Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands of cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multisubject, multicondition experiments to investigate cell-type-specific differential states (DS) between conditions. This can be performed by first identifying the cell types in all the subjects and then by performing a DS analysis between the conditions within each cell type. Naïve single-cell DS analysis methods that treat cells statistically independent are subject to false positives in the presence of variation between biological replicates, an issue known as the pseudoreplicate bias. While several methods have already been introduced to carry out the statistical testing in multisubject scRNA-seq analysis, comparisons that include all these methods are currently lacking. Here, we performed a comprehensive comparison of 18 methods for the identification of DS changes between conditions from multisubject scRNA-seq data. Our results suggest that the pseudobulk methods performed generally best. Both pseudobulks and mixed models that model the subjects as a random effect were superior compared with the naïve single-cell methods that do not model the subjects in any way. While the naïve models achieved higher sensitivity than the pseudobulk methods and the mixed models, they were subject to a high number of false positives. In addition, accounting for subjects through latent variable modeling did not improve the performance of the naïve methods. Single-cell RNA-sequencing (scRNA-seq) can be used to quantify transcriptomes of thousands of single cells simultaneously. scRNA-seq experiments comprise multisubject, multicondition setups, in which each condition includes samples collected from multiple subjects, cell lines or other biological replicates, and the researchers want to investigate transcriptomic changes between the conditions. Obtaining a large enough number of samples is crucial to ensure that the discovered gene markers are prevalent in the subject groups or treatment conditions, and not only in single subjects or biological replicates.The analysis workflow of multisubject, multicondition scRNA-seq data involves steps that are the same as in any scRNA-seq analysis. Quality control is important to remove poor-quality cells, such as doublets, empty droplets and dead cells . NormaliOnce the cell types have been confidently identified from all the samples, the next step is to perform a differential state (DS) analysis between two or more conditions within each cell type separately. DS changes can be divided into several subtypes , includiThe classical statistical tests for DS testing in scRNA-seq data, such as the Wilcoxon rank-sum test, naïvely assume the samples are statistically independent. However, this is usually not the case in multisubject scRNA-seq data, where cells from the same subject often have more similar gene expression profiles, which causes an error in the statistical testing known as the pseudoreplicate bias . To alleet al. [DE analysis in scRNA-seq data was first investigated in papers that did not address the issue of multisubject setup , 12. Sinet al.  comparedet al. , which iet al. . The autet al.   or averaging single-cell-normalized count values (mean). The sum aggregation is followed by bulk normalization, and it has achieved better performances in earlier studies than the mean aggregation . A recenet al.  recommenet al. . We seleet al. , 19, 20 et al. [Mixed models that account for the subjects as a random effect are gathering increasing interest. We included three mixed models in our comparison: MAST_RE, muscat_MM and NEBULA-LN. A recent paper by Zimmerman et al.  recommenet al.  that modet al. , 21, 22.et al. .FindVariables function. The four statistical tests of Seurat that support the use of latent variables are MAST, logistic regression, negative binomial generalized linear model (negbinom) and Poisson generalized linear model (Poisson). We included these four tests and their naïve versions in our comparison. In addition, we included the Wilcoxon rank sum test, which is the default method for DS analysis in Seurat. Other approaches for performing multisubject DS analysis, such as mixed models with random effects or pseudobulk methods, are not currently available in Seurat (version 4.1).Seurat is a popular R package for scRNA-seq data analysis, including a wide array of statistical tests for DS analysis , 23. TheSince it is in practice very difficult to ascertain which genes are differentially expressed between conditions in real scRNA-seq data, simulation is necessary to obtain an accurate benchmark. To simulate scRNA-seq count data, we used two different approaches. The first approach is based on a reference-free negative binomial generative model presented in the original study of one of the benchmarked tools (NEBULA). This approach can simulate DE and non-DE genes by controlling the average fold change between the groups but no other DS types. The second approach uses muscat, which is a recently introduced R package based on a reference-based negative binomial generative model that enables simulating multisubject, multicondition scRNA-seq data using real data as reference . It can We performed a reference-free negative binomial generative model simulation using the approach from the original paper of one of the benchmarked tools (NEBULA-LN) . This siTo generate gene expression data that included both non-DE and DE genes, we made changes to the original NEBULA simulation. As in the original simulation, we simulated non-DE genes by setting logFC = 0. Additionally, we simulated DE genes with logFC between 0.5 and 2.0. In total, our simulation included 1280 datasets, each containing 100 DE genes and 1900 non-DE genes. We simulated the 1280 datasets by adjusting five different parameters: the number of samples , the average number of cells per sample , the distribution for sampling the average number of cells , cell overdispersion  and sample overdispersion . The average expression term in the generative model ranged from −4 to 2.In our simulation with muscat, we considered reference data from four studies . The relative log-fold-change (logFC) values were set to 0.5, 1 and 1.25 for clusters 1, 2 and 3, respectively. Using Kang data as reference, four datasets were simulated: 20 000 cells and 4 replicates per condition, 20 000 cells and 8 replicates per condition, 5000 cells and 4 replicates per condition and 5000 cells and 8 replicates per condition. One dataset was simulated using Kallionpää data as reference: For Kang, Liu and Thurman reference data, we used the cell type annotation that was provided by the authors of the original studies in the muscat simulation. For Kallionpää data, we performed Seurat integration (v 4.0.3) with the default parameter values and used the resulting clustering in the muscat simulation.et al. [In both simulations, the datasets contained an almost even distribution for the number of cells between subjects. However, this assumption is not valid in many real situations, and a recent paper by Zimmerman et al.  suggesteP-values, and as the response, the ground truth provided by the simulation on which genes had DS. Since the methods had different gene filtering strategies (We performed receiver operating characteristic (ROC) curve analysis on the simulation results using the pROC R package . As the rategies , we onlyP-value threshold, and it can be interpreted as measuring the accuracy of ranking positive genes higher than negatives, it is possible to achieve a perfect AUROC score with statistically insignificant P-values. To assess the ability of the methods to provide well-calibrated P-values, we also calculated the sensitivity, specificity, precision, F1-score and MCC of the methods using the false discovery rate (FDR) of 0.05 as a cutoff. Before adjusting the P-values for multiple comparisons, we excluded the genes that were not included by all the methods  are within the range of the values of the other group, divide the counts by the number of samples in each group and then average the two ratios. Given two pseudobulk normalized gene expression vectors, A high average overlap close to one indicates that the data points of the two groups are mixed and the gene is not differentially expressed. In contrast, a low value close to zero indicates that the groups are differentially distributed and the gene is differentially expressed. We use the average overlap as one of the measures besides average expression and fold change to investigate the properties of the genes from the reference-based simulation that were classified differently by the pseudobulk methods and the mixed models.P-values for multiple comparisons, we excluded the genes that were not included by all the methods . The AUROC values were, on average, highest for the pseudobulk methods, followed by the naïve and latent variable methods. The number of cells and samples did not have a noticeable impact on the superiority of the method types (https://academic.oup.com/bib).We simulated data based on a reference-free negative binomial generative model from the original paper of the NEBULA method . To benchmark the methods in a way that is not limited to a single https://academic.oup.com/bib). However, the pseudobulk methods generally provided significantly better precision and specificity compared to all other method types (https://academic.oup.com/bib). When considering precision (https://academic.oup.com/bib) as the inverse of FDR (FDR = 1 – Precision), the pseudobulks were the only methods that were able to achieve FDR values that were close to the expected FDR of 0.05. The F1 score (https://academic.oup.com/bib) is the harmonic mean of sensitivity and precision that amplifies the impact of small values. Although the naïve methods achieved excellent sensitivity, their weak precision caused the F1 scores to be small, making the pseudobulks clearly the best methods based on the F1 score. The MCC is preferable to F1 score and accuracy for assessing overall performance when the binary labels (DS and non-DS) are imbalanced [https://academic.oup.com/bib) suggested that the pseudobulks once again outperformed the other method types. With Limma and ROTS, we also tested the effect of the aggregation method on the results, suggesting the systematically better performance of the sum over the mean aggregation (https://academic.oup.com/bib).In addition to the AUROC, we calculated the sensitivity, specificity, precision, F1 score and MCC using FDR of 0.05 as a cutoff to define the positives and negatives. Overall, the sensitivity was higher for the naïve methods and the latent methods compared to the pseudobulk methods and the mixed models, and it increased when the number of samples increased with all the methods, as expected . In this simulation, the imbalance had a negligible effect on the performance of all methods.Finally, we investigated how the imbalance in the number of cells between the samples affected the performance , to study the effects of different DS types, including changes in the mean expression, changes in the proportions of low and high expression-state components (DP), changes in modality and changes in both proportions and modality (DB). In total, 54 cell populations (clusters) were used in the benchmarking.We first calculated the AUROC for each method and cluster and grouped the results by the DS type . These rhttps://academic.oup.com/bib).In addition to the AUROC, we calculated the sensitivity, specificity, precision, F1 score and MCC using FDR of 0.05 as a cutoff to define the positives and negatives . The resTo take a closer look at the two best-performing mixed models (NEBULA-LN and MAST_RE) and the four pseudobulk methods that use the sum aggregation, we studied the genes that behaved differently among the six methods . We defihttps://academic.oup.com/bib). In general, the AUROC values of the methods were noticeably lower in the imbalanced datasets, but especially the sensitivity of the methods decreased in the imbalanced datasets. The imbalance did not affect the superiority of the method types.Finally, we investigated how the imbalance of the number of cells affected the performance  and analyzed the correlation