Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such an approach does not explain the biological features that contribute to correct predictions. To address this problem, here we investigate the interpretability of H&E-derived CNN features . While many studies have incorporated CNN features into predictive models, there has been little empirical study of their properties. We show such features can be construed as abstract morphological genes (“mones”) with strong independent associations to biological phenotypes. Many mones are specific to individual cancer types, while others are found in multiple cancers especially from related tissue types. We also observe that mone-mone correlations are strong and robustly preserved across related cancers. Importantly, linear mone-based classifiers can very accurately separate 38 distinct classes (19 tumor types and their adjacent normals, AUC =  However, they are black-boxes that do not directly reveal the morphological features they associate with labels, a significant concern for mechanistic analysis and clinical decision making2. Identification of biologically meaningful morphological features may be confounded by image artifacts3, such as blurring, noise, and lossy image compression4. Tissue damage, image quality, and dataset-specific artifacts have also been suggested to affect feature representation and prediction accuracy of neural networks6. Given the impact of such artifacts on deep learning-based predictors, it is of critical importance to be able to decompose CNNs into features that can be biologically interpreted.Deep learning has become an important methodology for analyzing biomedical images, and in particular for analyzing hematoxylin and eosin (H&E) stained whole slide images (WSIs). Deep neural networks have achieved classification accuracies higher than classical machine learning models7. However, such techniques tend to output different predictive regions, can be difficult to validate, or have been suggested to be “fragile”, i.e. extremely sensitive to small perturbations of the image8. Optimizing conventional deep learning techniques, such as self-attention, to identify regions informative of class labels is a current theme in digital pathology10. While most methods assess deep feature representations as a whole, recent work suggests deep learning features cluster together and encode distinct morphologies11. Other recent works have focused on visualizing individual deep learning features as heatmaps12. Finally, as the majority of interpretation methods have focused on identifying regions predictive of class labels, they requires a trained classifier and cannot be directly used in pipelines that employ unsupervised feature learning.The majority of models for visualizing, analyzing, and interpreting CNNs reveal “where” a network is “looking” to make its prediction, rather than revealing “what” information in the region of interest is important. Some methods output pixel patterns that affect the value of a neuron in a deep network13, biomedical image analysis is complemented by additional data modalities, such as multiplexed imaging, single cell and bulk sequencing, and clinical information15. These data may aid in interpreting the deep feature representations of the H&E slide. However, models integrating these diverse modalities are needed. The feasibility of doing so is supported by work establishing the connection between modalities, for example by using CNNs to predict expression values of specific genes from H&E images18. Because of the architectural complexity of CNNs, it has often been assumed that CNN-based decompositions of images into features are not interpretable. However, there has been little empirical study of this question, e.g. by testing whether CNN-derived features are correlated with simple biological features such as gene expression values.Unlike natural image analysis19 have referred to these by various names  whose use is not specific to biological image analysis. For clarity and because they represent morphological features in many ways analogous to genes, we refer to them as mones . We find that mones share statistical similarities with gene expression data, and hence, a mone can be conceptualized as an abstract gene with some expression value. Individual mones have strong linear associations with phenotypic features, making them directly interpretable, which we demonstrate in several analyses. We demonstrate that many mones can distinguish cancer tumors from adjacent normal slides. These mones can be linearly combined for reliable prediction of both pan-cancer and tissue-specific cancer phenotypes. Mone-mone correlation analysis identifies clusters of highly correlated mones within cancer types, and these correlations are strongly preserved among cancers from related tissues. The similarity of mone values to gene expression data allows immediate use of many interpretable bioinformatics tools and machine learning models to identify the underlying biology of morphologies encoded by CNNs. For example, integrative mone-gene expression correlation analysis reveals that collagen content and immune infiltration are linearly associated with morphologies encoded by mones in several cancer types, and we confirm these relationships by expert histopathological review. Our studies confirm individual deep learning features encode distinct and identifiable morphology, and demonstrate the power of mones for computationally deconstructing cancer images into interpretable biological features.The linear analysis of individual deep learning features versus expression values or interpretable morphology provides a simple and effective approach to interpreting deep learning models in biological image analysis, notably without the need for a trained classifier.In this work, we investigate the interpretability of CNN-derived image features. Prior works20 features of tiles from whole slide images of The Cancer Genome Atlas (TCGA)1 for 19 cancers . We use this terminology because mones have analogies to genes with individual expression values. Tile level mones can be combined to construct slide level mones  tumor and adjacent normal slides . A clustermap of the top 100 such mones was able to clearly separate these classes  slides Fig. c. It als1: pan-GYN, pan-KIDNEY, pan-LUNG, and pan-GI , and second by combining frozen and FFPE slides . The results were generally robust between the two methods-a few cancers had a non-trivial difference in the ratio of correlated mone pairs ascertained by the two methods , logistic regression is more interpretable than a multi-layer perceptron. Our slide level tumor detectors also produce meaningful tile level predictions. Independent review by our pathology team supports most tumor regions having high tumor probability, and most non-tumor regions have low tumor probability in these images, with the cases of misclassification tending to be prediction of non-tumor regions to be tumor  and pan-LUNG (LUAD and LUSC) cancers, with immune related gene sets taken fromd32 see “.33  with immune related genes in pan-GI cancers  genes. We considered the 52 mones correlated with mone 983 and which differentiate COAD tumor from normal “” section33 and IG score . Interestingly, however, B cells comprise only a small fraction of cells in each sample . Pathologist review suggests LUAD slides with high values of mone 1914 tend to have gland and micropapillary formations, and have finely vacuolated cytoplasm .Some prior linear analyses of deep learning features have enabled partial interpretation of CNNs trained on one cancer typer types “” sectionr types “” sectionPan-cancer mones and correlations between mones can facilitate and improve robustness of interpretation across cancer types. For example, mone983 encodes cellularity  are open research questions.Strong correlations of individual mones with image-derived features “d or geneA notable advantage of the mone approach is that it does not require a trained classifier, which is especially desirable when feature engineering and classification are decoupled . Interpretation models that require a trained classifier, such as attention-based models, are restricted to the morphologies that are predictive of a predetermined set of classes and are utilized by the classifier. Additionally, different classification architectures applied to a fixed learned feature space may use different features and morphologies for predicting a class label. Mone analysis does not require a classifier to determine if the learned features encode a given phenotype “” sectionOptimization of training classifiers that are robust to stain differences across datasets is an open research question. However, an advantage of mone analysis is that it can be more robust to stain differences than pure classification models, as we observed for cell classification, where our mone-based model enjoyed better generalization to external datasets than HoverNet “” section12. However, we believe Inception V3 mones are more appropriate for linear association studies because they are the direct inputs to the classification layer. A few other studies have explored correlations between deep autoencoder features and gene37 or protein expression43 for other architectures, but the relations between mones across architectures remains a broad and open research topic. We have found that Inception V3 mone 983 in BRCA can be reliably estimated via linear models using ResNet152V3 and DenseNet201 mones  images are primed for rapid progress, as recent work has shown that several IHC markers can be virtualized from H&E1. Consistent with1 non-overlapping 512 pixel-by-512 pixel tiles were used, and only tiles containing at least The 20X H&E WSIs of TCGA were pre-processed, tiled, and passed through an InceptionV3 model pre-trained on image-net data as described in21 for details) were used for statistical analysis. t-test, WRS test, and KS test use the Benjamini-Hochberg procedure47 for FDR correction. The scipy python package48 was used to implement t-test, KS test, and WRS test. The statsmodels49 implementation of the Benjamini-Hochberg procedure was used.Differential mone analysis identifies mones with statistically significant distributional differences across classes. Welch’s t-test, Kolmogorov Smirnov (KS) test, Wilcoxon Rank Sum (WRS) test, and optimal Bayesian Filter (OBF) (see21 for details) identifies mones with posterior probabilities larger than 50 for details) outputs the feature set that bounds the sample conditioned FDR by 50 for details). Assuming mone identities (marker or non-marker) are independent across cancers, the posterior probabilities can be multiplied to calculate the probability of a joint event.Minimal risk OBF (seea(m) for mone m. Similar to many ANOVA-based analyses this ratio measures distributional differences and is closely related to Bhattacharyya distance51. It converges to 1 for non-marker mones and converges to larger values for markers. Larger a(m) values denote larger distributional differences. Assuming balanced samples of sizes 200 and 100 we compute the a(m) values resulting in a posterior of 0.95 as thresholds to distinguish moderate [a(m) = 1.088] and strong [a(m) = 1.159] mones separating tumor from normal slides  considers the four possible relations (known as structures by OBF) between frozen normal, frozen tumor, and FFPE tumor slides: (A) a mone does not differentiate between slides (prior probability = 0.5), (B) a mone has one distribution for frozen slides  and another distribution for FFPE slides (prior probability = 0.5/3), (C) a mone has one distribution for tumor slides (both frozen and FFPE) and another distribution for frozen normal slides (prior probability = 0.5/3), and (D) a mone has one distribution for FFPE tumor slides and frozen adjacent normal slides and another distribution for frozen tumor slides. Mones with structure B for which frozen tumor and FFPE tumors lie on both sides of frozen adjacent normal slides  are considered ineffective due to FFPE/frozen differences.Structured multi-class OBF , inflammatory , and dead nuclei. Median number of cells nuclei across tiles were used as cell density. Median number of predicted inflammatory nuclei across tiles were used to characterize presence of immune cells.Cellpose60. We only used slide-gene expression pairs where both the slide and the expression profile were from the same vial. Log normalized FPKMs were used. Genes with zero counts in more than half the mone-gene pairs or expression standard deviation below 0.25 were removed. Given a set of mone-gene pairs, we stack the mone and gene vectors and compute the covariance matrix using the Ledoit-Wolf shrinkage method53 implemented in the scikit-learn python package54. Correlation values are computed given the covariance matrix similar to mone correlation analyses. Statistical significance tests are performed similar to mone correlation analyses.Gene expression data were downloaded from the GDC portal33. All T-cell and B-cell categories were summed to obtain T-cell and B-cell proportions, respectively. The fractions of T-cell and B-cells were summed to obtain lymphocyte fractions. Log normalization of fractions were used throughout. Correlation analysis of immune scores with mones and IG score was performed similar to mone correlation analysis. B-cell percentages above Leukocyte fractions of TCGA samples were obtained fromSupplementary Figures.Supplementary Information.


