Drug screening data from massive bulk gene expression databases can be analyzed to determine the optimal clinical application of cancer drugs. The growing amount of single-cell RNA sequencing (scRNA-seq) data also provides insights into improving therapeutic effectiveness by helping to study the heterogeneity of drug responses for cancer cell subpopulations. Developing computational approaches to predict and interpret cancer drug response in single-cell data collected from clinical samples can be very useful. We propose scDEAL, a deep transfer learning framework for cancer drug response prediction at the single-cell level by integrating large-scale bulk cell-line data. The highlight in scDEAL involves harmonizing drug-related bulk RNA-seq data with scRNA-seq data and transferring the model trained on bulk RNA-seq data to predict drug responses in scRNA-seq. Another feature of scDEAL is the integrated gradient feature interpretation to infer the signature genes of drug resistance mechanisms. We benchmark scDEAL on six scRNA-seq datasets and demonstrate its model interpretability via three case studies focusing on drug response label prediction, gene signature identification, and pseudotime analysis. We believe that scDEAL could help study cell reprogramming, drug selection, and repurposing for improving therapeutic efficacy. Single-cell RNA-seq data provide the opportunity to predict drug response in cancer while considering intratumour heterogeneity. Here, the authors develop a deep transfer learning framework - scDEAL - to predict single-cell drug responses in cancer by integrating single-cell and bulk RNA-seq data. However, cancer drug treatments suffer from low efficacies and high relapse rates caused by cancer heterogeneity among diverse states or cell fates. Such heterogeneity is responsible for differentiated responses of individual cells to a drug, leading to a minimal amount of cancerous residues remaining in the body, followed by, ultimately, cancer relapse3. The single-cell RNA-sequencing (scRNA-seq) technique provides an unprecedented opportunity to discover the heterogeneous gene expressions of cancer subpopulations in response to specific drugs4. Existing drug-response prediction methods developed for bulk data cannot be directly used for larger-scale and highly intricate single-cell data. Hence, computational methods to infer cancer drug responses at the single-cell level are urgently needed.Precision medicine has achieved remarkable success in understanding the complexity of the genomic landscape of cancer. The idea of tailoring cancer treatments to the particular genomic signature of an individual cell is gaining traction. Several in vitro drug screening studies have been conducted, giving rise to drug response data on various cancer cell lines7. The main obstacle in developing a deep learning-based tool for predicting single-cell drug responses is insufficient training power owing to the limited number of benchmarked data in the public domain. Intuitively, drug-related bulk RNA-seq data can be effective complementary resources to infer gene expression-drug response relations in support of the drug response predictions at the single-cell level9. Fortunately, deep transfer learning (DTL) can transfer knowledge and relation patterns from bulk data to single-cell data, which can be a means to overcome the issue of limited training data10. Using transfer learning, we can solve a particular task at the single-cell level using a preliminary trained model at the bulk level, either entirely or partially. The DTL model has been applied as an effective strategy in leveraging multiple bulk data sources for cancer drug response predictions11; however, thus far, its capabilities in transferring valuable bulk-level knowledge to the single-cell level are under-investigated.Deep learning methods have been deployed to tackle scRNA-seq data, redefining our capabilities to analyze large-scale data using sophisticated architectures of artificial neural networks. Deep learning models applied to scRNA-seq data have achieved competitive performances in gene expression imputation, cell clustering, batch correction, and similar taskssingle-cell Drug rEsponse AnaLysis) by adapting a Domain-adaptive Neural Network (DaNN)12 to predict drug responses from bulk and scRNA-seq data. scDEAL is very powerful at predicting single-cell level drug sensitivity as it establishes bridges among drug sensitivity, gene features in single cells, and gene features in bulk samples. scDEAL highlights the following aspects: (i) it can use a large amount of bulk-level drug response RNA-seq information from the Genomics of Drug Sensitivity in Cancer (GDSC) database14 and Cancer Cell Line Encyclopedia (CCLE)15 to train and optimize the model17; (ii) in order to account for data-structure differences between bulk and scRNA-seq data, scDEAL harmonizes single-cell and bulk embeddings to ensure that the drug response labels are transferable from bulk to single cells; (iii) in order to avoid losing heterogeneity in scRNA-seq data, scDEAL includes cell cluster labels for loss function regularization in each training epoch; (iv) scDEAL’s integrated gradient interpretation infers the signature genes of drug response predictions, which improves the interpretability of the model. We conduct comprehensive analyses and evaluations on six benchmark drug-treated scRNA-seq data22; scDEAL achieves high accuracy in predicting cell-type drug responses. We further identify gene signatures that are considered to directly contribute to drug sensitivity or resistance in a cell by tracing and accumulating the integrated gradients of each neuron in the DTL model. Finally, we prove that the predicted drug response aligned well with the expression trajectory of treatment procedures. Overall, we believe scDEAL enables the deployment of the DTL model in single-cell drug response prediction, which may benefit preliminary studies in drug development, repurposing, and selection in cancer treatment.In this work, we develop scDEAL  between gene features from two extractors, bridging the communication between bulk and scRNA-seq data. The second task is to minimize the difference between the prediction results and the database-provided drug responses via the cross-entropy loss. We expect the framework to be updated to harmonize bulk expression data and scRNA-seq data as well as to transfer the trustworthy gene-drug relations from the bulk level to the single-cell level. The output of scDEAL is the predicted potential drug response of individual cells.One of the critical challenges in model training is maintaining single-cell heterogeneity when harmonizing scRNA-seq data with bulk data. Two strategies were applied. First, as the noise characteristics in bulk RNA-seq and scRNA-seq data are quite distinct, we used a DAE model, rather than a common autoencoder or a variational autoencoder, to induce noises in bulk as well as scRNA-seq prior to the feature reduction. By this means, we could avoid the risk of imbalanced training that would only force gene expressions in scRNA-seq data close to bulk RNA-seq data. Second, we integrated cell-clustering results to regularize the overall loss function of scDEAL, so that cellular heterogeneity would be retained during the training process.We evaluated the drug response prediction performances on six public scRNA-seq datasets treated by five drugs, i.e., Cisplatin, Gefitinib, I-BET-762, Docetaxel, and Erlotinib  in scDEAL and compared the results with those from the final framework. The final scDEAL framework will be comprehensively validated if it can outperform all the alternative models.First, a comparison test was performed by training the model only on the bulk data and then directly using it for scRNA-seq data prediction without step 3 (transfer learning). For each data, the experiment was repeated 50 times (n = 50). Noted that the result of scDEAL is fully reproducible if use the same seed for the same data training. The results on all six datasets showed a significant increase in F1-scores when using the transfer strategy compared to without it Fig. . On averSecond, to evaluate whether the training power of the transfer model relies on bulk resources, we benchmarked scDEAL using bulk data from the GDSC database only, CCLE database only, and a combination of GDSC and CCLE databases . For all six datasets, using DAE and cell-type regularization in the framework achieved a better performance than the other two options Fig. . On averFurthermore, to validate whether relations between gene expression and drug responses at the bulk level have been successfully learned and transferred to the single-cell level, we calculated an integrated gradients (IG) score to reflect the potential contribution of each gene to the final drug response label prediction  on the six datasets   leukemic cells treated with a BET inhibitor (I-BET) ET) Fig. . Four trR2 = 0.90 for the sensitive DEG list, and R2 = 0.77 for the resistant DEG list  results showed p-values for bother-sensitive and resistant DEG score correlations are lower than 0.001, indicating that our correlation is significant and statistically meaningful  cell clusters. The hypothesis behind the score is that an accurate prediction assigns the correct response label to cells. Therefore, the gene scores of DEGs between the resistant and the sensitive states for an accurate prediction should be correlated to DEGs derived from the ground truth. In addition, our DEG showed gene score patterns that can separate resistant and sensitive cells better than DEGs identified using ground-truth labels Fig. . The corful Fig. .18. Cisplatin exerts its anti-cancer activity via the generation of DNA crosslinks by interacting with purine bases on DNA, interfering with DNA replication and causing additional deleterious DNA double-strand breaks, which, if not repaired, can lead to apoptosis of cancer cells23. Thus, any factor that can enhance DNA repair or/and inhibit cellular apoptosis is able to render cancer cells resistance to Cisplatin treatment. Using scDEAL, 85% of cells were correctly predicted as either sensitive or resistant to Cisplatin, with an F1-score of 0.92, AUROC of 0.92, and AP score of 0.97  that impact the drug response. We identified 936 drug-sensitive CGs in the HN120P (sensitive cell group) and 868 drug-resistant CGs in the HN120PCR (resistant cell group after Cisplatin treatment over four months) with significantly differential IG scores  treated by Cisplatin in Data 1ity Fig. . Overexpp-value = 0.039), which is one of the major Cisplatin resistance-related biological processes. Among the 26 DNA repair-related genes in the list of drug-resistant CGs in HN120PCR cells, we find strong literature evidences for eight of them RAD5128, EXO129, FANCL30, MSH331, RIF132, USP2833, FANCG34, and POLH35. These genes are critical to the DNA repair pathways that are used to deal with Cisplatin-induced DNA lesions, including inter-strand crosslinks36 and DNA double-strand breaks37, as well as the DNA damage tolerance pathway that is used to bypass Cisplatin-induced DNA damage to promote cancer cell survival38. On the other hand, another significantly enriched GO pathway, “cell division” , contains multiple genes involved in cell cycle checkpoint, e.g., CCNF39, BUB1B40, BUB140, and CDC25C41. The activated cell cycle checkpoint can protect cells from Cisplatin-induced cell death and plays a critical role in Cisplatin resistance42. In addition, a few pathways that also have associations with Cisplatin resistance, such as “negative regulation of cell death”43, “response to hypoxia”44, and “mitotic cell cycle checkpoint”45, are also identified from the resistant CGs, further validating that our scDEAL is able to identify genes that are important to drug responses (Supplementary Data Gene Oncology (GO) pathway enrichment analysis (Supplementary Data 46 for the trajectory inference on Data 6 (treated with I-BET) to validate whether our predicted drug response is correlated with the progression of drug treatment. The gene expression-based pseudotime analysis showed a trajectory trend starting from DMSO samples towards the 1000-ml I-BET treated samples  are needed. We performed comprehensive analyses to benchmark scDEAL on six drug-treated scRNA-seq data with experimentally validated drug-response labels. Our results indicate that scDEAL performs well and is robust in drug response label prediction and gene signature identification. We reason that AMI and ARI scores are sensitive to mislabeling, especially with only two categories in the data (sensitive and resistant); hence, they showed relatively lower scores in Data 1 and 2, and the variation of these two metrics is higher than that of the other five metrics  among six datasets. We also conducted comparative analysis to showcase the rationale of scDEAL framework design, including the contribution of DTL method, bulk data usage, autoencoder selection, and cell-type regularization. We identified CGs