The emergence of high-throughput RNA-seq data has offered unprecedented opportunities for cancer diagnosis. However, capturing biological data with highly nonlinear and complex associations by most existing approaches for cancer diagnosis has been challenging. In this study, we propose a novel hierarchical feature selection and second learning probability error ensemble model (named HFS-SLPEE) for precision cancer diagnosis. Specifically, we first integrated protein-coding gene expression profiles, non-coding RNA expression profiles, and DNA methylation data to provide rich information; afterward, we designed a novel hierarchical feature selection method, which takes the CpG-gene biological associations into account and can select a compact set of superior features; next, we used four individual classifiers with significant differences and apparent complementary to build the heterogeneous classifiers; lastly, we developed a second learning probability error ensemble model called SLPEE to thoroughly learn the new data consisting of classifiers-predicted class probability values and the actual label, further realizing the self-correction of the diagnosis errors. Benchmarking comparisons on TCGA showed that HFS-SLPEE performs better than the state-of-the-art approaches. Moreover, we analyzed in-depth 10 groups of selected features and found several novel HFS-SLPEE-predicted epigenomics and epigenetics biomarkers for breast invasive carcinoma (BRCA) , lung adenocarcinoma (LUAD) , and kidney renal clear cell carcinoma (KIRC) . Cancer has the characteristics of concealed onset, low cure rate, and high mortality. Traditional surgery, radiotherapy, and chemotherapy have limited effects on patients with advanced cancer. The cancer diagnosis is currently a hot research topic. With the acquisition of numerous gene expression profiles in various tissue samples, it is possible to perform cancer diagnosis at the molecular level. Early biology supposed that cancer is closely related to the mutations of protein-coding genes . Thus, mt-test and Euclidean distance, and via LLRFC criteria and then use the dynamic correlation analysis strategy to eliminate redundant features further. The emergence of large-scale RNA-seq data and DNA methylation data has offered unprecedented opportunities for developing cancer diagnosis approaches. However, integrating transcriptome profiling  and DNA methylation data for cancer diagnosis faces challenges. The diagnosis models are also essential for cancer diagnosis. Due to the diversity of classifiers, ensemble models tended to have better performance than single models . Generalvia FC and FDR. In stage 3 feature selection, we adopted the mRMR algorithm to select a compact set of superior features. At the model diagnosis level, we developed a second learning probability error ensemble model, named SLPEE. Specifically, we selected four individual classifiers with significant differences and apparent complementary effects to build heterogeneous classifiers and then obtain the classifiers-predicted class probability predictions. To thoroughly learn the nonlinear data, SLPEE integrated the classifiers-predicted class probability predictions and the actual class label in the validating set to construct a new training set, which implicitly included the probability error of each classifier. Furthermore, we utilized eXtreme Gradient Boosting (Xgboost) as ensemble learner to secondly learn the new training set. Via training on three cancers in The Cancer Genome Atlas (TCGA) based on 10-fold cross-validation, HFS-SLPEE achieved 100% multi-indicators of LUAD and kidney renal clear cell carcinoma (KIRC), and obtained 99.65% accuracy, 99.61% sensitivity, 100% specificity, and 99.81% F1-score of BRCA, outperforming previously published approaches. The results indicate that HFS-SLPEE is an accurate and robust approach for cancer diagnosis.To address the above limitations, we proposed a novel hierarchical feature selection and second learning probability error ensemble model, called HFS-SLPEE, for precision cancer diagnosis. At the dataset level, we integrated protein-coding genes expression profiles, ncRNAs expression profiles, and DNA methylation data to construct a triple dataset that provides a multiview perspective and diverse information. At the feature selection level, due to the significant differences in dimensions, abundance, and association relationships of the triple dataset, we designed a novel hierarchical feature selection algorithm. In stage 1, we developed a CpG sites aggregation feature selection algorithm, termed CSAFS, to non-destructively store the biological associations between ultra-high dimensions DNA methylation CpG sites and genes and rapidly reduce the nearly 500,000 dimensions DNA methylation data to nearly 30,000 dimensions methylated gene data. In stage 2 feature selection, we used different thresholds to further select significantly differentially features via the nonlinear ensemble of heterogeneous classifiers.Theoretically, the three contributions of this work are as follows: (i) we integrate protein-coding gene