of the  results  suggesteFinding DS between conditions from scRNA-seq data involves performing statistical testing between two or more groups of cells for each cell type separately. scRNA-seq experiments increasingly include multiple subjects or biological replicates to confirm that the transcriptomic changes are prevalent in groups and not only in single subjects. This requires specialized tools due to the hierarchical structure of the data. Cells from the same subject often have more similar gene expression profiles, which violates the statistical independence assumption of the basic statistical tests.This issue has been already addressed in recently published studies that have proposed new, improved methods for the DS analysis of multisubject scRNA-seq data , 9, 16. In this article, we compared 18 tools for the DS analysis of multisubject scRNA-seq data. These methods included both pseudobulk methods and mixed models, but also naïve single-cell methods that do not model the subjects in any way, as well as methods that model the subjects as a latent variable. Our benchmarking framework included both simulated and real data. For the simulation, we considered both a reference-free negative binomial generative model and a reference-based negative binomial generative model. The reference-free generative model simulated genes with changes in the mean expression (DE). The reference-based negative binomial generative model simulation was performed using the muscat R package, which is currently the only simulator that can simulate multisubject scRNA-seq data with four different DS types. Finally, we performed a mock comparison using 14 healthy control subjects from a large COVID-19 dataset,  which enP-values of the naïve methods accurately ranked the positives before the negatives, the main issue was that the P-values were poorly calibrated. With an FDR of 0.05 as a cutoff to define the positives and negatives, the methods can be expected to find at most 5% false positives from all positives. The naïve methods found high proportions of false positives, up to 40% in the mock comparison and the simulations. In the simulation, the precision values of the pseudobulk methods were in better accordance with the FDR cutoff than the precision values of the mixed models.The results indicated that the naïve methods were indeed subject to a higher rate of false positives than the pseudobulk methods and the mixed models. This conclusion is supported by all our analyses. While the naïve models generally provided higher sensitivity, this benefit was negated by the lower precision and specificity. In other words, the naïve models reported a lot of findings, but a large proportion of these were false positives. Although the AUROC results suggested that the We observed notable variation in the performances of the mixed models. Of the three mixed models that we considered in our comparison, MAST_RE and NEBULA-LN achieved considerably better overall performance than muscat_MM. However, the performances of the pseudobulk methods were mostly similar. Our results suggested that the pseudobulk aggregation by calculating the mean of single-cell normalized data provided inferior performance compared to the sum approach that cumulatively sums the count values and then uses bulk RNA-seq normalization. This is in line with at least one previous study that found that the sum aggregation outperformed the mean aggregation .We investigated how the number of cells and samples affected the performance of the methods. An earlier study  found thThe popular Seurat pipeline includes four statistical tests that allow for the incorporation of several latent variables in the models. The latent models test if the observed DE change between the conditions can be explained by the difference in one or several variables. According to the package manual, this is recommended if the data contains batch effects in the DS analysis, but no instructions are provided for any other variables. As of Seurat v4.1, using latent variables is currently the only way to account for the subjects in the DS analysis with Seurat. Our results strongly suggest against their use in the DS analysis when the subject is included as a latent variable. The latent models performed generally even worse than their naïve counterparts. A recent study came to the same conclusion when they used ComBat to correct the data for the subject effect before the DS analysis , 30. HowOur comparison includes limitations and details that can cause bias in the results. First, the reference-free simulation was originally presented in the original paper of NEBULA-LN, which is one of the methods compared in this article. The simulation uses the same generative model that is used by the NEBULA mixed model. Therefore, NEBULA could have an unfair advantage over the other mixed models in the comparison. Secondly, our comparison did not investigate the role of prepreprocessing, such as normalization or clustering. In this article, we focused solely on benchmarking DS detection methods for multisubject, multicondition data. More versatile comparisons are still needed to investigate the complex relationship between the different analysis steps of which the DS detection is only one step . While oOur findings opened many interesting research questions that require further investigation in future studies. Our results suggested that the mean aggregation generally outperformed the sum aggregation. Investigating why exactly the sum aggregation performed better was not investigated in this study. This would require adjusting the normalization workflows of the pseudobulk methods to test, for example, whether the TMM normalization  is a benhttps://github.com/elolab/multisubjectDSanalysis).To conclude, we performed a comprehensive comparison to benchmark 18 methods for DS analysis of multisubject scRNA-seq data. Our results suggest that the pseudobulk methods and the mixed models that model subjects as a random effect were superior compared to the naïve single-cell methods that do not model the subjects in any way. We also recommend not to perform DS analysis using Seurat’s statistical tests so that the subjects are modeled as a latent variable. Overall, the pseudobulk methods outperformed the mixed models. If the user wants to achieve high specificity and precision at the risk of losing some true positives, we recommend the pseudobulk ROTS with the sum aggregation. If sensitivity is more important than the false-positive results, then we recommend the pseudobulk methods Limma, DESeq2 or edgeR combined with the sum aggregation. We recommend that scRNA-seq analysis pipeline developers should begin to include pseudobulk methods and mixed models in their pipelines. To facilitate DS analysis of multisubject scRNA-seq data, the codes that implement all the methods in this article are freely available online (L.L.E. and S.J. conceived the study. L.L.E., S.J. and J.S. designed the study. J.S. and S.J. implemented the study. J.S. wrote the manuscript. L.L.E. and S.J. commented the manuscript. L.L.E. supervised the study. S.J. and J.S. share the first authorship.Naïve single-cell differential states analysis methods are subject to high proportions of false positives when analyzing data with multiple biological replicates.Latent variable models are not effective in reducing false discoveries when accounting for biological replicates.Pseudobulk methods and mixed models that account for biological replicates as a random effect are effective in reducing false discoveries.Overall, our results suggest that pseudobulk methods outperform mixed models.BiB-Supplementary_File-Benchmarking_27_04_2022_JS_bbac286Click here for additional data file.


The objectives of our study were to assess the association of radiological imaging and gene expression with patient outcomes in non-small cell lung cancer (NSCLC) and construct a nomogram by combining selected radiomic, genomic, and clinical risk factors to improve the performance of the risk model.  A total of 116 cases of NSCLC with CT images, gene expression, and clinical factors were studied, wherein 87 patients were used as the training cohort, and 29 patients were used as an independent testing cohort. Handcrafted radiomic features and deep-learning genomic features were extracted and selected from CT images and gene expression analysis, respectively. Two risk scores were calculated through Cox regression models for each patient based on radiomic features and genomic features to predict overall survival (OS). Finally, a fusion survival model was constructed by incorporating these two risk scores and clinical factors.  The fusion model that combined CT images, gene expression data, and clinical factors effectively stratified patients into low- and high-risk groups. The C-indexes for OS prediction were 0.85 and 0.736 in the training and testing cohorts, respectively, which was better than that based on unimodal data.  Combining radiomics and genomics can effectively improve OS prediction for NSCLC patients. Non-small cell lung cancer (NSCLC) is one of the deadliest diseases in humans. NSCLC occurs in approximately 80%–85% of lung cancer patients. Surgery is still the only potential cure for patients with early-stage NSCLC. However, 30% to 55% of NSCLC patients will relapse and die of the disease . TherefoRadiomics refers to the extraction of many quantitative image features from radiological data and data mining of these features for clinical tasks, such as disease diagnosis and prognostic analysis . Image fWith the advancement of high-throughput sequencing technology, using microarray gene expression profiling to analyze gene expression characteristics and establish prognostic gene signatures has also attracted significant research interest. Li et al. established a gene signature closely related to the tumor immune microenvironment that can effectively predict clinical outcomes .The above studies have greatly promoted the discovery of biomarkers for the prognosis of NSCLC. However, most methods are limited to a single-data model, and cross-modal comprehensive methods are relatively underdeveloped. By integrating multimodal data, such as gene expression, radiological and histological imaging, and clinical data, more detailed insights into the development and occurrence of disease can be provided . TherefoMultimodal approaches can extract more meaningful features from multiple perspectives, leading to more reliable characterization of tumors, and thus have great potential to address the limitations of single-modality models. According to this assumption, in this study, we tried to provide a complete view of NSCLC characteristics for survival prediction by integrating information from radiological imaging, gene expression, and clinical data. To this end, we first used CT images and gene expression data to develop their respective risk scores and combined them with clinical characteristics to construct the final survival analysis model. We then investigated whether the fusion model could improve the overall survival of NSCLC patients compared with the models based on unimodal data .An overview of our method is depicted in xi, ti, δi) denote each patient, where xi corresponds to the features extracted from CT or gene expression, ti is the survival time, and δi denotes the censoring indicator. δi=1 means a noncensored instance and vice versa. The purpose of survival analysis is to predict the time duration until a specific event occurs. In this study, the event was the death of a patient with NSCLC. The Cox proportional hazard model is one of the most popular methods to model the hazards function [h0(t) corresponds to the baseline hazard and f(x)=αTx is the risk function. α= is the regression parameter that can be estimated by minimizing the following log partial likelihood function:N denotes the number of patients and R(ti) is the risk set at time ti, which represents the set of patients who are still at risk before time ti.Let , including intensity features, shape features, texture features, and wavelet features . IntensiTo eliminate the differences in the value scales of the radiomic features, feature normalization was performed after feature extraction. For