Nowadays, many public repositories containing large microarray gene expression datasets are available. However, the problem lies in the fact that microarray technology are less powerful and accurate than more recent Next Generation Sequencing technologies, such as RNA-Seq. In any case, information from microarrays is truthful and robust, thus it can be exploited through the integration of microarray data with RNA-Seq data. Additionally, information extraction and acquisition of large number of samples in RNA-Seq still entails very high costs in terms of time and computational resources.This paper proposes a new model to find the gene signature of breast cancer cell lines through the integration of heterogeneous data from different breast cancer datasets, obtained from microarray and RNA-Seq technologies. Consequently, data integration is expected to provide a more robust statistical significance to the results obtained. Finally, a classification method is proposed in order to test the robustness of the Differentially Expressed Genes when unseen data is presented for diagnosis.The proposed data integration allows analyzing gene expression samples coming from different technologies. The most significant genes of the whole integrated data were obtained through the intersection of the three gene sets, corresponding to the identified expressed genes within the microarray data itself, within the RNA-Seq data itself, and within the integrated data from both technologies. This intersection reveals 98 possible technology-independent biomarkers. Two different heterogeneous datasets were distinguished for the classification tasks: a training dataset for gene expression identification and classifier validation, and a test dataset with unseen data for testing the classifier. Both of them achieved great classification accuracies, therefore confirming the validity of the obtained set of genes as possible biomarkers for breast cancer. Through a feature selection process, a final small subset made up by six genes was considered for breast cancer diagnosis.This work proposes a novel data integration stage in the traditional gene expression analysis pipeline through the combination of heterogeneous data from microarrays and RNA-Seq technologies. Available samples have been successfully classified using a subset of six genes obtained by a feature selection method. Consequently, a new classification and diagnosis tool was built and its performance was validated using previously unseen samples. Cancer is the second leading cause of death worldwide, just behind cardiovascular disease. Specifically, breast cancer is one of the five most dangerous cancers in the world, showing a high mortality rate according to World Health Organization (WHO), and being the cancer with the highest impact among the female population . NowadayMicroarray has been the main sequencing technology used in the last two decades until the arrival of Next Generation Sequencing techniques. The most extended microarray platforms are Affymetrix  and IlluAs a natural evolutionary step in the treatment of biological information from DNA, RNA-Seq is gradually replacing the widespread use of microarrays. Although its application was originally intended for genomic transcription study, it also allows achieving a mapping between the levels of transcription and gene expression . In thisRNA-Seq allows detecting the variation of a single nucleotide.RNA-Seq does not require genomic sequence knowledgement.RNA-Seq provides quantitative expression levels.RNA-Seq provides isoform-level expression measurements.RNA-Seq offers a broader dynamic range than microarrays.RNA-Seq offers an important number of advantages over microarrays, although the cost of RNA-Seq experiments is also higher than in microarray technology nowadays: In spite of these advantages, microarrays are still used due to their lower costs. Besides, as microarrays have been used for a longer period, there exist many robust statistical and operational methods for their processing –15.There are many significant microarray experiments already available to the research community, and there is also even a high number of microarray datasets that have not been analyzed so far. These datasets might have information that could reveal important facts and candidate biomarkers. In any case, there is no doubt that RNA-Seq is the present technology, but it can also take advantage of the available data from microarray technology. As Nookaew et al. explained , there iThe main objective of this work is to find possible breast cancer biomarkers from patient and control samples acquired via NCBI GEO web platform . To thisIn the case of RNA-Seq samples, cqn package  has beenMost of the previous studies in the selection of biomarkers perform this process through statistical tools over a given dataset and a given technology. However, this work takes an innovative step forward by combining different datasets and microarray technologies together with RNA-Seq data. Furthermore, this research also builds an smart breast cancer classifier with the aim of achieving early diagnosis when unlabeled samples are presented. To this end, the minimum-Redundancy Maximum-Relevance (mRMR)  feature This paper has been structured as follows. This section has shown the introduction and state of the art of this work. Next section explains the methodology followed in this study. It begins by describing the available data series that have been used for this research. Later, the pipeline for processing and classifying the data is shown. An innovative step for automatic sample classification is described using machine learning techniques. The results and discussion section shows the integrated gene expression, revealing those genes that remain unchanged regardless of the technology used in the gene expression identification process. Furthermore, this section underlines the validity of the proposed approach and its utility in breast cancer early diagnosis using the developed classification tool. Finally, the conclusions section summarizes the most important contributions of this study for breast cancer diagnosis and genetic profiling.The first issue that must be addressed concerns the definition of the kind of sample that is going to be used, along with the determination of the tissue or cell that the sample comes from. As a result, a wide search through the NCBI-GEO platform has been done with the objective of finding datasets belonging to both the selected cell lines and the considered technologies. In this study, control samples have been selected from the MCF10A cell line . This cehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=S.NAME where S.NAME is the name of each series at NCBI GEO.Once the requirements for selecting the desired samples were established, an exhaustive search of Affymetrix and Illumina series was carried out for microarray data. On the other hand, RNA-Seq data was selected from Illumina HiSeq technology. Only datasets containing the above-mentioned cell lines were selected. Table Ka between the distribution of each array and the distribution of the pooled data. Next, sample normalization was performed using the limma R package normalizedBetweenArrays function [The first step in the methodology for microarray data is to put together all the selected series, independently of their technology (Affimetrix or Illumina). Consequently, a quality analysis assessment was performed across the series, in order to detect and consequently remove any possible outlier. This outliers detection and removal was performed through arrayQualityMetrics R package , which cfunction , in ordeThe pipeline proposed by Anders et al.  has beenA new data processing pipeline is proposed in this work which extends the classical gene expression data analysis pipeline in two ways. On one hand, this pipeline integrates data from both microarray and RNA-Seq technologies. Furthermore, once the integration has been carried out, a gene selection process and an assessment through a classification process were performed, using separated training and test datasets. The workflow of the entire pipeline is shown in Fig. In a first step, sample integration of data from both microarrays and RNA-Seq technologies has been carried out using the merge function from base R package. Once the gene expression values have been obtained for each technology separately, a normalization of all joint technologies was applied using the normalizedBetweenArrays function cited before over all datasets available  and at integrated level (joined microarray and RNA-Seq data).SVM: These models are supervised learning algorithms which assign categories to new samples. This algorithm is based on the idea of separating data from different categories through a hyperplane. The algorithm calculates the maximum-margin hyperplane that maximizes the distance between different classes. For overlapped data, this type of models turn a reduced space into a higher dimensional space using a kernel function, in order to perform the classification in this new space. Moreover, the algorithm tolerates making classification errors, which are controlled by the γ hyperparameter, in order to improve the generalization capability of the model [he model , 21.RF: This method grows many single classification trees with the purpose of building a forest of classification trees. For the classification, the algorithm assigns the input vector to be classified to each tree of the forest. Once that each individual tree performs classification, the forest chooses the class having the largest number of votes over all the trees. After each tree is built, all of the data are run down the tree, and proximities are computed for each pair of cases. If two cases occupy the same terminal node, their proximity is increased by one. At the end of the run, the proximities are normalized by dividing by the number of trees. Proximities are used with the aim of replacing missing data, locating outliers and producing illuminating low-dimensional views of the data [the data , 37.k-NN: This supervised method is based on assigning to a new unseen sample, the class corresponding to the predominant one in the k nearest neighbors (most similar samples) from the known labeled data. It is a well-known fast and easy-to-use technique which however provides a comparable performance to other well-known more complex techniques [chniques , 38.Once a set of possible target genes which can be considered as biomarkers for breast cancer were identified, we proceeded to assess the results through three different classification technologies: SVM, RF and k-NN. The main objective of this stage is the validation of the behavior of the selected genes at the arrival of new unseen samples. The selected genes and the training dataset were used for designing the classification models, which were later evaluated over the test dataset. σ (kernel width) and γ for SVMs, number of trees for RF and k for k-NN.Ten-fold cross-validation was used over the training dataset to obtain the optimal hyperparameters for these methodologies: Additionally, a feature selection process was performed through the mRMR  algorithThis section will focus on presenting and discussing the obtained results coming from the experimentation process followed in this study. It is divided into two subsections: first subsection shows the results for the process of obtaining the set of DEGs; while second subsection will show the results of the classification process making use of the former set of genes.This subsection describes the process and results of the DEGs extraction. As it was previously stated in the methods section, series belonging to different technologies and platforms have been integrated. The objective of this integration is twofold: first, to increase the number of samples that will be used as input to our method, thus improving the robustness and stability of the results. Second, the obtained results will be independent of a single technology, as they proceed from different sources. The presence of RNA-Seq samples increases the dynamical midrange of the genes, making the results more accurate and robust. Furthermore, the number of available samples is greatly increased thanks to the availability of microarray data stored in public repositories.When working with heterogeneous data, normalization is one of the most sensitive steps in the whole process, as a mistake in this step could cause interpretation errors, and could lead to a false set of expressed genes. Figure p-value was set to be less or equal than 0.001. These constraints ensure that the chosen expressed genes are statistically significant, therefore showing different behavior between patient and healthy samples. These restrictions were applied to the three microarray, RNA-Seq and integrated datasets, so that three sets with different expressed genes were obtained. Finally, through the intersection of the three groups of expressed genes, a total of common 98 DEGs were found. These genes comply with the restrictions and they are differentially expressed in all datasets as the intersection shows  and for the integrated dataset. Several restrictions were imposed in order to determine the expressed genes: the fold change in the expression values of the selected genes was set to be greater or equal than 2 and the ows Fig. . ConsequA boxplot of the mean gene expression values of the 98 DEGs for the samples in the training dataset is shown in Fig. p-value, adj.p.val. and B). The log-fold change (logFC) represents the difference between breast cancer and control expressed values. If ∣logFC∣≥2 it means that there exists significant differences between cancer and control values. The second value in Table p-value  which represents the probability of obtaining a result equal or higher than what it was observed when the null hypothesis is true. The adjusted p-value indicates which proportion of comparisons within a family of comparisons (hypothesis tests) are significantly different. The B-statistic (B) is the log-odds that a given gene is differentially expressed.Table Figure Once the DEGs were identified in the previous subsection, this subsection assesses the performance of these genes through a classification process when new samples are presented. For that purpose, the classification algorithms SVM, RF and k-NN have been implemented. The whole training dataset formed by 132 samples has been used as the input data for the classifier  were designed from the training dataset and the selected DEGs and compared. These classifier were validated with the test dataset achieving outstanding results for the three algorithms when the complete set of 98 DEGs were used.In conclusion, results show that the expressed genes can be designated as robust biomarkers for breast cancer diagnosis when specific cell lines samples are used. Furthermore, even with a small subset of six of those genes, a great validation accuracy was reached (99%). Also, classification results over new unseen data show great accuracy, specially over SVM classification (96.8%). Five of these top six genes have been formerly reported as genes that show biological relation with breast cancer, which reinforce the designation of the expression profiles of these genes for breast cancer diagnosis.