expression profiles, ncRNA expression profiles, and DNA methylation data to solve the problem of lacking information on cancer diagnosis; (ii) we take the biological complex associations into account and develop a novel hierarchical feature selection approach, which integrates the proposed CSAFS, FC&FDR, mRMR, to efficiently select a group of superior and compact features; and (iii) we design an SLPEE model, which makes predictions by secondly learning the error rules between the predicted class probability values of the heterogeneous classifiers and the real values to realize the self-correction of the diagnosis errors via (ii) and optimize the parameters of heterogeneous classifiers via the grid search algorithm in the validating set. Finally, we developed the SLPEE model to ensemble the class probability predictions of the heterogeneous classifiers under the optimal parameters. SLPEE was utilized to predict the testing set in each fold and obtained the novel informative biomarkers.As shown in via different mechanisms that affected the activity of surrounding genes to induce cancer. These known mechanisms commonly included the regulation of ncRNA, DNA methylation, and histone modification, but there are many mechanisms that are unknown so far. Herein, we integrated protein-coding gene expression profiles, ncRNA expression profiles, and DNA methylation data to construct a triple dataset, which contained rich information and provided a multiview perspective for precision cancer diagnosis.The central dogma of classical genetics indicated that genetic information was stored in protein-coding genes . It had The triple dataset exhibits complex characteristics: (i) There were many-to-many complex modification associations between methylation CpG sites and genes; (ii) DNA methylation data were nearly 500,000 dimensions, while the transcriptome profiling were nearly 60,000 dimensions; (iii) there were many noise and redundancy features, and only a small part of the features was positively related to the two phenotypes as tumor and normal tissues; and (iv) the expression abundance of the transcriptome profiling was significantly different from the DNA methylation data. We proposed a novel hierarchical feature selection algorithm against the complex characteristics of the triple dataset.In this study, we used Illumina Human Methylation 450 array methylation chip data, which measures the level of methylation at known CpG sites as follows:M denotes the methylated array intensity, M+U denotes the unmethylated array intensity, and β represents the ratio between the methylated array intensity and the total array intensity, ranging from 0 to 1. A CpG site could modify multigenes, while a gene might be related to multi CpG sites. To rapidly reduce the dimensions of the ultra-high dimensionality DNA methylation data and preserve the many-to-many complex biological associations between CpG sites and genes in advance, we proposed a novel CpG sites aggregation feature selection method, called CSAFS, as the stage 1 feature selection algorithm to obtain methylated genes. Specifically, we defined the methylated genes as gjMG, which represents the arithmetic mean value of the methylation level of all CpG sites related to the gene jg. The gjMG is calculated as follows:where P denotes the dimensions of the DNA methylation data, P = 485,577, CpGi represents the level of methylation at the known ith CpG site, and jg.where The dimensions of methylated genes data and protein-coding gene and ncRNA expression profiles belonged to tens of thousands of dimensions after the stage 1 feature selection. However, the dimensions were still relatively high. Therefore, we utilized FC and FDR statistical methods to further perform overall rapid dimension reduction.gth gene is calculated as follows:FC is a well-known method to screen the differentially expressed genes of microarray data , which mT is the size of tumor samples, N is the size of normal samples, gth gene expression value of the tth tumor sample, gth gene expression value of the nth normal sample. Similarly, the FC values of methylated genes are obtained by calculating the ratio of the mean methylation level of methylated genes in normal and tumor samples. In this study, the thresholds for screening significantly differentially methylated genes and expressed genes are, respectively, (|log2FC| > 0.5) and (FDR <  0.05) and (|log2FC| > 3) and (FDR <  0.05). Although the differential features selected by FC method have strong repeatability, the false-positive results rate is relatively high in the absence of false-positive control  is the mutual information of the two random variables, S is a feature subset, and |S| is the number of features in S. I is defined in terms of the probabilistic density functions p(x),p(y), and p:where The input data of the mRMR algorithm needed to be discrete. We defined the discrete function as follows:j represents the standard deviation of the jth feature, jth feature, and k is the threshold parameter. In this study, we set k = 0.5, and the dataset has been discretized into three states{−2,0,2}.where