RNA-seq, wherein RNA transcripts expressed in a sample are sequenced and quantified, has become a widely used technique to study disease and development. With RNA-seq, transcription abundance can be measured, differential expression genes between groups and functional enrichment of those genes can be computed. However, biological insights from RNA-seq are often limited by computational analysis and the enormous volume of resulting data, preventing facile and meaningful review and interpretation of gene expression profiles. Particularly, in cases where the samples under study exhibit uncontrolled variation, deeper analysis of functional enrichment would be necessary to visualize samples’ gene expression activity under each biological function.We developed a Bioconductor package rgsepd that streamlines RNA-seq data analysis by wrapping commonly used tools DESeq2 and GOSeq in a user-friendly interface and performs a gene-subset linear projection to cluster heterogeneous samples by Gene Ontology (GO) terms. Rgsepd computes significantly enriched GO terms for each experimental condition and generates multidimensional projection plots highlighting how each predefined gene set’s multidimensional expression may delineate samples.The rgsepd serves to automate differential expression, functional annotation, and exploratory data analyses to highlight subtle expression differences among samples based on each significant biological function. RNA-seq is a revolutionary technology to measure genome-wide gene expression of biological samples at high resolution by sequencing messenger RNA (mRNA) molecules . Common After RNA-seq reads are generated using a sequencing instrument, gene expression abundance is estimated by mapping the sequencing reads to a reference genome if there is an available reference genome or by building a transcriptome assembly de novo , 10. DGEHowever, particularly when biological samples are not well separated , a direct two-group DGE analysis can result in unmanageable lists of DE genes with uncertain significance . FurtherIn these scenarios, list of DE genes and even significantly enriched biological processes would be hard to interpret for biologists. Alternatively, after computing significantly enriched biological processes, samples could be visualized based on their activity for each of these biological processes. Per biological process visualization would enable biologists to have a deeper understanding of the samples’ activity with respect to each significant biological process.To streamline the analysis of RNA-seq datasets to achieve the aforementioned goals, we developed a software toolkit GSEPD (gene set enrichment and projection display). GSEPD produces DE gene lists, significantly enriched gene ontology (GO) terms, and importantly their cross-product: a mapping of which genes are perturbed within each GO term, and how genes associated with those terms define the samples’ expression profiles in the context of the other RNA-Seq samples. GSEPD provides various plots and tables to summarize the results and give its users a comprehensive outlook of the underlying RNA-seq data.We demonstrated the usage of GSEPD on a time series dataset of H1ESC cell lines . GSEPD cGSEPD is implemented as a Bioconductor package named rgsepd and freely available under GPL-3 license.We built GSEPD as a Bioconductor package named rgsepd to ensure that it is readily available, simple to install, and bundled with both test data and documentation. The system architecture of GSEPD is shown in Fig. GSEPD requires two types of input data to run: the multisample RNA-seq raw counts matrix and sample information matrix. Input should be loaded as a matrix in R with RefSeq ID numbers as row and sample identifiers as column names. The sample information matrix is used to link sample identifiers with test conditions and short labels (for plotting into figures). Given input data, GSEPD automatically computes DE genes between two groups with default parameters of DESeq2, adjusted if necessary for small sample counts . GSEPD aOne of the novel features of GSEPD is to focus on each significantly enriched GO term and assess how samples are segregated with respect to the expression of genes in that GO term. In order to study if samples segregate in their original groups with respect to a particular GO term, GSEPD performs clustering of samples based on the expression of all genes in a significantly enriched GO term. GSEPD can also incorporate non-tested samples  in clustering to enable investigators label unclassified or indeterminate samples by their expression profiles among GO terms relevant to the experiment.k = 2. Briefly, for a given GO term with N genes, each sample is represented as an N-dimensional vector of expression of all genes in the GO term. To avoid broad GO terms associated with thousands of genes, only GO terms with less than m (m = 31 by default) genes are evaluated by GSEPD for clustering.GO term-based clustering of samples is performed by using k-means clustering where homogeneity and completeness. A cluster’s homogeneity is computed based on the entropy of class labels within the cluster, i.e., maximum homogeneity is achieved when all members of the cluster belongs to the same cluster. The completeness of a cluster is computed based on what percent of members of a class are assigned to the cluster. A cluster would have maximum completeness if it has all members of a class. In ideal cases, clusters should be homogenous and complete.To assess the quality of the clustering outcome, a validity score called V-measure  is compup-value for each GO term-based clustering by permuting sample group labels  and re-calculating the V-measure. The p-value is the proportion of random assignments that achieve a higher V-measure. By default, GSEPD performs adaptive permutation up to 400 times to resolve segregation by p < 0.01.In order to assess the significance of V-measure score, GSEPD computes an empirical GSEPD visualizes significant GO terms in scatter plots and subspace principle component analysis (PCA) figures to allow further exploration of the results by the user. Vector projection of samples is performed based on gene set of the GO term to score each sample’s similarity to the centroid of each group and to highlight any outlier samples for the gene set.alpha and beta. The alpha score is the distance between projected point on the axis to the origin and the beta score is the Euclidean distance between the sample and the projected point in the axis .We run GSEPD on a time series dataset (five time points with two replicates) along the differentiation of H1ESC cells into cardiomyocytes (NCBI SRA accession number SRP048993) . We usedThe heatmap of alpha scores (HMA) plot is shown in Fig. The results in the HMA plot also show that the day 3 samples were unique in GO terms “mesodermal cell fate specification”, “mesodermal cell fate commitment”, “negative regulation of cell fate commitment”, and “regulation of mesoderm development,” suggesting a unique spike of gene activation that deactivated on all other time points. With no biological systems background knowledge, the user of GSEPD can thus extract pathway activation knowledge from RNA-seq count data.PITX2 and NOTCH1 is shown in Fig. PITX2 is shown downregulated in class day 3 (green) versus class day 5 (red), whereas NOTCH1 is upregulated by 1.5 units of logged normalized counts. Colored lines (corresponding to cells of the heatmap in Fig. GSEPD also extracts significant GO terms into multi-page scatterplots of genes showing orthogonal views of samples on the high-dimensional clusters. For instance, for the “cardiac atrium morphogenesis” a 28-gene GO term in the HMA figure Fig. , a samplGSEPD is a user-friendly RNA-seq analysis toolkit. To enable rapid and simple installation and ensure reproducibility of results, GSEPD was implemented as an open source Bioconductor package. By utilizing the GO hierarchy through GOSeq, GSEPD can quickly identify significantly enriched GO terms with DE genes computed by DESeq2. Furthermore, GSEPD can visualize how each sample behaves with regard to each significant GO term. Byproducts including sample PCA figures save time and effort and can identify sample batch effects that may confound analyses and be obscured by rudimentary differential expression produced by other pipelines.https://bioconductor.org/packages/release/bioc/html/rgsepd.html.GSEPD is implemented as a Bioconductor package named rgsepd and freely available under GPL-3 license for academic and non-academic usage. The Bioconductor system will install required additional packages DESeq2, GOSeq, and the GO databases, available to any Mac, Linux, and Windows PC. Generating the input matrix will require other tools. Description of the 13 types of figures and 12 types of tables generated by each comparison run are available in the bundled package manuals. Instructions, manuals, and sample data are available in the online help files and the project website at


At the single cell level, the transcriptional fluctuations are much larger than the average of a cell population, and the low amount of RNA transcripts will increase the rate of technical dropout events. Therefore, scRNA-seq data are much noisier than traditional bulk RNA-seq data. In this study, we proposed the deep variational autoencoder for scRNA-seq data (VASC), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. VASC can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on over 20 datasets, VASC shows superior performances in most cases and exhibits broader dataset compatibility compared to four state-of-the-art dimension reduction and visualization methods. In addition, VASC provides better representations for very rare cell populations in the 2D visualization. As a case study, VASC successfully re-establishes the cell dynamics in pre-implantation embryos and identifies several candidate marker genes associated with early embryo development. Moreover, VASC also performs well on a 10× Genomics dataset with more cells and higher dropout rate.Single-cell RNA sequencing (scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities at the single cell level. It is an important step for studying cell sub-populations and lineages, with an effective low-dimensional representation and  Characterizing the cellular states at the single cell level is crucial for understanding the cell–cell heterogeneities and the biological mechanisms that cannot be observed in the average behaviors of a bulk of cells. Single-cell RNA sequencing (scRNA-seq) is a promising high-throughput technique to simultaneously profile the transcriptomes of a large number of individual cells In recent years, deep probabilistic hidden models have shown superior performances in representing complex features of high-dimensional data, especially for images and speeches X), by a set of latent variables z . The primary goal of VASC is to find the optimal z capturing the intrinsic information of the input data. In a probabilistic view, the posterior distribution P(z|X) could be treated as the best distribution of z given the observed data X. However, P(z|X) is usually intractable. Variational inference is thus proposed to solve this problem by designing another common distribution family Q(z|X)  to