the extracted features in the training cohort, each feature for a specific patient was subtracted by the mean value and divided by the standard deviation value from this cohort. The same normalization method was applied to features in the testing cohort using the mean and standard deviation values calculated based on the training cohort.Including too many features will increase the computational cost, and redundancy between features will reduce the accuracy of the model. Furthermore, the number of features is more than the number of samples in this work, which will increase the probability of overfitting. Therefore, we needed to select a small number of informative radiomics features to predict the survival risk of patients. In this study, we used the least absolute shrinkage and selection operator (Lasso)  for feati regularization term to l(α), so the Lasso-Cox model can be formulated ask=1p|αk| is the L1 regularization term and λ is a parameter to balance the two parts. We used 5-fold cross-validation to determine the optimal value of λ.The Lasso can shrink some regression coefficients to zero and select important variables by adding an LWe chose the autoencoder framework for processing gene expression data. Autoencoders aim to reconstruct the input values using combinations of nonlinear functions, and the bottleneck layers are considered as the representation of the inputs . UsuallyX ∈ RN×p, with N samples and p features. We used a multilayer neural network with parameter Φe as the encoder:An autoencoder can be divided into an encoder and a decoder. Let the original input be H is regarded as a compressed representation of input X. The encoder maps N samples from p-dimension space to k-dimension space. Another multilayer r neural network with parameter Φd is regarded as the decoder:X. The decoder maps N samples from k-dimensional space back to p-dimensional space. The whole process of the autoencoder can be expressed asThe parameters of the autoencoder can be estimated by minimizing the following reconstruction error:H can be regarded as the compressed form of X that can be used in subsequent tasks.Then, the latent representation i=1kβi × fi, where fi denotes the selected radiomic features or the compressed gene features, and βi represents the estimated coefficient of the corresponding features. According to this formula, the risk score of each patient was computed. Then, all patients in the training cohort were divided into high- and low-risk groups with the median of the risk score as the cutoff. Then, the survival differences between these two groups were evaluated by a Kaplan–Meier (KM) survival curve. We used the same category for the testing cohort. Accordingly, the samples in the testing cohort were also divided into two risk groups. The difference in the survival curves of the high-risk and low-risk groups was evaluated by the log-rank test.For the features selected from CT and gene expression data, we constructed Cox models. Therefore, a risk score of each patient can be computed by ∑The candidate prognostic indicators included age, sex, stage, grade, and the above two risk scores. We first used a LASSO Cox regression model to select the final features for constructing the fusion model. Then, a nomogram was built using a multivariate Cox proportional hazard model. We used the same validation methods as described in the validation of the radiomics and genomics models.The statistical analysis was performed with R software, version 4.1.2, and Python software, version 3.8.5. Cox regression was performed using the “scikit-survival” package . NomograM stage, or grade between the training and independent testing cohorts (P > 0.05).We randomly split the dataset, including 87 training and 29 testing samples. The clinical characteristics in the training and testing cohorts are summarized in λ selection in the LASSO Cox regression model is shown in There were 8 features with nonzero coefficients in the LASSO Cox regression model. The optimal P < 0.001) and confirmed in the testing cohort (P=0.045).The radiomics signature achieved a C-index of 0.79 for the training cohort, and 0.643 for the testing cohort, demonstrating the predictive performance of the model. Based on the risk score of patients in the training cohort, the optimal cutoff was −0.150. Then, patients in both the training and testing cohorts were stratified into low-risk and high-risk groups, as shown in P < 0.001) and confirmed in the testing cohort (P=0.083).In the autoencoder structure, the number of latent features is an important parameter. To determine this parameter, we preset the number of latent features to 5–12. For each set of potential features, we further compressed it into two dimensions using multidimensional scaling (MSD) , as wellN grade, and stage was finally selected.We used LASSO analysis to select the optimal feature combination in the multimodal analysis. A combination of the radiomics risk score, genomics risk score, age, p=0.0081, as shown in The combination nomogram for the prediction of 2-year survival is displayed in To further verify the effectiveness of our model, we performed 5-fold cross-validation for all data. All patients were randomly divided into 5 subsamples of equal size, and in each fold, 4 subsamples were used as training data and the remaining 1 subsample was used as validation data for testing. For each fold, we used the method presented in this paper separately. The average and standard deviation of the C-indexes over the 5-fold cross-validation are listed in p=0.15 for radiomics model, p=0.08 for genomics model, and p=0.015 for clinical features model). When using age, N stage, grade, radiomics risk score, and genomics risk score, the model can significantly distinguish high-risk and low-risk groups (p=0.0081).Gene expression data, imaging data and clinical factors each play an important role in the diagnosis and prognosis of diseases. Merging these multimodal data may lead to new prognostic cancer models and provide new support for patient treatment strategies. Accordingly, increased attention is given to statistical methods and algorithms to analyze and correlate multivariate imaging, clinical and gene expression data for disease diagnosis and prognosis. Our research demonstrated the ability to integrate radiomics data, genomics data, and clinical features for the stratification of lung cancer patients. Unimodal analysis shows that only using gene expression data, radiology data, or clinical features cannot effectively stratify patients. With unimodal data, the difference in survival between the two risk groups in the testing group was not significant (This study has some limitations. First, all the samples we explored are public and the sample size is relatively small, limiting the use of more advanced methods. Second, for molecular data, we only used gene expression data. More meaningful discoveries may be produced if data such as miRNA and DNA methylation data are fused. Third, we only discussed the handcrafted radiomic features. Many studies have shown that features based on deep learning have better predictive capabilities. We believe that the fusion of multiple modalities can help detect more effective biomarkers and improve lung cancer clinical decision-making. In future work, we will solve the above limitations and conduct further multicenter, large-scale researches to promote the application of multimodal fusion in the management of lung cancer patients.In this study, the risk score developed based on multimodal data has great potential to improve the determination of overall survival of NSCLC patients compared with the models based on unimodal data. We demonstrated that we could gain more significant insights into cancer prognosis by fusing imaging and genomic data. More studies that combine more data sources, such as multiomics data and digital pathology, are required to confirm the advantages of multimodal fusion.


In order to better understand the relationship between normal and neoplastic brain, we combined five publicly available large-scale datasets, correcting for batch effects and applying Uniform Manifold Approximation and Projection (UMAP) to RNA-seq data. We assembled a reference Brain-UMAP including 702 adult gliomas, 802 pediatric tumors and 1409 healthy normal brain samples, which can be utilized to investigate the wealth of information obtained from combining several publicly available datasets to study a single organ site. Normal brain regions and tumor types create distinct clusters and because the landscape is generated by RNA seq, comparative gene expression profiles and gene ontology patterns are readily evident. To our knowledge, this is the first meta-analysis that allows for comparison of gene expression and pathways of interest across adult gliomas, pediatric brain tumors, and normal brain regions. We provide access to this resource via the open source, interactive online tool Oncoscape, where the scientific community can readily visualize clinical metadata, gene expression patterns, gene fusions, mutations, and copy number patterns for individual genes and pathway over this reference landscape. Over the past several decades the scientific community has characterized and cataloged individual genes for their function and involvement in development and disease. More recently, several single-cell atlases from different tissues and organ sites for various species (for example human and mouse) have been created that provide deep insights into the relationships between different cell types in development and in adult tissues. In this study, we have blended publicly available RNA seq datasets of normal and neoplastic brain to derive similar insights into the relationship between various central nervous system (CNS) tumors and between neoplastic vs. normal brain. Using this reference landscape, expression of specific genes and gene ontology groups can be compared across all tumor types and normal brain regions.1 contains data from across 33 cancer types, including uniformly processed cancer genomic data (whole transcriptome RNA-Seq), microarray, gene fusions, gene mutations and copy number calls) from 702 glioma patients and 5 matched normal patients. The Chinese Glioma Genome Atlas (CGGA)2 contains RNA-seq, whole genome sequencing, DNA methylation, microarray data from over 2000 brain tumor samples. The Children’s Brain Tumor Tissue Consortium (CBTTC)3 contains whole genome sequencing and RNA-seq data across 23 different pediatric tumors. The Genotype Tissue Expression Project (GTEx)4, contains genomic data from 54 non-diseased tissue sites across nearly 1000 individuals, including 1409 brain tissue samples from 12 GTEx defined brain regions.A variety of omics approaches have been employed to characterize both tumor and healthy tissue at the molecular level by various large-scale international initiatives. The Cancer Genome Atlas (TCGA)Here, we present a visual integration approach for analyzing multiple diverse molecular datasets combining large numbers of samples across different brain regions and brain tumor subtypes. We combined transcriptional data from three different datasets for adult glioma, pediatric tumors and healthy normal brain regions, corrected for batch effects, and used a dimension reduction technique to construct a UMAP to find meaningful patterns in this pooled multi-disease dataset.5(https://oncoscape.sttrcancer.org/#project_bulkrnaseqbrainumap), can aid in analysis for translational research.This comparative study can help researchers and clinicians visualize similarities and differences in patient cohorts, study and compare alterations in gene expression, signaling pathways, gene fusions, copy number profiles and mutation calls across multiple tumor types. By adding normal healthy brain tissues from GTEx to our reference landscape, we also allow for comparisons between healthy and neoplastic states. Visualizing these similarities and differences in an opensource, interactive website, Oncoscape 4, 270 adult glioma samples from CGGA6, 1409 healthy normal brain samples from GTEx4 across 12 GTEx-defined brain regions (Supplementary Table 1a). Retrieving data from recount7 ensured that consistent bioinformatic