δAfter the hierarchical feature selection, we used four different models, i.e., SVM, decision tree (DT), random forest (RF), and AdaBoost, to capture the inherent information of the data with the characteristics of low dimension, nonlinear, imbalance class, and singular values. The large differences and obvious complementarity of the four models are as follows. First, SVM mapped data to higher dimensional space by kernel function and found the best separation hyperplane to maximize the margin between two types of training samples . SVM is via Xgboost, which used the residual between the real value and the predicted value as the next iteration of the learning goal. Xgboost could effectively learn errors and had a strong ability of nonlinear fitting, self-learning, and self-correction  on three cancers in TCGA to evaluate the prediction performance of HFS-SLPEE. In the 10-fold CV experiment, the triple dataset was randomly divided into 10-folds with equal size, 9 of which were taken as the learning set, and the remaining 1-fold was the testing set. The process is repeated 10 times until all samples are predicted once. In each learning set, we divided the learning set into We considered that the primary tumor was a positive class, and the solid normal tissue was the negative class. There were four results of cancer diagnosis in the testing set: true positive (TP), true negative (TN), false negative (FN), and false positive (FP). Among the results, TP represented the number of correctly classified tumor tissue samples, TN denoted the number of correctly classified normal tissue samples, FN indicated the number of samples predicted to be normal tissue but actually tumor tissue, and FP stood for the number of samples predicted to be tumor tissue but actually normal tissue. Therefore, accuracy, sensitivity/recall, specificity, and F1-score were defined as follows:n , the novel hierarchical feature selection algorithm, and SLPEE model. To examine the contribution of each component, we compared the proposed approach with several combinations.First, we compared the triple dataset with the other seven datasets, including mRNA, miRNA, lncRNA, ncRNA, DNA methylation, transcriptomic, and mRNA and DNA methylation, to inspect the contribution of the triple dataset. We found that the triple dataset achieved the best performance compared with the other seven datasets see . Specifin is 21, to compare the performance of diagnosis and the time of the feature selection process. On the one hand, the experimental results showed that the hierarchical feature selection with CSAFS achieved better performance, and the accuracy, sensitivity, specificity, and F1-score of BRCA are improved by 0.23, 0.12, 1.2, and 0.13%, respectively. In the final feature subset selected by the hierarchical feature selection with CSAFS, we screened out 11 methylated genes such as WT1-AS and AL513523.2, accounting for about 30% of the final feature subset. On the other hand, the time consumed by the hierarchical feature selection without and with CSAFS is 2.6 and 0.15 h, respectively. It indicated that CSAFS, as the stage 1 feature selection approach, is capable of non-destructively preserving the complex and essential many-to-many modification associations between methylation CpG sites and genes and improving the efficiency of cancer diagnosis.Next, to examine the contribution of CSAFS, which saves the information of all methylation CpG sites related to genes in an aggregated form, we compared the hierarchical feature selection algorithm without and with CSAFS. Owing to the BRCA with the largest number of samples among three cancers, we take BRCA as an example, when the feature variable Finally, to verify the contribution of SLPEE further, we compared the accuracy of SLPEE with DT, RF, SVM, and Adaboost by using the triple dataset and the proposed hierarchical feature selection algorithm that traversed each feature in the range of 5–30. The results showed that the four models have different accuracies for different cancers with different feature numbers and have significant differences and complementary effects see . We founWe compared the performance of HFS-SLPEE with state-of-the-art research . Table 4According to the optimal feature variable, we obtained 10 group features corresponding to the BRCA, LUAD, and KIRC with a total of 210, 120, and 160 features. After removing the overlapping features, the optimal features subsets were, respectively, reduced to 37, 35, and 40 features for the three cancers. The average repetition rate of the 10 group features for three cancers is 70.83–82.38%. On the one hand, the features selected in each fold is different, verifying that cancer does not generate along a fixed trajectory, but there are many different signal pathways. On the other hand, although the features subset selected in each fold changes dynamically, some feature genes recurring, and part of them will appear steadily in every fold.We organized the features into three categories: protein-coding genes, ncRNAs, and methylated genes see . We haveIn this work, we developed a novel hierarchical