approximate P(z|X). The minimization of the Kullback–Leibler (KL) divergence between the two distributions is usually adopted for the approximation. The variational distribution Q(z|X) should be sufficiently representative to model the complex information of P(z|X) in the scRNA-seq datasets, and on the other hand, should be tractable for efficient computation. In VASC, deep neural networks were used to explicitly model the variational distribution. Unlike the traditional variational inference methods, deep neural networks can approximate arbitrary functions and can be optimized efficiently using the stochastic gradient descent methods.VASC, a generative model based on the deep variational autoencoder (VAE) z|X) of latent variables z given the expression values X, of which mean and variance parameters could be generated by the encoder network. Then, the learned Q(z|X) was used to re-generate pseudo samples X’ by the decoder network, another three-layer neural network. Finally, a ZI layer, based on a double-exponential distribution, was designed to mimic the dropout events by randomly setting some data points as zero z|X) and P(z|X). After the auto-encoding procedure, a 2D representation was learned for visualization and other downstream analysis.Generally, VASC has three major parts, namely, the encoder network, the decoder network, and the zero-inflated (ZI) layer . The enchttps://hemberg-lab.github.io/scRNA.seq.datasets/), with ‘scater’ toolkit https://www.ebi.ac.uk/arrayexpress/; accession No. E-MTAB-3929). The PBMC3k dataset was downloaded from 10× Genomics (https://support.10xgenomics.com/single-cell-gene-expression/datasets).To demonstrate the performance of VASC, we analyzed 22 scRNA-seq datasets . The firX) of data points in a high-dimensional space χ, with the aid of low-dimensional latent variables z. The whole model is divided into two procedures, that is, (1) generating the samples of z in the latent low-dimensional subspace, and (2) mapping them to the original space χ. The critical point is to generate z having the high probability to recover the observed data matrix X. In this way, the generated z may be possible to capture the intrinsic information of the original data. The best choice to generate z, in theory, is the posterior P(z|X), which however, is usually too complicated and intractable. VAE tries to use a variational probability Q(z|X) to approximate the posterior, by minimizing the Kullback–Leibler (KL) divergence (D) between Q(z|X) and P(z|X):VASC is a deep VAE-based generative model and is designed for the visualization and low-dimensional representation of the scRNA-seq data. VAE aims to model the distribution P(X) is a constant and z|X from X to z and the decoder PX|z). The encoder network was used to estimate its posterior parameters. Usually, both the parameters z from Latent variables z to recover the original expression matrix, which was designed as a three-layer fully-connected neural network with dimensions of hidden units 32, 128, and 512, respectively, and an output layer. The first three layers used ‘ReLU’ activations and the final layer with sigmoid to make the output within  .The decoder network used the generated s from Gumbel-softmax distribution was obtained by:An additional ZI layer was added after the decoder network. Adapted from the model used by ZIFA The loss function as shown in the Equation The whole structure, now, could be optimized end-to-end using the stochastic gradient descent-based optimization algorithm. We chose the RMSprop method https://github.com/fchollet/keras) can be found at https://github.com/wang-research/VASC.Source codes implemented by keras  To measure the quality of visualization and low-dimensional representation, Suppose P is the predicted clustering results, and T is the known cell types (the same below), we denote the entropy of P and T as H(P) and H(T), respectively, and the mutual information between them as MI. NMI is computed as:n is the total number of samples, i-th cluster of P, j-th types of T, and i-th cluster of P and the j-th type and T. ARI is computed as:Suppose H(T|P) is the cross-entropy of cell types given the cluster P, the homogeneity score (h) is computed by:The measure homogeneity expects that every cluster only contains samples from one cell type. Suppose The measure completeness (c) expects that samples from one cell type are assigned to the same cluster, and is computed as:For all the measures including NMI, ARI, homogeneity, and completeness, larger values (up to 1) mean better performances.We filtered cells with less than three detected genes (UMIs > 3). Number of UMI counts was transformed to transcript-per-million (TPM)-like values by normalizing each cell through dividing total UMI counts and then multiplying by 10,000. Log2 transformation was applied after adding a pseudo-count 1 to obtain the gene expression matrix. Due to the serious dropout events present in this dataset, gene selection is used to reduce noises. We adopted the same procedure as previously reported We tested the visualization performance of VASC together with four state-of-the-art dimension reduction methods, including PCA Eleven different cell types were sequenced in the fourth dataset reported by Pollen and colleagues Kolodziejczyk et al. generated a dataset when examining embryonic stem cells grown under three different conditions: serum, 2i, and alternative 2i (a2i) The dataset reported by Baron et al. k-means clustering Next, to quantitatively assessing the performance of these methods in dimension reduction and visualization, we compared the cell sub-populations in the reduced subspaces . Loss function of the whole neural network decreased sharply during the first few epochs, and simultaneously, the NMI and ARI values increased sharply A and B. k-means clustering, slightly different results could be generated at different runs. We thus analyzed the four datasets with the smallest sample sizes, including Biase (56 samples), Goolam (124), Pollen (301), and Yan (90), to test the stability of VASC by 20 repeated runs. As expected, the two datasets with relatively more cells (Goolam and Pollen) showed much higher consistent results than the other two datasets , including 1529 cells with detailed annotations of developmental stages, inferred lineage, and inferred pseudo-time information  Compared to t-SNE, a sharper split in the grouping was observed in the E5 cells by VASC B and E. PGF, GCM1, CYP19A1, MUC15, CD24, CCR7, GREM2, CGA, GATA2, TDGF1, ESRG, GDF3, and DNMT3L mentioned in the original article q = 5.63E − 05 by DAVID 6.8 q = 4.87E − 05), and lipid metabolic process . Several metabolic pathways have been recently shown to play essential roles in regulating the stemness and differentiation of stem cells CYP11A1 , NR2F2 (encoding a member of the steroid thyroid hormone superfamily of nuclear receptors), PKM , PPARG (encoding a member of the peroxisome proliferator-activated receptor subfamily of nuclear receptors), and IDH1 .The candidate genes associated with the pre-implantation embryo development were identified by calculating the Spearman’s correlations between the gene expression and the two features shown in the reduced subspace. Many known regulators and markers were found in the top-correlated genes, such as + T cells, CD8+ T cells, and NK cells. Cells from common myeloid progenitors, such as dendritic cells, megakaryocytes, and monocytes, were separated from the cells derived from common lymphoid progenitors, like B cells, T cells, and NK cells. Then, we re-ran VASC on the population of monocytes, and consequently further classified them into FCGR3A+ monocytes and CD14+ monocytes  is fundamental to visualization and the downstream analysis of scRNA-seq data. In this study we report VASC, a method based on deep VAE, for dimension reduction and visualization of scRNA-seq data. We evaluate the performance of VASC by comparing with four other commonly-used methods, including PCA, t-SNE, ZIFA, and SIMLR. These methods are broadly divided into two categories. (1) PCA, ZIFA, and VASC aim at finding the representation that can best explain the variations of the original data; and (2) t-SNE and SIMLR try to find another embedded space that can preserve the neighborhood relationship of the samples in the original space. According to our data analysis, the former group of methods can better retain the basic shapes of the data distributions. ZIFA can be treated as a combination of the probabilistic PCA and the zero-inflated model. The major limitation of ZIFA is that it assumes a linear relationship between the hidden subspace and the observed data. Conversely, VASC can deal with complex non-linear patterns based on deep neural networks. Our data show that VASC has better performance than PCA and ZIFA, especially when the sample sizes are larger . The twoOne major application of scRNA-seq is to identify different cell types at a single cell level. According to the quantitative analyses shown in z|X). When the sample size is small, it is better to fix the co-variance matrix. However, when the size is large enough , a co-variance matrix learnt from the data can generate better results. It is expected that more complex variational distribution families should be tested in the near future, as the sample size of scRNA-seq dataset is quickly increasing.There are two parameters (the mean vector and the co-variance matrix) in the variational distribution Q(We also find that the inclusion of ZI layer improves the representation of VASC in terms of recovering the known cell types. Compared to ZIFA, the Gumbel distribution used by the ZI layer does not generate zeroes strictly, which may additionally model the near-zero dropout events. ZIFA is unable to deal with near-zero events, which could be a limitation of ZIFA The stochastic optimization algorithms, used in the VASC model learning, introduce variations in the dimension reduction. Repeated runs are thus recommended for more consensus performance, although such random effect is small if the sample size is over several hundreds. The running time is a common issue for deep models. For the large dataset with several thousands of cells, it costs several hours for the VASC model learning using a desktop-level computer with single GPU card, which may be acceptable for most scRNA-seq studies.In this study, a dimension reduction method, VASC, was developed for scRNA-seq data visualization and analysis. We systematically compared VASC with four state-of-the-art dimension reduction methods on 20 datasets. Results show that VASC achieves superior performance in most cases and is broadly suitable for different datasets with different data structures in the original space. Especially, VASC could make clearer separation of rare cell types than other methods according to our data analysis. The application on a dataset of the human pre-implantation embryo development shows that VASC can re-establish the cell dynamics in the reduced 2D-subspace and identify the associated marker genes.DW and JG designed this study and developed the algorithm. DW made the detailed implementation and performed the data analysis. DW and JG wrote this manuscript. Both authors read and approved the final manuscript.The authors declare that they have no competing interests.


