The classification of olive oils and the authentication of their biological or geographic origin are important issues for public health and for the olive oil market and related industries. The development of techniques for olive oil classification that are fast, easy to use, and suitable for online, in situ and remote operation is of high interest. In this study, the possibility of discriminating and classifying vegetable oils according to different criteria related to biological or geographical origin was assessed using cyclic voltammograms (CVs) as input data, obtained with electrochemical sensors based on carbonaceous nanomaterials and gold nanoparticles. In this context, 44 vegetable oil samples of different categories were analyzed and the capacity of the sensor array coupled with multivariate analysis was evaluated. The characteristics highlighted in voltammograms are related to the redox properties of the electroactive compounds, mainly phenolics, existing in the oils. Moreover, the antioxidant activity of the oils’ hydrophilic fraction was also estimated by conventional spectrophotometric methods  and galvinoxyl) and correlated with the voltammetric responses of the sensors. The percentage of DPPH and galvinoxyl inhibition was accurately predicted from the voltammetric data, with a correlation coefficients greater than 0.97 both in calibration and in validation. The results indicate that this method allows for a clear discrimination of oils from different biological or geographic origins. Olea europaea L.) is an ancient crop in Mediterranean countries and has been spread throughout all five continents. The reason for its worldwide diffusion is related to the high value of its oils, which are used in human consumption and have been demonstrated to be highly beneficial for human health [The olive  quality and the classification into different categories is based on the following criteria: acidity, peroxide value, UV absorption and organoleptic evaluation . On the The quality of the seed oils is established according to the Codex Standard for named vegetable oils 210  . In the Of the many commercial categories that exist for olive oil, EVOO has particularly high standards in terms of composition as well as sensory characteristics as assessed by recognized panels . ChemicaThere are at least thirty-six structurally different phenolic compounds that have been identified in EVOO . Not allThe quantitative determination of these compounds is usually performed according to the Folin–Ciocalteu spectrophotometric method . HoweverThe array of sensors coupled with multivariate data analysis, electronic tongues, based on potentiometric, voltammetric or impedimetric sensors, have been successfully used for discriminating different types of oils, depending on their phenolic and sensory characteristics ,46. ThesAn important category of electrochemical sensors and biosensors is based on carbonaceous nanomaterials and metal nanoparticles, which have a special affinity for the phenolic compounds . These nIn this study, screen-printed electrodes based on nanomaterials  modified with gold nanoparticles were used for the sensitive detection of phenolic compounds in oil hydromethanolic extracts. The synergic effects in the sensitive properties of nanomaterials increases the active area of sensors and improves the rate of electron transfer, increasing sensitivity. Electrochemical responses coupled with multivariate data analysis were used for discriminating and classifying vegetable oils according to different criteria, related to biological or geographical origin, or to establish correlations with antioxidant activity.KCl was purchased from Sigma-Aldrich  and dissolved in ultrapure water obtained from a Milli-Q system . Methanol was purchased from Merck .v/v) ethanol (Sigma-Aldrich). The resulting solution was kept at room temperature and in the dark until use.The 0.1 mM DPPH stock solution was prepared by weighing 0.0036 g DPPH  reagent (purchased from Sigma-Aldrich) and dissolving in 100 mL 96% (v/v) ethanol, the mixture being kept in the dark at room temperature for 20 min before determination.The 0.1 mM galvinoxyl stock solution was prepared by weighing 0.0042 g free radical galvinoxyl -p-tolyloxy) reagent (purchased from Sigma-Aldrich) and dissolving in 100 mL 96% , an auxiliary electrode consisting of a platinum wire, and a working electrode. The working electrode was, in turn, a screen-printed electrode (SPE) based on nanomaterials such as carbon nanotubes (CNT) and gold nanoparticles (GNP), nanofibers (CNF) and GNP and also, graphene (GPH) and GNP. These three working electrodes were purchased from Metrohm-DropSens .A Biologic SP 150 potentiostat/galvanostat (Bio-Logic Science Instruments SAS France) coupled with EC-Lab Express software operating in Windows was used to record, characterize and optimize the electrode signals. Software was also used to analyze and interpret the results: Origin, The Unscrambler and Microsoft Excel. For the weighting of the compounds, the Partner AS 220/C/2 analytical balance was used, as well as the Elmasonic ultrasonic bath  for the dissolution and homogenization of the solutions.For the spectrophotometric method based on the reaction of antioxidants with the stable free radicals DPPH and galvinoxyl, sample absorbances were measured using a Rayleigh UV2601 UV/Vis Double Beam Spectrophotometer .44 different commercial oils with year of production 2022 were analyzed in this study, divided into three types of edible vegetable oil, namely POO, EVOO from various countries , and unrefined sunflower oils (SFO). All oils studied were purchased from a local supermarket. A sub-criterion for EVOO sample classification was the country of origin. The studied samples were packaged in dark 0.5 or 1 L bottles and their price was not higher than 8 €/L at the time of the study. Oil samples were kept in a dry and dark place, at a constant temperature. v/v) and ultrasonicated for 10 min. The hydromethanolic extracts were separated using a separation funnel. For the measurements with the sensors, 5 mL of hydromethanolic extract was added to 45 mL 10−1 M KCl solution and the CVs were registered. Six replicates for all samples and sensors were registered. These data were used as input in multivariate data analysis.For analysis, 44 samples from different oils were prepared using liquid–liquid extraction . For eacThe DPPH assay for the evaluation of antioxidant activity is a simple, cheap and effective method, one of the most commonly used to determine the antioxidant capacity of a compound, an extract or other biological matrices  ,53,54. IDPPH has a significant absorption band in the range 515–520 nm, making spectrophotometry an easy tool for measuring this color change and determining the antioxidant activity of the sample. The more this color changes, the more DPPH is reduced and the better the antioxidant activity of the sample . The useThe galvinoxyl method of determining antiradical activity is based on the use of the stable O-centered radical galvinoxyl, which is known to associate with the physiological action of oxygen radicals rather than the stable N-centered radical of DPPH . GalvinoThe input data used in the multivariate data analysis were the CVs obtained with three electrochemical sensors immersed in the hydromethanolic extracts of all oils under study. Six replicates CVs of all electrochemical sensors were registered for all the oil sample hydromethanolic extracts and all CV data were used as input in the multivariate data analysis.−1. No signal pretreatment was applied. All CVs were imported in Excel and the constant variable  was removed. The current values of all replicates and samples from the anodic scan were included in the input matrix. From the analysis of the CVs it was observed that the main differences between the samples was in the anodic part. In order to respect the convention that in the data matrix the samples must be the lines and the variables the columns, the matrix containing the anodic current values was transposed. In this way, a matrix with 264 rows (44 samples × 6 replicates) and 480 columns  for the anodic part of CV was obtained.The cyclic voltammograms were registered between −0.4 and 1.3 V and the scan rate was 0.1 V·sMultivariate data analysis was performed to determine if there were significant differences between the oils studied and for their classification as a function of type of oil, or country of origin (in the case of EVOOs).Principal component analysis (PCA) was used to reduce the variables and discriminate into different classes. PCA is a multidimensional analysis that allows the transformation of the original variables into new ones, called principal components. The role of principal components is to explain the maximum amount of variance with the smallest number of components ,60.PLS-DA  has been used as a deterministic classification technique. The basis of PLS-DA consists primarily of applying a partial regression model using the least squares method on variables that are indicators of groups. The second step of PLS-DA is to classify the observations from the PLS regression results on the indicator variables . By projPartial Least Squares regression (PLS) is a method which reduces the variables used to predict to a smaller set of predictors, which is then used to perform a regression . PLS 1 cMultivariate data analysis was performed using Matlab, Excel and Unscrambler.