Mass spectrometry further validated the differential protein abundance of these genes . We also identified those proteins which correlate with overall survival. Finally, a support vector machine-based classification algorithm using the protein-level data was set up. We used transcriptomic and proteomic data to identify a minimal panel of proteins highly specific for clear cell renal carcinoma tissues. The introduced gene panel could be used as a promising tool in the clinical setting.Clear cell renal carcinoma is the most frequent type of kidney cancer, with an increasing incidence rate worldwide. In this research, we used a proteotranscriptomic approach to differentiate normal and tumor tissues in clear cell renal cell carcinoma (ccRCC). Using transcriptomic data of patients with malignant and paired normal tissue samples from gene array cohorts, we identified the top genes over-expressed in ccRCC. We collected surgically resected ccRCC specimens to further investigate the transcriptomic results on the proteome level. The differential protein abundance was evaluated using targeted mass spectrometry (MS). We assembled a database of 558 renal tissue samples from NCBI GEO and used these to uncover the top genes with higher expression in ccRCC. For protein level analysis 162 malignant and normal kidney tissue samples were acquired. The most consistently upregulated genes were IGFBP3, PLIN2, PLOD2, PFKP, VEGFA, and CCND1 ( Clear cell renal carcinoma (ccRCC) is the malignant transformation of epithelial cells of the kidney and is the most frequent form of kidney tumors with approx. 70% of all kidney cancer cases . In 2020Diagnosis of ccRCC is usually based on radiological imaging and tissue slide-based histopathological examination. Histopathological confirmation is essential before systematic therapy initiation.  TreatmenMS was introduced almost half a century ago in endocrinology and toxicology for drug, steroid, and organic acid quantitation and got its main medical application in the widespread newborn screening ,11. AlthUncovering a protein abundance-based panel specific to ccRCC could provide valuable support for the everyday clinical diagnostic and therapeutic decision-making process. Our study aimed to utilize large-scale transcriptomic studies to find genes showing higher expression in ccRCC. Then, by using our patient cohort with available proteomic and clinical data, we investigated the abundance of expressed proteins and the effect of these proteins on survival. By specifically focusing on markers with higher expression in tumor tissues, we aim to increase the specificity of our analysis to solidify future clinical application of the results.Altogether, we included 23 GEO series which contained 715 samples. Out of these 715 samples, 277 were from normal kidney tissues, and 438 were from ccRCC. Out of the entire gene array database, 414 samples were paired samples (207 pairs), and we used the paired specimens for the identification of differentially expressed genes. The entire analysis pipeline is summarized in p = 5.88 × 10−32). The most significant genes include previously established molecular targets like VEGFA (FC gene chip = 3.02 p = 5.1 × 10−31) and CCND1 . PLIN2 and PLOD2 also showed notable gene expression differences with FC values of 3.85 and 4.2 and adjusted p values of 3.09 × 10−31 and 5.24 × 10−32, respectively. The top differentially expressed genes are shown in We uncovered significantly differentially expressed genes between paired ccRCC and adjacent normal tissues. IGFBP3 was found to be the most upregulated gene in tumor tissues , PLOD2 , PFKP , IGFBP3 , CCND1 and VEGFA  shown in Proteomic analysis was performed using 162 normal and malignant tissue samples. Of the complete list of the 31 selected genes from gene chip results, we were able to successfully measure 22 in the targeted LC-MS/MS. Top differentially expressed genes include PLIN2 . Overexpression of further proteins such as TIMP1 , VIM , LGALS1 , and P4HA1 p < 3 × 10−2, HR = 2.6) also showed significant correlation with impaired overall survival. Kaplan–Meier curves of the best-performing proteins are shown in Supplemental Table S3 and as supplementary figuresTo estimate the potential effects of protein expression on patient survival, we performed a survival analysis using all available proteins. Five out of the investigated proteins showed a correlation with survival. Patients with elevated expression of PLOD2 protein showed significantly worse overall survival compared to subjects with lower expression , PLOD2 , PFKP , IGFBP3 , and VEGFA , had significant differences between normal kidney and ccRCC in the CPTAC study. Further results are displayed in To further support our analysis, we validated our results using CPTAC data from the study of Clark et al. . Out of MS-based protein abundance data of the investigated proteins in the 162 patient samples were used for establishing the most robust classification algorithm. We investigated multiple machine learning methods  to build a model which can differentiate between normal and malignant kidney tissues. For the proper estimation of the optimal gene panel, we performed recursive feature elimination. Of the four methods, SVM delivered the best performance in both test and training cohorts using nine proteins as input. SVM was able to identify tumor tissues from MS quantification data with a classification accuracy of 0.98 in the test set . Results of all four methods  in both training and test sets are displayed in Current clinical diagnostics of cancer rely mainly on pathological examination using tissue slide staining or immune histochemistry. The importance of tissue inspection is undoubted. However, with the increasing burden of workload in pathological diagnostics, the need for further potent diagnostic possibilities and tools capable to provide sufficient pathological decision support is necessary. While transcriptome-based methods are useful for this purpose, several studies with promising results were published recently in the proteome field as well. Establishing proteins with differential abundance in malignant samples compared to healthy tissues can provide valuable information in diagnostics and therapeutic target identification. For example, a breast cancer study comparing malignant breast cancer samples to adjacent normal samples using MS identified a novel luminal subtype . A compaLike other cancer types, early surgical intervention is the best solution for total recovery in ccRCC as well. Especially in the early stages, when the disease is localized, partial or radical nephrectomy is the most frequently performed treatment option . In the The final discriminative algorithm is based on the differential expression of nine proteins. Of these, VEGFA and CCND1 are well-known cancer biomarkers. VEGFA  is used as a target molecule in ccRCC treatment . CCND1  in multiple types of cancers ,29. WhilWe must note an important limitation of our approach. Although transcriptome-based examinations can provide valuable input of new potential biomarkers, due to mechanisms like alternative splicing, mutations, and post-translational modifications, RNA expression only moderately correlates with protein expression . A furthIn conclusion, we used a database of renal samples of paired normal and tumor tissues to identify biomarkers differentiating renal clear cell cancer (ccRCC) and normal kidney tissues. With a support vector machine-based machine learning algorithm using nine genes, we set up a model which can differentiate between normal and malignant ccRCC tissues using proteomic data. Finally, a set of proteins showed a significant correlation with poor survival outcomes and might serve as potential biomarkers of progression.https://www.ncbi.nlm.nih.gov/geo/, accessed on 21 January 2021) for potential ccRCC and normal specimens using keywords “ccRCC” AND “normal” OR “GPL570” OR “GPL571” OR “GPL96”. Only those datasets involved contained normal tissues adjacent to tumors from HGU133, HGU133A_2, and HGU133A platforms. We filtered the datasets to exclude xenograft experiments, pooled samples, and cell line studies. Samples with insufficient description, nonexistent raw data, and repeatedly published data with distinct identifiers have been removed. To achieve this, the expression of the first twenty genes was determined, and samples with identical values were identified. In each case, the first published version was retained in the dataset. After the manual selection, the remaining samples were normalized using the MAS5 algorithm by utilizing the Affy Bioconductor library [To set up the gene chip cohort, we searched the NCBI GEO repository . Wilcoxon test was used to compare the tumorous and adjacent normal samples. Genes showing significant differences according to the Wilcoxon test (p < 0.01) have been selected and ranked based on their fold-change values (FC). The Benjamini–Hochberg method was used for p-value adjustment. Finally, the top 31 genes with an FC over two were selected for further investigation.Data processing and analysis were performed in R version 4.1.0 .The expression of selected target proteins was verified by targeted LC/MS-MS. After isolation, protein samples were stored in guanidine isothiocyanate and stored at −80 °C. For targeted quantification, we used stable isotope labeled (SIL) peptides ; the peptide sequences of the 75 SIL peptides are listed in 7. Raw MS data were analyzed using the Skyline software and the MSstats statistical analysis tool. During the data processing steps, we performed the inbuilt normalization steps of the MSstats software package, which includes median polishing and log2 transformation.LC-MS/MS analysis was performed using an ACQUITY UPLC M-Class system  with HPLC coupled to an Orbitrap Fusion Lumos Tribrid  mass spectrometer on the mixture of the protein digests spiked with the mixture of the SIL peptides. Samples were loaded onto a trap column, ACQUITY UPLC M-Class Symmetry C18 Trap ; the sample loading time was 5 min; the flow rate was 5 µL/min, and separation was performed on an ACQUITY UPLC M-Class Peptide BEH C18  column with a flow rate of 400 nL/min. MS data acquisition was performed in an internal standard triggered parallel reaction monitoring fashion , where tt-test to see if any of the proteins show differential expression between male and female patients. To examine age as a covariate factor, we performed regression analysis to see if any of the examined proteins are influenced by age. Functional analysis was performed using the clusterProfiler R package [p-value adjustment. For survival analysis, we used the survminer and survival R packages. Further visualization has been done using the R packages ggplot2 [https://cran.r-project.org/web/packages/ggrepel/index.html, accessed on 13 December 2022).T-test was used to compare the log2 transformed protein intensity values between the tumorous and adjacent normal samples. In order to examine if any of the gene candidates are affected by covariates, we performed a  package . For eac ggplot2 , Complex ggplot2 , and ggrUsing the results of the targeted LC/MS-MS log2 intensity values, we tried four supervised AI methods, k-nearest neighbors (KNN), random forest (RF), logistic regression (LOGIT), and support vector machines (SVM), to set up the most accurate model for cancer detection. The data matrix from MS data was the input for the classification model, and we used the “caret” R package for data preparation and model establishment ,38. From


