Transcriptomics in Toxicogenomics, Part III : Data Modelling for Risk Assessment

nanomaterials Review Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment Angela Serra 1,2 , Michele Fratello 1,2 , Luca Cattelani 1,2 , Irene Liampa 3, Georgia Melagraki 4, Pekka Kohonen 5,6 , Penny Nymark 5,6 , Antonio Federico 1,2 , Pia Anneli Sofia Kinaret 1,2,7 , Karolina Jagiello 8,9, My Kieu Ha 10,11,12, Jang-Sik Choi 10,11,12, Natasha Sanabria 13 , Mary Gulumian 13,14, Tomasz Puzyn 8,9 , Tae-Hyun Yoon 10,11,12 , Haralambos Sarimveis 3, Roland Grafström 5,6, Antreas Afantitis 4and Dario Greco 1,2,7,* 1Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; [email protected] (A.S.); [email protected] (M.F.); [email protected] (L.C.); [email protected] (A.F.); pia.kinar[email protected] (P.A.S.K.) 2BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland 3School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; [email protected] (I.L.); [email protected] (H.S.) 4Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus; [email protected] (G.M.); [email protected] (A.A.) 5Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; [email protected] (P.K.); penny[email protected] (P.N.); grafstr[email protected] (R.G.); 6Division of Toxicology, Misvik Biology, 20520 Turku, Finland 7Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland 8QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; [email protected] (K.J.); [email protected] (T.P.) 9University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland 10 Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; [email protected] (M.K.H.); [email protected] (J.-S.C.); [email protected] (T.-H.Y.) 11 Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea 12 Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea 13 National Institute for Occupational Health, Johannesburg 30333, South Africa; [email protected] (N.S.); [email protected] (M.G.) 14 Haematology and Molecular Medicine Department, School of Pathology, University of the Witwatersrand, Johannesburg 2050, South Africa *Correspondence: dario.gr[email protected] Received: 10 March 2020; Accepted: 26 March 2020; Published: 8 April 2020   Abstract: Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety Nanomaterials 2020,10, 708; doi:10.3390/nano10040708 www.mdpi.com/journal/nanomaterials Nanomaterials 2020,10, 708 2 of 26 assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics. Keywords: toxicogenomics; transcriptomics; data modelling; benchmark dose analysis; network analysis; read-across; QSAR; machine learning; deep learning; data integration 1. Introduction Clarifying the toxic potential of diverse substances is an important challenge faced by scientists and regulatory authorities alike [ 1 ]. The rapid generation of genomic-scale data has led to the development of TGx, which combines classical toxicology approaches with high throughput/high content molecular profiling technologies in order to identify deregulated molecular mechanisms upon exposures as well as candidate biomarkers for toxicity prediction [2–7]. In the last years, many transcriptomics datasets have been generated to characterize the molecular MOA of chemicals, small molecules and nanomaterials exposure by transcriptomics profiling of the exposed biological systems [ 5 ]. At the same time, new ML algorithms have been proposed in order to better understand and eventually predict the genomic behaviour underlying the exposure. Indeed, TGx datasets have been exploited in the context of drug repositioning [ 8 , 9 ], toxicity prediction [ 10 – 13 ], definition of adverse outcome pathways (AOP), and as a valuable source to develop new approach methodologies [14,15]. Notably, TGx approaches have been used to analyze quantitative transcriptomic data, to determine the BMD and estimate the critical point of departure for human health risk assessment [ 16 – 19 ]. These approaches are applied in the framework of read-across analysis, with the aim of predicting the behaviour of uncharacterized compounds by comparing them to other substances whose molecular effects are known [20,21]. TGx shifts the focus from traditional end-point-driven analysis to a systems biology approach, allowing to better understand and predict the alterations in the molecular mechanisms leading to toxicity. In this context, different methods for the study of gene co-expression networks is of great interest to identify common patterns of expression among relevant genes [2–6,22]. Furthermore, different ML methodologies have been developed and applied to the analysis of TGx datasets for the purpose of identifying toxicogenomic predictors. ML includes both unsupervised and supervised methods. The unsupervised methods, such as clustering, do not require any prior classification of the samples, grouping them based on similarities of selected features. On the other hand, supervised methods require discrete or continuous endpoints. They are often combined with strategies to identify an optimal subset of features that can discriminate the endpoint values. This