In the context of biomedical and epidemiological research, gene-environment (G-E) interaction is of great significance to the etiology and progression of many complex diseases. In high-dimensional genetic data, two general models, marginal and joint models, are proposed to identify important interaction factors. Most existing approaches for identifying G-E interactions are limited owing to the lack of robustness to outliers/contamination in response and predictor data. In particular, right-censored survival outcomes make the associated feature screening even challenging. In this article, we utilize the overlapping group screening (OGS) approach to select important G-E interactions related to clinical survival outcomes by incorporating the gene pathway information under a joint modeling framework.Simulation studies under various scenarios are carried out to compare the performances of our proposed method with some commonly used methods. In the real data applications, we use our proposed method to identify G-E interactions related to the clinical survival outcomes of patients with head and neck squamous cell carcinoma, and esophageal carcinoma in The Cancer Genome Atlas clinical survival genetic data, and further establish corresponding survival prediction models. Both simulation and real data studies show that our method performs well and outperforms existing methods in the G-E interaction selection, effect estimation, and survival prediction accuracy.The OGS approach is useful for selecting important environmental factors, genes and G-E interactions in the ultra-high dimensional feature space. The prediction ability of OGS with the Lasso penalty is better than existing methods. The same idea of the OGS approach can apply to other outcome models, such as the proportional odds survival time model, the logistic regression model for binary outcomes, and the multinomial logistic regression model for multi-class outcomes.The online version contains supplementary material available at 10.1186/s12859-022-04750-7. It is believed that in the development of complex diseases such as cancer, diabetes, and so on, gene-environment (G-E) interaction plays a critical role beyond the main genetic (G) or environmental (E) factors ”, where the outcome variable can be a continuous, categorical, or survival time phenotype, Es represents a set of environmental factors such as environmental exposures, demographic, clinical, and socioeconomic variables, and G*(Es) represents the interaction between the G factor and all E factors under consideration. The significant G-E interactions can be selected based on the corresponding marginal u et al.  pointed On the other hand, models in the framework of joint analysis better describe disease biology given the fact that complex diseases are related to the combined effects of multiple genetic biomarkers. The conceptual joint model is “Outcome ~ Es + Gs + (Gs)*(Es)”, where Gs represents a set of G factors, including gene expressions, SNPs and other types of molecular measurements, and (Gs)*(Es) represents the interactions between all G and E factors. In this article, we focus on the joint analysis framework. A common challenge of joint analysis is its high dimensionality, which makes it difficult to identify significant interaction effects. Moreover, right-censored survival outcomes and contaminated biomarker data make the task even challenging.For survival outcomes, popular models include the accelerated failure time (AFT) model and Cox’s model. Based on the AFT model, several robust joint regression methods have been proposed. The Penalized trimmed regression (PTReg) method  uses thehttp://www.broadinstitute.org/gsea/msigdb. Wang and Chen [Based on the above rationale, we plan to adopt a two-step screening approach to detect G-E interactions by incorporating biological pathways information. The proposed method uses annotated gene sets collected in the molecular signatures database , which cand Chen  describeSimulation studies under various scenarios reveal that our method works well and outperforms existing methods in the model selection, estimation, and prediction accuracy. In the real data application, we combine gene expression profile data with prior pathway information from the Gene Ontology biological process (GO-BP) database and use the OGS approach to select several important environmental factors, genes, and G-E interactions that are associated with clinical survival outcomes of patients with HNSCC, and ESCA using TCGA clinical survival genetic data . Using tWe consider a study with For a subject Incorporating the grouping (pathway) information into the modeling process may improve the interpretability and prediction accuracy of the model. When groups overlap with each other, special techniques are required to account for the overlapping grouping information. According to Jacob et al. , we decomentclass2pt{minimOn the basis of the coefficient decomposition, the original regression model can be transformed into a new model, that is,We apply the OGS method to the environment and gene expression profile data with clinical survival trait to detect important main effects as well as interactions by incorporating prior pathway information. The steps of the OGS algorithm for G-E interaction selection are described as follows.Step1 We utilize the overlapping group Cox regression model to identify the candidate pathways based on the latent effect approach, which can be performed by the R package “grpregOverlap” [Overlap” . We defiStep 2 We utilize the sequence kernel association test (SKAT) to obtain the group-specific significance, where each group is formed by the interaction between the genes of each candidate pathway selected in the first step and the environmental factors in Es, where Es is a set of environmental factors. Following Chen et al. [n et al. , the SKAi-th variable in the data to that of all variables.Here ng power . Following power , we consBased on the null model by regressing survival outcomes on only the environmental covariates Es without gene covariates, let ,jl,…,Let CompQuadForm” [p-values p-value corresponds to a higher group importance and therefore leads to a higher priority of selection.We use the Davies method  to approuadForm” . GeneraluadForm” . The p-vStep 3 In the third step, we select significant G-E interactions based on the permutation procedure with the cutoff point determined by the soft-thresholding rule, where the permutation is applied to the covariate matrix consisting of both genes and environmental covariates. We randomly permute the original data p-values) To obtain a stable threshold, we repeat the above permutation process more times and define a cutoff point to select candidate pathway groups by using the median of the obtained desired thresholds, that is Note that he permutation procedure used to determine a data-driven threshold was similar to that proposed by Fan et al. , which iStep 4 Finally, in the framework of joint modeling, based on environmental covariates, and selected genes and G-E interactions, a penalized regression with an appropriate penalty is used to establish the final survival prediction model. Therefore, we apply the penalized Cox’s regression together with the Ridge or Lasso penalty to build the final prediction model based on all environmental variables, genes in glmnet” [“glmnet” .In the first step and the second step of the new OGS method, as the original OGS method in Wang and Chen , we stilgrpregOverlap” [In the following simulations, we study the performances of the proposed OGS approach in variable selection, estimation and prediction, and compare them with the performance of the “Oracle”, “SIS Lasso”, “Ordinary Lasso” and “GSIS SCAD” methods. The “Oracle” method is based on the underlying true model, which is known in the simulations but unknown in real applications. The “SIS Lasso” method  uses uniOverlap” .For performance comparison, we adopt the root mean squared error (RMSE) to measure estimation accuracy, defined asTo evaluate the estimation performance, we report RMSE.M, the mean of the root mean square errors of 200 simulations. To evaluate the performance of the selection accuracy, we consider various criteria: P.int is the proportion of the underlying effective G-E interaction variables contained by the selected G-E interaction variables; Sen. is the sensitivity, defined as the proportion of the underlying effective variables being selected; Spe. is the specificity, defined as the proportion of the underlying ineffective variables not being selected. We also report the median size of the selected models, S.model, in 200 simulations. To evaluate the performance of survival prediction, we consider three measures of prediction accuracy: the deviance, the c-index proposed by Harrell et al.  and timep-value of PI when PI is used as the covariate in the univariate Cox model for survival outcomes in the test data. Similarly, the LR-test is the p-value of the log-rank test for the null hypothesis of equal survival between the “good” and “poor” prognostic groups in the test data, where the “good” and “poor” prognostic groups are classified according to whether the PI value is higher or lower than the median PI value in the test data. Smaller Cox-test and LR-test values correspond to better predictive power.Let In simulations we consider survival data with a cohort size 300 in the training set, where each subject’s survival time follows the Cox proportional hazards modelwith the covariates In this simulation study, we consider 5 environmental variables and assume that the first 4 are related to the survival outcome, and the corresponding effects are 1.5, 2.25, 3, -1.5. On the other hand, the gene covariates considered contain 25 groups that have different group sizes (the numbers of genes) and may share with each other some of the genes. The group sizes and the overlapping structure (i.e. the number of the shared genes between two overlapping groups) are shown in Table From the simulation results shown in Table Furthermore, further simulation studies with a small cohort size are conducted under the scenario where all simulated settings are the same as those in the previous simulation study except for a cohort size defined as 150/50 (training/testing). We still obtain similar numeric results patterns; these corresponding results are shown in Additional file The TCGA HNSCC RNA-Seq expression data, together with the phenotype data containing the survival time and censoring status data, can be downloaded from the R package’TCGAbiolinks’ , or’UCSCSince the number of cancer-related genes is expected to be limited, we conduct prescreening using non-parametric inverse probability-of-censoring weighted (IPCW) Kendall’s tau correlation , which cGEInter” [The five E factors analyzed including AJCC pathologic stage nodes, AJCC pathologic stage tumor, age, gender, and ICD O3 site. Summary information for these clinical variables is reported in the Table GEInter”  to perfoThe PTReg method  was deveWe take ten random splits of the whole data into 413:104 training/test sets to evaluate the performances of all the methods considered in the TCGA HNSCC data application. Table CAMP” [DEFB1” [MAP2K7” [Based on one random split of the data, Fig. CAMP” , “DEFB1” [DEFB1” , “MAP2K7[MAP2K7”  have bee[MAP2K7” .Fig. 3KaThe TCGA ESCA RNA-Seq expression data, together with the phenotype data containing the survival time and censoring status data, can be downloaded from the R package’TCGAbiolinks’ , or’UCSCGEInter” to perform multiple imputation for the missing values in the clinical variables. Based on the GO biological process database, 1458 genes among the top 2000 genes are mapped into 4360 pathways and such prior pathway information is utilized in the OGS method. Excluding the genes without being mapped into any pathway, there are a total of 11,671 main and G-E interaction covariates in the proposed OGS method. On the other hand, a total of 16,007 main and G-E interaction predictors are considered in the “SIS Lasso”, “Ordinary Lasso”, and “PTReg” methods.Since the number of cancer-related genes is