We designed a systematic approach to evaluate how well different algorithms, trained on gene expression and drug response of CCLs, can predict CDR of patients. Using data from two large databases, we evaluated various linear and non-linear algorithms, some of which utilized information on gene interactions. Then, we developed a new algorithm called TG-LASSO that explicitly integrates information on samples’ tissue of origin with gene expression profiles to improve prediction performance. Our results showed that regularized regression methods provide better prediction performance. However, including the network information or common methods of including information on the tissue of origin did not improve the results. On the other hand, TG-LASSO improved the predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. Additionally, TG-LASSO identified genes associated with the drug response, including known targets and pathways involved in the drugs’ mechanism of action. Moreover, genes identified by TG-LASSO for multiple drugs in a tissue were associated with patient survival. In summary, our analysis suggests that preclinical samples can be used to predict CDR of patients and identify biomarkers of drug sensitivity and survival.Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and molecular profiles obtained prior to administration of the drug, can play a significant role in individualized medicine. Machine learning models have the potential to address this issue but training them requires data from a large number of patients treated with each drug, limiting their feasibility. While large databases of drug response and molecular profiles of preclinical  Cancer is among the leading causes of death globally and prediction of the drug response of patients to different treatments based on their clinical and molecular profiles can enable individualized cancer medicine. Machine learning algorithms have the potential to play a significant role in this task; however, these algorithms are designed based on the premise that a large number of labeled training samples are available, and these samples are accurate representations of the profiles of real tumors. However, due to ethical and technical reasons, it is not possible to screen humans for many drugs, significantly limiting the size of training data. To overcome this data scarcity problem, machine learning models can be trained using large databases of preclinical samples (e.g. cancer cell line cultures). However, due to the major differences between preclinical samples and real tumors, it is unclear how accurately such preclinical-to-clinical computational models can predict the clinical drug response of cancer patients. Here, first we systematically evaluate a variety of different linear and nonlinear machine learning algorithms for this particular task using two large databases of preclinical (GDSC) and tumor samples (TCGA). Then, we present a novel method called TG-LASSO that utilizes a new approach for explicitly incorporating the tissue of origin of samples in the prediction task. Our results show that TG-LASSO outperforms all other algorithms and can distinguish resistant and sensitive patients for the majority of the tested drugs. Follow-up analysis reveal that this method can also identify biomarkers of drug sensitivity in each cancer type. However century , such mo century  or are t century –10. In spatients , 11, witpatients –14.in vitro preclinical samples can predict the CDR of real patients for each drug and 2) what type of side information  might improve the CDR prediction.Various preclinical models of cancer have been developed to enable the study of cancer and its treatment in the laboratory. CCLs, which are 2D cell cultures developed from tumor samples, are one of the least expensive and most studied of these models. Recently, several large-scale studies have cataloged the molecular profiles of thousands of CCLs and their response to hundreds of drugs –17. AlthTo this end, we first formed a computational framework to systematically evaluate the prediction accuracy of different computational methods. We obtained preclinical training samples from the Genomics of Drug Sensitivity in Cancer (GDSC) database  and obtaNext, we developed a novel approach called Tissue-Guided LASSO (TG-LASSO) to explicitly include information on the tissue of origin of samples in the regularized regression model. This method outperformed all other approaches evaluated. Using this method, we showed that the CDR of cancer patients can be predicted using preclinical CCL training samples, for the majority of drugs. More specifically, out of 12 drugs, TG-LASSO separated resistant patients from sensitive patients for 7 drugs. In addition, for each tissue type and drug, TG-LASSO identified a small set of genes that may be used as tissue-specific biomarkers of drug response for each drug. We showed that genes selected by TG-LASSO for prediction of drug response are informative of patient survival when used as a gene signature, and also provide pathway-level insights into mechanisms of drug action. These results emphasize the clinical relevance of molecular profiles of preclinical samples cataloged in large-scale databases and demonstrate the importance of properly including information on the lineage of samples in follow-up analyses.In this study, our first goal was to determine whether commonly used machine learning algorithms are capable of predicting the clinical drug response (CDR) in cancer patients using computational models trained only on cancer cell lines’ (CCLs) basal gene expression profiles (i.e. before administration of the drug) and their drug response. For this purpose, we identified 23 drugs  values of resistant tumors  are significantly larger than sensitive tumors . One should note that due to the difference in the type of measured drug response in the training set ) and the test set , such an approach is necessary and other measures of performance such as concordance index or mean squared error are not suitable. In this evaluation, we only used 12 drugs that had at least 2 tumor samples in each category of resistant or sensitive and had at least 8 total samples with known CDR. 0 values explicitly in the predicting model using TG-LASSO can significantly improve the drug response prediction for paclitaxel.These results suggest several important points. First, consistent with the reports in , 22, we Various studies have suggested that including information on the interaction of the genes (and their protein products) or their involvement in different pathways can improve the accuracy of different bioinformatics tasks  such as implicitly (through their gene expression profiles) by training a single model for a drug on all CCLs of different lineages, and then using this global model to predict the response of patients with different cancer types. However, due to the importance of the tissue of origin in the efficacy of anticancer drugs observed in various studies [explicitly including the tissue of origin would improve the prediction of CDR, and if so, the best method for this inclusion. For our analysis, we focused on variations of LASSO (without including gene interactions), which previously yielded the best performance among all the tested algorithms  or not (‘0’) . We inclTissue-Guided LASSO (TG-LASSO). The idea behind this approach is to use all CCLs originating from different tissue types in training the LASSO model, but choose the hyperparameter of the LASSO model, α, in a tissue-dependent manner (α controls the number of features (i.e. genes) used by the LASSO model, this approach allows us to optimally select the number of predictive genes for each tissue type  drugs showing significant discrimination between resistant and sensitive tumors (p<0.05) and a combined p-value  of 2.25E-10 . These rth and 80th percentiles of the training samples’ log (IC50) may be good thresholds for deciding whether a patient is sensitive or resistant to these drugs.To better assess the ability of TG-LASSO in predicting whether a drug should be administered to a patient or not, we defined a measure which we called Precision@k%. Intuitively, this measure represents the precision of the method when test samples with predicted log(IC50) above the (100—k)th percentile of the training samples’ log(IC50)s are labeled as resistant and those below the kth percentile are labeled as sensitive see . These rOne interesting observation was that paclitaxel, the response of which could not be predicted accurately with the majority of methods reported in Since some of the drugs used in our study were administered in combination with other drugs, we asked how well TG-LASSO predicts the CDR in such cases of treatment with drug combinations. For this purpose, we evaluated its CDR prediction for a drug only on patients for whom that drug was administered over a period overlapping their treatment with at least one other drug. We limited our analysis to 9 drugs with at least two samples (patients) in each group (sensitive and resistant) and with at least 8 samples in total. Supplementary Next, we sought to evaluate the effect of batch-effect removal and preprocessing on the performance of TG-LASSO. For this purpose, we did not perform ComBat data homogenization or z-score normalization on the gene expression data. As expected, the performance of both TG-LASSO and LASSO deteriorated, with the former resulting in 4 drugs with p < 0.05 and the latter with only 3 . For example, EGFR, which is a direct target of both cetuximab and gefitinib [During its training phase, TG-LASSO automatically selects a subset of genes to be used in the regression model by tuning the hyperparameter efitinib , was selefitinib , was selefitinib .More importantly, the knockdown or overexpression of many of the identified genes has been shown to influence the sensitivity of cancer cells to these drugs. For example, the shRNA knockdown of CHI3L1, a gene identified for etoposide and cisplatin response in every tissue (but was not identified using LASSO for any of these drugs), has been shown to sensitize glioma cells to these two drugs, while its overexpression reduced their sensitivity . As anotWe hypothesized that genes that were identified by TG-LASSO as response predictors of many drugs in a single tissue , we sought to further characterize the identified genes that resulted in this significant patient stratification using functional and pathway enrichment analysis. For this purpose, we used the KnowEnG’s gene set characterization pipeline  and idenSeveral of the most significantly enriched GO terms were related to extracellular matrix (ECM), which plays an important role in the infiltration of glioma cells into the brain , 49. AnoThe enriched pathways included miRNA targets in ECM and membrane receptors (FDR = 2.0E-3) and Syndecan-1-mediated signaling (FDR = 0.04). Syndecan-1 is a cell surface heparan sulfate proteoglycan and its expression has been shown to be correlated with tumor cell differentiation in various cancers . In addiIdeally, a predictive model of CDR should be trained on data obtained directly from patients. Similarly, identification of biomarkers of drug sensitivity has the most potential clinical impact when based on patient data. However, since in practice most patients only receive the ‘standard of care’ treatment based on their specific cancer type, CDR data is scarcely available for the newly approved drugs or drugs that have not yet passed the clinical trial, limiting our ability to decipher the mechanisms of drug sensitivity for these drugs. An alternative approach is to train ML models on preclinical samples (e.g. CCLs) to predict the CDR of patients, then use these predictions to discover novel biomarkers and druggable targets.Recent large-scale studies that have cataloged the molecular profiles of thousands of CCLs and their response to hundreds of drugs –17 are gAnother important factor that played an important role in the performance of the ML models was data homogenization and batch-effect removal. The performance of TG-LASSO and LASSO both deteriorated when we did not remove the existing batch-effect between the training dataset and the test dataset. In spite of this, TG-LASSO could distinguish between resistant and sensitive patients for four drugs, when applied to non-homogenized data. This suggests two approaches when dealing with scenarios in which new test samples arrive. The first approach is to simply use the model trained on non-homogenized preclinical samples and accept the worse performance. The alternative is to retrain the model every time a new test sample arrives. This allows for training and prediction on homogenized data, but significantly increases the computational cost. An alternative could be developing a new data homogenization and batch effect-removal method that only transforms the gene expression of the test samples (keeping the gene expression profiles of training samples unchanged) by mapping them to the subspace spanned by the training samples. However, the development of such a method is beyond the scope of this study.We note that due to the major differences between CCLs and tumors (e.g. the greater heterogeneity of cells in a tumour compared to CCLs), obtaining more accurate results based on classical ML techniques may not be possible. The reason is that classical ML methods assume that the training samples and the test samples are drawn from the same or similar distributions. While batch-effect removal and other homogenization and normalization techniques help to alleviate this issue, more realistic preclinical models of cancer are necessary to significantly improve these results. Recent advances in developing human derived xenografts  and 3D hWe obtained the gene expression profiles  of 531 primary tumor samples of TCGA patients who were administered any of the 23 drugs