Except for a limited number of tissues with known pure cell type profiles, a majority of genomics and epigenetics data relies on the “reference-free deconvolution” methods to estimate cell composition. We develop a novel computational method to improve reference-free deconvolution, which iteratively searches for cell type-specific features and performs composition estimation. Simulation studies and applications to six real datasets including both DNA methylation and gene expression data demonstrate favorable performance of the proposed method. TOAST is available at The online version of this article (10.1186/s13059-019-1778-0) contains supplementary material, which is available to authorized users. There have been an increasing number of large-scale clinical studies using high-throughput technologies to profile biological samples collected from human subjects, in order to identify molecular biomarkers and therapeutic targets for different diseases , 2. ThesRegardless of the approach and goal, an important first step in the analysis of high-throughput data from complex tissues is to estimate the cell compositions. Experimental approaches including different cell sorting techniques , 13 are There have been some discussions and comparisons of RB and RF deconvolution methods. It was reported that the RB deconvolution in general provides more accurate and robust estimation than RF deconvolution , 24, 25.The high-throughput data from complex samples are weighted averages of signals from different cell types. To solve for cell compositions, most RF deconvolution methods are based on some type of factor analysis. They usually apply on the data for a subset of “informative” features, or the ones containing information for the cell composition. It has been reported that feature selection plays an important role in the deconvolution and has great impacts on the accuracy of cell composition estimation –35. IntuIn this work, we develop a straightforward and effective algorithm to improve RF deconvolution by better selecting features. The key idea is to identify features showing distinct profiles among different cell types, without knowing the pure cell type profiles or mixing proportions a priori. The feature selection procedure is purely data-driven, without requiring any additional information. The algorithm is based on a recently developed statistical framework, which provides functionality to detect cross-cell type differential signals for high-throughput data from mixed samples . The prohttps://bioconductor.org/packages/TOAST).We evaluate our method through extensive simulation and analyses of six real datasets and show that the proposed method can significantly improve the accuracy of proportion estimation based on existing RF deconvolution techniques. From our method, we observe substantial improvement in correlation and reduction in bias in the estimated proportions. In addition, there are significant improvements by using several other metrics including root mean squared error and goodness-of-fit of the deconvolution model, and precision of the selected features. Our method is applicable to both gene expression data and DNA methylation data as demonstrated in both simulation and real data applications. The method is implemented in the R package TOAST , which is freely available on Bioconductor  is to conduct an RF deconvolution to estimate mixture proportions. With estimated proportions, step (b) is to identify cell type-specific features using cross-cell type differential analysis. These features are then used for the RF deconvolution in step (a) in a new iteration. By iterating steps (a) and (b), the updated feature list can better capture the cell type distinction and improve RF deconvolution compared with initial features that are usually selected by choosing the most variable ones. The detailed notations and algorithm are described in the “In this work, we develop an iterative algorithm to improve feature selection. Figure deconf T, p=1,⋯,P. Denote the proportions obtained for sample s are θs=. With known proportions, the observed data can be modeled by a linear model: Now we briefly introduce the method for cross-cell type differential analysis for data from mixed sample, which is a special case of our previously proposed method . Assume where μpk represents the mean level for the p-th feature in the k-th cell type. Using this model, one can test the difference between cell type k versus other cell types in feature p by following hypothesis test: Regression coefficient Features with significant test results are cell type-specific features.μpi’s represent the mean observation levels for each cell type, it may not be reasonable to have negative estimators. As such, we provide options to bound negative estimated parameters in the TOAST software. In our experiments, we find bounding negative estimators has minimum impact on the results due to the small proportions of negative estimators .The above linear model can be fit using ordinary least squares (OLS) method. We find that sometimes extremely small variance estimation can lead to undesirable results for some features. To overcome this problem, we impose a data-driven lower bound  to stabilize the variance estimates. Similar methods are widely used in popular tools for differential expression analysis which has been proven to have good results . In addiM0, which could be selected using conventional methods, such as choosing the top variable features. In the “Y to detect cell type-specific features. In each iteration, the top features from this analysis with the same length as M0 are then used for another round of RF deconvolution. In our software implementation, the function “DEVarSelect” has an argument “nMarker” for users to specify the number of initial features and selected features in each iteration. The default value is 1000, as used throughout this paper. The feature selection and RF deconvolution are iterated for a number of times then stop. In the “With this method, we design the following iterative algorithm to improve feature selection in RF deconvolution. The algorithm starts with a list of initial features, denoted as deconf [RefFreeEWAS [BayesCCE [Note that Algorithm 1 is not constrained to a specific deconvolution method, so most existing RF methods can be applied in conjunction with this procedure. For example in our simulation study and real data applications, we use RF algorithm deconf  for geneFreeEWAS  and BayeBayesCCE  for DNA The proposed algorithm guarantees the improvements of proportion estimations from two aspects. The first is that RF deconvolution performs better with more “correct” or informative features. The second is that feature selection can be improved with more accurate estimated proportions. The first point has been empirically demonstrated by all our results and by discussions about feature selection in many RF deconvolution publications , 36. TheWe design two simulation studies based on real datasets, one for gene expression data and the other one for DNA methylation data, so that the simulation studies mimic the real data scenarios well.The first step of our simulation studies is to generate subject-specific reference panels. In the first simulation study, the four cell types in the reference panels for each individual are simulated from log-normal distributions, with cell type-specific means and variances estimated based on a real gene expression microarray dataset obtained from the Gene Expression Omnibus (GEO) with accession number GSE11058 . This daAfter subject-specific reference panels are generated, the simulated pure cell types are manually mixed using known mixture proportions, which are simulated from a Dirichlet distribution with parameters  for four-cell type setting or  for six-cell type setting. Randomly simulated measurement errors are added to the mixed signals. For all settings, results are summarized over 100 Monte Carlo datasets.We calculated a number of metrics to evaluate the proposed method. First, we compute the correlations and biases for comparison between estimated and true proportions. Correlations with true proportions is widely used in almost all studies that involve evaluating the deconvolution performance , 20, 24.r(·) represents the Pearson correlation and diag(·) is the diagonal operation extracting the diagonal elements from the variance-covariance matrix. RMSBias is the root mean squared bias of the estimated versus the true proportions, i.e., where Number of overlaps between selected and cell type-specific markers is to evaluate the agreement between our selected markers, and the best markers can be chosen if pure tissue profiles are known. The cell type-specific markers are selected by the largest log fold changes of the cell type-specific value against the mean value of other cell types, iterating over all cell types . Higher overlap usually leads to better proportion estimation.Goodness-of-fit score is the Pearson correlation of real observations and fitted values from the RF deconvolution results, which we find is a metric especially suitable when true proportions are unknown . Brieflyvec(·) is the vectorization operation.RMSE is also a widely used metrics calculated by the root mean squared error between the estimated and true proportions, i.e., FlowSorted.Blood.450k and are originally obtained by Reinius et al. [All the six datasets used in the study are publically available and can be downloaded from the Gene Expression Omnibus (GEO): Mouse-Mix data by Shen-Orr et al.  , for the deconf algorithm [RefFreeEWAS [https://cran.r-project.org/web/packages/RefFreeEWAS/index.html). The BayesCCE Matlab toolbox is downloaded from GitHub (https://github.com/cozygene/BayesCCE). In order to incorporate BayesCCE with our algorithm, we call the matlab function from R using the R.matlab package [We use the ged function from R package CellMix , downloalgorithm  implemenFreeEWAS  which is package .k = 6 and d = 10 and select the initial variables with ReFACTor as recommended in the BayesCCE paper. In addition to high-throughput data matrix, BayesCCE accepts known patient phenotype information. We accounted for age and gender for all analyzed DNA methylation, EPIC, Aging, and RA data. We also accounted for the smoking status when analyzing the RA data. For the prior information of cell proportions used in BayesCCE, we use the true parameter values in generating simulation proportions  for four synthetic cell types, when evaluating BayesCCE in simulation study. In real data analysis with blood samples, we use the parameter  for granulocytes, monocytes, CD4+, CD8+, B cells, and NK cells provided by the BayesCCE paper and originally obtained from the Perioperative Medicine at UCLA’s perioperative data warehouse (PDW) [When implementing BayesCCE, we set parameters se (PDW) .Additional file 1Supplementary Section S1 (Procedures to obtain cell type-specific features in simulation study); Section S2 (Simulation study for RNA-seq data); Figure S1-S17. (PDF 1526 kb)Additional file 2Review history. (DOCX 961 KB)