corresponding to the Cisplatin responses in OSCC, showing distinct predicted response patterns in drug-sensitive and -resistant cells. Our results demonstrated highly correlated drug response predictions with single-cell pseudotime analysis.47, can be included to predict combinatory drug responses. In addition, the genetic features between bulk and scRNA-seq data can be explained and biologically interpreted using the IG score and CG identification. The predicted CGs can be used as targets for experimental validations on drug–gene relations via single-cell Perturb-seq48.The accuracy of the prediction results in scDEAL could vary, depending on the collection of bulk gene expression of cell lines. We will update scDEAL training data by integrating additional bulk-level databases in the future. Furthermore, increased experimentally validated drug response scRNA-seq data would help determine better model hyperparameters and even help develop direct single-cell-to-single-cell deep transfer learning models. Databases, such as DrugCombDB49. To test the reliability of our method, we carried out a similar drug resistance analysis by re-assembling the HN120 and HN137 datasets as suggested in the above study. Specifically, we reassembled a new HN120 data with cells originally labeled as HN137P, HN120PCR, HN120M, and HN120MCR, and a new HN137 data with cells originally labeled as HN120P, HN137PCR, HN137M, and HN137MCR. A similar grid-search method was applied to optimize the reassembled data analysis using scDEAL. The new drug response prediction F1-score of re-assembled HN120 data is 0.750  and of re-assembled HN137 data is 0.764  50, and will be one focus of our future study along this research direction.One remaining challenge in single-cell drug response prediction is the prediction across different species. Considering the genetic variation between human and mouse, drug response in one species cannot be directly transferred to predict the other. In our case study of mouse scRNA-seq data (Data 6), only homologous genes were kept in the mouse data and the scDEAL model was still trained with human cancer cell lines. Due to the limited number of drug-treated mouse benchmark scRNA-seq data in the public domain, we could not systematically evaluate and optimize the trans-species reliability in the current study. Such a topic is of significant interest in the single-cell fieldIn summary, scDEAL has considerable potential for improving drug development at the single-cell level. First, it can be used to predict drug responses and link gene signatures with treatment effects. Second, the CGs are potential target signatures that can be used for CRISPR screening or cell reprogramming. Third, it can be applied to existing non-drug-treated scRNA-seq data to predict the potential drug response in multiple cell clusters that can be selected for animal drug tests. In the long run, we believe our work can contribute towards and provide insights into cell reprogramming, drug selection and repurposing, and combinatory drug usage for improving therapeutic efficacy.https://www.cancerrxgene.org/] is publicly available online. Drug response annotation, including half maximal inhibitory concentration (IC50) and area under the dose-response curve (AUC) are available online [https://www.cancerrxgene.org/downloads/bulk_download]. Gene expression data  for cell lines can be accessed on GDSC [https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources/Home.html]. We also collected the CCLE cell line expression profile and PRISM cell line viability assay. Expression profiles and viability assays can be downloaded from [https://depmap.org/portal/]. Considering the discrepancies between the two databases, we integrated the two databases by keeping the shared genes, as well as all cell lines and drugs without missing values. Overall, we collected bulk-level drug response data with 1280 cancer cell lines, 1557 drugs/chemical compounds, and their expression profiles on 15,962 genes (Supplementary Table The GDSC database [https://www.ncbi.nlm.nih.gov/geo/]). All data are accessible through the GEO Series accession numbers in Data Accessibility and Supplementary Table All scRNA-seq data analyzed in this study are publicly available. The following datasets are available from the National Center for Biotechnology Information’s (NCBI) Gene Expression Omnibus  for linear curves (whose regression line fitting has a Pearson correlation >0.95), the sensitive/resistant cutoff of AUC values is the median among all cell lines; 2) otherwise, the cutoff is the AUC value of a specific boundary data point, which has the largest distance to a line linking two datapoints having the largest and smallest AUC values.The drug response labels of cell lines in the bulk data are derived from the response AUC values in the GDSC and CCLE databases. For each drug, we used the same method in the CCLE study51. Up-sampling randomly duplicates samples in the minority, and down-sampling discards samples in the majority class to generate a training set with the same number of sensitive and resistant cell lines. SMOTE generates synthetic cell lines by selecting k nearest neighbors for a random sample in the minority class and synthesis of an artificial sample within neighbors in feature space. All sampling methods are implemented by the Python library imblearn52.The proportion of sensitive and resistant cell lines is different across different drug treatments. This imbalance issue of drug response labels in the training set may affect the model’s performance. We, therefore, introduce other sampling methods to balance the proportion of sensitive and resistant cell lines when training the prediction model at the bulk level. Three sampling methods as hyperparameters are introduced in the bulk model training, including up-sampling, down-sampling, and SMOTE-sampling53. Specifically, cells with less than 200 detected genes (indicative of no cell in the droplet), and genes detected in less than three cells were filtered out using the function “filter_cells” and “filter_genes”. Cells with a percentage of expressed mitochondrial genes higher than 10% were removed. Counts matrices were normalized by dividing by the total UMI count in each cell, multiplied by a factor of 10,000 using the function “normalize_total”, and log one plus transformed using the function “log1p”. Expression values are then scaled using “preprocessing.MinMaxScaler” in the package sklearn54.Quality control and preprocessing of the scRNA-seq data were performed using the Python package SCANPYThe whole framework of scDEAL can be treated as two parts: supervised learning to build a model to predict the response label classification at the bulk level and using this model for label prediction at the single-cell level. We first split 64%, 16%, and 20% of bulk RNA-seq data as the training set, validation set, and testing set, respectively. These subsets were used to train the initial model in Steps 1 and 2, detailed below. The trained bulk model and the complete scRNA-seq data followed Steps 3-5 to predict drug response at the single-cell level. Since there is no ground-truth label for single-cell data in this case, there is no need to split each scRNA-seq data into training, validation, and testing subsets. The split was performed by the function “train_test_split” in the package sklearn.P) in step 3.The first step of transfer learning is the gene feature extractor. Gene feature extractors are applied to extract variable gene features, reduce data dimensionality, and denoise data. Furthermore, it is a fined-tuned preliminary training step for determining the initial model weights in predictor B), which generates a noisy bulk expression matrix Xb based on a binomial distribution:pb is the probability of zero value assignment in each row;a noise operation ((ii)Eb), which subtracts an encoder ((iii)Db), which reconstructs an approximation matrix a decoder  between the input P) based on a fully connected multi-layer perceptron (MLP)55 on bulk RNA-seq data to estimate the correlation of drug response and bulk gene expressions. Parameters inside P are optimized with the classification loss  between the predictive drug responses classification per cell line Yb and the binary drug response label We train an encoder model  to estimate the similarity between output Eb and Es, which is defined as:n cell lines and m cells from the bulk and scRNA-seq data, respectively; ϕ(.) is referred to the feature space that maps to the universal Reproducing Kernel Hilbert Space (RKHS). The RKHS norm P to ensure that the feature space of Es and EB have similar distributions. The DaNN model is trained to update two gene extractors (EB and Es) and the predictor P, simultaneously, which can be defined as:α is a weight of lossMMD(.), β is the weight of the regularizer, c is cell, and CC is the cell cluster categories obtained from Louvain clustering results (using the igraph R package v1.3.4). By minimizing lossDaNN(.), the trained Es and P will then be used for predicting drug responses from the scRNA-seq data.The DTL training adapts the gene features extracted from bulk and single level to enable the sensitivity prediction for cells through the predictor Es and P in Step 4 will be assembled and transferred to predict the single-cell drug responses using all cells in the scRNA-seq data. The assembled Es and P will take scRNA-seq data Xs as input and output the continuous probability scores Ys for each cell in Xs. The binary label is determined by counting any cells with a continuous probability score between 0–0.5 as resistant cells and 0.5–1 as sensitive cells.The well-trained To evaluate the prediction of scDEAL, we applied seven metrics as shown below.Precision represents the ability of the model to correctly predict positive numbers among all positive predictions. We implemented Precision tests using the “precision_score” function in the package sklearn.Recall represents the model’s ability to correctly predict positivity from actual positive samples. We implemented Recall tests using the “recall_score” function in the package sklearn.F1-score can be interpreted as a weighted average of precision and recall. F1-score reaches its highest value at 1 and lowest score at 0. The equation for the F1-score is:“F1_score” function in the package sklearn54.We implemented F1-score tests using the AUROC score computes the area under the receiver operating characteristic (ROC) curve. The ROC curve’s x-axis is the true positive rate and the y-axis is the false positive rate derived from prediction scores. The curve is generated by setting different thresholds to binarize the numerical prediction scores. AUROC computes the area under the precision-recall curve with the trapezoidal rule, which uses linear interpolation. We implemented AUROC tests using the “roc_auc_score” function in the sklearn package.AP score summarizes a precision-recall curve (PRC) as the weighted mean of precisions achieved at each threshold, with the increase in recall from the previous threshold used as the weight. The AP score is given by:Pn and Rn are the precision and recall at a threshold n ordered by its value. We implemented AP tests using the “average_precision_score” function in the package sklearn.AMI is an adjustment of the Mutual Information (MI) score to account for chance. It accounts for the fact that the MI is generally higher for two clusters with a larger number of clusters, regardless of whether there is more information shared. We implemented ARI tests using the “adjusted_mutual_info_score” function in the package sklearn.ARI reaches its highest value at 1 and lowest score at 0. The equation for ARI is:N is the number of points in a given data, Nij is the number of points of class label Ci in partition P. Ni is the number of points of cluster Ci in partition P. Nj is the number of points of cluster Cj. We implemented ARI tests using the “adjusted_rand_score” function in the package sklearn.p-values <0.05) ranked by the z-score are selected to calculate the sensitive or resistant gene score. The sensitive or resistant gene score of each cell is calculated by subtracting the average DEG expression with the average expression of a randomly sampled reference set of genes in that cell. The raw gene score is then min-max scaled. The DEG score was evaluated with the “score_genes” function built in SCANPY53.To select the differentially expressed genes, we performed Wilcoxon signed-rank tests using the “rank_genes_groups” function. By default, the top 50 genes (BH adjusted 54. The number of output (n_samples parameter) is set to be 80% of the input, and the sampled data will not be sampled again with the setting “replace = False”.The stability test is performed by retraining the DTL model ten times with different random seeds and randomly sampled subsets of cells. To preserve the original sensitive and resistant cell ratio in the dataset, we choose to perform the stratified sampling of the sensitive and resistant cells in the