pipelines were used for these three datasets thus resulting in no batch effects between the three datasets.To characterize and better understand the molecular intricacies of brain tumors, we downloaded uniformly processed RNA-seq abundances values from recount-brain, a curated repository for human brain RNA-seq datasets, for three different uniformly processed datasets – 702 adult glioma samples from TCGA8. To adequately represent a wide range of CNS tumors in our reference landscape, we additionally included 802 pediatric tumor samples (Supplementary Table 1b) from the Children Brain Tumor Network (CBTN)3. Supplementary Table 1c)The most common solid tumors in children are brain tumors with approximately 1.15 to 5.14 cases per 100,000 children in the United States alone9. While both CBTN and recount used different bioinformatic pipelines (Supplementary Table 1c), in order to ensure that there were no batch effects we used combat10 method from the R package “sva” to remove unwanted variation in our combined dataset.Gene expression data from each of the datasets was converted to units of Transcripts per million (TPM) to avoid interpipeline difference and were limited to a common set of 19142 protein-coding genes.We explored three different dimension reduction techniques , t-distributed Stochastic Neighbor Embedding (tSNE) and uniform manifold approximation and projection (UMAP) for data visualization. We chose UMAP to build a Brain-UMAP  on batch5) where users can easily toggle among and compare different patient groups, while using a suite of interoperable tools.While 2-dimenisonal representation of the data is helpful, we also provide a 3-dimensional representation of the data on the interactive web-based platform OncoscapeAt a first glance, distinct clustering of samples is observed. The adult glioma samples from the different datasets, TCGA-LGG, TCGA-GBM and CGGA, co-localized closely within the same region of the UMAP, whereas the pediatric tumor samples clustered between the GTEx healthy normal brain and the adult glioma samples. .11  were not associated with distinct clustering patterns, suggesting that the sample clusters we observe are based on actual biological differences between these brain regions rather than sample preparation parameters. However, we observed that a few samples with much lower RNA integrity number (RIN) compared to all the other samples from different brain regions converged at a point .The 1409 normal brain samples segregated into two distinct clusters of multiple supratentorial regions and cerebellum, as we have previously shown11 . The sup12 et al. In line with previous reports, we observed that the age of the patients at diagnosis for the TCGA-GBM and TCGA-LGG samples revealed a sharp gradient illustrating the known correlation between age and outcome . Regarding chromosomal alterations, tumors with a gain of chromosome 7 and hemizygous deletion of chromosome 10  colocalized in the top area of the UMAP containing the TCGA-GBM samples  to color the adult gliomas from TCGA-LGG and TCGA-GBM also revealed distinct patterns across the adult glioma landscape. Selecting for common glioma mutations and copy number alterations clearly shows three distinct subtypes of glioma - IDHmut-1p19q co-deleted oligodendrogliomas, the IDH mutated astrocytomas with p53 and ATRX mutations, and the wild-type IDH (IDH-wt) glioblastomas with gain of chromosome 7 and loss of chromosome 10 molecular GBM.  and groups 3,4, Supplementary Fig. 4a). Similarly, Ependymomas (EPN) samples formed several clusters that correlated with the anatomic tumor location -EPN, spinal-EPN, and posterior fossa (PF)-EPN) 13. Pediatric pilocytic astrocytomas (PAs) and pediatric low-grade gliomas clustered closely together suggesting that they exert similar gene expression patterns. The schwannomas separated from the pediatric tumors and were located near the neurofibromas, which were localized adjacent to the malignant peripheral nerve sheath tumors (MPNST). The subependymal giant cell astrocytoma (SEGA) form a tight cluster as do the meningiomas. Interestingly, we observed that neurocytomas, DNET, ganglioglioma clustered near normal brain samples, specifically the hypothalamus and amygdala samples. Building a UMAP with just normal brain samples and the pediatric tumors, showed similar clustering profile . Taken together, these results suggest that pediatric tumors cluster by disease type and also form subclusters made by subtypes in the case of medulloblastomas and ependymomas.We added the 802 pediatric tumors from the CBTN dataset to our analysis to compare their gene expression patterns to both normal brain and adult glioma samples -b. We obAlterations in signaling pathways are a hallmark of cancer and understanding the extent to which these pathways are dysregulated in tumor samples compared to healthy normal brain can help inform researchers about the underlying mechanisms of different cancer types.14, KEGG15 and biocarta16 pathways. We then tested whether there is a difference between the GSVA enrichment scores from different pair of phenotypes using a linear model and moderated t-statistic.Bulk gene expression from adult glioma, pediatric brain tumors and healthy brain samples was subjected to a Gene Set variation Analysis (GSVA) and the gene set variation scores for each pathway were used to color in the Brain-UMAP. A score closer to 1 represented up-regulation of pathway in the given samples, whereas a score closer to −1 represented down-regulation of the pathway. We calculated GSVA scores for all pathways present in Reactome(Supplementary Table 2a-c) pathways were up-regulated in IDH-wt glioblastomas, IDFI-mut astrocytomas and oligodendrogliomas respectively compared to healthy brain samples from GTEx. A total of 456 pathways (Supplementary Table 2d) were up regulated in all three adult glioma subtypes compared to healthy brain. The top pathways which were enriched in all adult glioma subtypes were pathways enriched for cell cycle, DNA repair, translation, splicing, oncogenic signaling pathways such as RAS pathway, Notch pathway, MHC pathway, PI3K/AKT Signaling, Wnt pathway, SHH pathway . Additionally, neurotransmitter pathways, calcium channel, potassium channel opening pathways were up regulated in healthy brain regions compared to pediatric tumors and adult gliomas .As examples, we found that 605, 589, and 529  in comparison to the main adult glioma cluster.Interestingly, we noted that the two small clusters (IDH mutated grade 2 and grade3 oligodendroglioma and grade 4 IDH-wt glioblastomas) from the CGGA dataset were enriched in pathways related to olfaction, glucoronidation, ascorbate and aldarate metabolism and xenobiotics Supplementary Fig. 6). For example, almost all the genes in the mismatch repair pathway have elevated gene expression levels in medulloblastoma, except RPA3 and POLD4.While visualizing pathways across the reference Brain-UMAP is extremely informative, researchers exploring targets for drug development may be also interested in investigating individual genes of a particular pathway. For example, Reactome’s mismatch repair pathway (R-HAS-5358508) and Biocarta RELA pathway (M10183) were both found to be up-regulated in all adult glioma subtypes compared to healthy normal brain, coloring in the gene expression for individual genes over the Brain-UMAP show different gene expression patterns across members of the same pathway. , we first built a much smaller UMAP using only the bulk gene expression data from these two datasets. The resulting UMAP  showed aSupplementary Fig. 7a-b) in comparison to copy number profiles , whereas pediatric lower-grade gliomas frequently harbored BRAF fusions (KIAA1549 – BRAF in 32% of low-grade gliomas and in 60% of pilocytic astrocytomas). In addition, supratentorial ependymomas most commonly harbored the C11orf95 – RELA fusion  and meningiomas most frequently harbored fusions in NF2 and YAP1 (both 15% of tumors) .Of note, the IDH-mut oligodendrogliomas, astrocytoma and pediatric high-grade gliomas have the highest mutational burden and number of gene fusions across all brain diseases . We observe that different genes belonging to this pathway exhibit different trends, for example genes such as POLD1(chr19), RPA2(chr1), and LIG1(chr19) loose a copy in IDH-mut oligodendrogliomas, while genes RPA3(chr19), PMS2(chr19), PCNA(chr20) and POLD2(chr7) gain a copy in IDH-wt GBM. While most genes show elevated gene expression levels for all brain tumors, of interest is EXO1 and RPA3 which are only up-regulated in IDH-wt GBM samples. While members of this pathway do not form gene fusions, they get mutated in different brain tumors. For example, in pediatric high grade tumors, we observed mutations in all the members of the mismatch repair pathway - MSH2 (22%), MSH6 (15.71%), POLD3 (14.29%), MSH3 (12.86%), LIG1(11.43%), EXO1(10%), PMS2(10%), PCNA(10%), POLD1 (8.57%), RPA2(8.57%), MLH1 (7.14%), RPA1(7.14%), POLD2(5.71%), POLD4(5.71%) and RPA3(5.71%).Armed with additional genomic information such as gene fusions, copy number variation and somatic alternation, we investigated members of the Reactome mismatch repair pathway (R-HAS-5358508) (Supplementary Table 3a) ALK ((Supplementary Table 3b).This reference landscape is a useful research tool for the scientific community, where researchers can explore existing data to increase their understanding of oncogenic pathways and individual genes that make up these pathways, potentially uncovering candidates for novel therapeutic targets. By providing access to this reference landscape via an open source website like Oncoscape, we provide an interactive tool to researchers doing CNS research.Oncogenes show altered gene expression in tumor samples, leading to abnormal phenotype in samples. Understanding gene expression patterns across various cancers of the nervous system can further our understanding of the disease. When studying known oncogenes such as EGFR, PTEN and CIC at a gene level , as expe 3a) ALK  mutationAs costs for performing RNASeq continues to decline with increasing technological advances, more and more tumors will be sequenced, and additional tumor banks will be created with the underlying goal to understanding cancer’s complexity. The wealth of knowledge that that already exists in publicly available datasets such as those described here  is remarkable. Individually these datasets comprise of well-defined biologically similar set of patient samples and allow the analysis in exquisite resolution of genetic changes from one sample to the next. For example, comparing medulloblastomas to other medulloblastomas allows for precise characterization of molecular subtypes. As researchers, we can get so focused on comparing like with like that we lose sight of the proverbial forest for focusing too much on the leaves of a single tree. By integrating multiple datasets while correcting for batch effects, such as with the Brain-UMAP presented here, we can harness the power of multiple datasets.This landscape can be used to prospectively compare patients, similar to work that has been done using methylation arrays. Unlike methylation arrays, however, RNA seq is based on gene expression and therefore each cluster represented on the landscape contains granular information about the underlying tumor biology of the samples it contains. Because the overall expression pattern is identifiable for each tumor, this allows for cross comparison of various kinds of cancer to allow for characterization of tumor types not previously known. For example, asking questions about expression of particular genes or pathways across a wide panel of samples and tumor cohorts may uncover previously unknown roles or similarities between adult and pediatric tumor subtypes, which can possibly open new avenues for therapeutic discovery.The landscape that we present reveals correlations between tumor type and outcome. We have learned how diagnosis of a given tumor type can be confirmed using various pieces of genomic information. For example – the adult glioma subtype of oligodendroglioma can be confirmed by presence of co-deletion of chr1p/19q and somatic alteration in IDH1.We are also able to compare tumor samples