For EAA, we used PhenoAge, Wu, and the above mitotic clocks and found them to have distinct distributions in different tissue types, particularly between normal colon tissues adjacent to tumours and cancerous tumours, as well as between normal colon tissues adjacent to tumours and normal colon tissue from healthy individuals. Finally, we harnessed these associations to develop a classifier using elastic net regression (with lasso and ridge regularisations) that predicts CRC diagnosis based on a patient’s sex and EAAs calculated from histologically normal controls . The classifier demonstrated good diagnostic potential with ROC-AUC = 0.886, which suggests that an EAA-based classifier trained on relevant data could become a tool to support diagnostic/prognostic decisions in CRC for clinical professionals. Our study also reemphasises the importance of open access clinical data for method development and training of young scientists. Obtaining the required approvals for controlled access data would not have been possible in the timeframe of this study.Aberrant DNA methylation (DNAm) is known to be associated with the aetiology of cancer, including colorectal cancer (CRC). In the past, the availability of open access data has been the main driver of innovative method development and research training. However, this is increasingly being eroded by the move to controlled access, particularly of medical data, including cancer DNAm data. To rejuvenate this valuable tradition, we leveraged DNAm data from 1,845 samples  from 14 open access studies deposited in NCBI GEO and ArrayExpress. We calculated each sample’s epigenetic age (EA) using eleven epigenetic clock models and derived the corresponding epigenetic age acceleration (EAA). For EA, we observed that most first- and second-generation epigenetic clocks reflect the chronological age in normal tissues adjacent to tumours and healthy individuals [e.g., Horvath ( Colorectal cancer (CRC) is the third most common cancer in the world, with around 1.93 million new cases worldwide in 2020 . One of There are several methods developed for CRC diagnosis, with colonoscopy being considered the gold standard . Yet, otIn the past decade, epigenetic age predictors (“epigenetic clocks”) have been developed to estimate chronological and biological age based on DNAm levels in specific age-associated CpG sites . The firDeviation of the predicted epigenetic age (EA) from the chronological age (CA), known as epigenetic age acceleration (EAA), has been studied with respect to its association with age-related phenotypic changes and health outcomes, including cancer . Since DThis study was designed to be suitable for a Masters’s student project . Although the vast majority of DNAm data, including for CRC, are deposited in public databases such as EGA and dbGaP, they are classified as controlled access data which requires a data access agreement to be completed and to be approved by a data access committee before the data can be shared. This process can take months or even years  and is fWe obtained 14 open access datasets  and EMBL-EBI (European Molecular Biology Laboratory European Bioinformatics Institute) ArrayExpress . The lisp > 0.01, bead count minfi and ENmix R packages ; normal colon tissues from individuals with concurrent colon adenoma were included in this category,• normal: samples from normal colon tissues adjacent to the tumours of CRC patients,• tumour: samples from cancerous tumours obtained from CRC patients,• adenoma: samples from adenoma tissues of patients with observed colorectal adenoma (mostly sessile serrated adenomas).• All samples in our data contain information regarding chronological age, sex, and tissue types. We categorised samples into four different tissue types:For association analysis, we used two different datasets: (a) dataset with healthy, normal, tumour, and adenoma samples (Dataset 1) and (b) dataset with only healthy and normal samples (Dataset 2). A summary of the available cohort characteristics is given in methylClock library  were calculated for each sample using R  library , while e library . EstimatWe performed the analysis of outliers separately for Dataset 1 and Dataset 2 by using the differences between epigenetic and chronological age values, which we call epigenetic age acceleration differences (EAAd). This metric was only calculated for the first- and second-generation clocks, and not for the mitotic clocks. A sample was labelled an outlier if its EAAd value was more than three standard deviations away from the mean EAAd across the whole dataset . We removed all samples which were outliers in at least two clocks. In total, 142 and 38 samples were removed as outliers from Dataset 1 and Dataset 2, respectively.Step 1a: We regressed epigenetic age onto the chronological age and sex of healthy samples using the linear model (1).• Step 2a: Using the linear regression coefficients obtained in Step 1a in model (1), we calculated EAAs as the model residuals.• Step 3a: Based on the mixed-effect model (2), we adjusted EAAs obtained in Step 2a for the dataset and patient IDs using • All analyses in this study were conducted in R v. 4.2.2 . To evalIt is worth noting that traditionally EAAs for the first- and second-generation epigenetic clocks are calculated either as differences between EA and CA or as the residuals from linear regression of EA onto chronological age using the whole dataset . This wot-tests were performed to analyse the difference in EAAs between different tissue types. All graphs presented in this study were produced using ggplot and its extensions , and contained 341/215 healthy/normal samples. The testing dataset included data from four studies , and contained 321/113 healthy/normal samples. Samples originating from the same dataset were not split between training and testing sets in order to avoid potential data leakage through batch effect. The distribution of healthy and normal samples across the different datasets is provided in Only normal and healthy tissue samples were included when making the classifier (tumour and adenoma samples were excluded). Samples were excluded if there was no corresponding raw data (.idat) file or technical information (array identifiers and position of the sample in the array) available. Analysis of outliers using EAAd was done as described in Step 1b: We regressed epigenetic age onto the chronological age for healthy samples in the training dataset using model (3).• Step 2b: Using linear regression coefficients obtained in Step 1b, we calculated the EAA scores for all samples used in the classifier as the regression residuals.• Step 3b: We performed normalisation of the training dataset using standard normal distribution scaling.• Step 4b: Test data were scaled using the mean and standard deviation of the training data used in Step 3b.• To calculate EAAs for the classifier we used the following four-step procedure for each epigenetic clock:These steps were taken to prevent data leaks between the training and testing datasets. The choice of using only healthy samples in Step 1b was made to ensure a uniform EAA calculation for all epigenetic age scores, including mitotic clocks. Scaling was performed to unify the various scores’ distribution, making the classifier coefficients more interpretable. We also calculated platform-adjusted residuals by adding binary Illumina platform ID data (Illumina 450k or EPIC arrays) as a predictor in the model (3) in the first step.α and λ were identified through cross-validation. We manually selected folds for the cross-validation process. It was done by choosing two datasets for each fold testing data, and the remaining four for the fold training subset. By doing this, we ensured that the training and testing subsets in each fold included both healthy and normal samples, which resulted in 12 folds being used in the cross-validation process.Elastic net regression with ridge and lasso penalty terms was used when training our classifier. The optimal values for the elastic net parameters α ∈  with step 0.05, and λ ∈  with step 0.01. For each set of parameter values  we calculated two threshold-independent metrics [areas under the receiver operating characteristic (ROC-AUC) and precision-recall (PR-AUC) curves] to evaluate the model performance and identify optimal values for the parameters. For each pair of values {α, λ} we calculated the means of ROC-AUC across all folds and chose the optimal parameters based on the maximum mean ROC-AUC number.EAA calculation was performed separately at each fold, followed by training a classifier on the fold training set and calculating metrics on the fold testing set. This was done using a grid search for glmnet , normal (n = 522), tumour (n = 535), and adenoma (n = 72) samples from colorectal tissues (r = 0.46–0.79) compared to epigenetic mitotic age scores (r < 0.3).Our dataset consists of  tissues . We eval tissues . In termWe calculated EAAs following the procedure described in t-test. Among the different tissue types, tumour samples have the highest EAA variability. We also observed that Horvath AA, Pheno AA, Wu AA, EpiTOC AA, HypoClock AA, and MiAge AA captured differences between every tissue, except for healthy and adenoma  using the two-sample  adenoma . Interes adenoma . All EAA adenoma . Most EA adenoma .p-value of SkinBlood AA was around the borderline , hence, we may still consider SkinBlood AA for distinguishing between normal colon tissues from patients with and without CRC. This result slightly differs from comparing healthy and normal samples in the previous dataset, where PedBE AA was the only EAA that did not capture the difference between these tissues. Thus, all EAAs in our study, except for PedBE AA and Horvath AA, showed potential in discriminating between healthy and normal colon tissues in our datasets.We repeated this test using Dataset 2 to further investigate the ability of EAAs from different epigenetic clocks to distinguishing between healthy and normal colon tissues. The distribution of EAAs from this dataset is given in α = 0.05 and λ = 0.16 estimated through the 12-folds cross-validation process ] and 0.835 [95% CI ] on the testing data, respectively. The corresponding classifier coefficients for these values of regularisation parameters are presented in We also tried other values of the elastic net regression parameters α = 0.05 and λ = 0.68. This classifier demonstrated a higher ROC-AUC = 0.921 [95% CI ] than the platform-independent version, and was based on sex and ten EAAs. The corresponding plots and coefficients can be found in By using the EAAs adjusted for the Illumina platform ID (450k or EPIC), we trained a platform-dependent classifier. In this case, the cross-validation step was based on six folds , and theAbnormal changes in biological age, including epigenetic age, might reflect the underlying process of cancer development, including in CRC. In our study, we focused on evaluating the relationship between epigenetic clock measures (EA and EAA) and colon tissues from participants with and without CRC. We observed that most first- and second-generation epigenetic clocks reflect the chronological age very well in normal and healthy colon tissues, especially Horvath age. On the other hand, epigenetic mitotic clocks showed weaker correlations with CA. Our results align with findings from MLH1, CDKN2A, and SFRP2, hence promoting cancer growth and survival  were significantly different between the healthy and normal colon tissue in both datasets. Our results are in line with The main idea behind developing a classifier was an attempt to combine the abilities of several clocks to distinguish between normal colon tissue from individuals with and without CRC. To the best of our knowledge, this is the first effort to make a cancer status predictor based on EAAs in histologically normal tissues. We performed a thorough literature search and did not manage to find any similar studies, although there were several fairly successful attempts to create CRC diagnostic methods based on peripheral blood, stool blood, and colon tissue, which are well-summarised in the recent review on CRC diagnostic, prognostic and predictive DNAm biomarkers .α = 0.05, λ = 0.16) was Horvath AA, which is in line with the results reported in Our classifier demonstrated a very encouraging performance (ROC-AUC above 0.88), which is a clear indication of its diagnostic potential. The only EAA excluded from the regression by the elastic net (for ssNoob). We would like to point out that the heterogeneity of our data due to technical variability can be viewed as an advantage rather than as a shortcoming, since it reflects real-world data diversity.The results presented in this paper should be considered while taking into account several shortcomings. The analysed dataset comprises data obtained from multiple independent studies which were conducted in different countries; following diverse sample extraction, processing, and storage protocols; and using four different DNAm profiling technologies (two versions of Illumina 450k and two versions of EPIC arrays). The diversity in sample handling makes our dataset very prone to technical bias. In order to reduce the influence of this bias, where possible, we pre-processed the data using consistent unified techniques and methods designed to treat samples without the context of the dataset  and race are known to be associated with different distributions of EAAs , which, This open access-enabled study investigated the associations between eleven epigenetic age measures and the colon tissue of individuals with and without CRC. Our results indicate that CRC status might affect the association between epigenetic age and chronological age, as well as between colon tissue EAAs and clinical characteristics. We have also demonstrated that most EAAs, except for Horvath and PedBE AA, are able to distinguish between colon tissue with different CRC status, particularly between normal and healthy colon tissues. We developed a CRC status classifier based on sex and EAAs calculated using histologically normal colon tissue DNAm data, which performed well. Although further studies on a larger, more homogeneous, and more clinically described datasets are needed to acquire a deeper understanding of this association, our results provide valuable insights into the relationship between epigenetic age and CRC. In addition, our framework could be used for developing a more robust classifier.