dataset. The sampling is performed using the “resample” function in the sklearn package56 to characterize critical input genes features in the scDEAL model. An IG score represents the integral of gradients with respect to each gene expression as inputs along the path from zero expression as a baseline to the input expression level  / ∂xi represents the gradient of F(x) along the i -th dimension.It calculated the importance of the 57 to calculate IG values. The inputs are our expression matrix, trained model, and output labels. The outputs of the function are IG matrices of the same shape as the input expression matrix. Rows represent genes and columns represent cells. Values in a matrix are the corresponding IG values.We apply the “IntegratedGradients” class in the Python Captum libraryAs scDEAL is a binary classification deep learning model, it has two nodes in the output layer to predict sensitive and resistant probabilities. Based on the sensitivity or resistance for each gene contribute, we can obtain two separate IG matrices for each input data corresponding to the sensitive and resistant output. The IG matrix can be found in the model output file “attr_integrated_gradient.h5ad”. The IG matrix is stored with an “AnnData” object and can be read by the function “sc.read_h5ad”.p-values <0.05, log-fold changes > 0.1, and the percentage of cells with IG scores in either group higher than 0.2 as CGs.To select genes that have significantly higher IG values within the sensitive (or resistant) cell cluster, we utilized the Wilcoxon test using the function “sc.tl.rank_genes_groups” between sensitive (or resistant) cells in SCANPY. We considered the genes with Bonferroni adjusted p-value <0.1 on DAVID.Functional enrichment test was performed via DAVID online service. The GOTERM_BP_DIRECT database was used for GO pathways enrichment test. Results were filtered by the default 46. The read count matrix was projected to a 2-dimensional UMAP space using the “reduce_dimension” function. We then used the function “learn_graph” to construct a graph topology from the reduced dimension space based on the reversed graph embedding algorithm. Afterwards, we calculated pseudotime values for cells based on their projection on the graph learned in the “learn_graph” function using the “order_cells” function.The trajectory inference for the scRNA-seq data was preprocessed using Monocle3Further information on research design is available in the Supplementary InformationPeer review fileDescription to Additional Supplementary InformationSupplementary Data 1Supplementary Data 2Reporting Summary


Multiple Sclerosis (MS) is a disease attacking the central nervous system. According to MS Atlas’s most recent statistics, there are more than 2.8 million people worldwide diagnosed with MS. Recently, studies started to explore machine learning techniques to predict MS using various data. The objective of this paper is to develop an ensemble approach for diagnosis of MS using gene expression profiles, while handling the class imbalance problem associated with the data. A hierarchical ensemble approach employing voting and boosting techniques is proposed. This approach adopts a heterogeneous voting approach using two base learners, random forest and support vector machine. Experiments show that our approach outperforms state-of-the-art methods, with the highest recorded accuracy being 92.81% and 93.5% with BoostFS and DEGs for feature selection, respectively. Conclusively, the proposed approach is able to efficiently diagnose MS using the gene expression profiles that are more relevant to the disease. The approach is not merely an ensemble classifier outperforming previous work; it also identifies differentially expressed genes between normal samples and patients with multiple sclerosis using a genome-wide expression microarray. The results obtained show that the proposed approach is an efficient diagnostic tool for MS. Multiple Sclerosis (MS) is a chronic inflammatory disease that attacks the central nervous system. MS is characterized by lesions in the brain and spinal cord, which cause various neurological symptoms, including, but not limited to blindness, double vision, muscle weakness, and changes in sensation and balance . MultiplMultiple sclerosis is known to be one of the most common auto-immune disorders. The reports about the global burden of disease  revealedIn , a studyThis work is motivated by the fact that patients have an increased risk of being poorly diagnosed, hence increasing the risk factors and deterioration due to attacks. In addition to the availability of data and the increased diagnostic performance achieved by machine learning, machine learning has recently shown promising results for many neurological disorders. Proposed methods have successfully distinguished healthy subjects from patients with high accuracy. However, none of the proposed diagnostic methods have achieved reliable levels of accuracy to identify the genes associated with the disease as a basis for diagnosis. Moreover, none of the work in the literature has addressed the class imbalance nature of most of the MS expression data samples. Furthermore, most of these studies rely on data that are rarely used in clinical routine. In addition to that, ensemble learning has proven to be an effective approach for improving disease diagnosis accuracy.Many researchers addressed the problem of multiple sclerosis prediction from different perspectives. However, the literature still lacks efficient predictive models that benefit from all the available data. In this section, the most cited and recent work in multiple sclerosis is summarized. Furthermore, the section briefly covers the work related to the imbalance dataset problem in diseases. Some work in the literature addressed the association of certain genes with the disease. Random forests were used in  to identWeygandt et al.  used SVMBendefeldt et al., in , employeChen et al.  proposedOther methods used gene expression data for diagnosing multiple sclerosis. The study  proposedSweeney et al.  comparedA method for the diagnosis of multiple sclerosis using combined clinical data with lesion loads and magnetic resonance metabolic features was presented in . Three cMargineau et al.  used fouOstemeyer et al.  used staIn , machineMS could also be diagnosed with some biomarkers as shown in the work in . The bioIn 2020, Zhao et al.  extendedIn , supportShang et al.  used genAnother important aspect considered in this paper is imbalanced data. A dataset is considered to be imbalanced when the number of samples representing one class is significantly fewer than the other classes. The class with the fewest number of samples is called the minority class, and the others are the majority classes. Handling the class imbalance data problem is of great interest, as shows in many real-world problems, especially medical diagnosis and disease prediction. Training classifiers on these datasets makes the classifiers more biased towards the majority classes, as the rules that predict the majority class samples are positively weighted, whereas the rules that predict the minority class are usually treated as noise or ignored. This leads to the conclusion that the minority class samples are prone to misclassification . Class-iOne of the flaws of these algorithm-based methods is that they require specific modifications to the classification algorithm, which makes it lack generality and a systematic approach for evaluating the performance of different classifiers. Classification methods are developed to predict the class of the future samples based on the assumption that the training samples are good examples of the future samples, where matching the frequency of classes in training data would warrant a realistic performance. Moreover, data-driven down-sampling methods lead to missing some of the majority class samples, which could have significant information for the classifier.It is concluded from the background and related work discussed in the Introduction above that the diagnosis of MS is an extensible process with variable possible types of data that can be used, as well as several techniques. In the proposed model, we relied on gene expression data to diagnose MS based on the fact that gene expression identifies a unique group of genes in a cell that occurs as a result of an altered or unaltered biological process or pathogenic medical condition. As most of the expression profile datasets available for MS are imbalanced, our proposed model adapts an over-sampling technique to handle class imbalance expression data.In this paper, a hierarchical ensemble approach that employs voting and boosting ensemble techniques is proposed. Ensemble methods are used to improve the predictive classification performance of the constituent classifiers by using multiple learning algorithms . Two ensProposing a hierarchical ensemble approach to improve the classification accuracy for multiple sclerosis patients.Handling the class imbalance problem using over-sampling to improve the classifier performance when predicting the minority class.Applying three different feature selection algorithms, in addition to selecting genes using bioinformatics analysis on differentially expressed genes, to select relevant genes based on their importance to overcome the low number of samples compared to the number of features (microarray genes set).Identifying the differentially expressed genes between normal and multiple sclerosis patients’ expression profiles to use as one of the selected feature sets for evaluating our proposed approach.Evaluating the proposed ensemble approach to analyze the predictive accuracy on each selected feature set from the FS algorithms and DEGs. The evaluation metrics calculated are the accuracy, MCC, RMSE, F-score, AUC, and ROC curve.The contributions of this paper are summarized in the following points:The remainder of this paper is organized as follows. In this section, the methods and data used to conduct the study are discussed.https://www.ncbi.nlm.nih.gov/geo (accessed on 26 April 2022)) provides open access to many gene expression microarray datasets. The gene expression profile from GSE41850 [The GEO database  and to standardize the data range by normalization so that all features would have the same value range (between 0 and 1). Missing data could be handled in many ways, among which are weighted K-nearest-neighbor, expectation maximization, or local-least-squares approaches. In this study, missing data were replaced by the mean value of their represented class, as many studies proved that using the mean or median to replace missing values in gene expression datasets performs as well as other more complex strategies .x and y, where In most of the existing methods, under-sampling techniques are often used with gene expression datasets to balance the data. Under-sampling leads to missing important data; hence, the proposed approach adopts an over-sampling, specifically selective over-sampling based on the SPIDER algorithm presented by J. Stefanowski and S. Wilk . The newwhere x=x,x2,⋯,xn s using its k-nearest-neighbors in set D for correct and incorrect classification.Perfect —returns true or false based on the classification result of a sample D∈ class c and labeled as f.Label —returns a subset of samples from set s∈ set D∈ class c and labeled as f.Knn —identifies the samples among the k-nearest-neighbors of s by finding its knn  and adding this subset to set D.Increase —adds to sample The algorithm consists of two steps. The first step is recognizing the instances of the minority and majority classes as misclassified by KNN, then labeling these samples as noisy and excluding them or their nearest neighbors from generating the new samples. In the second step, the rest of the minority class samples are divided into weak and strong samples. Then, the new samples are generated by selecting from their nearest neighbors. The selection strategy differs for the weak and strong samples. Algorithm 1 shows the detailed algorithm for handling the class imbalance problem. Using this technique for over-sampling can ensure solving the class imbalance problem in gene expression datasets, by generating new samples through random interpolation of the minority and majority samples, which can avoid the overfitting problem. Algorithm 1 uses the following functions:Algorithm 1: Imbalanced class data handling.In this section, the estimation of the differentially expressed genes is outlined, as well as the three methods used for feature selection, namely Chi-squared, RFE-SVC, and feature importance with the XGBoost ensemble algorithm (BoostFS).http://oompa.r-forge.r-project.org (accessed on 26 April 2022)). Following the preprocessing step, the limma Bioconductor package [In order to obtain the DEGs, we first downloaded the raw data for the GSE41850  and GSE2 package  was usedThe Chi-squared test  is a nonRFE-SVC is a non-linear kernel algorithm used to rank features, and ranking results could