expected to be limited, we conduct prescreening using non-parametric inverse probability-of-censoring weighted (IPCW) Kendall’s tau correlation , which cWe take ten random splits of the whole TCGA ESCA data into 294:74 training/test sets to evaluate the performances of all methods for survival prediction in the TCGA ESCA data. Table CD40LG” [DEK” [IL6” [Based on one random split of the data, Fig. CD40LG” , “DEK” [G” [DEK” , “IL6” [K” [IL6”  have beeK” [IL6” , 38).Figp-value of each candidate gene pathway as well as the associated G-E factors, adjusting for the E factors. A pathway as well as the associated G-E factors is then selected when their group-level p-value is smaller than the one under covariate (both G and E factors) permutation. The final survival prediction model is constructed by a Cox model based on the E factors, the selected gene pathways as well as the associated G-E factors, subject to the Ridge or Lasso penalty. Simulation and real data studies demonstrate that, compared with the analysis that ignores pathway information, the new proposal can significantly improve the accuracy of gene and gene-environment interaction selection, as well as the resulting survival predictions.In this article, we propose a two-stage overlapping group screening procedure to identify important main and gene-environment (G-E) interaction effects efficiently for survival prediction. In the first stage, the new proposal utilizes the latent effect approach to identify candidate gene pathways for survival prediction, adjusting for the E and G-E interaction factors. Different gene pathways are allowed to overlap with each other, i.e., to share common genes. In the second stage, we utilize the SKAT approach , which iThe new OGS method aims at gene-environment interaction, while the OGS in Wang and Chen  aims at The OGS method is flexible. Although we focus on survival prediction based on the Cox proportional hazards model, the same idea can straightforwardly apply to other outcome models, such as the proportional odds survival time model, the logistic regression model for binary outcomes, and the multinomial logistic regression model for multi-class outcomes. For example, the SKAT statistic involved in the OGS method can be modified simply by using the residuals from the alternative model under consideration.Since the gene data is high-dimensional, following the conventional feature screening idea, the initial step of the OGS method is to use some univariate approach to screen gene variables for downstream analysis. Such a supervised screening procedure is common  structure and is limited to genes that can be assigned to at least one group (pathway). It is interesting to study how to relax these restrictions to improve the performance of feature selection and survival prediction in the presence some covariate network structure.In fact, the OGS procedure does not respect the hierarchy between main and interaction effects. We agree with that, if the hierarchy principle can be incorporated, the accuracy of interaction selection may have improved strength. Wu et al.  utilize Moreover, the OGS method does not select at both the pathway level and the gene level simultaneously. How to improve the OGS in selecting pathways and genes simultaneously will be investigated in our future work. The last step of the OGS is to apply penalized Cox regression together with Ridge or Lasso penalty to build the final prediction model, we can try to combine the other penalties like MCP, Adaptive Lasso to enhance the robustness of the estimation of the OGS (Jiang et al. , Ren et Additional file 1: The full detail of the latent effect approach, a series of simulation studies, simulated settings where some genes are shared by three groups, and the real data analysis.


Delayed cancer detection is one of the common causes of poor prognosis in the case of many cancers, including cancers of the oral cavity. Despite the improvement and development of new and efficient gene therapy treatments, very little has been carried out to algorithmically assess the impedance of these carcinomas. In this work, from attributes or NCBI’s oral cancer datasets, viz. (i) name, (ii) gene(s), (iii) protein change, (iv) condition(s), clinical significance (last reviewed). We sought to train the number of instances emerging from them. Further, we attempt to annotate viable attributes in oral cancer gene datasets for the identification of gingivobuccal cancer (GBC). We further apply supervised and unsupervised machine learning methods to the gene datasets, revealing key candidate attributes for GBC prognosis. Our work highlights the importance of automated identification of key genes responsible for GBC that could perhaps be easily replicated in other forms of oral cancer detection. Oral cavity cancer (OCC) is the tenth most common malignant tumor in the world and the third most common in southeast Asia. The common subsite recorded in OCC in third world countries, especially in Indian communities, is gingivobuccal cancer (GBC) constituting about 40% of all cases, whereas the cases diagnosed in the western world are about 10% . The GBCOver the last decade, several treatments have been utilized with the consistent use of effective gene therapies. Discoveries about how changes in the DNA of cells in the oral cavity and oropharynx cause these cells to become cancerous are being applied to experimental treatments intended to reverse these changes. For example, clinical trials are testing whether it is possible to replace abnormal tumor suppressor genes (such as the p53 gene) of oral cancer cells with a normal copy to restore normal growth control . MachineOral cancer prognosis is one of the burgeoning problems, and our work employing machine learning heuristics could lay emphasis on piloting candidate biomarkers. As diagnosis could be better aided for prognosis and theranostics, survival and therapies must be in place, and despite strategic improvements in these areas, this is still in infancy. Various phenotypes associated with oral cancer, such as oral squamous cell carcinoma (OSCC) and early-stage diagnosis, are still in the realm of early-stage detection. Alabi et al.  have revPIK3CA, KRAS, TP53 and Gingival. The dataset from NCBI  searches was screened with the following five features: (i) name, (ii) gene(s), (iii) protein change, (iv) condition(s), clinical significance (last reviewed). TP53 and Gingival have an additional Review Status feature. The number of samples varies for each dataset: PIK3CA has 544 instances, KRAS has 330 instances, TP53 has 2186 instances, and Gingival has 2107 instances . The first instance of data values of protein change, condition(s), clinical significance (last reviewed), and review status was used. Then, the data values of features such as gene(s), protein change, condition(s), clinical significance (last reviewed), and review status were converted into numeric keys using Preprocessing and Transformation classes in Scikit-learn. Binary and numeric weightages were assigned to each feature, including protein change, condition(s), clinical significance (last reviewed), and review status to evaluate the performance based on data annotations.We performed four experiments for PIK3CA and KRAS datasets and six experiments for TP53 and Gingival using different combinations of features. The following six experiments separately used one of the following four features: (i) all the features in a dataset, (ii) only binary features, (iii) only non-binary features, (iv) all features except review status  that contains review status as a feature), (v) only non-binary features with no review status  that contains review status as a feature), and (vi) only binary features with no review status  that contains review status as a feature).We used six major classifiers to train and test the model: (i) Support Vector Machine, (ii) Naïve Bayes, (iii) Decision trees, (iv) Perceptron, (v) Logistic Regression, and (vi) K Means (unsupervised). We randomly split the dataset to use 80% for training and 20% for testing. We used off-the-shelf algorithms implemented in Scikit-learn for these experiments and used other libraries, such as NumPy, Pandas, and Matplotlib available in Python 3.10.7. While unsupervised algorithms are hard to implement on such data, we used only K Means for a flavor of unsupervised learning. Further analyses with algorithms, such as K Medoids, PCA, etc., are left for future work.Evaluating the performance of learning algorithms is a central aspect of machine learning. We used an 80-20 train-test split to test the performance of the predictive and classification models. To mitigate the overfitting problem, the following measures were used to evaluate the performance of six classifiers based on accuracy, which is defined as the percentage of correct predictions for the test data. It can be calculated by dividing the number of correct predictions by the number of total predictions. The measure is defined as follows:One of the interesting findings we attempted in our study was to identify gene datasets that are significantly enriched from machine learning heuristics. We observe that there is a significant amount of attribute fitting with instances taken up from all datasets. While all instances were used and compared across all algorithms to further gain insight into this, the accuracies were tabulated accordingly . For PIKWhat we aimed to achieve from our pilot study is to employ gene selection and ask whether or not the lesser-known changes in attributes can, by choice, be ignored for further prognosis. In other words, in nature, are there any genes that are repetitively expressed with inherent changes attempted in our machine learning heuristics . The virhttps://www.kaggle.com/datasets/shivam17299/oral-cancer-lips-and-tongue-imageslast, accessed on 30 October 2022), the size is limited and might be underfitting in the present context. On the other hand, to overcome the overfitting and failed model as we postulated, deep-learning models could bring great promise for an accurate prognosis if in case the datasets have tumorigenic data, infiltrating lymphocytes and multiclass labeling, which can herald predicting disease states [Initially, we ran the experiments with the same data splits and the same machine learning algorithms using the java-based package Weka ,18. While states . Such daWe present a scatter plot for PIK3CA using the features ProteinChange_keys and Binary Scoring CS. We show five classes represented by colors red, blue, green, yellow, cyan, while centroids are represented by ‘X’. The classes represented by red, yellow, blue, and cyan form tight clusters around centroids, showing that the clusters capture the underlying data distribution well, while the cluster represented by green is slightly far from the calculated centroid . AdditioGiven the success of multimodal algorithms , we beliMachine learning and Artificial Intelligence (AI) aided methods have enhanced early detection in reducing mortality and morbidity. Indefatigably, there are not many metadata-based machine learning heuristics assessing the impedance of these oral carcinomas. In summary, we presented a machine learning-based approach to predict the gene dataset, which reveals key candidate attributes for GBC prognosis. We have attempted to fill these gaps by performing and labeling classes, and accurate identification of viable attributes for such cancers. Furthermore, we found that deterministic methods perform well with limited data. In contrast, non-deterministic methods excel in performance with large datasets, wherein supervised learning methods perform better than unsupervised methods. Nonetheless, our experiments had more supervised methods than unsupervised ones, which we wanted to establish the use case for such an analysis. We argue that a multitude of unsupervised and semi-supervised methods might be able to better model these data distributions that seldom have accurate annotations. However, this may be due to the lack of machine learning heuristics which could be used as models and vice versa for a better-modeled framework.