mentioned earlier. First, we removed genes that contained missing values. We also removed any gene that was not expressed (i.e. FPKM<1) for more than 90% of the samples. Then, we performed a log-transformation and obtained log2(FPKM+0.1) values for each gene. The resulting gene expression matrix contained 19,437 genes and 531 samples. We obtained the CDR of these patients from the supplementary files of  see the for 17,7To homogenize the gene expression data from these two datasets, we first removed genes not present in both datasets as well as genes with low variability across all the samples (standard deviation < 0.1), resulting in a total of 13,942 shared genes. Then, we used ComBat  for batchttps://github.com/KnowEnG/KN_Fetcher/blob/master/Contents.md). The details of each network including the number of nodes and edges are provided in Supplementary For the network-guided analyses, we downloaded four networks of gene interactions in humans from the KnowEnG’s knowledgebase of genomic networks   for the standard linear model.d and P are additional penalty weights for individual features and pairs of features, respectively. Our basic GELnet implementation sets P = L and d = 0. Furthermore, we used Network-Induced Classification Kernels (NICK), a method closely related to GELnet. The NICK framework is actually a special case of the GELnet, with P = (I + βL) for some β ≥ 0 and d = 0. The parameter β provides a trade-off between graph-driven regularization and the traditional ridge regression penalty of the SVMs.For the network-based algorithms , we usedIn addition to the above methods that utilize the graph Laplacian of each network in the regression algorithm, we used sparse group LASSO (SGL). This method takes a collection of pathways as input and induces sparsity at both the pathway and the gene level to generate the input. We performed community detection on each of the networks in Finally, we developed a heuristic method based on ssGSEA  followedIn the first approach  of drug d in only cell lines corresponding to tissue t (i.e. a subset of the training samples). For tumors originating in tissues without matching training CCLs, we used all the CCLs to train the model.In the second approach , while incorporating information on the tissue of origin of the samples. By utilizing all the training samples, it overcomes the lack of generalizability stemming from limited number of CCLs from each tissue type, a major issue in Method 2 above. In addition, by incorporating the information on the tissue of origin of the samples in the training step, it improves the performance of tissue-naïve regression methods, such as those in TG-LASSO is a method for predicting the CDR of tumors using the information in n is the number of training samples, y is the response vector of length n, X is an n × m feature matrix (m is the number of features), ∥ ∥2 denotes the L2 vector norm, ∥ ∥1 denotes the L1 vector norm, and α is the hyperparameter that determines the sparsity of the model (i.e. number of features used in training). The hyperparameter tuning is usually achieved independent of the structure of the training samples (e.g. their tissue of origin), for example using random cross-validation or a regularization path. However, we and others [α is selected in a tissue- and drug-specific manner, as explained below.During training, LASSO minimizes the objective function d others  have shoD be the set of all drugs and T be the set of all tissues in the test set (i.e. the TCGA dataset). To train a model to predict the CDR of tumor samples from tissue t ∈ T to drug d ∈ D, we identify all the training CCLs corresponding to tissue t and use them as the validation set. In addition, we use all other CCLs as the training set. Then, the hyperparameter α is selected as the one that obtains the best accuracy on predicting the log (IC50) values of the samples of tissue t in the validation set. Designing the hyperparameter-tuning step such that the validation and the test sets have the same tissues of origin ensures that the value of α is selected so as to generalize well to the test set. The obtained value of α is then used with all CCLs (including those from tissue t) to fit a model minimizing the LASSO objective function. In the prediction step, this fitted model is then used with the gene expression of tumor samples from tissue t to predict their CDR.Let K and t100-K, respectively. Then, given the predicted log(IC50) values of the tumors and their annotation as ‘sensitive’ or ‘resistant’ (based on their known CDR), we definedR100−K is the number of resistant tumors whose predicted log(IC50) is larger than t100−K, SK is the number of sensitive tumors whose predicted log(IC50) is smaller than tK, N100−K is the total number of tumors whose predicted log(IC50) is larger than t100−K, and NK is the total number of tumors whose predicted log(IC50) is smaller than tK. Intuitively, this measure shows the precision of predicting the tumors with predicted log(IC50) values larger than t100-K as resistant and those with predicted log(IC50) values smaller than tK as sensitive. Note that due to this definition of Precision@k%, for some values of k, the denominator may be equal to 0 and the measure may not be defined.To further assess the performance of TG-LASSO, we defined a measure called Precision@k% . To define Precision@k%, we first used the log(IC50) values of the preclinical cell lines form GDSC to find the Kth percentile (K< = 50) and the (100-K)th percentile of each drug (separately), denoted as tWe used the gene set characterization pipeline of KnowEnG analytical platform  for thishttps://github.com/emad2/TG-LASSO.An implementation of TG-LASSO in python, with appropriate documentation and input files, is available at: S1 Text(DOCX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S11 Table(XLSX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 FigPatients were clustered into two groups using the expression of genes identified by TG-LASSO for more than 5 drugs in each tissue type.(TIF)Click here for additional data file.


Lots of researches have been conducted in the selection of gene signatures that could distinguish the cancer patients from the normal. However, it is still an open question on how to extract the robust gene features.In this work, a gene signature selection strategy for TCGA data was proposed by integrating the gene expression data, the methylation data and the prior knowledge about cancer biomarkers. Different from the traditional integration method, the expanded 450 K methylation data were applied instead of the original 450 K array data, and the reported biomarkers were weighted in the feature selection. Fuzzy rule based classification method and cross validation strategy were applied in the model construction for performance evaluation.Our selected gene features showed prediction accuracy close to 100% in the cross validation with fuzzy rule based classification model on 6 cancers from TCGA. The cross validation performance of our proposed model is similar to other integrative models or RNA-seq only model, while the prediction performance on independent data is obviously better than other 5 models. The gene signatures extracted with our fuzzy rule based integrative feature selection strategy were more robust, and had the potential to get better prediction results.The results indicated that the integration of expanded methylation data would cover more genes, and had greater capacity to retrieve the signature genes compared with the original 450 K methylation data. Also, the integration of the reported biomarkers was a promising way to improve the performance. PTCHD3 gene was selected as a discriminating gene in 3 out of the 6 cancers, which suggested that it might play important role in the cancer risk and would be worthy for the intensive investigation. Biomarker based cancer diagnosis is a quite attractive and promising direction to improve the early cancer detection –3. As ithttps://portal.gdc.cancer.gov//) which provided multiple-omics data, the integration of gene expression and DNA methylation profiles could improve the molecular subtype classification , and divided into 3 linguistic terms with “small”, “medium” and “large”. We chose the membership function to be Gaussian function, and the distribution parameters were Similarly, the gene features selected based on only reported biomarkers, RNA-seq data, or gene features selected based on the integrative features of RNA-seq data and the original 450 K methylation data, or the integrative gene features that could be best combined with biomarkers, were also used to build the corresponding fuzzy rule based models.The performances of all the fuzzy rule based models were evaluated with cross validation strategy. The sample data of each cancer were divided into 10 folds, 9 folds were used to train the classification model, and the remaining fold was applied to test the performance of the training model, and the operation was repeated for 10 times.Cancer data were retrieved from TCGA, and RNA-seq data and 450 K methylation data of 6 cancers  were selected because of their reasonable sample size.The potential biomarkers for each cancer were derived from literature reports, the numbers of biomarkers used in the work for BRCA, PRAD, LIHC, HNSC, KIRP and THCA were 9, 6, 8, 7, 8 and 7, respectively, and were listed in Table ftp://ftp.ncbi.nlm.nih.gov/pub/geo/DATA/SeriesMatrix/GSE2109/). And the gene expression data were normalized to the RNA-seq expression levels based upon quantiles to reduce the batch effect.For the performance comparisons, the independent gene expression data for the 6 cancers were retrieved from Expression Project for Oncology  Model based on only reported biomarkers (BIO); 2) model based on gene features selected from only RNA-seq data (EXP); 3) model based on gene features selected based on the integrative features of RNA-seq data and the original 450 K methylation array data (EXP + METH); and 4) the gene features from model EXP + METH were further selected based on its combination performance with the reported biomarkers (BIO + EXP + METH). Their cross validation results were also shown in Fig. To evaluate the reliability of these gene feature selection strategies above, some independent gene expression data of these cancers were extracted and predicted with the trained models. The prediction performances of each model on the 6 cancers were shown in Fig. It is reasonable because what we emphasized in our strategy had obvious advantages over other models, firstly, the reservation of the reported biomarkers was a way to make use of the previous knowledge in cancer diagnosis; secondly, the expanded 450 K methylation data could introduce some important DMGs which missed in the original 450 K array data.Another feature of our strategy was the application of fuzzy rule based classification model, which was more robust than hard rule based classification model, and could output classification rules easily understood by biologists. Taking THCA as an example, 12 genes were finally selected in the model, and one of the corresponding fuzzy rule for thyroid carcinoma classification was: IF  ^ (CDKL2 is large) ^ (CLEC4F is medium) ^  ^ (HAPLN1 is medium) ^ (ITIH2 is medium) ^ (KLHDC8A is large) ^ (KLK13 is medium) ^  ^  ^  ^ (RBP4 is medium), THEN the sample is a tumor sample.Furthermore, we were interested in the selected gene features in each cancer (shown in Table Due to the complex nature of cancer, the investigation of potential biomarkers for cancer diagnosis is still far from the end. And the current feature selection methods suffer from the lack of robustness.Aim to improve the robustness of feature selection, a gene feature selection strategy was proposed in this work. The DEGs were firstly filtered with the requirement that they should be simultaneously differentially methylated. Because of the low coverage of 450 K methylation array, an expanding algorithm was applied to get 18 times more CpG loci, and produced more DMGs. Then these candidate gene features were further screened based on their combination performance with some reported biomarkers.Besides the high cross validation performance on TCGA data, our proposed gene feature selection strategy could get better prediction performances on six independent cancer gene expression data, which verified our conclusion that the proposed feature selection strategy would be more robust. And the PTCHD3 gene, which indicated discriminating ability in cancer prediction in three cancers, was worth for further exploring.From the prediction results on the independent data Fig. , the perIn our strategy, the expanded 450 K methylation data were retrieved based on an expanding algorithm, whose prediction accuracy is around 90%. It is the truth that the methylation values of some expanded CpG loci may not reflect their true methylation status, but our method could reduce its affection. Firstly, the definition of DMG was based on all the promoter CpG loci included in the expanded profile, the inaccuracy of one locus or small proportion of promoter CpG loci would not obviously affect the methylation status of the whole promoter; secondly, the candidate gene features should be not only differentially methylated, but also differentially expressed, therefore, the filtering requirements also helped filter the false differentially methylated genes caused by the inaccurate prediction of the expanding algorithm.In this paper, we proposed a novel multi-omics data integration strategy to retrieve the robust gene signatures in the classification of tumor and normal samples, and the proposed strategy could achieve the best prediction performances comparing with other models. The strategy could be applied in the integrative analysis of other omics data, especially the idea of the application of expanding the limited 450 K methylation array data.