be interpreted in terms of the degree of association between the features, their association with the target , as well as the association magnitude. In this study, Sanz et al.’s  estimatiEXtreme Gradient Boosting (XGBoost) is one of the dominant machine learning techniques. It implements gradient boosted decision trees designed to speed up and enhance the classification performance. In the training process of XGBoost, the importance score of each feature is calculated in order to improve the efficiency of trees’ generation . In thisIn this paper, we propose an ensemble learning approach that combines two main techniques, voting and boosting. The voting technique is based on using multiple models of different learners to obtain predictions for each data point. The predictions by each model are considered for voting, and the final prediction is chosen based on the majority of the models, the average of predictions from all models, or the weighted average predictions.On the other hand, boosting is a sequential process on the data, where each subsequent model attempts to correct the errors of the previous model. In the boosting technique, the weighting of the samples changes over time to allow the technique to optimize its decision by considering the results from the samples in proportion to their (positive) impact on overall system accuracy. Initially, the samples are equally weighted, and after each iteration, the samples that are correctly assigned are weighted lower than the incorrectly assigned samples. The proposed approach merges the idea of voting and boosting. Random Forest (RF) and Support Vector Machine (SVM) are used as the base learners for the voting models in each iteration of the voting step. In each voting step, the boosting technique is applied on each learner. The boosting starts by selecting a subset from the original dataset. All data points are assigned equal weights. Then, two base learners are trained on the subsets and used to make predictions on the whole dataset. The prediction errors are estimated, and the data points that are incorrectly predicted are given higher weights. The widely used boosting approaches are XGBoost , LightBoIn the proposed ensemble approach, two base classifiers were employed, namely random forest and support vector machine. The implementation details and parameters optimization for these techniques are explained below.Random Forest (RF) algorithm is an extension of decision trees [The on trees . Decisioon trees . A weak Support Vector Machines (SVMs) can be used for both regression and classification problems [N-dimensional space, where N is the number of features. The prediction is performed by discovering an exact hyper-plane that can define the target classes. An optimal plane is one that achieves the largest margin between the classes. SVM is best known for its scalability for large datasets, flexibility due to adjustment via a broad class of kernel functions, and its low computational complexity. In this study, SVM hyperparameters were optimized using the BayesSearchCV Scikit Optimize library [problems . The SVM library , which pBoosting Stage with LightBoost (LB): The purpose of the training boosting algorithms is to learn the best combination of class centroids and ensemble weights that minimizes a specific objective function [function , given aon trees . In geneon trees , which iIn this section, the results of the evaluation of the proposed approach are presented. The dataset is explained in the Multiple Sclerosis Data Description Section, and the performance metrics are outlined in the Performance Metrics Section. The experimental results are then presented. A discussion of all results obtained is given with the results of each experiment.The diagnostic ability of each ML model was assessed using the metrics below.Accuracy is calculated by dividing the number of correct predictions by the total number of samples to show how many times the model was correct. Accuracy is calculated using Equation  estimates the correlation coefficient between the actual and the predicted value for binary classification. The MCC uses all the values in the confusion matrix; this makes it a more reliable statistical measure, which yields a high score in all the confusion matrix values when the classifier achieves good results. The MCC value ranges from −1 to 1, where −1 signifies a completely false learning method and 1 is considered a completely correct learning method [g method . The MCCRoot-Mean-Squared Error (RMSE) represents the differences between the target samples to be diagnosed and the predicted ones. The RMSE can be estimated by taking the root of the Mean-Squared Error (MSE) for the average of the squares of the measured errors. If x is the target variable to be predicted, y is the predicted variable, and n is the total number of samples, then the RMSE is calculated as shown in Equation (The Equation .(4)RMSEArea Under the curve (AUC) is a graph that shows the sensitivity (true positive rate) plotted as a function of the specificity  using different cut-off values. The AUC is used as a measure of how well a parameter can differentiate between two classes . The AUC is a more accurate measure of the performance compared to the accuracy, especially with class imbalance datasets. A value of 0.5 for the AUC specifies random class prediction, while 0 is inverse class detection, and 1 represents perfect class detection.The The F-score is calculated using Equation  term, biological process, and KEGG Pathway enrichment analysis for the discovered DEGs in our study are presented.To evaluate the proposed method for handling imbalanced data, five datasets were selected from the KEEL imbalanced datasets. p < 0.005. Based on the analysis of the DEGs in three different MS stages, baseline (first time of diagnosis), the first-year follow-up, and the second-year follow-up, for up-regulated and down-regulated genes, the DEGs of each MS stage were identified. The Venn diagram for the numbers of DEGs of the three stages is presented in The microarray dataset used contains 816 samples of MS patients and 126 control ones. Following the raw data preprocessing steps, the differential gene expression analysis was filtered using the FDR adjusted with The proposed approach was evaluated on five different feature sets. Those sets are all features or feature sets selected using the chi-squared approach, RFE-SVC, BoostFS, as well as with deferentially expressed genes. For each set of features, the proposed ensemble approach was compared to random forest, KNN, and SVM. The Area Under the Curve (AUC) was calculated based on the ROC curve for each model to describe the quality of the work, which provides more accurate visual interpretation for the prediction performance. This section presents comparative experiments aiming to evaluate the feature selection algorithms used in this study. The accuracy and AUC were calculated to test the algorithm for the best features set selection on the final results reported in The identified DEGs were analyzed by their enriched O term, biological functions, and pathways using EnrichR R package version 2.0 , where Gp-values < 0.05.p-values < 0.05).Gene Ontology (GO) enrichment analysis for the identified DEGs in MS patient samples versus normal involved protein cytokine-mediated signaling and cellular response to lipopolysaccharide processes is shown in p-values < 0.05).This paper presented an ensemble approach using voting and boosting techniques for the prediction of multiple sclerosis patients using gene expression profiles. Two base learners, namely random forest and support vector machine, were employed with the voting and boosting techniques. Moreover, over-sampling was used to handle the class imbalance problem in the gene expression data. The proposed method for the class imbalance problem was evaluated on five KEEL imbalanced datasets, and the results obtained for the proposed over-sampling approach showed a higher classification accuracy than existing methods.The proposed classification approach for MS was tested with different feature sets, namely all features, differentially expressed genes set, and reduced feature subsets using three feature selection algorithms: Chi-squared, recursive feature elimination with support vector regression, and Extreme Gradient Boosting (XGBoost) for feature selection (BoostFS), as well as DEGs. The proposed method was compared to existing classifiers, namely random forest, KNN, and SVM. Experimental results showed that the proposed method achieved the highest accuracy of 92.81% and 91.8% with BoostFS and DEGs, respectively, in the diagnosis of MS. Hence, the proposed method outperforms the classification accuracy, MCC, AUC, and f1-score of all existing techniques.To the best of our knowledge, not much work in the literature has been directed towards the diagnosis of MS, especially using gene expression. Yet, the proposed approach outperforms the prediction accuracy reported in ,15,16,17


The present study introduces a new computational platform referred to as GATHER to conduct Geostatistical Analysis of Tumor Heterogeneity and Entropy in R. GATHER has several distinct advantages such as (a) a novel use of single-cell-specific spatial information for kriging to synthesize high-resolution and continuous gene expression landscapes of a given tumor sample, (b) the integration of such landscapes to identify and map the enriched regions of the pathways of interest, (c) the identification of genes that have a spatial differential expression at locations representing specific phenotypic contexts, (d) the computation of spatial entropy measures for quantification and objective characterization of intratumor heterogeneity, and (e) the use of new tools for the insightful visualization of spatial transcriptomic phenomena.Intratumor heterogeneity (ITH) is associated with therapeutic resistance and poor prognosis in cancer patients, and attributed to genetic, epigenetic, and microenvironmental factors. We developed a new computational platform, GATHER, for geostatistical modeling of single cell RNA-seq data to synthesize high-resolution and continuous gene expression landscapes of a given tumor sample. Such landscapes allow GATHER to map the enriched regions of pathways of interest in the tumor space and identify genes that have spatial differential expressions at locations representing specific phenotypic contexts using measures based on optimal transport. GATHER provides new applications of spatial entropy measures for quantification and objective characterization of ITH. It includes new tools for insightful visualization of spatial transcriptomic phenomena. We illustrate the capabilities of GATHER using real data from breast cancer tumor to study hallmarks of cancer in the phenotypic contexts defined by cancer associated fibroblasts. In their well-known paper in 2010, Hanahan and Weinberg noted that tumors exhibit an additional dimension of complexity through their “tumor microenvironment” that contributes to the acquisition of the so-called hallmark traits of cancer. Indeed, extensive studies in recent decades have uncovered a great diversity of cell populations in tumors, thus leading to the active research area of intratumor heterogeneity (ITH) ,4,5,6. IThe persistence of some of the drug-tolerant intratumor cell populations could be attributed to their high phenotypic plasticity. While hierarchies of differentiation also exist among normal cells in healthy tissues, the populations of tumor cells, in contrast, display a far greater cell-to-cell variability and the resulting phenotypic instability ,11. SuchWhile heterogeneous cell types are prevalent within the tumor microenvironment, and while some of them may account for cancer development and progression, it also contains different non-malignant components, including the cancer-associated fibroblasts (CAFs) ,17,18. Ahttps://humantumoratlas.org (accessed on 20 September 2022)) is increasingly enriched with data on human cancers based on scRNA-seq assays.In order to understand the spatial heterogeneity of gene expressions, including drug target genes, different sites of the same tumor were analyzed with multiregional RNA sequencing for different cancers ,33,34,35Despite the advancements and efficacy of scRNA-seq, the lack of spatial information in a scRNA-seq analysis is a significant shortcoming for typical scRNA-seq methods to capture cellular heterogeneity. On the other hand, while oncologic pathologists have long studied cell signaling within tumors by the manual scoring of discordance between individuals and variation between different batches using tissue immunostaining and microscopy, such techniques typically allow for only a few selected markers to be observed per cell, and thus offer a limited reporting of the extent of the potential heterogeneity. Combining high-resolution gene expression data with spatial coordinates can resolve these experimental shortcomings . For insIn order to approach and conceptualize