−1.To study electrochemical processes, reversibility of reactions and stability of the response of the three sensors, the CV electrochemical method was used. The determinations were carried out in the potential range between −0.4 and 1.3 V and the scan rate was 0.1 V·sIn the first step, the behavior of the three electrodes CNT-GNP/SPE, CNF-GNP/SPE and GPH-GNP/SPE immersed in the extract solutions was studied. It can be seen that each sensor has a particular response when immersed in a solution of the same extract. These differences among the responses of the three electrodes is attributed to the redox activity of the phenolic compounds present in the sample to be analyzed , the oxiIt was also observed that the highest intensity at which the oxidation peak occurs is obtained in the case of GPH-GNP/SPCE (276.22 µA), while the intensity at which the reduction peak occurs is higher in the case of CNF-GNP/SPCE (−98.16 µA). These increases in oxidation and reduction current intensity could be attributed to the association of nanomaterials CNT, GPH and also CNF with GNP, achieving a synergistic effect and contributing to increase the conductivity of the electrodes and the electron transfer rate, thus increasing the sensitivity of the sensors ,69. TherTo highlight the differences occurring between extracts from different samples, the following figure shows the response of the CNF-GNP/SPCE sensor when immersed in EVOO sample v2, POO sample p2 and SFO sample s2 .Important differences in peak intensity, position and shape were observed from one sample to another, depending mostly on the polyphenolic compound content of the respective samples. At the same time, a shift of oxidation peak potentials towards lower values is observed with increasing polyphenolic compounds content . The resThe nanomaterials from the sensitive element of the sensors facilitate the detection of phenolic compounds from the samples, the CVs being well defined, with reduced noise and high current peaks. The peak potentials and currents are related to the nature of the analyzed samples.In the first step, electrochemical data from the input matrix were used to perform the PCA. The data from the input matrix were firstly pre-processed by centering the variables and scaling them to unit variance. The purpose of this treatment was to give all variables included in PCA an equal chance to influence the model, regardless of their original variance. The normalization was performed with the 1/(Standard Deviation) method. For this data analysis, the entire data matrix was split into two different data sets including the specific samples and all the corresponding variables differentiated by different criteria: type of the oils  and the country of origin . The results are presented as three-dimensional score plots, differentiated by EVOO, POO or SFO—all samples were included in the model , and by The graph in Another principal component analysis was carried out to explore the structure of the data and the possibility of discriminating EVOO samples by country of origin. The scores plot of PCA based on the CVs of all voltammetric sensors immersed in the extracts of EVOOs is presented in In the PCA score plot, four distinct groups of EVOOs could be found, grouped according to country of origin . The memThe PLS-DA technique, a supervised classification method that allows verification of the assignment of a sample to a particular group and calculation of the calibration and validation error, was used to confirm the groups observed in the principal component analysis.pred), which estimates the class membership of the samples [PLS-DA modeling involves two main procedures, PLS-DA component construction (dimension reduction) and prediction model construction . The output of the PLS-DA algorithms is the X-score (PLS-DA scores), which represents the original data X in a lower-dimensional subspace, and the predicted class membership matrix (Y samples . PLS-DA PLS-DA aims to maximize the covariance between the independent variables X (sensors data) and the corresponding dependent variable Y (classes of samples) of highly multidimensional data by finding a linear subspace of the explanatory variables . This neIn The quantitative data of all PLS-DA models are collected in From these results, it can be concluded that the electrochemical data obtained with the voltammetric sensors based on different carbonaceous nanomaterials and gold nanoparticles can be successfully used for the classification of the oils based on different criteria such as type of oil for EVOO, POO and SFO samples, or country of origin for EVOO samples.An evaluation of antioxidant activity with the DPPH spectrophotometric method was performed according to the method previously described by Gali et al. . The assFor the application of the galvinoxyl method for the analysis of oil samples, volumes of 1 mL extract solution were added to 3 mL 0.1 mM galvinoxyl solution in the spectrophotometer cuvettes and the absorbance for each sample was then measured at 860 nm towards ethanol after 20 min .For both methods, the percentage of DPPH inhibition and the percentage of galvinoxyl inhibition were calculated according to the following equation :%InhibitThe % inhibition results obtained by both methods are shown in As can be seen from the table above, the percentage inhibition values are mostly close in value, but the highest inhibition percentage was obtained for Costa d’Oro L’Italiano EVOO. This result can be correlated with a higher intensity of the anodic peak and, therefore, with a higher content of polyphenolic compounds.The next step of the study was to establish correlations between the electrochemical signals provided by the sensors and the data obtained from the spectrophotometric determination of antioxidant activity by the DPPH and galvinoxyl methods.PLS1 was used as a prediction technique to correlate the voltammetric data obtained with the sensors with the antioxidant activity quantified as capacity of free radical scavenging. The independent variables (X matrix) were all CVs obtained with the sensors and the dependent variable was the percentage of DPPH inhibition or the percentage of galvinoxyl inhibition, respectively. The normalization of the data was performed with the 1/(Standard Deviation) method.The results of PLS1 regression models are presented in the form of the dependences between predicted antioxidant activity and measured antioxidant activity.As can be observed in From the data included in In this study, models were developed for discriminating and classifying oil samples based on different criteria, using an electrochemical method based on SPE modified with different carbonaceous nanomaterials and GNP. The electrochemical signals were used as input variables in multivariate data analysis studies. At the same time, it was established that there was a very good correlation between the electrochemical responses of the sensors when immersed in solutions from the 44 oil samples studied and the data obtained from the DPPH and galvinoxyl spectrophotometric assays for the assessment of antioxidant activity. As a result of this study, it can be concluded that electrochemical sensors can be useful for the estimation of antioxidant activity of different types of oils, including EVOO, but also that they can be used for the discrimination and classification of vegetable oils.The use of sensors and biosensors in industry and quality control laboratories is still limited, even though these have numerous advantages and adequate sensitivity. Further studies will include biosensors capable of detecting biomarkers that could improve the performance of the analytical system for discrimination, classification or authentication. Thus, the development of databases using analytical information obtained by electroanalytical methods, together with the use of appropriate statistical tools could be useful in increasing the performance of systems for the quality control of EVOOs. Another research perspective would be the development of a lab-on-a-chip device useful in the routine analysis of olive oil at different stages of production, from harvesting to marketing, and also to analyse oxidative stability.