Fruit juices are one of the most widely consumed beverages worldwide, and their production is subject to strict regulations. Therefore, this study presents a methodology based on the use of headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) in combination with machine-learning algorithms for the characterization juices of different raw material . For this purpose, the ion mobility sum spectrum (IMSS) was used. First, an optimization of the most important conditions in generating the HS was carried out using a Box–Behnken design coupled with a response surface methodology. The following factors were studied: temperature, time, and sample volume. The optimum values were 46.3 °C, 5 min, and 750 µL, respectively. Once the conditions were optimized, 76 samples of the different types of juices were analyzed and the IMSS was combined with different machine-learning algorithms for its characterization. The exploratory analysis by hierarchical cluster analysis (HCA) and principal component analysis (PCA) revealed a clear tendency to group the samples according to the type of fruit juice and, to a lesser extent, the commercial brand. The combination of IMSS with supervised classification techniques reported an excellent result with 100% accuracy on the test set for support vector machines (SVM) and random forest (RF) models regarding the specific fruit used. Nevertheless, all the models have proven to be an effective alternative for characterizing and classifying the different types of juices. The production of fruit juices is regulated by the European directive 2012/12 EU, which defines fruit juice as the product derived 100% from the squeezing of healthy and ripe fruit , lipid mHowever, fruit juices are one of the products most subject to adulteration, mainly because their main components are water and sugars. For this reason, it is necessary to have adequate analytical tools to characterize the samples and guarantee the quality of the product. In recent years, numerous techniques have been developed for the analysis of fruit juices, such as those based on DNA ,7, nucleFor this reason, other alternatives, such as headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) techniques, have emerged. However, HS-GC-IMS has been scarcely applied in the analyses of fruit juices. Recent studies have demonstrated the potential of this technique for monitoring the fermentation process of sour cherry juice  as well In terms of data analysis, all these studies are based on the identification of target compounds. This approach has major limitations, such as the complexity of automating the process. Furthermore, in complex matrices such as juices, the differences between different spectra are sometimes very subtle, so identifying of a small number of markers can be difficult and unsuccessful for discrimination purposes.In this context, the ion mobility sum spectrum (IMSS), obtained through HS-GC-IMS analysis, shows a great potential for food characterization and quality control. This is achieved by combining it with machine-learning algorithms. IMSS has been successfully applied to characterize and detect adulterations of different foods such as honey ,24, oil Among the most commonly used machine-learning algorithms are exploratory techniques such as principal component analysis (PCA) and hierarchical cluster analysis (HCA). Regarding supervised analysis, parametric techniques such as linear discriminate analysis (LDA) are the most used techniques for classification purposes, and partial least squares (PLS) for regression purposes.In specific scenarios, when there is no apparent correlation between the spectra and the response variable or when the response variable exhibits non-linearity, the application of more complex non-parametric algorithms becomes essential. Support vector machines (SVM) and random forest (RF) models are notable examples of these techniques. Both SVM and RF can be applied for classification and regression purposes, and they have shown excellent results in the characterization of different fruit juices using different spectroscopic data ,28,29,30Therefore, this study aims to evaluate the feasibility of IMSS in combination with different machine-learning algorithms for the discrimination of different types of fruit juices. For this purpose, an optimization of the most important conditions in the creation of the HS was performed for subsequent analysis by HS-GC-IMS. Finally, both classical parametric techniques (LDA) and more complex non-parametric techniques (SVM and RF) were evaluated and compared.Four types of pure fruit juice were selected, and the distribution of the samples was as follows: (I) 9 samples of apple juice (3 brands and 3 batches of each brand), (II) 9 samples of pineapple juice (3 brands and 3 batches of each brand), (III) 9 samples of grape juices (3 brands and 3 batches of each brand), and (IV) 11 samples of orange juice . All the samples were taken from local markets. Different brands  and different batches were selected to increase heterogeneity within the same type of juice. In addition, each one of the samples was analyzed in duplicate, so the total number of samples was 76. All samples were named by the first initial of the juice used  followed by the first two initial letters of the brand  and the batch number . Furthermore, the number of the replica was indicated with “R1” or “R2”. In this way, an orange juice of the HC brand belonging to batch 2 would be labeled as O_HC_3_R1 for the first replica and O_HC_3_R2 for the second replica.The samples were directly analyzed by using the FlavourSpec system . This system has a static headspace (HS) creation system followed by a gas chromatography column (GC) and an ion mobility spectrometry detector (IMS). The GC column was multicapillary MCC OV-5  of 20 cm length and 0.2 µm diameter. The drift and carrier gas selected was nitrogen at 99.999% purity. The HS conditions  were optimized as it is described in In this case, the injection volume was kept at a minimum of 100 µL to avoid possible saturation of the equipment. In addition, to ensure no cross-contamination between the analyses of each sample, a blank  was introduced and the syringe was flushed with a stream of nitrogen for 5 min after each injection.Based on the previous experience and the literature , the mosAs mentioned above, the variables to be optimized were as follows: sample volume, temperature, and incubation time. As can be seen in MD), are summarized in The response variable used for the optimization study was the Manhattan distance in the IMSS between samples of two different juices (orange and pineapple). In this way, it ensured that the conditions employed allowed the greatest separation between the groups. In addition, 3 center points were added to the experimental design, with a total of 15 experiments for each juice. Therefore, the total number of samples analyzed was 30. All the experiments were randomly carried out. The conditions used, as well as the value of the response variable , and statistical significance, which were determined through an analysis of variance (ANOVA).The RSM model was applied to the results obtained from the BBD design; specifically, a second-order polynomial function was used. Therefore, the correlation between the independent variable and the dependent variables is given by the following Equation (1):Ion Mobility Sum Spectrum  was used, which has already been successfully applied for similar purposes ,27. Thernxp) where n refers to the number of samples and p to the number of variables (drift times); i.e., the final complete array was D76x881. All analyses were performed using RStudio software version 4.2.2 . The rsm package [ggplot2 [stats package [caret package [The data were stored in two-dimensional arrays  in order to maximize the IMSS differences among the juices based on their volatile organic compounds (VOCs) profile. For this purpose, a BBD  was carrp-value less than 0.05) were: the temperature, volume, and the quadratic effect of temperature. The results show a very good regression between the difference of the spectroscopic data and the variables in the experimental design, with a very high quadratic coefficient of regression (R2 = 0.981). In addition, the lack of fit test was not significant , indicating that the model obtained fits the data well. Therefore, it can be concluded that the approximation of the curvature in the response variable by this model was adequate. In this way, the complete Equation (2) was as follows:MD represents the response variable (Manhattan distance in the IMSS) and X1, X2, and X3 correspond to temperature, volume, and time, respectively, representing the three optimized variables. In accordance with the results, a new model was fitted using only the coefficients that had been found to be significant. The new model also shows a correlation between the response and the experimental variables with a quadratic coefficient of regression (R2 = 0.956) and also a non-significant lack of fit . Therefore, the second-order polynomial equation is defined only by the three significant variables, giving results similar to those obtained with the full equation. The new reduced Equation (3) was as follows:As can be seen, the variables that were influential in the model at the 95% confidence level . It is observed that apple juice samples (colored in red) were close to pineapple samples (colored in yellow) and orange samples (colored in orange). However, the grape juice samples seem to be the most different (colored dark and light green). Moreover, these samples constitute two independent clusters: one cluster contains the samples of the “GR” and “CA” brands while the other cluster comprises only samples of the “HC” brand. This fact also demonstrated the strong tendency of grouping the samples according to the brand. This tendency was also observed, although to a lesser extent, in the other types of juices. For example, in the case of pineapple (colored in yellow), three almost perfect sub-branches are observed, each one of them containing a single brand.Based on the HCA, grape juice was the most different from other fruit juices based on the volatile profile. This indicated a greater difference in IMSS and, consequently, in the VOCs that make it up. The other types of juices showed more similarity with each other, especially the apple juice, which sometimes was very close to the orange and pineapple juices. These results indicated that the main clustering trend in the IMSS from the different juices was based on the raw material (type of fruit in the juice), followed by the brand influence that generally divided the main branch into sub-branches. Therefore, the volatile profile obtained by HS–GC–IMS could be useful for fruit juice quality control. In addition, to corroborate this grouping trend, a principal component analysis (PCA) was performed. https://rpubs.com/JoseLuisPCalle/1051330 (accessed on 1 June 2023). This tool offers greater versatility and includes a third dimension (PC3) that explains 13.1% of the data variability. The graph allows for dynamic rotation to facilitate the viewing of the sample distribution, and, by hovering the cursor over the samples, displays their names. Furthermore, clicking on the legend