Unwanted variation can be highly problematic and so its detection is often crucial. Relative log expression (RLE) plots are a powerful tool for visualizing such variation in high dimensional data. We provide a detailed examination of these plots, with the aid of examples and simulation, explaining what they are and what they can reveal. RLE plots are particularly useful for assessing whether a procedure aimed at removing unwanted variation, i.e. a normalization procedure, has been successful. These plots, while originally devised for gene expression data from microarrays, can also be used to reveal unwanted variation in many other kinds of high dimensional data, where such variation can be problematic. There are many causes of such variation . An RLE plot is constructed as follows:j, calculate its median expression across the m samples, i.e. Med(yj*), then calculate the deviations from this median, i.e. calculate yij − Med(yj*), across the is.For each gene For each sample, generate a boxplot of all the deviations for that sample.We suppose that our microarray expression data (after log transformation) is organized into a matrix with We use the median, a robust measure of centre, to protect against outliers.To furnish an example we consider data from a study by Vawter et al. . The aimgender data. We processed the data, performing background correction and summarisation , with the RMA package , mθ = 2 for i ∈ , and λ = 0 for all i.Additive and non-additive effects:mθ = 0 and λ = 1 for all i.Additive and non-additive effects, in two batches:mθ = 0 for i ∈ , mθ = 2 for i ∈ , and λ = 1 for all i.With this model, we simulate four different data sets each with In all instances, we set data set .only kinds of statistical effects that produce “bad” RLE plots—there are clearly others. These effects, however, seem to be some of the most important, as we will see next.These results show, perhaps not surprisingly, that shifting boxplots are produced by additive sample effects. However, perhaps more surprisingly, we see that additive sample effects are not sufficient to produce variation in the boxplot widths; this feature only appeared when non-additive sample effects were present. We do not claim that these are the real data? The answer is “yes”, in many instances. Take, for example, the gender data again. Let Y be the matrix containing the gender data, where yij is the log expression for gene j in sample i. First, we try removing the additive sample effect by calculating Y′  matrices:r is the rank of D′, σk is the kth singular value, and uk and vk are kth left and right singular vectors, respectively. Now observe that the entries of these matrices have the form [Mij] = σkuivj, i.e. a product with one factor indexed by i, the other indexed by j, all scaled by σk. In other words, we have decomposed the non-additive component of the data into a sum of non-additive effects of simple multiplicative type. So, by subtracting Mk matrices from Y′ we can remove non-additive sample effects from the data, in addition to the additive sample effect removed previously. Given this, define p = 1, …, r. We generate RLE plots of p = 1, …, 6 . Thus, lidation .replicate samples are an immediate sign of unwanted variation. In the gender study, for example, samples 5 and 26 are from the same individual and brain region; nominally, the samples are identical. The large disparity between the two can only be explained by unwanted variation, most likely resulting from being analyzed at different laboratories.Lastly, we mention two important points about assumption (A), i.e. that expression of a majority of genes are unaffected by the biological factors of interest. Firstly, this assumption is not always needed to infer the presence of unwanted variation from an RLE plot. Large differences between safe to make. There may be instances where different biological factors of interest elicit a shift in the expression levels of a large majority of genes. In that case, a bad RLE plot might not be a reliable sign of unwanted variation, but a sign of a genuine shift in expression levels for some of the samples. Thankfully, however, these instances are rare, and when they do occur the effect is often expected.Secondly, assumption (A) is not always We have seen that RLE plots, i.e. boxplots of deviations from gene medians, provide a simple, yet powerful, tool for detecting and visualizing unwanted variation in high dimensional microarray data, the presence of which is often problematic. The only assumption we need to interpret sample heterogeneity in an RLE plot as a sign of unwanted variation is that expression levels of a majority of genes are unaffected by the biological factors of interest. We noted, however, that while this assumption is often plausible, it is sometimes not safe to make, and sometimes not even needed. We have seen that RLE plots can reveal unwanted variation in two ways, i.e. varying boxplot position and varying boxplot width, and that additive and non-additive sample effects can produce these features, although additive effects alone cannot produce variation in boxplot width. We showed how simulated data with these effects produce these features, and that these effects provide an explanation of these features for real data. We have emphasised that due to their ability to detect unwanted variation, RLE plots are particularly useful for assessing whether a normalization procedure has been successful, with a “bad” plot usually suggesting that the procedure has failed. However, we cautioned that while bad looking RLE plots are excellent evidence that a normalization procedure has failed, good looking RLE plots are only weak evidence that a procedure has succeeded, in the sense of not also removing signal of interest. Although our discussion has been framed in terms of microarray expression data, the original context in which RLE plots were devised, we hope we have conveyed how RLE plots might be useful for revealing unwanted variation in many other kinds of high dimensional data, where such variation can be problematic.