The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require multiple samples to identify less frequently mutated driver genes. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumor progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify personalized driver genes based on changes in expression. Our method is the first machine learning model to classify genes as tumor suppressor gene (TSG), oncogene (OG), or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT , to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalized driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labeling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared with mutation and expression data, achieving an accuracy  Alterations in the genome drive the progression of cancer . MutatioVarious computational methods exist for identifying driver genes. Tools classify either mutations as driver events , or the Other tools exist that predict driver genes instead of mutations and use background mutation rate (BMR) or ratio-metric features for prediction. Computational methods using BMR such as MutSigCV  assume hMethods using the concept of mutual exclusivity of genes overcome the challenge by identifying a set of mutually exclusive genes in samples . Somaticpersonalized driver genes, i.e., driver genes for individual samples rather than a cohort. Cohort-based studies rely on a large sample size to identify patterns consistent across samples, while identification of personalized driver genes is especially relevant for sub-types of cancers where large cohort studies are not possible or show very few mutations.While a large number of genomic events in the cancer genome are single-nucleotide variations (SNVs), other genomic rearrangements such as CNVs, gene fusions, and epigenomic changes are also known to occur. While the above-mentioned methods help identify many genes, many samples remain with no mutations in known driver genes . This imIdentification of personalized driver genes helps identify actionable targets in patients without known driver mutations. The methods for the identification of personalized driver genes are based on unsupervised algorithms because we lack the ground truth. Instead, the methods use a network-based algorithm to identify perturbed pathways. The graph, along with somatic alterations and differential gene expression profile of the patient, is used to predict driver genes. DawnRank  and SCS Network-based personalized driver gene tools integrate somatic mutation and gene expression data and identify genes using an unsupervised method. A subset of mutations, SNV, are used in PRODIGY though the method can be extended to other mutation types. Furthermore, the functional impact of mutations is ignored when mutation data are converted into presence/absence calls. Data ingested are limited to network, mutation, and expression data, though methods such as DawnRank also analyze CNV data. With many high-throughput multi-omic data available, the prediction of driver genes can be improved by including multi-omic data. Moreover, the expression data are included as differentially expressed genes (DEGs), calculated based on the cohort and not an individual sample. These methods rank the driver genes but do not classify genes as TSG or OG.In this study, we define a machine learning (ML) classification problem to identify personalized driver genes and address the challenges. We define strategies for labeling genes as driver or neutral and identify the best model that classifies them. We employ features based on mutation, expression, CNV, and miRNA expression data and understand their contribution to classification. We finally build mutation, RNA, and multi-omic models to identify personalized diver genes and assign functional classes. We classify genes of three TCGA cancer cohorts as TSG or OG for individual samples and identify new driver genes.Our method, PIVOT , is the first ML-based supervised approach for identifying personalized driver genes to the best of our knowledge. Unlike previous methods that distinguish between driver and non-driver genes, PIVOT can also identify whether it functions as a TSG or OG in the specific cancer type. We build separate models using features extracted from different modalities of -omics data: SNV, gene expression, and multi-omics. The SNV model is trained on mutation data not limited to single-nucleotide variation but all mutation types. The multi-omic data integrates SNV, gene expression, CNV, miRNA data, and also network information. One of the significant challenges in formulating a supervised classification problem for driver gene identification is the lack of a labeled gold standard dataset. We define four labeling strategies to assign labels and build models using the SNV data, two at the mutation level and two at the gene level. We used the CIViC  databaseal. list . Althougal. list . We concCancer Gene Census (CGC)  is the ghe score . Given t dataset .The best classification performances for identifying personalized driver genes were obtained using the labels derived from the list of TSGs and OGs published by Bailey et al. The method consistently performs well across all four cancer types with the best model accuracy of 0.89 , though approach . We concWe used PIVOT on four TCGA datasets [Breast Cancer: BRCA , ColorecNo data was labeled using CIViC or Martelotto et al. driver genes for the other three datasets . Models SIFTscore consists of a prediction score between 0 and 1, while the corresponding feature SIFTpred is a categorical feature with “D” defining damaging and “N” for neutral mutations. We cannot impute missing values for SIFTscore and SIFTpred, as the given mutation type might be out of the scope of the tool. Hence in the smaller feature subset, we drop SIFTpred feature and encode SIFTpred as an ordinal feature with the lowest value for missing data (explained in Methods).SNV features are based on the functional impact of the mutation, domains mutated, and the prediction scores by various driver or mutation impact predicting tools. These tools cannot score all mutations and are hence dropped while training. We train our models using two feature sets, one that uses all features and the second that uses a subset of these features. The advantage of a smaller feature set is an increased number of training data. Since features with a large number of missing data are dropped, the number of data points dropped because of missing data reduces. The statistics for labeled data used for training and testing is available in We ran the SNV models with two feature sets, “all” and “small,” where “small” consists of categorical features. It is to be noted that the number of features in the two feature sets vary between the datasets because of the difference in domains associated with a cancer type. In BRCA, irrespective of the labeling strategy, the models consistently perform better on “all” features . Since oWe include onco-domain information into the SNV features. For each cancer type, we define features to capture the presence or absence of mutations in cancer-type-specific known onco-domains. Not all domains contribute equally to the classifier’s performance, with a majority having no contribution. We identify the top contributing domain in each cancer type. The subset of domains identified during SNV classification intersects with domain features contributing to multi-omic models . BRCA idWhile mutation data helps classify genes into TSGs and OGs, there is scope for improvement. We used expression data and PPI networks for generating features and predicting personalized driver genes. We built models for BRCA, COAD, and LUAD cancer types using all feasible labeling strategies. In contrast to SNV models, we find er types . The F1 e labels . An anale labels . While tThe SNV and RNA models are sufficient to predict the driver genes, but including both improves the prediction. We used features extracted from SNV, RNA features, CNV, and miRNA data to build the multi-omic models. The best prediction model uses Balanced Bagging across all cancer types using Bailey et al. genes for labeling . Unlike We used the multi-omic model to predict TSGs and OGs for BRCA, COAD, and LUAD cancer types. Most samples predicted at least one TSG or OG. Out of 984 samples, we generated features for 972 samples and identified driver genes in 963. Similarly, only three samples in COAD predicted no driver genes and the number was larger at 62 for LUAD. Surprisingly, the number of unique genes predicted in each cancer type was large. In BRCA a total of 1,342 unique genes were identified, followed by 1,155 and 1,152 in LUAD and COAD, respectively. The distribution of genes identified across samples was similar in all cancer types with most samples with  drivers . A large drivers . In cont drivers  as previ drivers .PRKCA . While models built on labeled mutations score high on classification metrics, their ability to predict genes not observed may vary. Furthermore, ML algorithms predict consistently only when trained on large datasets that mimic all driver genes. We increased the training data by employing gene-based labeling methods and dropping SNV features with large missing data. Our final multi-omic models built on fewer features and many cancer-type specific driver genes perform the best.The lack of gold standard further makes comparison difficult among different methods. Most driver gene lists are either pan-cancer or cancer-type specific. Comparison for personalized driver genes requires a list of driver genes specific for a patient. In the absence of any ground truth most methods use CGC gene list to compare predictions. While building models we show the performance is poorest on CGC genes as the list of genes is not specific to cancer type. There is a need for establishing gold-standard for personalized driver genes to further the field.Compared with previous methods that employ only mutation and expression data, we include known biological knowledge regarding domains and onco-miRNA expression. Furthermore, the previous network-based unsupervised methods consider the presence or absence of mutations in the gene. The functional impact of the mutation is lost in the compression. We use mutation type and prediction scores by multiple tools, and we observe functional impact-based features ranking high in the absence of network or RNA data. Furthermore, the methods use expression data to identify DEGs to map onto the network. The method to identify DEGs is based on cohort, making it difficult to use in a clinical setting. We identify DEGs for an individual sample based on pre-computed values of biological variation for the cancer type. Any new sample can be processed to feature and predict TSG and OG.Our method has its limitation commonly observed with ML-based models. ML algorithms assume the training data covers all possible driver genes and the features capture all the required information to predict the genes. Given the lack of experimental data on personalized driver genes, we assume known driver mutations and all mutations in the curated driver gene list are drivers in the observed tumor. Furthermore, it is to be noted that PIVOT may also produce false positives along with true personalized driver genes. Our tool generates reasonable candidate driver genes in a personalized fashion, which help shortlist genes for experimental studies to understand the progression of cancer. PIVOT uses logCPM values from expression data, which is biased to genes with longer length. Calculating transcripts per million (TPM) might be a better feature as it is normalized for gene length. Including new features that improve the prediction while not increasing computation cost are potential directions in this field. We use network data from STRING , and othIn this study, we make three significant contributions. First, we define the identification of personalized drivers as a supervised problem by defining and studying various labeling strategies. We conclude that the best labeling strategy is using gene-based labeling that is cancer-specific. Second, we build multiple models on mutation, RNA, and multi-omic features to identify the contribution of individual omic-based features. We show network-based and expression features contribute the most to models, with the multi-omic models performing the best. In case of missing omic data, mutation or RNA models can be used for predicting driver genes. Lastly, our models can label genes as TSG and OG in a tumor. The functional labeling of genes is helpful for the identification of potential treatment strategies. Our method, PIVOT, is capable of predicting TSG and OG for individual samples, with multiple models available for predicting based on the availability of data.The models for the identification of personalized TSGs and OGs are built using different modalities of data, feature sets, labeling strategies and imbalance algorithms. The data are labeled using four different strategies. The data are split into training and test and models are tuned using cross-validation on the training data. The best models are selected using metrics on the test data. The overview of model building is shown in The TCGA data were downloaded from GDC for the four cancer types, BRCA, COAD, LGG, and LUAD. For each cancer type the mutation , expression , CNV, and miRNA data were downloaded . The mutation file was downloaded as a maf file generated using Mutect2. The data were annotated using ANNOVAR  and procDriver genes are defined as a gene that contains driver mutations or is expressed aberrantly such that it confers a selective growth advantage . We labeFor SNV features, the mutation data were annotated using ANNOVAR   to incluneigh_FC and neigh_normFC. For a given gene, neigh_FC is calculated as the sum of logFC of all neighbors of the gene, with fold change neigh_normFC is normalized for the number of genes differentially expressed in the neighborhood.For RNA features, the data were processed for each sample to generate a list of DEGs using edgeR (v3.32.1). Differential genes are usually reported for a cohort. It is advisable to have more than one sample in each condition. In a clinical setting, multiple samples may not be available. We generate DEGs for each sample. Not all samples contain paired adjacent normal for a cancer type. First, we calculate the common biological coefficient of variation (bcv) for all tumor samples against normal—this is the coefficient of variation with which the (unknown) true abundance of the gene varies between replicate RNA samples . It reprneigh_CNV_FC and neigh_CNV_normFC similar to RNA features. All genes with copy number variation ≠ 0, the sum of logFC values neigh_CNV_FC. The neighborhood is defined by all genes that can be accessed with n = 1 for this analysis. The list of miRNA associated with cancer type was downloaded from OncomiR Cancer Database [OCMD; Multi-omic features concatenate the SNV, RNA features, CNV, and miRNA data. The effect of CNV on neighborhood is calculated by se OCMD;  and Oncodriver, while all other methods label genes as TSG or OG. CGC and Bailey et al. label all mutations in a gene with the same label. Furthermore, we labeled genes with copy number variations in driver genes based on labels assigned by gene-based labeling strategies for multi-omic analysis. Classification of CIViC labels was a binary classification problem , while the rest were multi-class classification problems . We used 70:30 that split the samples into train and test data  curve and the receiver operator curve (ROC) were plotted for all classes and all models including randomized labels (The data is labeled using four different gene lists. CIViC and Martelotto et al. list the gene as well as the mutation location. Only mutations with the exact location and base change are used labeled. CIViC labels genes as est data . All theest data . The datd labels . FurtherFor each cancer type, the consensus feature contribution was calculated as the average feature contribution for all models built on the dataset. The top domains for SNV and multi-omic datasets were identified based on all domains with feature contribution The predictions of multi-omic models were compared with DawnRank, another personalized driver tool. The mutation and mRNA expression data were run using DawnRank. The complete network published with the tool was used for the analysis. For comparable results, we only consider the labels predicted by PRODIGY for mutated genes and not genes altered by CNV. For each sample, the precision was calculated for increasing ranks. Precision is calculated as the true positives divided by all predictions. True positives are genes predicted by tool and also present in the CGC list of driver genes. We calculate and plot the average rank for samples in a cancer type for varying ranks.