feature selection and second learning probability error ensemble model, called HFS-SLPEE, for cancer diagnosis. HFS-SLPEE is a precision cancer diagnosis framework, constituted by the integrated data of protein-coding gene expression profiles, non-coding RNA expression profiles, and DNA methylation data, the novel HFS algorithm, and the SLPEE model. We experimentally studied three high-incidence cancer as BRCA, LUAD, and KIRC in the TCGA database. The results have demonstrated that HFS-SLPEE achieves higher performance in comparison to several state-of-the-art methodologies. Therefore, HFS-SLPEE could be a powerful tool for cancer diagnosis. Moreover, HFS-SLPEE is universal, not limited to the field of cancer diagnosis. It is also suitable for cancer subtype classification, tumor origin detection, etc.Herein, we acknowledge some limitations of our proposed method. Since a recent related study  has demohttps://github.com/luckymengmeng/HFS-SLPEE.Source data and code can be downloaded from YM and MJ conceived the concept of the work. YM collected data, performed the experiments, and wrote the manuscript. MJ helped in revising and supervising the manuscript. Both authors contributed to the article and approved the submitted version.The 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.


Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings. The last two decades can be characterized as the “Post Genomic Era”, moving from hypothesis-driven approaches based on molecular and cellular methodologies  to discovery-driven approaches with the emergence of high-throughput methodologies and the area of functional genomics. Functional genomics is a field of molecular biology, which aims to understand the dynamic relationships between an organism’s genome and its phenotype, by applying different omics technologies that utilize the continuously growing body of sequence information. The term omics describes a comprehensive quantitative characterization of a class of molecules in a given biological sample or specimen, aiming to understand the molecular mechanisms and underpinnings underlying the functioning of an organism ,2. CurreIn the context of translational cancer research, high-throughput methodologies—and more recently the wave of NGS technologies—have highlighted significant genomic alterations in distinct solid tumors, and proposed perturbed molecular networks with potential impact on cancer pathophysiology ,12,13. MThe majority of diseases and human disorders have extremely complex phenotypes, with confounding variables making it difficult to detect a clear causality . SimilarThe multi-omics analysis approach follows the core principle that any biological condition or disease such as cancer constitutes multiple molecular phenomena, and only through the detailed understanding of the interactions between the different molecular layers can one understand holistically the significantly perturbed biological entities that characterize the specific disease ,22.These approaches have been employed to predict vaccine response  or to liPersonalized medicine can benefit from the implementation of multi-omics data integration methods, as the profound diversity in disease onset, progression and treatment outcome across cancer patients makes it difficult to decide on the optimal patient-specific treatment. Thus, joint analysis of multiple omics layers (‘multi-view learning’) may lead to a better understanding of heterogeneity and thus personalized treatment decisions ,27,28. Fhttps://portal.gdc.cancer.gov/) from the National Cancer Institute (NCI) is the largest scale consortium that enables the retrieval, download, comprehensive analysis and exploitation of multimodal cancer genomics studies. Within the GDC, scientists can access over 3 petabytes of data from programs like the NCI’s Clinical Proteomic Tumor Analysis Consortium (CPTAC), the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative and The Cancer Genome Atlas (TCGA). Both harmonized datasets and legacy data on older genome versions are available. Furthermore, it includes various data visualization tools to enhance the exploration of specific projects and cancer types, and available bioinformatics pipelines. Based on the latest data summary , this data-driven platform contains 67 projects, 68 different cancer types with more than 84 thousand cases, along with clinical data [GDC: The Genomic Data Commons Data Portal  currently holds 86 projects with more than 80 million somatic mutations (data release 28). Its flagship projects are the Pan-Cancer Analysis of Whole Genomes (PCAWG) and ARGO (Accelerating Research in Genomic Oncology). Launched in 2019, ARGO is the next phase of ICGC, which aims to uniformly analyze specimens from 100,000 cancer patients with high quality clinical data to address outstanding questions in genomic cancer research (https://www.icgc-argo.org/).ICGC: The International Cancer Genome Consortium was established in 2008 as an international effort to harmonize the large number of ongoing and future projects on cancer genomics. Members include the NIH, the Wellcome Trust Sanger