the diversity in the spatial omics information, the concept of a habitat and its locations have been studied in association with genetic heterogeneity in a tumor ,52. In fThe present study introduces a new computational platform referred to as GATHER to conduct Geostatistical Analysis of Tumor Heterogeneity and Entropy in R. GATHER uses geostatistical modeling and spatial entropy measures for the quantification and objective characterization of intratumor heterogeneity, as well as to identify different transcriptomic patterns in the molecular landscapes of a tissue sample. Geostatistical models provide a well-established theoretical framework for the prediction and interpolation of spatial data. Kriging, for example, is a generalized least-square regression approach to predict spatial attributes at unobserved locations . GATHER The concept of entropy in information theory, defined as strings of symbols by Claude Shannon in 1948 , has beehenomena  and landhenomena , researcangement . This haIn 2020, a paper listed eleven grand challenges in single-cell data science, which included the challenge of “finding patterns in spatially resolved measurements” . Towardshttps://www.10xgenomics.com/resources/datasets/human-breast-cancer-whole-transcriptome-analysis-1-standard-1-2-0 (accessed on 15 December 2020). The data were generated using the Visium Spatial Gene Expression protocol run on an invasive breast cancer tissue sample that is estrogen receptor (ER)-positive, progesterone receptor (PR)-positive, and human epidermal growth factor receptor 2 (HER2)-negative. RNA sequencing data were generated with a paired-end, dual-indexed process using Illumina NovaSeq 6000, with a sequencing depth of 72,436 mean reads per cell. After filtering the downloaded dataset for average gene expression value >1, the data matrix contained 1981 rows (genes) and 4325 columns (single cells). The spatial transcriptomics data were downloaded from the 10x Genomics online resource  was used to interpolate the expression value of each gene distance . Typicalrd error  at the sOur platform allows us to identify a spatial phenotype in terms of differential expression of one or more “marker” genes that are known to characterize the phenotype. This allows us to demarcate and map the regions in the tissue space where the phenotype is significant. To map the co-occurrence of more than one phenotype, distinct colors were used. Furthermore, the presence of these spatial phenotypes could serve as specific contexts within which certain genes of interest may show a differential expression. Indeed, we developed methods for identifying such genes as well as measuring the contextual enrichment of gene sets and curated molecular pathways.Differentially expressed genes were detected using the semi-parametric 2-Wasserstein distance test for single-cell data . In thisIn our second approach, the entire tissue space was partitioned into regions according to different levels of enrichment of a phenotype of interest, e.g., characterized by expression  .Let For a selected gene set This allows us to construct a gene set enrichment landscape, which extends the idea of a single-gene expression landscape.We downloaded gene sets  from theG subareas of size Given our interest to characterize the heterogeneity of a given tumor sample in terms of the spatial phenotypes therein, Batty’s entropy measure was computed to evaluate the distribution of a candidate phenotype over the given tissue area by allowing for its partitioning into subareas of different sizes and shapes. Let a tissue area of size Batty’s spatial entropy for phenotype The maximum value of spatial entropy is Batty’s spatial entropy of a variable . (2018) , an entr., 2009)  is definhttps://david.ncifcrf.gov/ (accessed on 15 September 2022) platform.All statistical analyses were performed in R version 4.0.4. We used the Seurat package  for dataThe present study yields a new computational tool, GATHER, for the geostatistical modeling and heterogeneity analysis of molecular landscapes in tumors and tissue samples. The different modules of GATHER are outlined in TBX3, which encodes for the transcription factor T-box 3 (TBX3), was found to be overexpressed in different types of carcinomas, including breast cancer. TBX3, a mostly cytoplasmic protein in both normal and breast cancer tissues, is significantly overexpressed in the latter, and thus, could serve as a potential diagnostic marker of breast cancer cells [TBX3, which, along with the corresponding standard errors of the local kriging predictions, are mapped and shown in We begin with an illustration of the gene-specific expression landscape construction via kriging-based geostatistical prediction. In a past study of 100 breast tumors to understand the complexity of intratumor genetic heterogeneity, driver mutations were observed in several cancer genes . For inser cells . Yet, TBer cells . In ordehttps://mortezahaji.github.io/Landscape-Project/ (accessed on 15 September 2022)).Notably, the geostatistical modeling-based transcriptomic landscapes could also be viewed using the 3D interactive visualization tool of GATHER C. Using CXCL12 (CAF-S1), FBLN1 (mCAFs), C3 (inflammatory CAFs), S100A4 (sCAFs), and COL11A1, which is a fibroblast-specific “remarkable biomarker” that codes for collagen 11-α1 and shows expression gain in CAFs [GATHER analyzes spatial differential gene expression in single-cell transcriptomic data using two different approaches. An illustrative example is provided using a selected set of five CAF phenotypes, which were represented by the expression of the corresponding marker genes (the respective phenotypes are noted in parentheses):  in CAFs . For det in CAFs ,90.In the first approach, at each point distance . It outpIn the absence of ancillary external data, we performed an internal validation of the lists of differentially expressed genes as identified by GATHER based on a pathway over-representation analysis. Using this popular approach, the genes in p-value < 0.001) that were identified by DAVID based on the results of GATHER are (a) protein digestion and absorption, (b) ECM–receptor interaction, and (c) focal adhesion. Clearly, each of these pathways are relevant to the known effects of fibroblasts on tumor microenvironment. Importantly, fibroblasts bio-chemically and bio-physically modify the extracellular matrix (ECM), such as by the production of ECM-degrading proteases  that degrade tumor-associated ECM, thereby remodeling the ECM to support tumor growth [The most recurrent (across the 10 lists) among the over-represented KEGG pathways  has been shown to be involved in the functional interplay among various mediators such as MMPS, mesenchymal markers, and focal adhesions ,97. SincIn the second approach, GATHER partitions the tissue space into regions according to different levels of enrichment of a phenotype of interest, and identifies all genes that are expressed differentially across these regions. The regions of the landscape are characterized by Furthermore, we used GATHER to compute the spatial enrichment z scores For comparative analysis, we employed two commonly used methods for RNAseq data analysis, namely, edgeR  and DESeTBX3 gene in the given tumor sample. While the median spatial entropy tends to decrease as the heterogeneity is likely to reduce within smaller polygons generated by higher values of TBX3) as 0.942.GATHER computes Batty’s spatial entropy index with a variable partitioning of the tissue space to output a quantitative measure of ITH. The tissue space is randomly partitioned into a fixed number of  and a sixth context  wherein none of those phenotypes occur significantly. We test their co-occurrence with the enrichment of the selected hallmarks of cancer.G were set at 0.5. Taking combinations of the different CAF marker genes and cancer hallmark gene sets, the spatial heterogeneity of their co-occurrence is mapped. At each point, the combination with the most dominant phenotype is depicted. The map in FBlN1, PI3K_AKT_MTOR), blue , purple , and grey (no significant CAF phenotype). The regional diversity of co-occurrence is clearly visible, which could be further analyzed by selecting other combinations with the platform’s interactive 3D visualization tool.At each point In the 19th century, Rudolph Virchow, the “father of modern pathology”, observed the pleomorphism of cancer cells within tumors. In the 1970s, G.H. Heppner, I.J. Fidler, and others showed the existence of distinct subpopulations of cancer cells within tumors, which differed in terms of their tumorigenicity, their resistance to treatment, and their ability to metastasize. ITH has been shown to be associated with poor outcome and decreased response to cancer treatment in multiple human cancer types, implying a universal role in therapeutic resistance ,101. To As noted above, GATHER has many practical advantages. The kriging estimates are based on a geostatistical model that allows GATHER to predict the expression value of a gene at any point of the transcriptomic landscape, which allows it to be represented as a surface that is both high-resolution and continuous. Thus, such landscapes can be visualized by an isopleth or contour map. Importantly, GATHER also computes and maps the standard errors of the gene-specific kriging estimates. Furthermore, as the gene-specific landscapes are synthesized over a common grid, they can be easily aligned and systematically combined to produce surfaces that can depict the spatial enrichment of gene sets or pathways of interest. Moreover, a quantitative measure of error associated with the kriging predictions is available as a spatial measure of quality—and mappable at every location—of the transcriptomic landscape. As yet another advantage, since the kriging prediction at any point is based on every available observation in any given neighborhood, the synthesis of a gene’s expression landscape by GATHER is in general not affected by the missing value problem that commonly afflicts single-cell RNAseq data.Invasive and metastatic tumors often present with thorough tissue disorganization, leading to a microenvironment defined by cellular and paracrine interactions that allow for the selection and diversification of certain phenotypes that are not otherwise observed. For instance, blood and lymphatic vasculature in tumors are disorganized with significant functional, spatial, and temporal heterogeneity ,103. TheIn the present study, we used different CAF phenotypes as illustrative examples. The significance of such phenotypes could be understood from several experimental models of breast cancer and human tumors that reveal the spatial separation of the CAF subtypes attributable to different origins, including the peri-vascular niche, the mammary fat pad, and the transformed epithelium. Indeed, not only do the cancer cells and CAFs share location-specific signaling pathways, but the gene expression profiles for each CAF subtype indicate distinctive functional programs and hold independent prognostic capabilities in clinical cohorts by association to metastatic disease. GATHER is able to effectively identify at the single-cell level the genes with significant differential expression across the diverse spatial contexts as defined by the complex phenotypes that occur in heterogeneous tumor microenvironments.Notably, an innovative quantitative feature of GATHER is its use of spatial entropy measures to evaluate ITH in a given tumor sample. It computes Batty’s entropy to evaluate the distribution of a particular phenotype, as determined by the expression of the corresponding markers, over a given tissue area. Furthermore, as a tissue area could provide the locations for more than one phenotype or the expressions of multiple markers of a complex phenotype, GATHER also computes a co-occurrence-based spatial entropy measure based on Leibovici’s. The randomization over the tissue space allows the resulting spatial entropy to yield a robust measure of ITH.Next-generation genetically engineered mouse models can more accurately mimic human cancers , new mulWe note that our present work has some limitations. The assumption of stationary mean by ordinary kriging may not always hold true in real data, although the method is known to yield relatively unbiased estimates despite non-stationarity . AlternaOur computational platform, GATHER, is a new addition to the emerging field for spatial single cell omic data analysis. It introduces the use of geostatistical modeling for synthesis of high-resolution and continuous gene expression landscapes of a given tumor sample. Such landscapes allow GATHER to map the enriched regions of molecular pathways of interest in the tumor space and identify genes that have spatial differential expressions at locations representing specific phenotypic contexts using measures based on optimal transport. GATHER provides novel applications of spatial entropy measures that could be used for quantification and objective characterization of ITH.