Metabolomics is a powerful approach for discovering biomarkers and for characterizing the biochemical consequences of genetic variation. While untargeted metabolite profiling can measure thousands of signals in a single experiment, many biologically meaningful signals cannot be readily identified as known metabolites nor compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To overcome these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets and to generate metabolic pathway annotations for these signals. To pair up signals measured in different datasets, where retention times (RT) are often not comparable or even available, we implemented an imputation-based approach that only requires mass-to-charge ratios (m/z). As validation, we treated each shared known metabolite as an unmatched signal and showed that PAIRUP-MS correctly matched 70–88% of these metabolites from among thousands of signals, equaling or outperforming a standard m/z- and RT-based approach. We performed further validation using genetic data: the most stringent set of matched signals and shared knowns showed comparable consistency of genetic associations across datasets. Next, we developed a pathway reconstitution method to annotate unknown signals using curated metabolic pathways containing known metabolites. We performed genetic validation for the generated annotations, showing that annotated signals associated with gene variants were more likely to be enriched for pathways functionally related to the genes compared to random expectation. Finally, we applied PAIRUP-MS to study associations between metabolites and genetic variants or body mass index (BMI) across multiple datasets, identifying up to ~6 times more significant signals and many more BMI-associated pathways compared to the standard practice of only analyzing known metabolites. These results demonstrate that PAIRUP-MS enables analysis of unknown signals in a robust, biologically meaningful manner and provides a path to more comprehensive, well-powered studies of untargeted metabolomics data. Untargeted metabolomics can systematically profile thousands of metabolite signals in biological samples and is an increasingly popular approach for discovering biomarkers and predictors for human traits and diseases. However, currently, a significant portion of the measured signals cannot be identified as known metabolites or easily compared across datasets, and thus are usually excluded from downstream analyses. Here, we present PAIRUP-MS, a suite of computational methods designed to analyze unknown, unidentified signals across multiple mass spectrometry-based profiling datasets. Specifically, PAIRUP-MS contains a flexible imputation-based approach for pairing up unknown signals across datasets, allowing for meta-analysis of matched signals across studies that would otherwise be incompatible. PAIRUP-MS also offers a pathway annotation and enrichment analysis framework that links metabolite signals to plausible biological functions without using their chemical identities. Importantly, we validated both components of PAIRUP-MS using genetic data and applied them to study an example trait, body mass index. Overall, our results demonstrate that PAIRUP-MS enables previously infeasible analyses of unknown, unidentified signals across multiple datasets, thereby greatly improving power for discovery and biological inference. Metabolomics is a powerful systematic approach for studying circulating molecules in biological samples , 2. ProfWhile metabolomics has led to many new discoveries, a significant portion of the generated data have not been used to its full potential. Untargeted profiling platforms collect measurements for thousands of metabolite signals and compare their data to standards in compound databases to identify known metabolites . CurrentAnalyzing unknown signals poses several challenges. Distinguishing biological signals from noise and adjusting for technical artifacts are difficult, because unknowns are often associated with noisier measurements compared to known metabolites . Meta-anTo overcome the challenges described above, we developed and implemented a suite of computational methods, PAIRUP-MS , for analyzing unknown signals. PAIRUP-MS can be applied to MS-based profiling datasets in which signal peaks have been detected, aligned, and quantified, and where some peaks have been identified as known metabolites. PAIRUP-MS is used to further process, match, and annotate the unknown signals and to perform meta-analysis and pathway-based analysis that include unknown signals. We utilized genetic associations to validate the methods and demonstrated their application by studying metabolite signals associated with body mass index (BMI). Our framework can be applied to diverse untargeted profiling datasets produced by typical MS-based profiling platforms and will help to advance metabolomics as a powerful approach for elucidating biology underlying human traits and diseases.S1 Fig). After QC, we leveraged the correlation structure among signals to impute any remaining missing values within a dataset. In the end, the OE dataset contained 298 samples and 13,613 metabolite signals ; MCDS contained 821 samples and 7,136 signals ; BioAge contained 583 samples and 14,617 signals . Covariate adjustment (if appropriate) and rank-based inverse normal transformation were performed on each metabolite signal and the resulting abundance z-scores were used in downstream analyses. QC’ed cohort and dataset characteristics are summarized in Table 1. Known metabolites in QC’ed datasets are listed in S1 Table.We developed and tested PAIRUP-MS using LC-MS profiling data generated from three independent cohorts: (1) Obesity Extremes cohort (OE): 300 individuals  sampled from the Estonian Biobank, (2) Mexico City Diabetes Study cohort (MCDS): 865 individuals in a prospective type 2 diabetes study, and (3) BioAge Labs Mortality cohort (BioAge): 583 individuals selected from the Estonian Biobank for a retrospective mortality study. In order to reduce noise in the profiling data and to improve power in analyses, we implemented a quality control (QC) pipeline to adjust each dataset for measurement variation associated with technical artifacts and remove samples, metabolite signals, and data points with noisy trends . In preliminary analysis, we observed that known metabolites measured across our three datasets had moderately similar correlation structures despite substantial differences in study populations and designs . We took advantage of this observation and imputed the abundance of unknown (or unshared known) signals from one dataset to another using shared known metabolites as predictors in linear regression models. The imputation then allowed us to calculate the correlation between signals measured in different datasets across a common set of samples and to pair up signals based on agreement in m/z and this correlation.An approach for comparing unknown signals measured in different untargeted profiling datasets is crucial for increasing power in metabolomics studies. Instead of using RT information, which can vary greatly across profiling methods and platforms, we developed a method that utilizes m/z and imputation to match up signals likely to represent the same metabolites across MS-based profiling datasets (2 > 0.8) with the correct match in observed data. The parameter choices we tested included: (1) whether to consider different adduct ions when checking for m/z agreement, (2) which set of samples to use for calculating correlation between signals, (3) whether to allow multiple-to-1 signal matching or require unique 1-to-1 matching, (4) whether to match only signals measured by the same profiling method or allow matching across different methods, and, optionally, (5) which correlation cutoff threshold to use for calling a match.In order to calibrate parameters in our method, we treated known metabolites shared across two datasets as unmatched signals and applied our method to match each of them in turn. We compared the performance of different parameter settings by calculating the percentage of matches that were either the correct match or highly correlated  with the correct match. However, the “multiple” setting also performed relatively well  and identified more correct or highly correlated matches overall, so both match types can be useful, depending on the analysis context.The calibration results for OE-MCDS, OE-BioAge, and MCDS-BioAge matching are shown in S2A Table). An important advantage of the imputation approach is that, even when the matches made for known metabolites were incorrect, ~60–80% of them were strongly correlated (r2 > 0.8) with the true known matches. In contrast, incorrect matches made by the RT approach sometimes showed much weaker correlation. These results suggest that our method can be used to discover “proxies” for metabolites in a dataset even when an exact match does not exist or could not be identified. Furthermore, when we allowed matching across profiling methods, the RT approach showed a bigger dip in performance compared to the imputation approach (S2B Table). Thus, imputation-based matching may be a more flexible framework for comparing datasets generated by diverse profiling platforms, especially when directly comparable RT data is not available.We compared our method against a m/z and RT-based matching approach, where signals with agreeing m/z were mapped to each other based on predicted RT calculated from shared known metabolite data. The imputation method generally performed as well as or slightly better than the RT method, despite not using any RT information . We observed that the fraction of correct or highly correlated matched knowns tended to increase with sample size, stabilizing after each dataset contained at least ~300 samples. Similarly, when we performed matching using subsets of MCDS-BioAge shared known metabolites as predictors during imputation, performance improved as the number of shared knowns increased . Finally, to check how overall similarity in metabolite correlation structure influences matching performance, we assessed OE-MCDS, OE-BioAge, and MCDS-BioAge similarities by first calculating pairwise correlations between shared known metabolites within each dataset and then calculating the correlation of these pairwise correlations across each pair of datasets . We observed that the most similar dataset pair, MCDS-BioAge (r2 = 0.625), had the best matching performance, while the least similar pair, OE-MCDS (r2 = 0.400), performed the worst (S2A Table). Together, these results indicate that the imputation-based matching procedure is more effective at matching larger datasets with more known metabolites and composed of similar samples. Based on these analyses, we suggest that having ~300 samples per dataset, ~200 shared known metabolites, and r2 > 0.4 in terms of metabolite correlation similarity should be sufficient to achieve matching performance similar to what we describe in this paper. However, empirical assessment of matching performance using shared knowns is still recommended for new datasets.Dataset characteristics such as sample size, number of shared known metabolites, and similarity in metabolite correlation structure can influence performance of the imputation-based matching approach. We ran additional analyses using random subsets of MCDS and BioAge samples to assess matching performance across a range of sample sizes ; we also analyzed an additional 207 pairs of shared knowns and 20 sets of 4,432 randomly matched pairs to serve as positive and negative controls, respectively. To assess how well we had selected matching pairs, we determined the overall directional consistency of association for the best-associated single nucleotide polymorphisms (SNPs) with the matched signal pairs. Specifically, for each matched pair, we identified the SNP with the best p < 5 × 10−8) SNPs in the meta-analysis, 66 pairs  showed directional consistency; in comparison, 478 out of 672  “reciprocal” or 1,119 out of 1,772  “multiple” matched pairs were consistent, both of which were significantly better than randomly matched signals . Importantly, at more stringent p-value thresholds, as many as 91.4% of the “reciprocal” matched pairs had directional consistency, compared to a ceiling of 90.3% for the shared knowns, indicating that these matches for unknown signals show similar consistency as shared known metabolites . In addition, we estimated true positive rate for the matched signals by assuming that true matches would show same degree of consistency as the shared knowns, and found that the “multiple” setting generated more true positive matches overall , albeit at a lower confidence level . In summary, the most stringent application of our method  identified ~7 times as many high-confidence matches compared to the shared knowns. The method can also be used to generate many more potential matches under more lenient settings .For the 94 pairs of shared knowns that had genome-wide significant (p < 0.05) and directionally consistent associations in MCDS. In comparison, 156 “reciprocal” matched signal pairs had genome-wide significant hits in OE, 56 of which had replicated associations in MCDS. Furthermore, after meta-analyzing the two cohorts (this time taking direction of effect into account), we found 71 shared knowns and 512 matched signal pairs with genome-wide significant associations. Among the significant matched signal pairs, 93 (18.2%) contained potentially redundant signals as the measured known metabolites: 48 pairs directly contained known metabolites; 45 pairs had m/z matching adduct masses of the known metabolites recorded in the Human Metabolome Database (HMDB) + and [M+H]-) is considered, or (2) “Adduct”: multiple adduct ions are considered.  