Oncobox Atlas of Normal Tissue Expression (ANTE). Our data will be useful to all those working with the analysis of human gene expression.Comprehensive analysis of molecular pathology requires a collection of reference samples representing normal tissues from healthy donors. For the available limited collections of normal tissues from postmortal donors, there is a problem of data incompatibility, as different datasets generated using different experimental platforms often cannot be merged in a single panel. Here, we constructed and deposited the gene expression database of normal human tissues based on uniformly screened original sequencing data. In total, 142 solid tissue samples representing 20 organs were taken from post-mortal human healthy donors of different age killed in road accidents no later than 36 hours after death. Blood samples were taken from 17 healthy volunteers. We then compared them with the 758 transcriptomic profiles taken from the other databases. We found that overall 463 biosamples showed tissue-specific rather than platform- or database-specific clustering and could be aggregated in a single database termed   Growing amount of transcriptomic data is deposited in the special public repositories like Gene Expression Omnibus, GEO8 and Array-Express9 which have already accumulated over two million individual transcriptomic profiles obtained in over 100,000 series of experiments10. The data cover a wide spectrum of specific human physiological conditions, including most of known diseases and developmental features12.High throughput gene expression (transcriptomic) analyses can be used in every aspect of biomedicine17. The NGS methods have become the standard in the field of gene expression studies because of higher reproducibility and lower platform bias18.The enclosed transcriptional profiles were obtained using different experimental platforms of either microarray hybridization or next-generation sequencing (NGS) of mRNA. However, the gene expression may be poorly comparable across the different platforms because of use of different equipment and reagents20. Ideally, they should represent normal tissues from healthy donors, profiled in a single series of experiments using the same equipment and reagents. Nowadays, there is a shortage of such gene expression data. The largest published dataset, GTEx21 , lacks publicly available data on the donors’ age, and this puts limits on data analysis and makes impossible age-matched studies, which are crucial for many practical applications and aging research. For that reason, we didn’t include GTEx data in this study.Comprehensive studies of molecular pathology or tissue specific patterns require a collection of reference samples22 (625 samples), ENCODE23 polyA RNA-seq (38 samples), and ENCODE total RNA-seq (89 samples). However, they lack one or several of the previously mentioned features. For example, in The Cancer Genome Atlas (TCGA) project database, specimens of histologically normal tissue adjacent to surgically removed tumors24 are considered normal. However, these tissues may not be completely normal due to numerous effects tumors may have on the neighboring cells, including biased growth factors and cytokine balances25, pathological inflammation26, and altered vascularization27. The ENCODE polyA RNA-seq and ENCODE total RNA-seq datasets were generated based on the normal tissues subjected to NGS using different library preparation methods. They have only 1–4 samples profiled per tissue type  and most of the cases can not form a statistically significant reference group.The other relevant databases of significant size do include the information on the donors’ age: TCGA28. We also compared our data for consistency with the transcriptomic profiles taken from databases The Cancer Genome Atlas (TCGA), ENCODE polyA RNA-seq, and ENCODE- total RNA-seq. We found that 463 biosamples showed tissue-specific rather than platform- or database-specific clustering. These have beeen aggregated in a single database named Oncobox Atlas of Normal Tissue Expression (ANTE) , including 11 sex-matched statistically significant tissue groups. Our data will be useful to all those working with the analysis of human gene expression.During our study we have designed and assembled the gene expression database of normal human tissues based on uniformly obtained original sequencing data using Illumina HiSeq-3000 engine. A total of 142 solid tissue samples representing 20 organs were taken from post-mortem human healthy donors, who had died in road accidents, no later than 36 hours after death. Blood samples were taken from 17 healthy volunteers. The materials, that had been being collected since 2012, were stored until gene expression profiles were obtained in one series of experiments using the same reagents and protocols. The gene expression profiles were then submitted to Gene Expression Omnibus under accession id GSE120795In the period from March 2012 to May 2018, we obtained 142 solid tissue samples from 20 human organs at the Department of Pathology at the Faculty of Medicine, Moscow State University, Russia, from autopsies taken from 23 non-related adult healthy donors killed in road accidents, no later than 36 hours after death. We also collected normal blood samples from 17 healthy volunteers. For each biosample an informed written consent to participate in the study was obtained from the patient’s legal representative. The consent procedure and the design of the study were approved by the ethical committees of the Faculty of Medicine, Moscow State University, and of the National Research Center for Hematology. The biosamples were evaluated by a pathologist to confirm the tissue origin of every specimen prior to analyses.Overall, blood and solid tissues were taken from 8 male and 9 female and 14 male and 9 female donors, respectively. The mean age was 33.47 years old (range 23–75 y.o.) and 37.39 y.o. (12–54 y.o.), respectively. The full list of the tissues and biosamples obtained is given in Online-only Table Solid tissue samples were either immediately stabilized in RNAlater  and then stored at −70 °C or fixed in formalin and embedded in paraffin blocks. RNA extraction was performed immediately before the preparation of sequencing libraries using QIAGEN RNeasy Kit (Qiagen) or Direct-zol™ RNA MiniPrep (Zymo Research) with TRI Reagent (MRC) for tissues in RNAlater and the RecoverAll™ Total Nucleic Acid Isolation Kit for FFPE (Invitrigen), following the manufacturer’s protocol. In cases of whole blood normal samples, mononuclear cells were extracted shortly after peripheral blood collection. Alternatively, a fraction of CD138 + cells was isolated from bone marrow with Ficoll Paque Plus (Sigma) followed by enrichment using CD138 MicroBeads (Miltenyi Biotec) and MS Columns (Miltenyi Biotec). Cells were counted by Scepter™ 2.0 Handheld Automated Cell Counter (Merck Millipore) and immediately subjected to RNA extraction. For RNA extraction, cells were resuspended in TRI Reagent (MRC) and then Direct-zol™ RNA MiniPrep (Zymo Research) was used for the RNA extraction. RNA was quantified using Nanodrop (Thermo Fisher Scientific), ethanol-precipitated, and stored in liquid nitrogen until sequencing.RNA Integrity Number (RIN) was measured using Agilent 2100 bioanalyzer. Agilent RNA 6000 Nano or Qubit RNA Assay Kits were used to measure RNA concentration. KAPA RNA Hyper with RiboErase (KAPA Biosystem) Kit was used for further depletion of ribosomal RNA and library preparation. Different adaptors were used for multiplexing samples in one sequencing run. Library concentrations and quality were measured using Qubit ds DNA HS Assay kit (Life Technologies) and Agilent Tapestation (Agilent). RNA sequencing was performed using Illumina HiSeq 3000 equipment for single end sequencing, 50 bp read length, for approximately 30 million raw reads per sample. Data quality check was conducted using Illumina SAV. De-multiplexing was performed using Illumina Bcl2fastq2 v 2.17 software.30 in ‘GeneCounts’ mode with the Ensembl human transcriptome annotation (Build version GRCh38 and transcript annotation GRCh38.89). Ensembl gene IDs were converted to HGNC gene symbols using Complete HGNC dataset . The mapping statistics for corresponding dataset can be found on Figshare29 in the file “QC and additional meta information”. The available RNA sequencing profiles for normal and cancer tissues matched normal samples were extracted from the websites of projects TCGA and ENCODE . Combined meta information for compatible gene expression profiles from different databases can be found on Figshare29 in the file “ANTE overview”.Gene expression profiles were deposited at the Gene Expression Omnibus database (GEO) under accession number GSE120795FFPE or tissue in RNAlater for solid tissue, and RNA in ethanol for blood samples.To assess if the obtained gene expression profiles are in correlation with the biological nature of the biosamples tested and identify samples that might be of low quality, we performed cluster analysis of all the RNA sequencing data we had obtained Fig. . The colThe FFPE and RNAlater samples formed several mixed clusters on the dendrogram, which suggests that the clustering was independent of the sample preparation technique for solid tissues. The blood samples formed a clearly distinct cluster on the left side of the dendrogram, which is in line with the histological status of this tissue Fig. , which sHowever, we then observed a mixed cluster next to the one formed by the blood samples, which included both blood and solid tissue specimens Fig. , red, an29 in the file “QC and additional meta information”.The established threshold effectively marked samples with low quality values of other QC metrics, e.g. proportion of genomic counts, high rate of mismatches, number of reads spanning splice junction, high percentage of ribosomal counts. Full list of mapping statistics generated by STAR aligner and additional QC metrics generated by NCBI MAGIC is available on Figshare(i)RIN, that shows the level of RNA degradation (lower RIN points to more degraded RNA). All samples with high RIN RIN > 4) passed the QC RNA concentration. We found no correlation between RNA concentration and number of uniquely mapped reads Fig. . TherefoWe then looked for a correlation between whether a biosample passes the QC and its internal characteristics. The following parameters were investigated:Only the samples that had more reads than the QC threshold were analyzed in further comparisons.Finally, we assessed the reproducibility of the RNA sequencing output data for the characterization of normal human tissues. We performed RNA sequencing of four different fragments of each of the following tissue specimens: two human liver (ID 16_5) and esophagus (ID E_3). The tissue fragments were blinded and sent for sequencing separately. In each case, one of quadruplicates was sequenced and mapped in a separate batch. In both cases we observed high pairwise correlation coefficients between quantile normalized gene expression values  Fig. . We conc22 of ‘normal’ tissues adjacent to tumors , ENCODE23 database, poly(A) priming library preparation , and ENCODE23 database, random priming library preparation . All the above mentioned gene expression profiles, both obtained in our experiments and published earlier, were merged and quantile normalized34. We then performed cluster analysis of these data and found that both ENCODE databases showed clustering similar to that of the current experimental database, with clear tissue specific, rather than database specific, clustering patterns . The database overview is presented in the Table 29 in the file “ANTE overview”. In total, the ANTE database contains 463 transcriptomic profiles for 11 tissue types. All the types of tissue are represented by seven samples or more, with the mean representation of 42 samples per tissue.After that we checked if our gene expression data are compatible with previously published transcriptomic datasets. The largest published datasets of RNA sequencing data for normal tissues were TCGA databaserns Fig. . Contrarrns Fig. , probablrns Fig. . We thenOncobox Atlas of Normal Tissue Expression (ANTE). The database includes 67 original experimental and 396 previously published gene expression profiles for 11 normal human tissues. The experimental profiles that are published here for the first time can be considered a golden standard data from the histopathological point of view as they had been obtained for the post-mortem human healthy donors, killed in road accidents, no later than 36 hours after death. Blood samples were taken from 17 healthy volunteers. The ANTE database provides unparalleled source of normal human tissues for transcriptomic research. The data in the database are in a machine-readable format and annotated by the available patients’ data which include tissue type, sex, and age of a donor. Besides comparisons of individual gene expressions, e.g. of pathological v normal human tissues, this collection of molecular data could be beneficial for a more complex level of data analysis such as molecular pathway activation scoring37 and bioinformatic ranking of drugs38. Additionally, the database can be used to revise lists of tissue specific gene expression biomarkers and housekeeping genes.We have assembled the gene expression database of normal human tissues termed Download metadata file