Doxorubicin is a common treatment for breast cancer. However, not all patients respond to this drug, which sometimes causes life‐threatening side effects. Accurately anticipating doxorubicin‐resistant patients would therefore permit to spare them this risk while considering alternative treatments without delay. Stratifying patients based on molecular markers in their pretreatment tumors is a promising approach to advance toward this ambitious goal, but single‐gene gene markers such as HER2 expression have not shown to be sufficiently predictive. The recent availability of matched doxorubicin‐response and diverse molecular profiles across breast cancer patients permits now analysis at a much larger scale. 16 machine learning algorithms and 8 molecular profiles are systematically evaluated on the same cohort of patients. Only 2 of the 128 resulting models are substantially predictive, showing that they can be easily missed by a standard‐scale analysis. The best model is classification and regression tree (CART) nonlinearly combining 4 selected miRNA isoforms to predict doxorubicin response (median Matthew correlation coefficient (MCC) and area under the curve (AUC) of 0.56 and 0.80, respectively). By contrast, HER2 expression is significantly less predictive . As the predictive accuracy of this CART model increases with larger training sets, its update with future data should result in even better accuracy. How well can the response of breast cancer patients to doxorubicin‐containing treatments be currently predicted? An unusually broad analysis  reveals that only 2 of the 128 resulting classifiers are predictive (MCC > 0.3). Therefore, considering fewer algorithms and/or fewer profiles could easily result in not being able to anticipate doxorubicin response. Doxorubicin is an intravenous chemotherapy, within the anthracycline class of drugs, used in both the early and the advanced setting of various cancer types, including BC. Doxorubicin works essentially by intercalating between neighboring DNA base pairs. The resulting doxorubicin–DNA complex inhibits topoisomerase II activity, which subsequently disrupts DNA replication and transcription, leading to both cytotoxic and apoptotic cell death. Unfortunately, primary resistance to treatment is common across cancer types and drugs. Such de novo resistance is also commonly observed with doxorubicin‐containing treatments in BC patients.Breast cancer (BC) has the highest global incidence and mortality rate amongst all cancer types affecting women.1, 2 This anticipation would result in the identified patients receiving without delay alternative treatments more likely to stop cancer progression. In the case of doxorubicin, which may cause cardiotoxicity, such predictors would also be helpful to avoid resistant‐predicted patients taking unnecessary risks. Historically, this goal has been approached by searching for a single‐gene marker, which is a molecular feature able to distinguish between sensitive and resistant tumors to the treatment. While many efforts have employed preclinical data with this purpose, much less abundant clinical datasets are by definition the most relevant for patients. An example of the latter clinical studies is one where high HER2 expression in BC patient tumors was reported to be predictive of patient response to anthracyclines, including doxorubicin. A major limitation, however, is that many single‐gene markers only provide modest predictive accuracy restricted to a very small fraction of patients. As an example, 16% of EGFR‐mutant non‐small‐cell lung cancer (NSCLC) patients were found to respond to Erlotinib, but the prevalence of this mutation was low and 7% of EGFR‐WT NSCLC patients also responded to this drug. Thus, at least in this cohort, the Matthew correlation coefficient (MCC) of this FDA‐approved single‐gene marker is just 0.11,, i.e., slightly more predictive than random guessing. These practical limitations of single‐gene markers may be due to biological reasons , but also technical reasons . Multigene expression signatures of drug response are also a common approach, but these are limited by neglecting epistatic effects and profiles other than messenger RNA (mRNA) expression.A central goal of precision oncology is to anticipate which patients will be resistant to a given drug.15 Thi especially data from cancer cell lines but also data from primary tumor cultures or from patient‐derived xenografts. When systematically and directly compared to single‐gene markers, multigene ML models mostly offer higher MCC and almost always much higher recall. However, while some progress on leveraging preclinical data with this purpose has been made, preclinical models still tend to struggle to predict drug response in patients with a useful level of accuracy.These limitations are now being overcome by machine learning (ML), which can generate computational models exploiting multiple pretreatment features of patients to predict their response to that drug treatment. There are many of such computational studies using pharmaco‐omic data from preclinical models,31 esp Therefore, as these are unlikely to represent the pretreatment molecular features of the tumor faithfully, one aim of curation is to discard tumors that were profiled after the drug is administered . Following such curation, Ding et al. built binary classifiers for six drug‐cancer type binomials, each exploiting four molecular profiles: copy number alteration, DNA methylation, mRNA, and microRNA (miRNA). An elastic net with bootstrapping was used for each profile to select the most predictive molecular features, and a final ensemble classifier was built based on these features. Using fivefold cross‐validation (CV) predictions of drug treatment response, the area under the curve (AUC) of the receiver operating characteristic (ROC), hereafter AUC for short, was above random‐guess level in 4 of the 6 binomials in at least one of the considered profiles. In Bomane et al., our lab focused on a single drug‐cancer type binomial, paclitaxel‐BC, but expanded the analysis to 6 profiles and 10 ML algorithms. Interestingly, miRNA and DNA methylation profiles were revealed to be highly predictive for this binomial. However, the latter only occurred with two algorithms: classification and regression tree (CART) and, to a lesser extent, extreme gradient boosting (XGBoost) integrated with feature selection. Overall, there are still few studies on the application of ML to predict drug treatment response, in part due to the scarcity of suitable clinical samples for a given drug‐cancer type binomial, although its application to predict other patient outcomes requiring less curation has received more attention .ML models exploiting clinical data are hence attractive in this context. However, such studies also have their challenges, e.g., the scarcity of suitable datasets and the need for substantial curation before these datasets can be used for this purpose. For instance, a patient tumor's molecular features are likely to be altered upon drug treatment.51 The This is particularly true when considering algorithms that incorporate some form of feature selection to mitigate the impact of high‐dimensionality in the training data. The latter may have the additional advantage of providing interpretable molecular hypotheses for interpatient doxorubicin response variability. Integrating optimal model complexity (OMC) strategies with ML algorithms has resulted in improved predictions with problems spanning a range of drugs and cancer types. Another novel aspect of this study is that we will also investigate for the first time multivariate predictors based on patient response and profiles other than mRNA expression for this drug. The US National Cancer Institute (NCI) Genomic Data Commons (GDC) datasets have been harmonized across different cancer genome programs, providing a range of clinical drug response data and omics profiles. Thus, the data used in this study were obtained from the GDC repository . Beyond single‐gene and/or multigene expression predictors with modest performance, we considered data from multiple molecular profiles arising from the application of various next‐generation sequencing technologies to patient samples and processed via various GDC workflows with their annotated treatment and biospecimen information. Drug response data of BC patients required the most intensive curation  predictions for the patients by each model is quantified by its MCC. The median MCC (mMCC) of five 10CV