When identifying all signal pairs with agreeing m/z values across two datasets, we also implemented a third “combined” setting, where the “adduct” setting was used only when the “no adduct” setting failed to identify any pairs with matching m/z values. We tested all 3 settings in Parameter calibration to identify the optimal setting to use for matching signals across each pair of datasets.We implemented an algorithm to check whether the difference between 2 m/z values are within Parameter calibration), we built a leave-one-out model for each known metabolite m, where all shared knowns except for m were used as predictors. After imputation, we calculated pairwise correlation between Dataset 1 and Dataset 2 signals using 3 different settings: (1) “Dataset 1 correlation”: correlate Dataset 1 (observed) and Dataset 2 (imputed) signals across Dataset 1 samples, (2) “Dataset 2 correlation”: correlate Dataset 1 (imputed) and Dataset 2 (observed) signals across Dataset 2 samples, or (3) “All correlation”: merge observed and imputed data and correlate signals across all samples. We evaluated these settings in Parameter calibration to identify the optimal approach to use for Matching based on m/z and correlation.To impute a signal from Dataset 1 to Dataset 2, we fit a linear regression model for the signal using Dataset 1 data, which included all known metabolites shared by the two datasets as predictors, and then applied the resulting model to Dataset 2 data to predict the signal’s abundance in Dataset 2 samples. The reversed process was done to impute signals from Dataset 2 to Dataset 1. When performing imputation for the shared knowns , we distinguished between 3 types of matches, each type being a subset of the previous type: (1) “Multiple”: all matches that can be made as described by the general guideline above, which allows multiple-to-1 matching , (2) “Unique”: unique 1-to-1 matches that only keep the best (in terms of correlation) Dataset 1 signal mapped to each Dataset 2 signal, or (3) “Reciprocal”: unique, bidirectional 1-to-1 matches that appear both when matching from Dataset 1 to 2 and from 2 to 1. We also allowed 2 partition settings: (1) “Within method”: only look for matches among signals measured using the same profiling method, or (2) “Across method”: look for matches across datasets regardless of differences in profiling methods. Lastly, we implemented an optional correlation cutoff for calling matches (i.e. only keep matched pairs whose correlation is greater than the cutoff).In general, to match a Dataset 1 signal, s, to a Dataset 2 signal, we would predict the RT of s in Dataset 2 using the fitted model, find all Dataset 2 signals whose m/z agree with s, and then select the one whose RT is closest to the predicted RT to be the final match. Just as we did for Matching based on m/z and correlation, we tested different match types  and partition settings (“within method” or “across method”) in Parameter calibration.We implemented a crude RT-based matching procedure to serve as a comparison to our imputation-based method. Here, to match signals from Dataset 1 to Dataset 2, we would first use RTs of shared knowns to fit a linear model for predicting the RT shift between the two datasets. Then, to match a Dataset 1 signal, 2 > 0.8) with itself in Dataset 2, and (3) incorrect but highly correlated matches: matches in (2) but not in (1).To calibrate all of the parameters described above , we treated known metabolites shared across two datasets as unmatched signals and applied our procedures to match them up. For each pair of datasets , we calculated the number and percentage of (1) correct matches: Dataset 1 metabolite is matched to itself in Dataset 2, (2) highly correlated matches: Dataset 1 metabolite is matched to a signal that is highly correlated (rS4 Text for genotyping and imputation details). We used the linear mixed model and kinship matrix calculation methods in EPACTS (v3.2.6)  where c = fraction of consistent pairs and s = fraction of consistent shared knowns, and then derived the number of true positive pairs using this fraction.For each pair of shared knowns or matched signals, we performed an alternative meta-analysis that ignored direction of effect , identified the SNP with the best meta-analyzed database:name” label to each pathway, where database refers to the source database and name is the pathway name as shown in that database. We further consolidated the pathways into metabolite sets, with each set representing all pathways sharing the same metabolite combination, excluding metabolites not profiled in BioAge. The resulting metabolite set annotations were used as input data in Signal-metabolite set annotation matrix. CPDB also contains annotations that link genes to pathways based on previous literature evidence, which we utilized in Genetic validation of pathway annotations.We collected metabolic pathway annotations from CPDB (release 31) , which ap-value threshold, P, to be:  / . This FDR was used to filter MCs as described in Parameter calibration.We calculated pairwise Spearman correlation for all metabolite signals (known and unknown) across BioAge samples, and then performed principal component analysis on the correlation matrix to derive “metabolic components”  and a corresponding signal-MC score matrix. In order to assess if the MCs captured biological patterns contained within metabolite sets derived from CPDB, we performed enrichment analysis using the MC scores of known metabolites included in the metabolite sets. For each MC and each set, we performed a two-tailed Wilcoxon rank-sum test to compare the MC scores of metabolites in the set vs. other metabolites. We repeated the tests using 20 iterations of null, permuted MC scores and estimated the FDR for each observed rank-sum z-scores. Next, we used the signal-MC matrix (from Metabolic components) and the metabolite set-MC matrix to calculate the Spearman correlation between each signal-metabolite set pair across the MCs, resulting in a signal-metabolite set annotation matrix of correlation scores. To avoid overfitting, when calculating the matrix scores of a known metabolite originally annotated in a set, we repeated the reconstitution procedure by leaving out the data for this metabolite.We used the MC scores of metabolite signals to reconstitute the CPDB metabolite sets, adopting a previously described framework for gene set reconstitution . Again, Metabolic components). After generating annotation matrices using different parameters, for each matrix, we converted correlation scores of known metabolites into p-values, and then evaluated how well these p-values could be used to classify metabolites into their original metabolite sets using a receiver operating characteristic (ROC) curve and area under the curve (AUC) approach. We also performed post-reconstitution enrichment analysis to identify reconstituted metabolite sets enriched for metabolites originally curated to be in the set . The enrichment p-values from this analysis were used as “label confidence scores” for the metabolite sets.A calibration procedure was carried out to determine which metabolite sets and MCs to use for constructing the optimal signal annotation matrix. We tested metabolite sets containing > = 2, 5, or 10 known metabolites profiled in BioAge. For MCs, we tested the top 50, 100, or 583 MCs (in terms of variance explained), or filtered for any MCs that were enriched for > = 1 metabolite set at 1% or 5% FDR (see A subset of 281 OE samples were genotyped using the Illumina HumanOmniExpressExome BeadChip (v1.2). We performed association analysis for each OE metabolite signal using this genetic dataset to identify exome SNPs associated with signal abundance. We restricted our analyses to 57,220 exome SNPs with minor allele count > = 3 and call rate > = 0.5 in OE, using the linear mixed model and kinship matrix calculation methods in EPACTS (v3.2.6) .p < 1 × 10−5, (2) cause missense or nonsense mutations in genes, and (3) affect genes linked to > = 1 CPDB pathways used for reconstitution. If multiple SNPs were found for the same gene, we only kept the SNP with the best p-value. To perform validation analysis for each locus, we first used the BioAge signal annotation matrix to prioritize metabolite sets enriched for the SNP-associated signals . Next, we determined if any pathways linked to the affected gene showed up as enriched across a range of enrichment p-value thresholds. For each threshold, we counted the number of loci for which > = 1 gene-associated pathway was enriched, and then repeated the procedure using 20 iterations of null exome chip association results  to generate null counts for comparison. We calculated the empirical p-value for the observed count to be the proportion of null counts > = the observed count.We filtered the OE exome chip association results using OE-BioAge “multiple” matched signals to identify associations between OE SNPs and BioAge signals. We grouped the associations by SNPs and identified SNPs that (1) were associated with > = 5 signals at z-scores for both OE and MCDS samples by adjusting raw BMI for age and sex and performing rank-based inverse normal transformation on the residuals. For OE, all samples in the Estonian Biobank were included during z-score calculation; for MCDS, only samples in the cohort were included. We performed linear regression to test for association between BMI z-scores and signal abundance z-scores in both cohorts and identified BMI-associated signals at Bonferroni-corrected significance thresholds .We calculated BMI Metabolite set enrichment analysis. After identifying enriched metabolite sets at 5% FDR in both analyses, we performed a Fisher’s exact test to evaluate the statistical significance of the overlap.We identified BioAge shared knowns or “multiple” matched signals corresponding to the BMI-associated OE and MCDS signals from the previous section, and then used these signals and the BioAge signal annotation matrix to perform two separate enrichment analyses as described in S11 Fig.A schematic overview of how we calculated statistical significance in metabolite set enrichment analysis is shown in