subset of features can then be used for the prediction of the class or the effect of a new sample. A wide range of algorithms has been proposed to build robust and accurate predictive models, including linear and logistic models, support vector machines (SVM), random forests (RF), classification and regression trees (CART), partial least squares discriminant analysis (PLSDA), linear discriminant analysis (LDA), artificial neural networks (ANNs), matrix factorization (MF) and k-nearest neighbours (K-NN) [ 23 – 26 ]. Classic techniques such as linear and logistic models have been the first to be applied in such modelling tasks and can still be considered the methods of choice, especially when analyzing small datasets. More recently, novel methodologies based on artificial intelligence (AI) and deep learning (DL) have been used with great success in a wide range of applications, including image analysis, and also for the development of TGx-based predictive models [ 27 ]. These new approaches are envisaged to produce more accurate predictions and open new horizons to the identification of biomarkers with discrimination performance and predictive ability [ 5 , 28 ]. One of the biggest challenges faced in TGx is the limited amount of samples in the available data, especially in specific fields such as nanotoxicology. Nanomaterials 2020,10, 708 3 of 26 ML techniques can be very useful to overcome small sample sizes in TGx studies by combining several gene profiles from multiple related biological systems or by applying transfer learning techniques [ 27 ]. In this review, we present the state-of-the-art methodologies developed to interpret, analyze and model already preprocessed omics data. These include BMD analysis, gene co-expression network and ML methods for predictive modelling. We discuss the main ML methodologies, highlight scenarios where each methodology is most suited, the pros and cons of the different approaches, and which are the best validation strategies. We also provide a brief overview of data integration methodologies for multi-omics data analyses. 2. Benchmark Dose Modelling One of the main goals of toxicity assessment is the study of exposure–response relationships that describe the strength of the response of an organism as a function of exposure to a stimulus, such as chemical exposure, after a certain time. These relationships can be described as dose-response curves where the doses are represented on the x-axis and the response is represented on the y-axis. From these curves, a BMD value is calculated as the dose (or concentration) that produces a given amount of change in the response rate (called BMR) of an adverse effect. Normally, the BMR value is 5% or 10% change in the response rate of an adverse effect relative to the response of the control group. Furthermore, estimations of the lower and upper confidence interval for the BMD value are also computed and are called BMDL and BMDU, respectively [29–31]. In the last years, dose-response studies have been integrated with microarray technologies, thus introducing gene expression as an additional important outcome related to the dose. Indeed, the genes whose expression changes over the dose are of particular interest, since they provide insights into efficacy, toxicity and many other phenotypes. A specific challenge is to identify genes with expression level changing according to dose level in a non-random manner, identifiable as potential biomarkers [32]. The combination of microarray technology with BMD methods results in a bioinformatic tool that provides a comprehensive survey of transcriptional changes together with dose estimates at which different cellular processes are altered, based on a defined increase in response [ 33 ]. A classic BMD modelling pipeline involves fitting the experimental data to a selection of mathematical models, such as linear, secondor third-degree polynomial, exponential, hill, asymptotic regression, Michaelis–Menten models etc. Among all, the best model is selected by using a goodness of fit criteria, such as the Akaike information (AIC) or the goodness-of-fit p-value. A predefined response level of interest, called BMR, is identified and the optimal model is used to predict the corresponding dose (BMD) [ 34 ]. Moreover, the European Food Safety Authority (EFSA) suggests reporting both the lower and upper 95% confidence limit on the BMD [ 35 ]. The most popular tool to perform BMD analysis is BMDS (Table 1), which is developed by a U.S. Environmental Protection Agency’s publication [ 29 ]. It implements the following pipeline: first, the BMR value is selected. A set of appropriate models and their parameters for which the model fit are assessed. Then, the BMDs and BMDLs values for the adequate models are estimated. The optimal values coming from the model with the lowest AIC are selected. BMD approach is also implemented by the PROAST software (https://www.rivm.nl/en/proast) (Table 1), developed by the Rijksinstituut voor Volksgezondheid en Milieu institute (RIVM). Although