DNA methylation is a fundamental epigenetic modification involved in various biological processes and diseases. Analysis of DNA methylation data at a genome-wide and high-throughput level can provide insights into diseases influenced by epigenetics, such as cancer. Recent technological advances have led to the development of high-throughput approaches, such as genome-scale profiling, that allow for computational analysis of epigenetics. Deep learning (DL) methods are essential in facilitating computational studies in epigenetics for DNA methylation analysis. In this systematic review, we assessed the various applications of DL applied to DNA methylation data or multi-omics data to discover cancer biomarkers, perform classification, imputation and survival analysis. The review first introduces state-of-the-art DL architectures and highlights their usefulness in addressing challenges related to cancer epigenetics. Finally, the review discusses potential limitations and future research directions in this field. Genetic and epigenetic alterations in DNA contribute to modify gene expression (GE) and are heavily implicated in human diseases, particularly in cancer . Epigenehttp://bib.oxfordjournals.org/) [Changes in DNA methylation, either hypo- or hypermethylation, play an important role in carcinogenesis. DNA hypomethylation, the decrease in global DNA methylation, is recognized as a key feature of cancers , 6, 7. Mls.org/) , is alsols.org/)  in a numls.org/)  . Alreadls.org/)  and therls.org/) .The DNA methylation profile across the genome is vital to understand the influence of epigenetic mechanisms and modifications in cancer. Genome-wide epigenetic pattern of DNA methylation is explored and evaluated through developed platforms based on array and sequencing . DNA metDetecting cancer-specific DNA methylation patterns as unique signatures  and captML, developed historically as a subset of artificial intelligence (AI), has played a significant role in the development of autonomous cancer and disease diagnosis. The goal in ML is to develop models based on statistical associations and mathematical rules among features from a given dataset. ML algorithms are generally categorized into supervised learning and unsupervised learning . In supeK-means clustering and hierarchical clustering, principal component analysis (PCA) and variants as well as dimensionality reduction techniques.In unsupervised methods, no target is given and the algorithm tries to find natural clustering of similar data points. In this context, ML methods are specifically designed to discover hidden patterns or data groupings with little or no human intervention. Methods used include DL is a further specialized subset of ML that works in the same way as ML, but with vastly enhanced capabilities and approaches. DL algorithms can be regarded as the complex evolution of ML algorithms. DL models typically use a network of interacting layers to learn and discover information in data . The intThe focus of this systematic review is to investigate DL applications that address the challenges in cancer epigenetics by leveraging multi-omics datasets, specifically those involving DNA methylation data, and DL applications that analyze unimodal data obtained from both methylation array and sequencing platforms. The review was conducted using MEDLINE as the sole online database to find studies published until 23 December 2022. The following search terms consisted of a combination of DL-related keywords, DNA methylation and cancer including: ‘deep learning’, ‘artificial neural network’, ‘convolutional neural network’, ‘deep autoencoder’, ‘recurrent neural network’, ‘long-short-term-memory’, ‘gate recurrent unit’, ‘capsule neural network’, ‘transformer’, ‘cancer’ and ‘DNA methylation’. After an initial screening process, 35 studies were selected for further analysis from a total of 118 studies identified. For literature screening, a PRISMA flow diagram was prepared that displayed the number of studies included and excluded at each stage of the review, which is illustrated in http://bib.oxfordjournals.org/, of this article in DL Architecture which we have provided as a resource.With the significant improvement of computational power and the advancement of big data in recent years, DL has become one of the most successful approaches for dealing with the explosion of next-generation sequencing genomic data . HoweverThe design of artificial neural networks (ANNs) was inspired by the structure of the human brain: information is transmitted through layers of weighted, interconnected computational neurons . ANNs tyConvolutional neural network (CNN)  is a DL h. The decoder is used to reconstruct the initial input Autoencoder (AE)  is an uner (DAE) , the Spaer (DAE) , Contracer (DAE)  and Varier (DAE) .Capsule neural networks (CapsNets) were introduced  as an alRecurrent Neural Networks (RNNs) possess internal memory, enabling them to capture and remember important information from previous inputs. This memory allows RNNs to make precise predictions for sequential data, such as time series, speech, text and DNA sequences , 34. UnlLong Short-Term Memory (LSTM)  is an adThe Gated Recurrent Unit (GRU)  is a typhttp://bib.oxfordjournals.org/.Transformer-based models  have achhttp://bib.oxfordjournals.org/) along with the calculation and definition of these metrics .In this study, we identified a total of 35 research articles that utilized various DL architectures. The articles were categorized into four groups: (1) diagnosis or classification of cancer epigenetics and diseases, (2) prediction of CpG methylation states and imputation of missing values, (3) biomarker discovery in tissue and cfDNA samples and 4) estimation of patient survival and survival-sensitive subtypes in cancer. The findings of these studies are summarized in  estimatiMetaCancer  is a DL The CVAE was employed to automatically extract features from the multi-omics datasets, while the DNN was trained in a supervised manner to discriminate between the two classes. The encoder and decoder parts of the variational autoencoder (VAE) architecture consisted of two convolutional layers and one dense layer . This architecture enabled the recognition of local patterns independent of their position in the data, facilitating effective feature extraction. Subsequently, the latent vector extracted by the CVAE was used as input to train the DNN for the accurate classification. This study introduced a novel methodology for integrating DNA methylation and GE data in uterine cervical cancers. The MetaCancer model achieved a high accuracy of 88.85%, with an AUC of 91.19%, precision of 91.65%, sensitivity  of 87.69%, F1 score of 90.44% and specificity of 89.61%.This approach utilized a multistep process, starting with the removal of outliers and LR to predict GE data from preprocessed methylation data, using TCGA GE data as a reference. The study then identified significantly differentially expressed genes (DEGs) with an FDR cut-off of less than 0.001, using Empirical Bayes test and Limma downstream analysis. Finally, the proposed approach employed DL techniques, specifically feed-forward neural networks (FNNs), to interpret the DEGs . The methttps://www.disgenet.org/) for supervised classification. The feature matrix is normalized and reshaped, serving as input to a CapsNet classifier with two convolutional layers and one fully connected layer. The first layer operates on the reshaped matrix, while the second layer works on feature maps generated from the first layer. These feature maps are transformed into 32 channels of 8D capsules in PrimaryCap. To enhance the classifier 's learning ability, the method applies instantiation parameters and dynamic routing mechanism settings. The study achieved an AUC of 94.6%, a sensitivity rate of 88.7% and a specificity rate of 90%. This research provides a promising methodology for the classification of breast cancer patients using multi-omics datasets.The CapsNeTMMD method, developed by , aims toR2 = 0.96 and a correlation coefficient of 0.68. Additionally, the proposed model achieved high accuracy 95.1%, precision 96%, recall 95% and F1-score 95% on the classification task.A DL approach called DDAE-MLP  was propThe HI-DFNForest  methodolt-test methods, and the construction of a DL network as a classifier. The DL network had two input layers for GE and DNA methylation data, six hidden layers with the Relu activation function and 100 nodes each, and an output layer with one node using the Sigmoid activation function. The model aimed to identify DEGs and differentially methylated positions while avoiding redundancy in a new dataset created from tumor and normal samples. The GCBMI model achieved excellent performance with AUC 98%, accuracy 98%, precision 99%, sensitivity  98% and F1 score 99%.A novel GCBMI model  was applA two-stage DL approach was proposed  for clask-nearest neighbor, mean and iterative imputation. The synthetic minority oversampling technique was used to address class imbalance in the dataset. In the second phase, dimensionality reduction methods, namely ANOVA and RF, based on ML and statistical approaches, were applied to reduce the dataset's dimensionality. Finally, two variants of a sequential deep neural network (DNN) were implemented, considering the dataset size (27 K and 450 K), to classify breast cancer patients. The study achieved high accuracy 98.75% and precision 99.99% in classifying breast cancer patients. The AUC was 98.75%, indicating excellent performance. The sensitivity  and F1 score were 97.6 and 98.73%, respectively [A three phase method was developed to classify breast cancer patients using methylation data. In the first phase, preprocessing steps were performed, including the removal of specific CpG sites and handling missing values using various methods such as zero, 27 K and 0 K, to cAn ML- and DP-based method was developed to classify MCD patients using methylation data. The study used classical ML algorithms, such as SVM, KNN, RF and tree classifier to classify MCD patients. Additionally, the study proposed a stacking ML algorithm, which combines the predictions of several ‘weak’ classifiers to improve prediction robustly and accurately. Furthermore, the study modeled a DL network consisting of three subsequent layers. To be consistent with plots of the disease clusters, the study transformed the predictions of the ML and DL models into a 2D space via Uniform Manifold Approximation and Projection dimensionality reduction. The DL network had an AUC of 100% and an accuracy of 94%. The precision and sensitivity were both 98%  .MethylCapsNet and MethylSPWNet  are DL aR2 value of 0.96 and an MAR of 3.0 [Methylnet, a pretrained VAE , was utiR of 3.0 .d-score value, and estimating the fraction of tumor-derived reads by maximizing the posterior probability of a plasma sample using all d-scores predicted by the DL-based model in the previous stage. In this study, DISMIR was applied to methylation information of cfDNA and DNA sequence data in HCC patients, achieving an impressive AUC 99.69%, a sensitivity  of 93.94% and a specificity of 100%. Furthermore, the Pearson correlation between tumor size and the ratio of tumor-derived reads was found to be 0.88, with a P-value of 6.68 × 10−5 [DISMIR was developed to determine the source of reads in plasma cfDNA WGBS data and estimating the proportion of tumor-derived reads. DISMIR comprises four main phases: identifying cancer-specific DMRs as candidate biomarkers, screening out reads located in those DMRs, building and training a CNN-Bidirectional LSTM model to predict a 8 × 10−5 .iCancer-pred  was emplA DL methodology utilizing VAE was developed to analyze DNA methylation datasets for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) obtained from the TCGA. VAEs were employed to generate a biologically significant latent space by merging the datasets, accurately capturing the distribution of distinct sample subtypes. The high-dimensional features were encoded to a lower dimensional intermediate layer and then decoded back to the original