p-value. For post-reconstitution enrichment analysis , the positive and negative lists consisted of known metabolites in an original CPDB metabolite set vs. not in the set, respectively. For identifying trait-associated metabolite sets, the lists consisted of signals significantly associated with the trait  vs. all other signals .To calculate the enrichment of each metabolite set in the signal annotation matrix given a “positive” and a “negative” list of signals, we used a two-tailed Wilcoxon rank-sum test to compare the metabolite set scores (absolute correlation) of the positive vs. negative signals to get a nominal Genetic validation of pathway annotations and BMI-associated metabolite signals and metabolite sets, we generated null lists of signals ranked by their association with 1000 sets of permuted trait scores and repeated the rank-sum tests using the null lists . We used the null enrichment results to adjust the observed rank-sum p-values for metabolite set-specific biases, calculating a permutation p-value for each metabolite set to be the proportion of null rank-sum p-values < = the observed rank-sum p-value.For BMI-associated metabolite signals and metabolite sets, we implemented another level of permutations to estimate analysis-wide FDR that accounts for multiple hypothesis testing across metabolite sets. We generated 20 additional null signal lists and calculated their corresponding rank-sum p-values. By comparing these new null rank-sum p-values against the 1000 sets of null rank-sum p-values computed previously, we calculated 20 sets of null permutation p-values. The FDR for an observed permutation p-value threshold, P, was then estimated as:  / . Finally, we forced the final FDR estimates to be monotonic  to smooth out random fluctuations in null iterations.For p-value. Null permutations were performed as described in Metabolite set enrichment analysis (except using Fisher’s exact test instead of rank-sum test) to calculate permutation p-value and FDR.Binary metabolite set annotations from CPDB were used to perform overrepresentation analysis given a list of trait-associated known metabolites. For each metabolite set, we created a 2 × 2 contingency table that categorized known metabolites based on trait association and metabolite set membership and used it to calculate a one-sided Fisher’s exact In order to determine potential identities of unknown signals prioritized in either the matching or pathway results sections, we performed m/z queries using the “LC-MS Search” tool in HMDB . All addS1 Fighttps://github.com/yuhanhsu/PAIRUP-MS for details on input and output data format at each step).Data processing steps performed externally by Broad Metabolomics Platform or using existing software package (i.e. MICE) are highlighted in blue. Steps performed separately on data generated from each profiling method vs. on merged dataset containing data from all profiling methods are indicated by red text. All components of PAIRUP-MS were designed to be run in a streamlined manner, but each component can also be used independently depending on the characteristics of different datasets and analysis needs (see source code and documentation on (PDF)Click here for additional data file.S2 FigPairwise correlation between shared known metabolites were calculated within each dataset then compared across (a) OE vs. MCDS, (b) OE vs. BioAge, and (c) MCDS vs. BioAge. R-squared (Rsq) of the pairwise correlation values were calculated to assess overall similarity.(PDF)Click here for additional data file.S3 Fig2 > 0.8) with the correct known in observed data; “Incorrect Rsq0.8 Fraction”: fraction of incorrect matches strongly correlated with the correct known.Matching was performed using different adduct ion (a), correlation (b), match type (c), partition (d), and correlation cutoff (x-axis) settings (see (PDF)Click here for additional data file.S4 Fig2 > 0.8) with the correct known in observed data; “Incorrect Rsq0.8 Fraction”: fraction of incorrect matches strongly correlated with the correct known.Matching was performed using different adduct ion (a), correlation (b), match type (c), partition (d), and correlation cutoff (x-axis) settings (see (PDF)Click here for additional data file.S5 Fig2 > 0.8) with the correct known in observed data; “Incorrect Rsq0.8 Fraction”: fraction of incorrect matches strongly correlated with the correct known.Matching was performed using different adduct ion (a), correlation (b), match type (c), partition (d), and correlation cutoff (x-axis) settings (see (PDF)Click here for additional data file.S6 FigS2A Table were used for all analyses.MCDS-BioAge matching was performed using (a) random subsets of MCDS and BioAge samples (and 217 shared known metabolites) or (b) random subsets of shared known metabolites (and 821 MCDS and 583 BioAge samples). For each matching analysis, the fraction of matched knowns that was correct (“Correct”) or highly correlated (“Rsq > 0.8”) with the correct match is plotted. In (a), “Number of Samples” indicates sample size per dataset; red and blue dashed lines indicate the correct and highly correlated fractions, respectively, when using all 821 MCDS and 583 BioAge samples to perform matching. Optimal MCDS-BioAge matching parameters shown in (PDF)Click here for additional data file.S7 Figp-value was selected to assess directional consistency of its association in the two cohorts. (a) “Total # of Pairs”: number of signal pairs with best SNPs below p-value threshold; (b) “# of Consistent Pairs” and (c) “Fraction of Consistent Pairs”: number and fraction of pairs with directionally consistent best SNPs below p-value threshold; (d) “# of True Positive Pairs” and (e) “Fraction of True Positive Pairs”: number and fraction of true positive pairs were estimated as described in Methods. X-axes start at genome-wide significant p-value threshold (p < 5 × 10−8). Error bars for “Random” pairs were excluded due to low visibility .GWAS were performed for “multiple” matched signal pairs , shared known pairs , and randomly matched pairs  in OE and MCDS, followed by meta-analysis that ignored direction of effect. For each pair, the SNP with the best meta-analyzed (PDF)Click here for additional data file.S8 FigPrincipal component analysis was performed on the BioAge metabolite signal correlation matrix to derive the MCs. (a) Cumulative variance explained (left y-axis) or Cronbach’s alpha (right y-axis) of the MCs. (b) Number of CPDB metabolite sets that were enriched for each MC at 5% FDR.(PDF)Click here for additional data file.S9 FigAnnotation matrices generated using different combinations of (a) metabolite sets and (b) MCs were used to classify known metabolites into their original metabolite sets, generating a ROC curve  for each matrix (see (PDF)Click here for additional data file.S10 FigN MCs (N = sample size) were included for reconstitution in each analysis.BioAge signal-metabolite set annotation matrices were generated using (a) random subsets of BioAge samples (and 603 known metabolites) or (b) random subsets of known metabolites (and 583 samples). For each resulting annotation matrix, the area under the curve (AUC) and the number or fraction of confidently reconstituted metabolite sets (i.e. label confidence score < 0.05) are plotted (see (PDF)Click here for additional data file.S11 Figp-value: Metabolite signals were split into positive (hatched pattern) and negative lists based on association with a variable of interest (e.g. BMI). The lists were used to perform two-tailed Wilcoxon rank-sum test to calculate observed (obs.) rank-sum p-value for each metabolite set. 2 rounds of null permutations  were used to calculate null rank-sum p-values. (b) Permutation p-value: Observed permutation (perm.) p-value for each metabolite set was calculated by comparing the observed rank-sum p-value against 1000 sets of null rank-sum p-values (from permutation round 1); 20 sets of null permutation p-values were calculated by comparing 20 sets of null rank-sum p-values (from permutation round 2) against another 1000 sets of rank-sum p-values (from permutation round 1). (c) FDR: The analysis-wide FDR for an observed permutation p-value threshold, P, was estimated by comparing the observed permutation p-values against the 20 sets of null permutation p-values.(a) Rank-sum (PDF)Click here for additional data file.S1 Table“NA”: m/z and RT information not available.(XLSX)Click here for additional data file.S2 Table(a) Optimal parameter settings and corresponding calibration statistics for matching each pair of datasets. (b) Optimal parameter settings and corresponding calibration statistics for cross-method matching.(PDF)Click here for additional data file.S3 TableMetabolite signals with profiling methods  as prefix in their names are unknowns. Lipid name abbreviations: CE (Cholesteryl ester), DAG (Diacylglycerol), TAG (Triacylglycerol), LPC (Lysophosphatidylcholine), LPE (Lysophosphatidylethanolamine), PC (Phosphtatidylcholine), PE (Phosphtatidylethanolamine), PI (Phosphtatidylinositol), PS (Phosphtatidylserine), SM (Sphingomyelin). Other metabolite name abbreviations: ADMA (Asymmetric dimethylarginine), NMMA (N-Monomethyl-L-arginine), SDMA (Symmetric dimethylarginine).(XLSX)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table“Potential identity”, “Chemical formula”, “Adduct”, and “HMDB ID” information was retrieved by performing m/z query in HMDB see .(XLSX)Click here for additional data file.S7 Tablep-value; “Perm_P”: permutation p-value; “FDR”: false discovery rate of the permutation p-value; “Label_P”: label confidence score for the reconstituted metabolite set; “CPDB_Pathways”: labels for the original pathways from CPDB, individual pathways are separated by ‘|’.“SET_ID”: unique identifier assigned to each metabolite set; “Nominal_P”: rank-sum (XLSX)Click here for additional data file.S8 Tablep-value; “Perm_P”: permutation p-value; “FDR”: false discovery rate of the permutation p-value; “Label_P”: label confidence score for the reconstituted metabolite set; “CPDB_Pathways”: labels for the original pathways from CPDB, individual pathways are separated by ‘|’.“SET_ID”: unique identifier assigned to each metabolite set; “Nominal_P”: rank-sum (XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Tablep-value; “Perm_P”: permutation p-value; “FDR”: false discovery rate of the permutation p-value; “Label_P”: label confidence score for the reconstituted metabolite set; “CPDB_Pathways”: labels for the original pathways from CPDB, individual pathways are separated by ‘|’.“SET_ID”: unique identifier assigned to each metabolite set; “Nominal_P”: rank-sum (XLSX)Click here for additional data file.S11 Tablep-value; “OE_FDR” or “MCDS_FDR”: false discovery rate of the permutation p-value; “Label_P”: label confidence score for the reconstituted metabolite set; “CPDB_Pathways”: labels for the original pathways from CPDB, individual pathways are separated by ‘|’.“SET_ID”: unique identifier assigned to each metabolite set; “OE_Perm_P” or “MCDS_Perm_P”: permutation (XLSX)Click here for additional data file.S12 Tablep-value; “OE_FDR” or “MCDS_FDR”: false discovery rate of the permutation p-value; “CPDB_Pathways”: labels for the original pathways from CPDB, individual pathways are separated by ‘|’.“SET_ID”: unique identifier assigned to each metabolite set; “OE_Perm_P” or “MCDS_Perm_P”: permutation (XLSX)Click here for additional data file.S13 Table(a) Rules for checking for m/z agreement between 2 ions. (b) m/z values of different adduct ions included in “adduct” settings.(PDF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 Text(PDF)Click here for additional data file.S4 Text(PDF)Click here for additional data file.