RIVM and EPA aim to achieve consistency between these tools, there are still differences in some of their default settings and functionalities [ 36 , 37 ]. For example, PROAST allows for the statistical comparison of dose-responses between subgroups (covariate analysis) and offers larger flexibility in plotting [ 37 , 38 ]. PROAST can be run as a library in R, but also using two web applications that offer only basic functionalities for quick access (https://efsa.openanalytics.eu/,https://proastweb.rivm.nl/). A similar pipeline for toxicogenomics applications is implemented in the java-based US National Toxicology Program’s BMDExpress 2 tool, where a dose-response model is fitted for every gene, whose expression value is the response variable for the different doses [ 39 – 41 ] (Table 1). Furthermore, Nanomaterials 2020,10, 708 4 of 26 an R package for the dose-response analysis of gene expression data, called ISOgene has been proposed [ 42 , 43 ]. It implements the testing procedure proposed in, in order to identify a subset of genes showing a monotone relationship between the expression values and the doses [ 44 ]. This tool does not compute any BMD or BMDL values but returns a set of genes with a statistically significant monotone relationship (Table 1). More recently, a novel user-friendly software based on R/Shiny, BMDx, has been introduced [ 31 ]. In addition to the evaluation of dose-response of each gene expression pattern, BMDx also provides ways to compare multiple exposures or multiple time points along with suggesting functional characterization of the identified dose-response genes (Table 1). Table 1. Tools available for benchmark dose analysis. BMDS PROAST BMDExpress 2 ISOgene BMDx EPA Models * X X Probe id - - X Gene id - - X BMD/BMDL X X X X BMDU X X X IC50 X EC50 X Enrichment Analysis - - X X Interactive enriched maps - - X Comparisons at different time points - - X GUI X X X X X * Models approved by the US Environmental Protection Agency. 3. Gene Co-Expression Network Analysis Gene co-expression network analysis is a systems biology method used to describe the correlation patterns among genes across different experimental samples. It allows representing, investigating and understanding the complex molecular interactions within the exposed system [ 22 , 45 ]. The genes and their interactions are represented as a network (or graph) where the genes are the nodes of the network and their strength of similarity is represented as weighted edges between the nodes [46]. To understand the nature of cellular processes, it is necessary to study the behaviour of genes by means of a holistic assessment [ 47 ]. Thus, the inference of gene co-expression networks is a powerful tool for better understanding gene functions, biological processes, and complex disease mechanisms. Indeed, co-expression network analysis has been widely used to understand which genes are highly co-expressed within certain biological processes or differentially expressed in various conditions. They are also used for candidate disease-related gene prioritization [ 48 ], for functional gene annotation and the identification of regulatory genes [ 49 ]. For example, Kinaret et al. systematically investigated the transcriptomic response of the THP-1 macrophage cell line and lung tissue of mice after exposure to several nanomaterials by using a robust gene co-expression network inference method [ 2 , 50 ]. Subsequently, they ranked the genes in the network by computing different topological measures, identified and functionally characterized a set of genes that play a key role in the adaptation to exposure. Other approaches focus on identifying gene network modules associated with specific patterns of drug toxicity [45]. Studying the topology of the gene co-expression network allows identifying communities of genes that show similar behaviour. Moreover, the use of centrality measures facilitates the identification of genes that are hubs in the network [ 49 ]. A classical analysis performed on inferred gene co-expression networks is the identification of functional modules, such as groups of co-expressed genes. This is usually carried out by means of standard clustering algorithms, such as k-means, hierarchical clustering, spectral clustering, or by means of community detection algorithms [ 51 ]. The clustering method needs to be chosen with consideration because it can greatly influence the outcome and Nanomaterials 2020,10, 708 5 of 26 meaning of the analysis [ 49 ]. Modules can subsequently be interpreted by functional enrichment analysis, a method to identify and rank overrepresented functional categories in a list of genes. Algorithms to Infer Gene Co-Expression Networks The first challenge in this type of analysis is the identification of the best algorithm used to infer the gene co-expression network. Indeed, starting from the preprocessed TGx data, different methods to construct the gene co-expression network can be applied [ 52 – 54 ]. These methods differ on how they calculate the similarities