validated using samples obtained from two independent clinical trials. The abundance of HNF4A and PTBP1 mRNAs significantly decreased and increased, respectively, in PD patients during a 3-year follow-up period suggesting that these biomarkers may be useful for monitoring disease-modifying therapies for PD.Santiago and Potashkin  implemenet al. [In the previously discussed cross-platform normalization approaches , the majet al.  developeet al. [et al. [p-values of the candidate biomarker list from each study using Fisher’s and maxP methods. Statistical approaches to allow synthesis of regression slopes in meta-analysis have been described [Modelling confounding factors with variable selection in meta-analysis has recently been shown to improve robustness and sensitivity of DE gene detection  and inteet al.  produced [et al.  applied escribed  and applescribed .biological heterogeneity will become a central focus.Gene signature discovery for prognostic and diagnostic purposes is improved with knowledgeable selection and appropriate application of integration methods on microarray data performed on multiple platforms. While no consensus for the best implementation of cross-platform integration is currently available, previous benchmarking and comparative analyses have established the strengths and limitations of many of the existing methods. The recent evidence suggesting improved performance of cross-platform normalization methods over meta-analysis may lead to an increasing proportion of studies in the literature implementing the former method. Further refinement of existing methods and development of new methods for cross-platform normalization and classification to exploit the vast quantity of microarray data currently available are expected. As elimination of platform specific bias becomes well-established with these methods, future studies addressing the performance of prognostic signature discovery in light of the existing


The great utility of microarrays for genome-scale expression analysis is challenged by the widespread presence of batch effects, which bias expression measurements in particular within large data sets. These unwanted technical artifacts can obscure biological variation and thus significantly reduce the reliability of the analysis results. It is largely unknown which are the predominant technical sources leading to batch effects. We here quantitatively assess the prevalence and impact of several known technical effects on microarray expression results. Particularly, we focus on important factors such as RNA degradation, RNA quantity, and sequence biases including multiple guanine effects. We find that the common variation of RNA quality and RNA quantity can not only yield low-quality expression results, but that both factors also correlate with batch effects and biological characteristics of the samples. High-throughput transcriptome profiling is an essential technique in biomedical research. Countless gene expression studies have been performed using various available microarray platforms, including large-scale and meta studies comprising hundreds of experiments. The latter are enabled by the large and ever-growing number of samples archived in public data repositories. For example, about 250,000 new microarray samples have been publicly released and indexed by the ArrayExpress database  each yeaThe general aim of gene expression experiments is to find systematic expression changes relating to variations in biological or environmental conditions. Systematic expression changes due to technical factors, however, constitute a bias that can negatively affect the reliability of the expression results. Technical factors often change between groups of samples (“batches”) where the technical variables, for example, could be that two different lab workers handle the sample groups, or that the experiments are carried out at different dates or locations.et al. reanalyzed a large microarray-based study intending to assess gene expression variation between human populations [et al. concluded that the data possesses a systematic and confounding technical bias, and that the reliability of the obtained results is at least questionable.These batch effects are a major issue in microarray data analysis, and correlation of such factors with the biological variables of interest can prevent identification of the true biological source of variation and render the results of a microarray experiment worthless . An aneculations . They foi.e., as an unspecified source of variation) that can be detected within experimental data using a hypothesis- and thus model-based approach [In face of the widespread occurrence and the negative impact on the reliability of the outcome, it raises the question about the origins of batch effects. Currently they are often handled merely as a statistical issue  constant in an ideal experiment and (b) largely independent of the biological variation of interest. Consequently, specific metrics for these factors represent covariates potentially explaining the unwanted technical differences in the expression results. We will not consider general quality-control (qc) metrics such as GNUSE  which arHumanArraySet representing a large number of publicly available microarray samples of this platform  compiled by Lukk et al. [expression space, the combined and processed gene expression data from this diverse collection of human samples, can be queried using the dedicated database ArrayExpress Atlas [We have downloaded the k et al.  consistiss Atlas .scaling factor, average background, percentage of present calls, RNA degradation from whole array, Normalized Unscaled Standard Errors, and Relative Log Expression computed from the array data using Bioconductor [The 5372 samples have been selected from a larger data set of 8268 samples after