Helicobacter pylori infection and inflammatory processes. The overexpression of TGFBI and S100A2 was associated with poor disease-free survival while the down-regulation of NR5A2, SLC4A4, and CD177 was linked to worse overall survival of the patients. In conclusion, novel transcriptome signatures to improve the diagnostic accuracy in CRC are introduced for further validations in various clinical settings.The advancement of bioinformatics and machine learning has facilitated the discovery and validation of omics-based biomarkers. This study employed a novel approach combining multi-platform transcriptomics and cutting-edge algorithms to introduce novel signatures for accurate diagnosis of colorectal cancer (CRC). Different random forests (RF)-based feature selection methods including the area under the curve (AUC)-RF, Boruta, and Vita were used and the diagnostic performance of the proposed biosignatures was benchmarked using RF, logistic regression, naïve Bayes, and k-nearest neighbors models. All models showed satisfactory performance in which RF appeared to be the best. For instance, regarding the RF model, the following were observed: mean accuracy 0.998 (standard deviation (SD) < 0.003), mean specificity 0.999 (SD < 0.003), and mean sensitivity 0.998 (SD < 0.004). Moreover, proposed biomarker signatures were highly associated with multifaceted hallmarks in cancer. Some biomarkers were found to be enriched in epithelial cell signaling in  Colorectal cancer (CRC) is the fourth most frequently diagnosed cancer . The 5-yvita. A strong point of this method is that it has comparable power with better stability in feature selection while requiring a significantly shorter computation time, which is appropriate to handle large datasets [Several systematic approaches and guidelines have been proposed for selecting important biomarker candidates across experiments . In the datasets .In the current study, we implemented AUCRF, Boruta, and Vita for selecting novel biomarker candidates from microarray-based gene expression of 340 CRC patients and 134 paired noncancerous controls. Afterward, only genes that are early dysregulated in the tumorigenesis of CRC, at the adenoma stage, were selected to produce the final sets of biomarker candidates. Various prediction models of the three biosignatures were then built, validated, and compared using 762 CRC and non-cancerous samples from processed and batch effects removed whole-transcriptome shotgun sequencing (RNA-seq)-based gene expression derived from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) cohorts. Finally, the mode of action in cancer hallmarks and impact of the biomarker candidates in the prognosis of the patients were also examined. Our comprehensive analysis using statistical learning on gene expression patterns successfully introduced and validated novel biomarkers. Proposed predictive transcriptome signatures revealed strong classification power and might be utilized to support the diagnostic decision making in clinical practice to improve the management of CRC patients. Our study covered the biomarker discovery, prediction model building and validation, and established a foundation for the clinical validation step.Samples from heterogeneous ethnics with different patients’ bio-parameters were utilized in data-driven biomarker discovery. The data set GSE44861 comprised 56 CRC tissues and 55 adjacent noncancerous tissues from the United States . The datADH1C, GBA3, and CD177 were not found in the RNA-seq and were eventually removed from the classification and validation process. Ultimately, there were 39, 41, and 68 individual markers from AUCRF, Boruta, and Vita, respectively.According to the results, 747, 179, and 953 genes passed the selection criteria for AUCRF, Boruta, and Vita, respectively. They were redundant and not representative for the early stage of the disease, which is essential for diagnostic biomarkers. Hence, we filtered them with dysregulated genes in adenomas. After being filtered, only 41 , 42 , and 71  genes remained. Interestingly, the Boruta-derived biosignature was a part of the Vita-derived biosignature. There were 5 up-regulated genes and 21 down-regulated genes in common among three methods. Thus, AUCRF possessed only one distinct up-regulated gene and one distinct down-regulated gene b. ADH1C,RNA-seq data set including CRC samples (TCGA-T-coad and TCGA-T-read) noncancerous samples  was subjected to data exploration and visualization. It is worthy to mention that no or limited batch effects were shown in the original processed RNA-seq data set . After pPotential biomarker candidates derived from conventional statistical biomarker selection are often highly collineared. Thus, we conducted correlation network analysis to check whether the multicollinearity occurred among predictors in three signatures: AUCRF-derived signature, Boruta-derived signature, and Vita-derived signature. As shown in the correlation network, the predictors are highly correlated to each other in the non-cancerous group but possess a small correlation network in CRC group a. The coWe performed computational experiments with the aid of machine learning techniques built upon three sets of biosignatures. Due to a paucity of external datasets, we trained classification models and validated them by dividing available data randomly, which was repeated 20 times. Tuning parameters, if relevant, of statistical models were tuned through 10-fold cross-validation with 5 times repetitions.RF, our primary classifier, showed highly promising performance at the validation step regardless of gene sets a; mean aSensitivity analyses were conducted to inspect the robustness of our findings. First, we examined the effect of balancing the proportion of samples in each group. Second, we tested if the unbalance between cancer and non-cancerous samples had a significant impact on the performance of the predictors by training and testing samples derived only from TCGA cohorts. Finally, we tested if the performance was heavily dependent on the dominance of non-cancerous samples from GTEx. Interestingly, there was not much difference regarding the prediction powers of utilized models in these three designs from that of the main analyses. Moreover, we found that the biomarker set derived from the Vita method gave more robust results than those from AUC-RF or Boruta .CXCL1 and CXCL8 that are associated with colorectal cancer risk and overall survival, appeared to be included in epithelial cell signaling in Helicobacter pylori (H. pylori) infection, chemokine signaling pathway and cytokine-cytokine receptor interaction [We conducted pathway enrichment analysis using 73 individual biomarkers (19 up-regulated and 54 down-regulated) that appeared in at least one biosignature to get better insights into the associated biological processes. There were only eight enriched pathways in the up-regulated group. eraction . There wANXA3 (OS), IL8 (CXCL8) (OS), and CXCL11 (DFS) were associated with the preferred outcome of CRC patients. On the contrary, the up-regulation of TGFBI (DFS) and S100A2 (DFS) was related to the poor outcome of the patients. When investigating 54 individual down-regulated biomarker candidates, the deletion of ADAMDEC1, CEACAM7, GCG, AQP8, BEST2, SLC26A2, and SLC26A3 was all related to a better OS of the patients. Finally, down-regulations of NR5A2, SLC4A4, and CD177 were the indicators of poor OS prognosis of the CRC patients. The Kaplan-Meier plots of TGFBI, S100A2, NR5A2, SLC4A4, and CD177 are shown in The combined cohorts of CRC from TCGA Colon Adenocarcinoma (TCGA-T-COAD) and TCGA Rectum Adenocarcinoma (TCGA-T-READ) were used for the survival analysis. The relationships between gene expression and both overall survival (OS) and disease-free survival (RFS) were examined . Among tThe explosion of high-throughput data and literature database has facilitated integrative data- and knowledge-driven biomarker exploration and validation processes . PioneerSST, SCGN, NFE2L3, MMP7, KIAA1199 (CEMIP), CLDN1, CDH3, and ADH1B were consistently among the most important features from the RF models of AUCRF-derived signature, Boruta-derived signature, and Vita-derived signature. Importantly, the biomarker list derived from Vita exhibited an extremely stable performance in all tested analyses. In addition, the biological functions of the biomarkers in cancer were also revealed to suggest further mechanistic and translational studies to move forward to the applications. Network-based pathway enrichment analysis suggested that the proposed predictors and closely associated genes are enriched in several well-known cancerous processes, such as chemokine signaling pathway or cytokine-cytokine receptor interaction. Moreover, some of these genes belong to the epithelial cell signaling in H. pylori infection. These pathways, together with previous clinical and laboratory findings, indicate a potential oncogenic interaction between H. pylori and colorectal mucosa [H. pylori infection causally triggers colon tumor formation.The predictive transcriptome signatures derived from an early phase translational research may be further developed and eventually implemented to assist the clinicians in making clinical decision. For instance, a previous investigation aimed to establish a novel profile from 12 public microarray data sets using 17 known CRC-associated genes suggested a seven-gene model with encouraging results for further investigations with blood-based assays . Anotherl mucosa ,35,36. TNotwithstanding, our study has several limitations. For instance, there is a lack of the detailed patients’ bio-parameters, which may affect the measurement of the potential biomarkers . HoweverWe successfully introduce a solid workflow for biomarker discovery and validation that is able to overcome some current technical and experimental limitations of high-throughput gene expression platforms. Novel transcriptome signatures that may improve the diagnostic accuracy in colorectal cancer are introduced and subjected to additional translational evaluation and application. Further studies are required to address the limitations of our study and validate proposed predictive signature in various clinical settings.Colorectal cancer and matched non-neoplastic mucosal tissues of GSE83889 (HumanTH-12 V4.0), GSE44861 (HG-U133A), and GSE41258 (HG-U133A) were collected from Gene Expression Omnibus (GEO) and served as the samples for selecting novel biomarker candidates. In addition, colorectal adenomas and corresponding colonic mucosa samples from GSE8671 (HG-U133 Plus 2) were collected to analyze the dysregulated genes in colorectal adenomas. To measure the classification performance of the biomarker candidates, the batch effects corrected RNA-seq data of TCGA (TCGA-READ and TCGA-COAD) and GTEx that are available from literature were utilized . The useAffymetrix and Illumina microarray derived data sets were pre-processed by either