As a result, 35 RNAseq profiles (18.9%) corresponded to a normal, inflamed pancreas or non-PDAC neoplasms. This information was difficult to obtain. By considering gene expression data as continuous values, the expression of the 5312 and 4221 genes were significantly associated with the progression-free and overall survival respectively. Considering the cohort was not curated, only 4 and 14, respectively, had prognostic value in the PDAC-only cohort. Similarly, mutations in key genes or well-described miRNA lost their prognostic significance in the PDAC-only cohort. Therefore, we propose a web-based application to assess biomarkers in the curated TCGA_PAAD dataset. In conclusion, TCGA_PAAD curation is critical to avoid important biological and clinical biases from non-PDAC samples.Data from the Cancer Genome Atlas (TCGA) are now easily accessible through web-based platforms with tools to assess the prognostic value of molecular alterations. Pancreatic tumors have heterogeneous biology and aggressiveness ranging from the deadly adenocarcinoma (PDAC) to the better prognosis, neuroendocrine tumors. We assessed the availability of the pancreatic cancer TCGA data (TCGA_PAAD) from several repositories and investigated the nature of each sample and how non-PDAC samples impact prognostic biomarker studies. While the clinical and genomic data ( Finally, multiple web-based platforms were launched with “one-click” capabilities to give users direct access to the prognostic role of the gene expression level, the frequency of any mutation, the protein level expression and the networks of genes and proteins, etc. Main platforms include TCGA (https://gdc.cancer.gov), the Broad Institute (https://gdac.broadinstitute.org), the University of California Santa Cruz (http://xena.ucsc.edu), cBioportal  and the Human Protein Atlas (https://www.proteinatlas.org). These platforms have given everyone access to use these data as exploratory or validation sets.Consortium efforts, such as those of the Cancer Genome Atlas (TCGA) or the International Cancer Genome Consortium (ICGC), to massively sequence thousands of tumors from multiple types have led to a much better understanding of tumor biology. While the data were freely accessible, their use was restricted in practice to teams with great expertise in bioinformatics. Further efforts from centers such as the Broad Institute . Other malignant tumor entities such as the neuroendocrine neoplasms or the acinar cell carcinomas can also be found in the pancreas . While tIn this study, we aim to gather and compare the available data concerning the TCGA_PAAD from all the main repositories and clearly establish a list of suitable TCGA_PAAD samples for the PDAC centered studies. We then compare the TCGA_PAAD cohort to a large multicentric consecutive cohort of surgical PDAC. Using the key DNA mutation and the whole transcriptome, we then assessed the potential for bias based on survival analyses when using an uncurated sample list. Finally, we designed a web-based tool to assess the prognostic value of any gene expression on the curated TCGA_PAAD dataset.n = 185, UCSC Xena n = 185, Broad Institute Firehose n = 185, The Human Protein Atlas n = 176 (only patients with available RNAseq data were considered) and cBioportal n = 185). In all platforms, clinical data were available for the 185 patients. Depending on the platforms, mutation and copy number data were available for 184 or 185 samples, DNA methylation data were consistent and available for 184 samples, and RNAseq data were the most discordant, ranging from 176 to 185 samples . TCGA-derived RNAseq data were the most frequently used. As a result, we carefully investigated the nature of the samples to explain the discrepancy in the available number of samples depending on the platforms. RNAseq data were not available for seven patients and the number of patients with RNAseq data  did not help in further assuring the diagnosis. The flow chart presenting the TCGA-PAAD sample curation is presented in The recent study from the TCGA group focused on pancreatic ductal adenocarcinoma and only included 150 samples. Therefore, we carefully reviewed the clinical and histological data of all the cases gathered through the repositories and by viewing the virtual slides. Ten samples presented the pancreas as normal with atrophy, eight samples were neuroendocrine neoplasms, four samples were tumors derived from other organs (duodenum-ampulla in three cases and undefined location in one case), two samples were intraductal papillary neoplasms, one sample was an acinar cell carcinoma, one was a ductal adenocarcinoma but had received neoadjuvant chemotherapy and one had a normal ampulla. It should be noted that for several patients, while the analyzed specimens were not PDAC , these patients did have a PDAC. These clinical data may therefore be used, but not the omic data. One additional case was excluded, as no single nucleotide variation data was available (TCGA-L1-A7W4-01). This sample was listed as a PDAC and treated with adjuvant gemcitabine, a classical drug for this tumor. The examination of the frozen section showed a poorly differentiated tumor, which was difficult to clearly identify as a PDAC or a neuroendocrine carcinoma. Copy number abnormalities (p < 0.05). Progression-free survival (PFS) was comparable in both cohorts  (p < 0.0001)  a. In con 0.0001) b.n = 150)  , the non-PDAC patients had a much longer PFS and OS compared to the pure PDAC cohort (. 19.7%) a.. When . 19.7%) b. In the. 19.7%) . Using aKRAS and TP53 were strongly associated with the overall survival in the uncurated cohort, these mutations lost their prognostic significance in the pure PDAC cohort (Similar findings were observed when key mutations in PDAC were assessed. Here we included the seven PDAC cases with the DNA data available (but no RNAseq). The results were similar when using the minimal 150 PDAC cohort. While the mutational status of C cohort b,c. In aC cohort d.interval in which only the patients with the highest expression levels would be shown against the patients with the lowest levels of expression of the analyzed gene. By default, the upper quartile (>75%) was shown against the lower quartile (<25%). In both types of survival analysis, the p-value of a log-rank test was shown, as well as the median survival in each group. The log-rank test p-value and the Hazard Ratio of the continuous gene expression level were also shown.In order to quickly assess the prognostic significance of a gene in the curated TCGA_PPAD, we developed a web-based application . The appClinical validation of their findings is often a bottleneck step for basic scientific laboratories. Consortium efforts such as those of the TCGA and the ICGC have been a tremendous help for this purpose. In addition, it facilitated alternative approaches based on in silico discovery completed by clinical validation. Here, we presented a thorough analysis of the TCGA dataset, which was dedicated to pancreatic neoplasms and highlighted the heterogeneity of the data sources and samples. In addition, we demonstrated that the curation of this dataset led to the exclusion of almost 20% of the cases but was a mandatory step, as it prevented false results on prognostic analyses.TCGA data may be retrieved through numerous platforms. Confusion for researchers may arise from the fact that while data for a particular sample are homogeneous across all platforms, the list of available samples is heterogeneous with little to no information on the sample of most platforms. In addition, while the genomic data for a sample may be exploitable, the RNAseq data may not. The TCGA data portal provided the most comprehensive information on the nature of the samples, but it required a deeper exploration within the platform to find it. Due to most platforms providing the official clinical data, but not a detailed manifest of the samples analyzed, it is important to retrieve the final list of “good samples” and to only assess these, a function available on some platforms but not all.In the TCGA_PAAD, there were three main reasons for sample exclusion. The first reason, valid across all the data  was the histology of the tumors. PDAC have a well-described biology  and a clinical behavior, which is very distinct from other pancreatic neoplasm such as neuroendocrine neoplasms (NEN), acinar cell carcinomas or intraductal papillary neoplasm (IPMN). Well-differentiated NEN for instance, have a completely different mutational and transcriptional pattern from PDAC and a much longer survival rate. Therefore, any alteration specific to PDAC will artificially see its prognostic value increased. The second reason was that the sample analyzed was not a tumor. It was either a normal pancreas or from a PDAC-look alike histological lesion, often atrophic fibrosis with a stroma-like appearance. These patients had a prolonged survival compared to PDAC patients. As a result, the prognostic value was strongly biased in any alteration present in PDAC. Finally, for one sample, the single nucleotide variation data were not available (TCGA-L1-A7W4-01).Peran et al. compared the clinical characteristics of the TCGA_PAAD cohort with that of the SEER and the national cancer database and reported that the TCGA_PAAD cohort had less locally advanced and metastatic tumors and therefore a much better chance of survival . This hiThe importance of the curation is also highlighted by the massive prognostic bias introduced by the samples (tumor or non-tumor) with a different molecular profile from the PDAC and a prolonged survival. We observed that many molecular alterations  had a prognostic value only in the uncurated cohort. This is not surprising as most of these were absent from the good prognostic non-PDAC subgroup. Improper data curation led to the description of many PDAC prognostic factors that lost their value in the curated dataset ,10. ThisWe therefore developed a free web-based app to quickly assess