between the expression profiles and how they remove the non-relevant connections. The dependence between the expression profiles is usually computed by means of information-theoretic methods such as the pairwise correlation coefficient and mutual information (MI) [ 45 , 55 ]. The main difference between the two methods is that the first one is able to identify only linear dependence between the profiles, while the second is also able to identify non-linear dependencies. Various algorithms have been proposed based on information theory. Some of the most important ones are RelNet [ 56 ], ARACNE [ 57 ], CLR [ 58 ], PANDA [ 59 ] and WGCNA [ 60 ]. RelNet works on two steps: it first creates a completely connected gene co-expression matrix where the mutual information between all genes is computed [ 56 ]. Subsequently, a threshold is defined, called TMI, that identifies which are the associations to be considered as significant. ARACNE computes the mutual information for all gene pairs of a gene expression dataset and excludes all the mutually independent gene pairs. Consequently, the ARACNE algorithm reduces the number of false positives connections, by cutting the less strong association between every triplet of genes in the network [57]. The CLR algorithm is an extension of the relevance network, but there is a correction step to eliminate false correlation and indirect effects [ 58 ]. Similarly to RelNet and ARACNE, this algorithm uses the matrix of MI values between all regulators and their potential target genes. In the next step, the CLR calculates the statistical likelihood of each MI value within its network context. This algorithm compares MI values of gene pairs with the background distribution of MI values. The interactions whose MI scores stand significantly above background distribution of MI scores are considered as the most probable interactions. This step eliminates many of the false correlations in the network (e.g., when transcription factors co-vary weakly with a lot of genes or a gene co-varies weakly by transcription factors of different factories). Unfortunately, applying different methods to the same omic dataset may not always result in consistent co-expression networks. For this reason, Marwah and collaborators recently proposed a tool, called INfORM (Inference of NetwOrk Response Module), able to infer a more stable and robust network by applying an ensemble strategy [ 50 ]. INfORM derives gene networks by employing a two-level ensemble strategy that combines models proposed by multiple network inference algorithms (ARACNE [ 57 ], CLR [ 58 ], MRNET, MRNETb [ 61 ]), to ensure the robustness of gene–gene associations. Network topology information and user-provided biological measures of significance (e.g., differential expression scores) are used together to obtain a robust rank of genes in the network, by means of the Borda method. Community detection methods are used to identify modules of closely correlated genes. Such modules are characterized by the importance of member genes in the network and GO enrichment is performed for functional characterization. Finally, the user can assess the characteristics of the modules and the functional similarity between modules to define the response module that represents the best network properties. The biological significance of this response module can be inferred from the summarized representation of enriched GO annotations clustered by their semantic similarity. A different approach is to infer directed graph networks, which allow not only to reveal the systematic coordinated behaviour of sets of genes but also the identification of causal and regulatory relationships between them [ 61 ]. These models can overcome the pitfalls of correlation networks, which are sensitive to technical and biological noise and may produce artefacts due to the loss of the direction of the correlation between each pair of genes during the network construction [ 62 – 65 ]. There are a number of methods for learning directed acyclic graphs (DAGs) available [ 66 ], but significant effort is Nanomaterials 2020,10, 708 6 of 26 being made to modify them in order to produce meaningful results by analyzing high-dimensional omics data [62–67]. 4. Read-Across The main assumption of read-across studies is that structurally similar compounds are likely to share a similar toxicological profile. These approaches are used to fill toxicological data gaps by relating to similar chemicals for which test data are available [ 68 ]. Traditional read-across studies rely only on the similarities between the chemical structure of the compounds. Different measures have been proposed to compute the chemical structure similarity and also multiple tools for read-across, mainly based on the nearest neighbour algorithm, have been developed [ 69 , 70 ]. However, these approaches are limited to the fact that the chemistry cannot explain the complex biological processes that are activated by substance exposure [71]. TGx datasets, such as DrugMatrix [ 72 ], Connectivity