affy package or lumi package when applicable. For batch effects removal, we applied the Empirical Bayes cross-study normalization method implemented in NetworkAnalyst .2 fold change of 1 was applied as the feature selection in adenoma versus normal tissue data set. The analysis was conducted using NetworkAnalyst [Differential gene expression analysis with a blocking factor as the second metadata set was applied to analyze gene expression of the paired design samples using NetworkAnalyst. The mean was used as the gene-level summarization in the annotation step. Data filtering was performed to remove genes with 15% lowest variance. A LogkAnalyst .Principal component analysis (PCA) was used for gene expression (log transformed) data visualization and to detect potential outliers prior to the class assignment analysis using ggfortify 0.4.5 . A heatmp-value’ of 0.01. Vita was implemented using the Vita package 1.0.0 with a selection criterion of ‘threshold for p-values’ of 0 [Variable selection was conducted using the combined microarray data set of CRC and adjacent noncancerous samples. AUCRF was implemented using party package 1.3 with a selection criterion of genes with the top 20% highest importance score. Boruta was implemented using the Boruta package version 5.3.0 with a selection criterion of ‘es’ of 0 .A random forest was chosen as our main decision maker in the task of classification between cancer patients and non-cancerous controls since all variable selection methods were based on it. The model was constructed and validated using 762 transcriptome samples by splitting training and test samples by the ratio of 7:3 while the relative proportion of non-cancer and cancer groups was kept balanced within each dataset. With a training data set, the number of variables used in each split of a tree was tuned by random grid search through 5 times repeated 10-fold cross-validation, and the consequent final model determined by AUC was evaluated on the test set to gauge various performance measures. We repeated this procedure 20 times to reduce uncertainty in random splitting. To check how robust three signatures are in prediction, three commonly used classification techniques  were applied in the same manner described above. Logistic regression does not have parameters to be tuned, but naïve Bayes has the use of kernel to be used, and kNN should be tuned by the number of neighbors.A unified framework of model training and validation was applied using the Classification and Regression Training (caret) package 6.0.80 . Accuraccorrr [Correlation analysis was conducted for gene expression level based on the R package corrr , which pThe overall survival and disease-free survival were investigated using the Kaplan-Meier method with a log-rank test. The median value was utilized to set the high and low gene expression level groups. The hazard ratio and the 95% confidence interval information were measured. The process was implemented in the GEPIA web tool .Cancer Hallmarks Analytics Tool (CHAT) was applied to find the association between biomarker candidates and documented evidence of these molecules in hallmarks of cancer . Networkp-value of 0.05 was used as the cut-off for significance. A false discovery rate (Benjamini-Hochberg method) threshold of 0.10 was utilized in pathway enrichment analysis and of 0.05 for all other multiple hypothesis tests. Finally, R statistic 3.5.1 was used to implement the statistical analysis except otherwise stated [A e stated .


REdiscoverTE, a computational method for quantifying genome-wide TE expression in RNA sequencing data. Using The Cancer Genome Atlas database, we observe increased expression of over 400 TE subfamilies, of which 262 appear to result from a proximal loss of DNA methylation. The most recurrent TEs are among the evolutionarily youngest in the genome, predominantly expressed from intergenic loci, and associated with antiviral or DNA damage responses. Treatment of glioblastoma cells with a demethylation agent results in both increased TE expression and de novo presentation of TE-derived peptides on MHC class I molecules. Therapeutic reactivation of tumor-specific TEs may synergize with immunotherapy by inducing inflammation and the display of potentially immunogenic neoantigens.Profound global loss of DNA methylation is a hallmark of many cancers. One potential consequence of this is the reactivation of transposable elements (TEs) which could stimulate the immune system via cell-intrinsic antiviral responses. Here, we develop  Treatment with demethylation agents can reactivate transposable elements. Here in glioblastoma, the authors also show that this is accompanied by de novo presentation of TE-derived peptides on MHC class I molecules. Neoantigens stemming from point mutations in the coding exons alone, however, likely underestimate the true mutational burden in the tumor3; other cancer-specific antigens might also exist and contribute to immune response against the tumor. Indeed, there is growing evidence from in vitro and preclinical studies that DNA demethylation inhibitors can trigger innate antiviral response to human endogenous retroviral (HERVs) expression in the tumor7. Such retroviral transcripts also have the potential to produce antigens that activate adaptive immunity8. HERVs, together with other classes of transposable elements (TEs) comprise about 45% of the human genome, a sequence space that vastly eclipses that of the coding genome9. Although in normal tissue, much of TE activity is under tight epigenetic control, in cancer, we hypothesized that wide-spread TE expression occurs, particularly in those with extensive epigenetic dysregulation10. Investigation of the extent of expression by these evolutionarily sequestered sequences in cancer, and their antigenic potential, has thus far been very limited.Over the past few years, we have come to understand that tumor-associated neoantigens provide important targets for anticancer T-cell responses. This conceptual breakthrough has led to efforts to therapeutically target patient-specific mutations using personalized vaccines11. To shed light on these hidden parts of the transcriptome, we have developed and benchmarked a new method, REdiscoverTE, to comprehensively quantify expression by all repetitive elements including TEs in RNA-seq data, then applied it to 7750 cancer and normal tissue samples from The Cancer Genome Atlas (TCGA) and a replication study12. Here, we provide the first landscape analysis of TE expression in cancer, reveal that the rate of tumor DNA demethylation proximal to TEs is far greater than global demethylation, and identify multiple ways in which TE expression may impact cellular and immune responses to the tumor. We show that tumor cells present potentially immunogenic peptides derived not only from HERVs, but also other classes of TE: long interspersed nuclear elements (LINE), short interspersed nuclear elements (SINE), and SINE-VNTR-Alu (SVA). TE-derived tumor-specific antigens that are conserved and not patient-specific may be evaluated as “off-the-shelf” vaccine targets both for therapeutic intervention and, even more provocatively, for cancer prophylaxis.A major impediment to understanding TE expression and its potential relevance to tumor immunity is the analytic challenge of accurate quantification of short-read sequences from repetitive regions in the transcriptome. Standard pipelines typically discard repetitive readsREdiscoverTE was devised to simultaneously quantify expression by all annotated genes defined in Gencode13 and all RepeatMasker sequences (over five million) in the human genome14  algorithm to assign multi-mapping reads probabilistically to transcripts, based on evidence from uniquely mapped reads15. Post-quantification, we restricted our downstream analysis to only TEs sequences (over four million), which are classified into 1052 distinct TE subfamilies in five classes: LINE, SINE, long terminal repeats (LTR), SVA, and DNA transposons  ≤ 0.05), likely by reducing mapping noise observed at the individual element level  across 25 cancer types. For validation of our findings in select cancer types, we also analyzed an additional 405 tumor and matched normal RNA-seq samples across five cancer types  and most families (N = 43), including both retrotransposons and DNA transposons  and L1PA2—the two youngest L1 subfamilies—were overexpressed in the most number of TCGA cancer types (n = 7), followed by fewer recurrences by the increasingly older subfamilies of L1PA3 (n = 5 cancer types), L1PA5 (n = 4), and L1PA8 (n = 3). This trend is not strictly replicated in other well-defined TE families. For example, the six SVA subfamilies are named A–F in the order from the oldest to the youngest. In TCGA data, we observed that SVA_A, SVA_B, and SVA_F were recurrently overexpressed in four cancer types, whereas SVA_D, SVA_E, and SVA_C were overexpressed in 2–3 cancer types  and TE-proximal level using TCGA Illumina 450 K array data, which captured 70 K CpG sites overlapping with 1007 TE subfamilies  CpG sites being significantly demethylated in tumor compared with normal tissue . We examined DNA methylation status at six recurrently downregulated TEs but found no clear association between methylation and TE expression , six related to DNA damage response (DDR)  and eight related to immune response 30 (Supplementary Data 31: (1) a cellularity-only model that relates pathway scores to estimates of sample tumor purity plus lymphoid and myeloid cell content32  of model 2 and model 1 was interpreted as the total contribution from all TE expression to each pathway (variance explained). As Lasso is a statistical technique that selects representatives of correlated variables, the models also helped to identify representative top TE contributors to the variable expression of each pathway in question  than the cellularity-only model (0.09 ± 0.10). MER75 (piggyBac DNA transposon), MER4A (ERV1 family), MER54A (ERV3), and MER67A (ERV1) were identified by the true TE model as top predictors for DDR activities , THE1C-int (ERVL), LTR21B (ERV1), and MER57F (ERV1) were identified as TEs with the strongest positive association to type I IFN response  expression , were also identified as the top TE correlates to gene expression levels of type II IFN response, CD8 T effector and immune checkpoint activity inferred from tumor bulk tissue. Direct correlation analysis with estimated immune infiltrates confirmed positive association of LTR21B and MER57F to CD8 + T cells expression  on GBM cell lines. Applying REdiscoverTE to the GBM transcriptome generated with rRNA depletion library prep, we discovered a much higher proportion of TE transcripts (~2.5%) and an enrichment of intronic TE expression in comparison with the data from poly-dT library preps, independent of treatment group , L1 (n = 2), and Alu (n = 9) families , we also detected peptides derived from other TE classes: SVA (n = 17), SINE (n = 7), and