allows for group removal, providing a closer analysis of each group.However, to enhance understanding, an interactive plot has been created, which can be accessed via the link Regardless of the plot, both showed a tendency for samples to group according to the type of juice used. Again, the most different group is the grape juice, obtaining the highest scores for PC1. Furthermore, it is noteworthy that PC2 divided this group into subgroups, with negative values associated with the “HC”-brand grape juices, while positive values were found for the “CA” and “GR” brands. Moreover, a clustering trend was observed for the remaining juices. Orange samples showed positive values for PC2 and negative values for PC1, allowing for their differentiation from pineapple samples, which showed negative values for PC2 and values close to 0 for PC1. Additionally, apple juice samples had negative values for PC1 and values close to 0 for PC2.Thus, similar to the results obtained with HCA, the samples tended to cluster according to the type of juice analyzed. However, it is important to note that, while satisfactory results were obtained in both HCA and PCA, these analyses do not allow for the perfect classification and prediction of future observations. Therefore, the use of supervised classification techniques is necessary for the accurate classification and characterization of future samples.76x881). This matrix comprised 881 drift times and 76 samples, including samples of four juice types . The classification models evaluated include both parametric (LDA) and non-parametric techniques (SVM and RF). The classification models were applied to the complete data matrix  is used. The value of C determines the balance between bias and variance. Additionally, SVM is combined with a radial basis function (RBF), which allows for non-linear separation limits. Therefore, a new hyperparameter, gamma (γ), is introduced to control the behavior of the Gaussian kernel, which, in turn, controls the model flexibility [SVM is a supervised method based on the concept of hyperplanes. The hyperplane is used to separate the observations, but the separation is not always perfect, allowing for some misclassifications, which are known as support vectors. To control the number of allowed misclassifications, a hyperparameter called Cost . 2γ values (y-axis) versus the log2C values (x-axis) and the corresponding accuracy (z-axis) obtained from the cross-validation.To optimize the SVM model, a five-fold cross-validation was performed using a grid search method with exponentially growing γ approximately below 0.031 (log2γ = −5) and values of C above 1 (log2C = 0). Lower values of γ yielded the best results, suggesting that the groups were linearly separable. The optimal γ value was set at 0.0028 (log2γ = −8.5). On the other hand, accuracy increased with higher values of C, indicating that the hyperplane will allow fewer misclassified observations and resulting in a less biased model but with higher variance. The optimal value of C selected in the optimization was 1 (log2C = 0).As can be seen, the accuracy reaches its maximum for values of With the selected combination of hyperparameters, the model achieved a 100% correct classification rate during the cross-validation. Furthermore, the model was able to correctly predict 100% of the samples for both the training and test set. Based on the above, it can be stated that the SVM model with the selected hyperparameters is highly accurate and able to effectively discriminate between the different types of fruit juices based on their VOCs profiles.ntree hyperparameter) that make up the model and the value of mtry must be chosen. On the one hand, the number of trees must be large enough for the error to stabilize, so, to determine the number of trees to be used, the ntree values in this study were checked from 2 to 100 at a two-tree interval. On the other hand, the mtry controls the number of variables evaluated before the division of each tree and, in classification problems, it is recommended to use the square root of the total number of variables [ntree hyperparameter was performed by five-fold cross-validation. The result is shown in Prior to training an RF model, the number of trees , which, respectively, accounted for variances of 66.32%, 21.30%, and 12.38%.The samples were visualized in a partition plot, showing the combination that encompasses the greatest variance (LD1 versus LD2), as depicted in Although the performance of the LDA model was satisfactory, it was lower than the SVM and RF models. Therefore, the use of this one would be discarded. It should be noted that other authors, such as Mansour Rasekh et al., reported similar results, with superior performance of the SVM model compared to LDA, for the detection of adulterations in fruit juices using data from an electronic nose . In contTo the authors’ knowledge, this is the first time IMSS in combination with ML is applied for the characterization of juice samples from different raw materials. This study successfully optimized the key conditions for creating HS using a BBD design coupled with RSM. The incubation temperature (46.3 °C) and the volume of the sample (750 µL) were the most influential variables. Exploratory techniques like HCA and PCA, combined with IMSS, revealed a tendency to classify the juice samples according to the raw material (type of fruit in the juice) and, to a lesser extent, the brand used. The combination of IMSS with supervised machine-learning algorithms allowed for the accurate characterization and classification of the samples according to the type of juice. The non-parametric models (SVM and RF) reported an accuracy of 100%, outperforming the parametric model (LDA), which achieved an accuracy of 97.76%.To summarize, the results obtained in the present study demonstrate the potential of IMSS in combination with machine-learning algorithms for the characterization of fruit juices. Moreover, this methodology provides a promising alternative to traditional methods of sample interpretation and analysis, being faster, cheaper, cleaner, and more objective than other traditional methods.


Genome-wide DNA methylation (DNAme) profiling of the placenta with Illumina Infinium Methylation bead arrays is often used to explore the connections between in utero exposures, placental pathology, and fetal development. However, many technical and biological factors can lead to signals of DNAme variation between samples and between cohorts, and understanding and accounting for these factors is essential to ensure meaningful and replicable data analysis. Recently, “epiphenotyping” approaches have been developed whereby DNAme data can be used to impute information about phenotypic variables such as gestational age, sex, cell composition, and ancestry. These epiphenotypes offer avenues to compare phenotypic data across cohorts, and to understand how phenotypic variables relate to DNAme variability. However, the relationships between placental epiphenotyping variables and other technical and biological variables, and their application to downstream epigenome analyses, have not been well studied.Using DNAme data from 204 placentas across three cohorts, we applied the PlaNET R package to estimate epiphenotypes gestational age, ancestry, and cell composition in these samples. PlaNET ancestry estimates were highly correlated with independent polymorphic ancestry-informative markers, and epigenetic gestational age, on average, was estimated within 4 days of reported gestational age, underscoring the accuracy of these tools. Cell composition estimates varied both within and between cohorts, as well as over very long placental processing times. Interestingly, the ratio of cytotrophoblast to syncytiotrophoblast proportion decreased with increasing gestational age, and differed slightly by both maternal ethnicity (lower in white vs. non-white) and genetic ancestry (lower in higher probability European ancestry). The cohort of origin and cytotrophoblast proportion were the largest drivers of DNAme variation in this dataset, based on their associations with the first principal component.This work confirms that cohort, array  batch, cell type proportion, self-reported ethnicity, genetic ancestry, and biological sex are important variables to consider in any analyses of Illumina DNAme data. We further demonstrate the specific utility of epiphenotyping tools developed for use with placental DNAme data, and show that these variables (i) provide an independent check of clinically obtained data and (ii) provide a robust approach to compare variables across different datasets. Finally, we present a general framework for the processing and analysis of placental DNAme data, integrating the epiphenotype variables discussed here.The online version contains supplementary material available at 10.1186/s13072-023-00507-5. The placenta is an organ derived from cells of the conceptus, and is genetically identical to the fetus. The placenta is essential for fetal growth and development, and plays an important role in mediating maternal exposures that may influence newborn and child health. To better understand these roles of the placenta, genome-wide DNA methylation (DNAme) profiling has been widely applied, often using Illumina Infinium Methylation bead arrays. Alterations in placental DNAme have been reported in association with maternal exposures such as smoking , 2, gestPart of the issue underlying incomplete replication between studies is inter-dataset heterogeneity. Prior to performing epigenome-wide analysis, it is important to understand and account for the factors driving variability in each DNAme dataset. Relevant technical factors may include differences in sample processing techniques, batch effects, and poor data quality control, which can all lead to false positive EWAS results, or low signal-to-noise ratios . BiologiData interpretation and replicability across studies from diverse populations and using different sample collection methods can be improved if we can better assess and account for key variables and systematic differences between datasets. “Epiphenotyping” approaches have been developed whereby the (epigenetic) DNAme data are used to extract information about phenotypic variables, such as age, sex, or cell composition –28. WhilRecently, epiphenotyping tools have been developed to estimate placental cell composition, ancestry, and epigenetic age from DNAme data. Since the development of these placental-specific epiphenotyping tools, and their implementation in the PlaNET R package , the assThe placental epiphenotypes investigated here include: (1) a placental cell-specific deconvolution method developed to estimate major cell types in bulk tissue DNAme data , which cIn this study, we use three cohorts of placental samples  to assess factors contributing to within- and between-cohort variation in placental DNAme data. We specifically apply the PlaNET R package to estimate gestational age, ancestry, and cell composition epiphenotype variables, and we evaluate the utility of these epiphenotyping approaches, assess inter-cohort differences, and examine their relationships to technical and biological variables. In addition, we explore how technical and biological variables