which were sequenced years apart (TCGA and CGGA) in two different continents and confirm that these differences did not contribute to expression changes in majority of the tumor samples. We are also able to identify novel entities and gain insight into genes that drive their unique character, such as in the case of the two adult glioma subtypes seen only in the CCGA data that appear to strongly and uniquely express genes involved in olfaction.Using this study as an example, we have seen how one diagnosis can be comprised of more than one expression entity, such as in the case of medulloblastoma and ependymoma. The UMAP also indicates room for better classification of tumors. For example, we found tumors which were documented as one type in the publicly available database, but in our UMAP were found to cluster with tumors of a different type. For example, some embryonal tumors ended up clustering with the medulloblastoma samples indicating that they may be medulloblastomas, or at least share many common features with medulloblastoma. We also have seen that multiple diagnoses may really be one entity as in the case of tumors diagnosed as either pilocytic astrocytoma (PA) or pediatric low-grade glioma.Reference landscapes like the Brain-UMAP, will be informative for researchers who wish to obtain a quick diagnosis or characterization of newly obtained tumor samples. For example, if we look at the medulloblastoma subtypes from CBTN, there are 14 group 3, 49 group 4, 30 SHH, 9 WNT and 19 unclassified medulloblastoma samples. For the 19 unclassified samples, we can predict which subtype they belong to, based on which medulloblastoma subtype samples they cluster with. .By combining results from both RNA-seq data  as well as whole genome sequencing  and with the help of a few examples, we illustrate the knowledge that can be mined from this resource, at both a pathway level as well as a gene level. The work presented here demonstrates the utility of reference landscapes for combining genomic data across multiple tumor types for both diagnosis, prognosis and better understanding the biology of the tumors that are similar to a given patient collected prospectively. The power of visualizing gene expression changes, regulation of pathways, chromosomal alteration, gene fusions across multiple tumor types is informative to every researcher, especially those who may not have immediate access to computational biology experts.The reference landscape described here provides a useful tool for researchers interested in gene level questions across large scale patient data, while the methods used for integrating data sources highlight the tremendous potential for combining future datasets with existing resources to address complex biological questions.https://www.r-project.org/) using Bioconductor (https://www.bioconductor.org/) packages. We have deposited all scripts, associated data, at: https://zenodo.org/badge/latestdoi/584982012 to maximize transparency and reproducibility. TCGA data from GDC was downloaded using R/Bioconductor package TCGAbiolinks19, SummarizedExperiment20 was used to store adult glioma and pediatric tumor data. All plots were made using ggplot221, RcolorBrewer22.All analyses were performed in R .23.For consistency, we converted all FPKM gene expression data to TPM data using the formula21.Uniform Manifold Approximation and Project (UMAP) values were generated using the R function umap using all protein-coding genes and visualized with R package ggplot2https://xenabrowser.net/datapages/?cohort=TCGA%20Lower%20Grade%20Glioma%20(LGG)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443 and https://xenabrowser.net/datapages/?cohort=TCGA%20Glioblastoma%20(GBM)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443) for adult gliomas. Copy Number calls for pediatric tumors estimated from the GISTIC2 pipeline were downloaded from https://github.com/AlexsLemonade/OpenPBTA-analysis.Gistic2 thresholded gene level copy number variation estimated using the GSITIC2 method were downloaded from UCSC Xena. Gene fusions for pediatric tumors were downloaded from https://github.com/AlexsLemonade/OpenPBTA-analysis. Only high-confidence gene fusion calls were retained from ARRIBA.Gene fusions for adult gliomas estimated using Arriba and Star-Fusion as per the GDC RNA-Seq pipeline were downloaded from the GDC portal  files containing somatic variants from MuSE, VarScan2, MuTect2 and Somatic Sniper were downloaded from GDC portal for TCGA-GBM and TCGA-LGG. The variants for each patient were combined based on custom script present in our github repository. Somatic Variant calls for pediatric tumors was obtained from 24. https://www.gseamsigdb.org/gsea/msigdb/collections.jsp#C2 Batch corrected log2(TPM) counts from each pipeline were used to conduct a Gene Set Variation Analysis (GSVA)25 using Biocarta, KEGG and Reactome pathways. GSVA scores obtained from 1 and − 1 for each sample, were visualized using ggplot2.Gene sets for all the pathways from Biocarta, Kyoto Encylcopedia of Genes and Genomes (KEGG) and Reactome pathways were downloaded from Molecular Signature Databases (MSigDB)(v7.2)lifelines 26 using survival data and glioma subtype labels downloaded from GDC, where the world health organization (WHO) 2016 criteria for the classification of adult diffuse gliomas was used to determine glioma subtype27.To generate Kaplan-Meier curves for TCGA and CGGA patients, all samples labeled recurrent, secondary, or normal tissue were removed. Duplicate samples were also excluded so that each patient was represented by exactly one sample. Kaplan-Meier curves were drawn by the Python package To annotate the Brain-UMAP with survival data, each TCGA and CGGA sample was colored with the median survival of a cohort of patients (nearest neighbors) close to the sample in question on the UMAP landscape. The notion of nearest neighbors was defined as a number of samples within a radius of 2 from the sample in question under the constraint that all nearest neighbors must be of the same glioma subtype and from the same dataset (TCGA or CGGA) as the sample in question. The radius parameter was chosen qualitatively. The number of nearest neighbors was defined as 25% of the total number of samples of the same glioma subtype and dataset as the sample in question. If the median survival of a sample’s cohort of nearest neighbors is undefined, or if a sample has fewer than 10 nearest neighbors, the point is not colored in. Non-primary and duplicate samples were excluded in the same manner as was done for the Kaplan-Meier curve analysis.https://github.com/FredHutch/OncoscapeV3/blob/master/docs/upload.mdGene expression and survival data analysis result files were prepared according to the oncoscape instructions here: All statistical analyses and plots were done in R (v.3.3.1) as implemented in Rstudio (v.1.0.136). Plots were created using the R basic graphics. The following R packages were used: GenomicAlignments53 (v.1.24), reshape64 (v.0.8.8), png65 (v.0.1–7), ape66 (v.5.3) and seqinr67 (v.3.6–1).


Using latent variables in gene expression data can help correct unobserved confounders and increase statistical power for expression quantitative trait Loci (eQTL) detection. The probabilistic estimation of expression residuals (PEER) and principal component analysis (PCA) are widely used methods that can remove unwanted variation and improve eQTL discovery power in bulk RNA-seq analysis. However, their performance has not been evaluated extensively in single-cell eQTL analysis, especially for different cell types. Potential challenges arise due to the structure of single-cell RNA-seq data, including sparsity, skewness, and mean-variance relationship. Here, we show by a series of analyses that PEER and PCA require additional quality control and data transformation steps on the pseudo-bulk matrix to obtain valid latent variables; otherwise, it can result in highly correlated factors (Pearson's correlation r = 0.63 ~ 0.99). Incorporating valid PFs/PCs in the eQTL association model would identify 1.7 ~ 13.3% more eGenes. Sensitivity analysis showed that the pattern of change between the number of eGenes detected and fitted PFs/PCs varied significantly in different cell types. In addition, using highly variable genes to generate latent variables could achieve similar eGenes discovery power as using all genes but save considerable computational resources (~ 6.2-fold faster).The online version contains supplementary material available at 10.1186/s13059-023-02873-5. Inferring latent variables that explain the variations in the gene expression data has been an essential step for expression quantitative trait locus (eQTL) analyses. It can be used to identify the unobserved confounding effects and potential cellular phenotypes . Popular methods include principal component analysis (PCA) , surrogaAs the scale of scRNA-seq studies rapidly grows, eQTL analyses that use pseudo-bulk approaches have emerged. Pseudo-bulk refers to the aggregation of the gene expression profiling of all cells from one sample into a single pseudo-sample; thus, the expression matrix dimension will be assimilated as the bulk RNA-seq data as a “sample x gene” matrix. However, due to the nature of scRNA-seq data structures, the bulk expression matrix and scRNA-seq pseudo-bulk expression matrix can be very different. There are three main differences: matrix sparsity, distribution normality or skewness, and mean–variance dependency. First, since the scRNA-seq matrix is sparse and many elements are zero, the pseudo-bulk gene expression matrix still contains many zeros. Second, some evidence showed that the underlying distribution of gene expression across cells largely follows non-normal distributions, such as Gamma, Point-Gamma, or non-parametric distributions , and intMoreover, how many PFs/PCs should be fitted in the eQTL association model to improve the discovery power for pseudo-bulk data is unclear. Previous bulk eQTL analysis either chose a fixed number  or a cerHere, we identify some common scenarios where pitfalls occur when inferring latent variables in single-cell eQTL analyses and how they can be avoided with data-driven approaches. We have performed analyses using PEER factors and PCA, where we have observed spurious correlations among the inferred factors. To help with the future application of PEER and PCA to single-cell RNA-seq data, we propose guidelines for the quality control and scaling of the pseudo-bulk expression matrix, diagnosing and troubleshooting the inferred latent variables, and a new way to select the optimal number of latent variables to improve the eQTL discovery.N = 980) and the others from fibroblast cells and iPSCs [N = 79 and 31). To construct a pseudo-bulk expression matrix for each cell type, the gene expression level per individual was calculated as the intra-individual mean counts across cells  and the correlations among PFs 5–7 are equal to 1. Although the hidden factor model of PEER allows for non-orthogonal components, the mean of the pair-wise Pearson’s r across the first 10 PFs were all larger than 0.5 in all 14 cell types, suggesting that many PFs are redundant and overfitted. Additionally, we found that the variance explained by the first PF was overwhelmingly larger than the rest of the PFs, where the latter’s contributions seem negligible  and transformation options on the pseudo-bulk matrix using three independent scRNA-seq datasets: one from peripheral blood mononuclear cells  transformation; (3) standardization, which scales the distribution to mean = 0 and standard deviation = 1; (4) rank-based inverse normal transformation (RINT); (5) selecting the top 2000 highly variable genes  to generate the PFs (see the “x + 1) transformed  + standardization + To alleviate the impact of these properties, we mixed and matched different options in combinations  to generate PEER factors: (1) excluding the genes with zero expression in more than a certain % across the individuals . Strong correlations among PFs were also observed in four iPSC subtypes (after the 4th or 5th PF) but not in fibroblast subtypes  when N = 40, suggesting that we might need at least five samples per factor in such a dataset.To expand