application of strict quality control (qc). This was based on the quality measures onductor . Qualityonductor  adjustedHumanArraysSet therefore is a representative sample of available human microarray experiments.We obtained a full list of the 8268 samples from the authors and downloaded the remaining 2896 (“qc-excluded”) samples from public databases. From these, 137 samples could however not be retained as they were removed from the databases, leaving in total 8268 − 137 = 8131 samples. The full set of 8268 unique samples represents virtually all HG-U133a data publicly available in the two major public databases GEO and ArrayExpress in 2006 with no restrictions on the type of samples. This The key process in any microarray experiment is the hybridization of nucleic acids on a solid surface. Modeling this process based on physico-chemical principles has helped to improve the understanding of microarray surface hybridization and has been applied many times to precisely describe the response function of microarray probes ,13,14,15In the first step of our approach we disentangled the complex nature of microarray hybridization process by addressing a series of effects in separate studies in order to understand their nature, to establish causal relations between experimental factors and microarray measurements, to judge the effect size, and finally to develop models and algorithms that allow suitable calibration of the raw data. Particularly, we studied the character of the hybridization isotherm ,18,19,20versus the logged mean intensity of these probe pairs provides curves of characteristic hook-like shape, the dimensions of which enable parameterization of the Langmuir isotherm in a chip- and probe-specific fashion and finally allow to extract expression measures from the data.In the next step we developed practical algorithms such as the “Hook Curve” formalism ,27 to esMoreover, the dimensions of the hook-curve, e.g., its starting point, its width and height, provide quality measures characterizing the hybridization of each particular array in terms of, e.g., the amount of RNA and the fractions of specific and non-specific hybridization in large scale experimental series. The hook approach was also adapted to assess RNA-quality using microarray data . Here, pWe applied the hook approach for quality control and the identification of batch effects and of outlier samples in large scale cancer data sets ,29. For As mentioned above, the hook-quality measures make use of internal standards available for arrays of the GeneChip-type such as the PM/MM pairs and sets of several probes interrogating different positions of the gene of interest. Due to the special design of these arrays we were able to monitor the hybridization characteristics of large experimental series. In this publication we focus on arrays of the Affymetrix HG-133 design . For comparisons between GeneChip arrays of different generations we refer to our previous work ,25. It hImportantly, the study of hybridization artifacts of “non-Affymetrix” microarrays, e.g., of the Illumina or Agilent products, would require first the development of appropriate methods of quality control which estimate analogous effects as discussed here. In general, we expect gradual differences in effect size but no principal difference due to the common working principle of microarrays based on sequence-specific surface hybridization . Also thk,k+1 ∈ {A, C, G, T}2 and all positions k = 1...24 of the 25-meric probe sequence ξ [The intensity of a microarray probe is modeled in dependency of specific/non-specific target concentration, saturation intensity and a sequence effect δA as described in . We emplquence ξ (1)δA(ξ)maximum sensitivity amplitude as the maximum difference between all pairs of NN-sensitivity profilesWe define the p of a probe set p relative to the baseline of non-specific binding Np of this probe set. Averaging these R values over all probe sets of a microarray exceeding a given expression threshold provides the relative specific transcript level, or mean log S/N ratio,In  we definWe further defined β as the mean negative decadic logarithm of the non-specific signal of all probe sets of the arrayIt was previously shown that β has a geometric interpretation as the width of the hook curve and describes the measuring range of specific hybridization .Good RNA quality is an important prerequisite for obtaining reliable results from a microarray gene expression experiment. RNA quality propagates from the hybridized sample to the obtained gene expression estimates and consequently also to differential expression results. Combined with the previous detection of a noticeable degradation effect upon the majority of microarray samples , variabik degradation index provides a sensitive estimate of RNA-quality that can be computed from raw GeneChip microarray data. It was shown to correlate well with RIN [k ≥ 0.45. Note that dk values are not only sensitive, but also specific for RNA quality since only RNA degradation and amplification have such a systematic effect on this value estimated by means of the probe intensity decay with increasing distance between the probe location and the transcription start site [The RNA Integrity Number (RIN) provides a measure for RNA quality that is determined for most microarray samples before hybridization . RIN valwith RIN . For freart site .k values for all 8131 samples of the HumanArraySet which were either included or excluded from the HumanExpressionAtlas data set as described above. k for the qc-included/qc-excluded sample sets. Most samples included after quality control have a degradation index between 0.5 ≤ dk ≤ 0.8 referring to acceptable RNA quality. On the other hand, a large fraction of the qc-excluded samples exhibits values of dk < 0.45 referring to critically low RNA