However, RNA-seq experiments are still small sample size experiments due to the cost. Recently, an increased focus has been on meta-analysis methods for integrated differential expression analysis for exploration of potential biomarkers. In this study, we propose a RAD51 in GBM compared to healthy controls, which has recently been shown to be a target for inhibition to enhance radiosensitivity of GBM cells during treatment. Pathway analysis identified multiple aberrant GBM related pathways as well as novel regulators such as TCF7L2 and MAPT as important upstream regulators in GBM.The proposed method generalizes the inverse-normal method without an increase in statistical or computational complexity and does not pre- or post-hoc filter genes that have conflicting direction of expression in different studies. Thus, the proposed method, as compared to the inverse-normal, has better potential for the discovery of differentially expressed genes (DEGs) with potentially conflicting differential signals from multiple studies related to disease. We demonstrated the use of the proposed method in detection of biologically relevant DEGs in glioblastoma (GBM), the most aggressive brain cancer. Our approach notably enabled the identification of over-expressed tumour suppressor gene The proposed meta-analysis method generalizes the existing inverse-normal method by providing a way to establish differential expression status for genes with conflicting direction of expression in individual RNA-seq studies. Hence, leading to further exploration of them as potential biomarkers for the disease.The online version contains supplementary material available at 10.1186/s12859-022-04859-9. Over thogenesis , 3. Despogenesis , 5, RNA-ogenesis , 7. In togenesis , 9 and sogenesis , 11.p-values obtained from per-study analysis into a single combined p-value per gene [For microarray gene expression studies, apart from vote-counting and direct merging of datasets, meta-analysis methods can mainly be classified into three types based on the combined statistic . First aper gene . Lastly,per gene . Howeverper gene . Hence, per gene .p-value combination methods, namely Fisher and inverse normal (IN) or Stouffer’s methods, previously proposed and used for meta-analysis of microarray studies [p-value combination approaches have been implemented in several R packages, e.g., metaRNASeq [In case of RNA-seq data, Poisson or Negative-Binomial distributions are typically used to model gene counts . Kulinsk studies , 9, 13 a studies . These ttaRNASeq , metaseqtaRNASeq  and metataRNASeq  and are p-value combination methods  allow f p-value , 11. Hen p-value , particup-value combination methods such as Fisher method and Bayesian Hierarchical Model [p-value combination and assess its performance by comparing it with the IN method based on an extensive simulation study. Next, to overcome the limitation of MIN method, we further propose a fused inverse normal (FIN) method for p-value combination and assess its performance by comparing it to IN and MIN methods in a simulation study. Then an application to a set of real glioblastoma  studies has been conducted. Moreover, we assessed the relevance of the identified differentially expressed genes (DEGs) by FIN method for GBM by using Ingenuity Pathway Analysis  for pathway analysis and upstream regulator analysis (URA).Recently, importance of inclusion of direction of expression information for genes in RNA-seq meta-analysis has been recognized leading to a generalization of some existing al Model –23. Howep-value for per-gene and per-study obtained using the individual differential expression analysis within a given study Let on 3.6.0  with lik. (2014)  were adoLet nd Mayer  as folloc and p-value obtained for gene Here, p-values which are roughly uniformly distributed under the null hypothesis [It is assumed that mentclass2pt{minimseq data . Filteripothesis . Similarmentclass2pt{minimLet Using first principle,Now, if For Thus, p-value is given by  at a desired level approach .To address the conservative nature of MIN method (see simulation study results), we propose a mixture method which is a mixture of IN and MIN method for integrated differential analysis. In contrast to formula  we definHere, posed in ) can be mentclass2pt{minimp-value combination method (IN with post-hoc filtering), we performed a simulation study. The simulation study has been divided into two parts. In first part of the simulation study, we compare MIN with IN to understand the behaviour of MIN in comparison to IN and emphasize the need for FIN method. Next, we assess and compare the performance of FIN method to that of IN and MIN methods.To investigate the performance and compare the MIN and FIN methods with state-of-the-art https://www.ncbi.nlm.nih.gov/geo/) [p-value < 0.05 being used to classify a gene as a DEG. This dataset was chosen as it has considerable number of samples for both biological conditions, namely case and control. Simulation settings for inter-study variability parameter   with accov/geo/)  with BH mentclass2pt{minimt}σ∼0.5) . We chosmentclass2pt{minimp-values obtained from differential expression analysis using edgeR (version 3.26.5) were combined using MIN, FIN and IN methods. A gene was considered differentially expressed if the BH adjusted combined p-value (FDR) For each simulation setting, individual To demonstrate how the MIN and FIN method can be adapted in practice for differential meta-analysis of RNA-seq data and compare it with IN method, an application to real glioblastoma (GBM) studies has been conducted.https://www.ncbi.nlm.nih.gov/geo/) and TCGA databases . Datasets were selected based on a selection criterion that at least 3 GBM patients and 3 normal tissue samples are available for analysis. Three different GBM RNA-seq datasets, two from GEO (with accession ID: GSE123892 and GSE151352) and one from TCGA (TCGA-GBM) matched our selection criteria and were considered for analysis  [GBM RNA-seq datasets were searched in GEO (axy.eu/)  to obtaiaxy.eu/) . The paraxy.eu/)  where otaxy.eu/)  to get rp-values under p-values under p-values were then obtained from the differential analysis for case/control conditions.The raw counts of each of these datasets  were processed separately for quality control and differential expression analysis using edgeR package (version 3.26.5) in R. Raw counts data (transcript) were annotated by mapping Ensembl IDs to Entrez Gene IDs and gene symbols . Ensemb) method . Common p-values were obtained from the individual differential expression analysis for each dataset, IN, MIN and FIN methods were applied for p-value combination. Since the TCGA-GBM dataset  is much larger in terms of number of samples as compared to GSE123892  and GSE151352 , we considered two different combination scenarios. First, all TCGA-GBM samples were used for individual analysis to obtain the raw p-values. Second, 20 cases and 5 normal samples randomly selected from TCGA-GBM dataset were considered for individual analysis to get raw p-values and then considered for meta-analysis with the other two datasets (GSE123892 and GSE151352). 10 different random selections were made, and individual differential expression analysis were conducted respectively. Second scenario ensured that the datasets included in meta-analysis had comparable sample sizes.Once the raw p-value p-value combination methods were then compared based on number of DEGs identified and unique DEGs identified by each method.For each of the combination methods, we assessed the number of DEGs based on average absolute log fold-change www.qiagen.com/ingenuity) tool was used to identify biological pathways in which DEGs were enriched and upstream regulator analysis (URA) identified upstream regulators for GBM. We performed pathway analysis and URA separately for DEGs that were up-regulated and down-regulated and were present in all three datasets.DEGs obtained by the FIN method were further explored to assess their biological relevance to GBM. QIAGEN’s Ingenuity Pathway Analysis (IPA) (Both MIN and FIN methods were compared to the IN (with post-hoc filtering) method in a simulation study and a real data application.p-value Based on AUC Table , Fig. 1,As expected, with increase in inter-study variability and number of studies to be combined, the number of genes with mismatched direction of expression was significantly higher  comparable in terms of number of replicates for the meta-analysis , a total of 18,315 unique gene pool was considered for meta-analysis which was the combination of genes identified in each RNA-seq data analysis after quality control and filtering , the identified DEGs were consistent with scenario 1. For instance, on average using the FIN method, about 94% of the DEGs obtained when randomly selected subset was considered were also found in DEGs identified using the full TCGA-GBM dataset . These include Heptatic Fibrosis Signaling Pathway . Synaptogenesis Signaling Pathway  that has been experimentally observed to affect gene expression. It identifies these regulators by analysing linkage to DEGs through coordinated expression . Among togenesis , 40 are p-value < 0.01, see Additional file TCF7L2 and MAPT as the top two. 14 of the 32 upstream regulated were predicted to be inhibited and two among the inhibited are DEGs. TCF7L2 is a diabetes risk-associated gene which plays a key role in the Wnt-signaling pathway and is shown to be frequently mutated in colorectal cancer [MAPT is also a DEG observed in our analysis and is one of the two hallmarks of AD [IDH1/2 wild-type and mutation in human gliomas [MAPT in central nervous system disorders.On the contrary, for the down-regulated DEGs, IPA identified 32 potential upstream regulators  method, details of RNA-seq raw data processing using GALAXY, brief description of the simulation method, results of scenario 2 considered for meta-analysis and Ingenuity pathway analysis results.Additional file 2. Full list of differentially expressed genes (DEGs) identified in scenario 1 by the inverse-normal (IN) method in glioblastoma (GBM) data application.Additional file 3. Full list of differentially expressed genes (DEGs) identified in scenario 1 by the modified inverse-normal (MIN) method in glioblastoma (GBM) data application.Additional file 4. Full list of differentially expressed genes (DEGs) identified in scenario 1 by the fused inverse-normal (FIN) method in glioblastoma (GBM) data application.