dimensionality using a nonlinear combination. To gain insights into the resulting 100D space, t-Distributed Stochastic Neighbor Embedding was applied to reduce the dimensionality further to 2. Logistic regression classifiers were trained using the 2D features to evaluate the performance of the VAE-generated latent space in classifying among four groups . The study accomplished useful results, achieving an AUC and accuracy rate both reaching 91.50%, with a sensitivity rate of 78.75% and an F1 score of 81.25% .A DL-based classifier model was developed to predict cancer origins using DNA methylation data. Statistical techniques such as one-way ANOVA and Tukey's HSD test were applied to reduce dimensionality and eliminate noise in the data. These techniques filtered CpG sites with insignificant differences across tissues and removed CpG sites with minimal variation in mean beta values. The resulting dataset included 10 360 CpG sites, which were used as input for an MLP model. The MLP model consisted of 2 hidden layers with 64 nodes each, using a Relu activation function and an output layer with 18 nodes, employing a softmax activation function. The results demonstrated a model with exceptionally high sensitivity and specificity, scoring 94.48 and 99.80%, respectively .The imputation of missing values in GE and DNA methylation data (DM) was explored using VAE methodology . The stuDeepCpG  is a comThe BiLSTM-5Mc  is a metThe CpG transformer  is an arβ value of all the probes located in the promoter regions of a certain gene to determine its corresponding promoter methylation level. The model applies this promoter methylation level to the landmark genes to predict the methylation profile of target. The D-GPM was compared against the performance of classical ML models, including RT, LR and SVM, by evaluating the mean absolute error (MAE) and Pearson correlation coefficient (PCC) criteria. The results demonstrated that D-GPM outperformed classical ML methods in terms of both MAE and PCC, achieving an MAE of 0.03 and a PCC of 0.82.A multilayer neural network called the D-GPM  was deveThe MRCNN  is a speThe INTERACT model  is a recTo classify circulating cell-free DNA (cfDNA), methylation data in ovarian cancer were evaluated using two stages. The first stage involved selecting the top 2000 and 5000 CpGs in promoter regions using the Recursive Feature Elimination method and logistic regression for classification. In the second stage, six AI models were employed on all CpGs located in intragenic regions, including RF, SVM, ANN, Prediction Analysis for Microarrays (PAMs), Linear Discriminant Analysis (LDA) and Generalized Linear Models (GLMs). Finally, a set of CpG markers were proposed in two parts as binary classification in ovarian cancer. The performance of the AI models was evaluated using AUC, sensitivity and specificity. The DL model achieved an AUC of 100% with a sensitivity of 100% and specificity of 88%  .In addition, a method for detecting lung cancer using cfDNA methylation analysis and AI was developed by Bahado-Singh et al.  that conA diagnostic prediction model based on DNA and cfDNA methylation data was applied to 27 cancer types of TCGA database. To achieve this, they used moderated t-statistics to identify the most differential methylation expression between whole-genome and promoters’ CpGs. They then utilized two ML methods, Lasso and RF, to recognize the markers with the biggest methylation beta value difference. Finally, two multilayer FNNs were implemented to distinguish between cancer samples of different subtypes and normal samples. The performance of the method was evaluated based on the detection of cancer from methylation data in tissue and cfDNA. The findings demonstrate that the method consistently delivered outstanding performance in cancer detection, boasting an impressive average across key metrics AUC, accuracy, sensitivity and specificity of 98, 91, 93.65 and 92.8%, respectively, for both tissue and cfDNA samples .A novel computational method, known as ML_ordCOX , was desK-means clustering method. The results showed that the SdA method was effective in distinguishing KIRC subtypes with P-value = 4.37 × 10−7, and average Pearson correlation was 0.13 between the original input and the reconstructed input from LJHL.The stacked denoising AEs (SdA)  is a comK-means clustering based on the reduced features associated with survival. In the second part , DEGs between survival subgroups were calculated using DEseq2 and ANOVA F-values methods for each omics data type, and random forest models were constructed using these genes. The performance of the model was evaluated on validation dataset with log-rank test P-value 9.3 × 10−3 and C-index value of 0.64 [A DL-based model was developed to predict the prognosis of lung adenocarcinoma patients using multi-omics data. The method consisted of two parts. In the first part (training), the model stacked the four unit-norm scaled matrices and used them as input for an AE to extract new features. Univariate Cox proportional hazard (Cox-PH) models were then applied to these new features to identify those significantly associated with overall survival (OS). A covariate Cox-PH model with Lasso regression was performed to select features by minimizing the log partial likelihood. Survival subgroups were identified using  of 0.64 .The Multi-view Factorization Autoencoder (MFAE) method  was desiP-value < 0.05). K-means clustering was used to group samples based on these informative features. The Wilcoxon test was applied to continuous features and the Chi-square test or Fisher’s exact test to categorical features to determine the multi-omics features most correlated with a patient's risk subgroup. The top-ranked features were used to construct multiple prediction models, including FC layers, SVM, RF, LogitBoost (LGB) and Naive Bayes (NB) with optimized parameters determined through a grid search. This method achieved an impressive AUC of 99% for multi-omics data, and it demonstrated high accuracy 98%, sensitivity 99% and specificity 96% for the FC layers prediction model.The i-Modern methodology  developeK-means clustering, the tumor samples were divided into two groups based on survival-related features. The resulting clusters (G1 and G2) showed distinct survival rates, suggesting their potential for predicting survival outcomes in CRC patients. The evaluation yielded a C-index value of 0.78 and log-rank test P-value of 1.53 × 10−7, indicating its potential as a powerful and effective tool for CRC cancer prognosis [AE is a model to integrate DNA methylation, RNA-seq and miRNA-seq data in CRC patients. The AE model concatenated the three data matrices and reduced their dimensionality into a lower dimensional representation. The transformed features were then evaluated using a COX-PH model to assess their predictive effect on the hazard rate. Using rognosis .K-means clustering with K = 10 showed a significant difference in survival rates (log-rank test: P = 0.003) too [Utilizing AEs, the correlation between the LUAD and LUSC subtypes of non-small cell lung cancer (NSCLC) and survival outcomes has been explored . The dat003) too .P-value of less than 0.0001 and a Brier score of 0.16.An AE-based model  was deveTwo principles, known as ConcatAE and CrossAE, were introduced to integrate multi-omics data based on using multiview learning approach . ConcatAThe process of developing a deep survival Epigenome-Wide Association Study (EWAS) method  was desiAn alternative categorization approach has been implemented for a total of 35 research articles, which explored diverse DL architectures, based on the similarity of their DL models. These articles were subsequently grouped into nine distinct categories. A concise summary of the outcomes from this categorization is presented in It is worth noting that terms such as FNN, FC layer, DNN, MLP and ANN, while often interconnected, signify distinct aspects and variations within the realm of neural network models. These models find primary application in DNA methylation analysis, categorized into two main domains: multi-omics datasets and unimodal data. Within these domains, they serve purposes ranging from binary and multiclass classification tasks aimed at label recognition to regression tasks focused on predicting target values. The performance metrics reveals varying degrees of success among the studies. In the classification domain, AUC values range from 85% to a remarkable 100%, suggesting diverse levels of discriminative power in the models developed. In contrast, for regression tasks, the MAE is 0.03 and a C-index of 0.70.The AE and its variants such as DAE, VAE, CVAE, multiple AEs and stacked denoising AEs or AEs are considered as a group of deep AEs initially designed for dimensionality reduction. They were primarily developed for applications in two key domains: multi-omics datasets and unimodal data. Deep AEs offer the flexibility to incorporate multiple data modalities, allowing the exploitation of complementary information. This integration can be achieved through two main approaches. The first method involves early integration, where features from each dataset are concatenated at an initial stage. The second approach is intermediate integration, wherein each data modality undergoes dedicated processing layers. Subsequently, the outputs of these layers are concatenated, followed by further layers for the integration of features extracted from each data modality. The performance metrics exhibited a range of values, including AUC (60–99%), C-index (0.63–0.92) and accuracy (74–97%).The CapsNet model is utilized either in isolation or in conjunction with other DL models for classification tasks. The performance of this specific DL model type, applied to both multi-omics datasets and unimodal data, yielded reported metrics of 94.6% for AUC and 98% for accuracy, respectively.Ensemble models based on DL architectures, such as BiLSTM, BiGRUs and transformerare all DL architectures commonly used for sequential data processing, are employed in tandem with other DL models such as CNNs for pattern detection in sequencing data or FC layers for regression tasks, and even for integrating the output of other modules. These categories exhibit a notably higher level of complexity compared with other groups. While BiLSTM, BiGRU and Transformer are all used for sequential data processing, they differ in terms of architecture, parallelization, attention mechanisms and parameter efficiency. These models play a pivotal role in identifying long-term dependencies within CpG neighborhoods or DNA sequencing. In addition, the performance metrics exhibited a range, with AUC values spanning from 82 to 99%, with a C-index of 0.72.The success of DL-based methodology in processing of DNA methylation data is of particular importance because these approaches has the potential to be applied in a wide range of biological processes, including chromosome instability, X-chromosome inactivation, cell differentiation, cancer progression and gene regulation . ConsequBlack boxes: DL architectures are often criticized for lacking transparency in how they produce outputs. In the field of bioinformatics, it is crucial to interpret these models to comprehend the biological and computational patterns they detect. Consequently, there is a rising preference for ‘white-box’ approaches that offer more interpretability and transparency. By enabling interpretation, researchers can gain a deeper understanding of the biological relevance of the results, which enhances the usefulness and dependability of DL models in bioinformatics research.Overfitting: DL architectures have a complex nature with numerous interconnected parameters. To tackle this issue, several techniques have been developed, including dropout, batch normalization, weight decay, data augmentation and data corruption. These methods aim to improve model performance and generalizability, especially when training data are limited. Insufficient training data are identified as the primary cause of overfitting, and the mentioned techniques address