our exploration into other cell types, we tested the data from Neavin et al. , who notanalysis , 18, whiAs principal components (PCs) are also commonly used to control for confounding factors in the eQTL analysis, we also conducted the same exploration for PCs in the OneK1K cohort. The PCs inferred without proper QC and scaling also showed spurious correlations but were very modest compared to PFs  in single-cell pseudo-bulk data and conducting sensitivity analysis to select the optimal number of latent variables to be incorporated in eQTL mapping for each cell type. As we are moving towards the era of identifying single-cell, context-dependent, and dynamic eQTL –21, learApplying methods designed for bulk RNA-seq data to scRNA-seq pseudo-bulk data could be challenging as the assumptions might not be fully satisfied. This work highlights the pitfalls when learning PEER factors and principal components from scRNA-seq data. It presents diagnostic guidelines for performing further QC and normalization on single-cell data matrices to avoid spurious correlations among the inferred factors. Optimization for the number of latent factors included in the eQTL association model should be carried out by a data-driven approach. Using highly variable genes to generate the latent factors could achieve similar eGene discovery power as using all genes.sctransform [scPred [z-transformed.Three single-cell datasets were used in this study to explore the performance of the PEER and PCA methods. The OneK1K consortium  is a popransform , and cla [scPred  (see mor [scPred ). We fur [scPred  collecteRankNorm in the R package “RNOmni” [PEER factors are latent variables that can explain the variability in gene expression. The original method  was prop“RNOmni” . The trax + 1) transformation per gene. STD indicates z-score scaling per gene. RINT indicates rank-based inverse normal transformation per gene. HVG2000 indicates whether to use top 2000 highly variable genes or all genes.The cis-region of the gene within the 1 Mb from either upstream or downstream and with minor allele frequency > 5%. A local false discovery rate (LFDR) was calculated to control the false-positive rate for each chromosome tested by the R package “qvalue” [The eQTL association analysis was performed by Matrix eQTL (v2.3) . We fit “qvalue” . An eGenTo choose an optimal number of latent variables fitted in the eQTL association model, we propose a local greedy detection algorithm. We first calculated the percentage change of eGenes with every incremental latent variable added. Then, we performed a LOESS  of the percentage change against the number of latent variables. Instead of choosing the number of PFs/PCs maximizing the number of eGenes, we selected the number of PFs/PCs right before the LOESS curve became negative. The rationale behind this algorithm is that if the number of eGenes reaches saturation, the percentage change is expected to be symmetrically distributed around 0. By this balanced strategy, we only need to adjust 2 ~ 10 PFs rather than 20 ~ 50 but can retain the most power gain of eGene discovery (Additional file To investigate whether the strong correlation of PEER factors in iPSC data from Neavin et al.  arose duAdditional file 1: Fig. S1. Correlation among inferred PEER factors and global intra-individual mean–variance dependence. Fig. S2. Correlation among known covariates, including sex, age, and first six genotype PCs. Fig. S3. Different transformations of highly expressed and lowly expressed genes. Fig. S4. Performance of the 8 candidate QC options of input matrix for PEER factor generation. Fig. S5. Sensitivity test for eQTLs and eGenes discovery power between no QC and QC option #11. Fig. S6. The computational time for eQTL association analysis when adjusting a different number of PEER factors. Fig. S7. Detection of the optimal number of PCs. Fig. S8. Detection of the optimal number of PEER factors using the local greedy algorithm. Fig. S9. Detection of the optimal number of PCs using elbow detection method and the local greedy algorithm. Fig. S10. The correlation plot of fibroblast and iPSC clusters from Drew et al. Fig. S11. Distribution of mean pair-wise correlation coefficient among PEER factors in down-sampling of fibroblast clusters. Fig. S12. Performance of different QC options on the generation of expression PCs. Fig. S13. Sensitivity test for eGene detection using a different number of latent variables inferred from PCA and PEER. Table S1. The maximum of eGene detection power gain by incorporating PEER factors.Additional file 2. Review history.


Understanding molecular features that facilitate aggressive phenotypes in glioblastoma multiforme (GBM) remains a major clinical challenge. Accurate diagnosis of GBM subtypes, namely classical, proneural, and mesenchymal, and identification of specific molecular features are crucial for clinicians for systematic treatment. We develop a biologically interpretable and highly efficient deep learning framework based on a convolutional neural network for subtype identification. The classifiers were generated from high-throughput data of different molecular levels, i.e., transcriptome and methylome. Furthermore, an integrated subsystem of transcriptome and methylome data was also used to build the biologically relevant model. Our results show that deep learning model outperforms the traditional machine learning algorithms. Furthermore, to evaluate the biological and clinical applicability of the classification, we performed weighted gene correlation network analysis, gene set enrichment, and survival analysis of the feature genes. We identified the genotype–phenotype relationship of GBM subtypes and the subtype-specific predictive biomarkers for potential diagnosis and treatment. Glioblastoma multiforme (GBM), which is the grade IV of glioma as defined by the World Health Organization (WHO), is a highly invasive and devastating primary form of brain cancer. The complexity and molecular heterogeneity of GBM pose the challenge for accurate diagnosis and therapy . The proRecent advances of sequencing technologies have helped generate massive omics data in cancer, leading to a deep understanding of the molecular mechanisms in both common and rare cancers . Data frWe have implemented ML and DL algorithms for the precise and accurate classification of GBM subtypes in the present work. Each data type  and its integrated subsystem were separately used for classification. We found that the performance of the convolutional neural network (CNN) was superior  compared with the other ML models. In addition, we examined the biological relevancy of features using weighted gene co-expression network analysis (WGCNA) and Gene Ontology (GO) analysis. Results show distinct co-expression modules are linked to each GBM subtype and are associated with subtype-specific biological functions. Moreover, several genes in the co-expression module are associated with patients’ survival. Overall, our findings suggest that a combination of LASSO feature selection and CNN can classify the subtype of GBM with higher accuracy and be used for clinical diagnosis.https://xena.ucsc.edu/) (n = 155) were divided into three categories based on cancer subtype, i.e., classical (n = 42), mesenchymal (n = 55), and proneural (n = 39) for transcriptome data. Similarly, based on the clinical information, we divided the methylome data (n = 84) into a particular subtype, i.e., classical (n = 29), mesenchymal (n = 32), and proneural (n = 23). Next, based on the clinical information, patients with both transcriptome and methylome profiles in TCGA were screened to integrate the transcriptome and methylome data. The total number of these patients with omics data was 52, including classical (n = 16), mesenchymal (n = 22), and proneural (n = 14). Due to the unavailability of healthy patient data for both transcriptome and methylome, we used the Z-score to classify higher and lower expression of genes and hyper- and hypo-methylated CpG sites. We calculated the Z-score for each gene or CpG site in a specific subtype using the following formula:In this study, we analyze TCGA GBM transcriptome (RNA-seq) and methylome (Illumina Infinium HumanMethylation450 platform) data. The data set was retrieved from UCSC Xena (sc.edu/) . Log2 , mesenchymal (n = 14) and proneural (n = 9). Models built on methylome data were further validated using GSE128654, which consisted of classical (n = 11), mesenchymal (n = 8), and proneural (n = 10) subtypes.Here, ectively . We applanalysis . We collThe subtype-specific clustering of patients using transcriptome, methylome, and integrated data was visualized by t-distributed stochastic neighbor embedding (t-SNE) and principal component analysis (PCA) . t-SNE whttps://scikit-learn.org) package in Python.We performed feature or variable selection to improve the performance of ML and DL algorithms. The least absolute shrinkage and selection operator (LASSO) was performed on all types of preprocessed data . We usedk independent folds, where k-1 folds were used to train the network, and the remaining one is reserved for test purposes. This procedure is then repeated until all folds are used once as a test set. The final output is then computed by averaging over the obtained performance parameters from each test set.We performed classification on the subtype of GBM as a multiclassification problem using gene expression levels as covariates. Several ML and DL algorithms were used for classification: support vector machine (SVM), random forest (RF), naïve Bayes (NB), logistic regression (LR), k-nearest neighbors (kNN), and CNN. SVM is used for the classification between the classes to find the optimal hyperplane . The optThe performance of ML and deep learning models was evaluated using accuracy, recall, precision, F1-score, FPR, GM, and MCC. At first, we generated a confusion matrix to compute these performance scores. The confusion matrix is a table that categorizes the model’s prediction of whether it matches the actual value. We calculated true positive (TP), true negative (TN), false positive (FP), false negative (FN) from the confusion matrix . Then, wThe sensitivity or TP rate of an ML model was measured using the following equation:The specificity or TN rate of an ML model was measured using the following equation:The precision or positive predicted value was measured using the following equation:A measure of model performance that combines precision and recall into a single number is known as the F measure or F1-score. The following equation was used to compute the F1-score:nth root of the product of their values.Geometric mean (GM) is the average value or mean, which signifies the central tendency of the set of numbers by taking the Mattews correlation coefficient (MCC) measures the correlation of the true classes with the predicted labels.We used the sklearn metrics library in Python to calculate the above score by importing functions such as confusion_matrix and classification_performance. Finally, we visualized the model performance across a wide range of conditions using receiver operating characteristic curve (ROC) plots using the roc_curve function.Algorithm performance was compared using multi-criteria decision analysis (MCDA)/multi-criteria decision making (MCDM). The technique for order of preference by similarity to ideal solution (TOPSIS), an established MCDM method, was used to rank. Multiple criteria, such as accuracy, sensitivity, precision, G-mean, F-measure, FPR, and MCC, were used in TOPSIS .We identified co-expressed gene modules and analyzed the module-trait relationship using the WGCNA package in R . First, p-value was less than 0.05. The gene list from each positively correlated module was used to examine the enrichment of GO biological processes and molecular function terms. We performed overall survival and log-rank test of a co-expressed module using the survminer and survival package in R. We calculated the average expression of all genes in the module. Survival was compared between two groups: patients with higher (⋝75 percentile) and lower (⋜25 percentile) gene expression levels. Furthermore, we performed the overall survival analysis of specific genes