Transcriptome profiling is a mainstay of translational cancer research and is increasingly finding its way into precision oncology. While bulk RNA sequencing (RNA-seq) is widely available, high investment costs and long data return time are limiting factors for clinical applications. We investigated a portable nanopore long-read sequencing device  for transcriptome profiling of tumors. In particular, we investigated the impact of lower coverage than that of larger sequencing devices by comparing shallow nanopore RNA-seq data with short-read RNA-seq data generated using reversible dye terminator technology (Illumina) for ten samples representing four cancer types. Coupled with ShaNTi , a newly developed data processing pipeline, a turnaround time of five days was achieved. The correlation of normalized gene-level counts between nanopore and Illumina RNA-seq was high for MinION but not for very low-throughput Flongle flow cells . A cost-saving approach based on multiplexing of four samples per MinION flow cell maintained a high correlation with Illumina data (r = 0.56–0.86). In addition, we compared the utility of nanopore and Illumina RNA-seq data for analysis tools commonly applied in translational oncology: (1) Shallow nanopore and Illumina RNA-seq were equally useful for inferring signaling pathway activities with PROGENy. (2) Highly expressed genes encoding kinases targeted by clinically approved small-molecule inhibitors were reliably identified by shallow nanopore RNA-seq. (3) In tumor microenvironment composition analysis, quanTIseq performed better than CIBERSORT, likely due to higher average expression of the gene set used for deconvolution. (4) Shallow nanopore RNA-seq was successfully applied to detect fusion genes using the JAFFAL pipeline. These findings suggest that shallow nanopore RNA-seq enables rapid and biologically meaningful transcriptome profiling of tumors, and warrants further exploration in precision cancer medicine studies. Using the digitized current-level information, pretrained artificial neural networks can predict the sequence of very long DNA fragments or full-length transcripts with high accuracy. Because this principle does not require imaging, unlike methods based on reversible dye terminator technology (Illumina), sequencing devices could be substantially downsized2. Of particular interest to many investigators is the small, portable MinION sequencer (Oxford Nanopore Technologies), which enables rapid and decentralized sequencing with low investment costs but also has lower throughput than previous methods. In cancer research, MinION-based nanopore sequencing has been successfully employed for mutation detection5, DNA methylome analysis8, DNA copy number profiling9, and the identification of gene fusions12. In addition, full-length cDNA sequencing has been used to detect aberrant splicing in cancer and to perform differential expression analysis14.Nanopore sequencing is an emerging third-generation DNA and RNA sequencing (RNA-seq) technology. It is based on the phenomenon that a single DNA or RNA molecule in an electrophysiological solution passes through a nanometer-scale protein pore, accompanied by ions in varying concentrations depending on the nucleotide composition. This causes quantifiable patterns of current fluctuations attributable to the nucleotide sequence15. For example, mRNA expression analysis of the receptor tyrosine kinase genes FGFR1-3 identified a larger patient population eligible for treatment with the pan-FGFR inhibitor rogaratinib16. More broadly, the increasing understanding of the target spectrum of clinically available kinase inhibitors17 allows the predictive value of kinase expression for response to these drugs to be systematically studied20. In addition, inferring pathway activities from RNA-seq data is becoming increasingly important as they have been shown to outperform the expression of single genes as biomarkers22. However, the functional taxonomy of tumor ecosystems based on their transcriptomes is not limited to the cancer cell compartment. For example, deconvolution algorithms are widely applied to estimate the composition of the immune cell microenvironment25.Despite these developments, the full utility of nanopore RNA-seq for transcriptome analysis of human cancers remains elusive. This could be highly relevant for the clinical implementation of precision oncology approaches. Compared to DNA-based stratification approaches alone, the detection of aberrantly expressed genes has the potential to substantially increase the proportion of patients whose care can be individualized based on molecular information26. We investigated the feasibility of using a MinION sequencer for rapid transcriptome profiling of human tumors. In addition to a comparison with matching Illumina RNA-seq data, we explored four applications for precision cancer medicine, i.e., (1) pathway activity inference, (2) expression quantification of kinases targeted by approved inhibitors, (3) immune cell deconvolution, and (4) fusion gene detection. Our results show that shallow nanopore RNA-seq enables biologically meaningful transcriptome profiling of tumors and warrants further development as a stratification tool in the clinic.MinION-based nanopore RNA-seq can be considered “shallow” RNA-seq since its yield is considerably lower than that of standard Illumina RNA-seq. However, predictive in silico modeling suggests that biomarkers for precision cancer medicine can be developed with drastically reduced sequencing depth27  should align to the reference genome in the opposite direction  as the gene model because it is the reverse complement of the coding sequence. Such alignments are called reverse-oriented. The second cDNA strand should then align in the same direction  as the gene model. Such alignments are called forward-oriented. However, direct cDNA sequencing deviates from this theoretically perfect case. Empirically, we observed that reverse-oriented alignments were twice as many as forward-oriented alignments Figure a. This iAnother reason we exclude forward-oriented reads is overlapping gene pairs in the genome Figure b. These Second, we decided to use reads per million (RPM) and transcripts per million (TPM) normalizations to estimate gene expression levels based on nanopore and Illumina data, respectively. The expression level of a gene is estimated based on the total number of reads aligned to its exonic regions in the reference genome. The total length of all exons of a gene approximates its effective transcript length. The total read counts of all genes in a sample define its library size. While both RPM and TPM normalize read counts to library size, the TPM metric also normalizes them to effective transcript length.In short-read sequencing, reads are often shorter than transcripts. Therefore, multiple reads tandemly align to the gene locus in the reference genome. This creates a bias in gene expression levels measured as RPM because shorter genes appear to be expressed less Figure a. The TPWe examined different sequencing depths Fig. b and obs29. In the original PROGENy framework, 100 so-called footprint genes per pathway were used to compute the activity of a pathway. To simulate the impact of shallow nanopore RNA-seq on the performance of PROGENy, we compared the accuracy of determining an active pathway based on nanopore or Illumina RNA-seq with different coverages. Using the default number of 100 footprint genes per pathway resulted in a drop in nanopore performance relative to Illumina with decreasing coverage  and intrachromosomal  rearrangements. In samples DDLS1 and DDLS2, we mainly observed intrachromosomal rearrangements affecting chromosomes 6 and 12. Finally, in LCNC1, we found a large number of rearrangements between chromosomes 3 and 11 in addition to rearrangements within either of these chromosomes.Many cancers are associated with recurrent gene fusions that drive tumorigenesis and may also be diagnostic biomarkers, such as ts Table . Only inMYB and NFIB loci in ACC samples and to SSX2 and SS18 in SS1, we observed reads that did not align beyond the breakpoint , was already sufficient for the downstream applications that we performed. This result is consistent with a comprehensive computational simulation of RNA-seq data generated within The Cancer Genome Atlas initiative, which showed that a 10- to 100-fold reduction in sequencing depth resulted in no change in the performance of predicting patient outcomes based on transcriptome profiling48. Based on single-cell RNA-seq data, it has been shown that the number of footprint genes used to infer the activity of a given pathway needs to be tuned49. We found that such parameter tuning is also crucial for our shallow RNA-seq approach. We observed that when we used a smaller number of footpring genes per pathway (100–500), the performance of PROGENy dropped as the number of covered genes decreased. However, it remained stable when we included 1,000 genes or more. This suggests that at least 1,000 footprint genes should be used to reliably infer pathway activity based on shallow RNA-seq data. Using this optimized parameter, we observed high similarity between the pathway activities predicted by nanopore and Illumina RNA-seq. These results are consistent with previous data on the activity of specific pathways in the entities studied, e.g., androgen signaling in ACC33. However, because our study is the first application of PROGENy in ACC, LCNC, DDLS, and SS, a more comprehensive comparison is not possible.Following this technical benchmarking, we pursued a first translational application and derived signaling pathway activities from nanopore RNA-seq data and compared them with the corresponding Illumina RNA-seq results. To this end, we applied PROGENy, an emerging methodology used to better understand oncogenic signaling and, of particular relevance to precision oncology, to relate pathway activities to drug responses37. Similarly, the DDLS sample showed CDK4 overexpression, which, due to amplification of the CDK4 locus, occurs in more than 90% of DDLS cases and provides a rationale for using CDK4 inhibitors, which have been associated with prolonged progression-free survival in clinical trials39.In addition to pathway activity, we determined in each sample the most highly expressed kinase genes whose protein products are within the target range of clinically approved inhibitors. We observed that findings based on Illumina RNA-seq were confirmed in all cases by nanopore RNA-seq. For example, FGFR family members were commonly overexpressed in ACC, consistent with the