Whole exome and gene panel sequencing are increasingly used for oncological diagnostics. To investigate the accuracy of SCNA detection algorithms on simulated and clinical tumor samples, the precision and sensitivity of four SCNA callers were measured using 50 simulated whole exome and 50 simulated targeted gene panel datasets, and using 119 TCGA tumor samples for which SNP array data were available.On synthetic exome and panel data, VarScan2 mostly called false positives, whereas Control-FREEC was precise  at the cost of low sensitivity (<40% detected). ONCOCNV was slightly less precise on gene panel data, with similarly low sensitivity. This could be explained by low sensitivity for amplifications and high precision for deletions. Surprisingly, these results were not strongly affected by moderate tumor impurities; only contaminations with more than 60% non-cancerous cells resulted in strongly declining precision and sensitivity. On the 119 clinical samples, both Control-FREEC and CNVkit called 71.8% and 94%, respectively, of the SCNAs found by the SNP arrays, but with a considerable amount of false positives (precision 29% and 4.9%).Whole exome and targeted gene panel methods by design limit the precision of SCNA callers, making them prone to false positives. SCNA calls cannot easily be integrated in clinical pipelines that use data from targeted capture-based sequencing. If used at all, they need to be cross-validated using orthogonal methods.Scripts are provided as supplementary information.Bioinformatics online. Although copy number variation only represents about one tenth of a percent of an individual patient’s genetic variation, it affects more base pairs than single nucleotide polymorphisms or short indels . Copy nuIn clinical laboratories, well-studied, recurrent SCNAs are traditionally identified using immunohistochemistry or fluorescence in situ hybridization, which are highly sensitive and specific to the queried biomarker .Despite its theoretical and practical appeal, the detection of SCNAs from massively parallel sequencing methods is far from straightforward. Ideally, SCNAs are determined from whole genome sequences with uniform coverage across all positions, yet currently such comprehensive sequencing remains beyond the means of most clinics and patients. Whole exome and targeted panel approaches offer higher coverage at a much reduced cost, but suffer from several drawbacks caused by (i) non-homogeneous coverage, (ii) unequal distribution of targeted sites across the genome, (iii) the inability to find CNV breakpoints falling outside covered regions, and, particularly for panel data, (iv) sometimes extreme GC bias  find all copy number variants in all of the sequenced genomic segments (sensitivity), (ii) reliably detect breakpoints and (iii) avoid falsely calling sequencing errors, spurious fluctuations in coverage, or other systematic biases (precision). These challenges have spurred a rich body of research that has produced a wide variety of CNV tools, only a few of which were specifically designed to deal with the vagaries of tumor genetics  and targeted gene panel (panel) data. First, we use simulated datasets with known mutations (gold standard), which allows comprehensive analysis of both false positive and false negative rates. Here, we specifically test the influence of low frequency variants that can be the consequence of tumor admixture, impurity, or clonality (heterogeneity). Second, to gain more realistic insights into clinical data, we also evaluate the performance of the methods on 119 whole exome endometrial cancer datasets for which SNP array data were available as a gold standard.The construction of realistic tumor samples consisted of several steps, each of which is explained in more detail in the next paragraphs. First, to mimic realistic human genomes, a diploid normal (control) and five tumor genome reference sequences were generated by implanting germline SNPs and somatic SNVs, germline and somatic indels and germline CNVs and tumor SCNAs. Second, to simulate the heterogeneity of tumor samples, the five tumor clones were mixed at random proportions. Finally, to simulate admixture of tumor samples with normal (control) samples, each subclone mixture was further mixed with reads sampled from the control samples. This entire process was repeated five times.In step one, a control genome was created by implanting 3 894 338 germline SNPs and indels  into thein silico probe capture by hybridization. In addition, it reproduces known biases resulting from GC-content and fragment length variation, and platform-specific errors. Read simulations were conducted once for a whole-exome approach using the Agilent SureSelect Human All Exon kit v5 probes, and once for a targeted gene panel, using the Molecular Health Cancer Gene Panel containing 619 genes  proportions of 0.27, 0.29, 0.36, 0.06 and 0.02.In the third and last step, this clonal mixture was used to create ten admixed tumors, by adding 0% to 90% of reads from the control sample. The entire process, starting from the construction of the genome sequences, was repeated five times, such that in total we obtained 50 admixed tumor-control sample pairs with a total of 5800 SCNAs.www.novocraft.com), and realigned around indels using GATK IndelRealigner 2.2  project . SamplesBecause our focus was on the evaluation of SCNA callers in the context of cancer, we only included callers that were designed to call SCNAs from tumors, and that could deal with targeted (exome and gene panel) capture data. We included CNVkit . Parameter optimization was not performed for CNVkit, as the approach of this tool relies on off-target reads which were not present in the simulated data. Parameters included base and mapping quality thresholds, choice of segmentation algorithm, and several tool-specific modulations. In ONCOCNV, we also tested the option to use three non-matched controls, which greatly increases precision by adding more non-cancer samples (that do not need to be from the same patient). These three non-matched controls were randomly chosen from the control samples not used to simulate that sample. A complete overview of the parameters tested for each tool is provided in For the synthetic data, SCNAs obtained from the caller were compared with the known ‘gold standard’ variants that were implanted into the datasets. For TCGA cases, the output of the bioinformatic tools was compared with the variants determined by the SNP array, which for our purposes can be considered the ‘gold standard’.Using sparsely and unequally distributed targeted genome regions to call copy number variants creates several technical difficulties. For example, it can lead to oversegmentation due to experimentally inherent coverage inhomogeneity, and can cause larger CNVs encompassing several target regions to be missed or falsely considered separate by the caller. Moreover, the highly variable size of SCNAs, which can range up to a full chromosome arm, renders simple interval comparisons difficult and inaccurate. Given these complexities, all comparisons between predicted and gold standard SCNAs were done using three metrics: nucleotide-based, gene-based and interval-based agreement. For the gene panel analysis, the latter was only done for genes covered in the target panel. In the manuscript we will concentrate on the nucleotide-based approach, which avoids setting an arbitrary threshold on the fraction of overlap, and also accounts for complex cases where several smaller predicted SCNAs are contained within a larger gold standard SCNA. In our experience, nucleotide-based and gene-based approaches yield similar values for sensitivity and precision, while an interval-based approach with an 80% overlap criterion leads to strongly decreased sensitivity and precision estimates.Using the definitions listed in First, we investigated the reliability of SCNA calling algorithms for whole exome and targeted gene panel data across a range of known tumor admixture levels. Simulated data offer the advantage that the exact size, position and copy number of each SCNA is known; therefore true positives, false positives and false negatives can be directly determined. Nevertheless, whether a given copy number variant is indeed identical to the one that is known to be present in the data can be determined in different ways (see methods). The interval-based estimates led to significantly decreased precision estimates across all simulated datasets, whereas the nucleotide-based and gene-based approaches yielded higher, more similar estimates of precision. For clarity and simplicity we therefore further concentrate on the nucleotide-based approach—the other measures lead to the same conclusions see .On the 50 exome datasets we tested VarScan2, Control-FREEC and CNVkit, and on the 50 gene panel datasets we additionally evaluated ONCOCNV, which was developed specifically for deep-targeted amplicon sequencing data. Because our simulated data did not contain off-target reads (outside of the capture regions), CNVkit was hampered in its performance, as it uniquely exploits such off-target reads to establish a genome-wide coverage baseline. For that reason CNVkit could not be fairly appraised using our synthetic data; the results are nevertheless included in Across all of our experiments, VarScan2 showed surprisingly poor performance. Compared to Control-FREEC or ONCOCNV it called far more SCNAs, but most of these were false positives. Moreover, VarScan2 found almost none of the known SCNAs . In contTo further explain the low sensitivities of Control-FREEC and ONCOCNV, we investigated sensitivity with regard to the allele fractions at which the variants occurred in the tumors. As shown in The precision and sensitivity of ONCOCNV (on panel data) and Control-FREEC (on gene panel and exome data) were relatively robust up to 60% contamination with normal cells, but both measures dropped off steeply at higher admixtures , presumaNext we analyzed deletions and amplifications separately . Both onFig. 3C, D;Finally, we compared the length distribution of detected SCNAs across the different methods . The 50 Taken together, these analyses show that both Control-FREEC and ONCOCNV are conservative methods with good sensitivity and precision for deletions, but limited sensitivity to low-frequency amplifications. This applied to both exome and panel data. Nevertheless, Control-FREEC and ONCOCNV generated few false positives on the simulated data, although this could be the result of the relative ‘cleanliness’ of simulated data (simulation cannot capture the full complexity of heterogeneous tumors).To evaluate the performance of SCNA detection algorithms on realistic clinical data, we obtained exome sequencing data of 119 endometrial cancer samples from TCGA. For these samples, SNP array data were also available as an independent proxy for ‘true’ SCNAs. The TCGA samples used in this study exhibit a wide range of SCNA numbers, and it has been established previously that the presence of SCNAs in endometrial tumors correlates with progression-free survival: most endometrioid tumors have few SCNAs, whereas serous and serous-like tumors have many . On averOur analyses showed that Control-FREEC reached median precision of 29% and median sensitivity of 71.8% across the 119 clinical samples, although these values varied greatly depending on the patient Fig. 5A;. In contContrary to the simulation results, in half of the samples Control-FREEC detected at least 60% of the amplifications, although with many false positives (20% median precision). However, it identified few deletions  control samples were pooled, which enables better GC and library size normalization (et al. found around 30% Jaccard concordance between Control-FREEC calls and SNP arrays on four breast cancer samples (A different, even worse, picture emerged when we analyzed the 119 clinical endometrial carcinoma exome samples. Control-FREEC produced many more false positive amplifications and deletions on real than on simulated data, but found only 30% of deletions and only about 60% of amplifications determined by SNP arrays. CNVkit tended to correctly identify more gold standard amplifications and deletions than Control-FREEC, but at the cost of close to zero precision. Control-FREEC was biased to mostly medium-sized amplifications, while CNVkit preferentially produced medium-sized and large deletions. These data suggest that all of the tested algorithms called too many spurious SCNAs. This confirms an earlier study on 50 kidney chromophobe tumor exomes (The discrepancy between our simulated data and the TCGA cases can be explained by several factors. First, clinical datasets feature the full biological complexities of the tumor environment and potential sample preparation and processing biases, which cannot be captured by simulations. The much-reduced precision in the TCGA data may thus be partially explained by confounding complexity, or by the presence of artifacts such as sequencing errors or differential coverage at GC-rich regions. Second, Control-FREEC had much lower sensitivity for amplifications in the simulated exome data than in the tumor samples, presumably because the real data also contained more high-copy number amplifications, which are easier to detect than duplications. On the other hand, detection of deletions was easier in the simulated than in the real data. Deletions, as an absence of signal, are expected to be easier to find. It is therefore puzzling that the precision of the tools on deletions in the TCGA data was low. Possibly this effect was negligible compared to the alltogether high number of false positives. Third, the use of SNP arrays as gold standard also introduces important limitations compared to the absolute ground truth in the simulated data. A comprehensive comparison of array-based CNV calling methods found less than 50% concordance among algorithms, and less than 70% reproducibility among replicates when the same raw data was processed multiple times with the same array platform and CNV calling algorithm (Because with simulated data we were able to rely on an absolute gold standard, we investigated how low allele fractions of SCNAs affect the sensitivity and precision of the SCNA discovery algorithms. Low allele fractions are common in clinical tumor samples, for two related reasons. First, they are caused by the presence of non-cancerous cells such as stromal cells, immune cells and blood cells present in the tumor , or can Whole exome and targeted gene panel sequencing are increasingly finding their way into clinical practice. Their focus on a smaller and presumably more cancer-relevant part of the genome at higher coverage offers a compelling alternative to whole genome sequencing, at a much reduced price. Although these methods are primarily used in precision medicine to detect single nucleotide variants and small indels, they offer the opportunity to simultaneously call somatic copy number aberrations. This is an attractive option considering that the presence of SCNAs such as ROS or MET amplifications offers clear therapeutic options. However, our analysis of SCNA callers using 50 simulated datasets and 119 endometrial tumors clearly shows that considerable caution is needed with targeted sequencing approaches. On such data, SCNA methods are error-prone: they both missed a considerable portion of SCNAs known to be present in our test datasets, and called a large number of false positives, especially in the TCGA data. Therefore copy number aberrations called from WES or targeted gene panel sequences should be considered hypotheses that require cautious interpretation and cross-validation with more reliable methods such as FISH, PCR, or SNP arrays. In cases where SCNA discovery is explicitly desired, researchers are recommended to contemplate appropriately powerful experimental alternatives.Supplementary DataClick here for additional data file.