Map (CMAP) [ 73 ] and LINCS 1000 [ 74 ], can be used to profile the biological fingerprint of multiple chemicals and allow to compare the measured compound with a huge number of tested chemicals at the transcriptomic levels. Thus, the assumption underlying related read-across studies could be that if two chemicals have similar biological profiles they have a similar adverse outcome. Biological-based read-across could be complemented to the structure-based read-across. For example, Zhu et al. developed a read-across method based on a consensus similarity approach starting from different biological data, to assess acute toxicity in the form of estrogenic endocrine disruption [ 68 ]. Moreover, Serra et al. proposed a network-based integrative methodology to perform read-across of nanomaterials exposure with respect to other phenotypic entities such as human diseases, drug treatments and chemical exposures [ 20 ]. In particular, they integrated gene expression data from microarray experiments for 29 nanomaterials, with other gene expression data for drug treatments and data available from the literature that relates differentially expressed genes to chemical exposures and human diseases. They created an interaction network that was used to contextualize the effect of the nanomaterials exposure on the genes by comparing their effects with those of chemicals and drugs with respect to particular diseases. With this approach, the authors identified potential connections between metal-based nanoparticles and neurodegenerative disorders. Furthermore, the toxFlow web-based application is available to perform read-across and toxicity prediction by integrating omics and physicochemical data [ 21 ]. The implemented workflow allows to filter omics data with enrichment scores and then merge them together with the physicochemical data into a similarity-based-read across method to predict the toxicity level of a substance by inferring information from its most similar ones. However, the user still needs to define an initial grouping/read-across hypothesis regarding the variables that will be considered important and the threshold values, that set the boundary to the neighborhoods of similar ENMs. Apellis web application updates the toxFlow methodology by automating the process of searching over the solution space in order to find the read-across hypothesis that produces the best possible results in terms of prediction accuracy and number of ENMs for which predictions are obtained [ 75 ]. To do so, a stochastic genetic algorithm that serves the selection of both the appropriate variables and the threshold values simultaneously was developed and is trained during the first step of the procedure, while the predicted toxicity endpoint is retrieved during the second part of it [75]. 5. Adverse Outcome Pathways Adverse outcome pathway (AOP) is a conceptual framework that couples existing knowledge on the links between a molecular initiating event (MIE), such as contact of nanomaterial with Toll-like receptors on the cell surface, with the activation of a chain of causally relevant biological processes or key events (KE), e.g., the production of inflammatory cytokines, with the resulting adverse outcomes (AO) at the level of the organ or the organism (e.g., lung fibrosis) [ 76 , 77 ]. Coupling of gene expression profiling with bioinformatics-driven placement of the results into AOP descriptions has the potential Nanomaterials 2020,10, 708 7 of 26 for quantitative analysis of adverse effects that combines in vitro-derived mechanistic analyses with causally relevant modes-of-action and related key events [ 77 , 78 ]. As AOPs can span different cell types, numerous in vitro assays may need to be associated with a single one [ 14 , 76 ]. The details of the coupling are still being worked out by the community but mapping the results of pathway analyses to KEs is a simple alternative. For example, if the bioinformatics results cover all of the KEs in the chain leading up to an AO, then the AOP could be considered as active. The point-of-departure concentration might be defined as the lowest concentration where all of the KEs are activated. Naturally, this depends on the type of model systems used and its limitations (see Part I of this article series). However, early activation of molecular mechanisms in vitro have been shown to be predictive of phenotypic effects taking place later or in other systems [ 12 ], providing a basis for use of TGx data to cover largely all information blocks in an AOP [ 78 ]. Pathway annotations that can be considered include the WikiPathways, Molecular Signatures Database, KEGG, Reactome, Gene Ontologies Biological Process but also the Predictive Toxicogenomics Space (PTGS) components and other dedicated descriptions of toxicity pathways [ 12 , 79 ]. A more refined POD concentration could be provided by coupling the aforementioned AOP-based transcriptomics data analysis with BMD modelling to evaluate the dose-response nature of the exposure to ENMs at gene or pathway level. Subsequently, these transcriptional BMD values may be used to rank the potency of the nanomaterials to induce changes related to specific adverse outcomes of interest at the lower levels of biological organization, and group them according to the severity of the biological effects they cause [18]. 