this challenge by modifying model parameters, architecture and manipulating input data.High dimensionality and low sample size: This issue poses significant challenges for DL. The data requirements of DL models are significantly higher than those of other ML algorithms, making access to more data crucial for achieving optimal performance. Collaborative data repositories can be an effective solution for addressing this challenge. However, it is important to consider the appropriate algorithm type and performance measures during data collection can help to avoid erroneous data interpretations.Imbalanced data: Imbalanced datasets are characterized by an uneven distribution of observations among the target classes, meaning that one class label has a notably higher number of observations, while the other exhibits a significantly lower count. Training DL models using imbalanced data can lead to undesirable results. Fortunately, transfer learning can offer a viable solution for addressing the class imbalance problem. By initially training the model using a general dataset, transfer learning can effectively mitigate the challenges associated with imbalanced data in epigenetic data analysis.Fine-tuning a DL model: The issue is a complex process that requires careful analysis of initial results, as it depends on the dataset and research question. Hyperparameters, including learning rate, batch size, momentum and weight decay, are critical for every DL architecture and can be adjusted during training. These hyperparameters control various aspects of the model, such as step size, number of training samples per iteration, optimization of training paths and weight multiplication after updates. It is crucial to correctly set these hyperparameters to avoid under-fitting or over-fitting problems.Limitation of resource-constrained environments: This issue poses significant challenges due to the computational complexity and memory requirements of DL models. To address this, novel hardware solutions such as Graphics Processing Units and Field Programmable Gate Arrays have been proposed, alongside model compression techniques. These compression methods encompass diverse approaches including parameter pruning, knowledge distillation, compact convolutional filters and low rank factorization, all aimed at reducing the computational overheads of the models.Balancing accuracy and interpretability: the application of ML and DL methods in studying epigenomics can aid in explaining the underlying mechanisms of diseases and developing new treatments. However, many of these methods prioritize accuracy or efficiency over interpretability, hindering our ability to understand the relationship between epigenomic markers and diseases. This limitation can restrict the use of ML and DL in clinical settings where decisions rely on transparent reasoning. Therefore, it is crucial to establish clear reasoning behind any computational model before employing it in high-stakes clinical situations .DL methods are nonlinear algorithms that are highly flexible and can identify patterns in complex, high-dimensional data. These methods can model data more accurately than most other classical ML algorithms and are used for dimensionality reduction, clustering and identifying correlations between data types. A key benefit is the ability of DL methods to integrate multiple steps of data processing into a single model, saving time and minimizing errors. Another advantage is its capacity to incorporate various types of data, such as electronic health records, molecular data, digital pathology and radiographic images, enabling a comprehensive understanding of the heterogeneity seen in complex diseases. This integration facilitates tailoring medical care and advancing personalized medicine . FinallyDL will continue to be a crucial tool in the diagnosis, screening, treatment and prognosis of disease, including cancer. Emerging application areas, for example, include drug discovery and pharmacogenomics . The futIn this work, we first introduced current state-of-the-art DL architectures. We then reviewed the current state of the DL methods and its application in cancer epigenetics, specifically those involving DNA methylation data. This review provides a comprehensive overview of recent advances in DL-based approaches in four categories: diagnosis or classification of cancer epigenetics and diseases, prediction of CpG methylation states and imputation of missing values, biomarker discovery in tissue and cfDNA samples, and estimation of patient survival and survival-sensitive subtypes in cancer. We also discussed the challenges in processing DNA methylation data and multi-omics data using DL methods, including interpretability, overfitting, the curse of dimensionality, imbalanced data, parameter and hyperparameter tuning, the deployment of DL models in resource-constrained environments, and the attention to improving accuracy which sometimes lacks transparency in the clinical context. Finally, we highlighted emerging DL tools that are likely to play a key role in future DNA methylation analyses.Deep learning (DL) methods are increasingly being used to facilitate analyses of genome-wide DNA methylation patterns.DL methods have been applied to multi-omic datasets and unimodal data to inform biomarker discovery, classification of disease status, data imputation and survival analysis.DL methods have shown impressive performance on these tasks, but successful application depends critically on the DL architecture.The challenges of DL include interpretability and overfitting, as well as the technical issues of parameter tuning and dealing with imbalanced datasets.The ongoing and rapid development of DL, for example in large language models, heralds a promising future for the analysis of DNA methylation data and its application to clinical genomics and precision oncology.supplementary_files_bbad411Click here for additional data file.


AE-GCN transfers the AE-specific representations to the corresponding GCN-specific layers and unifies these two types of deep neural networks for spatial clustering via the clustering-aware contrastive mechanism. In this way, AE-GCN accommodates the strengths of both AE and GCN for learning an effective representation. We validate the effectiveness of AE-GCN on spatial domain identification and data denoising using multiple SRT datasets generated from ST, 10x Visium, and Slide-seqV2 platforms. Particularly, in cancer datasets, AE-GCN identifies disease-related spatial domains, which reveal more heterogeneity than histological annotations, and facilitates the discovery of novel differentially expressed genes of high prognostic relevance. These results demonstrate the capacity of AE-GCN to unveil complex spatial patterns from SRT data.Spatially resolved transcriptomics (SRT) provides an unprecedented opportunity to investigate the complex and heterogeneous tissue organization. However, it is challenging for a single model to learn an effective representation within and across spatial contexts. To solve the issue, we develop a novel ensemble model, AE-GCN ( Spatially resolved transcriptomics (SRT) technologies, such as spatial transcriptomics (ST) , 10x VisCurrently, the spatial domain detection methods could be mainly divided into two categories: non-spatial and spatial clustering methods. Some non-spatial methods originally developed for single-cell RNA-sequencing (scRNA-seq) studies, e.g., Seurat  and ScanThough these methods have provided useful information on the usage of expression profiles and spatial information, they usually depend on single models, which center on either expression data itself or spatially neighboring structure, thus probably resulting in the preferred usage of the focused data type. For example, the non-spatial clustering methods only models the gene expression itself, while the spatial clustering methods often take spatial neighbors prior as a hard constraint to ensure spatial clustering continuity, which may lead to over-smoothing of expression  and missautoencoder-assisted graph convolutional neural network), which combines the autoencoder (AE) and graph convolutional neural network (GCN), for accurate and fine-grained spatial domain identification. Specifically, AE-GCN relies on AE for learning expression data-based representations and GCN for spatial graph-constrained learning. AE-GCN orderly transfers the AE-specific representations to GCN-specific layers and unifies these two types of neural networks for spatial clustering via a clustering-aware contrastive mechanism. In this way, AE-GCN combines the advantages of the two models and takes full integration of the expression data and spatial information during the representation learning process.Here, we develop a novel combined model, AE-GCN  based on gene expression and select the top Assume that there are original expression matrix Then, the weighted adjacency matrix Limited by the transcript capture rate of SRT technologies, expression data are often sparse and noisy. AE-GCN generates the enhanced expression data We employ AE to learn the useful representations from the expression data itself and assume that there are The output  for 13 10x Visium datasets, one ST dataset, and one Slide-seqV2 dataset are selected using scanpy.pp.highly_variable_genes from the Scanpy Python package. The log-transformation of the expression profiles is performed using scanpy.pp.log1p on the original gene expression data.AE-GCN uses the combined latent representation y Leiden  algorithFor the enhanced expression matrix We use adjusted Rand index (ARI)  and clusdatasets .We use bulk expression data with patient survival information to evaluate the prognostic significance of genes via the Kaplan–Meier plotter  in the IWe evaluated the ability of AE-GCN to detect spatial domains using 12 human dorsolateral prefrontal cortex (DLPFC) slices generated using 10x Visium. The DLPFC dataset obtained from spatialLIBD  is manuaGenerally, most of the spatial clustering methods performed better than non-spatial algorithms . Slide-seqV2 can measure gene expression at near-cellular resolution  image , ventricle (AE-GCN domain 12), and habenula (AE-GCN domain 11) sections . These sPcp4 ) and indH) image . For venentricle  and Gabbhabenula ) or staihabenula . Thus, fn = 428 spots). The histopathological image and annotations were taken as references  ST dataset (ferences . We asseferences .S100P and TNS4) appeared more spatially smoothed on spots in situ . In this manner, we used the AE-GCN-reconstructed data to denoise the low-quality measurements and evaluated the effectiveness in recovering gene spatial expression patterns. Based on the denoised data, we selected the top 50 DEGs of each domain from the reconstructed data enoising . Overall in situ . These t in situ . For exarognosis  . The histopathological annotations from the original paper  Visium dataset  demonstrates that AE-GCN reveals more functional regions and novel cancer prognostic genes for interpreting cancer heterogeneity, suggesting that AE-GCN has great capability of unveiling tissue heterogeneity from SRT data.The effectively combined modeling is key to the superiority of AE-GCN in the SRT study. Generally, AE models learn the representations from expression data itself, while GCN models learn the structured representations from the sample graph structure by providing an approximate second-order graph regularization, which may suffer from over-smoothing issues. AE-GCN combines the characteristics of these two deep learning methods and integrates them to learn effective representations so that AE is used to weaken the problem of overfitting while simultaneously learning the structured representations in GCN. Additionally, the proposed clustering-aware contrastive module in AE-GCN further promotes the combined model from processes independent of clustering targets to the model that achieves effective spatial clustering. Thus, AE-GCN can not only effectively use the information of the expression data itself but also reasonably regularize the learned information from expression data by spatial structure between spots, which has better advantages than the spatial domain detection methods based on a single-model design in SRT studies.Currently, AE-GCN only models gene expression and spatial information from SRT data and cannot utilize histological images which are also provided by several SRT technologies, e.g., 10x Visium. Although some methods have used histological images in spatial domain detection, histological images are mainly used to enhance the quality of expression data and lack of modeling image data separately, e.g., stLearn . Compare