Microsatellite instability (MSI), an important biomarker for immunotherapy and the diagnosis of Lynch syndrome, refers to the change of microsatellite (MS) sequence length caused by insertion or deletion during DNA replication. However, traditional wet-lab experiment-based MSI detection is time-consuming and relies on experimental conditions. In addition, a comprehensive study on the associations between MSI status and various molecules like mRNA and miRNA has not been performed. In this study, we first studied the association between MSI status and several molecules including mRNA, miRNA, lncRNA, DNA methylation, and copy number variation (CNV) using colorectal cancer data from The Cancer Genome Atlas (TCGA). Then, we developed a novel deep learning framework to predict MSI status based solely on hematoxylin and eosin (H&E) staining images, and combined the H&E image with the above-mentioned molecules by multimodal compact bilinear pooling. Our results showed that there were significant differences in mRNA, miRNA, and lncRNA between the high microsatellite instability (MSI-H) patient group and the low microsatellite instability or microsatellite stability (MSI-L/MSS) patient group. By using the H&E image alone, one can predict MSI status with an acceptable prediction area under the curve (AUC) of 0.809 in 5-fold cross-validation. The fusion models integrating H&E image with a single type of molecule have higher prediction accuracies than that using H&E image alone, with the highest AUC of 0.952 achieved when combining H&E image with DNA methylation data. However, prediction accuracy will decrease when combining H&E image with all types of molecular data. In conclusion, combining H&E image with deep learning can predict the MSI status of colorectal cancer, the accuracy of which can further be improved by integrating appropriate molecular data. This study may have clinical significance in practice. Colorectal cancer (CRC) is a common digestive tract malignancy. CRC is the third largest cancer in the world, and the second leading cause of cancer-related death; the incidence rate and mortality rate of CRC were third and fifth, respectively, among all cancers in China, with more than 250,000 new patients and 140,000 deaths annually . SporadiMSI refers to the change in the length of normal microsatellites caused by the deletion or insertion of repeated bases compared with normal tissue cells . In 2001There is some evidence to support the use of pre-diagnostic MSI in clinical decision-making. First, MSI detection is recommended for the diagnosis of Lynch syndrome. Lynch syndrome is the most common hereditary colon cancer syndrome, which is associated with germline mutations in the MMR gene  . MSI staIn recent years, the deep learning method has become a newly developing method, which has shown excellent performance in the fields of computer vision , 22, speWith the increasing availability of high-throughput genomic and transcriptional data, there are several molecular biomarkers in The Cancer Genome Atlas (TCGA), including somatic mutation, copy number variation, gene expression, microRNA expression, and DNA methylation, which were used to track cancer –39 and pBased on the feasibility of cancer prediction and multimodal fusion from the pathological image level, our goal was to compare these unimodal data and combinations to predict the MSI ability of CRC in a unified context and to explore whether multimodal data fusion can significantly improve prediction accuracy compared with single-mode data.We overlapped the H&E images data and omics data to obtain 353 sample sizes, of which 63 were labeled MSI-Hs, which were marked as 1; 290 cases were labeled MSSs, which were labeled as 0.Pathological image. We used the method of Kather et al. to publish the CRC with hematoxylin and eosin stabilized (CRC-HE) dataset, including 100,000 pieces of 224 × 224 pixel H&E-stained pathological images that were divided into blocks; each pixel in the block corresponds to 0.5 μm × 0.5 μm organization. To eliminate the color difference of slices from different data sources in the process of production and scanning, all H&E images have been dyed and standardized according to the method of Macenko et al. , microRNA expression (miRNA), long non-coding RNA (lncRNA), DNA methylation (Met), and gene copy number variation (CNV). Their forms include Counts and FPKM. The difference between FPKM and Counts is that Counts is the original expression quantity that is not processed in the data background, although FPKM and Counts are data processing methods. In the analysis of this paper, the difference analysis part adopts the form of Counts, and the modeling analysis part adopts the form of FPKM. CNN is the latest algorithm for image recognition and classification because of its stable learning performance . CNN incResNet is a large-scale CNN constructed from residual blocks. We used ResNet34 Figure 1A common problem with high-throughput sequencing datasets is the so-called “Curse of dimensionality” . Variabln-dimensional vector calculated the outer product to obtain the n2-dimensional vector. In this work, our fusion method was the multimodal compact bilinear (MCB) model. MCB maps the result of the outer product to low-dimensional space without explicit calculation of the outer product.The most common fusion methods are concatenation, element-wise product, and element-wise sum. These simple operations are not as effective as the outer product, and complex relationships can be established between the two modes. However, the complexity of outer product calculation is too high. The p-value < 0.1 and a log2foldchange (LFC) > 0 were classified as upregulated genes, whereas those with an adjusted p-value < 0.1 and an LFC < 0 were classified as downregulated genes. Taking |log2 (foldchange)| ≥ 1 and the corrected p-value < 0.05 as the threshold, the genes with significant differences were selected. The R-Pack “heat map” shows significantly different genes. The R-Pack “cluster analyzer” is used for Gene Ontology (GO) enrichment analysis and calculation. R-Pack ggplot2 is used to generate enrichment pathways in significantly different genes.The R package “Deseq2” was used to identify differentially expressed genes (DEGs) in mRNA, miRNA, and lncRNA gene expression profiles. Genes with an adjusted Fivefold cross validation (5-f cv) is used to evaluate the accuracy of the algorithm.5-k cv: Divide the dataset into five equally, and take turns using four of them as training data and one as test data. The performance of the classification algorithm is estimated by averaging 5 test sets. For binary classification, the area under the subject operating characteristic curve (AUC), Accuracy (Acc), Precision, Recall, and F1_score are used to evaluate the performance of the model.In this work, we studied the data in two parts. In the first part, the differences of mRNA, miRNA, and lncRNA were analyzed. In the second part, in the modeling analysis, we conducted two experiments Figure 2p-value, we obtained 663 significantly differentially expressed lncRNAs, 61 significantly differentially expressed miRNAs, and 1,898 significantly different mRNA genes  is a protein-coding gene. MUCL3 may regulate NF kappa B signaling and play a role in cell growth.GO analysis was used to annotate the function of DEGs between MSI-H and MSI-L/MSS. In the biological process (BP) category, genes with significant differences were mainly enriched in organic acid, organic anion, and carboxylic acid transport. For cell component (CC) categories, genes with significant differences were mainly clustered in the apical part of the cell. In the binding molecular function (MF), significantly different genes were mainly involved in signaling receptor activator activity and receptor–ligand activity , the Natural Science Foundation of the Higher Education Institutions of Anhui Province, China (KJ2019A0085), the Academic Foundation for Top Talents of the Higher Education Institutions of Anhui Province (gxbjZD2016041), and the Educational Commission of Anhui Province (KJ2019ZD05).Authors WQ, JLY, GT, and MY were employed by Geneis Beijing Co., Ltd., Beijing.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Resistance to gemcitabine is the main challenge of chemotherapy for pancreatic ductal adenocarcinoma (PDAC). Hence, the development of a response signature to gemcitabine is essential for precision therapy of PDAC. However, existing quantitative signatures of gemcitabine are susceptible to batch effects and variations in sequencing platforms. Therefore, based on within‐sample relative expression ordering of pairwise genes, we developed a transcriptome‐based gemcitabine signature consisting of 28 gene pairs (28‐GPS) that could predict response to gemcitabine for PDAC at the individual level. The 28‐GPS was superior to previous quantitative signatures in terms of classification accuracy and prognostic performance. Resistant samples classified by 28‐GPS showed poorer overall survival, higher genomic instability, lower immune infiltration, higher metabolic level and higher‐fidelity DNA damage repair compared with sensitive samples. In addition, we found that gemcitabine combined with phosphoinositide 3‐kinase (PI3K) inhibitor may be an alternative treatment strategy for PDAC. Single‐cell analysis revealed that cancer cells in the same PDAC sample showed both the characteristics of sensitivity and resistance to gemcitabine, and the activation of the TGFβ signalling pathway could promote progression of PDAC. In brief, 28‐GPS could robustly determine whether PDAC is resistant or sensitive to gemcitabine, and may be an auxiliary tool for clinical treatment. Using within‐sample relative expression ordering of pairwise genes, we developed a qualitative gemcitabine signature containing 28 gene pairs, named 28‐GPS. The 28‐GPS outperformed previous signatures in classification accuracy and prognostic performance for pancreatic ductal adenocarcinoma (PDAC). Compared with gemcitabine‐sensitive PDAC samples, gemcitabine‐resistant PDAC samples classified by 28‐GPS showed poorer overall survival, higher genomic instability, lower immune infiltration, higher metabolic level and higher‐fidelity DNA damage repair. The 5‐yA phase III clinical trial showed that the clinical benefit rate of gemcitabine in the treatment of advanced pancreatic cancer reached 23.8%, and gemcitabine was widely used in clinics . As a stKRAS, TP53, SMAD4 or CDKN2A failed to predict whether patients are sensitive or resistant to gemcitabine [et al. [Resistance to gemcitabine in PDAC is a complex biological process, and the underlying mechanism of resistance is not clear. It is well known that PDAC is a quite complex disease characterized by molecular and clinical heterogeneity. Focussing on a single PDAC driver gene such as citabine , 12. Gencitabine  and GemPcitabine . Howevercitabine . Previoucitabine . Cheng e [et al.  have dem [et al. . Thus, oIn this study, based on REO, we developed a qualitative transcriptional signature to predict the response to gemcitabine for PDAC. The prognostic performance of the signature was validated in multiple independent data sets. Finally, we conducted a systematic and comprehensive analysis to further explore the underlying mechanism of gemcitabine resistance.22.1https://dcc.icgc.org/), ArrayExpress (https://www.ebi.ac.uk/arrayexpress/) and Gene Expression Omnibus   