F1 measure compared to the existing scoring methods.We discuss a novel diagnostic method for predicting the early recurrence of liver cancer with high accuracy for personalized medicine. The difficulty with cancer treatment is that even if the types of cancer are the same, the cancers vary depending on the patient. Thus, remarkable attention has been paid to personalized medicine. Unfortunately, although the Tokyo Score, the Modified JIS, and the TNM classification have been proposed as liver scoring systems, none of these scoring systems have met the needs of clinical practice. In this paper, we convert continuous and discrete data to categorical data and keep the natively categorical data as is. Then, we propose a discrete Bayes decision rule that can deal with the categorical data. This may lead to its use with various types of laboratory data. Experimental results show that the proposed method produced a sensitivity of 0.86 and a specificity of 0.49 for the test samples. This suggests that our method may be superior to the well-known Tokyo Score, the Modified JIS, and the TNM classification in terms of sensitivity. Additional comparative study shows that if the numbers of test samples in two classes are the same, this method works well in terms of the  Liver cancer is one of the refractory cancers, and overcoming it is of national concern. Despite complete surgical resection, the problem with refractoriness lies in the high percentage of recurrences of liver cancer . If recuThe difficulty with cancer treatment is that even if the types of cancer are the same, the cancers vary depending on the patient. Even a wide variety of tests such as blood tests or CT scans reveal only certain aspects of cancer, and there is no test that can perfectly detect cancer. Nevertheless, by using various combinations of laboratory data , the Tokyo Score , the ModThe diagnosis of leukemia was made possible using levels of gene expression, and this triggered the diagnosis of cancer by machine learning . As a reGenerally, with laboratory data, quantitative data (metric data or continuous data), which are represented by numerical numbers, intermingle with qualitative data . Unfortunately, as with the machine learning described above, the conventional Bayes classifier, which is particularly popular in statistical pattern recognition , cannot M markers as candidates, the range of each marker is divided into divisions so that they become mutually exclusive events. Suppose that x1, x2,…, xd are selected as d targeted markers from among M markers and then the discretized laboratory data of a patient in marker xj belongs to the division xj(rj), j = 1,2, …, d. Here, xj(rj) denotes the rjth division of marker xj. The subscript j is a discrimination number representing a marker. Then, the patient is described as the pattern vector x = [xr1)12]. The class-conditional probability P(xj(rj)∣ωi) of the division xj(rj) for class ωi is defined asnj(rj)i represents the number of patients who belong to the division xj(rj) among ni patients from class ωi.As previously mentioned, the discrete Bayes classifier is characterized by the fact that it can handle discrete data. Given P(x∣ωi) is described as follows:Assuming that, in general, the events in which the discretized laboratory data belong to any of the divisions are mutually independent, the class-conditional probability P(ωi∣x  )  in the two-class problem is provided by Bayes' theorem as follows:P(ωi) is the prior probability for class ωi. To equally deal with the recurrence class and nonrecurrence class, we assume that the prior probability P(ωi) is an equal probability of 0.5. Then, the posterior probability is simplified as follows:x is classified into a class ωi where the posterior probability P(ωi∣x) is maximum.The posterior probability x2 is divided into three divisions of x2(1), x2(2), and x2(3), and among these divisions the division x2(2) includes n2(2)i patients from class ωi. Here, the total sum of the number of the patients in each division is the following: n2(1)i + n2(2)i + n2(3)i = ni. As previously mentioned, ni is the number of all patients from class ωi.Cases of the relationship between the divisions and the number of patients are shown in d = 2 markers x1 and x2 are used and the discretized laboratory data of a patient belong to x1(1) in marker x1 and x2(3) in marker x2. x1(1) and x2(3) is 7. Then, the equations are the following: P(x1(1), x2(3)∣ω1) is as follows:P(x1(1), x2(2)∣ω2) is obtained and from . Meanwhile, in any of the discrete Bayes classifiers, which deal with discrete data alone, computation is only scalar computation. Even if a dimension is high, the discrete Bayes classifier can be easily calculated, indicating that the classifier is practical. To simplify the discussion, we have so far dealt with a two-class problem, but this decision rule can easily be extended to multiclass problems. d markers useful for discrimination is selected from among M candidate markers. The small number of training samples in marker selection often causes the overfitting problem [ Knowing which markers are used for the discrete Bayes classifier is essential in discrimination. This is a problem of feature selection in the statistical pattern recognition fields . Here, w problem  that whi problem . The floP(x∣ωi) is calculated from the discretized laboratory data of the virtual training samples, and a virtual test sample is classified based on the posterior probability P(ωi∣x) calculated by using the class-conditional probability. In discrimination, we investigate which divisions the discretized laboratory data of a virtual test sample belong to according to the markers of interest. Next, using the class-conditional probability corresponding to the divisions to which the data belong, the posterior probability is obtained from  and nonrecurrence samples (n = 177) are each randomly divided into halves, and one-half is assigned to training samples and the other half to test samples. Consequently, the number of recurrence training samples is 29 and that of nonrecurrence training samples is 89, whereas the number of recurrence test samples is 28 and that of nonrecurrence test samples is 88. The flow of classifier design and evaluation is shown in The holdout method  splits tS1 consists of 5 recurrence training samples and 15 nonrecurrence training samples. The next subset S2 contains 23 training samples by adding one true recurrence training sample and two nonrecurrence training samples. In this manner, a discrete Bayes classifier is designed for each subset with increased true training samples, and the discrimination abilities of 6 discrete Bayes classifiers for the same test samples are obtained. This trial was independently repeated 30 times, and the influence of an increase in training samples on discrimination ability was investigated.Next, the optimal combination of markers was fixed, and the number of training samples is discussed. Generally, an increase in the number of training samples results in an improvement of the discrimination ability of the classifier. Therefore, a designer is interested in how many training samples are needed for classifier design. Then, as a subset of the increased number of training samples, we assume that a series of 6 training subsets, as shown in F1 measure, and diagnostic odds ratio [Then, a comparison of the performance of the classifiers with the existing scoring formulae was conducted. For comparison, we adopted accuracy, sensitivity, specificity, the Youden index   selected from among the 10 candidates was optimal. Next, when the influence of the training sample size on discrimination performance was investigated, as shown in F1 measure, we assumed that the numbers of recurrence test samples and nonrecurrence test samples were the same. Then, using 28 recurrence test samples and 28 nonrecurrence test samples that were derived by the resampling method, we independently reevaluated them 100 times. The results are shown in F1 measure. Next, as shown in Meanwhile, in terms of accuracy and the In addition, we point out an advantage of the discrete Bayes classifier over the existing scoring systems. Because the existing scoring systems require the use of specific markers, they cannot be used when the data of the markers are insufficient. However, the proposed technique can be used by selecting an optimal combination of markers from the laboratory data that a patient already has. Moreover, the physician is presented with the markers that should be added to improve discrimination performance for each patient. In this way, on the basis of the technique proposed here, the best personalized medicine can be expected.In this paper, a discrete Bayes decision rule that predicts early recurrence of highly refractory liver cancer with a high degree of accuracy was proposed. This discrete Bayes decision rule can deal not only with qualitative data but also with quantitative data by discretization. Discrimination experiments enabled us to predict early recurrence of liver cancer with higher sensitivity than that of the Tokyo Score, the Modified JIS, and the TNM classification, which are existing representative scoring systems. Realization of personalized medicine via this discrete Bayes decision rule may be expected.One of the main limitations of this paper was the use of a small amount of sample data from a single institution. This limited the evaluation of the performance. Further study is needed to evaluate the proposed method using data collected from other institutions. In addition, this method uses the hypothesis of independence without discussion to simplify the calculations. Although verification of independence is difficult, a study on independence such as the adoption of Bayesian networks will be a future challenge. Also, because the discrimination ability of this method depends on the cutoff value used when marker values are discretized, optimization will also be an interesting challenge.