6. Machine Learning in Toxicogenomics ML methods have significantly advanced in recent years and are proven to be important alternatives to experimental testing for chemicals and nanomaterials [ 80 – 82 ]. The value of TGx-derived biomarkers of toxicity lays in the fact that they can be detected earlier than histopathological or clinical phenotypes [ 83 ]. The development of ML methods and tools for omics data analysis has also been proposed and several algorithms have been successfully applied to the analysis of omics data also in the TGx field [ 84 ]. For example, Rueda-Zarate, Hector A., et al. proposed a strategy that combines human in vitro and rat in vivo and in vitro transcriptomic data at different dose levels to classify the compound toxicity levels [ 85 ]. They combined machine learning algorithms, with time series analysis by taking into account the genes correlation structure across the time. Furthermore, Su et al. developed a drug-induced hepatotoxicity prediction model based on biological feature maps and multiple classification strategies [ 86 ]. They use a biology-context based gene selection to identify the most discriminative genes and showcased their methodology on the Open TG-GATEs and DrugMatrix datasets. ML algorithms use data-driven approaches to develop predictive models. Data derived from empirical experiments is first analysed to assess its quality, and, if necessary, it is preprocessed to improve the stability of the ML models. Common preprocessing techniques include filtering out features that are not informative, removing anomalous observations (outliers, noisy data) or filling data gaps. After this step, the dimensionality of the training data can still be excessive, so the data can be further preprocessed using dimensionality reduction techniques. The preprocessed data is used to train ML models that can predict a variable of interest (supervised learning) or detect patterns in the dataset (unsupervised learning). In addition to estimating parameters fitted to the data, most models also provide a set of hyper-parameters that must be optimized to achieve best performances, like the number of clusters in k-means, or the number of trees in a random forest. After training, the capability of the model to generalize beyond the training data is evaluated on an independent and identically distributed test set. In the case of multiple competing models, the best model of each family of predictors is evaluated and the optimal model is deployed in the real-world environment. A graphical representation of the process is shown in Figure 1. Nanomaterials 2020,10, 708 8 of 26 Data Acquisition Preprocessing Hyperparameter Tuning TrainingValidation Model Selection Testing Figure 1. Example of ML pipeline for TGx data. Data Acquisition and Preprocessing: Data is collected and analyzed to ensure the quality of the dataset. During the preprocessing, feature selection and/or feature transformations may be applied to improve stability. Training-Hyperparameter tuning-Validation loop: candidate models are fit to the data. This is embedded in an iterative process where for each candidate model the best hyperparameters are optimized through the validation step. Model Selection and Testing: Optimized candidate models are identified and the best ones are tested on a final hold-out dataset to evaluate generalization capabilities. 6.1. Dimensionality Reduction and Feature Selection Since TGx data usually present a large number of measured molecules compared to the number of samples, they can suffer from the curse of dimensionality. Thus, the model overfitting, the spurious correlations and a trade-off between accuracy and computational complexity have to be taken into account when modelling these data [87]. Dimensionality reduction techniques and feature selection methods can mitigate these issues and can be used in combination with ML approaches to build predictive modeling [ 24 ]. Some examples of dimensionality reduction techniques are principal component analysis (PCA) [88], multidimensional scaling (MDS) [ 89 ], t-distributed stochastic neighbour embedding (t-SNE) [ 90 ] and Uniform Manifold Approximation and Projection (UMAP) [91]. Probabilistic component modelling can be a powerful technique as it combines dimensionality reduction with highly interpretable ML models [ 10 , 92 ]. It can be used in unsupervised mode to identify response modules that describe aspects of cellular response to chemicals or in a supervised way to predict responses based on omics input data. The Predictive Toxicogenomics Space (PTGS) scoring concept tool is based on modules derived from Latent Dirichlet Allocation (LDA) probabilistic component model analyses of the entire CMAP dataset [ 73 ] of over 1300 