common to placental DNAme studies are related to each other, and to the imputed epiphenotype variables. Finally, based on these extensive studies, we provide a set of recommendations for the use of these epiphenotyping tools in placental EWAS.In this study, we use DNAme data from 204 placentas across three independent cohorts to investigate the relationships between placental epiphenotype variables computed with the R package PlaNET, and other technical and biological characteristics associated with these samples. The placentas from the V-NORM and V-SSRI cohorts were obtained and sampled in one processing lab located in Vancouver, Canada. The QF2011 cohort placentas were collected and sampled in Brisbane, Australia, and subsequently snap-frozen and shipped to Montreal, Canada for DNA extractions. Samples from all three cohorts were assayed on the Illumina Infinium MethylationEPIC (850K) array at one center in Vancouver, Canada, and were randomized during array processing for key variables including cohort, SSRI and flood-related stress exposure, and infant sex. The samples from all cohorts were run on the EPIC array in three technical “batches”, with batch referring to all stages of array processing from bisulfite conversion, to array hybridization and staining, to scanning. More details are presented in Table p < 0.001), with almost all mothers from the QF2011 cohort identifying as white. Infant birth weight standard deviation  was also slightly higher in the QF2011 cohort (p = 0.04). Finally, sample processing and storage in the QF2011 cohort differed in subtle but potentially significant ways (see Methods). These key differences could contribute to cohort differences that were important to consider in data analysis.In addition to the analyses conducted on PlaNET epiphenotype variables, we note that although these three cohorts underwent similar sampling protocols and were processed for DNAme analysis at one center, there are some key between-cohort demographic differences Table . First, Before performing DNAme array data analysis, it is useful to assess the main drivers of DNAme variation in the raw and processed data. To that end, we used principal component analysis (PCA) in combination with linear models to assess the relationship between variation in the data (PCs) with major technical and biological variables Fig. .Fig. 1Prp < 0.001 for Cohort = V-NORM, V-SSRI, or QF2011) and technical array variables   and cytotrophoblast proportion , while PC2 was also associated with cell type proportions. Array batch and PlaNET-derived ancestry were also weakly associated with PC1 and PC2 in the processed dataset . The fact that PC1 was more strongly associated with cohort than any other variable suggests that there are unidentified technical and/or biological variables contributing to between-cohort variability. In summary, data normalization and probe filtering are essential for reducing DNAme variance associated with technical factors, and cohort, array batch, cell type proportion, self-reported ethnicity, ancestry, and sex are all important variables to consider in any downstream analyses of these data.Our first main finding was that data processing  attenuated the association between DNAme variance and technical factors, and the reduced DNAme variance in the processed data was instead related to cohort and cell-type differences rather than technical factors. This was evidenced by the fact that in the raw data, the first two principal components PC1 and PC2) accounted for nearly half (45.2%) of the DNAme variability across all samples, and were associated with cohort  had a high estimated probability (score > 75%) of European ancestry and for most of these  the maternal self-reported ethnicity was “white”/European descent. All 17 placentas with a high probability (score > 75%) of East Asian ancestry had maternal self-reported “Asian” ethnicity, as did 4 additional samples (n = 21/204). No cases had a probability score > 75% of African ancestry, but of the 2/204 cases with a probability score > 50% of African ancestry, the maternal self-reported ethnicity was “Black” in one case. While there is a strong relationship between PlaNET ancestry estimates and maternal self-reported ethnicity, importantly, 24 placental samples (14% of the combined cohort) did not have values > 75% in any one ancestry dimension. This suggests a high degree of genetic diversity, which is important to consider in downstream analyses and cannot be captured by assigning samples to discrete ethnicities or ancestry groups. Looking at demographic and DNAme-derived variable relationships, we found that as expected, across all cohorts the various ethnicity and ancestry measures were associated (R2 0.22–0.95) . The median deviations between predicted and reported gestational age were < 1 week in all three cohorts   Fig. . ConsideR of 0.54 and 0.37, respectively)  samples due to localized heterogeneity within the placenta, or due to systematic differences in sampling techniques between cohorts. As DNAme profiles vary markedly between cell types, they can be used to estimate the relative cell type proportions in whole chorionic villi samples . Cell type proportions can then be compared between cohorts, datasets, or disease status groups to identify systematic between-group differences.In assessing the inter-relationships between the different cell type proportions calculated with PlaNET, we found that cytotrophoblast proportion was inversely correlated (Pearson correlation) with syncytiotrophoblast proportion, and that there were no strong relationships between cytotrophoblasts or syncytiotrophoblasts and any other cell type proportions . Beyond this small impact on stromal cells, however, processing time appeared to have little effect on placental cell composition estimates.When comparing relative cell proportions across cohorts, some subtle but significant differences were noted. A slight decrease in stromal cells was observed in V-SSRI samples as compared to the other two cohorts Fig. B. As thep = 0.0012), and also observed a decrease in cytotrophoblast:syncytiotrophoblast ratio with increasing gestational age (p = 0.00052). However, when the linear model testing for association between white/non-white ethnicity and cytotrophoblast:syncytiotrophoblast ratio was adjusted for gestational age, trophoblast ratio remained associated with ethnicity (higher ratio in non-white ethnicity) (p = 3.18e−7).Samples from the QF2011 cohort displayed a slightly higher median estimate of syncytiotrophoblasts, and a lower median estimate of cytotrophoblasts, as compared to the other two cohorts Fig. B, leadinBefore performing statistical analysis, it is important to assess inter-relationships and possible collinearities between demographic and technical variables in a dataset, including any relevant epiphenotype variables, and any necessarily related demographic variables, such as birth weight and gestational age. As the datasets originally used to construct the PlaNET epiphenotyping tools may have inherent biases towards different technical or biological variables, investigating the relationships between these epiphenotypes and other dataset metrics in these three well-characterized cohorts could provide useful knowledge for future applications of these tools. Reassuringly, beyond the factors already discussed, we did not detect further associations between PlaNET epiphenotype variables and other biological or technical variables , and in cases with maternal white ethnicity . We found that fetal:placental weight ratio was higher at lower gestational ages as reported in [Of the remaining variables of interest, birth weight Z-score and placental efficiency  are both metrics of fetal growth during gestation, and are interesting to evaluate relative to the PlaNET tools for their relationships to both successful gestation and pathologic conditions such as preeclampsia or fetal growth restriction. Birthweight Z-score characterizes fetal growth by contextualizing infant birthweight relative to population-based reference groups of sex- and gestational-age-matched peers, while placental efficiency is a metric reflects the growth (mass) of a fetus relative to the growth (mass) of its own placenta. In principle, larger placentas can support larger infants, but the most “efficient” placentas are those that support adequate fetal growth with less relative placental mass. Birthweight Z-score and placental efficiency were significantly associated with each other, but were not strongly associated with other variables , in all samples (n = 204). We also ran a series of equivalent models in only the V-NORM and V-SSRI samples (n = 99) as there was a systematic difference in the data collection method for gestational age between QF2011 (rounded to the nearest week) and the other two cohorts, see Methods.As a final investigation into the utility of epiphenotype variables in analysis of placental DNAme data, we evaluated whether accounting for epiphenotypes during statistical analysis may help attenuate test statistic inflation (lambda) . To thatε. We computed the lambda value across all p values obtained from this model, and compared this lambda to models iteratively adjusted for gestational age, RRPC gestational age epiphenotype, PlaNET ancestry, cell composition, and the combination of RRPC, ancestry, and cell composition. In the full dataset (n = 204), accounting for epiphenotypes of ancestry and cell type led to the largest reductions in lambda . In the series of models run on the V-SSRI and V-NORM samples only, accounting for reported gestational age had a similar magnitude of effect on lambda as did accounting for the RRPC gestational age epiphenotype (approximately − 0.5). See Additional file The base model we assessed was adjusted for the additive covariates of sex and Sentrix Position (array row), and took the form: DNAme ~ Cohort + Sex + Sentrix Position + The models run on the full dataset all had higher lambda values (median lambda = 19.26) than models run on the V-SSRI and V-NORM samples only (median lambda = 4.08). This example scenario does not reflect a complete analysis of the data, and is only meant to illustrate the impact of adjusting for epiphenotypes on epigenome-wide DNAme analysis. Typically, analysts would be investigating other outcomes with possibly different lambda values (beyond cohort effects), and given the lambda values obtained here, should also consider adjusting for unmeasured technical effects .In this analysis, we have demonstrated the applicability of placental epiphenotyping tools to independent data, and our findings indicate that these tools are appropriate for routine use in placental DNAme data processing and analysis pipelines in a variety of contexts. We have also reported how these epiphenotypes vary in association with extended sample characteristics such as gestational age, ethnicity, and processing time; our major findings in this regard are presented in Table PlaNET can first be used to estimate ancestry along three continuous axes of variation. The main utility of these PlaNET ancestry probabilities is that they can account for ancestry-driven genetic variation that influences DNAme, particularly when genetic ancestry data are absent. Self-reported