Here, we introduce CIDR , an ultrafast algorithm that uses a novel yet very simple implicit imputation approach to alleviate the impact of dropouts in scRNA-seq data in a principled manner. Using a range of simulated and real data, we show that CIDR improves the standard principal component analysis and outperforms the state-of-the-art methods, namely t-SNE, ZIFA, and RaceID, in terms of clustering accuracy. CIDR typically completes within seconds when processing a data set of hundreds of cells and minutes for a data set of thousands of cells. CIDR can be downloaded at The online version of this article (doi:10.1186/s13059-017-1188-0) contains supplementary material, which is available to authorized users. Single-cell RNA sequencing (scRNA-seq) enables researchers to study heterogeneity between individual cells and define cell types from a transcriptomic perspective. One prominent problem in scRNA-seq data analysis is the prevalence of dropouts, caused by failures in amplification during the reverse-transcription step in the RNA-seq experiment. The prevalence of dropouts manifests as an excess of zeros and near zero counts in the data set, which has been shown to create difficulties in scRNA-seq data analysis , 2.Several packages have recently been developed for the various aspects of scRNA-seq data analysis, including cell cycle  on an Euclidean distance matrix derived from the data set. We posit that as long as we can reliably estimate the dissimilarity between every pair of samples  in the presence of dropouts, there is no need to estimate explicitly the values of the dropouts.Ci= and Cj=, where oki and okj represent the gene expression values of gene k in cells Ci and Cj, respectively: Let us begin by examining the squared Euclidean distance between the expression profiles of two single cells, oki and okj if they are both non-zero values. This term is not affected by dropouts. The second term is the sum of squared differences of oki and okj if they are both zeros, so this term is zero .For simplicity, we refer to all zeros in the gene expression data as dropout candidates. In general, our argument remains valid even when a dropout candidate is allowed to have near zero values. We note that the squared Euclidean distance in Eq. Therefore, we observe that the main impact of dropouts comes from the third term, which deals with when one value is zero and the other is not. A zero can either represent a lack of gene expression in the ground truth or a dropout event in which a non-zero gene expression value is observed as a zero. If we treat all observed zeros as a lack of gene expression , which is the case if we directly apply PCA to scRNA-seq data, this term will tend to be inflated. Nonetheless, it has been observed that the probability of a gene expression value being a dropout is inversely correlated with the true expression levels , 2. ThisThe CIDR algorithm can be divided into the following five steps: 1) Identification of dropout candidates, (2) estimation of the relationship between dropout rate and gene expression levels, (3) calculation of dissimilarity between the imputed gene expression profiles for every pair of single cells, (4) PCoA using the CIDR dissimilarity matrix, and (5) clustering using the first few principal coordinates  gene expression for each cell. The distribution of the log-transformed expression values in a scRNA-seq data set is typically characterized by a strong peak at zero, and one or more smaller non-zero positive peaks representing the expression of expressed genes , 15, 16.Ci, CIDR finds a sample-dependent threshold Ti that separates the zero peak from the rest of the expression distribution; Additional file Ti. The entries for cell Ci with an expression of less than Ti are dropout candidates, and the entries with an expression of at least Ti are referred to as expressed. We call Ti the dropout candidate threshold. Note that dropout candidates include true dropouts as well as true low (or no) expressions.For each cell u be the unobserved true expression of a feature in a cell and let P(u) be the probability of it being a dropout. Empirical evidence suggests that P(u) is a decreasing function [P(u), illustrated by the tornado plot , which we denote as The next step of CIDR involves estimating the relationship between dropout probability and gene expression levels. Let function , 2. CIDRCi and Cj, and their respective observed expressions oki and okj for a feature Fk, and let Ti and Tj be dropout candidate thresholds defined as above. Imputation is applied only to dropout candidates, hence when oki≥Ti and okj≥Tj no imputation is required. Now consider the case in which one of the two expressions is below Ti, say oki<Ti and okj≥Tj. Then oki needs to be imputed and the imputed value W(u), defined as To achieve a fast speed in the implementation of the above step, we replace TW is by default 0.5. We refer to W(u) as the imputation weighting function, as it gives us the weights in the weighted mean in the imputation, and we refer to the jump of W(u), i.e., where mentclass2pt{minimoki. Lastly, if oki<Ti and okj<Tj, we set both where We have also implemented CIDR directly using mentclass2pt{minimCi and Cj is calculated using Eq. Then, the dissimilarity between Dimensionality reduction is achieved by performing PCoA on the CIDR dissimilarity matrix. It is known that clustering performed on the reduced dimensions improves the results . CIDR peFigure As a comparison, we have also considered an alternative method in which dropout candidates were imputed to the row mean (IRM) of the expressed entries. This is a straightforward and commonly used approach for dealing with data with missing values. When applying IRM to our toy data set, we observe that both the BC and WC distances shrink very significantly  of a library, CIDR finds the minimum point between the two modes in the density curve of the distribution. The R function density is used for kernel density estimation, and the Epanechnikov kernel is used as the smoothing kernel. For robustness, after calculating all the dropout candidate thresholds, the top and bottom 10 percentiles of the thresholds are assigned the 90th percentile and the 10th percentile threshold values, respectively. CIDR also gives the user the option of calculating the dropout candidate thresholds for only some of the libraries and in this option the median of the calculated thresholds is taken as the dropout candidate threshold for all the libraries.In the kernel density estimation, CIDR uses the default bandwidth selection method nrd0 of the R function density with adjust = 1. We have varied the adjust parameter and re-calculated the adjusted rand indices for both the human brain  and humaPCoA is performed on the CIDR dissimilarity matrix to achieve dimensionality reduction. Because the CIDR dissimilarity matrix does not, in general, satisfy the triangle inequality, the eigenvalues can possibly be negative. This does not matter as only the first few principal coordinates are used in both visualization and clustering, and their corresponding eigenvalues are positive. Negative eigenvalues are discarded in the calculation of the proportion of variation explained by each of the principal coordinates. Some clustering methods require the input dissimilarity matrix to satisfy the triangle inequality. To allow integration with these methods, CIDR gives the user the option of a Cailliez correction , implemeCIDR implements an algorithm that is a variation of the scree  method fMore specifically, CIDR assigns eigenvalues into groups based on the differences in consecutive eigenvalues. A new group is created each time a consecutive difference is greater than a cutoff point determined as a fraction of the largest difference. If the size of the current group exceeds a predetermined threshold, the sum of sizes of all but the current group is returned as the number of principal coordinates used in clustering.Users are encouraged to inspect the proportion of variation plot output by CIDR, and possibly alter the number of principal coordinates used in clustering.Hierarchical clustering is performed using the R package NbClust. CIDR’s default clustering method for hierarchical clustering is ward.D2 , and theSimulated log tags are generated from a log-normal distribution. For each cell type, an expected library, i.e., the true distribution of log tags, is first generated, and then dropouts and noise are simulated. For each cell type, the expected library includes a small number of differentially expressed features  and markers. By markers we mean features that are expressed in one cell type and are zeros in all other cell types.π(x), where x is an entry in the expected library, is used to simulate dropouts. π(x) specifies how likely an entry is to be a dropout, so intuitively it should be a decreasing function. In our simulation, we use a decreasing logistic function. The parameters of the logistic function can be altered to adjust the level of dropouts. After simulating dropouts, Poisson noise is added to generate the final distribution for each library.A probability function Tag tables from three recent scRNA-seq studies , and has a higher expected shrinkage rate for WC distances than for BC distances. This property ensures that the CIDR dissimilarity matrix better preserves the clustering structure in the data set.For simplicity of discussion, let us assume that dropouts are zeros. We will now explain why imputation by Eq. F has true expression levels x1, x2, and x3 for three cells C1, C2, and C3, respectively. Let us assume x1≤x2≤x3. Let P be the true dropout probability function, and P and Suppose that a particular feature C1 and C2 contributed by feature F is The true dissimilarity between C1 and C2 contributed by feature F is In the presence of dropouts in the observed data, the expected value of dissimilarity between C1 and C2 contributed by feature F is The expected value of the CIDR dissimilarity between E)≤E) for the pair of cells C1 and C2. This demonstrates that CIDR shrinks the expected distance between two points in the presence of dropouts.Comparing Eqs. C1 and C2 contributed by feature F: Furthermore, let us consider the expected rate of shrinkage between Eshrinkage rate and Eshrinkage rate . Since CIDR always shrinks the expected distance between two points, and that Eshrinkage rate is likely smaller than or equal to Eshrinkage rate. In other words, we hypothesize that the shrinkage rate between two closer points is larger than or equal to the shrinkage rate between two points that are further apart. It is very complex to prove this property algebraically, so we have conducted an extensive computational study on the rate of shrinkage. Additional file P and x1, the expected rate of shrinkage becomes smaller when x2 becomes larger. In particular, Additional file Let us consider ge rateC,C2,F ange rateC,C2,F an