Pediatric low-grade gliomas (pLGG) show heterogeneous responses to MAPK inhibitors (MAPKi) in clinical trials. Thus, more complex stratification biomarkers are needed to identify patients likely to benefit from MAPKi therapy. Here, we identify MAPK-related genes enriched in MAPKi-sensitive cell lines using the GDSC dataset and apply them to calculate class-specific MAPKi sensitivity scores (MSSs) via single-sample gene set enrichment analysis. The MSSs discriminate MAPKi-sensitive and non-sensitive cells in the GDSC dataset and significantly correlate with response to MAPKi in an independent PDX dataset. The MSSs discern gliomas with varying MAPK alterations and are higher in pLGG compared to other pediatric CNS tumors. Heterogenous MSSs within pLGGs with the same MAPK alteration identify proportions of potentially sensitive patients. The MEKi MSS predicts treatment response in a small set of pLGG patients treated with trametinib. High MSSs correlate with a higher immune cell infiltration, with high expression in the microglia compartment in single-cell RNA sequencing data, while low MSSs correlate with low immune infiltration and increased neuronal score. The MSSs represent predictive tools for the stratification of pLGG patients and should be prospectively validated in clinical trials. Our data supports a role for microglia in the response to MAPKi. The MAPK pathway is a key driver of pediatric low-grade gliomas (pLGG); however, response to MAPK inhibitors (MAPKi) in pLGG patients is not consistent. Here, the authors develop MAPKi sensitivity scores (MSS) to predict response to MAPKi and apply them to bulk and single-cell sequencing datasets from pLGG patients and preclinical models. They comprise a variety of entities2 and are defined as grade 1 or 2 by the World Health Organization (WHO)3. They are characterized by a generally favorable outcome, with a 20-year overall survival (OS) ranging from 80 to 90%4. Event-free survival, however, is low when incompletely resected5, and 10-year progression-free survival ranges between 40 and 60% after adjuvant therapy6. This makes pLGG an often chronic progressive disease, with some patients needing several lines of systemic therapy and accumulating therapy-related sequelae5.Pediatric low-grade gliomas (pLGG) are the most common brain tumors in children7, such that they are now considered to be a single-pathway-driven disease, with virtually all driving alterations occurring mutually exclusively in the MAPK pathway8. The most common alterations are KIAA1549:BRAF fusions (~50%), BRAF V600E mutations (~10%), NF1 mutations (~15%) and FGFR1/2 mutations (~10%)2.In the past decade, the mitogen-activated protein kinase (MAPK) pathway was identified as the main driving force in pLGG9, the MAPK pathway has been thoroughly described, and many inhibitors of its main mediators  have been synthesized and characterized13. Many inhibitors have been approved by the FDA to treat MAPK-driven diseases, such as the MEK inhibitor (MEKi) trametinib14, binimetinib15, selumetinib16 and cobimetinib17, and the type I1/2 BRAF inhibitor (BRAFi) encorafenib18, dabrafenib19 and vemurafenib20. Consequently, several (pre-)clinical studies were initiated to test the efficiency of such MAPK inhibitors (MAPKi) in pLGG. Studies using the MEKi selumetinib21 and trametinib22 and the BRAFi dabrafenib24 showed very encouraging results, although with less extensive or durable responses than initially hoped for. Overall, 33–50% of treated patients had at least a partial response (PR) of their tumor to the treatment. Stable disease (SD) was observed in a further substantial fraction (24–67%), while progressive disease (PD), despite treatment, was observed in 4–28% of patients24. This highlights the fact that patients harboring a pLGG will not respond homogeneously to a MAPKi despite sharing a common MAPK alteration. This is consistent with studies conducted in other MAPK-driven entities, mainly KRAS-driven tumors, where it was shown that MAPK alteration status alone does not necessarily correlate with MAPKi response27. For instance, it was shown that the treatment of MAPK pathway-driven tumors with MEKi can induce the activation of parallel pathways  via the release of negative feedback regulation of upstream partners, such as CRAF30. There is therefore a need to improve the stratification of patients based on their potential sensitivity to a given MAPKi in order to treat them with the therapy with the highest probability of response, i.e., significant tumor volume reduction.Since its first discovery more than 30 years ago31. This highlights the potential role that overall MAPK pathway activity level may have in predicting MAPKi sensitivity in pLGG, as opposed to genetic MAPK alteration status, which has been shown to be a poor predictor of MAPKi sensitivity32. In addition, phospho-ERK (pERK) is often used as a surrogate marker to assess MAPK pathway activity on formalin-fixed paraffin-embedded (FFPE) samples and to predict MAPK pathway activity levels in the clinical setting33. However, an increasing number of studies showed that pERK is not an accurate readout to asses MAPK pathway activity34, most likely because of negative feedback loops triggered by ERK activity and involving the DUSP and SPRTY family proteins35. Instead, investigators suggested that measuring MAPK pathway output by expression of key MAPK target genes, such as the MAPK Pathway Activity Score (MPAS), derived from aggregated gene expression of 10 genes that have been reported in multiple gene signatures predictive of sensitivity to MAPK inhibition, would be a more accurate way to assess MAPK pathway activity36.Cell lines harboring the same MAPK alteration possess different levels of MAPK pathway activation37. These NGS techniques are being used in current recruiting and upcoming clinical trials, such as the LOGGIC/FIREFLY-2 trial38 and its associated LOGGIC Core BioClinical Data Bank39, generating valuable tumor-derived sequencing data. Using transcriptome signatures that could predict sensitivity to several types of drugs is therefore a realistic and clinically relevant stratification approach that could be used prospectively in upcoming clinical trials. Several studies have shown the relevance of gene expression-based signatures from cell lines in the accurate prediction of drug sensitivity41. Initiatives such as the Genomics of Drug Sensitivity in Cancer (GDSC) study, in which sensitivity to over a hundred compounds was measured in over 900 cell lines with known gene expression profiles, greatly helped develop such drug sensitivity signatures42.As gene expression data from clinical sequencing analyses of patients’ tumors are becoming a routine diagnostic method in precision oncology, gene expression-derived signatures are becoming more readily available as a new data type suitable for clinical applications such as predictive stratification. The pediatric precision oncology registry INFORM has shown the potential of using Next-Generation Sequencing (NGS) approaches to provide a more precise diagnosis by identifying driving alterations, thus refining treatment and improving progression-free survivalIn this work, we show that drug class-specific MAPKi sensitivity scores (MSS) derived from gene expression signatures predict MAPKi sensitivity in pLGG in vitro and in vivo, warranting further validation in clinical trials. Our data also supports a role for microglia in the response to MAPKi.BRAF V600E pLGG samples had the highest MPAS levels.We first investigated the differential MAPK activation level in pLGG using the MPAS measure in the Open Pediatric Brain Tumor Atlas (OPBTA) dataset  from MAPK-altered cancer cell lines (n = 234) from the Genomics of Drugs Sensitivity in Cancer (GDSC) dataset. This showed a moderate positive correlation (r > 0.3) of the MPAS with IC50z-scores for the MEKi and the BRAFi type I 1/2 and weak positive correlation coefficients (r < 0.3) with the BRAFi type II and ERKi : ulixertinib; dual inhibitor ERKi : SCH-772984), samples from the Discovery set were grouped together based on their drug sensitivity, as determined by their IC50z-score across the entire GDSC dataset  to calculate corresponding predictive sensitivity scores in the Testing set. The prediction ability of those scores was evaluated in the Testing set , a consensus ranking was used to sort the signatures based on their overall performance  , the GDSC dataset was randomly split regardless of the underlying genetic background. Sensitivity data from specific NTRKi , FGFRi , and mTORi  were used. GSEA was performed using pathway-specific gene signatures . The same metrics as for the MSS were used to rank the signature, except for the concordance with MAPK pathway activity. Gene signatures topping the ranking in both Discovery and Testing sets were identified, potentially identifying signatures predicting sensitivity to mTORi nly Fig.  were usenly Fig. , binimetnly Fig.  or tramenly Fig. .Fig. 2InBoth BRAFi Type I 1/2 and MEK1/2i MSSs showed good concordance (concordance index from 0.62 to 0.66) with treatment response and a statistically significant correlation with treatment response Fig. . When grNone of the BRAFi Type I 1/2 and MEK1/2i MSS correlated with tumor progression in the untreated group, allowing to suggest that the signatures are not affected by a confounding effect driven by the spontaneous evolution of the tumor  comprising pilocytic astrocytoma (PA) cell lines harboring a KIAA1549:BRAF fusion , a PA and a pleomorphic xanthoastrocytoma (PXA) cell line harboring a BRAF V600E mutation , melanoma cell lines (n = 55) with BRAF mutations and known high MAPK pathway activity36 , and multiple myeloid cell lines (n = 11) with MAPK wild-type and known low MAPK pathway activity36 . The pLGG cell lines with a BRAF V600E mutation showed BRAFi MSSs at the same level as BRAF-mutated melanoma cells and had significantly higher MSSs compared to the pLGG cell lines with a KIAA1549:BRAF-fusion  and DKFZ-BT317 cells (KIAA1549:BRAF fusion) transduced with this reporter and determined the IC50 of several MAPKi belonging to of each class investigated in this study . Consistently, in pLGG entities only, the samples harboring a MAPK alteration showed higher MSSs compared to those with an unaltered MAPK pathway and normal tissue  were considered to have a high predicted sensitivity score , samples with an overall MSS significantly lower than the average (z-score < −2) were considered to have a low predicted sensitivity , and samples with an in-between overall MSS (z-score between 0 and −2) were considered to have an intermediate predicted sensitivity . Hence, we identified sample clusters with several levels of predicted sensitivity  to identify potential overall MAPKi-sensitive clusters of pLGG samples in the OPBTA dataset Fig. . Samplesity Fig. . The pro55  and for which treatment response was available by MRI using the RAPNO cirteriaTo address the current lack of clinical response data of pLGG patients to MAPKi paired with RNAseq data from baseline, we analyzed a publicly available RNA sequencing dataset from melanoma patients, with mutually exclusive MAPK alterations and which were treated with the BRAFi Type I 1/2 vemurafenib. Notably, the data showed that patients who responded well to the treatment had a higher median BRAFi Type I 1/2 MSS than those who did not respond, and this difference was almost significant despite the small sample size  tumors and their corresponding normal tissue  with the first FDA