quality. This applies to 25% of the qc-excluded samples and to 10% (868) of all investigated samples. Note that some of these samples might be part of an experiment that is entirely based on low input RNA and multiple rounds of amplification, respectively. Such an experiment can provide valuable information when analyzed individually.We have computed the dFurthermore, 3% (162) of the qc-included samples are so severely degraded that they should have been excluded by RIN analysis. Expression estimates of these samples are biased, with negative consequences for the reliability of downstream results. That these samples were included in the HumanExpressionAtlas suggests that a more rigorous assessment of RNA-quality should be applied in quality control procedures. Note that our results agrees well with a previous estimation that about 2% of samples of large microarray series have low RNA-quality .k values larger than 0.8, which obviously represents an upper limit referring to the “weakest possible intensity decay”. This limit can be attributed either to the insufficiency of the clean-up assays to fully stop RNAase activity or, alternatively, to the incomplete amplification of aRNA fragments. On the other hand, a fraction of 8.1% of the qc-excluded samples has values of dk > 0.8 which could be also due to uncertainties in estimating dk at large signal noise levels [Interestingly, only few qc-included samples have de levels .k parameter changes together with different batches of samples for which we suspect technical variation. To this end, sample groups were defined using a combination of experiment id and experimental date as extracted from the HumanExpressionAtlas. For the experimental date we use the month in which the microarray sample has been hybridized, which can be extracted from Affymetrix raw data files. k parameters for the samples ordered by these batches. Using a simple linear model we obtained a generalized R2 statistic with a value R2 = 0.67 indicating that a considerable covariation between the batches and the degradation index dk.We next investigate how the dk. The resulting plot in k and the second principal component is r = −0.44. Hence, on average the RNA used for hybridization lost quality with increasing malignancy of the samples.To test for confounding of the HumanExpressionAtlas with the effect of variable RNA quality we plot its first two principal components of the “expression space” referring to the pre-processed data as available in ArrayExpress in Before continuing the analysis of potential sources of batch effects, let us first introduce suitable metrics for the assessment of the amounts of RNA available for hybridization in a given sample. To this end, we computed so-called λ and β chip summary measures estimating the relative specific (λ) and non‑specific (β) transcript abundance levels in negative decadic logarithmic scale see  for all r = 0.71 for liver tissue and r = 0.78 for SNB‑19. However, λ does not increase further for a RNA mass of 20 µg which can be explained by the up-down effect: increasing RNA concentrations result in a larger non-specific background accompanied by a smaller effective specific binding constant due to bulk dimerization [Panel a of rization . The λ sThe β parameter from Equation 5) characterizes the dynamic range of the specific hybridization signal  of the samples that passed stringent quality control exhibit values smaller than λ = 0.95, which we consequently consider a conservative threshold for samples of critically low quality due to decreased specific RNA amounts. We find that 133 (1.6%) of all samples exhibit λ values below this threshold. This equals a fraction of 4.6% from the qc-excluded samples.The parameter λ describes the average logged specific transcript level in units of the non-specific one. Low expression levels of some genes can have other origins than low RNA amounts, for example local surface deficiencies appearing as weakly shining blurs. Low RNA amounts can also be a result of degradation due to the overlap between the effects of RNA quality and RNA quantity.The density distribution of the β parameters describing the measurement range of the microarray hybridization is shown in r = −0.61 of λ with the fourth principal component  referring to the maximum pairwise difference between all positional-dependent NN-sensitivity profiles (see Methods diff) = 5 for a particular hybridization means that, on the average, two hypothetical probes (most likely with the sequences AAA…A and CCC…C) would differ in their intensity values by 5 orders of magnitude. log(Kdiff)can thus be thought of as the maximum strength, or impact, of the sequence effect.We assess the sequence effect using the parameter log(Kdiff) for all 8131 samples of the HumanArraySet plotting the respective density distribution in diff) = 3 is chosen as conservative threshold selecting samples with critically low sequence effect size. This applies to a fraction of 4.1% of all samples.As previously we computed log(Kdiff) parameter with the first five principal components of the HumanExpressionAtlas  is r = −0.17 with the third principal component. Correlations for the remaining principal components are smaller than |r| = 0.11. In conclusion, the sequence effect size is not a technical variable with a large impact on the most common patterns in the expression space.The largest correlation of the log as follows1 = 0.3 thus reflects an on the average 100.3 ≈ 2 times as large intensity of probes containing the (GGG)1 motif compared to probes containing (TTT) anywhere in their sequence. The average intensity of (TTT) containing probes here serves as appropriate baseline.We here define the strength of the guanine effect in terms of the 1 parameter which varies between 0 < δI(GGG)1 < 0.3 for qc-included samples. We consider samples with