LINE (n = 2), which have not been reported before. Two subfamilies (SVA_D and LTR12C) representing half of the 39 peptides. In addition, we identified 19 peptides derived from five of these subfamilies within the whole proteome data, adding further confidence in the translation potential of these TEs , allograft rejection (FDR = 1.3 × 10−2), but also a number of immune and cellular pathways that is consistent with above TCGA finding: inflammatory response (FDR = 4.0 × 10−7), TNFα signaling (FDR = 1.5 × 10−3), EMT (FDR = 6.4 × 10−2), and P53 response .Simultaneous to TE expression increase, decitabine treatment also resulted in a striking induction of host gene expression, including many cancer testis antigen (CTA) genes such as the Our study revealed extensive TE expression in tumors, which strongly associated with the expression of innate immune genes and triggered the production of polypeptides that are processed and presented on MHC-I molecules. These finding were made possible by the development of a novel computational approach that simultaneously quantified the expression of host genes and all annotated repetitive elements. Our extensive computational characterization demonstrated that the repetitive transcriptome, which had been previously ‘hidden’ to RNA-seq studies contained features strongly correlated to cancer cell phenotypes. These features are potentially cancer transcriptional fingerprints and may provide insights for future therapies.REdiscoverTE permitted the demonstration that TE expression is tightly linked to proximal DNA demethylation in tumors. Using data from 7000 + patient tumors from TCGA, we provide the first the statistical demonstration that total TE expression accounts for substantial fractions of variance-explained for many gene expression signatures including type I interferon response, DNA damage repair response, cell cycle, and P53 signaling. By expanding the reference cellular proteome and MHC class I peptidome to include TEs, we uncovered that TE transcripts can be translated and presented on the MHC-I molecules of tumor cells, with peptides stemming from intronic and intergenic HERV, LINE, SINE, and SVA loci. Tumor presentation of TE-derived MHC-I peptides represent an antigen space for T-cell recognition that is seldom explored. Consistent with a growing body of recent studies, these findings suggest TE expression in the tumor, whether spontaneous or induced under epigenetic therapy have important clinical implications for cancer immunotherapy40. Synergistic effect between epigenetic therapy and immunotherapy38 have been observed in preclinical models, where induction of antiviral immunity to retroviral expression is proposed to be a mechanism of tumor regression. In particular, for renal clear cell carcinoma (ccRCC or KIRC), where tumors have low mutational burden yet are highly immunogenic, HERV activity has been proposed to be a source of inflammation6 and recently associated with response to immunotherapy8. It remains to be seen, however, if epigenetic agents can be safely combined as a strategy to achieve improved efficacy in humans. Finally, T-cell populations recognizing the same HERV antigens have been identified in renal clear cell carcinoma tumors from multiple patients8, which offers evidence that TE antigens can be shared tumor antigens and should be explored as potential vaccine targets. In sum, our comprehensive in silico characterization of TE activities in tumors offers a number of predictions for experimental validation.Key findings included the demonstration of transcriptional activities from all five classes of TE, including DNA transposons, with an enrichment seen in the evolutionarily youngest TEs. Much of the TE expression was derived from intergenic loci, supporting autonomous expression of many TEs rather than read-through resulting from host, protein-coding genes. Advancing well-established observation of global demethylation in many cancer types, we discovered a much higher rate of DNA demethylation in regions of TE; REdiscoverTE does not rely on consensus sequences, traditional short-read aligners, exclusion of multi-mapping reads, nor does it utilize step-wise operations that potentially introduce read-assignment bias42. In this study, we have focused on TE expression in cancer, however, the method automatically quantifies expression of all repeats including those that are not transposable, e.g., satellites, which have been implicated in epithelial tumors43. This method is broadly applicable to landscape profiling of the RE/TE expression in research areas beyond cancer, including autoimmune44, and neurodegenerative diseases46, as well as normal embryonic stem cell development where TE activation is a hallmark of cellular identity and pluripotency50. At last, REdiscoverTE can be adapted to apply to transcriptomic analysis of TEs and REs for any organisms whose genome contain such elements .Regarding the computational methods presented herein, REdiscoverTE uses the light weight-mapping method, Salmon15, for repetitive element expression quantification.REdiscoverTE reference transcriptome: Salmon version 0.8.2 was used to generate quasi mapping index. The reference transcriptome includes:n = 98,029) from the GENCODE release 26 basic13Distinct RNA transcript sequences  from the standard chromosomes, excluding all polyA repetitive elements RepeatMasker elements  and excluding any regions overlapping with exons on either strand since we analyzed non-strand-specific RNA-seq.Distinct sequences representing GENCODE RE-containing introns  and the other with (index 2) the inclusion of RE-containing introns. We showed with simulation significant performance improvement by index 2 over index 1  and number of reads. We have chosen to use read counts for all downstream analysis.Salmon quantification, RE, and host gene transcripts were aggregated separately. Host isoforms were aggregated to the gene level according to ensembl gene ID. All aggregation and downstream analysis of the aggregated expression were performed using R53. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org.).Post-Salmon quantification of read counts for individual REs were aggregated to the level of RE subfamily, family and class according to hierarchies defined by the human Repeatmasker for Hg38   was used to define REs’ relationship with respect to host genes. Gencode GTF/GFF file defines the following basic categories of features: gene, transcript, coding exon (CDS), exon, UTR. We inferred intronic and intergenic regions from these features , their locations are assigned with the following priority: exon > intron > intergenic. For example, a RE residing at the exon-intron boundary is considered as an exonic RE.Aggregation of RE expression to the subfamily level: owing to the high degree of sequence homology among numerous copies of REs from the same subfamily, quantification at the individual locus level is highly susceptible to noise. Therefore, REdiscoverTE, we carried out RSEM16 simulations to create fastq files with ground truth on expression levels (TPM) of all features in the transcriptome. To create realistic gene and RE expression levels we first used RSEM to learn sequence statistics from two TCGA RNA-Seq samples with different proportions of RE reads estimated by REdiscoverTE: one with 5.4% of reads derived from REs as estimated by REdiscoverTE , and one with 11.5% of reads derived from REs , then generated corresponding simulated fastq files based on learned models , each is a concatenation of all repetitive elements in the subfamily with additional flanking sequences and spacers.REdiscoverTE against RepEnrich on RSEM simulated RNA-seq data. We followed default workflow of RepEnrich. Performance of REdiscoverTE and RepEnrich were evaluated using the metric MARD at the level of subfamily, where MARD is defined as in Salmon publication:15N is the total number of features, where features could be individual RE transcripts or aggregated features such as RE subfamily.We benchmarked performance of 6, we created a Salmon reference transcriptome that included 90 k human transcripts and the same 124 sequences for the 66 ERVs analyzed by the authors (from ref. 56). RNA-seq from 5217 TCGA samples (20 cancer types) were quantified by Salmon (Version 0.6.0). ERV read counts were normalized by total counts mapped to genes, similar to Rooney et al. where ERV expression was normalized by total counts of reads mapped to genes.To directly compare the ERV quantification results from ref. SalmonTE18, SalmonTE v0.4 was downloaded from https://github.com/LiuzLab/SalmonTE.To compare with http://cutadapt.readthedocs.io/en/stable/) from both TCGA and CGP. REdiscoverTE was run as described above for whole-transcriptome expression quantification.TruSeq adapters were trimmed by Cutadapt  at least 10% of samples that have counts greater than zero, and (2) a log2(CPM) threshold. The log2(CPM) threshold was determined independently for each cancer type based on visual inspection of the mean-variance trend (estimated by the voom function in limma) to ensure variance was monotonically decreasing for low mean expression. Within each cancer type, raw p values resulting from differential expression (t tests on log2 fold change was different from 0) are adjusted by Benjamini–Hochberg (BH) approach to control false discovery rate (FDR)57. Differentially expressed genes/TEs were determined at the threshold of: abs(log2 fold change) > 1 and FDR < 0.05.To control for potential batch effect and patient-to-patient variation, only tumor and matched adjacent normal samples are used for differential expression analysis. Cancer types with fewer than 10 normal samples were excluded from this analysis; 13 TCGA cancer types and five CGP cancer types satisfied this threshold. The R packages 58. Raw array data were background corrected (lumiB method) and variance stabilized and normalized (lumiT and lumiN methods). Beta values were calculated per CpG site by flooring intensity values at zero and then calculatingTCGA Illumina 450k Infinium methylation arrays were processed using the “lumi”59. M values were transformed from Beta values by:by Illumina60. IlluminaHumanMethylation450kanno.ilmn12.hg19: Annotation for Illumina’s 450 K methylation arrays. R package version 0.6.0.) Using the liftOver utility provided by UCSC genome browser, physical coordinates of 450 K probes in hg19 annotation were lifted to hg38 reference genome. 485,441 out of 485,512 CpGs were successfully converted to Hg38, 71 failed owing to position removal in Hg38 assembly.Liftover of CpG sites in 450 K array to hg38: Hg19 annotation of 450 K probes was obtained using R package “IlluminaHumanMethylation450kanno.ilmn12.hg19”M values of DNA methylation data were used to fit the linear regression model with tumor/normal status and patient ID as covariate using lmFit from the R package limma21. To control for potential batch effect and patient-to-patient variation, only tumors (N = 237) with matched normal (N = 236) samples were included for analysis in 10 TCGA cancer types , ors N = 27 with maDMCs were defined as CpGs with the average absolute beta value change (Δbeta) ≥ 10% and FDR < 0.05. DMCs are called demethylated if the average beta value in tumor is lower than normal samples, and methylated if the average beta value in tumor is higher than normal.