and FIREHOSE (http://gdac.broadinstitute.org/). Each chemotherapy drug was accompanied with corresponding response information in the TCGA drug information. We only retained samples with response information to gemcitabine chemotherapy, and for samples with multiple rounds of gemcitabine treatments, we only kept the samples with first response information to gemcitabine. Pancreatic cancer‐CA (PACA‐CA) patients’ chemotherapy information was obtained from the ICGC. In our study, only PDAC samples with survival information were used. Silent mutations were excluded from TCGA mutation data. In PACA‐CA expression data, only tumour‐related specimens were retained. The probe‐level expression values of pancreatic cancer‐AU (PACA‐AU) and GEO data sets were annotated to gene‐level with matched platform information according to the following criteria: if multiple probe‐sets were mapped to the same gene, the gene expression values were averaged, and multiple gene expression values were mapped to the same probe‐set were excluded. In addition, the gene expression data were filtered by the HUGO Gene Nomenclature Committee database to retain the protein‐coding genes.The transcriptome data were downloaded from publicly available databases, including The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium  Table . PDAC sa2.2https://depmap.org/portal/) and corresponding annotation files were obtained from the Cancer Cell Line Encyclopedia  (Table https://www.cancerrxgene.org), the Cancer Therapeutics Response Portal  and the work of Cancer Genome Project (CGP) [Gene expression data of PDAC cell lines were downloaded from the Cancer Dependency Map (e) Table . Only prct (CGP)   was used for data preprocessing and subsequent analysis [Single‐cell data of 24 PDAC tumour samples were obtained from Peng et al. . r packaanalysis . All funanalysis , 22, 23.2.4i and Gj were used to represent the expression value of gene i and gene j, respectively. Fisher's exact test was used to evaluate whether the frequency of a specific REO pattern (Gi > Gj or Gi < Gj) in the resistant group was significantly higher than the frequency in the sensitive group (P < 0.05). A panel of DEG‐related gene pairs was screened out by pairwise comparisons of all DEGs. To improve the accuracy of the prediction, DEG‐related gene pairs were filtered by maintaining the consistency of the REO pattern and the direction of average rank difference. Using univariate Cox proportional hazards model, the DEG‐related gene pairs were further filtered in TCGA, PACA‐CA and PACA‐AU data sets to obtain a set of prognosis‐related gene pairs. Finally, the common gene pairs of the three data sets were defined as the gemcitabine signature.In the TCGA data set, PDAC samples were classified into the resistant and sensitive groups based on their response to gemcitabine, where patients with complete response (CR), partial response (PR) and stable disease (SD) comprised the sensitive group and progressive disease (PD) belonged to the resistant group. Differentially expressed genes (DEGs) were identified between gemcitabine‐resistant and ‐sensitive groups using the Wilcoxon rank‐sum test. For a gene pair composed of DEGs, G2.5r package ‘pROC’. A single sample was classified into the resistant group when the number of 28‐GPS voting for resistance was no less than the classification threshold; otherwise, the sensitive group. Specifically, for a given gene pair in 28‐GPS, if Gi was greater than Gj, it would be scored as 1. Then, the scores of all gene pairs in 28‐GPS were added and recorded as resistance score. The classification threshold should be correspondingly adjusted with the number of matched gene pairs within the sample.The classification threshold was obtained from the receiver operating characteristic (ROC) curve, which was drawn by the 2.6P value was adjusted using the Benjamini–Hochberg (BH) procedure. Tumour mutational burden (TMB) was calculated based on the number of nonsynonymous somatic mutations. Homologous recombination defect score (HRDscore) was derived from the work of Thorsson et al. [Fisher's exact test was used to identify genes with significantly differential mutation frequencies between gemcitabine‐resistant and ‐sensitive groups. The Wilcoxon rank‐sum test was applied to detect DEGs and differentially expressed methylations (DEMs) between the two groups with a false discovery rate (FDR) < 0.05. The n et al. . TMB, HR2.7r package ‘clusterProfiler’ (version 4.0.5) [Functional enrichment analysis of the genes with corresponding deregulation direction of DEGs and DEMs was performed using the n 4.0.5) . Metaboln 4.0.5) , 27. Sub2.8https://cibersortx.stanford.edu/) and TIMER 2.0 (http://timer.cistrome.org/). T‐cell receptors (TCR) richness was obtained from Thorsson et al. [et al. [et al. [The proportion of infiltrating immune cells in the tumour immune microenvironment was estimated using transcriptome‐based algorithms CIBERSORTx , EPIC 2, TIMER , QUANTISn et al. . Immune‐ [et al. . The Wil [et al. . Differe2.9r¦ > 0.4 and P < 0.05, was used to calculate the correlation between DEGs and differentially expressed immune checkpoint genes or differentially expressed DDR genes. The correlations among metabolic pathway, immune pathway and DDR type were also calculated by Spearman rank correlation. cytoscape software  was used to visualize the correlation network.Spearman rank correlation, with ¦2.10The overall survival time curves were estimated by the Kaplan–Meier method, and tested using the log‐rank test.2.11r Package ‘InferCNV’ [CNV analysis was carried out using the nferCNV’ . The CNV2.12r package ‘CellChat’ (v1.1.3) [Cell–cell communication was investigated via the (v1.1.3) . After c2.13r software (v 4.1.1). The significance of the P value is shown in the following way: *P < 0.05; **P < 0.01; ***P < 0.001; ns: No significance.All statistical analyses in this study were performed using 33.1P < 0.05, Wilcoxon rank‐sum test). Among all gene pairs composed of DEGs, we screened out 2516 candidate gene pairs , of which 1028 gene pairs had REO patterns consistent with the direction of average rank difference. Subsequently, 407, 153 and 164 prognosis‐related gene pairs were identified from the TCGA, PACA‐CA and PACA‐AU data sets, respectively . Finally, the common 28 gene pairs (28‐GPS) of the three data sets were defined as gemcitabine signature . As a frequent KRAS mutation type observed in PDAC, we found that p.G12D mutation exhibited significantly higher resistance scores of 28‐GPS than other types of mutation  cells, and immature dendritic cells (DC). In addition, PDAC cell lines with high TNFSF14 expression were accompanied by high AUC or IC50 values of gemcitabine  and metabolism. Using ssGSEA, resistant samples classified by 28‐GPS reached significantly higher DDR and metabolic pathway enrichment scores than sensitive samples  likewise serve as a hot spot for immunotherapy and two CAR‐T drugs Kymriah and Yescarta have been approved by the Food and Drug Administration (FDA) [+ cells with targeting KRAS mutation showed effective treatment against cancer with mutant KRAS G12D [KRAS G12D in our study achieved a higher 28‐GPS resistance score. Therefore, gemcitabine combined with CAR‐T may be a promising approach for PDAC treatment. Genomic analysis in TCGA indicated that resistant samples classified by 28‐GPS displayed higher genomic instability, such as high HRDscore. In addition, as a tolerant DNA damage repair process accompanied by mutagenesis [In this study, the results suggest some possible combination regimens for PDAC. In the TCGA data, resistant samples classified by 28‐GPS showed lower immune infiltration and down‐regulation of the immune checkpoint genes. PDAC cell lines with higher expression of immune activating gene on (FDA) , 45. An RAS G12D . And, PDagenesis , TLS wasagenesis .et al. [Although some immune cells showed inconsistent infiltration proportion between gemcitabine‐resistant and ‐sensitive samples, most of the immune cell infiltration presented no statistical significance. For example, Tregs exhibited high immune infiltration by CIBERSORTx and low immune infiltration by XCELL and QUANTISEQ in gemcitabine‐resistant samples, but the differences in Tregs infiltration showed no significance between resistant and sensitive samples detected by CIBERSORTx and XCELL Fig. . A compret al.  showed tet al. [ERCC1 gene is involved in multiple DDR pathways and overexpression of ERCC1 is well documented in poor gemcitabine responders [et al. [CXCL13 and CCL21) could potentiate antitumour activity of chemotherapy and increase the infiltration of CD8+ T cells. Xiao et al. [+ T‐cell infiltration. Indeed, in our results, the sensitive PDAC samples achieved high enrichment scores in the chemokine signalling pathway and consistently high infiltration proportion in CD8+ T cells.Compared with the gemcitabine‐sensitive PDAC samples, the gemcitabine‐resistant PDAC samples achieved higher enrichment scores in the DDR and metabolic pathways and lower enrichment scores in the immune pathways. As reported by Jain et al. , gemcitaet al. . In addiet al. . For exasponders . Delvecc [et al.  observedo et al.  found thet al. [ANGPTL4 gene and TGFβ signalling pathway, were reflected in cell communication of the resistant group. ANGPTL4 has been found to play an important role in the process of tumour metastasis [in vitro [In single‐cell analysis, cancer cells in the same PDAC sample had both resistant and sensitive cancer cells, which could be used to explain why PDAC patients who responded to gemcitabine at the beginning will develop secondary resistance later. Zou et al.  also protastasis . The actin vitro .Although our work was limited by the independent PDAC data sets with gemcitabine information to investigate the robustness of 28‐GPS, we used PDAC samples with prognostic information and PDAC cell lines with gemcitabine‐used information to validate the 28‐GPS. In addition, resistant samples in our work showed high metabolic levels and high‐fidelity DNA damage repair, which warrants our future detailed biological experiments to validate those discoveries.5In summary, we developed the 28‐GPS for gemcitabine based on transcriptome, which could be applied to predict response to gemcitabine chemotherapy for PDAC. The resistant samples classified by 28‐GPS in TCGA showed multidimensional resistance‐related characteristics compared with the sensitive samples. Collectively, it is worthwhile to further evaluate the clinical applications of 28‐GPS, which may assist clinicians to make a suitable strategy for PDAC patients.The authors declare no conflict of interest.YYG and KDL conceived the research; KDL, YDG and LZW performed the data processing and analysis; HHX, MZ and YWL carried out the data collection; KDL wrote the manuscript; YYG, ZXZ and TTC revised the manuscript; SLW, LS and FYX provided valuable suggestions for this manuscript; All authors have approved the final manuscript.Fig. S1. The workflow of this study.Fig. S2. The genomic landscape between gemcitabine‐resistant and ‐sensitive samples classified by 28‐GPS in TCGA.Fig. S3. The differences in immune infiltration assessed by five algorithms.Fig. S4. Single‐cell annotation and copy number variation.Table S1. The PDAC cell lines used in this study.Table S2. The pharmacological data sets used in this study.Table S3. The composition of 28‐GPS.Click here for additional data file.