the prognostic value  of the expression of any gene on the curated dataset. This is to our knowledge the only “click and play” tool to reliably assess the prognostic value of any gene using either a cut off based on the median expression value or any interval, and retrieve the survival and expression data.KRAS, TP53, SMAD4) and clinical data: TCGA (https://gdc.cancer.gov), the Broad Institute (https://gdac.broadinstitute.org), the University of California Santa Cruz (http://xena.ucsc.edu), cBioportal  and the Human Protein Atlas (https://www.proteinatlas.org). The sample list and the clinical data used in the TCGA group publication were also retrieved from the supplemental data [The following data repository were queried to retrieve a sample list and biological annotations, whole normalized level 3 RNAseq and miRNA data, specific DNA mutation data KRAS, TP3, SMAD4 For comparison with the TCGA_PAAD cohort, we used a previously published cohort of 471 consecutive patients who underwent curative intent surgery for PDAC at 5 university centers between September 1996 and August 2009. Subjects were excluded if they had received preoperative treatment and macroscopically incomplete resection (R2) or if their tumor histology was not a ductal adenocarcinoma. Patients who died of postoperative complications within 30 days following surgery were also excluded.survival package. The gene expression or miRNA expression association to survival was evaluated by fitting a Cox proportional hazards regression model. The explanatory variable of the survival model fitted for each gene was either the continuous expression values or the discretized expression value . The association to survival in the curated or uncurated cohorts was assessed for 17,302 genes. For each of them, the p-value of the log-rank test was retrieved and corrected using the Benjamini-Hochberg multiple comparison approach for controlling the false discovery rate. When defining the number of prognostic genes, the log-rank test was used and adjusted to obtain a False Discovery Rate of 5%.Survival analyses with gene expression were performed in R using the Clinical characteristics of the two cohorts were compared using a Chi2-based test of equal proportions for discrete variables and a Student’s t-test for continuous variables.ggsurv function from the GGally R package.Survival curves were drawn using the A web app to associate gene expression to overall and progression-free survival in the 144 TCGA patients  is available at  . The PDAC survival web application can be used to associate gene expression to overall and progression-free survival in three different ways: by splitting the series of patients into two subseries around a gene expression threshold , by separating the series of patients into two extremes intervals  or by directly associating the continuous gene expression values to the survival. For the splitting and interval analysis, the thresholds can be modified by the user which will update the results accordingly. Kaplan-Meier curves are used and the log-rank test p-values are shown. In addition, the raw survival and gene expression values are shown in order to let anyone reuse the curated data with any software.The TCGA data used for the application were obtained from the Broad’s Institute firehose portal (In conclusion, we highlighted in this study the heterogeneity of the data available through the main repositories and the lack of a proper sample description leading to the inclusion in many studies dedicated to PDAC of non-PDAC tumor samples or even non-tumor samples. We confirmed that it introduced a major bias in biomarker prognostic value analysis and we provided a comprehensive list of the curated dataset together with a free web-based app to assess on the curated dataset the prognostic value of gene expression levels.


TM platform allows for targeted transcriptomic analysis and is currently used by many groups to perform high-throughput gene expression analysis. Herein we performed a comparison of gene expression characteristics measured using 45 purified RNA samples from the livers of rats exposed to chemicals that fall into one of five modes of action (MOAs). These samples have been previously evaluated using AffymetrixTM rat genome 230 2.0 microarrays and Illumina® whole transcriptome RNA-Seq. Comparison of these data with TempO-Seq analysis using the rat S1500+ beta gene set identified clear differences in the platforms related to signal to noise, root mean squared error, and/or sources of variability. Microarray and TempO-Seq captured the most variability in terms of MOA and chemical treatment whereas RNA-Seq had higher noise and larger differences between samples within a MOA. However, analysis of the data by hierarchical clustering, gene subnetwork connectivity and biological process representation of MOA-varying genes revealed that the samples clearly grouped by treatment as opposed to gene expression platform. Overall these findings demonstrate that the results from the TempO-Seq platform are consistent with findings on other more established approaches for measuring the genome-wide transcriptome.The TempO-Seq TM technology from BioSpyderTM  is increasingly being adopted for screening in chemical and toxicological genomics in part due to advances in technological  and greater efficiency  in the liver are represented in the samples including orphan nuclear hormone receptors (CAR/PXR) activation, aryl hydrocarbon receptor (AhR) activation, peroxisome proliferator-activated receptor alpha (PPARA) activation, cytotoxicity, and DNA Damage /MicroArray Quality Control III (MAQC3) toxicogenomics study in which the transcripts from the latter two platforms were matched for a fair comparison  samples, preparation of them, RNA extraction and microarray and RNA-Seq analyses are as previously described  algorithm . Raw data files and processed data in various file formats are available in the Gene Expression Omnibus (GEO)  whole genome GeneChip® Rat Genome 230 2.0 Array as previously described  cycles for library generation. The library size distribution was validated on the Agilent Bioanalyzer  using a DNA 1000 kit. The final library was generated from a band between 200 and 500 bp with a peak at ~260 bp. Using Illumina TruSeq RNA Sample Preparation Kit and SBS Kit v3 , samples were prepared for sequencing. Paired-end RNA-Seq cluster generation and sequencing by synthesis was performed using Illumina HiScan or HiSeq 2000 sequencers according to the manufacture's protocol. Depths of 30–130 million of paired 100 bp reads were generated for each sample. Details of the methods are as previously described  to the following references:Rattus norvegicus genome build RGSC v3.4The The RefSeq and AceView 2008 External RNA Control Consortium (ERCC) RNA spike in control sequences R. norvegicus genome bases , but not reversing the order. As such, the control genome has exactly the same composition as the reference genome but alignments to it are false positives and removedA control genome constructed by complementing the Alignment, quantification and normalization of the RNA-Seq data are as previously described  the Index was imputed with 5.0. The preprocessed data (not imputed) is available in GEO under accession number GSE55347.Where n = 28, 975) mapping to at least 8 probes within a probe set unambiguously (meaning not mapping to any other probes from other probe sets) are considered a one-to-one match in terms of them being representative of the same transcript probe set. These were then mapped to UniGene .The sequencing library for the rat liver RNA samples , those with a total read count ≤214 across all the samples were removed leaving 2,055 genes. The counts per gene were normalized to counts per million (CPM) by dividing it by the total read count per sample and multiply by 106. The CPM normalized data was then transformed with log2 using an offset of 1.Of the 2,284 genes targeted in the rat S1500+ beta gene set .For each gene/transcript in a data set, the average of the logPrincipal Variance Components Analysis  is the standard procedure to accomplish this and was specified in the lmer function of the lme4 R package  matrix of logPerform PCA on the correlation matrix to obtain eigenvaluesDetermine the first K principal components (PCs) to explain ≥58.76% of the variation in the dataFit all factors as random effects in a mixed linear model using the K PCs and REML to obtain unbiased estimates of varianceStandardize the variance component estimates from the modelCompute weighted proportions of the standardized variance component estimates. Here the weights are the proportions of variation explained by the PCsCompute weighted average proportions of the standardize variance component estimates by averaging model effects according to the proportion of total variance across all estimates including the residualThe following steps comprise of PVCA:x and y isRoot mean squared distance (RMSD) is a measure of the gene expression distance between pairs of biological replicates  between treated rats within a MOA were averaged. This MOA-RMSD can be interpreted as a measure of the difference among the chemicals within a MOA. To compare the three gene expression platforms, the five MOA-RMSD measures were averaged to give a Platform-RMSD.where To obtain genes from each platform that vary significantly by MOA, we modeled the gene expression data with a MOA analysis of variance (MOA-ANOVA)Yijkl represents the lth log2 ratio gene expression observation on the ith MOA (M), jth route (R) and kth chemical (C). μ is the grand mean for the whole experiment and εijkl represents the random error. The errors are assumed to be normally and independently distributed with mean 0 and standard deviation δ for all measurements. Chemical is a random effect. Multiple testing correction was controlled at a false discovery rate (FDR) of 0.05  co-expression patterns.From p-values and were imputed with 1.0. –Log10p-values, a min of 5 genes per GO BP, FDR < 0.05, Pearson correlation similarity metric and Ward algorithm for clustering, cluster slicing using correlation (r) of 0.1 and a minimum of 5 GO BP terms per cluster for subtree generation were used as input and parameters. Clusters (those with a 1– r ≥ 0.9) of GO BP terms (n ≥ 5) were labeled according to the node having the maximum number of paths to it within the GO BP subtree directed acyclic graph derived from the terms in the cluster.To compare each platform in terms of enrichment of the genes that vary by MOA, we used GO subtrees to tag and annotate genes (goSTAG) within a set , followed by RNA-Seq (n = 141), and then TempO-Seq (n = 99). Some of the enriched GO biological processes that overlapped related to fatty acid metabolism, apoptosis, liver development, and lipid metabolism (Table r > +0.9).Enrichment of GO biological processes by the platforms' varying genes yielded 49 significant categories (FDR < 5%) that overlapped Figure . MicroarComparing and contrasting gene set enrichments can be challenging when there are many categories to consider. To more formally compare the three platforms in terms of biology, we used goSTAG to identify subtrees of enriched GO BPs from the MOA varying genes and then find the categories that are shared or differ between platforms. As shown in Figure Over the last two decades gene expression analysis has advanced to permit genome-wide transcriptomics. Affymetrix microarray and Illumina RNA-Seq are two platforms that have gained popularity for gene expression analysis. Each has its own advantages and disadvantages but currently, the platform of choice for gene expression analysis seems to be RNA-Seq.Comparison of the two platforms was performed using liver RNA samples from rats exposed to chemicals that have particular modes of action  varying, respectively (Supplemental Table It has been proven that reproducibility between gene expression is higher when the data are compared on the pathway level than the gene level (Guo et al., Having another tool for biologists to survey genome-wide gene expression is a luxury for scientific experimentation. With microarray fully matured and easy to analyze, and RNA-Seq flexible to interrogate complex transcriptional machinery, scientists have diverse platforms to investigate biological consequences that regulate gene expression genome-wide. The emerging TempO-Seq platform adds to the genomics tool chest and with comparable performance capabilities to its predecessors, will undoubtedly play a pivotal role in high-throughput screening efforts.PB conceptualized the analysis strategy, performed the analyses, interpreted the results, and wrote parts of the paper. SA provided the samples that the data were generated from, interpreted the results, provided biological, toxicological context, and wrote parts of the paper. RP helped to interpret the results and provided biological, toxicological context.