ethnicity is often collected and used as an estimate genetic ancestry, but even when both maternal and paternal ethnicity data are available for prenatal samples such as placenta, ethnicity is a categorical identity and a poor proxy for genetic ancestry, which is continuous in nature and more closely reflects genetic variation –46. Whilr = 0.26 with an absolute mean deviation of 7 days) [The PlaNET placental gestational age clock (RRPC) was less strongly correlated with birth weight than was clinically reported gestational age, which implies reduced accuracy of the DNAme-derived estimate. Nonetheless, the RRPC gestational age on average deviated only by − 4.3 days from the reported gestational age, which is less than reported in the original publication of this tool ( 7 days) . The Lee 7 days)  study ut 7 days) , 49); weR = 0.94) between PlaNET-predicted gestational age (RPC) in a separate dataset with a broad range of gestational ages (25–38 weeks), also with median absolute error of 4 days [While the present study focused on evaluating tools presented in the PlaNET R package, two other placental gestational age clocks, exist, developed by Mayne et al.  and Haftf 4 days . Althougf 4 days . In our f 4 days . If resef 4 days . That saf 4 days , 50.Regarding cell composition, the placenta is a heterogenous solid tissue with multiple cell types, derived from all three components of the blastocyst: (1) trophoblast from trophectoderm; (2) placental endothelial and endodermal cells from hypoblast , 55, andThe cytotrophoblast:syncytiotrophoblast ratio was found to be strongly associated with gestational age over the last few weeks of gestation, which is consistent with the decrease of cytotrophoblast populations over time as these cells fuse to form the multinucleated syncytiotrophoblast, which in turn becomes increasingly abundant towards full term , 60. TheThe inter-relationships between variables that affect placental DNAme are important to understand before undertaking further analysis. While we identified a small number of variables that differed between cohorts, including differences in ancestry composition and slight differences in trophoblast ratios, none of these factors individually explained as much variation in DNAme as the “cohort” variable itself did (significantly associated with PC1). This is an expected result, and cohort-level differences likely arise from the combination of many factors including different procedures used in sample processing, storage and DNA extraction, and differences in environmental exposure between samples comprising each cohort . This is particularly relevant as in this study, the QF2011 cohort was exposed to an acute environmental stressor (flood), which will be explored in a future study for its effect on DNAme. Although a subset of the V-SSRI cohort was exposed to SSRIs and gestational maternal depression, in a previous study we found no consistent signature of altered placental DNAme in association with these exposures, and thus this particular exposure variable is likely not a large driver of cohort-level differences .n = 343 [Obstetrical outcomes can differ by the sex of the conceptus; for example, male (XY) placentas tend to be larger and more prone to proinflammatory response than female (XX) placentas . Accordin = 343 , implyinn = 343 , 62, 63 n = 343  have been = 343 . Addition = 343 . Thus, ip < 0.05). While it still needs to be experimentally confirmed, this suggests that PlaNET ancestry estimation will still be a valid tool if applied to placental DNAme data collected with the v2 array.Cohort of origin, array batch, cell type proportions, self-reported ethnicity, genetic ancestry, and biological sex are important variables to consider in any analyses of Illumina DNAme data –68. HoweWe suggest integration of epiphenotype variables during both the processing and analysis of placental DNAme data; see Fig. As a proof-of-principle experiment, we demonstrated that accounting for epivariables in differential DNAme analysis of placental data can help attenuate test statistic inflation. This was demonstrated by running a series of linear models with “Cohort” as the primary variable of interest, and iteratively adjusting for the epiphenotype variables as additive covariates in these models. Small but progressive decreases in the lambda values, i.e., decreasing inflation of the p values, was observed with adjustment for epiphenotypes . This reWe note that the method by which biological and epiphenotype variables are accounted for in analyses following data processing should be carefully considered, given the associations observed here between cell composition, ethnicity, genetic ancestry, and gestational age/birth weight Z-score. Adjusting or “controlling” for these factors in statistical models can mask important relationships between these variables and the outcomes of interest. As factors such as cell composition, ethnicity, genetic ancestry, and gestational age/birth weight may all interact with fetal health in unique ways, they should be studied directly when possible. If sample size is sufficient, for example, data should be analyzed separately by maternal self-reported ethnicity groups, and by sex, since DNAme alterations associated with other variables of interest may differ within these groups. Though not explored here, it is also worth noting that epiphenotypes could be used for metadata harmonization across cohorts with different reporting standards . We also note that these epiphenotype variables can be analyzed directly in relation to outcome variables of interest, such as disease status . We acknowledge that an important limitation of our study is the relatively small sample size limited to pregnancies at or near term. It will be interesting to explore these questions and further validation of the epivariables in larger and more diverse cohorts in the future.Overall, we recommend the application of epiphenotyping approaches, followed by detailed exploration the interrelated nature of biological, technical, and epiphenotype variables in any dataset before beginning analysis, and further recommend that analysts exercise due caution in interpreting results.n = 35), as part of a study on Epigenetics in Pregnancy Complications (EPIC) [n = 64), with/without clinical depression, and with/without the use of selective serotonin reuptake inhibitors (SSRIs) [n = 105) [204 placentas from three cohorts were processed for DNAme arrays in Vancouver, Canada. The three cohorts consisted of: (i) V-NORM, a normative population of pregnancies recruited at BC Women’s Hospital (BCWH) in Vancouver, Canada (s (EPIC) , 74, 75; (SSRIs) , 76; andn = 105) . Ethics The V-SSRI and QF2011 were prospectively recruited cohorts, and, except for four individuals giving birth between 35.7 and 37.0 weeks, all births occurred at term. Cases for V-NORM were thus retrospectively selected for having similar gestational ages at birth  to match the other two cohorts, with four samples included between 36 and 37 weeks gestational age. Of note, gestational age was only available to the nearest week for the QF2011 cohort, with three missing values in the 105 placentas that were imputed to the median of all other measurements (39 weeks). Exclusion criteria applied to all cohorts included pregnancies with multiple fetuses and chromosome abnormalities. Additionally, V-SSRI excluded mothers with bipolar illnesses, hypertension, current substance abuse, any diabetes, or infants with congenital brain malformations or fetal growth. V-NORM excluded any pregnancies affected by preeclampsia, while QF2011 was not subject to any additional specific exclusions. The respective exclusion criteria were applied to all cohorts to enable examination of key variables of interest without the presence of large confounding factors .Self-reported ethnicity and/or race are increasingly recognized as important variables to consider in health research, but there have not been consensus definitions of race or ethnicity –80. Furtn = 75, Spearman’s Rho = 0.97, p < 2.2e−16). Between-cohort differences were evaluated by ANOVA for continuous variables and Chi-square tests for categorical variables.Infant birth weight is presented as standard deviation Z-scores from the mean sex- and gestational age-specific birth weights, based on Canadian birth charts . Placent3 samples of chorionic villi were taken from each of four distinct cotyledons (sites) from below the surface of the fetal-facing side of the placental disc at a depth that targeted intermediate and tertiary villi and well-washed of blood. Any potential contaminating maternal tissue  was carefully avoided. Placental processing time (number of hours from placenta delivery until sampling) ranged from 0.5 to 288 h (with 5 samples missing data). The samples were washed thoroughly to remove blood, and any thick vessels were removed. Samples were frozen at − 20 °C until use. DNA was then extracted from all four cotyledon samples using a salting-out DNA extraction procedure [3 each) representing different cotyledons were sampled across the fetal-facing side of each placenta. These samples were snap-frozen in liquid nitrogen and subsequently shipped to Montreal, Canada. Pools of five samples were ground over dry ice, and DNA was extracted using the DNeasy Blood & Tissue Kit  in Montreal before being shipped to Vancouver on dry ice for DNAme processing.Placental sampling after delivery followed two similar but distinct sampling processes. First, the V-NORM and V-SSRI cohorts were sampled by a single lab in Vancouver, Canada, using a standardized sampling protocol . Brieflyrocedure , and ext(n = 11) of QF2011 samples were received in Vancouver at a later date and were included in Batch 2. All samples from V-NORM were run in EPIC array batches 2 and 3, along with placental samples that were part of other, related projects.DNA samples from all three cohorts were run on Illumina Infinium MethylationEPIC v1 arrays in Vancouver, BC, Canada. Processing included DNA purification after extraction using the DNeasy Blood & Tissue Kit , bisulfite conversion using the EZ DNAme Kit , and hybridization to and processing of the Illumina Infinium MethylationEPIC BeadChip arrays according to the manufacturer’s protocol . Samples from the three cohorts were distributed and run in 3 array batches across 44 eight-sample chips as illustrated in Additional file , and ewastools [X and Y chromosome probes, normalized to the per-sample mean autosomal total fluorescence intensity, and was confirmed to match the clinically reported sex of the infant in all cases. Sample genetic identity was assessed using the 59 SNP (‘rs’) probes on the EPIC array with the “call_genotypes” and “enumerate_sample_donors” functions (ewastools) [DNAme data from raw IDAT files were read into R v4.2.2  and annowastools . First, wastools , using tastools) . FinallyR package [PlaNET-derived genetic ancestry can be described as a continuous variable on three axes of variation that sum to one, representing contributions of African, East-Asian, and European ancestry [PlaNET-derived cell composition was calculated using the robust partial corrections method, which yields six compositional estimates of the major placental cell types  [PlaNET-derived gestational