repetitions, each with a different initial random seed, is presented in Figure 58 whiLooking at the seven models with an mMCC of at least 0.2  and fivefold CV (5CV) experiments, in addition to the five 10CV runs from the previous section, using different random seeds. Both models were robust to this type of variability given the similar MCC values returned within each set of experiments summarized by a boxplot Figure 3. HigheCV and fi2.2.1Figure We next look at whether adding or considering other sets features to describe patients improves prediction further. CART using clinical data was barely predictive  out of the 1881 considered miRNAs . A total of 13898 genes are predicted to be targeted by the 7 predictive miRNAs  databases, respectively. To enhance the BC‐specificity of the analysis, we focus on the 23 genes that we found to be common to these three gene lists 64 and2.4.2 ORA was used to identify the biological pathways significantly enriched with the 13898 targeted genes from the cancer‐related repository of WikiPathways (WikiPathway cancer). The four cancer‐associated pathways that were found  ≤ 0.05) are DNA damage response, ErbB  signaling pathway, endometrial cancer, and chromosomal and microsatellite instability in colorectal cancer  method implemented in the WEB‐based Gene SeT AnaLysis Toolkit .67 ORA However, defect in these regulators can lead to doxorubicin failing to induce cell cycle arrest and apoptosis. The 23 BC‐specific genes regulate processes associated with doxorubicin's mechanism of action and the breast cancer pathways in WikiPathway cancer . Owing to CART's embedded feature selection, these CART models only employ either three or four features of the about 1–2 thousand considered. Another sign of the importance of feature selection when training on high‐dimensional datasets is that OMC models were generally more predictive than their all‐features counterparts. Overall, only 2 of the 128 models in this large‐scale analysis were substantially predictive .The predictive accuracy of the isomiR‐based CART model is high in the context of this problem despite the strong class imbalance. First, the mMCC of its tenfold CV predictions across five independent repetitions is 0.56, which corresponds to a mAUC of 0.80. Importantly, while all models with a substantial mMCC also have a substantial mAUC, the opposite is not true. For instance, the miRNA‐based LGBM model returned an mAUC of 0.71, but its mMCC is practically zero. In class‐imbalanced problems like this, we recommend MCC as a more appropriate alternative to AUC, as the latter has here overestimated the ability of some models to discriminate between responders and nonresponders. Second, the best ML models are much more predictive than their respective permuted versions Figure . Third, 61 butTo go beyond these retrospective validations, we performed clustering on an additional 1078 BC patients profiled for both miRNAs and isomiRs Figure . Here we and hereditary BRCA2 BC, respectively. Also, hsa‐miR‐514a‐1 negatively correlates with BC recurrence. On the other hand, the second CART model presents predictive combinations of 4 isomiRs: hsa‐miR‐450a‐1, hsa‐miR‐19b‐2, hsa‐miR‐92a‐2, and hsa‐miR‐92a‐1. This model is also supported by the relevant individual roles of these four molecules. hsa‐miR‐450a‐1 is upregulated in BC. Also, overexpression of hsa‐miR‐19b family members are candidate prognostic biomarkers of BC and their involvement in the tumor progression through the PI3K/AKT pathway has also been documented. Downregulation of hsa‐miR‐92a family members is also associated with aggressive BC and high tumor macrophage infiltration. Hsa‐miR‐92a family members are also involved in the formation of blood vessels and the development of some mammalian organs, with their aberrant expressions being highly associated with different malignant human tumors. Therefore, they have been reported as a potential therapeutic target and novel diagnostic biomarker of human tumors. In addition, hsa‐miR‐92a is involved in apoptosis, cell proliferation, and doxorubicin chemosensitivity in gastric carcinoma cells, with the suppression of hsa‐miR‐92a leading to DNA damage foci and thus sensitivity to doxorubicin treatment.An additional advantage of the identified models is that they are interpretable at the patient level. For example, a patient is predicted to be a nonresponder because her/his expression levels for the 3 selected miRNAs, hsa‐miR‐4421, hsa‐miR‐4680, and hsa‐miR‐514a‐1, are nonlinearly combined in a way that has been accurately associated to nonresponders by the CART algorithm Figure . This 3‐78 and As miRNAs play critical roles in gene regulation and drug resistance, their dysregulation could promote cancer development, recurrence, and chemoresistance. MiRNAs can be used as tools or targets for the treatment of different cancers because of their essential roles as gene regulators for various human cancers. Hundreds of genes, including oncogenes and tumor suppressor genes, can be regulated by a single miRNA binding to their mRNA transcripts. Conversely, a single mRNA transcript can bind different miRNAs. That is, one single miRNA usually targets many genes and different miRNAs might regulate the same gene.In addition to doxorubicin response, individual miRNAs are often associated to other cancer patient outcomes. They play significant roles in RNA silencing as well as post‐transcriptional regulation of gene expression.92 As  For example, the deregulation of our predicted target genes in DNA damage and apoptotic pathways could be a mechanism that promotes patients resistance to doxorubicin, whose mechanism of action include inducing DNA damage to trigger apoptotic cell death. This EA provides starting points for studies investigating the molecular mechanisms of primary resistance to doxorubicin in BC patients.To reveal the most predominant BC‐specific biological processes underpinning primary resistance to doxorubicin, EA for the 23 predicted BC‐associated genes targeted by our predicted miRNAs revealed ten significantly (FDR < 0.05) enriched cancer‐associated biological pathways Figure , includi99 ForOur study has some limitations to point out. While clustering analysis has shown that the best ML models are likely predictive on an additional cohort with over a thousand BC patients whose tumors were profiled for miRNAs and isomiRs, this is still to be confirmed prospectively. In such prospective clinical trial, each BC patient would only have to be profiled pretreatment for the 4 isomiRs selected by CART, which would represent a large saving in time and cost with respect to determining the full profile. Another limitation is that the TCGA BRCA project collected treatment response data on a voluntary basis from hundreds of submitting institutions. Thus, the level of curation and harmonization of these datasets is likely to be much lower than that of the molecular profiles. With that said, there is a strong signal in treatment response data, at least for the 96 patients used for training, given that models trained on permuted response data were significantly less predictive.Overall, this large‐scale ML analysis has led to the discovery of highly predictive, robust and even interpretable predictors of BC patient response to doxorubicin. These are CART models nonlinearly combining selected miRNAs and their isoforms in a predictive manner . These CART models achieved median MCC values that are at least four times higher than those based on HER2 expression and response‐permuted data. Importantly, the MCC of these models increased with larger training sets, therefore these should become even more predictive as more data are available in the future.4 to its generic name (doxorubicin), were first standardized. Next, the 332 BC patients who received doxorubicin were identified. Among these patients, those without annotated doxorubicin responses, those missing sample collection and treatment start dates were excluded. Thus, 236 (71.1%) of patients were excluded from the study for these reasons. After these filtering steps, 96 BC patients treated with doxorubicin were retained . 