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Analysis of RNA sequencing (RNA-seq) data from related individuals is widely used in clinical and molecular genetics studies. Prediction of kinship from RNA-seq data would be useful for confirming the expected relationships in family based studies and for highlighting samples from related individuals in case-control or population based studies. Currently, reconstruction of pedigrees is largely based on SNPs or microsatellites, obtained from genotyping arrays, whole genome sequencing and whole exome sequencing. Potential problems with using RNA-seq data for kinship detection are the low proportion of the genome that it covers, the highly skewed coverage of exons of different genes depending on expression level and allele-specific expression. In this study we assess the use of RNA-seq data to detect kinship between individuals, through pairwise identity by descent (IBD) estimates. First, we obtained high quality SNPs after successive filters to minimize the effects due to allelic imbalance as well as errors in sequencing, mapping and genotyping. Then, we used these SNPs to calculate pairwise IBD estimates. By analysing both real and simulated RNA-seq data we show that it is possible to identify up to second degree relationships using RNA-seq data of even low to moderate sequencing depth. RNA sequencing is used in genetics for a variety of purposes such as to test the heritability of gene expression traits , to searDuring sample and data processing, sample mislabeling can occur due to human error. This is detrimental for downstream analysis, especially for family studies, and has been estimated to affect at least 4% of published samples ,7. GivenThere are several methods to predict or confirm familial relationships and build heritability estimates from genetic data. Although these methods were initially based on microsatellites  and amplHere, we report the results of our assessment of the utility of human RNA-seq data for kinship detection and pedigree reconstruction. Our input data is a set of raw RNA-seq data files and a set of known common variant positions. First, we filter both the RNA-seq data and the known variants to obtain reliable predicted genotypes at common variant positions for each RNA sample. We then use the predicted genotypes to detect and represent familial relationships by estimating pairwise probabilities of identity-by-descent (IBD) ,25. By aWe used three types of empirical data Figure . First, To simulate the genotypes of family members we used SNP data from the 1000 Genomes Project . We seleTo simulate gene expression data from real or simulated individuals, we generated paired-end RNA-seq reads with flux-simulator v1.2.1 . We usedftp://ftp.ccb.jhu.edu/pub/infphilo/hisat2/data/grch37_tran.tar.gz . We then used SAMtools to remove duplicates (command markdup). We retrieved 14.8 million (M) common SNPs from the UCSC Genome Browser (dbSNP Build ID 150) (http://www.geneimprint.org/ accessed: 8 March 2018) and 8.2 M SNPs in repeats (RepeatMasker annotation downloaded from the UCSC Genome Browser), ending up with 6.2 M SNPs. Genotypes were obtained with SAMtools mpileup v1.7   . We used ID 150)  to identt AD,DP)  and BCFtt AD,DP) , using ut AD,DP)  to select AD,DP)  37). Th. Thftp:/et al.  meaning that none, only one or both alleles of the pair are inherited from the same recent common ancestor. In ternary diagrams, the coordinates of each data point are the three IBD estimates for each pair of individuals in the dataset. We observed that the different relationships were grouped around their theoretical values with no overlap between them. As expected, parent-offspring pairs are clustered at the Z1 corner , unrelated individuals at the Z0 corner , second degree relationships on the Z0-Z1 axis  and full-siblings in the middle of the graph  Figure .The actual pedigree was also correctly reconstructed with PRIMUS  from theIn addition to the data from the 17-member family, we assessed kinship detection for a family trio  using transcriptome sequencing data from cardiomyocytes differentiated from induced pluripotent stem cells , for a pAccording to these results, we can conclude that kinship detection is possible with RNA-seq data, allowing us to detect and represent first and second degree relationships. Although the sensitivity decreases as the degree of relatedness increases, pedigree reconstruction is robust. As an indication of the time required to perform kinship detection, the full RNA-seq analysis of nine samples from the trio  took 11The sequencing depth of the real RNA-seq data from the extended human family ranged from 37M to 67M reads, with a mean depth of 52M. To understand the effect of sequencing depth on kinship detection, we randomly subsampled different numbers of reads from the FASTQ input files. We subsampled 37M reads  to homogenize the sequencing depth for all individuals and then also 30M, 20M, 10M, 8M, 6M, 4M and 2M reads from each sample to determine the minimum sequencing depth for kinship detection and pedigree reconstruction.Visual inspection of estimated probabilities using ternary diagrams Figure  revealedPedigree reconstruction was only possible for RNA-seq data with 52M, 37M, 30M and 20M reads because, when decreasing the sequencing depth, the probability to share one IBD allele (Z1) of some full siblings decreased way below the expected values .Taken together, these results demonstrate that sequencing depth affects the number of SNPs used for pairwise comparison, but overall its effect on kinship detection is small, providing acceptable IBD estimates (no overlapping between relationships) even with 6M reads, where the number of SNPs is as low as 811–1,282 Figure . As IBD To further assess the feasibility of estimating relationships from RNA-seq samples from related individuals, we simulated RNA-seq data from real related individuals with known genotypes. We used variation data from six pairs of first and second degree relatives from the CDX population of the 1000 Genomes Project Figure . We simuIn agreement with the results obtained from the real RNA-seq data, visual inspection of estimated probabilities in ternary diagrams revealed that all relationships are clearly separated Figure . SimilarType 1: A 16-member pedigree with the same relationships as CEPH/UTAH family 1463.Type 2: A 16-member pedigree with all first and second degree relationships from Type 3: A 16-member pedigree with first, second and third degree relationships.Type 4: A 12-member pedigree with all first, second and third degree relationships from We then asked whether the complexity of the pedigree could affect kinship detection and pedigree reconstruction. To address this, we generated additional RNA-seq data from different types of pedigrees, this time simulating both gene expression and also the genotypes of individuals in the pedigrees. We used genotypes of real unrelated individuals from the 1000 Genomes Project as founders  Figure :Type 1: We made 32 simulations, one for each pedigree type and population. Sex was not simulated as sex chromosomes are not used for IBD estimation. After data filtering, we observed that the number of SNPs used for pairwise comparison was higher in African populations (ACB and LWK) ranging from 5,180 to 5,974 and lower in Asian populations (KHV and CDX) ranging from 4,289 to 5,084 Figure . To furtVisual inspection of IBD probabilities Figure  revealedReconstruction of the actual pedigree was possible for all simulated type 1 pedigrees, seven type 2 pedigrees and four type 4 pedigrees , but for none of the type 3 pedigrees Figure .We conclude that family structure affects kinship detection, providing good IBD estimates for simple families with up to second degree relatives, but in pedigrees with third degree relatives there is overlap between different relationships. Accordingly, entire pedigree reconstruction works better for simple families, getting worse when the number of third degree related pairs increases . Population specific differences in genetic variability lead to different number of SNPs that pass all filters, however these differences do not affect the results.We wondered whether the addition of unrelated individuals would improve kinship detection. Presumably, they include more variability and make related individuals look more similar to each other. To test this, we added five unrelated individuals from the same population to each simulated family. The result was just as good for all families that were previously correctly reconstructed but it improved for four families, reconstructing correctly all type 1, seven type 2, all type 4 pedigrees but none of type 3  and found that type 1 and type 2 pedigrees were reconstructed equally well but also that seven out of eight type 3 pedigrees and all type 4 pedigrees could also be correctly reconstructed . Furthermore, we have shown that samples from first degree relatives can be distinguished from replicates and samples from unrelated individuals even with targeted RNA-seq data. Reconstruction of full pedigrees requires higher sequencing depth but is still possible with at least 20M reads.An issue that remains to be investigated in future studies is whether gene expression data from other species would provide similar results to the ones from humans. Genetic variability among different individuals from a particular species or a particular population could play an important role in kinship detection with higher genetic diversity between individuals leading to better results.In conclusion, we have shown here that RNA-seq data can be used to identify samples from closely related individuals using estimated identity by descent probabilities calculated from predicted genotypes at common variant positions. It turns out that the effect of calling genotypes correctly at the single nucleotide level due to allele-specific expression is not large enough to impede kinship detection from IBD estimates. We therefore recommend the estimation of IBD probabilities and visualization of the clustering of samples in ternary graphs or the use of pedigree reconstruction programs such as PRIMUS as a quality control step in studies that generate multiple RNA-seq samples from related individuals and population based studies.http://github.com/vavouri-lab/RNA-kinshipThe scripts used for kinship detection and pedigree reconstruction are available through the GitHub repository gkz776_Supplemental_FileClick here for additional data file.