Response to antidepressant treatment in major depressive disorder (MDD) cannot be predicted currently, leading to uncertainty in medication selection, increasing costs, and prolonged suffering for many patients. Despite tremendous efforts in identifying response-associated genes in large genome-wide association studies, the results have been fairly modest, underlining the need to establish conceptually novel strategies. For the identification of transcriptome signatures that can distinguish between treatment responders and nonresponders, we herein submit a novel animal experimental approach focusing on extreme phenotypes. We utilized the large variance in response to antidepressant treatment occurring in DBA/2J mice, enabling sample stratification into subpopulations of good and poor treatment responders to delineate response-associated signature transcript profiles in peripheral blood samples. As a proof of concept, we translated our murine data to the transcriptome data of a clinically relevant human cohort. A cluster of 259 differentially regulated genes was identified when peripheral transcriptome profiles of good and poor treatment responders were compared in the murine model. Differences in expression profiles from baseline to week 12 of the human orthologues selected on the basis of the murine transcript signature allowed prediction of response status with an accuracy of 76% in the patient population. Finally, we show that glucocorticoid receptor (GR)-regulated genes are significantly enriched in this cluster of antidepressant-response genes. Our findings point to the involvement of GR sensitivity as a potential key mechanism shaping response to antidepressant treatment and support the hypothesis that antidepressants could stimulate resilience-promoting molecular mechanisms. Our data highlight the suitability of an appropriate animal experimental approach for the discovery of treatment response-associated pathways across species. Major depression is the second leading cause of disability worldwide. However, only one-third of patients with depression benefit from the first antidepressant compound they are prescribed. It is a fundamental problem that the outcomes of individual antidepressant treatments are still highly unpredictable. In clinical studies, discovery of biomarkers for antidepressant response is hampered by confounding factors such as the heterogeneity of the disease phenotype and additional environmental factors, e.g., previous life events and different schedules of psychopharmacological treatment, which reduce the power to detect true response biomarkers. To overcome some of these limitations, we have established a conceptually novel approach that allows the selection of extreme phenotypes in an antidepressant-responsive mouse strain. In the first step, we identify signatures in the transcriptome of peripheral blood associated with responses following stratification into good and poor treatment responders. As proof of concept, we translate the murine data to a population of depressed patients. We show that differences in expression profiles from baseline to week 12 of the human orthologues predict response status in patients. We finally provide evidence that sensitivity of the glucocorticoid receptor could be a potential key mechanism shaping response to antidepressant treatment. A “one size fits all” approach is not effective or efficient in the treatment of major depressive disorder (MDD). Although it would be ideal to tailor available treatments to individual patients , patientConceivably, useful biomarkers for treatment response in depression could be developed through blood-based biomarkers, including genetic approaches, although psychophysiological and neuroimaging approaches are also promising . HoweverAmong the potential barriers to the development of clinically useful biomarkers in depression, the following 3 have been identified as being most important. First, current symptom-based diagnoses likely group pathophysiologically distinct patients , leadingIn addition to the aforementioned problems, major psychiatric disorders, including MDD, are primarily viewed as brain disorders, so the question of whether peripheral measures can be informative for treatment response to centrally acting compounds such as antidepressants continues to be matter of debate . During To overcome some of the limitations of past approaches, we here present a conceptually novel approach that allows the selection of extreme phenotypes in an antidepressant-responsive mouse strain (DBA/2J ) and useAll animal experiments were approved by the committee for the Care and Use of Laboratory Animals of the Government of Upper Bavaria, Germany (AZ 55.2.-1-54-2532-127-11). All experiments were carried out in accordance with the European Communities Council Directive 86/609/EEC.PREDiCT: The study was designed and conducted in accord with the latest version of the Declaration of Helsinki. The Emory Institutional Review Board and the Grady Hospital Research Oversight Committee gave ethical approval for the study design, procedures, and recruitment strategies (Emory IRB numbers 00024975 and 00004719).Citalopram versus CBT: Written informed consent was obtained from all participants, with the protocol conducted as approved by the Emory Institutional Review Board and registered at clinicaltrials.gov (NCT00367341).We hypothesized that within a large group of antidepressant-treated mice (antidepressant-responsive strain DBA/2J), we would be able to stratify animals into subgroups that are either responding exceptionally well (“responder”) to the antidepressant treatment or that do not show a response at all  from Charles River, France. On the day of arrival, the animals were 6–8 weeks old and from that day on were singly housed in standard cages under a 12L:12D cycle (lights on at 0800 h) and constant temperature (23 ± 2°C) conditions. Food and water were provided ad libitum. Pharmacological treatment of all animals started at an age of 9–11 weeks. Behavioral testing was performed at an age of 11–13 weeks. The experiments were carried out in the animal facility of the Max Planck Institute of Psychiatry in Munich, Germany, and approved by the committee for the Care and Use of Laboratory Animals of the Government of Upper Bavaria, Germany. All experiments were carried out in accordance with the European Communities Council Directive 86/609/EEC.Experiments were carried out with male DBA/2J mice (n = 90), a commonly used selective serotonin reuptake inhibitor (SSRI) antidepressant or a vehicle (n = 50). On treatment day 15, the animals received their last drug administration at 6 AM and were subjected to a FST 4 hours later. Directly after the FST, the animals were anesthetized with isoflurane and decapitated.The sequential steps and experimental procedures are summarized in The DBA/2J mouse strain has previously been shown to respond to oral treatment with the commonly used SSRI paroxetine under basal stress-free conditions , and thin = 29, vehicle n = 11) twice daily for comparison. Consumption was monitored on a daily basis. Animals that did not voluntarily consume the mouse pellets were excluded from the analysis.Animals were randomly distributed to the vehicle or paroxetine-treated experimental group. Paroxetine  or vehicle was voluntarily self-administered twice daily via customized palatable pellets , with a concentration of 5 mg/kg body weight. To evaluate the minimum effective dosage in our mouse strain, we included an additional group of animals treated with 1 mg/kg paroxetine  filled with tap water (22 ± 1°C) to a height of 15 cm, so that the mouse was not able to touch the bottom with its hind paws or tail. The duration of the test was 5 min. The parameters floating, swimming, and struggling were scored by an experienced observer who was uninformed regarding the treatment of the animals. Animals with the highest 20% of time floating were categorized as poor treatment responders, whereas animals with the lowest 20% of time floating were categorized as good treatment responders .Animals were anesthetized with isoflurane and killed immediately following the FST. Trunk blood was collected individually in 1.5-mL tubes. Brains were rapidly dissected and frozen at −80°C.Due to the complex character of the study, limitations in available specimens, stringent quality control (QC), and exclusion of outlier data, we could not always achieve fully identical sample and group compositions throughout all data analysis levels. This also explains the sporadic appearance of nonconcordant group sizes, which we consider a minor but unavoidable drawback.Brain and plasma paroxetine concentrations were measured after extraction by high liquid chromatography and quantifications. Paroxetine plasma concentrations were considered as a covariate in the analysis of the microarray data. For details of the respective protocols, see .High-performance liquid chromatography (HPLC) analysis was performed using a Beckman 166 variable-wavelength ultraviolet (UV) detector , a Merck L-7480 fluorescence detector , and a Beckman gradient pump 126 Solvent Module  equipped with a Beckman autoinjector 508 autosampler  . A Luna Calibration of plasma samples was performed using spiked samples at different concentrations . QuantifFor determination of brain tissue concentrations of paroxetine, tissue from the cerebellum was dissected and rapidly frozen on dry ice. The remaining trunk blood of each animal was collected in labeled 1.5-mL EDTA-coated microcentrifuge tubes . All blood samples were kept on ice until centrifugation at 8,000 rpm at 4°C for 15 min. After centrifugation, the blood plasma was transferred to new, labeled 1.5-mL microcentrifuge tubes. All plasma samples were stored frozen at −20°C until the determination of corticosterone by radioimmunoassay .t test in case of normal distribution of the data; otherwise, nonparametric comparisons were applied (Mann–Whitney U test). For variables with more than 2 groups, 1-way ANOVA was performed followed by Bonferroni post hoc testing. Correlations were analyzed with a 2-tailed, bivariate Pearson’s correlation analysis. As nominal level of significance, p < 0.05 was accepted. Values outside the 95% confidence interval (CI) were defined as statistical outliers and excluded from the analyses.The data presented are shown as means + standard error of the mean, analyzed by the commercially available software SPSS 16.0. For comparing 2 independent groups, data were analyzed with 2-tailed, independent samples Student Part of the blood was processed according to the PAXgene blood miRNA Kit manufacturer’s instructions. Briefly, 350 μL of freshly collected trunk blood was immediately transferred into 1.5-mL tubes filled with 966 μL PAXgene solution (RNA stabilizer reagent), gently inverted 10 times, incubated at room temperature (RT) for 2–24 hours, and then stored at −20°C before ribonucleic acid (RNA) isolation. Volume ratio of RNA stabilizer reagent to blood samples was kept at 2.76, according to the manufacturer’s protocol.Blood consists of a heterogeneous cell population of erythrocytes, granulocytes, and other peripheral mononuclear cells (PBMC). This heterogeneity makes it difficult to detect differences in gene expression levels. Furthermore, it is worth