Phage ImmunoPrecipitation Sequencing (PhIP-Seq) is a recently developed technology to assess antibody reactivity, quantifying antibody binding towards hundreds of thousands of candidate epitopes. The output from PhIP-Seq experiments are read count matrices, similar to RNA-Seq data; however some important differences do exist. In this manuscript we investigated whether the publicly available method edgeR :139–140, 2010) for normalization and analysis of RNA-Seq data is also suitable for PhIP-Seq data. We find that edgeR is remarkably effective, but improvements can be made and introduce a Bayesian framework specifically tailored for data from PhIP-Seq experiments .The online version contains supplementary material available at 10.1186/s12864-022-08869-y. Because of their high abundance, easy accessibility in peripheral blood, and relative stability ex vivo, antibodies serve as excellent records of environmental exposures and immune responses. While several multiplexed methods have been developed to assess antibody binding specificities, Phage Immuno-Precipitation Sequencing (PhIP-Seq) is the most efficient technique available for assessing antibody binding to hundreds of thousands of peptides at cohort scale –3. PhIP-We and others have utilized PhIP-Seq to successfully identify novel autoantigens associated with autoimmune diseases –9, to br1 versus n2 two-group comparison in RNA-Seq. In addition, genes with low read counts are presumed to have little biological relevance, and RNA-Seq data workflows typically filter out lowly expressed genes (measured as counts-per-million) prior to analysis. In PhIP-Seq experiments however, peptides with low read counts may have biological relevance and are not filtered out in advance. That said, under suitable assumptions, such as equality of variances in both groups, a two-group comparison with a single sample in one group can still be carried out.The output from PhIP-Seq experiments are read count matrices, similar to RNA-Seq data, but important differences in the data structures, experimental design, and study objectives exist between the two sequencing-based methods. RNA-Seq experiments typically focus on differentially expressed genes or transcripts between experimental groups, rather than identifying expressed genes for any particular sample. The objective of PhIP-Seq experiments however is typically just that: detecting peptide antibody reactivity in an individual sample. Thus, in contrast to RNA-Seq experiments, the design of PhIP-Seq experiments requires the use of negative controls (i.e. “mock” immunoprecipitations (IPs) lacking antibody input, also referred to as beads-only samples), which are typically included as 4 to 8 wells of a 96-well plate. This generates a “n versus 1” mock IPs versus sample comparison, in contrast to the most common nSignificant advances in normalization and analysis methods for RNA-Seq data have been made in recent years, with edgeR, DESeq2, and voom among the most popular open-source software packages available –23. ThesIn this manuscript we investigated whether the publicly available method edgeR  for normSimulation. Regardless of the approach used to estimate prior parameters, BEER has high discriminatory power for identifying enriched peptides  control, BEER has a higher probability of correctly identifying enriched peptides than edgeR across all fold-changes, and the difference in probability is most pronounced for moderate fold changes between 2 and 8 Fig.  were betHIV elite controllers. Both BEER and edgeR had no false positives across the six mock IP samples using a posterior probability cut-off of 0.5 for BEER and an FDR control of 5% for edgeR  were produced by BEER or edgeR across the eight mock IPs in the CoronaScan data using a posterior probability cutoff of 0.5 for BEER and an FDR control of 5% for edgeR  was generally above 0.80 . In particular for peptides showing weaker reactivity, we saw an improvement of sensitivity with lower false positive rates when standard cut-offs were employed . This comparison might be perceived as somewhat unfair, as the data were simulated from a model similar to that underlying BEER, which we recognize. However, BEER was implemented in a way we believe reflects the true data generating mechanism, which is also corroborated by the posterior predictive assessment [https://github.com/athchen/beer_manuscript), may yield worthwhile additional discoveries.In this manuscript we investigated whether the publicly available method edgeR  for normsessment  of the HIt was initially surprising to us how well edgeR fared on PhIP-Seq data despite being designed specifically for RNA-Seq data. While important differences between PhIP-Seq and RNA-Seq data structures exist, as previously described, edgeR captures some of the most important effects that exist in both types of data. For example, unlike RNA-Seq, the PhIP-Seq experimental protocol requires the use of negative controls (i.e. samples with no serum) on a 96-well plate. The observed read counts mapped to the peptides among those negative controls show a very strong peptide-dependent bias in library representation and/or “background” binding to the beads, such that some peptides consistently draw a much higher proportion of reads than others . The prior is a Bernoulli with success probability j, i in sample j. It is equal to 1 if i does not elicit an enriched antibody response in sample j. Enriched peptides are expected to pull a larger proportion of reads, so only fold changes larger than 1 are considered. Here, we model the fold-change as a shifted gamma distribution  scale factor used in edgeR [i pulls a read in sample j, and j. This Binomial probability is modeled using a Beta prior distribution, and the shape parameters depend on the expected peptide read counts observed in the mock IP samples (estimation procedures described below), the fold change in edgeR , with \dShape parameter estimation. We define two functions, a and b given a mean i in a beads-only sample  . We noteMarkov chain Monte Carlo. The model was implemented in JAGS (4.3.0) and run using the R interface for JAGS, rjags [j. As described above, S, rjags –33. JAGSS, rjags . We use In PhIP-Seq experiments we commonly observe very reactive peptides , 35. AllPeptide reactivity detection with edgeR. To identify reactive peptides, each serum sample is compared to all beads-only samples from the same plate. Differential expression in edgeR is assessed for each unit  using an exact test analogous to Fisher’s comparing means between two groups of negative binomial random variables, but adapted for overdispersed data [p-values were subsequently converted to one-sided p-values as the alternative to the null of no reactivity (fold change = 1) is reactivity, leading to read count enrichment and thus, fold-changes larger than 1. Multiple comparisons corrections were based on the Benjamini-Hochberg procedure, using false discovery rates to delineate enrichment across all peptides.sed data . Two-sidSimulation study. We simulated ten data sets based on the read counts observed in the HIV elite controller data described below. Each of these data sets had eight beads-only samples and twelve simulated serum samples. The twelve samples contain one beads-only sample run as an actual sample and two technical replicates (samples generated from the same parameters). For each simulated serum sample, we randomly selected 50 peptides as reactive. Among those, 10 peptides each had fold changes between 1 and 2, between 2 and 4, between 4 and 8, between 8 and 16, and between 16 and 32. Each data set was analyzed using the first two, four, and all eight beads-only samples to assess the sensitivity of the results to the number of beads-only used for analysis. For each data set and number of beads-only sample combination, we ran BEER with the true beads-only Beta p-values were addressed using the Benjamini-Hochberg procedure to ensure a 5% FDR. Cut-offs for the posterior probabilities were selected in each data set to achieve 5% false positive calls.Performance was primarily assessed using ROC and PR curves on the full data and fold-change subsets of the data. For each fold-change bin, curves were generated using all non-enriched peptides and enriched peptides within the specified fold-change group from the simulated serum samples (no peptides from beads-only samples were included). To ensure that all curves had the same support points, we used linear interpolation to approximate the sensitivity or positive predictive value respectively at each support point for each simulation. ROC curves started at 0 sensitivity and 0 false-positive rate, while PRC curves started at 0 sensitivity and perfect positive-predictive value. The interpolated curves were averaged point-wise to generate an average curve for each condition. The area under each ROC curve was calculated using trapezoidal approximation from the interpolated data points. We also used logistic regression to model the probability of identifying and enriched peptide by fold-change in each data set. Multiple comparisons for edgeR Examples. Antibody reactivity counts for eight plates of data were generated using the PhIP-Seq assay and the VirScan library on serum samples from HIV elite controllers with HIV subtype A and B infections, and analyzed by Kammers et al. [s et al.  to assesThe CoronaScan data consists of counts for 6,932 peptides for 10 serum samples and 8 beads-only samples from one plate of data [The example in the Discussion to highlight the strong peptide-dependent background binding to the beads was from a previous study to evaluates HIV antibody responses and their evolution during the course of HIV infection  and to gAdditional file 1: Supplementary Materials.