approved systemic therapy for first-line treatment of pediatric patients with pLGG with a BRAF V600E mutation, namely trametinib and dabrafenib. Since dabrafenib is not recommended for the treatment of KIAA1549:BRAF fusion pLGG, we also tested a combination of the MEKi trametinib with the currently clinically investigated BRAFi Type II tovorafenib (DAY101). For both of these BRAFi+MEKi combinations, we could show an inverse correlation between the measured combination log10(IC50) and the predicted sensitivity to both combined drugs, i.e., increased MAPKi sensitivity correlated with increased MSS 67. Using these data, we could show the same correlation between increased sensitivity to the combination treatment and increased MSS  in the pLGG samples from the OPBTA, such as age at diagnosis, tumor site, sex and molecular subtype (predictors). We also included the prediction of immune and stromal infiltration in order to evaluate whether the tumor microenvironment could have an impact on the predicted MAPKi sensitivity using the ESTIMATE signaturesSSs Fig. , indicatThe MSSs showed a strong correlation with the ESTIMATE score  in the pLGG samples from the OPBTA cohort Fig. , and in n = 5) and neuronal-type (n = 1) tumors, as well as lymphoid and myeloid cell populations. As expected, the ESTIMATE immune score was exclusively limited to the lymphoid and myeloid compartments  data from 6 pLGG primary samples  did not correlate with the best MAPKi response type , indicating that the correlation is not biased because of gene overlap Fig. .Fig. 7MS69. We tested the extent to which our gene signatures are confounded by the microenvironment transcriptome using the ConfoundR platform. We compared our signatures’ enrichment in the stroma compared to tumor epithelium and then within cell populations in the stroma only, using datasets derived from colon, breast, pancreas, ovarian and prostate cancers  in the microglia clusters, of which one was expressed in only 2/4 MSS signatures (SPP1), and the other also had an elevated average expression in the monocyte cluster, while our MSSs were specifically elevated in microglia in pLGG. The MPAS genes were also lowly expressed with similar expression proportions across clusters. In contrast, the genes comprising the immune score had similar percentages of expression in the immune clusters  only, with 23/200 (12%) genes highly expressed (log(CPM + 1) >2) in at least one of these clusters. This highlights that the high predicted sensitivity toward MAPKi in microglia might be specific to tumor-activated microglia and not to healthy microglia.Since the ConfoundR platform uses data from non-CNS tumors and therefore lacks data on microglia, we decided to assess the relative expression level of the genes comprised in our signatures in a published scRNA-seq dataset with non-tumor-associated microglia Of note, PDX-derived sensitivity signature analysis did not yield signatures superior to our MSS see .We finally tested the correlation between the MSS and the predicted immune infiltration in other tumors and tissues from the OPBTA and the TCGA Fig. . The corTaken together, these data indicate that our MSSs are not biased by genes specifically expressed in stromal/immune cells, and in particular not from healthy microglia, and seem to be specifically elevated in tumor-associated microglia in pLGG and potentially other CNS entities.Hot and a Neuronal cluster in a mutually exclusive manner. Since our MSSs positively correlated with the proportion of immune infiltration and were the highest in microglia, i.e., in Hot pLGG samples, we hypothesized that pLGG samples with low MSSs might be pLGG with neuronal features71. To test this, we used a neuronal signature from a scRNA-seq study on glioblastoma72 to measure a neuronal score reflecting the proportion of neuronal infiltration. We could show a significant negative correlation between the MSSs and the neuronal score in the pLGG samples from the OPBTA  that would represent patients with stable or progressive disease Fig. . PetraliBTA Fig. . The samBTA Fig. , suggestBTA Fig. . While tBTA Fig. . While t36. In addition, the MPAS poorly predicted MAPKi response in the XevaDB PDX dataset.In the present study, we observed that MAPK pathway activity, as measured by the MPAS, is not an accurate enough surrogate marker to predict responsiveness to any type of MAPKi. We showed that MAPK pathway activity prediction did not correlate well with actual response to MAPKi other than MEKi in the GDSC dataset, in accordance with the fact that the MPAS genes were validated in models treated with MEKi trametinib and cobimetinib73. In our case, the risk of using such unsupervised methods was to select genes that would not only differentiate between MAPKi-sensitive and non-sensitive cells but genes that would also relate to the tissue of origin the cell lines were derived from. To avoid such gene selection bias, we decided to use a biologically supervised approach, focusing on MAPK pathway-related genes only . This rationale was strengthened by the work of Wagle et al., which showed that a biology-driven gene selection performed at least as good as the E-net model36. A common pitfall of such supervised prediction, however, is the misuse of split datasets, where the selection of genes is made on the full dataset74. We made sure the split of the GDSC dataset was proportional and that the signatures were generated using the Discovery set only. The second common pitfall of using such split datasets is the risk of confounding factors hidden in the dataset75. To avoid this, we used an independent PDX dataset to validate the robustness of our MSSs. Another shortcoming when using gene expression-derived datasets has recently been discussed concerning gene-expression signatures derived from bulk tumors being biased by the expression of stromal and microenvironment cells69. To control for this bias, we used the published ConfoundR platform that showed that none of our signatures were confounded by gene expression from the microenvironment and stromal cells. This was of particular importance since the Hallmark_KRAS_Signaling_UP has been shown to be confounded by gene expression from the stroma  in all five tumor entities where this was investigated69. It appears that our selection of genes specifically upregulated in MAPKi-sensitive tumor cell lines was enough to correct this bias, as our signatures poorly correlate with the ESTIMATE stromal score. All this allowed us to generate biologically robust signatures used to calculate their corresponding MSSs.We therefore generated gene expression-based MAPKi sensitivity scores derived from in vitro drug sensitivity from a cohort of more than 200 MAPK-altered cancer cell lines (GDSC dataset). Many studies aiming at identifying gene signatures use unsupervised approaches, such as classifiers using elastic net logistic regression (E-net). Such classifiers have proven useful in generating cell type-specific gene signaturesWe were able to apply this approach to other pLGG-relevant drugs , generating signatures that could be further explored in future studies. This highlights the relevance of such a biology-driven approach to identify new drug-sensitivity signatures from large pharmaco-genomics databases. A more comprehensive analysis of the whole GDSC dataset, which includes hundreds of compounds, is warranted, as it could lead to deeper discoveries and further validate this approach. While our approach didn’t use nor generate new tools to measure sensitivity score per se, the combination of the methods used allowed us to generate specific and unbiased gene signatures that may capture MAPKi sensitivity in pLGG tumors. In particular, our signatures were derived focusing on MAPK-related genes only  in order to avoid the inclusion of any genes that would be specific to the tissue the tumor cells were derived from. By doing so, we aimed to generate MAPKi sensitivity signatures that would be free of any tissue/cell type-specific genes  to make the signatures virtually applicable to any cell type.Our four signatures showed only a small overlap with already published signatures related to MEKi and BRAFi response, as well as MAPK activation . Interestingly, the overlap across all signatures (overlap MSS) did not perform as well as the single signatures to predict sensitivity to any MAPKi. This allows to suggest that each class of MAPKi, despite targeting the same pathway, probably has a distinct output requiring specific predictive signatures.23, with scores indicating a proportion of predicted responsive patients of around 59% and non-responsive patients of about 4% in a cohort of primary pLGG samples. We demonstrated that the MEK1/2i MSS correctly predicted treatment response in five primary pLGG samples, with increasing MSS correlating with a better response. Furthermore, we were able to demonstrate a similar pattern in an independent melanoma dataset in response to the BRAFi Type I 1/2 vemurafenib. This indicates that, beyond pLGG, the MSSs described here could potentially also be applied to predict MAPKi sensitivity in melanoma patients with mutually exclusive MAPK alterations. Although these data warrant further validation in larger datasets, this clearly highlights the potential for such scores to be further explored and validated in future clinical trials. It is worth noting that in the context of combination therapy, our MSS was also positively associated with the response to several types of MAPKi in vitro and in PDXs in vivo. It appeared the best responders were the samples with the highest MSS for both drugs (as opposed to high MSS for only one drug), which may suggest that patients with high MSS for two drugs might be more likely to benefit from combination therapy. This observation will certainly need further validation in upcoming clinical trials.Our MSSs showed consistent results in the pLGG background, showing a clear dependency toward MAPK alterations, and accurately predicted MAPKi sensitivity in a set of three pLGG cell lines in vitro. These signatures, derived from in vitro cell lines, precisely recapitulated pLGG biology in primary tumor samples. Indeed, the MAPKi sensitivity signatures showed the highest scores in MAPK-altered gliomas, and more particularly in low-grade gliomas/astrocytomas. The MSSs also captured the proportion of responding patients observed in recent clinical studies80. However, translating the signatures’ output into normalized scores, applicable to patient-derived gene expression data from any cohort, remains a challenge for their translation into clinical studies. For instance, the MPAS uses the sum of gene expression’s z-score, hence normalizing the scores across the whole dataset, making its value vary when one or several samples are added or removed. To circumvent this, we preferred the ssGSEA approach81, allowing us to generate an enrichment score per sample independent of the cohort composition and variability in the number of samples. However, one pre-requisite to allow comparison is that every sample has to be comparable, i.e., went through the same sample processing and data processing pipeline, in order to obtain meaningful enrichment scores, making scores comparable across several datasets/studies a challenge. Another challenge is the comparison of scores among themselves, as it is at the moment not possible to compare ssGSEA scores  with each other, which would allow to estimate whether one MSS is higher or lower than another. Some approaches could be envisioned to circumvent these pitfalls, such as a resampling procedure to generate null distributions for each of the MSSs82, which will need further validation in prospective studies.To date, several studies have shown the potential of such gene signatures derived from preclinical studies and large datasets such as the GDSC or TCGA83 and CD8 + T lymphocyte84 activity. In addition, the MAPK pathway is known to be active, in particular in macrophages/microglia cells, where it is involved in microglia/macrophage polarization, metabolism, and pro-inflammatory activities87. A recent