78 (81.3%) of whom had a tumor biopsy taken, while the remaining 18 (18.7%) had their tumors surgically resected instead. Patient responses to doxorubicin‐containing treatments, complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD), were provided by the TCGA‐BRCA project. As it is common practice, such responses were further categorized into two classes: responder (CR or PR) and nonresponder (SD or PD). This process resulted in a total of 87 responders and 9 nonresponders . Note that this TCGA acronym stands for Breast Invasive Carcinoma, not the BRCA gene. To avoid confusion, BC was employed to refer to this cancer type, for which TCGA contains data for 1098 patients. To curate these datasets, misspellings, formulations, and synonyms  of the drug as annotated in the DrugBank database100 toThe open‐access molecular profiles for the 1098 TCGA‐BRCA patients were also downloaded from the GDC portal. This was restricted to baseline primary tumor samples . A few patients had multiple tumor samples sequenced. In this case, the first labeled sample was chosen, which corresponds to the A‐level sample . To reduce technical variability, the primary analysis of profiling data was harmonized by the GDC . All the nonrestricted‐access profiles were considered for these patients. Thus, eight molecular profiles per patient were considered: mRNA(FPKM) are the messenger RNAs as Fragment Per Kilobase of transcript per Million, mRNA(FPKM‐UQ) are the messenger RNAs as Upper‐Quantile‐normalized FPKM, miRNA are the log2‐transformed Reads Per Million mapped‐normalized microRNAs, isomiR are the log2‐transformed Reads Per Million mapped‐normalized miRNA isoforms expression at a given locus that are distinguished by their location within the locus, CpG are the DNA methylation beta values of 450k probe at known CpG sites, CGI are the averaged beta values of all the probes at the CpG sites of the corresponding CpG Island, CNV(mean) are the Copy Number Variants calculated using CNTools R packages as the mean of DNA copy number across the segments of the considered gene, and CNV(median) is calculated in the same as CNV(mean) except using the median instead of the mean. Each molecular profiling dataset was used as a set of features for building models using a range of ML algorithms. CV, with K values being 3, 5, and 10. The predictions from each left‐out CV fold were merged prior to calculating a given evaluation metric , instead of calculating the metric for each left‐out fold and average them. This provides a more robust estimation of the metric, while ensuring that the prediction of each instance from the CV was not used in any way for the training or selection of its corresponding model.Only some doxorubicin‐treated BC patients have both treatment responses and molecularly profiles annotated. The number of doxorubicin treated BC patients along with the number of features of each dataset was reported in Figure 102 CVhttps://www.python.org/) with packages from scikit‐learn (https://scikit‐learn.org).The median results from the 5 seeds are reported for each of the 128 ML models in Figure  random forest, XGBoost, LGBM, Logistic regression, Linear Support Vector Machine and Radius Support Vector Machine, and K‐Nearest Neighbors. The model hyperparameters were set to their default values.8 ML algorithms were employed: CART,103 raStandard stratified K‐fold CVs were performed to measure the model performance for each algorithm‐profile pair using all the available features for that profile. This is called an all‐features model . During CV, K‐1 folds were used for model training, whereas the remaining partition was used to test the trained model. Thus, each fold was used exactly once as a test set. Therefore, for any given model, the response of a patient was predicted using a model trained with data from other patients. variants of each of the 8 ML algorithms were implemented to build models using only the most relevant features of the doxorubicin response. This can remove noise from the training data and improve the model performance. To select models with the OMC while estimating their performance, nested CV was carried out for each algorithm‐profile pair. In brief, OMC is comprised of three steps. First, features were ranked according to their relevance to doxorubicin response by increasing p‐values from Analysis of Variance (ANOVA). The p‐value, one per each feature, indicates the discriminative power to distinguish between responders and nonresponders, the informative features associated with small p‐values. Then, an ML model was trained with considered subset of features . Finally, the best‐performing model was selected among all n/2 trained model as the one with the highest MCC in the inner loop of the nested CV and evaluated that model performance in its outer loop.Due to the high dimensionality of the datasets , OMC53 varAs a baseline, CVs for a top model was also run with the same algorithm and features, but after randomly shuffling doxorubicin response labels across patients. This was called a permutation model. To compare the accuracy of the ML models at this task with that of using HER2 only, an HER2‐CART model was built using standard tenfold CV with HER2(ERBB2) expression data derived from the mRNA(FPKM) profile.On the other hand, HER2 status has been identified as a single‐gene marker for BC patient sensitivity to doxorubicin in several studies.61 To For MCC, as it is customary, the operating threshold was set to 0.5. Patients with class probabilities above this threshold were predicted to be responders, otherwise they were predicted to be nonresponders. To estimate predictive performance, the true and predicted classes were compared. The numbers of true positive, true negative, false positive, and false negative instances were used to calculate performance metrics. The AUC of the ROC, AUC for short, was also calculated for comparison purposes.BC patients were clustered using the predictive miRNA features (either miRNAs or isomiRs) with an agglomerative clustering algorithm . A dendrogram heatmap was used to visualize the results of this clustering analysis, marking the 95 patients with high‐quality response and profiling data. Both clustering and its visualization were carried out with clustermap in the seaborn package (version 0.11.2). This online miRNA target prediction tool identify mRNA by matching the desired miRNA seed region to the conserved complementary sites (sequences) of their mRNA targets. It retrieves the highest number of target genes of any database and ranks the predicted targets of each miRNA based on these matching algorithms. Then, the list of BC‐driving genes was download from IntOGen, and COSMIC. Of those, the subset of genes in common of TargetScan, IntOGen, and COSMIC were considered as potential BC miRNA targets. They were used as input data of EA to explore the biological relevance of predictive miRNAs. WEB‐based Gene SeT AnaLysis Toolkit   was utilized for EA for WikiPathway cancer, KEGG, and GO database. WebGestalt uses a hypergeometric test for statistical significance, which further employed the Benjamin–Hochberg FDR multiple‐testing correction. The enriched pathways were identified based on FDR threshold which was set to 0.05. For each pathway, input genes that are part of the pathway are counted and enrichment ratio was also calculated.To reveal the biological pathways related to predictive miRNAs, the target genes of predictive miRNAs were predicted using the TargetScan database (version 7.0).63 ThiPreprocessing of data: See subsections entitled “Acquisition, Retrieval, and Preprocessing of Drug Response Data”, “Acquisition, Retrieval, and Preprocessing of Molecular Profiling Data”, and “Preparing Datasets for ML” at the start this section.Data presentation: Whenever relevant, Figures Sample sizes for each statistical analysis: Each sample is formed by 5 runs whose results are summarized by boxplot Figures , 4, 5. Et‐tests for each considered pair of samples. A performance difference was considered to be significant if p‐value < 0.05.Statistical methods: A two‐tailed Welch's Software used for statistical analysis: Statistical analysis was carried out using the statannot package in the python 3.7.3 software.https://github.com/adeolu1/BRCA_CART_model.Code availability: The python codes and processed datasets are provided to build the best classifiers and the HER2 baseline, evaluate them and facilitate their application to other cohorts of miRNA‐profiled BC patients: The authors declare no conflict of interest.P.J.B. conceived the idea and designed the experiments. A.Z.O. collected and analyzed the patients’ data, developed the ML process and interpreted the results. A.Z.O. and P.J.B. wrote the manuscript with the assistance of C. Piyawajanusorn, A. Gonçalves, and G. Ghislat. All authors contributed to the discussion.Supporting InformationClick here for additional data file.