using GEPIA  data can provide novel insights into biological function and disease mechanisms. In this work, we first separately analyzed the transcriptome and methylome, and then we integrated both data types to identify the molecular feature and classify the GBM subtypes.n = 42), mesenchymal (n = 55), and proneural (n = 39). These genes were taken for further analysis. However, 14,125 genes could not be used as variables for prediction as the data is high-dimensional, leading to the inaccurate classification of subtypes. Therefore, we performed the LASSO to reduce the dimension of data and subsequently for selecting top key feature genes to enhance the prediction accuracy of the DL and ML model. LASSO performs L1 regularization and adds a penalty to the loss function. This penalty contains the absolute value of the regression coefficients. It attempts to minimize the cost function and automatically selects relevant features that are useful, and the remaining features are discarded with a coefficient equal to zero. The coefficients of the regression variables having nonzero values were selected as an optimal feature for further processing. A total of 201 feature genes were obtained after performing the LASSO analysis  and ML algorithms  to classify subtypes of GBM using these feature genes as variables. We divided the data into training (70%) and test (30%) data sets. Seventy percent of the data was used for parameter optimization and to assess the performance of each model. The remaining 30% of data was used for independent predictors. Additionally, an external data set was also used for the final validation of models . In the In the previous section, we classify the GBM subtype using the transcriptome data (or gene expression data) because the alteration of gene expression is a hallmark of oncogenesis. However, the level of gene expression is regulated by DNA methylation. Therefore, changes in DNA methylation patterns can play a crucial role in GBM development. Recent studies show that DNA methylation biomarkers are essential for improving and designing cancer therapy . Hence, z-score, i.e., z > 1 and z < -1 . A z-score greater than 1 or less than -1 indicates the expression and methylation is greater or less than the population mean. We identified common genes whose expression and methylation both are z > 1 or z < -1 in each subtype. Next, we combined all these genes  and used their gene expression level to find the most variable features (n = 75) using LASSO  based on scale independence and mean connectivity . In contrast, it was negatively associated with the mesenchymal , and weakly correlated with the classical subtype . Similarly, we found a distinct pattern of association between other modules  and subtypes . The green module is positively associated with the classical  and negatively associated with the proneural . The blue module is strongly and positively correlated with the mesenchymal subtype , whereas it was negatively associated with proneural . However, the feature from the integrated data showed a more specific module–subtype association. At least one module was strongly and positively correlated with a specific subtype. The red , turquoise , and blue  were explicitly and positively associated with classical, mesenchymal, and proneural, respectively (p < .05) associated with several BP and MF that are linked to oncogenesis. For example, the turquoise module from the transcriptome data in the classical subtype is involved in the RIG-I signaling pathway that elicits RIG-I-like receptors’ expression and activity (RLRs) . Therefore, further analysis of these features genes can link the genotype to phenotype. We performed WGCNA to understand genotype-to-phenotype relationships. WGCNA can find the module of highly correlated genes and their association with a specific subtype of GBM . We consectivity . We thenectivity . To undeectivity . Resultsectivity  was signsubtypes . We obsesubtypes , the broectively . The modectively . We obsey (RLRs) . These ry (RLRs) . In conty (RLRs)   . Phosphay (RLRs)   . Similary (RLRs) . These py (RLRs) . Even moy (RLRs)   . Furthery (RLRs)   . The geny (RLRs) . The MF y (RLRs)   .p = .029) associated with the patient survival. Hence, we performed survival analysis of all genes separately present in these modules using GEPIA web tools (p < .05). The higher expression of most of the genes was associated with worse survival of the patients, except DUOX1 (FIGURE 5O) and FOXN2 (Our results show that most of the positively correlated modules in GBM subtypes were involved in several BP and MF. Besides this, many of these BP and MF are involved in oncogenic processes. This shows a possibility of identifying these modules’ genes as cancer biomarkers for therapy or diagnosis. We performed survival analysis of positively correlated modules . The tureb tools . We founnd FOXN2 . Howevernd FOXN2 . Furthernd FOXN2  (Figure nd FOXN2 ; GCNT1, nd FOXN2  (Figure nd FOXN2 ; and TOLnd FOXN2  in pronend FOXN2  are linknd FOXN2 . FurtherThe present study indicates that DL and ML can be powerful tools for finding patterns in large-scale genetic and epigenetic data sets related to human cancer. In general, efficient DL and ML tools work like a black box; researchers or clinicians may not be confident in diagnosing or classifying cancer patients using these approaches. However, if the basis of classification is biologically relevant and has higher accuracy, the diagnosis and patient management are more assured and systematic. Here, we present a biologically relevant DL- and ML-based framework to classify the subtype of GBM to increase accuracy in diagnosis; in turn, it can lead to better patient management. Previous studies try to develop the cancer classification model using a single type of omics data. Models are mainly developed for binary classification to identify healthy and cancer patients. However, we use two types of high-throughput data, i.e., transcriptome and methylome; integrated forms of these data were explored to develop the classification framework. Most importantly, we successfully separate three subtypes, classical, mesenchymal, and proneural, of GBM. Although we dealt with multiclass classification problems, we still achieved classification accuracy >90%. We also compared DL and ML techniques to identify the most suitable method for interpreting the transcriptome, methylome, and integrated data. The DL method, i.e., CNN, outperforms other ML models. Using CNN, we were able to classify the tumor into the correct subtype from the test and external cohort. We observed that overall classification performance was higher using the transcriptome and integrated data than the methylome data.Another significant aspect of our findings is the biological relevance of features and the identification of subtype-specific prognostic biomarkers. To find the association of feature genes with specific subtypes, we performed WGCNA. The gene co-expression module-subtype relation analysis revealed how a subset of features is strongly and positively correlated with a particular subtype of GBM. In addition to that, the gene set enrichment analysis revealed that all positively correlated modules are biologically relevant even those that are linked to oncogenic processes. Among all data types, a strong module–trait relationship was observed in feature genes from integrated data. Furthermore, we identified several genes present in these co-expressed modules, which were linked to patient survival. Our study explained how the feature genes from the DL/ML framework could be used to find the subtype-specific biomarkers. Good agreement was found when comparing prognostic markers from this work against published experimental data. The feature genes of this study and CNN can provide assured and clinically relevant deep learning-based diagnostic tools for the proper treatment of GBM patients. Furthermore, the results of this work unravel and shed light on the understanding of genotype-phenotype relationships of the GBM subtype. Last, much of the research presented in this work can be applied to other human cancers to design DL-based diagnostic tools using high-throughput experimental data.


Tumor-infiltrating immune cells (TIICs) form a critical part of the ecosystem surrounding a cancerous tumor. Recent advances in radiobiology have shown that, in addition to damaging cancerous cells, radiotherapy drives the upregulation of immunosuppressive and immunostimulatory TIICs, which in turn impacts treatment response. Quantifying TIICs in tumor samples could form an important predictive biomarker guiding patient stratification and the design of radiotherapy regimens and combined immune-radiation treatments. As a result of several limitations associated with experimental methods for quantifying TIICs and the availability of extensive gene sequencing data, deconvolution-based computational methods have appeared as a suitable alternative for quantifying TIICs. Accordingly, we introduce and discuss a nonlinear regression approach  and a machine learning algorithm for approximating the solution of the resulting constrained optimization problem. This way, the deconvolution problem is treated naturally, given that the gene expression levels of pure and heterogenous samples do not have a strictly linear relationship. When applied across transcriptomics datasets, our approach, which also allows the coupling of different loss functions, yields results that closely match ground-truth values from experimental methods and exhibits superior performance over popular deconvolution-based methods.  In recent times, the continuous rise in the global cancer burden has emphasized the need for increased efforts in cancer treatment strategies. In 2020 alone, there were 19.3 million new cases and almost 10 million cancer deaths worldwide, with the number of new cases projected to climb to 28.4 million by 2040 . In simple terms, radiotherapy involves using ionizing radiation, usually high-energy X-rays, to kill cancer cells or, at least, limit their proliferation by damaging their genetic code of life, known as deoxyribonucleic acid (DNA). In doing this, the goal is to achieve tumor control without introducing severe damage to surrounding normal tissues, enhancing treatment outcomes and minimizing adverse effects. Precision radiotherapy aims to reach this goal by stratifying and precisely treating “each individual cancer patient, using state-of-the-art new radiotherapy technology and biomarkers” .Biomarkers are objectively evaluated and measured characteristics indicative of normal  biological processes, pathogenesis, or therapeutic response . Their rTraditionally, experimental methods such as immunohistochemistry (IHC), cytometry, and recently, single-cell RNA sequencing (scRNA-seq) have been the gold standard for quantifying TIICs in samples. Although these methods precisely quantify TIICs in samples, there are limitations in terms of the technicality and range of applicability associated with each method. On the one hand, scRNA-seq is not just expensive and laborious for routine use but also highly prone to bias due to variations in the dissociation efficiencies of single cells . On the Recently, the sharp reduction in the cost of next-generation sequencing (NGS) technologies has encouraged its routine application in clinical settings, resulting in the availability of large amounts of transcriptomics datasets from patients’ tumor samples, such as The Cancer Genome Atlas (TCGA) . Althougconvolution of the gene expression levels of the different cell components 23], the http://epic.gfellerlab.org) accessed on 28 September 2022. It can also be accessed from the Gene Expression Omnibus (GEO) repository [The other portion of the single-cell suspensions was used for bulk RNA sequencing (RNA-seq). We downloaded this RNA-seq data for the four Melanoma patients from the “example data from EPIC” link on the EPIC web application , expressed for AMLA is then applied to approximate the solution.2.Linearized nonlinear model (LNM), expressed for However, we do not apply AMLA directly to Equation (16). Rather, we linearize it to obtain the form3.Nonlinear model one (NM1), expressed for 4.Nonlinear model