age can be calculated using 3 different built-in tools: the robust placental clock (RPC), the control placental clock (CPC), and the refined-robust placental clock (RRPC). The RRPC is most appropriate to the present dataset as it was developed using exclusively samples > 36 weeks of gestational age  to improve prediction over the narrow age range at term [The PlaNET  package  was used package . PlaNET-ancestry . PlaNET-oblasts) . To avoioblasts) . PlaNET- at term .p value > 0.01 or bead count < 3 or missing values in > 5% of samples) were removed from the dataset (n = 4783), as were cross-hybridizing probes and probes overlapping single-nucleotide polymorphisms . Technical replicates of 12 genetically distinct samples (11 replicate pairs and one sample run in quintuplicate) were used to assess data processing by calculating the correlation between all DNAme beta values of replicate sample pairs in the raw and processed datasets. The highest quality replicate from each pair was retained for the rest of the analysis, and all others removed (n = 15 replicate samples removed). One additional non-replicate sample was removed for failing probe quality checks (> 1% of array probes failed detection P/bead count). After data processing and quality control, a total of 746,608 probes in 204 samples remained for analysis. Of note, we elected not to apply ComBat [ComBat can lead to false positive discoveries [After estimation of epiphenotype variables, raw data were normalized for analysis, using noob and dasen combined normalization methods , 88. Sevolumn of , n = 99,y ComBat , a commocoveries .Principal component analysis (PCA) was used to assess the primary drivers of DNAme variance in the data using the R package irlba [ge irlba . Linear ge irlba , and visge irlba .Ancestry informative markers (AIMS) were used as an independent assessment of genetic ancestry. Genotypes at 57 single-nucleotide polymorphisms informative to assess African, East Asian, and European ancestry [ancestry , 97 wereancestry . Brieflyn = 746,261) were converted to M values, and subjected to linear modeling, testing for between-cohort DNAme differences, with sex and Sentrix Position (array row) included as additive covariates. Subsequent linear models were run that included additional additive covariates beyond sex and Sentrix Position: reported gestational age, RRPC gestational age epiphenotype, PlaNET ancestry estimates as continuous values, and PlaNET cell composition estimates as continuous values. The two compositional epiphenotype variables (ancestry and cell composition) were adjusted for using a “leave one out” structure, where all but one continuous variable from each epivariable set were included in the linear models , as recommended in the package vignette [R package [R package [DNAme data at all filtered autosomal CpGs (vignette ). Linear package , and lam package .Additional file 1: Fig. S1. Sample map for EPIC array processing. Depiction of sample distribution across Illumina Infinium MethylationEPIC array chips, colored by randomization variables . Chips are grouped by batch. Fig. S2. Heatmap of the strength of association between pairs of covariates. R2 values of linear models run on Covariate ~ Covariate demographic variables. “Ethn” denotes ethnicity, “P(African/Asian/European)” refer to the continuous PlaNET ancestry probabilities, “SD” refers to standard deviation, “wt” refers to weight, “GA” refers to gestational age at birth, “Cyto” refers to cytotrophoblast, and “nRBC” refers to nucleated red blood cells. Fig. S3. Relationship between processing time and cell type proportions. (A) Placental processing time in hours after delivery (Proc time) is plotted along the Y axis, with cohort plotted along the X axis. (b) Estimates of cell type proportions (Y axis) were plotted against placenta processing time (hours) from all cohorts. Significant Pearson correlations (Estimate ~ Cell Type) are indicated with p < 0.05 in the figure legend. (C) Samples from the V-SSRI cohort were excluded, to evaluate the impact of processing time on cell type proportions independent of the few samples in V-SSRI with unusually long processing times. Significant Pearson correlations are indicated with p < 0.05 if the figure legend. Fig. S4. Relationship between cell type proportions and sex, self-reported maternal ethnicity, and PlaNET ancestry.  All Cohorts,  Vancouver-collected cohorts only, QF2011 cohort excluded. Significance of comparisons are indicated when p < 0.05. Fig. S5. Relationship between cell type proportions and placental to fetal weight ratio and residual. (A) Fetal to placental weight ratio association with cell type proportions. Significant correlations are indicated with p < 0.05 in the legend. (B) Residual of fetal weight regressed on placental weight showed no significant association with any cell type proportion. Fig. S6. Distribution of all nominal p values for linear models run with adjustment for epiphenotype variables. “Base” refers to the base linear model of DNAme ~ Cohort + Sentrix Position + Sex + ε. Additional models refer to the base model plus the specified additive covariate. For example, GA  refers to DNAme ~ Cohort + Sentrix Position + Sex + GA + ε. P values investigated are those associated with the term “Cohort”. RRPC indicates robust refined placental clock, Ancestry refers to adjustment for PlaNET ancestry continuous values, Cells refers to adjustment for continuous PlaNET cell composition estimates. Listing > 1 variable indicates additive adjustment for all indicated variables (such as adjustment for both ancestry and cell composition as indicated by the notation Ancestry_Cells). A horizontal dashed line indicates p = 0.05. The p values shown in this plot arise from linear models run on V-SSRI and V-NORM (n = 99) at all filtered autosomal CpGs. Table S1. Lambda values from linear models for differential DNAme by Cohort. Lambda was calculated in each case from all nominal p values associated with the Cohort term in each model. GA refers to gestational age, RRPC refers to the robust refined placental clock gestational age, Ancestry and Cell Types refer to the PlaNET epiphenotype variables for ancestry and cell composition, included as continuous additive covariates.


Identifying target promoters of active enhancers is a crucial step for realizing gene regulation and deciphering phenotypes and diseases. Up to now, several computational methods were developed to predict enhancer gene interactions, but they require either many epigenomic and transcriptomic experimental assays to generate cell-type (CT)-specific predictions or a single experiment applied to a large cohort of CTs to extract correlations between activities of regulatory elements. Thus, inferring CT-specific enhancer gene interactions in unstudied or poorly annotated CTs becomes a laborious and costly task.Here, we aim to infer CT-specific enhancer target interactions, using minimal experimental input. We introduce Cell-specific ENhancer Target pREdiction (CENTRE), a machine learning framework that predicts enhancer target interactions in a CT-specific manner, using only gene expression and ChIP-seq data for three histone modifications for the CT of interest. CENTRE exploits the wealth of available datasets and extracts cell-type agnostic statistics to complement the CT-specific information. CENTRE is thoroughly tested across many datasets and CTs and achieves equivalent or superior performance than existing algorithms that require massive experimental data.https://github.com/slrvv/CENTRE.CENTRE’s open-source code is available at GitHub via  Promoters and enhancers are the two major cis-regulatory elements that control the context-dependent gene transcription. Eukaryotic enhancers are bound by various transcription factors (TFs) and when activated they upregulate the expression of target genes by forming chromatin loops with their target promoters . It is eAs more mutations and genomic alterations of the noncoding genome become associated with regulatory elements, identifying gene targets of active enhancers is crucial for deciphering diseases and other phenotypes. Experimental methods such as Hi-C  which requires for the CT under study only RNA-seq results and ChIP-seq data for H3K27ac, H3K4me1, and H3K4me3. This CT-specific information is combined with statistics derived from enhancer and promoter signals across many CTs by means of a machine learning method, extreme gradient boosting. Through this combination of generic, across-cell-type information with CT-specific information we obtain a prediction accuracy which is on par or better than established tools. At the same time the requirement for genomic data about the new CT is low enough to allow for easy and routine use of CENTRE for predicting ET interactions in a new CT.http://owww.molgen.mpg.de/~CENTRE_data/BENGI_processed_datasets.zip.We used the All-Pairs.Natural-Ratio BENGI collection of datasets for the training and evaluation of CENTRE. BENGI uses the hg19 annotation of cCREs-ELS and GENCODE v19 TSS. We updated all ET pairs of the BENGI datasets on the hg38 annotation using the UCSC Genome Browser liftover facility . Then weAll features used for the training of CENTRE are listed in ENCODE cCREs-ELS, and GENCODE TSSs were extended on both sides to have a length of 500 bp. We downloaded H3K4me1, H3K4me3, and H3K27ac ChIP-seq data from the ENCODE portal  for all CRUP-EP uses a combination of two binary random forest classifiers and assigns enhancer probabilities to each 100-bp bin . The firWe used the complementary probability of the second classifier that distinguishes enhancers from promoters such that it can return the probability of the bin to be an active promoter, given that the bin is active . We callWe overlapped the obtained probabilities with the 500 bp enhancer and target regions [findOverlaps function from GenomicRanges library  the number of bins where CRUP-EP > 0.5 (reg_dist_enh), (ii) the number of bins where CRUP-EP > 0.5 divided by the total number of bins in the ET window (norm_reg_dist_enh), (iii) the number of bins where CRUP-PP > 0.5 (reg_dist_prom), and (iv) the number of bins where CRUP-PP > 0.5 divided by the total number of bins in the ET window (norm_reg_dist_prom).RNA-seq TPM values for all biosamples considered in the study were downloaded from the ENCODE portal .https://fantom.gsc.riken.jp/5/datafiles/reprocessed/hg38_latest/extra/). We averaged TPM values for the enhancers and for the genes across replicates resulting in 848 different CTs. We intersected cCRE-ELS with enhancer CAGE peaks [findOverlaps function from GenomicRanges library  from liftOver  to obtaihttp://big.databio.org/papers/RED/supplement/. We used the processed DNA-seq dataset for the enhancer regions and we applied the Wilcoxon rank-sum test to compare the target gene expression in samples where the enhancer has higher DNAse activity (top quantile) than the rest of the CTs.We downloaded the normalized microarray gene expression for 112 CTs (exp112.bed) that match the DNAse-hypersensitive dataset from http://owww.molgen.mpg.de/~CENTRE_data/In_house_constructed_datasets.zip.We downloaded H3K4me1, H3K4me3, H3K27ac, ChIP-seq data, and RNA-seq TPM values for 66 matched CTs from the ENCODE portal . We applP-values into a