study also described the dependency of migrating glial progenitors toward MAPK pathway activity in the development of NF1-associated optic pathway gliomas, where MAPK inhibition was capable of preventing the expansion of such glial progenitors88. This indicates that the MAPK pathway is a key pathway in microenvironmental cells, particularly in microglia cell biology. Since we showed that healthy human microglia and infiltrating macrophages had low MSSs, it could be that microglia become sensitive to MAPKi only following interaction with tumor cells, either via direct contact or stimulation by secreted factors, such as the senescence-associated secretory phenotype (SASP) factors89. This predicted MAPKi sensitivity of tumor-associated microglia in pLGG will need further validation. In vitro validation might represent a challenge, however, considering that all the current microglia cell models present limitations90, the most important being their immortalization with the SV40-largeT antigen, preventing any TP53-related apoptosis, as already observed in SV40-transformed pLGG models45. scRNA sequencing on short-term cultured primary samples treated with MAPKi, or pLGG organoids might represent an alternative to investigate this further. If confirmed, and considering the importance of microglia in the support of glioma development91, this finding could change the way we see pLGG treatment with MAPKi, and, more generally, in the way we consider pLGG biology: patients treated with MAPKi therapy show signs of tumor volume reduction; however, the main cell populations targeted are only hypothesized as being exclusively pLGG tumor cells. In addition, the fast rebound often observed upon treatment cessation is a counter-intuitive phenomenon to happen in tumors that are thought to be mainly in a senescent status. Based on our results, it is conceivable to imagine that pLGG is partly driven by immune cells, which are recruited and supported by senescent tumor cells91. If microglia are at least as sensitive to MAPKi as the glial tumor cells, such therapies might induce a reduction of the proportion of the microglia compartment (which can represent up to 30% of a pLGG tumor92) as at least a part of the reduction of tumor volume. Upon MAPKi withdrawal, microglia might be able to be quickly recruited by the remaining surviving tumor cells via the production of SASP factors, at least partly explaining the fast tumor rebound. This hypothesis is worth further investigation in the future.We observed that the MSSs strongly correlated with the ESTIMATE score, particularly its immune signature in primary pLGG samples. On a single-cell level, the MSSs were the highest in the microglia compartment. This was unexpected, considering that our signatures were exclusively derived from gene expression from tumor cell lines and did not show indications of stromal-related gene expression bias. Several studies have shown an interconnection between tumor microenvironment and resistance to MAPKi in certain tumor entities, mainly via dysregulation of macrophagesBRAF wt cells). It is well known that such BRAFi generally induces paradoxical activation of the MAPK pathway in non-BRAF-altered cells94. Therefore, the immune cell population should not be detected as a putative sensitive population. This false positivity probably came from the fact that, by design, the signature relates to MAPK pathway activity, which is increased in both tumor and microglia cells , while the paradoxical activation induced by BRAFi Type I 1/2 is purely intrinsic to the drug’s mode of action10. Therefore, a cell population with an activated MAPK pathway could be wrongly predicted as being sensitive to a BRAFi Type I 1/2. However, this signature still accurately predicted the degree of sensitivity to a BRAFi type I 1/2 in our GDSC-derived Testing set and the independent PDX dataset, advocating that this signature could still be relevant under the condition that the type of genetic MAPK alteration is known before treatment initiation93.The MAPKi sensitivity prediction in the immune compartment also underlined a limitation of one of our signatures: the BRAFi type I 1/2 sensitivity signature predicted sensitivity in the normal microglia compartment 96. These NSG have been shown to promote glioma cell progression and survival through the activation of several pathways, such as the Akt/mTOR pathway and the MAPK pathway, which are mediated by N-methyl-D-aspartate (a.k.a. NMDA) and amino-3-hydroxy-5-methyl-4-isoaxazolepropionate acid (a.k.a. AMPA) receptors97. If validated in a larger dataset, this could potentially pave the way for a new type of therapy for the treatment of pLGG, where MAPKi could be used in combination with neurotransmitter inhibitor, which could potentiate and increase the effects of MAPKi in low responders/resistant pLGG patients.Our signatures also allowed us to investigate potential molecular and cellular mechanisms driving pLGG tumors with low sensitivity toward MAPKi. We found that pLGG with neuronal features seems to be less sensitive to MAPKi, as estimated with our MSSs. Petralia et al. described a dichotomous classification of pLGGs in either Hot tumors with a high degree of immune cell infiltration or Neuronal with a low degree of immune cell infiltrationLarger gene expression datasets derived from patients that received a MAPKi treatment, coupled with their respective response to the treatment , are now urgently needed to identify and validate sensitivity score thresholds to assess the applicability of our MSS as stratification tools for clinicians. Such large datasets will also allow further investigation of the involvement of the microglia in treatment response and the relationship between pLGG with neuronal features and MAPKi resistance. This will ultimately allow a better understanding of pLGG biology and the identification of new treatment modalities that could improve the treatment and quality of life of all pLGG patients.Legal guardians provided written informed consent on behalf of all pediatric patients for the use of tissues for research without compensation. All samples from McGill collaborators (CHU Sainte-Justine biobank) were collected under a protocol approved by the ethical committee of CHU Sainte-Justine. All samples from Charité (archives from the SIOP-LGG 2004 interim protocol) were collected under a protocol approved by the ethical committee of Charité Universitätmedizin Berlin.46 were cultured in Astrocyte Basal Medium  plus supplement  with 1 µg/ml doxycycline  to induce cell proliferation as described previously46. The BT40 cell line, kindly provided by Prof. Houghton, was grown in RPMI  supplemented with 10% FCS . All cells were cultured under a 5% CO2 atmosphere at 37 °C. Were indicated, DKFZ-BT317 cells transduced with the pDIPZ reporter48 were seeded in a 384-well plate (1 × 106 cells per well) and treated with the corresponding MAPK inhibitor  of this mixture were used to draw the combination dose-response curve and measure the IC50 of the combination.Pilocytic astrocytoma cell lines  generated in our previous studieshttps://hgserver1.amc.nl/cgi-bin/r2/main.cgi) and MAS5.0 normalized.pLGG cell lines samples (untreated) and primary samples RNA extraction was performed using the RNeasy Mini Kit  following the manufacturer’s protocol. RNA was submitted to the DKFZ genomics core facility (Microarray Unit) to generate gene expression profiles with Affymetrix Gene Chip human U133 Plus 2.0. Gene expression data were then uploaded on the R2 online database  therapy was collected from two partner institutes  (cohorts’ details in Supplementary Data n = 3), RNA extraction was performed using the RNeasy Mini Kit  following the manufacturer’s protocol. cDNA synthesis and library preparation were done using the Agilent SureSelect XT HS2 RNA kit, and sequencing was done on an Illumina NovaSeq 6000 platform. For the samples from McGill University (n = 2), total RNA was isolated using AllPrep DNA/RNA/miRNA Universal kit (Qiagen), according to the manufacturer’s instructions. RNA samples were rRNA depleted using QIAseq FastSelect (Human/Mouse/Rat 96rxns). cDNA synthesis was achieved with the NEBNext RNA First Strand Synthesis and NEBNext Ultra Directional RNA Second Strand Synthesis Modules (New England BioLabs). The remaining steps of library preparation were done using the NEBNext Ultra II DNA Library Prep Kit for Illumina (New England BioLabs), and sequencing was done on an Illumina NovaSeq 6000 platform. There was no material left over after extraction.For the samples from Charité  was subjected to papain digestion to generate a single-cell solution. Non-vital cells were removed by 7-AAD staining  using a FACSAria II cell sorter (BD Biosciences), and approximately 10,000 vital cells were used as input for scRNA-seq. Single-index libraries were generated by Chromium Single Cell 3’ v3.1 technology (10x Genomics) and sequenced using an Illumina NextSeq 2000 sequencing system at the Genomics Core Facility . The samples were analyzed with the 10x Genomics CellRanger v6.0.2 pipeline and Seurat R package v4.0.5. Raw data were converted to Fastq format with the CellRanger mkfastq function and subsequently aligned against the human reference transcriptome GRCh38 v2020-A with CellRanger count and default values. Seurat objects were created for both samples based on the following filter criteria: a minimum of 3 cells, a minimum feature number of 200, and cells with <25% of mitochondrial genes. Outlier cells with a high nCount_RNA value were classified as doublets and removed . The filtered data were then normalized, integrated and clustered with Seurat’s SCTransform routine using a resolution parameter of 0.5. Feature plots, UMAPs and dotplot visualizations were created with Seurat functions; a cluster-based cell type annotation was conducted based on the expression of characteristic marker genes per cell type.Fresh tumor material from the Münster pLGG samples  dataset was used. RMA-normalized data can be downloaded on the GDSC website (see “Data availability” section).99. FPKM values were converted to TPM values in order to make sample comparison possible for subsequent single-sample Gene Set Enrichment Analysis (ssGSEA), as recommended by the Broad Institute (https://software.broadinstitute.org/cancer/software/gsea/wiki/index.php/Using_RNA-seq_Datasets_with_GSEA). The type of response followed the mRECIST criteria described in the original publication. All PDXs showed a primary response, almost always followed by the emergence of treatment resistance induced by newly acquired genetic alterations. Since this is extremely rare in pLGG100, we only considered the primary response in our study.For signature validation in the PDX cohort, RNAseq data from the “Database For PDX Pharmacogenomic Data”  was usedhttps://github.com/AlexsLemonade/OpenPBTA-analysis).Processed genomic data from the Open Pediatric Brain Tumor Atlas dataset is available through the Open Pediatric Brain Tumor Atlas portal (101 (https://figshare.com/articles/dataset/Data_record_3/5330593), and mutation status was retrieved from already published work102.Finally, unified TCGA sequencing data is accessible on figshareSince we aimed to identify gene signature that would be applicable for pLGG, where all driving MAPK alterations are mutually exclusive, all non-pLGG datasets  were pre-filtered to only keep samples with mutually exclusive driving MAPK alterations.42. Both GDSC1 and GDSC2 drug sensitivity runs were kept in the analysis. As both GDSC1 and GDSC2 datasets were included, the same cell line could have been tested with the same inhibitor several times. For clarity in that case, we will henceforth describe these as “samples” treated with MAPKi. The data from the GDSC2 dataset was used preferentially in case of conflicting results across datasets 1 and 2, as suggested by the GDSC authors42. The list of cell lines comprising both sets can be found in Supplementary Data MAPKi sensitivity signatures were generated from the Genomics of Drug Sensitivity in Cancer (GDSC) datasetz-score) and predicted sensitivity  were