mentioning that blood consists of a high amount of globin mRNA transcripts. This high amount of globin mRNA transcripts can mask differences in other mRNA transcripts. After the RNA isolation from whole blood, we applied the Ambion GLOBINclear-Mouse/Rat Kit. Globin reduction was performed according to the manufacturer’s protocol. Input RNA was quantified before the globin reduction with a Nanodrop spectrophotometer. Shortly, custom biotinylated complementary oligonucleotides were mixed with globin RNA sequences from RNA isolated out of whole blood and then annealed the oligonucleotides to α- and β-globin transcripts. Streptavidin-coated paramagnetic beads were added to bind the biotinylated duplexes and therefore removed the captured globin transcripts from the preparations of total RNA. This globin-reduced RNA was then further processed and amplified and then used for the microarray experiments.n = 12), poor treatment responders (n = 12), and vehicle-treated animals (n = 12)  was performed on globin-depleted RNA extracted from 3 groups of animals: good treatment responders  and both quantified and quality controlled by capillary gel electrophoresis . The obtained RNA integrity numbers (RIN) were greater than 7.5 in all total RNA samples derived from blood before globin depletion and dropped slightly after globin depletion .Globin-depleted RNA was labeled and linearly amplified to cRNA in a commercial form of the classical procedure by Eberwine. As input for the Illumina TotalPrep-96 RNA Amplification Kit , 250 ng of RNA was used, and sample processing followed the manufacturer’s protocol exactly. cRNA was again quantified and quality checked as performed with total RNA. All samples underwent photometric analysis  and a selected cross section of the samples was additionally checked on the Bioanalyzer .Following the manufacturer’s instructions (WGGEX Direct Hybridization Assay Guide 11322355A), 1,500 ng of labeled cRNA was hybridized onto whole-genome gene expression microarrays . Sample randomization and alternating processing between the experimental groups were applied in order to avoid technical bias being correlated with group comparisons.Microarrays were scanned and intensity extractions were computed using a BeadArray Reader  via the BeadScan Software with activated internal outlier detection and a scan factor of 1 . The extracted bead summary data provide gene expression levels from 45,281 array features per sample. QC of microarray data was based on visual inspection of scan images, data distributions, internal Illumina controls, pairwise scatter plots, and statistical outlier detection of samples. Thereby, 3 samples (1 from the good responder group and 2 from the control group) were excluded from further analysis.For the samples fulfilling QC criteria, bead summary scan data were filtered for detected probes with p-detection < 0.05 in at least 4 samples in the whole data set. Variables remaining numbered 20,412 and were variance stabilizing normalization (vsn) transformed and normalized using the bioconductor R package “beadarray.” Normalized and filtered data were imported into Qlucore Omics Explorer 2.3  for exploration of batch effects and for inference testing. Principal component analysis was used to identify batch effects and artifacts according to correlations of the sample structure with putative confounders. Consequently, a mild technical bias was removed by using bead chip ID as a covariate in ANOVA from blood samples analyses. Paroxetine concentrations were used as a second covariate to remove unwanted variance introduced by differences in the bioavailability of paroxetine. For some animals, blood paroxetine concentrations were not available due to limitations in total blood amount. To impute those missing values, 4 blood concentrations used in the differential gene expression analysis were predicted from regressing paroxetine concentrations from brain against blood. In the brain microarray data, covariates were paroxetine concentration in brain, and “amplification batch.” For correlation analyses of paroxetine concentrations with microarray data, paroxetine concentration as covariate was omitted.q-values (q < 0.1 and q < 0.05).For inference testing, variables  were further filtered for variance of >5%, which left 4,966 variables for the good versus poor responder comparison and 6,664 for the paroxetine versus vehicle comparison. Statistical tests of microarray data were based on 2 group comparisons using ANOVA with Benjamini-Hochberg based false discovery rate (FDR)-analogue Annotation of microarray probes was done using the manufacturer’s annotation file (MouseWG-6_V2_0_R2_11278593_A.bgx).q-values.Microarray results were visualized as a volcano plot; all 4,966 microarray probes that have been filtered for detection over background and variance in the paroxetine blood data have been plotted. The x-axis displays the difference in residual gene expression for normalized microarray data . The y-axis indicates the negative value of log 10 transformed Gene regulation pattern analysis between tissues was performed using the R “stats” package . All significant probes from blood were mapped to the brain array results. After removing probes with missing data in one of either tissue, 214 microarray probes remained for the analysis. Clustering was only performed for microarray probes that are represented as rows in the plot. The agglomerative clustering is based on the method “complete” and the use of a distance matrix with euclidean distances as a dissimilarity parameter.We assessed whether the observed gene expression profiles of good treatment responders and poor treatment responders were related to changes in blood cell proportions in the mice using CIBERSORT . The inpn = 86) consisted of a subset of MDD patients treated with antidepressant drug treatment over 12 weeks from 2 samples recruited at Emory University School of Medicine . In bot 12 from  and to C 12 from . Only thRNA was isolated from peripheral blood in a 96-well format using the magnetic bead-based technology MagMAX for Stabilized Blood Tubes RNA Isolation Kit, compatible with Tempu Blood RNA Tubes . RNA was quantified using the Nanophotometer, and quality checks were performed on the Agilent Bioanalyzer . Only samples with RIN ≥ 6 with clear 18S and 28S peaks on the Bioanalyzer were used for amplification; the average RIN was 6.3 (SD of 0.668). RNA was further processed for generation of biotin-labeled amplified RNA using the Amplification Kit . cRNA was hybridized to Illumina Sentrix Arrays HT-12 v4.0 arrays using the Illumina TotalPrep-96 RNA  and incubated overnight for 16 hours at 55°C. Arrays were washed, stained with Cy3 labeled streptavidin, dried, and scanned on the Illumina BeadScan confocal laser scanner . QC was performed using the bead-array package in R for 86 samples and 47,282 probes. Probes with p-detection values of <0.01 in at least 10% of the samples in the whole data set were removed. Remaining probes were normalized and transformed using the vsn package in R. Not all samples were hybridized on the same batch, and thus we corrected for chip number using COMBAT. A total of 17,725 transcripts and 86 samples remained after QC.For the full drug-treated sample, 63 patients were classified as responders and 23 as nonresponders, according to percent changes in HDRS-17 scores from baseline to week 12 .n = 259) resulting from the microarray analysis and described in n = 241). Because some genes are represented by more than one probe, 288 probes were included in final analyses. Prediction models were built as soft margin support vector machines for classification using the e1071 packages in R with the parametrization gamma = 0.001; cost = 10. Further analyses included only mouse transcripts at FDR of 5% (n = 85). These were also mapped to their human orthologue genes (n = 77); 66 genes passed QC in the human study, which were represented by 92 probes. The sample was equally divided into training and test data sets for each of the analyses (probes at q < 0.1 and q < 0.05). Gene expression repeated measures from the patients at baseline and week 12 were available; we computed the absolute difference between the expression levels of the transcripts between those time points and tested whether these differences were able to predict response to antidepressant treatment in the test data set. We permuted the response-status labels 10,000 times in the training data set and predicted the response status in our test data. In addition, we compared the obtained prediction accuracy of our selected classification features against 1,000 classification models derived from randomly sampled features. Random feature sets also consisted of absolute difference in expression between baseline and week 12 of treatment and were size matched to the selected feature set. Those data were the input for soft margin support vector machine training and testing as indicated above.Mouse gene expression transcripts  and Pathway-Express  = 12, n[good] = 12, n[poor] = 12). We evaluated both treatment effect and response status with respect to antidepressant treatment and with respect to paroxetine plasma concentrations. We also investigated whether paroxetine brain or plasma concentrations might have an effect on gene expression levels. Linear and quadratic regression analyses did not reveal any microarray probe that showed significant correlations with the related plasma paroxetine levels when controlling for multiple testing. No significant influence of paroxetine concentrations on gene expression profiles was observed. Nevertheless, identified technical batch effects in the data and measured paroxetine drug concentrations in blood were used as covariates in an ANOVA-based statistical model.To identify signature gene expression profiles characteristic of the animals’ responder status, gene expression data sets of vehicle-treated animals, good responders, and poor responders were created by whole-genome gene expression microarray analysis on blood samples and analyzed (q < 0.1) (q < 0.05 (Although no robust gene regulation was apparent when the treatment group (independent of response) was compared to the control group, there was a pronounced effect within the treatment group. We were able to detect a set of 259 transcripts that showed a significant difference in expression due to antidepressant response status at a false discovery controlled significance level of 10% (q < 0.1) , of whicq < 0.05 .We then aimed to see whether the observed gene regulation patterns in peripheral blood might overlap with effects observed in the PFC from the same animals. To test this, we first performed a cluster analysis on the difference in expression between the responder groups in the set of differentially regulated genes in blood. We then compared the results for these transcripts to the difference in expression between these 2 groups measured in PFC brain tissue in the same animals. The results are summarized in a heat map in No significant differences in immune cell subtypes between the different response groups were