Recent progress in Single-Cell Genomics has produced different library protocols and techniques for molecular profiling. We formulate a unifying, data-driven, integrative, and predictive methodology for different libraries, samples, and paired-unpaired data modalities. Our design of scAEGAN includes an autoencoder (AE) network integrated with adversarial learning by a cycleGAN (cGAN) network. The AE learns a low-dimensional embedding of each condition, whereas the cGAN learns a non-linear mapping between the AE representations. We evaluate scAEGAN using simulated data and real scRNA-seq datasets, different library preparations , and several data modalities as paired scRNA-seq and scATAC-seq. The scAEGAN outperforms Seurat3 in library integration, is more robust against data sparsity, and beats Seurat 4 in integrating paired data from the same cell. Furthermore, in predicting one data modality from another, scAEGAN outperforms Babel. We conclude that scAEGAN surpasses current state-of-the-art methods and unifies integration and prediction challenges. The maturation of the single-cell genomics field has produced methods to profile multiple data modalities, such as single-cell RNA sequencing (scRNA-seq) and chromatin profiles (scATAC-seq), even on the same cells at the same time. This development has provided rich opportunities for a deep understanding cell states and transitions while presenting severe computational challenges . One of In contrast, Liger , iMAP 1, scMergeFurthermore, these approaches implicitly assume that differences between datasets arise entirely from technical variation, thus potentially masking the biological signal. For example, the Mutual Nearest Neighbors (MNNs) method  effectivThere is a need for scalable and robust integrative methods for omics data. Preferentially general enough to encompass multiple integration tasks in one systematic framework. From this standpoint, we can also expect the scale and the number of different data modalities to increase further .Here we present a novel integrative method that has been designed to take these requirements into account. The critical insight motivating our approach is that we do not force all experimental samples into a single joint representation, regardless of their library protocol, data modality, paired or unpaired design. Instead, we use an autoencoder (AE) to represent and respect the distributional characteristics of each dataset and condition. The integration is performed in the latent space by learning a mapping between the different latent space representations. Inspired by recent progress in image-to-image translation, we use a cycleGAN (cGAN) architecture for obtaining a translation between the latent spaces corresponding to different datasets. Conceptually, our method reformulates the integration challenge from a problem to be addressed in raw data space into a learning challenge between different data-specific latent space representations. We denote our method scAEGAN, a coupled AE—cycleGAN architecture. Our results demonstrate that scAEGAN can target single-cell multi-omics integration tasks with performances similar to or superior to other state-of-the-art tools. Furthermore, we provide evidence that the mapping between different latent spaces is essential for effective integration by contrasting scAEGAN against the simplified approach of directly concatenating latent spaces, which forces the data into a shared latent space without learning a mapping.scAEGAN is a unifying architecture combining AE  and cGANX, where rows (m) represent the cells and columns (n) indicate genes/transcripts. The AE task involves learning the encoding representation through an encoding function e(x) and then mapping back e(x) to the original input space through a decoding function d. For faster convergence and better accuracy, Rectified Linear Unit (ReLU) has been used as an activation function, which is given as a function f applied to the input x:First, each given dataset is provided as input to an AE in a matrix Hidden1 with l1 nodes following the input Xi (row vector) is formulated as follows:The first hidden layer w1 is of l1 × n dimensions and the bias term b1 is l1 length vector. Each subsequent middle layer k is formulated as:The weight matrix e and d, i.e., d(e(x)) = X′ is called the reconstruction function, and the reconstruction loss function penalizes the error made, which is given as:The composition of GAB: A-> B and GBA: B-> A. In addition to these generative functions, two discriminators DA and DB were used to regularise the generators to generate samples from a distribution close to the latent representation of A or B. We used the Wasserstein GAN adversarial loss introduced in [K-Lipschitz continuous function, making the neural network gradient smaller than a threshold value K, such that ‖∇f‖ ≤ K. The primary rationale for applying this condition is that gradient behaves better, making generator optimization easier [B denoted by GAB: A-> B:f is a K-Lipschitz continuous function, {fw}w∈W, parameterized by w, A and B. The cycle consistency loss ensures that the learned mappings are cycle consistent, i.e., bringing back to the original domain. It acts as a regularization and reduces the space of possible mapping functions. Which is given as:The low-dimensional space representation from the AEs captures the underlying manifold of the data. Secondly, we utilize a cGAN to learn relationships between the different domains/datasets (A and B). Specifically, learning two generative mapping functions duced in . In the n easier . As the To train the cGAN on the latent subspaces of the two domains, the entire objective function is:1 represents dataset1 from the same modality and same library protocol. Sample S2 represents dataset2 from the same modality same protocol; likewise, for Library L1, L2 represents data from the same modality but different library protocols. The different types of lines in 1 represents the input to the encoder, the same bold line from the decoder to Sample S1 represents the reconstructed output, and likewise for other domains with a different representation of dashed lines. The first step in the scAEGAN integration algorithm is training an AE independently on both domains A and B to find a low-dimensional embedding that preserves each domain’s key defining features. This step is necessary since direct translation between scRNA-seq domains via cGAN, while possible, is hampered by increased technical variation or dataset complexity. AE is particularly suitable due to its ability to reduce random noise while still maintaining essential features. Moreover, it turns out that AE generates more biologically meaningful embeddings compared to variational autoencoders (VAE) when learning across latent spaces, which is most likely due to a poor match between the unimodal prior and the inherently multimodal scRNA-seq data [The scAEGAN architecture is provided with a scRNA-seq and a scATAC-seq data set  as illustrated in seq data . A cGAN λ1, λ2 and training epochs.We have performed a series of analyses to generate the best configuration for scAEGAN hyperparameters based on the nature of the single-cell data, that resulted in the optimal configuration. The hyperparameters to be adjusted for scAEGAN are batch size, learning rate, the embedding space dimensions, and set of weighted parameters used to control the cGAN loss, i.e Hidden1 (300), Hidden2 (50), Hidden3 (300). ReLU has been used as an activation function for the hidden layers followed by a linear activation function in the bottleneck layer. The embeddings from this bottleneck layer are used as input to the cGAN. A dropout value of 0.2 has been used to prevent overfitting. We used Adam [The AE model consists of three hidden layers with the dimensions of sed Adam  as an oplr = 0.0005 and r = 0.0002, beta1 = 0.5, beta2 = 0.999, epsilon = 1e − 7, decay = 0. And for the simulated data, the cGAN is trained with Adam optimizer with parameters: lr = 0.0002, beta1 = 0.5, beta2 = 0.999, epsilon = 1e − 6, decay = 0.0. We use the hyperparameters as weights for the cyclic loss and identity loss in all our experiments, i.e., λ1 = 0.3 and λ2 = 0.3, which were chosen to check a couple of combinations for verifying that our optimization process generates the translated data similar to the starting ones. Also, to maintain the K-Lipschitz continuity of fw we used the hyperparameter c = 0.1 during the training, which helps in resulting in compact parameter space. In addition to these, the cGAN is trained with a batch size of 4 for 200 to 400 epochs.The architecture of cGAN consists of two generators and two discriminators. The generators consist of one residual block and one dense layer of 50 dimensions each, and the discriminators consist of two dense layers. A dropout value of (0.2) has been used for the residual block, followed by batch normalization, which stabilizes the learning process. In addition to this, batch normalization has a slight regularization effect; for this reason, we have used a small value (0.2) for the dropout. LeakyReLU  has beenlr = 0.0005 for 200 epochs using a batch size of 16. The concatenated AE uses the mean square error as a loss function to minimize the input and output loss.The AE concatenated architecture is used for the comparison with scAEGAN. The AE-Concat architecture consists of two encoders of one hidden layer, concatenated and projected down to the bottleneck layer. The first encoder takes the input from the first domain, and the second encoder takes input from the second domain. The first encoder and second encoder dimensions are 30 each, summing up to 60 dimensions after concatenating, projected down to a low dimensional space of 50 dimensions in the bottleneck layer. This layer contains the integrated low-dimensional representation of the two domains. ReLU is used as an activation function and a dropout value of (0.2). This concatenated network is trained with Adam optimizer with a learning rate of Firstly, the overlap between datasets before and after integration was visually assessed in low-dimensional representations using the UMAP R package v0.2.3.1. In the case of scAEGAN, integration quality was measured by transferring labels between domains. A support vector machine is first trained to classify cell types in one domain using the cluster assignments obtained from Louvain clustering as implemented in Seurat3. This step is followed by the prediction of cell type in the other domain and a comparison with the original