These loci are identified as Common Insertion Sites (CISs), i.e. regions with more transposon insertions than expected by chance. However, the identification of CISs is affected by biases in the insertion behaviour of transposon systems. Here, we introduce Transmicron, a novel method that differs from previous methods by (i) modelling neutral insertion rates based on chromatin accessibility, transcriptional activity and sequence context and (ii) estimating oncogenic selection for each genomic region using Poisson regression to model insertion counts while controlling for neutral insertion rates. To assess the benefits of our approach, we generated a dataset applying two different transposon systems under comparable conditions. Benchmarking for enrichment of known cancer genes showed improved performance of Transmicron against state-of-the-art methods. Modelling neutral insertion rates allowed for better control of false positives and stronger agreement of the results between transposon systems. Moreover, using Poisson regression to consider intra-sample and inter-sample information proved beneficial in small and moderately-sized datasets. Transmicron is open-source and freely available. Overall, this study contributes to the understanding of transposon biology and introduces a novel approach to use this knowledge for discovering cancer driver genes.Transposon screens are powerful  Carcinogenesis is an evolutionary process that results from the interplay of somatic mutations and selective pressure . DiscoveTransposon-based forward genetic screens based on insertional mutagenesis have contributed to this task by identifying large sets of driver genes in mouse models ,5. PiggyDepending on the adjustable transposon cargo sequence, the reintegration can cause the activation or inactivation of nearby genes. For instance, gene activation can be caused if the transposon contains an internal promoter. Conversely, transposons can also function as bidirectional loss-of-function gene traps by inserting internal splice acceptors and bidirectional poly(A) sites ,16. HencIn forward mutagenesis screens, tumour cells are enriched for insertions conferring selective advantages: Cancer develops when oncogenic insertions ultimately cause uncontrolled proliferation. Consequently, tumour tissue will contain many clones of the cell carrying the oncogenic insertions that induced carcinogenesis. Therefore, cancer genes can be determined from transposon screens by identifying common insertion sites (CISs), i.e. regions with more insertions than expected by chance across multiple independent samples. In order to distinguish genuinely positively selected (driver) insertions from non-oncogenic (passenger) insertions that clonally expanded along with driver insertions in a particular tumour, evidence from multiple tumours is required .There are two major classes of statistical methods for detecting CISs in transposon-based screens. On the one hand, so-called locus-centric methods consider all possible regions in the genome with enrichment for insertions, independently of genome annotation. This includes methods based on counting insertions in windows of fixed sizes ,19–22 oret al. , a novel computational method to identify CIS controlling for transposon integration biases, which can be applied either in a locus-centric or in a gene-centric fashion. Our method comprises two steps Figure . First, et al.  developed in PB ITP mice  and SB ITP mice (n = 11) as well as tail tissue as a control tissue (n = 12 for PB and n = 11 for SB). All experimental mice were maintained on a mixed C57BL/6 × 129Sv × FVB background. Mice were kept in the animal facilities of the Wellcome Trust Sanger Institute, Hinxton/Cambridge, UK under specific-pathogen-free (SPF) conditions on a 12-h light/dark cycle, receiving food and water ad libitum. All animal experiments were carried out in compliance with the requirements of the European guidelines for the care and use of laboratory animals and were approved by the UK Home Office and the Institutional Animal Care and Use Committees (IACUC). Genotyping was performed according to Friedrich et al. , Blmm3 mice and ITP transposon lines have been described earlier ,44,45. Ih et al. .et al.  for subsequent DNA isolation. The histological procedure and immunohistochemical analysis to diagnose diffuse large B cell lymphomas were performed as described in Weber et al. .DNA was isolated using the Qiagen DNeasy Blood & Tissue Kit according to the manufacturer's instructions.et al. , cutaneous squamous cell carcinoma (cuSCC), hepatocellular carcinoma (HCC), and gastrointestinal tumours  ,25,46, aet al., ,,2), thatOn top of local insertion biases, which are modelled with the mutagenesis model, we control for two global biases that influence insertion counts in any particular experiments, namely chromosome biases  and sequEquation .Based on the estimates for quations  and 5)  \documenet al. (https://github.com/NKI-CCB/cimpl/blob/master/inst/doc/cimpl.pdf), CIMPL was implemented with the scale parameter ranging from 10 kb to 150 kb in 10-kb increments and the iterations parameter set to 10,000. For benchmarking, we used results obtained by setting the scale parameter to 10 kb, as this yielded the highest precision measured against the CGC. CIS were mapped to genes using CIMPL’s built-in mapping functionality. If multiple associated gene identifiers were found for a CIS, the gene was chosen at random.We benchmarked Transmicron against three established methods  ,21,27. Fet al. . As suggSimilarly, PRIM was implemented on window sizes from 10 kb to 150 kb in 10-kb increments. Because we lacked the required data, we ran the PRIM version without the PRI-correction factor that accounts for restriction site biases. As PRIM provides a built-in function to aggregate results over window sizes, the aggregated list of CIS was used for benchmarking. PRIM-CIS were mapped to genes by overlapping insertion sites with gene annotations from the UCSC RefSeq mm10 track. If a CIS overlapped with multiple genes, the assigned gene was chosen at random. Moreover, we enabled PRIM to run on PB datasets by supplying the number of TTAA nucleotides instead of TA nucleotides.As suggested by the authors, SB Driver was implemented with the promoter cut-off set to 0 kb. No read depth filtering was applied. We excluded genes that are not hit in at least a certain number of tumours  . To ensuAs mentioned above, all results from running Transmicron presented here are based on running the method on the UCSC RefSeq mm10 annotation unless stated otherwise. To investigate to what extent our results are influenced by gene-based implementation we also ran Transmicron on 10 kb genomic windows and compared the results to CIMPL  and PRIM (window size set to 10 kb).Transmicron comprises two conceptual steps: the mutagenesis model and the selection model. To delineate the contribution of each step, we start by assessing the added value of the selection model before systematically exploring the effects of integrating our mutagenesis model into Transmicron.To analyse the selection model, we implemented a simplified version of Transmicron controlling only for the number of TA / TTAA sites per region (TransmicronNULL). Specifically, we set the insertion rate per region quations  and 5)))\documens Figure  and B.Reassuringly, all methods were enriched for known cancer genes in both DLBCL datasets. Moreover, we found TransmicronNULL to detect a considerably higher number of genes (FWER < 0.05) than existing methods in both the SB screen and the PB screen while demonstrating a higher CGC gene fraction across the top-ranked genes Figure  and B. SHaving demonstrated that our selection model offers a competitive approach to detect selected genes, we turned to explore the effects of controlling for known transposon insertion site biases (mutagenesis model). As a first step, we analysed our DLBCL datasets with regard to several genomic and epigenomic features previously shown to affect transposon insertion likelihood. In accordance with previous research ,38,39,41First, we found that both transposon systems display a significant bias regarding the local sequence context around their insertion sites Figure . This bip-values for PB and SB < 10−4).Second, we found that SB and PB favour proximity to transcription start sites, transcription termination sites, and gene bodies Figure –F. Specip-values for PB and SB < 10−4). Moreover, we found transposon insertions to be strongly associated with chromatin states in mESC  and an equal number of TA/TTAA sites randomly sampled from the genome (negatives).A potential caveat with training a mutagenesis model using data from mutagenesis screens is that insertion counts reflect both mutagenesis and selection. Our approach to limit the confounding effect of selection when training the mutagenesis model was two-fold: First, we considered unique insertion sites and discard coverage information, assuming that the read coverage of insertion site reflects clonal expansion (selection). Second, we used predictive features that a priori have a role in the mutagenesis (insertion) process but not in the selection process. Moreover, in the few datasets for which matching unselected samples were available, we assessed whether the mutagenesis model yielded similar predictions be trained on unselected samples or on selected samples.First, we investigated the predictive power of the individual features in our DLBCL datasets, finding interesting differences between the SB and the PB datasets. For instance, we found that SB insertions  can be wTo assess the generalizability of our models, we repeated this analysis in multiple previously described datasets ,25,37,46p-value < 10–4, Figure Even though the specificity of the unselected mESC dataset can be argued to explain the much different behaviour of the mutagenesis model, this raises the concern that the mutagenesis model trained on the selected data is confounded due to oncogenic selection. Therefore, we investigated the validity of the concern that the mutagenesis model learns a background distribution biased by oncogenic processes. To this end, we trained the mutagenesis model both on the DLBCL PB samples and on the corresponding tail samples and compared the gene-wise insertions rates. We found a high correlation  and PB insertions (AUC = 0.75) with high accuracy Figure . Second,B Figure . This avA major consequence of this averaging effect might be that the plain number of TTAA / TA sites remains a major determinant of expected insertion counts in a gene under neutral selection, potentially mitigating the need for a mutagenesis model.p-value < 10−4)  Figure . This su) Figure . Among he Figure . This suHowever, comparing the final sets of genes identified by Transmicron and TransmicronNULL  and window-based  methods, we ran Transmicron based on non-overlapping genomic windows of 10 kb (Transmicron10kb), finding its precision to drop considerably . Howeveret al.  to compaet al. . SimilarTo further demonstrate that Transmicron can correct for differences in the insertion site preferences of different transposon systems, examined whether Transmicron increases the similarity between results from SB and PB screens, relative to existing methods. We reasoned that statistical methods that correct for transposon system biases better than other methods should report more consistent gene lists when applied to two distinct transposon systems. Therefore, we examined whether Transmicron increases the gene list similarity between results from SB and PB screens, relative to existing methods. Specifically, we compared the Jaccard Index (Intersection over Union of two lists) of the genes detected by Transmicron in our two DLBCL screens (SB and PB) to the Jaccard Index found using CIMPL  and PRIMThe mutagenesis model of Transmicron is, in part, based on cell-type-specific data. The results presented so far were generated by training the mutagenesis model with DNA accessibility data from mESC. As we have seen in our analysis of individual features, mESC-based ATAC-seq and DNAse-seq data constitute reasonable surrogates for the chromatin accessibility in our DLBCL dataset. To investigate to what extent the results of Transmicron are affected by the choice of cell-type-specific DNA accessibility data, we replaced the mESC DNA accessibility data with B-cell-specific DNA accessibility data and examined the impact on our result Figure .First, we observed the ranks of genes identified by both versions of the background model correlate very strongly. Second, we observed interesting differences between the two versions of the background model regarding the ability of Transmicron to remove putative false positive CIS. We found that using the more appropriate B-cell features removed a considerable number of genes that had been significant when mESC features were used while adding few extra genes. Notably, the genes impacted by the differences in cell type tend to be highly ranked genes, suggesting that controlling for cell type-specific features does not affect the high-confidence candidate genes identified by Transmicron. This is perhaps unsurprising, given that the mutagenesis model showed only limited impact on the ranking of the high confidence genes, even when compared to TransmicronNULL.p-value < 10−4, Since we found Transmicron results to be robust against the choice of DNA accessibility data, we intended to provide a pre-trained version, enabling the end-user to implement Transmicron without the need to acquire dedicated DNA accessibility data and retrain the computationally intensive mutagenesis model. To this end, we compiled Transmicron with a mutagenesis model that was trained on insertion data generated in mESC without selective carcinogenic pressure (Transmicron pre-trained) and compared it to a model that was trained on insertion data from our DLBCL PB data (Transmicron retrained). We found that the pre-trained and retrained results show a high degree of correlation, with differences only in the highly ranked genes Figure . LikewisOverall, this analysis suggests that Transmicron can take advantage of retraining on specialized experimental data while not affecting the detection of high-priority cancer driver genes.Identifying CISs from transposon mutagenesis screens is an important task in the quest to understand the molecular dynamics of oncogenesis. Previous studies suggested that CIS detection suffers from biases arising from benign transposon insertion site biases ,29,39. Tet al.  a , has a significant effect only on lower-priority candidate cancer genes. Similarly, we found cell-type-specific mutagenesis features to have limited impact on the high-priority cancer genes in our data. This suggests that researchers primarily interested in high-priority candidate cancer genes may as well use the pre-trained version of the mutagenesis model. Researchers investigating genes that are less obviously implicated in oncogenesis may benefit from retraining Transmicron on their experimental data or use cell-type specific features to control for.Transmicron is publicly available as a flexible tool accommodating different user requirements. We provide a precalculated background distribution based on unselected mESC insertions which we expect to be sufficient for most users. Moreover, Transmicron enables users to retrain the mutagenesis model using our predefined features, or, alternatively, supplying any BED-formatted feature they wish to control for. This option to supply user-defined features is aimed at use cases for which cell-type specificity is more important than for our DLBCL screens. For users interested only in the selection model, we offer the option of running TransmicronNULL correcting only for the distribution of TA/TTAA sites. Moreover, while previous tools call CISs either on genes or genomic windows, users can choose to run Transmicron on genes, genomic windows of any size, or supply their own GTF-formatted annotation . Finally, Transmicron can be run on both SB and PB without modification. One limitation of our current model is that we excluded the donor chromosome, following previous approaches : TransmiIn sum, Transmicron provides a valuable tool for cancer gene detection from mutagenesis screens. The selection model behind Transmicron makes conceptual advances by explicitly considering both the inter-sample as well as the intra-sample distribution of insertions. The mutagenesis model offers a way to systematically analyse the impact of benign insertion biases of transposon systems and Transmicron is the first method to demonstrate how these biases can be controlled in a statistically robust way.https://github.com/gagneurlab/transmicron). A permanent DOI pointing to the exact version used for this manuscript is provided at: https://doi.org/10.5281/zenodo.7388676. Sequencing data have been deposited at GEO (accession number: GSE214379).Transmicron is freely available as an open-source software tool in the GitHub repository (gkac1215_Supplemental_FileClick here for additional data file.