detected using CIBERSORT  and ImmuNo significant change in immune cell subtypes using CellCODE  was assopperm = 0.0328). When a more stringent FDR of 5% cutoff was applied to the mouse transcripts, the corresponding human orthologues predicted response status with an accuracy of 81% in the human sample. The prediction persisted after we permuted the response-status labels 10,000 times (pperm = 0.0018).In the next step, we determined whether this transcriptional profile identified in the mouse model would also be relevant in the human data set. Therefore, we tested whether changes in the mRNA expression of the human orthologues of transcripts at FDR of 10% and at FDR of 5%, separately, are associated with response to antidepressant treatment. Differences in expression profiles from baseline to week 12 when using human orthologues of transcripts at FDR of 10% allowed prediction of response status (at least 50% improvement in HDRS-17 from baseline to week 12 for responders) with an accuracy of 76%, using all patients treated with antidepressant. The prediction persisted after we permuted the response-status labels 10,000 times (pperm = 0.026).After showing that expression levels of the antidepressant response genes identified from mice are also informative for classification in a human sample, we further analysed the quality of the mouse-based feature selection in the human data set. For this, we compared the classification accuracy of our identified antidepressant-response features to classification accuracy given by randomly chosen and size-matched sets of gene expression probes in the human sample . In analFor functional annotations of the microarray results, we performed pathway analyses and included an overrepresentation analysis with DAVID, and we conducted a second analysis using Pathway-Express. The latter accounts for pathway topology and biological effect size. In both approaches, no significant results passing our threshold criteria were found. The top overrepresented categories in DAVID were entities associated with general gene transcription and did not reach significance levels. Although Pathway-Express showed formally significant results for a few specific Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, we excluded them because less than 2% of the pathway genes were regulated.p-value of 5.6e-4, further supporting an enrichment of GR-responsive transcripts among response-associated genes.We next integrated our results with another microarray data set that we had previously generated. Those data originated from mouse blood samples taken from animals that had been treated with the glucocorticoid receptor (GR) agonist dexamethasone (dex ). To tesN = 96) are regulated in the same direction in both conditions, and based on a binomial distribution, such a result could not be observed if both outcomes (same and opposite regulation) had the same probability (p = 7.2e-07). Thus, we can conclude that there is a common direction of gene regulation for dex treatment and paroxetine response.Standard enrichment analysis does not take into account the direction of gene regulation, and we were interested to see whether paroxetine response and dex regulation showed a directional overlap. Of the 134 array probes that are significantly regulated by dex and are, at the same time, between the paroxetine response groups, only 38 had a mismatch in the direction of the putative regulation. The majority of the regulated genes  the lack of biosignatures predicting antidepressant response and (2) the lack of knowledge of the molecular mechanisms mediating the response to antidepressant pharmacotherapy. The latter is of particular importance for the eagerly awaited discovery of conceptually novel antidepressant treatment strategies, which can only be rationally realized with a deeper understanding of the molecular mechanisms underlying clinical response .In recent years, the unbiased, i.e., genome-wide, screening to identify genetic factors that could assist in the prediction of an individual’s drug response has been a major focus in depression research. Despite tremendous efforts, however, the results are fairly modest in identifying predictive genes in large genome-wide association studies –29 and eHowever, studies to investigate the neurobiology of antidepressant treatment response have been hampered by the fact that no appropriate animal model addressing this issue had yet been described. Therefore, we embarked upon the development of an animal experimental approach modeling the heterogeneity in response to antidepressant treatment as closely as possible. In contrast to studies in patients, this model approach both enables an in-depth analysis of the neurobiological mechanisms shaping individual antidepressant response in the central nervous system and searches for peripheral biosignatures associated with treatment response. There are different approaches to model depression-like phenotypes  in the mouse. While induction of depression-like symptoms following exposure to different types of stress, e.g., chronic social defeat or chronic mild stress is one possible approach, the use of mouse strains with high innate anxiety- and depression-like behavior is also commonly accepted. The selection of the DBA/2J mouse strain, with its well-described high innate anxiety and responsiveness to antidepressant treatment , enabledOral treatment with the SSRI paroxetine significantly reduced—as expected—depression-like behavior. Remarkably, in addition to the overall antidepressant-like effect on promoting active coping strategies in the FTS, we detected a high variability in the behavioral outcome. Although the neurobiological mechanisms underlying antidepressant-induced behavioral changes in the FTS still are not fully understood , we hereThe selection of a rodent approach for biomarker discovery in psychiatric disorders has the advantage of minimizing potentially confounding variables, which, in clinical depression studies, so far have impeded biomarker discovery . Due to Aiming to enable a translational approach, we focused on the identification of transcriptome signatures in the periphery, because only those are relevant for clinical application. Several studies have investigated the use of human peripheral blood cells as surrogate material for different organs and tissues, including the central nervous system –41. HoweTo address issues of cross-tissue relevance, we compared peripheral transcriptome signatures with expression profiling data of the PFC of the same good- and poor-responding animals. We did not find any major common response status-associated gene regulation pattern between both tissues. We thus hypothesize that in depression treatment, blood cells might act as sentinels of treatment response but are not generally informative about central regulation processes, at least not in the PFC.In the next step and as a proof of concept, we sought to evaluate the relevance of the murine transcriptional signature associated with antidepressant treatment response in a human data set. Using a powerful within-participant approach investigating longitudinal transcription changes between baseline and week 12 of antidepressant treatment, we tested whether mRNA expression of the human orthologues of these transcripts changes with antidepressant treatment in peripheral blood in a subset of 2 human studies , 22. DifThe available evidence makes a compelling case implicating dysregulation of the stress hormone system, the so-called hypothalamus-pituitary-adrenocortical (HPA) system, in the pathogenesis of MDD , 43. MorWe could not detect any difference in corticosterone plasma concentrations between good and poor responders to paroxetine treatment directly after the FTS challenge, although assessment of plasma corticosterone concentrations at 1 time point, i.e., 5 min after the FST, does not exclude potential dynamic changes in HPA system response . Evidence from measurements of HPA system activity in depressed patients, however, supports the notion that in vivo challenges such as the combined dexamethasone/corticotropin releasing hormone challenge test (Dex-CRH test) are superior to single baseline measurements of peripheral glucocorticoid concentrations in discriminating between depressed patients and healthy controls as well as treatment responders versus nonresponders. In addition, recent investigations have shown that dex-stimulated gene expression is a sensitive marker of GR-resistance in MDD  and thatBiomarkers or biosignatures, respectively, would not only allow monitoring of antidepressant treatment response in clinical practice but they also could assist in the evaluation of drug actions at an early stage in clinical trials of novel agents that are frequently marred by late attrition . In partIn conclusion, we expect this translational approach to serve as a template for the discovery of improved and tailored treatment modalities for depression in the future.S1 Data(XLSX)Click here for additional data file.S2 Data(XLSX)Click here for additional data file.S1 TableGenes are ordered according their functional classes. Fold changes are normalized to poor responders.(XLSX)Click here for additional data file.S2 TableMDD, major depressive disorder.(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S1 Figp < 0.05. All raw data for S1 Fig are available in (A) Twenty-eight days of 1 mg/kg BW paroxetine treatment led to an increase in body weight in the paroxetine-treated animals. (B) Corticosterone levels were not altered due to the treatment. (C) Paroxetine treatment led to a trend in reducing the time spent floating in the treated animals compared to the control group. (D) Chronic treatment did not alter the time spent struggling in the paroxetine-treated group. * significant correlation, (TIF)Click here for additional data file.S2 Fig(A) After 14 d of paroxetine treatment, animals treated with the SSRI gained significantly more body weight compared to the vehicle-treated control group. (B) No significant difference in body weight gain was found between the responder groups due to the paroxetine treatment. (C) Corticosterone levels were assessed in blood plasma during the circadian nadir in the morning directly after the FST. We did not find any significant difference in corticosterone levels between vehicle- and paroxetine-treated animals. (D) While comparing the corticosterone levels in the different responder groups, no difference was found between the groups. Data are represented as mean + SEM. * significantly different from vehicle treated animals, p < 0.000. All raw data for S2 Fig are available in (TIF)Click here for additional data file.S3 Figr = 0.94). All raw data for S3 Fig are available in  ANOVA analysis showed a significant association of responder status with both plasma and paroxetine concentrations. In the post hoc analyses, only brain tissue concentrations of paroxetine showed a significant difference between good responders and poor responders. (C) Paroxetine brain and plasma concentrations were closely correlated ((TIF)Click here for additional data file.