chemicals and drugs. To begin, 100 response components were derived. Expression data was then integrated with the NCI-60 DTP cellular screening database (222 chemicals) to identify 14 components that corresponded with cytotoxicity at 50% growth-inhibitory level or above. Further ML assessment selected 4 of these 14 components that were able to predict liver toxicity. The multi-view Group Factor Analysis (GFA) and Bayesian Multi-tensor Factorization (MTF) are in some ways more advanced versions of probabilistic component modelling but also have their own limitations, so decisions on their use have to be made individually. If the goal of the analysis is to reduce the dimensionality by preserving the original features, feature selection approaches can be a better alternative. Indeed, it allows to reveal significant underlying information and to identify a set of biomarkers for a particular phenotype [ 24 ]. Examples of these are filter approaches such as information gain, correlation feature selection (CFS) [ 93 ], Borda [ 94 ], random forests [ 95 , 96 ], FPRF [ 26 ], and Varsel [ 97 ]. More advanced modelling based on genetic algorithms, such as GALGO [ 98 ] and DIABLO [ 99 ], GARBO [ 100 ], allows taking into account the non-linear correlations between candidate biomarkers. Feature selection methods for the identification of biomarkers of toxicity can be used in combination with ML approaches to predict the toxicity of different drugs and chemicals [ 12 , 101 , 102 ]. For example, Eichner et al. [ 103 ] applied an ensemble feature selection method in conjunction Nanomaterials 2020,10, 708 9 of 26 with bootstrapping technique to derive reproducible gene-signature from microarray data for the carcinogenicity of drugs. The application of stable feature selection methods is particularly important since it may accelerate the screening for promising candidates and hence have more efficient and less costly processes for drug development. Su et al. proposed a multi-dose computational model to predict drug-induced hepatotoxicity based on gene expression for toxicogenomics data [ 104 ]. Their methodology is based on a hybrid feature selection method, called MEMO, which uses the dose information after drug treatment based on a dose response curve to deal with the high-dimensional toxicogenomics data after. They validated their model using the Open TG-GATEs database and they show that the drug-induces hepatotoxicity can be predicted with high accuracy and efficiency. 6.1.1. Stability and Applicability Domain TGx data are the result of an experimental measure that is prone to both technical and biological noise, due to the complexity of the exposed system. Thus, stability and reproducibility play a key role in the analysis [ 105 ]. For example, multivariate methods can identify different subsets of candidate biomarkers with equal or similar accuracy, even if the feature selection algorithms are used on the same data [ 26 , 106 , 107 ]. Other challenges that should be taken into account when creating models from TGx data involve the applicability domain (AD) of the predictors, the number of predictors in the models. According to the OECD principle of validation [ 108 ], one of the essential steps in model implementation is the definition of the AD. Indeed, predictions extrapolated outside of the model’s AD may be less accurate [ 109 ]. Even though different methods have been proposed to compute and evaluate the model AD, there is still a lack of a uniform definition. One of the most common methods to compute AD is based on the leverage methods such as the Williams plot [ 110 ]. This method can be used to compute AD for linear regression models and is also useful to identify outliers in the data. Other methods can be the standardization approach [ 111 ] and the euclidean and city block distance methods [ 112 ]. The AD of non-linear models can be computed with kernel-based estimators or k-nearest neighbours method [ 112 ]. The AD for classification models can be computed by using the PCA-based and range-based methods [113]. Recently, a new methodology for feature selection from complex data, called MaNGA has been proposed [ 114 ]. MaNGA uses a multi-objective optimization strategy to identify the minimum set of predictive features with the widest AD, better predictivity capability and high stability. Even though the MaNGA strategy has been implemented for the development of robust and well validated predicting QSAR models, it could be easily applied to identify biomarkers of toxicity from TGx data. 6.2. Clustering Clustering is an unsupervised learning exploratory technique, that allows identifying structure in the data without prior knowledge on their distribution. The main idea is to classify the objects based on a similarity measure, where similar objects are assigned to the same class [ 51 , 115 , 116 ]. Transcriptomic data are characterized by a huge number of features (genes), thus the first step in gaining some understanding of microarray data is to organize them in a meaningful