a threshold of δI(GGG)1 > 0.25 to be significantly affected by GGG1 effect—this reflects an intensity increase of +75% and would lead to a significant bias in the expression estimates of transcripts being targeted by the respective probes. Accordingly, 254 (3.1%) of the samples have a GGG1 related intensity bias. Many of them are removed by strict quality control; only 63 (1.1%) of qc-included samples are above the threshold.1 with the first five principal components of the HumanExpressionAtlas expression data we observe correlation coefficients between 0.14 < |r| < 0.19. Consequently, guanine effects have only a minor impact on the common patterns in the expression space. Note that the RMA method was used for the preprocessing of the HumanExpressionAtlas calibration, and we showed previously that its expression estimates are in general susceptible to GGG1 effects [Assessing the correlation of δI(GGG) effects . We have studied the general prevalence and impact of a RNA quality, RNA quantity and sequence effects using a large and representative set of microarray samples from the Affymetrix HG-U133a platform. We defined parameters, or used previously defined ones, that quantify each technical artifact based on systematic changes in the microarray intensity signals. We determined appropriate thresholds indicating samples of questionable quality, which associate with or even potentially cause biased expression estimates due to the respective factors. The impact of the technical variables on the expression estimates was analyzed by computing correlations with the first five principal components of the expression space of the HumanExpressionAtlas. The results are summarized in We found that a large fraction of 10% of the 8131 samples are so severely degraded that they should be excluded from further analysis. While most of these samples where indeed excluded from the HumanExpressionAtlas, further 3% of the low-quality RNA samples passed quality control, highlighting the need for a more rigorous assessment of RNA quality in microarray data analysis.A fraction of 1.6% of the samples has decreased specific transcript levels λ, relating to low amounts of hybridized RNA. Samples excluded by quality control in general have smaller λ values. Analysis of the β parameter shows that about 4% of the samples have low measurement ranges and that β has almost no impact on the predominant variation patterns in the expression space.k correlates with the second principal component for which previously a biological interpretation was found. The relative specific transcript level parameter λ is highly correlated with another principal component. Together with the observed high prevalence of these artifacts, these technical factors thus constitute major sources of batch effects.Importantly, RNA quality and RNA abundance variation were found to have potentially an unexpectedly high impact on the gene expression results. Both technical variables strongly correlate with the most common patterns of expression variation in the HumanExpressionAtlas. The RNA quality measure d1) effect with critical impact on some transcripts [We found that sequence effects are highly variable in the investigated Affymetrix HG-U133a platform. The sequence effect size is particularly low among low-quality samples where 4% of the samples are affected. 3% of the samples have a strong guanine-run  is affected or even determined by systematic errors caused by the respective technical factors. In best case, the gradient of expression values is “correct” in the sense that it is not affected by the technical factors. In this case the associated gradient of quality measures can reflect an independent variation of the respective technical parameters, which can have their origin also in biological factors such as the malignancy of the tumor samples. In the former situation the expression values and their interpretation is questionable whereas the latter situation potentially indicates unexpected and possibly interesting systematic changes of biological factors such as global changes of total expression level in the cells which, for example, can shift the relation between absent and present genes. Unfortunately the “worst” case is much more common according to our experience and it is hard to explain what, e.g., the Guanine-effect or truncated transcripts have to do with changes of transcriptional regulation. On the other hand, the second situation cannot be excluded owing to interferences between the different quality measures and/or with other, still unknown biological factors.A more detailed analysis should extract consequences on gene level to identify the particular causes of the observed biases of gene expression and of the quality measures in the samples studied. Such forthcoming studies are of high interest because they not only can help to improve the calibration of microarrays. They will also enable new insights into biological mechanisms underlying the observed biases such as the systematic variation of the total expression level as a function of biological factors.Methods to gradually correct these biases are required at the level of data analysis. A natural first choice are the existing batch effect removal methods  as part of a comprehensive quality control.What can be done to alleviate unwanted technical variation and its effects? The importance of quality control for microarray gene expression experiments is already well accepted—partly thanks to initiatives such as the MAQC. Independent measurements, for example of RNA quality, are de-facto standard protocol. These measures ensure that all samples fulfill at least a minimum quality criterion prior to microarray hybridization. We here however showed how several technical factors affect expression results to a degree that goes beyond simple good-quality