Institute, Cancer Research UK, RIKEN, and many more. Its data portal (https://dcc.icgc.org/repositories) as the main dissemination platform for ICGCThe ICGC Data Portal (https://xenabrowser.net/) is the main exploration tool for the included multi-omics resource data, in order to identify any putative correlations among all primary results. Additionally, it includes the possibility of performing survival analysesUCSC Xena (https://www.ebi.ac.uk/gxa/) is an open science resource hub hosted by EMBL/EBI. It provides information about gene and protein expression across species and biological conditions such as different tissues, cell types, developmental stages and diseases.EBI Expression Atlas (https://pcawgscout.bsc.es) is another analysis platform to visualize and explore PCAWG data. It consists of a portal that presents the original omics data and sample annotation along with the results from different analysis working-groups, whereas its main focus lies on providing information on driver mutations and resulting proteins.BSC PCAWG Scout (http://compbio.med.harvard.edu/chromothripsis/) is a tool that provides highly interactive Circos plots for all tumors in the PCAWG cohort. Each Circos plot reports the point mutations, small insertions and deletions, structural variations and copy number profiles detected in each tumor. On this premise, the user can exploit large-scale alterations such as chromosome arm deletions, and complex mutational patterns such as chromothripsis.Chromothripsis Explorer  is an ongoing collaborative project between the Broad Institute and the Novartis Institutes for BioMedical Research. Established in 2008, its main goal is to conduct a thorough genetic and pharmacologic characterization of a large panel of 1457 cancer cell lines. Its aims are to capture the genomic heterogeneity of the preclinical models and link them with the molecular heterogeneity in cancer patients. Additionally, it can unravel clinically actionable molecular targets that might be associated with drug response and ultimately link them to cancer survival, enhancing personalized medicine. Collectively, the multi-omics cell lines dataset includes gene expression from microarray and RNA-Sequencing experiments, reverse-phase protein arrays, copy number, gene methylation and mutation data. In parallel, the database also stores legacy data, which include pharmacological profiles of 24 anticancer drugs across 504 cell lines. Besides data the web page also includes tools for data visualization, including box plots, scatter plots and bubble maps for methylation data [CCLE: The Cancer Cell Line Encyclopedia  is an open-source resource platform developed at Memorial Sloan Kettering Cancer Center, whereas the software is developed and maintained by various research institutes. Its main goal lies in the interrogation, interactive visualization and integrated analysis of clinical and complex multimodal cancer genomics datasets. While the major focus of the platform lies on genomic alterations , it also hosts mRNA and microRNA expression, protein and phosphoprotein level data (RPPA or mass spectrometry based), DNA methylation and microbiome data, especially for TCGA data. Additionally to TCGA projects, cBioPortal includes other large-scale cancer genomics projects to advance translational cancer research, such as immunogenomic and pan-cancer studies. Overall, whereas cBioPortal is considered mainly as an exploratory analysis tool, GDC would be a more appropriate choice if the user requires full access to raw data from various cancer projects . Additionally, cBioPortal is currently using only data aligned to the hg19/GRCh37 reference genome, and it doesn’t provide normal tissue samples for any study [cBioPortal: The cBioPortal for Cancer Genomics –developed at the Wellcome Sanger Institute—is the largest and most comprehensive resource for mining publicly available cancer sequence data, aiming to investigate the impact of somatic mutations on cancer progression and pathophysiology . The latAs multi-omics approaches require high-dimensional data, portals hosting these data have high standards for normalization and transparent harmonization of different molecular omics modalities. In this section we would like to highlight different data repositories for multi-omics purposes and their utility for translational cancer research. For a more detailed and broader overview on leveraging distinct omics databases for personalized oncology see .GDC: Thehttps://pypi.org/project/rpy2/) and reticulate (https://rstudio.github.io/reticulate/) which have been used for multi-omics data integration, especially for combining machine learning computations and data mining approaches [Development of platforms and packages for accessing, configuration and preparation of data in the field of multi-omics data integration makes tools easier applicable and saves time for the major integration and analysis of data but is also limited to the use of specific runtime environments. The mainly used programming languages in this field are R and python . The main source of development for bioinformatics