way. Cluster analysis has been used as an extremely helpful method to analyze and visualize this data. The main objective of performing cluster analysis with transcriptomic data is to group together genes that share the same pattern of expression but differ from the genes in other clusters. The main assumption is that the genes in the same cluster may be involved in similar or related biological functions [117–119]. Different clustering algorithms are available and some well known algorithms are listed in Supplementary Table S1. Some entries (e.g., biclustering) refer to families of algorithms stemming from a common structure. Different clustering algorithms can produce different results starting from the same input data, and even a single algorithm may produce two different results in two different runs if featured with a random component. Different metrics, such as the Davies-Bouldin index, Nanomaterials 2020,10, 708 16 of 26 In conclusion, TGx methodologies have a good potential to become part of the regulatory hazard assessment when all aspects from data generation to data preprocessing and modelling will be harmonized, and openly available for the scientific and regulatory communities. Furthermore, we believe that some methodologies and techniques implemented in other fields (e.g., QSAR) could be translated in the contest of TGx. Eventually, future methods could combine ML algorithms and dose-dependencies methods in order to identify biomarkers of toxicity. This review can be considered the starting point to identify the best downstream analysis methodology to apply to TGx data depending on the problem in hand. It is important to highlight that each one of the described methods can be used individually, but they can also be concatenated in a pipeline to perform a more comprehensive TGx analysis. Moreover, it is important to note that all the modelling methodologies strongly rely on careful planning of the exposure conditions and robust data preprocessing, discussed in detail in the first and second parts of this review series. Author Contributions: Conceptualization, D.G., A.S. Methodology, A.S. Investigation, A.S. Writing—original draft preparation, A.S., M.F., L.C., I.L., G.M., P.K., P.N., A.F. P.A.S.K. Writing—review and editing, A.S., M.F., L.C., I.L., G.M., P.K., P.N., A.F., P.A.S.K., K.J., M.K.H., J.-S.C., N.S., M.G., T.P., T.-H.Y., H.S., R.G., A.A., D.G. Visualization, A.S., M.F., L.C., and A.A. Supervision, D.G. Funding acquisition, D.G., A.A., R.G., H.S., T.-H.Y., T.P., M.G. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Academy of Finland [grant number 322761] and the EU H2020 NanoSolveIT project [grant number 814572]. Acknowledgments: The authors would like to thank David Winkler for critical comments to the manuscript. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations The following abbreviations are used in this manuscript: AD applicability domain AI artificial intelligence AIC Akaike criterion ANNs artificial neural networks AOP adverse outcome pathways ATHENA Analysis Tool for Heritable and Environmental Network Associations BMD benchmark dose BMDL benchmark dose lower bound BMDU benchmark dose upper bound BMR benchmark regulation CART classification and regression trees CFS correlation feature selection CNN convolutional neural network CNV copy number variation CMAP Connectivity Map DAGs directed acyclic graphs dGCs donkey granulosa cells DL deep learning DT decision trees EFSA European Food Safety Authority FN false negative FNN feedforward neural network FP false positive GCN graph convolutional network GENN grammatical evolution neural network GFA group factor analysis Nanomaterials 2020,10, 708 17 of 26 GO gene ontology GTEx Genotype-Tissue Expression KE key events K-NN k-nearest neighbors IC50 half maximal inhibitory concentration L1000 Library of Integrated Network-Based Cellular Signatures 1000 LDA linear discriminant analysis LDrA Latent Dirichlet Allocation LR logistic regression MDS multidimensional scaling MF matrix factorization MI mutual information ML machine learning MOA mechanism of action MOE molecular initiating event MVDA multi-view data analysis miRNA microRNA MTF bayesian multi-tensor factorization NAM novell assessment methods NB naive bayes OECD Organisation for Economic Co-operation and Development Open TG-GATEs Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System PCA principal component analysis PLSDA partial least squares discriminant analysis POD point of departure PPI protein-protein interactions PTGS Predictive Toxicogenomics Space QSAR quantitative structure activity relationship ReLU Rectified Linear Unit RF random forest RIVM Rijksinstituut voor Volksgezondheid en Milieu institute RNA-Seq RNA sequencing SNF similarity network fusion SNP single nucleotide polymorphism SVM support vector machines tSNE t-distributed stochastic neighbour embedding TGx Toxicogenomics TN true negative TP true positive UMAP Uniform Manifold Approximation and Projection References 1. 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