single P-value using Fisher's method  classifier. We used analogous parameters with the XGBoost for the RF training. RF parameters: n_estimators = 300, class_weight = ‘balanced_subsample’, random_state = 0, max_samples = 0.9, max_depth = 5. Area under precision recall curves (AUPRCs) and F1-scores comparing the two models are illustrated in CENTRE features were calculated based on the ENCODE Registry of cCREs on hg38 and the basic gene annotation of GENCODE Release 40 (GRCh38). However, for the comparison with TargetFinder on the BENGI datasets we used the hg19 annotation of cCREs-ELS and GENCODE v19 TSS that the authors originally used. CENTRE feature values were averaged for hg38 ET pairs corresponding to the same hg19 pair.https://github.com/shwhalen/targetfinder). Regarding the five tissue datasets where not all genomic datasets required by the TargetFinder method were available, we implemented a TargetFinder reduced model using a subset of 13 features coming from DNase, H3K4me3, H3K27ac, and CTCF experimental assays and distance. We used experimental assays downloaded from the ENCODE portal  by performing a Wilcoxon rank-sum test between target transcriptional signals between cells where the enhancer is active and those where it is not active. We use its associated P-value for all enhancers and target pairs within 500 KB. In addition we employ enhancer probabilities generated by CRUP-EP .We further extend this approach by including other descriptors of enhancer activity for cCREs and GENCODE TSS activity. Descriptors for enhancer activity comprise DNAse accessibility and eRNA expression as measured by CAGE tags. On the side of the target promoter/gene activity we use DNAse accessibility and downstream transcript level as measured by microarrays and CAGE-seq. We use three publicly available datasets . We downloaded H3K4me1, H3K4me3, and H3K27ac ChIP-seq data for 104 CTs from the ENCODE portal , and we Aggregate statistics obtained from many biosamples are insufficient to delineate CT-specific links. Thus, computational ET prediction methods also use CT-specific assays to capture CT dependent interactions. We designed two kinds of features. Firstly, we want to capture whether an enhancer and promoter are each active. To this end, we again apply the CRUP-EP and CRUP-PP functions (Section 2) to compute from HMs the probabilities for cCRE-ELS and TSS to be active as enhancers or promoters, respectively. Additionally, we use the expression level of the respective target gene as given by the CT-specific RNA-seq data.A very important feature in predicting whether cCRE-ELS might target a certain promoter is the genomic distance between the regulatory elements. However, this is a generic feature carrying no information about a particular CT. We again exploit CRUP predictions for regulatory elements to upgrade the genomic distance feature to a “regulatory distance” which describes whether there are many active regulatory elements in the genomic region between the ET pairs, or whether that region is largely devoid of regulatory activity. We apply CRUP-EP and CRUP-PP to the window between the two regulatory elements and extract the fraction of regions classified as enhancers or promoters, respectively. Note that regulatory distance (RD), in contrast to genomic distance alone, captures CT-specific information.Taken together we form features from the following information: (i) Statistics among regulatory element activities in many CTs, as described by the Wilcoxon rank sum test and Pearson’s correlation coefficient, (ii) genomic distance and RD, and (iii) CT-specific regulatory element activity predictions obtained using CRUP and gene expression . The CENFor a proof of concept we tested the ability of individual features to correctly classify ET interactions as derived from GM12878 RNAPII-ChIAPET data  importance ranking of the features. Here, for the reduced model the F1-score gets computed. The result is shown in With versions of RD scoring high in feature importance, we further checked whether RD is in fact more informative than mere genomic distance. To this end, we collected enhancer–promoter pairs which interact in LCLs but not in HeLa cells, based on the annotation provided by An example of an ET pair where RD makes a difference with respect to the final CENTRE prediction score is given in Benchmark of candidate Enhancer-Gene Interactions (BENGI) established by For the validation of CENTRE on multiple CTs, we used the P-value of a t-test comparing the two distributions. Generic features and CT-specific features for the target promoter/gene are generally the most significant ones presenting a consistent significant difference between interacting and noninteracting ET pairs across BENGI datasets.Before testing the machine learning prediction, we investigated how informative are individual features with respect to distinguishing positive from negative ET pairs. The full TargetFinder model uses roughly 101 epigenomic and transcriptomic experiments from histone modification ChIP-seq, TF ChIP-seq, DNase-seq, and CAGE-seq, yielding 303 features. Our CENTRE method computes 28 features stemming from only four experiments, namely three ChIP-Seq HMs and RNA-seq. For the initial comparison we used ET datasets derived from five commonly used CTs where all experiments required by TargetFinder are available, which then also includes the four experiments used by CENTRE. We extended our evaluation in five tissue datasets where the positive pairs were extracted from eQTL mapping. Since not all TF ChIP-seq datasets needed for training of TargetFinder were available, we reimplemented a reduced TargetFinder model (Section 2) using a subset of 13 features coming from DNase, H3K4me3, H3K27ac, and CTCF experimental assays and genomic distance, as suggested by Training a classifier is a complicated process and rather than requiring the user to train a classifier a pre-trained classifier should be provided. Therefore, we provide a pre-trained classifier software that we have trained on a dataset of known ET interactions and that is then ready to make new ET predictions in any CT and context. To this end, the selection of the training dataset is a critical factor for the algorithm's ability in future predictions. Based on the information summarized in For the classifiers trained on this information, we performed extensive performance evaluation on 11 ET datasets coming from three cell lines and six different experimental assays, using the 12 CV scheme to avoid overfitting. According to the results shown in Once we homed in on the training set, CENTRE was trained and optimized on the whole consensus LCL dataset (Section 2). The feature importance for the pre-trained classifier is provided in In this work, we have put forward the CENTRE method to predict interacting enhancer–promoter pairs in a CT of interest. In a real-life application, that CT of interest will typically be a less studied CT and our method requires only a limited set of experimental data to base the prediction on. When training the method, however, we include a wealth of available data to establish a space of feasible interactions. These interactions get reweighted according to the CT-specific information. This process is transparent to the user who does not need to retrain the program but instead only provides the CT-specific data to the trained algorithm. Despite this simplicity in applying CENTRE, the quality of its predictions is generally comparable to, and in some cases better, than the best existing methods.Designing a machine learning method not only yields the benefit of the final product, the program, but also allows to study which features contribute and improve the quality of the predictions. We did not attempt to assemble large numbers of features but put a lot of effort into the careful design of the features. In this process we experienced a few surprises. Firstly, in the context of distilling the information from available data across CTs, we found that a rank-sum test between two regulatory features of many CTs adds valuable information on top of that provided by correlations. Clearly, enhancer and promoter activity across many CTs need not be linearly related. For an active ET link both enhancer and promoter/gene intensities  will be high. In an inactive link the relationship between the intensities will be largely random while generally lower than for an active link. A rank-based measure like the rank-sum test appears to detect this more robustly than a correlation coefficient.The other insight from the feature design concerns the RD. While genomic distance clearly plays an important role in enhancer–promoter interaction, RD describes how much regulatory activity there is in the interval between enhancer and promoter in the CT under study. We have shown that inclusion of this feature improves prediction, and that RD is connected to the probability of an ET link. This suggests that for the cell it is easier to establish a specific chromatin interaction when in the loop that is excluded there is little other regulatory activity. The concept of inspecting various signals in the window between ET pairs is not new, TargetFinder also uses window signals in its model. However, TargetFinder investigates numerous signals while CENTRE relies only on CRUP probabilities.Although CENTRE performs well and compares favorably with TargetFinder, the F1 score on many datasets is still low. To a certain degree this simply reflects the inherent difficulty of the problem of enhancer target prediction. Inclusion of more HMs could possibly improve the annotation of cCREs and RD, but this comes to the cost of extra experiments and makes practical application of our method harder. We also tried to put our method to the toughest tests and present fair comparisons and results, avoiding overfitting issues due to dependent genomic regions in training and test sets. Still, even the choice of validation data strongly influences any performance measurement. We notice that the Hi-C datasets consistently exhibited the lowest overall performance. One possible reason is that Hi-C maps even at 5 kb resolution are too coarse and cannot be used to link distal regulatory elements to their target genes (Given the difficulty of the enhancer target prediction problem, there is clearly room for future improvement of our method. In the current version we derive the space of feasible interactions from an analysis of large amounts of epigenetic data. Here the question is whether and how to capitalize on Hi-C data when it is available. Also, we still have to limit the predicted interactions to a distance of 500 kb to avoid large numbers of false positives. Future methodological improvements will hopefully allow extending this interval.The pre-trained CENTRE framework is provided as ready-to-use R software where the user gives target genes along with CT-specific H3K4me1, H3K4me3, H3K27ac, ChIP-seq, and RNA-seq TPM data and receives all the interacting and noninteracting predicted enhancers for the genes of interest. Thus, CENTRE provides an accurate, pragmatic framework to distinguish genomic interactions without the need for extensive and costly experiments.btad687_Supplementary_DataClick here for additional data file.