Δbeta value for each CpG site is defined for tumor and matched normal sample pairs as:Distribution of 450 K CpGs in different genomic featuresSimilar to RE expression analysis, CpGs were classified into exonic, intronic and intergenic CpGs according to the genomic features defined above. The genomic distribution of all 450 K CpGs and CpGs overlapping with TEs were visualized in Supplementary Fig. RepeatMasker was used as proxy for transcription start site (TSS).Spatial profile of methylation around TEs were analyzed by extracting CpG sites near intergenic TEs. CpGs located outside but nearby individual intergenic TE elements were binned into two categories for further analysis: those within 1 kb of TEs and those within 10 kb of TEs. Owing to the lack of functional annotation for all TE transcripts, the most 5′ bp of each individual TE as annotated by findOverlaps function in the R package GenomicRanges with strand information taken into account. This resulted in 90,950 TE-proximal CpG sites for 1007 TE subfamilies.For the 1 kb methylation spatial analysis, all 450 K Array CpG sites within 500 bp ± of TSS for each intergenic TE were extracted using the For these 1007 TE subfamilies, CpG sites from a bigger, 10 kb window: 5 kb up- and downstream from the start and end coordinates of individual intergenic TEs, were extracted, resulting in 155,360 CpG sites.For spatial profile analysis  across the 1007 tests within each cancer type. Significant correlation was defined as FDR < 0.05 and |cor| ≥ 0.4.Two types of correlation analyses were carried out, both using Pearson correlation on aggregated intergenic TE expression at the subfamily level and TE demethylation enrichment score. We defined methylation state as the ratio of number of demethylated vs. number of over-methylated DMC sites. Methylation state is 1 when there are equal number of demethylated and over-methylated DMCs, >1 when there is bias in the direction of demethylation, <1 when there is bias toward over-methylation.We then computed a TE demethylation enrichment score (Supplementary Table multiGSEA (https://github.com/lianos/multiGSEA) was used to score gene signature expression based on singular value decomposition.Twenty-four gene signatures associated with major cellular pathways related to cancer, DDR and immune response were selected from previous publications  and related gene signature scores  computed with multiGSEA , with tumor purity as a covariate. Benjamini–Hochberg approach was used to control FDR within each cancer type separately.For each cancer type, Spearman correlation coefficients between log2CPM expression of 1052 TE subfamilies and 24 gene signature scores were computed using the R package for partial correlations xCell scores were computed using the same method.In addition, Spearman correlation coefficients between 1052 TEs and 64 glmnet31. In order to account for variations of cellular content that existed between tumor samples, we included tumor purity as well as abundance of total lymphoid and myeloid content (xCell section above) as parameters in the lasso model. To avoid any possible bias introduced by normal-tumor status, only tumor samples were used in the regression model.To identify top TE subfamilies associated with each of the 24 cellular pathways and gene signatures, we exploited Lasso regularized regression–generalized linear model via penalized maximum likelihood using the R package N = 24) and each cancer type (N = 25):Three Lasso models were implemented for each gene expression signature (Model (1): Cellularity-onlyModel (2): Cellularity + true TE data:N samples.where true TE data is a Nx1052 matrix containing 1052 TE expression in units of log2CPM from Model (3): Cellularity + permuted TE datawhere permuted TE data is a row-wise scrambled version of the true TE data.Ten rounds of tenfold cross-validation was performed for each regression. Lasso coefficients at one standard error of the minimum mean cross-validation errors (lamda 1SE) were used. Each Lasso fit yielded a sparse set of predictors—variables with non-zero coefficients, corresponding to TE subfamilies with significant contributions to the variability of a given gene signature. We then ranked all 1055 dependent variables (TEs and three cellularity scores) by their average absolute coefficient values across cancer types to select the top 20 predictors associated with the gene signature of interest. To produce the Lasso rank coefficient heatmap  were averaged across the 10 rounds of cross-validation and used as MS raw data files for the global proteome (unenriched peptides) and MHC-bound peptidome (pan-MHC-I enriched peptides) were obtained from PRIDE (PXD003790) and SysteMHCAtals (SYSMHC00007), respectively.To enable identification of TE-derived peptides in the GBM proteome and MHC-bound peptidome data, we collected nucleotide sequences at all individual loci for the 62 TE subfamilies that were significantly overexpressed at either the intergenic or intronic regions upon 5′aza-treated condition, performed six frame translations (both forward and reverse direction), then fragmented the resulting amino-acid sequences at all stop codons. This yielded ~ 1.1 M peptide fragments, ranging 7–1321 amino acids in length. The peptide fragments were combined with the human protein sequences in Uniprot  and common contaminant proteins to create a database used for searching non-MHC-enriched MS data. TE-derived peptide fragments were further reduced into 4.6 M 11-mers generated with a moving window of eight amino-acid overlaps, with duplicates removed. This 11-mer database was also combined with the human protein sequences in Uniprot and common contaminant to create a database used for searching MHC-enriched MS data.61. In brief, raw MS data were refined and sequence tags were identified by a de novo search algorithm. Identified sequence tags were used in the assignment of peptide sequences to MS data through a database search. For all database searches, the following parameters were used: precursor tolerance = 25 ppm, fragment ion tolerance = 0.02 Da, enzyme = none, variable modifications include deamidation  and oxidation , and max number of variable mods = 3. Data were filtered to 1% FDR at the peptide level, but owing to TE peptide fragments being represented as multiple “protein” entries within the database protein level FDR was not performed. For MHC-bound peptidome data a median of three peptide spectral matches (PSMs) were identified for non-TE peptides, owing to this TE peptides were considered high confidence if they had been identified in ≥ 3 spectra.Raw MS data were analyzed using PEAKS Studio In addition, we performed an identical search against a database that did not include the TE peptide 11-mers in order to determine whether TE PSMs mapped to alternative sequences  with peptides separated over a nano-LC column  by a gradient delivered by a Waters NanoAqcuity nano-LC. For each synthetic TE peptide, 250 fmol was injected and analyzed by MS utilizing various collision energies  in order to match fragmentation spectra of Shraibman et al. Synthetic peptide MS data were analyzed in PEAKS in an identical manner to the MHC-bound peptidome data. Annotated spectra for the synthetic and experimental spectra were manually compared with validate peptide identifications. Through this process we were able to confirm 17 of 18 peptide spectra as visual matches, adding further confidence to TE peptide identifications.62. The resultant purified, stable complex was incubated in the presence of 100-fold molar excess of synthetic TE-derived peptides of interest. HLA-A*03:01 was present at a concentration of 50 µg/ml (1.04 µm) in 25 mm TRIS pH 8.0, 150 mm NaCl, 2 mm EDTA, 5% DMSO. The peptide exchange reaction mixture was incubated for 25 min under a UV lamp set to 365 nm to induce cleavage of the UV-sensitive amino-acid 3-amino-3-(2-nitro)phenyl-propionic acid. Samples were then incubated at room temperature, overnight, to allow for peptide exchange to occur. Upon cleavage of the conditional peptide ligand, synthetic TE-derived peptides with suitable properties  exchanged into the complex displacing any fragments of the cleaved conditional ligand.Recombinant HLA-A*03:01 MHC-I was refolded in the presence of a conditional peptide ligand that contains a UV-sensitive amino acid, as previously describedm TRIS pH 8.0, 150 mm NaCl for 10 min. A sampling valve collected the entirety of the complex peak that eluted between 1.90 and 2.13 min in a volume of 160 µl and injected it onto the second dimension reversed phase column . The second dimension column was exposed to a gradient of 5–50% mobile phase B in 4.7 min at 0.55 ml/min with the column heated to 80 °C. Mobile phase A was 0.05% TFA. Mobile phase B was 0.05% TFA in acetonitrile. The column eluent was sent to an Agilent 6224 TOF LCMS for MS data acquisition.To determine successful exchange of TE-derived peptides into HLA-A*03:01 complexes, a 2-dimensional liquid chromatography MS method was used. The first dimension LC method employed an analytical SEC column  to separate intact complex from excess peptide run at an isocratic flow of 0.7 ml/min in 25 mHLA-A*03:01 complex peak area (detected at 280 nm) in the first dimension and mass spec detection of the peptide in the second dimension are used to determine successful exchange. Successful exchange of a peptide into the complex after cleavage of the conditional ligand during the peptide exchange reaction stabilizes the complex and results in nearly complete recovery of the starting complex measured in the first dimension SEC analysis. The peptide that has exchanged into the complex can then be detected in the second dimension, where the complex is run under denaturing conditions with mass spectral analysis allowing for direct detection of the peptide of interest. Unsuccessful peptide exchange reactions result in destabilized complex after the cleavage of the conditional ligand when a peptide fails to bind to and stabilize the complex. This is measured as a reduction in A280 peak area of the complex on SEC and an absence of peptide in the second dimension. In some cases, such as for peptide RLAPRPASR, no reduction in peak area was observed, however the peptide was not detected by MS. A small number of peptides, due to their properties, are not captured by the second dimension chromatography column and method. In these cases, the peak area recovery is enough to suggest successful exchange when the proper experimental controls are used.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Reporting Summary