clustering in this domain. In the case of AE integration, direct label transfer between input space and low-dimensional representation of the integrated dataset is not applicable. Accordingly, cell types are again assigned to input and integrated datasets via clustering with the Louvain algorithm and are then directly compared.Furthermore, Seurat was used to transfer labels using its TransferData function. Cell type assignments, i.e., clusterings, are compared using the Adjusted Rand Index (ARI) in R package pdfCluster v1.0.3. and the Jaccard Index (JI) in R package clusteval v0. In addition to ARI and JI, we used Preserved Pairwise Jaccard Index (PPJI), a non-symmetric distance metric between two clusterings, for evaluating the clustering results.Since Seurat is the most widely used tool, we compare our integration results with Seurat version 3 and 4 for the different library protocols on paired/unpaired data.The JI calculates a 2 by 2 contingency table of agreements and disagreements between the two finite subsets and evaluates the stability of clustering. Given two subsets Ai and Bj, the JI is computed as:nij refers to the number of common cells between two partitions and ai = ∑k (nik), bj = ∑k (njk) are the number of cells in estimated cluster i and in true cluster j, respectively.The ARI measures the similarity between the two partitions of the same datasets by the proportion of the agreement between the two partitions. The metric is adjusted for chance, such that the independent have an expected index of zero and identical partitions have an ARI equal to 1. The ARI is computed as:PredRNA. RNA prediction was carried out by training the cGAN on the scRNAseq/scATACseq paired dataset and predicting on the held-out set.Evaluation for the quality check was performed by computing Pearson correlation between each pair of cells from predicted RNA and original RNA training input data. This computation was performed using cor function from the stats package.Clustering for integrated and independent omic modalities. The Seurat Louvain clustering implementation was used for all of the clustering analysis [analysis . VariousSingle-cell RNA-seq data: PCA components.Single-cell ATAC-seq data: LSI components.Cells were clustered based on shared components generated by the methods studied .For integrated subspaces, the Louvain resolution has been set to the default value of 0.6. The number of nearest neighbors has been used as K = 20.For developing and testing this computational approach’s performance and quality, four different datasets  have been used. The summary of the datasets used is given in Two datasets containing 600 cells from 5 populations and with 3000 genes each were simulated using SymSim  with theTwo more datasets were simulated for analysis to examine the sufficiency of scAEGAN when there is a cell type unbalance in two datasets. For dataset A, we simulated multiple versions with all cells, 100, 50, and 10 cells for the largest cluster, and for dataset B, we opted to remove the largest cluster, which had about 200 of the 600 genes in it.https://github.com/quon-titative-biology/scalign [The pre-processed mouse hematopoietic stem cell dataset of young and old individuals presented by was downloaded from /scalign . Seurat’https://github.com/immunogenomics/harmony2019 [Four human pancreatic islet cell datasets sequenced using different platforms were obtained pre-processed as described in from scRNAseq/scATACseq paired dataset. We selected an existing paired scRNA-scATAC dataset from the SNARE-seq protocol (a droplet-based single nucleus over mRNA expression and chromatin accessibility sequencing) [uencing) . The datQuality filter—low-quality features: removes low-quality features and cells from both modalities. We excluded all cells with an overall abundance level of "number of features per cell" and "number of counts per cell" less than quantile 0.1 and greater than quantile 0.9. For mRNA (ATAC), minimum abundance filtering was used: genes (peaks) profiled in less than 4 cells (3 cells) and cells with fewer than 201 genes quantified were filtered. There was no requirement for a certain number of peaks per cell. Following quality and abundance filtering, we considered a total of 8,086 cells for scRNA and 8,214 cells for scATAC adult samples for analysis.ATAC-derived gene activity. To compute ATAC-derived gene activity, the Seurat3 ’CreateGeneActivityMatrix’ function with "upstream = 2000" bases was used. In addition, the GRCh38 genome was used as a reference to later identify marker genes across the integrated expression subspace.Quality filter—mitochondrial: 5 percent mitochondrial filtering was used for the expression matrices of scRNA and scATAC, with activity from peaks used in the ATAC case.Component parameters. For scRNA reduction, 15 principal components (PCA) were chosen, and 50 latent semantic indexing components (LSI) were chosen for scATAC.The final number of cells: from the resulting pipeline, a total of 6,735 paired cell profiles were considered for the downstream analysis.Integration. On this dataset, Seurat 3 (unpaired) and Seurat 4 (paired) were used to generate a reference integrated version for further processing and later integration. Using standard normalization and integration guides for Seurat3 and Seurat4 Weighted Nearest Neighbor Analysis vignettes . In Seurat 3, the FindTransferAnchors function was used to generate anchorsets using RNA as the reference and ATAC as the query modalities, with CCA as the reduction method. This was followed by the TransferData function, where the anchorset generated was used to transfer the RNA derived information into the ATAC modality using LSI dimensional reduction for the weighting anchors. Seurat 4 was used to identify multi-modal neighbors using the FindMultimodalNeighbors function.We propose an integrated AE and cGAN architecture , allowinTo evaluate this concept’s viability and performance, we first tested the scAEGAN by simulating scRNA-seq data using SymSim . Cells wWe systematically assessed the ability of scAEGAN-derived feature representations to integrate different library protocol datasets. To this end, we evaluate and compare scAEGAN with Seurat3 as Seurat3 has demonstrated that it can integrate two datasets using different library protocols. It has performed better than Liger  and scMeTo assess the GAN cycle consistency loss contribution, we fused the two latent representations by concatenation instead of learning a mapping . This caNext, we asked whether we could learn to integrate the input datasets using a cycleGAN without employing an Autoencoder first to project the data into a latent space. This would conceptually correspond to a pixel-by-pixel translation between images. The cycleGAN performed better on the simulated datasets . But when using a real scRNA-seq SMARTseq dataset from two mouse strains , a reducInterestingly, the dataset contains several less-informative PCA components likely representing noise in the original data, making it challenging to learn a stable non-linear mapping between the two domains . The effAiming for generality, we investigated the integration of multi-omic datasets. To this end, we integrated scRNA-seq and scATAC-seq data as a case study. When the scRNA-seq and scATAC-seq data are collected from different cells, referred to as unpaired data, it also includes the challenge of having different samples. Both data modalities are collected from the same cell in the paired case. Thus, the integration of scRNA-seq with scATAC-seq data could be either paired or unpaired. Recent progress has mainly targeted unpaired data. Tools such as Seurat3 and MOFA+ have demonstrated promising results. A recent upgrade, Seurat4, is the first attempt to our knowledge targeting the paired data-integration challenge. We evaluated the architectures using paired  and unpan = 53850) and testing (n = 1350) sets and trained scAEGAN jointly on scRNA-seq and scATAC-seq training sets. After training predictions were inferred from the test set. We used Pearson correlation as an evaluation metric as used in Babel, for cross-domain inference between the empirical expression(scRNA-seq) and the scAEGAN’s inferred (translated expression) between each pair of cells. scAEGAN outperforms Babel, where scAEGAN achieved a Pearson correlation (0.60) compared to Babel (0.55).To further investigate the efficacy of scAEGAN, we attempted to predict one modality from another modality. We trained the scAEGAN on scRNA-seq and tried to predict the scATAC-seq. To this end we divided our scRNA-seq and scATAC-seq data into training have set the stage to discover, catalog, and characterize cell types at an unprecedented level using various profiling techniques and library protocols. In addition, such community efforts have increasingly produced single-cell atlases at an unprecedented resolution and scope . Yet, weMoreover, the investigator wants to predict missing data or data modalities from the available data samples. Such predictions are helpful since they can be subject to validation in downstream experiments. However, it is challenging and time-consuming to navigate and potentially combine different tools and their results to perform a holistic integrative and predictive biological analysis.To address this challenge, we developed scAEGAN, a unifying end-to-end unsupervised single-cell data integration and predictive method combining an AE architecture for efficient representation of scRNA-seq data with a CycleGAN network for translation across datasets. We demonstrate the sufficiency in that such a unifying machine learning architecture can achieve state-of-the-art or better performance by tackling seemingly “different” integration challenges. Anchoring-based methods, such as Seurat , have a As the community progresses with developing powerful data integration methods, we may be able to revisit the early vision of system biology . CombiniS1 Fig(TIFF)Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.S3 Figa) scAEGAN results shows its outperformance as compared to AE-Concatenated, integrating data CelSeq2, SmartSeq platforms, b) The results from the AE-Concatenated shows its bad performance while integrating the datasets from CelSeq2, SmartSeq platforms, c) scAEGAN results shows its outperformance as compared to AE-Concatenated, Seurat and cGAN for integrating data across different platforms.Integration results with across platforms data from CelSeq2, SmartSeq and its quantitative comparison, (ZIP)Click here for additional data file.