used to evaluate the efficiency of each signature to classify each sample into a sensitive or non-sensitive category. The IC50 z-score threshold was set at 0 to allow to differentiate between the samples that would respond to MAPKi  and the samples that would not respond . Homogenously distributed predicted sensitivity thresholds (n = 100) ranging from the lowest to the highest ssGSEA score and ROC curves were generated. The optimal predicted sensitivity threshold was then selected using Youden’s J statistics, and the following metrics were used to assess the signature’s efficacy: Youden’s J statistics (to measure the difference between true positive rate (TPR) and false positive rate (FPR)), and F1-score to assess model performance considering TPR and precision.Measured IC50  but are also capable of accurately capturing the different degrees of sensitivity of a given sample to a MAPKi .Pearson’s correlation coefficient was measured to assess the linear correlation between MAPKi IC50 31. We used the concordance index to control for a link between the MAPK pathway activity score (MPAS) and predictivity scores, which indicates the probability of equidirectional ranking of the signature score and the MPAS when comparing pairs of samples.Ideally, a MAPKi predictive signature should be related to the MAPK pathway activity level. However, MAPK pathway activity itself does not correlate well with MAPKi sensitivityz-score thresholds were assigned to define each measured sensitivity category . We subsequently identified the predicted ssGSEA sensitivity score threshold that allowed to differentiate between sensitive, medium-sensitive and medium-resistant cells in the GDSC dataset  was calculated based on the measured sensitivity and the predicted sensitivity: IC50 For each class of MAPKi , the nine signatures were ranked per drug and metric. The ranks were then aggregated across drugs using average ranks to give the smallest Euclidean distance. This led to the generation of a consensus ranking for the nine signatures across drugs for each of the five metrics separately. This was followed by a consensus ranking across all five metrics, allowing to generate an overall ranking of the signatures based on their performance to predict sensitivity to all MAPKi included in the given MAPKi class.36. In order to palliate the cohort-dependent effect induced by the use of gene expression z-score used in the original paper, ssGSEA was performed to generate signature scores on a single-sample basis.To measure MAPK pathway activity, the previously described MPAS signature was usedAll statistical analyses were performed in R Studio (R Version 1.4.1103) using the following packages:Correlation analysis and Pearson coefficient were carried out using the “stats” package (v4.2.1), and ROC analysis with the “pROC” package (v1.18.0).Concordance indices were calculated using the “survival” package (v3.4-0).103.Consensus ranking and related figures were done using the “challengeR” package (v1.0.2)Signatures overlap and Venn diagrams were generated using the “VennDiagram” package (v1.7.3).Significance between groups was calculated using ANOVA followed by Tukey’s ‘Honest Significant Difference’ test in the “stats” package. Multiple linear regression analysis was performed with the lm function after ensuring that the data followed a normal distribution .104, using the parameters recommended in the documentation. Of note, ssGSEA scores were not normalized and are therefore considered as measured in arbitrary units.Gene Set Enrichment Analysis (GSEA) was performed using the Broad Institute software (GSEA_4.0.3). The ssGSEA module (v10.1.0) from Gene Pattern was used to measure ssGSEA scoreshttps://confoundr.qub.ac.uk/)69.The testing of the extent to which our signatures are confounded due to the microenvironmental transcriptome was done using ConfoundR . scRNAseq data from Reitman et al. was re-analyzed with the “Seurat” package using the parameters described in the original publication, and signature scores were measured using the “UCell” package (v2.0.1).Graphical representations were done in R Studio using the “ggplot2” package (v3.4.1) for the correlation plots, waterfall plots and boxplots. The “ComplexHeatmap” package (v2.12.1) was used to generate the heatmaps.50 calculation was performed using GraphPad Prism 5 software , using a 4-parameter dose-response model.ICn = 5 samples, and scRNA seq was performed on n = 6 samples, as these were the only patient samples available at the time of the study. The rest of the analyses were carried out on publicly available datasets.For all in vitro experiments carried out in this study, experiments were carried out in three independent biological replicates as standard practice. Bulk RNAseq was performed on Data were excluded from the publicly available datasets used in the study based on criteria defined beforehand. These criteria were chosen to stay relevant to pediatric low-grade glioma biology  and to stay clinically relevant .All measures performed experimentally were always done in three independent biological replicates. All replicates are shown in the manuscript. All attempts at replication were successful. For in vitro experiments, cells were randomly allocated into control and experimental groups. The investigators were not blinded to allocation during experiments and outcome assessment.Sex and/or gender were not a criterion for the study design or data interpretation. This information was not collected.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary SoftwareReporting Summary


These methods can be separated into two groups: post hoc analyses of black box models through feature attribution methods and approaches to build inherently interpretable models through biologically-constrained architectures. We argue that these approaches are not mutually exclusive, but can in fact be usefully combined. We propose PAUSE (The online version contains supplementary material available at 10.1186/s13059-023-02901-4. For instance, tools like scVI use variational autoencoders (VAEs) to help analyze single cell RNA-seq data [Pseudomonas aeruginosa and yeast datasets [As technologies to measure gene expression in biological samples have advanced over the last several decades, the tools and methods to extract biological meaning from these high-dimensional measurements have developed in parallel . The incseq data . Similarseq data . Determidatasets , 8. Whildatasets .Two competing trends have emerged to “open the black box” and elucidate the biological mechanisms learned by these models, and have so far been framed as orthogonal approaches. The first trend could be described as post hoc interpretability, and aims to identify which genes were the most important contributors to each latent variable learned by an autoencoder. A variety of methods have been used to do this interpretation, and range from the use of feature attribution methods –12 to thbiologically-constrained modeling, and aims to create models with latent spaces that are inherently interpretable. These models use prior information to define sparse connections between input nodes corresponding to genes, and latent nodes corresponding to biological pathways (or other pre-defined groups). These biologically-constrained networks have been used in a supervised setting to improve the prediction of survival or treatment resistance from cancer gene expression or mutational status [Another recent trend could be described as l status , 18, andl status . In partl status –22.fully unsupervised analysis of gene expression data. We next show how existing, feature-level attribution approaches still provide useful information for annotated, unsupervised models. Finally, we apply our approach to a large transcriptomic compendium of post-mortem brain data from patients with Alzheimer’s disease and demonstrate how our approach can identify Mitochondrial Respiratory Complex I as a potential drug target for this disease.Our paper aims to demonstrate that these two trends in biological interpretability are not mutually exclusive, and that principled attribution methods can improve the analysis of unsupervised models of gene expression analysis by quantifying the importance of pathways. We first outline a general workflow for unsupervised analysis of gene expression data, comparing classical linear approaches like principal component analysis (PCA) to more contemporary deep learning-based approaches. This outline allows us to identify a step in the workflow that has been neglected by previous methods. We then propose a novel, fully-unsupervised attribution method and demonstrate how it can be used to identify important pathways in transcriptomic datasets when combined with biologically-constrained autoencoders. This allows for To understand how game-theoretic attributions can improve the unsupervised analysis of gene expression data, it is first helpful to understand a representative unsupervised workflow Fig. a 23, 2424. In thWhile deep learning-based autoencoders are able to reconstruct gene expression with high fidelity, they fall short at steps (2) and (3) in the workflow described above. Both the relative importance of the different latent dimensions, and the biological meaning of these dimensions are opaque in deep autoencoders. “Interpretable” autoencoders Fig. b aim to principled attribution for unsupervised gene expression analysis (PAUSE), aims to improve the utility of “interpretable” deep autoencoder models using techniques from the area of feature attribution  architecture  learns mays Fig. c.imputation benchmark, and measures the extent to which the reconstruction error of a trained biologically-constrained autoencoder model increases when the learned embeddings for each pathway are replaced with an uninformative mean imputation. The better an attribution method ranks important pathways, the more quickly the reconstruction error will increase. The second benchmark is a retrain benchmark, and measures the extent to which pathways identified as important can reconstruct gene expression space when used to train a new model. Attributions that do a better job of ranking pathways will decrease the reconstruction error more quickly  stimulated with interferon-cument}β , a datasolygyrus , and a dolygyrus   trained on a dataset of mouse intestinal epithelial cells where control cells were untreated, and stimulated cells had been exposed to the parasitic roundworm olygyrus . Again, olygyrus . Previouolygyrus , 37. TheIn addition to identifying important pathways, the interpretability of biologically-constrained models can be enhanced through the application of gene attribution values. These gene attribution values help identify which gene expression values are important determinants of each learned pathway representation. For example, previous work has analyzed the expression of Jurkat cells, an immortalized human T lymphocyte cell line, when stimulated with anti-CD3 and anti-CD28 antibodies to induce T cell activation . This prgene attributions to understand the expression programs represented by the multiple nodes present within given pathway modules. The two most important genes for each of the four latent nodes in the TCR signaling module were PTPRC and PTPN22 . We can plot gene attributions for the top two genes for Node 1 . AD is the most common form of dementia, and accounts for an estimated 60–70% of worldwide cases . Clinicahe brain . We therhe brain , Adult Che brain , Mount Sconditional pathway module VAE (cpmVAE)  , which mfully deep architecture similar to that proposed in the pathway module variational autoencoder (pmVAE) approach [both have multiple non-linear layers. To better describe these networks, we can start with a description of a typical, unmodified VAE [In our experiments, we primarily used a approach . This aufied VAE . Standarassignment mask, which ensures that each gene is only connected with non-zero weight to the hidden nodes of the modules corresponding to its pathways. Each subsequent layer is masked with a binary separation mask, which is a block diagonal matrix