clinical efficacy of FGFR blockade in this entity40, the six ACC cases displayed an immune-excluded microenvironment, with an overall low fraction of immune cells. The high proportion of CD4 + T cells in the LCNC case may be an unfavorable prognostic factor for recurrence-free survival, as previously described41. Further studies correlating immune cell fractions estimated by quanTIseq with immunohistochemical staining of the same tumor tissue are needed to validate these findings.Next, we explored the application of nanopore RNA-seq to predict the tumor microenvironment composition using the CIBERSORT and quanTIseq algorithms. Because the shallow RNA-seq approach cannot capture sparsely-expressed genes, we first investigated the average expression of the gene sets used in CIBERSORT and quanTIseq in our Illumina RNA-seq data. We observed that the reference genes used in CIBERSORT were significantly less expressed and more often undetectable than those used in quanTIseq. This is a potential reason why the concordance between the immune cell estimates derived from Illumina and nanopore RNA-seq data was inferior when using CIBERSORT. In line with a recent study11. In the interest of rapid turnaround, which can be critical in certain clinical situations, automatic detection of gene fusions is desirable.In a third application of diagnostic and therapeutic relevance, we demonstrated that shallow nanopore RNA-seq data can be used to detect gene fusions using tools such as JAFFALIn summary, we showed that shallow nanopore RNA-seq might enable biologically meaningful transcriptome profiling of human cancers and thus has the potential to complement short-read-based sequencing workflows, especially in applications where rapid processing is required. Our next steps will be to develop our workflow further and, in particular, increase the cohort size via testing it prospectively alongside the diagnostic workup of the MASTER precision oncology trial.27. Library preparation for nanopore RNA-seq was performed with the same analyte previously used for Illumina RNA-seq. Samples were selected based on RNA availability and known entity-defining gene fusions , dedifferentiated liposarcoma (DDLS), large-cell neuroendocrine carcinoma (LCNC), and synovial sarcoma (SS), were studied. All patients provided written informed consent for banking of tumor tissue, molecular analysis and the collection of clinical data under a protocol S-206/2011) approved by the Ethics Committee of the Medical Faculty of Heidelberg University. This study was conducted in accordance with the Declaration of Helsinki. All samples were subjected to quality control, verification of the respective entity, and estimation of tumor cell content by experienced pathologists. RNA was extracted in the Sample Processing Laboratory of the German Cancer Research Center (DKFZ). Illumina RNA-seq had been performed within the MASTER (Molecularly Aided Stratification for Tumor Eradication Research) trial of the National Center for Tumor Diseases (NCT), DKFZ, and the German Cancer Consortium (DKTK)1 approvehttps://github.com/DKFZ-ODCF/RNAseqWorkflow). First, FASTQ reads were aligned via two-pass alignment using STAR 2.5.3a50. The STAR index was generated from the 1000 Genomes assembly and GENCODE Version 19 gene models with a sjdbOverhang of 200. Alignment call parameters are listed in Table 51 using eight threads. The chimeric file was sorted using samtools 1.652, and duplicates were marked using sambamba. BAM indexes were also generated using sambamba. Quality control was performed using samtools flagstat and the rnaseqc tool version 1.1.853 with the 1000 Genomes assembly and GENCODE Version 19 gene models. Depth-of-coverage analysis for rnaseqc was turned off. Gene-specific read counting was performed using featureCounts (from Subread 1.5.1)54 over exon features based on GENCODE Version 19 gene models. Both reads of a paired fragment were used for counting, and the quality threshold was set to 255, indicating that STAR found a unique alignment. Strand-specific counting was also used. For RPKM and TPM calculations, all genes on chromosomes X and Y, the mitochondrial genome, as well as rRNA and tRNA genes were omitted as they are likely to introduce library size estimation biases. All computations were performed on a high-performance compute cluster.Reads were processed with the RNA-seq workflow 1.3.0 developed by the DKFZ Omics IT and Data Management Core Facility (Direct cDNA sequencing was performed using the SQK-DCS109 kit (Oxford Nanopore Technologies). For analysis of a single sample on a MinION flow cell (version R9.4.1), 5 µg RNA was used as input. For multiplexing on a MinION flow cell, 2.5 µg total RNA per sample was used as input, and the native barcoding expansion kit EXP-NBD104 was employed in conjunction with SQK-DCS109. After reverse transcription with Maxima H Minus Reverse Transcriptase (Thermo Scientific), second-strand synthesis was performed using the 2 × LongAmp Taq Master Mix (New England Biolabs). The resulting double-stranded cDNA was subjected to end-repair and dA-tailing using the NEBNext Ultra End Repair/dA-Tailing Module (New England Biolabs). For multiplexed libraries, this step was followed by barcode ligation and library pooling. Next, libraries were quantified with a Qubit Fluorometer 3.0 (Life Technologies). Finally, sequencing adapters were added to the library preparations and ligated with Blunt/TA Ligase Master Mix (New England Biolabs), followed by further quality control using a Qubit. Samples ACC1 and ACC2 were analyzed on individual MinION flow cells, while the remaining eight samples were sequenced as multiplexed libraries on two MinION flow cells by pooling four samples for each run. Five ACC samples were also analyzed individually on Flongle flow cells , alignment, and read summarization  used for Illumina RNA-seq data preprocessing. Alignment was performed with minimap2 2.2256. Alignment parameters were optimized to achieve the highest rate of reverse-oriented protein-coding genes with the lowest alignment error. SAM-to-BAM conversion, BAM sorting, indexing, and extraction of basic alignment statistics were performed with SAMtools 1.13. Read summarization was performed with featureCounts (from Subread 2.0.3), similar to Illumina data preprocessing. Strand-specific (forward/reverse) and -unspecific count tables were merged for each sample. Quality reports from basecalled, untrimmed reads before demultiplexing, filtered and demultiplexed reads, and aligned reads were generated using NanoPlot 1.28.255.We developed a custom pipeline for processing nanopore RNA-seq data, available at n Figure . Custom 57. As a comparable metric for long-read sequencing, we calculated reads per million (RPM) by normalizing reverse-oriented long-read alignment counts to the corresponding library size. Briefly, the total library size was estimated after excluding all genes on chromosomes X and Y, the mitochondrial genome, as well as rRNA and tRNA genes. Gene-wise read counts were divided by the total library size and multiplied by 1,000,000 to calculate RPM values, whereas normalization to total exon length as in TPM calculation was not desired for long-read direct cDNA sequencing.Raw transcript counts from all Illumina and nanopore sequencing runs were combined into one table for downstream analysis, and only the counts of reverse-oriented alignments were used. Illumina sequencing was performed with the TruSeq Stranded mRNA Library Prep system (Illumina), which effectively estimates the abundance of coding cDNA strands. Therefore, only the counts of reverse-oriented alignments were relevant in these data. A similar stranded sequencing approach is not available for nanopore cDNA sequencing, although it is possible with direct RNA sequencing, which, however, requires a large amount of starting material and multiplexing is not officially supported by Oxford Nanopore Technologies. To estimate the abundance of coding cDNA strands, we used only the counts of reverse-oriented alignments. Similarly, we used the transcripts per million (TPM) values derived from reverse-oriented alignment counts, which normalize raw read counts to the total exon length and library size for each gene and accurately represent mRNA abundanceprogeny R package was used29. PROGENy is based on a list of pathway response genes collected from publicly available perturbation experiments. Version 1.10 of the R package contains data on 14 pathways involved in cancer biology . The input for PROGENy were the TPM and RPM matrices for Illumina and nanopore sequencing, respectively. Pathway scores were scaled to have a mean of 0 and a standard deviation of 1, the default for the function. Different numbers of footprint genes per pathway (default is 100) were investigated.To infer signaling pathway activities from RNA-seq data, the PROGENy algorithm implemented in the 24 and quanTIseq23, implemented in the immunedeconv R package58, were employed. CIBERSORT estimates the abundance of 22 immune cell types using 547 genes, whereas in quanTIseq, ten immune cell types are deconvoluted using a set of 170 genes. The input for both algorithms were the TPM and RPM matrices. As recommended by the CIBERSORT authors, quantile normalization was disabled for RNA-seq data, and fractions were calculated in relative mode. QuanTIseq was run with default parameters.For immune cell deconvolution from RNA-seq data, CIBERSORT11. High- and medium-confidence fusions were defined as hits, although no medium-confidence fusions were reported.The JAFFAL pipeline was used with standard settings to detect gene fusions59. Subsequently, detailed visualizations with coverage and alignment data were generated using the Gviz package in R60. Gene models for the GRCh37 human genome assembly were downloaded from ENSEMBL using the BioMart service. Only protein-coding transcripts with the GENCODE Basic tag were used to create a metagene model including all possible exons. Fusion breakpoints were determined by whole-genome sequencing and acquired from the clinical bioinformatics workflow of the MASTER cohort27.Gene fusions were first confirmed using Integrative Genomics Viewer (IGV)ComplexHeatmap package in R61. No repeated measurements were performed. Comparison of TPM and RPMs across all protein coding, quanTIseq and CIBERSORT gene sets were done after logarithmic transformation of TPM/RPM values to the normality assumption of t-test.All statistical analyses were performed on R statistical environment version 4.2.0. All heatmaps were created using the Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.