Patient subgroups are important for easily understanding a disease and for providing precise yet personalized treatment through multiple omics dataset integration. Multiomics datasets are produced daily. Thus, the fusion of heterogeneous big data into intrinsic structures is an urgent problem. Novel mathematical methods are needed to process these data in a straightforward way.We developed a novel method for subgrouping patients with distinct survival rates via the integration of multiple omics datasets and by using principal component analysis to reduce the high data dimensionality. Then, we constructed similarity graphs for patients, merged the graphs in a subspace, and analyzed them on a Grassmann manifold. The proposed method could identify patient subgroups that had not been reported previously by selecting the most critical information during the merging at each level of the omics dataset. Our method was tested on empirical multiomics datasets from The Cancer Genome Atlas.Through the integration of microRNA, gene expression, and DNA methylation data, our method accurately identified patient subgroups and achieved superior performance compared with popular methods.The online version contains supplementary material available at 10.1186/s12911-022-01938-y. The rapid development of high-throughput technologies has produced massive amounts of multiomics data, including genome, transcriptome, and proteome data as well as many more types. The analysis of these data enables researchers to improve basic research on cancer, including oncogene identification , recogniAccording to the manner of integration, methods for clustering multiomics data can be classified into three categories, namely, early, intermediate, and late integration , 6. EarlInspired by the advantages of intermediate integration, we developed a novel integration method for handling high-dimensional multiomics data. We first learned low-dimensional representations for each view by utilizing principal component analysis (PCA). These representations can be regarded as new features of the patients within each view. With the obtained representations, we constructed a patient-to-patient graph for each view by using the k-nearest neighbors (k-NN) algorithm; these graphs served as intermediate representations, which we projected onto a Grassmann manifold. Finally, a new representation of the patients was obtained by merging these projections on the Grassmann manifold.Its status as an intermediate integration method makes our method more advantageous than other methods based on early or late integration because it can accurately preserve the properties of each data type, thus making it a powerful approach for scaling different data. We tested our method by applying it to three types of data for many cancers, and we found it very suitable for multiple data that require integration for clustering. The method can be extended to include image datasets.Our work focuses on the use of a well-known PCA technique based on Grassmann manifold theory that can be used to align different bases from different sources via a nonlinear alignment method. Nonlinear alignment methods, such as the Grassmann manifold method, can effectively improve the performance of clustering. Regarding multiomics data clustering, our work shows high superiority through the use of linking graphs and subspace theory.The rest of this work is organized as follows. We first review recent progress in “The increasing significance of analyzing multiomics data has motivated many studies –13. FaciTo reduce the effects of bias and regular noise in heterogeneous genomic profiles, Shi et al.  proposedTo further enhance the robustness of clustering , 18, theThe proposed method consists of five steps, which are summarized as follows: (1) normalization, (2) dimensionality reduction, (3) construction of patient-to-patient graphs, (4) embedding of the k-NN graphs, and (5) merging on the Grassmann manifold.M omics, let m, where N and D are the numbers of patients and features, respectively. For all analyses, we perform the z-score transformation:f for patient p and f. Standardizing the expression values for each feature across all patients through z-score transformation [Given ormation  is necesThe PCA technique has much wider applicability than other techniques, such as independent component analysis (ICA) and nonnegative matrix factorization (NMF). It is ideal for recognizing patterns and reducing dimensions; for more details, see the Additional file m-th normalized matrix k eigenvectors of We perform the PCA calculations as described in , 21. Conm-th graph i and j and is computed as follows:t is the normalization factor [We construct a patient-to-patient graph in the PCA space to model the specific structure within each view –24. For n factor . The higk nearest neighbors of patient i. The parameter k depends on the sample size. Different omics have distinct structures. Thus, the k-NN graph is more representative than the original similarity Next, we select the k-nearest neighbors of each patient to preserve the local structure of each graph:To further extract crucial omics features, we project all the graphs into low-dimensional subspaces and obtain their associated embeddings in those spaces.We first calculate the normalized graph Laplacian matrix iW~ij(m) .k eigenvector of the normalized Laplacian matrix With the learned Laplacian matrix, its embedding M embeddings of the omics is a natural way to obtain a fused representation:Minimizing the Euclidean distance between the integrated embeddings and the G [k-dimensional linear subspaces. Each point on G represents a set of orthonormal bases Y that can span a span(Y). Thus, the distance between the spaces span(Y) and The Grassmann manifold G  is a setment}Y~i , 27.U to be close to all the embeddings k largest eigenvectors of the modified Laplacian matrix Based on this measurement, the distance between subspace embeddings can be formulated as follows:In this section, we discuss the main results of our study. First, we introduce the datasets used in our work. Next, we explain the experimental procedures. Then, we compare our results with those of recent methods. Finally, we analyze the performance of all methods.We used datasets processed using the SNF method, which were downloaded from the TCGA website and include data on breast invasive carcinoma (BIC), colon adenocarcinoma (COAD), kidney renal clear cell carcinoma (KRCCC), glioblastoma multiforme (GBM), and lung squamous cell carcinoma (LSCC). For each cancer type, three types of data are provided, namely, DNA methylation, gene expression, and miRNA expression. All these datasets contain stage III cancer data, as shown in Table k-dimensional spectral embeddings to further encode their information. Then, we merged all the representations on the Grassmann manifold. Finally, the patients were clustered in the fused representation, and we evaluated the clustering results via a post hoc analysis.As shown in Fig. p values to compare the results of our method with those of SNF and the GrassmannCluster method, and the results are shown in Table To prove the efficiency of the proposed method, we compared it with SNF and the GrassmannCluster method. We used Cox survival p values, as shown in Table Regarding the GrassmannCluster method, there was no COAD among the types of cancer. However, we used its code to obtain the Cox survival p values, as shown in Table Regarding the Grassmann clustering method, there was no COAD among the types of cancer. However, we used its code to obtain the Cox survival Great care was taken to test a synthetic omics dataset extracted from , 28 to pp value in the Cox log-rank test, to evaluate the significance of the differences in survival profiles between subgroups . Additionally, group 4 has the highest survival among all groups, with a hazard ratio (HR) of 0.02, a p value of 0.008.We compared the obtained subgroups against other known subgroups , for which we downloaded the data of already known subgroups from the TCGA website for the same patients. Kaplan–Meier and multivariate Cox analyses were performed to compare the known subgroups and the newly obtained subgroups. Significant survival differences were obtained for the newly obtained subgroups compared to the already known subgroups, as shown in Additional file p value for breast cancer was In general, the differences in the survival profiles between subgroups were assessed by means of Cox log-rank tests using the survminer R package. The entclass1pt{minimaIn this paper, we proposed a novel multiomics clustering method. Our method is based on subspace representation and manifold integration. We projected each type of omics data into a low-dimensional space via PCA and built a graph for the patients. Then, all the constructed graphs were represented by their spectral embeddings and subsequently merged on a Grassmann manifold. The proposed approach can effectively identify patient subgroups with distinct survival rates by combining microRNA, gene expression, and DNA methylation data. Our method was more accurate in addressing the patient subgroup problem than recent methods.Our approach can be extended to include images, for which multiple types of properties need to be integrated. Moreover, our method can be used for other applications that require the integration of multiple types of features.Additional file 1. More details about using PCA, k-NN and k-means algorithms.