ensuring that non-zero connections only occur within pathway modules. In addition to the sparse masking modifications made to the encoder and decoder architectures, the pmVAE method also alters the training objective to add a local reconstruction loss:p), p), N, and K is the total number of modules considered.In order to learn a low-dimensional latent space c, from the biological variation of interest, we wanted to train a conditional pathway module VAE (cpmVAE), and therefore maximize the conditional variational lower bound objective:In our experiments on bulk RNA-seq brain data, the brain gene expression samples were compiled from multiple data sources. In order to disentangle the batch effects, represented by a vector These condition labels are passed to each pathway module in the encoder, and again to each pathway module in the decoder. In order to ensure that the learned embedding is fully independent of the unwanted sources of variation encoded in the conditional labels, we add a regularization term to the loss based on the Hilbert Schmidt independence criterion (HSIC) between the latent embedding cument}c , 60. TheWe implemented our cpmVAE model (and the pmVAE models) using the PyTorch deep learning library . For bothttps://github.com/theislab/intercode), and for both experiments the network was trained using the hyperparameters described in (https://github.com/theislab/intercode/blob/main/notebooks/intercode-api-Kang18.ipynb): 80 epochs with a batch size of 62 samples, a learning rate of 0.001, and the regularization hyperparameters of For our experiments involving models with linear decoders , we can therefore apply the following formula to get a local pathway importance value:z is the value of the learned pathway embedding for a particular sample, We propose a novel attribution method to understand which latent factors are important in unsupervised neural networks, based on Integrated Gradients  and the completeness, meaning that the attributions for each pathway node sum to the difference between the reconstruction error of the model at the input z (when the pathway information is “present”) and the baseline implementation invariant, meaning the attributions are always identical for two functionally equivalent networks. This is important, in that it ensures the attributions reflect real functional differences between networks, rather than relying on artifacts based on particularities of the model employed.These local attributions have a variety of desirable properties , such asTo go from local pathway importance (the contribution of a particular pathway for a particular sample) to global pathway importance (the contribution of a pathway over an entire dataset), we can take the expected value of the local attributions over the samples in the original data:Since these attributions can be calculated so efficiently, rather than sampling we calculate the local attributions for every point in the dataset. Because the local attributions allocate the reduction in the sample-level reconstruction error to each pathway, the global attributions allocate the reduction in the dataset-level reconstruction error to each pathway. Since the mean squared error is proportional to the variance in the original expression space explained by the model, allocating the reconstruction error to each pathway tells us how much variance is explained by each pathway.We also note that our method is agnostic to the exact form of the loss used to train the model. For example, approaches like scVI model RNA-seq data as discrete counts under a negative binomial  distribution by optimizing the decoder to parameterize one of these distribution types. For a model like this, rather than using the mean squared reconstruction error as the function k that we wish to explain, we define the function kth node in the encoder network’s latent space. We can therefore quantify gene j’s contribution to pathway node k by applying the Integrated Gradients formula to this function to get:x is the observed expression for a particular sample, For our gene-level attributions, we want to understand which genes are important contributors to each of the latent pathway nodes. For a particular node in the latent space increase the loss.To summarize local gene attributions into global gene attributions, we take the average of the magnitude of the local attributions over the samples in the original data:nt signs . This wahttps://github.com/suinleelab/path_explain), which is a Python library for explaining feature importances and feature interactions in deep neural networks using path attribution methods [All attributions were calculated using the Path Explain repository , and the Harvard Brain Tissue Resource Center (HBTRC) study was made available at the following Synapse ID (syn3159435). In each study, we used the protein-coding genes that have a nonzero RNA-Seq read count in at least one-third of the samples. Overlapping these genes across the three studies resulted in 16,252 genes which we used in our experiments. We used normalized and log-transformed RNA-Seq read counts for all datasets. For the subset of samples with neuropathology data, MSBB neuropathology data was made available by the AMP-AD Knowledge Portal of Sage Bionetworks through synapse.org with Synapse ID syn6101474. We accessed neuropathology data from the ROSMAP and ACT studies through data use agreements.For experiments with AD, we used data from the ROSMAP, ACT, HBTRC, MAYO, and MSBB studies. Around half of the people in each cohort had been diagnosed with dementia by the time of death. ROSMAP RNA-Seq data and MSBB RNA-Seq data were made available by Sage Bionetworks on the AMP-AD Knowledge Portal with Synapse IDs syn3505732 and syn7391833, respectively. The ACT RNA-Seq data20 was collected by the Allen Institute for Brain Science, Kaiser Permanente Washington Health Research Institute (KPWHRI), and the University of Washington (UW), and it was made available with Synapse ID syn5759376. Mayo Clinic Brain Bank data was made available at Synapse ID , but then trains new models using only modules corresponding to the top pathways. In this benchmark, methods that perform better will decrease the reconstruction error more quickly as pathway modules are added. Finally, to benchmark the standard, unconstrained VAE models and the models with linear decoders, we followed the same steps as the impute benchmark, but trained the intercode models and VAE models as described above in the section on Model Architectures, rather than pmVAE models.To benchmark how well our PAUSE pathway attributions identified important factors of latent variation explaining the observed gene expression measurements, we adapted two benchmarks of feature importance from the feature attribution literature . The firAE model  AE model . The sechttps://github.com/suinleelab/path_explain), which is a Python library for explaining feature importances and feature interactions in deep neural networks using path attribution methods. All attributions were generated using the “attributions” method of the PyTorch explainer; the argument “num_samples” was set to 200, while the argument “use_expectation” was set to “FALSE. With this experimental setup, the mean time to compute PAUSE attributions was 22.54 s. In contrast, it took an average of 1.34 s to get supervised attributions using logistic regression.We have recorded the compute time and resources needed for our analyses. We will focus our compute time analyses on a typical dataset used in these analyses, in this case, the Zheng et al. BMMC dataset  where wep = 0.049).Before interpreting the learned embedding for our conditional pathway module VAE (cpmVAE) model trained on the AD dataset, we wanted to perform several sanity checks to ensure the model was sufficiently plausible and reliable. We first wanted to ensure that our proposed architecture (cpmVAE) was able to reconstruct the data with at least the same accuracy as the previously proposed, non-conditional pmVAE model. We therefore randomly split the data into a train set (75% of samples) and a test set (25% of samples), trained both a pmVAE and a cpmVAE on the training set for 50 epochs, and measured the reconstruction error on the held out test set for each model. We repeated this procedure a total of 10 times with different random train/test splits, and compared the distribution of test reconstruction errors for the two models , associated amyloid-mentclass2pt{minimWhile these results are certainly suggestive of an improvement in the control of nuisance variables and an improvement in one biological signal represented, they also do not necessarily demonstrate that all of the nuisance variation has been removed, or that all of the biological signal is optimally preserved, as there are likely signals in the dataset from biological processes not related to density of Aompendia . The simFinally, to compare the overlap between supervised approaches and pathway attributions from PAUSE on the AD dataset, we generated a ranked list of pathways using the supervised LR score method for the 664 brain expression samples with associated AB IHC protein density measurements . We took into account only the protein pairs mapped from human to C. elegans with a BLAST e-value smaller than C. elegans isoform, we identified the corresponding human genes, again using the NCBI BLAST tool, and used only the orthologs that achieved a BLAST e-value smaller than C. elegans.To enable biological testing of the human genes identified using our computational analysis, we obtained the Reciprocal Best Hits (RBHs) between human and  on CRAN , 76. Thesequence , 78. We C. elegans strain maintenance and manipulation were used, as previously described [E. coli at 15C [Standard procedures for escribed , 80. Allpromoter . Experimi at 15C , 81. CarC. elegans RNAi-feeding libraries . Each bacterial clone was struck-out onto LB plates containing carbenicillin (50 μg/ml) and tetracycline (10 μg/ml). Single colonies were then seeded into 5 ml LB + carbenicillin (50 μg/ml) and tetracycline (10 μg/ml) for growth overnight on a 37 °C rotator. One hundred microliters of each overnight culture was then inoculated into 10 ml of LB containing carbenicillin (50 μg/ml) and tetracycline (10 μg/ml) and IPTG (5mM) and incubated on a 37 °C rotator for 4 h. Each bacterial growth was then centrifuged at 3500 X G for 25 min, decanted, and the pellet resuspended in 0.5 ml of LB containing carbenicillin (50 μg/ml), tetracycline (10 μg/ml), and IPTG (5 mM). To verify that the RNAi plasmid DNA contained the expected gene target, each RNAi clone was purified and assessed through PCR (polymerase chain reaction) with sequence-specific primers or through Sanger sequencing.The gene-specific RNAi clones were obtained from the commercial Ahringer or Vidal Paralysis assays were performed by visually inspecting recordings of the animals daily to determine if they were capable of normal locomotion or if they were paralyzed and unable to transit the agar plate. A custom-built robotic system, the WormBot , was equank test , 84. AllE. coli. Worm populations were developmentally synchronized by hypochlorite treatment, and the remaining eggs were deposited on unseeded plates overnight. Synchronized larval stage 1 animals were washed off unseeded plates and moved onto standard C. elegans RNAi plates containing carbenicillin (50 mg/ml), tetracycline (10 mg/ml), and IPTG (5 mM) 48 h at 20 °C. These developmentally synchronized, late larval stage 4-populations were then washed and transferred to their respective RNAi conditions on 12-well plates. We used standard RNAi conditions plus FuDR (100 ug/ml) to prevent progeny and nystatin (200 mg/ml) to prevent fungal growth [Prior to loading on the experimental plates, animal populations were propagated on high-growth plates seeded with NA22 l growth . Each RNAdditional file 1. Figures S1-S12.Additional file 2. Table S1 (ranked list of pathways for the 664 brain expression samples with associated AB IHC protein density measurements).Additional file 3. Table S2 .Additional file 4. Review history.