two (NM2), expressed for AMLA is then applied to approximate the solution.For all four versions enumerated above, we set the variable We have already emphasized that the nonlinearity of our model (Equation (11)) strictly depends on the value of Our empirical approach relies on the observation that the relationship between the gene expression profiles of pure and heterogeneous samples is not strictly linear. Guided by this, we interpret the expression “not strictly linear” as slightly different from one, and this difference can be either side of one, i.e., greater than or less than one. Because http://epic.gfellerlab.org) accessed on 30 September 2022. The default setting comprises tab-delimited inputs of bulk RNA-seq dataset as described in We present two results when estimating the cell-type fractions in the four melanoma samples using EPIC. The first result which we denote as “EPIC1” was obtained from the default setting of the EPIC web application (https://cibersortx.stanford.edu/) accessed on 30 September 2022. The gene expression reference profile used was as described in Furthermore, we estimated the cell-type fractions in the four melanoma samples using the CIBERSORTx web application . RMSE is excellent for comparing the prediction error of different models for a specific variable. As a result, it is an incredibly good measure of model accuracy. The most accurate model would have an RMSE of zero, which is far from possible. Therefore, the model accuracy is determined by how close the RMSE is to zero, although this determination is made relative to the values of the observations or predictions. Here, we calculated the RMSE for NM1, LNM, ELM, CIBERSORTx1, CIBERSORTx2, and EPIC2 for all cell types of the four melanoma samples and then for TIICs only. In the latter, we also included the RMSE calculation for EPIC2. We present these results in NM1 can be seen to have the lowest RMSE in both charts of The linear framework has been the dominating paradigm in the computational deconvolution of TIICs and other tumor cell subsets from bulk gene expression data of tumor samples. Efforts toward improving the accuracy of cell fraction estimates from the computational deconvolution of bulk gene expression data have been centered on modifications still built around the basic linear framework. For instance, in both charts of It is clear from As shown in Furthermore, AMLA’s design allows it to exhibit faster rates of convergence and runtime in comparison to other traditionally used algorithms in machine learning, which is highly advantageous in the event of enormous amounts of bulk gene expression data of many tumor series. Verifying these with simple numerical experiments on For each of the Equations (20)–(23), we approximated their solutions using AMLA, the well-known classical gradient descent algorithm (CGDA), and Nesterov’s accelerated gradient (NAG) widely used in machine learning. Since the optimal solutions of the equations are known, we measured the convergence to the solutions from AMLA, CGDA, and NAG using variations of From A very noteworthy affirmation of the high robustness of AMLA can be seen in Naturally Estimating Cell-type Subsets from Tumor Gene Expression Profiles). NECSTGEP will be equipped with an additional rigorous machine learning algorithm that will be able to automatically fix the model parameters and the initialization values for AMLA, using pattern analysis of the input bulk gene expression profiles, as well as the reference profile. This is very essential as this proof of principle has shown a crucial role in yielding highly accurate estimation results. Similar problems in biology, oncology, and medical physics, such as the optimal scheduling of combined cancer therapies and reconstruction of gene regulatory networks, parade similar levels of complexity. Thus, they can benefit from an application of the technique described thus far, to yield highly accurate results. Overall, this proof of principle demonstrates that fully accurate and efficient computational deconvolution of tumor bulk gene expression data for estimation of the proportions of cell-type fractions, including TIICs, is best achievable using a nonlinear optimization framework, whose solution can be approximated by an accelerated machine learning algorithm (AMLA). This nonlinear optimization framework truly captures the natural state of the deconvolution problem. Consequently, in the future, we will implement the entire deconvolution workflow, described in We introduced and discussed a nonlinear constrained optimization framework for the computational deconvolution of TIICs and other tumor cell-type subsets from tumor bulk gene expression profiles, in addition to an accelerated machine learning algorithm (AMLA) for directly approximating its solution. Our analysis using real tumor transcriptomics datasets concluded that this nonlinear approach yields values closely matching ground-truth values from experiment, because it treats the problem in its natural state. Models NM1 and NM2 produced the “best” values for the estimated cell-type fractions in this study and were significantly different from those obtained using the traditional linear modeling approach. However, one main limitation of the study is the empirical choice of model hyperparameters, which will be addressed in future studies. This study, therefore, heralds a paradigm shift away from the traditional linear modeling of the TIIC deconvolution problem.


Cancer progression is linked to gene-environment interactions that alter cellular homeostasis. The use of biomarkers as early indicators of disease manifestation and progression can substantially improve diagnosis and treatment. Large omics datasets generated by high-throughput profiling technologies, such as microarrays, RNA sequencing, whole-genome shotgun sequencing, nuclear magnetic resonance, and mass spectrometry, have enabled data-driven biomarker discoveries. The identification of differentially expressed traits as molecular markers has traditionally relied on statistical techniques that are often limited to linear parametric modeling. The heterogeneity, epigenetic changes, and high degree of polymorphism observed in oncogenes demand biomarker-assisted personalized medication schemes. Deep learning (DL), a major subunit of machine learning (ML), has been increasingly utilized in recent years to investigate various diseases. The combination of ML/DL approaches for performance optimization across multi-omics datasets produces robust ensemble-learning prediction models, which are becoming useful in precision medicine. This review focuses on the recent development of ML/DL methods to provide integrative solutions in discovering cancer-related biomarkers, and their utilization in precision medicine.  This can be subjected to a trained CNN classifier for cancer-type prediction based on gene expression data Medical image categorization is essential in clinical diagnosis, prognosis, and educational activities KRT7) as a biomarker of MIBC. Recently, Hira et al. showed that DL-based variational autoencoders could be utilized for the classification of ovarian cancer transcriptional subtypes using multi-omics data from the cancer genome atlas (TCGA) datasets with model accuracies ranging from 87% to 93% et. al. suggested a hybrid approach using a novel DL and ML framework called “DeepProg” which utilized multi-omics data to build a highly predictive model for patient survival risks among various cancer subtypes DL may assist in the in-depth study of cancer by enabling the integrative analysis of multi-omics datasets The promising outcomes of DL-based studies have accelerated broader interest in combining metabolomics and other omics data for similar applications, such as potential biomarker discovery Furthermore, the benefits of such findings show that including multi-omics data in a DL model improves its accuracy when compared to using only a single omic dataset 3de novo drug design (DNDD) and compound optimization de novo design techniques include inverse QSAR10 de novo design owing to their ability to learn the characteristics of real-world training examples and to create entirely new synthetic entities with similar characteristics de novo compound design using generative models and autoencoders. Despite the lack of theoretical evidence from other ML approaches, the results suggest that a DL approach may hold potential comparable to well-established conventional ML and statistical methods in cancer studies de novo molecular generation method using a latent-vector-based GAN that did not require SMILES for compound synthesis de novo drug creation methodologies de novo synthesis and compound characteristics prediction for the development of cancer drugs.Drugs designed using synthesis-based predictions have a long history, dating back to the early 1960 s. It is exceedingly difficult to design novel compounds using conventional tools 4Petrovsky et al. used 1D and 3D CNNs to differentiate patients with kidney, ovarian, and prostate cancers from healthy controls by analyzing metabolomics datasets Lilikoi v2.0Lilikoi v1.0Lilikoi algorithm can transform and incorporate metabolomics data from samples to generate personalized pathway profiles, which can aid downstream classification or development of a prognosis model based on the metabolites linked with the corresponding pathway A recent study by Indeed, the application of DL in metabolomics and lipidomics has been relatively slow, mainly because of the scarcity of reference standards, accurate analytical methods, computational resources, expertise, and interpretability. Because DL models require large datasets for training, low sample sizes are likely to decrease the sensitivity and accuracy of DL models to predict metabolic phenotypes A graph convolutional neural network (GCNN) is a type of deep neural network that works directly with a graph structure. A GCNN can be a DNN with fully connected convolutional layers. Node categorization is commonly used in the GCNN in vitro cancer cell growth Metastasis is the most common cause of cancer-related death. Monitoring variations in cancer gene expression is crucial for understanding metastatic pathways for tumors and cellular activities. A GCNN was recently used to combine the image features of metastasis to local lymph nodes for cancer trajectory prediction and clinical decision-making 5One of the major aims of cancer precision medicine is to identify the molecular traits responsible for certain clinical outcomes. At present, a strategy involving molecular traits through drug-specific informative genes remains a popular means to investigate sensitivity or resistance to cancer drugs 6DL models learn from a training dataset and then apply that knowledge to infer classes in an entirely new dataset, whereas reinforcement learning (RL) models learn dynamically using continuous feedback to optimize a predesignated reward 7de novo drug synthesis, candidate gene clustering, and clinical outcome prediction based on a set of biomarkers discovered by processing complex features from multi-omics datasets.Clinical research is increasingly demonstrating the broad applicability of DL methods, including in cancer diagnosis and therapy. Recent studies have demonstrated the potential of DL-based ensemble models to augment personalized patient treatment strategies based on medical histories and diagnostics. Advancements in high-throughput and large-scale omics assays have generated large volumes of data that can be utilized for biomarker discovery. Furthermore, precision medicine-based therapeutic interventions for cancer have shown promising efficacy when using a DL-based multimodal ensemble strategy that involves gene-protein-metabolite interaction . These aHowever, DL models have several limitations, including small sample sizes, lack of interpretability, reliability of computational resources, and scarcity of professional expertise Vivek Bhakta Mathema: Conceptualization, Writing – original draft. Partho Sen: Conceptualization, Writing – original draft. Santosh Lamichhane: Writing – original draft. Matej Orešič: Writing – original draft. Sakda Khoomrung: Conceptualization, Writing – original draft.None.