packages in R is the Bioconductor software platform. It is an open source and open development project, which provides tools for the exploration and analysis of high-throughput omics data. It is based on the R programming language, and among its main priorities are reuse and interoperability, along with high-quality documentation. In R, the fundamental unit of sharable code is the R package, which combines code, data, tests and vignettes, which are extensive documents illustrating how to use the corresponding package. The latest version (3.12) includes 1974 packages, covering a broad range of bioinformatics and statistical applications for sequencing data , microarrays, flow cytometry, imaging and proteomics [In addition, recent implementations in R led to integration of functionalities from those two big platforms to make ML-development also available for R users , xenaPython [https://pypi.org, accessed: 4 March 2021). Finally, besides the Bioconductor and Python projects, also other web based curated platforms provide tools to perform multi-omics data analysis. The most representative example is the online platform Galaxy [On the other hand, python-based development in this field has also led to numerous useful tools for accessing, preprocessing, analyzing, and integrating multi-omics data from cancer repositories. Such tools like TCGAIntegrator , PyGDC  generally exhibit distinct statistical properties , which could be attributed also to inter-patient individual genomic diversity, cell type composition and other technical factors. Additionally, this complexity is further enhanced by the inherent correlation structures and hidden confounders  introduced by each different omic layer ,58,59. ASingle-cell multimodal omics represents the recent technological advancement from single-cell RNA-sequencing (scRNA-seq) to the acquisition of multiple molecular data types such as genome, transcriptome, methylome or proteome from single cells.This includes the combination of multiple next-generation sequencing-based methods such as DR-Seq (gDNA-mRNA sequencing)  and G&T-This powerful technology enables the investigation of complex biological states and processes of multicellular organisms. In cancer research it can be used to explore tumor heterogeneity, tumor evolution or the identity of infiltrating immune cells ,73,74. AThe aim of analyzing multimodal single-cell data is the unification of different data modalities to uncover complex biological mechanisms on the cellular level such as the reconstruction of gene-regulatory and signaling networks . ParticuOften not all modalities of a data set stem from exactly the same cell but cells from the same sample or tissue, leading to batch effects from unmatched data. To remedy this projection into a common latent space (Feature Projection) can be applied. Canonical correlation analysis (CCV) and Manifold alignment are both feature projection-based dimensionality reduction techniques. CCV, which is a multivariate analysis technique for estimating a linear relationship between two sets of measurements, can be performed using Seurat3 . VDJViewBayesian Modeling is a stochastic variational inference method based on Bayesian modeling . ClonealRegression Models include least absolute shrinkage and selection operator (LASSO) regression with sci-CAR , gradienIn addition, single-cell multimodal autoencoders for mapping to a shared latent space are emerging ,94.For the unsupervised integration of single-cell multimodal data a widely used method is Matrix Factorization. Here, the data matrices are decomposed into two lower dimensionality matrices. Methods include integrative non-negative matrix factorization (iNMF) by algorithms such as Wishbone  or LIGERAdditionally, MIMOSCA  and MUSIFinally, an interesting new implementation is included in the new release of the DESeq2 R package: the function integrateWithSingleCell integrates bulk differential gene expression analysis results from DESeq2 with public single-cell datasets. This facilitates the investigation of which types of cells might be responsible for the relative expression differences in the bulk samples .The general literature search in PubMed was performed in November 2020 and resulted in 753 publications about multi-omics data integration. We started our search with basic search terms to discover the entire complexity of multi-omics publications and continued with more specific filtering for supervised methods, unsupervised methods, reviews, tools, and cancer related papers. A detailed description of the literature mining parameters is available in the In this section, we will give a summary of user-friendly tools suited for defined general research purposes, using tools identified in our literature research as well as tools classified by Nicora et al. (2020)  and HuanWe used common general research purposes as defined by Nicora, et al. (2020) , but addMulti-omics data integration tools for patient stratification see  are findTools for the discovery of biomarkers see  are aimiTools for pathway analysis see  are dealThe following tools in The tools in this category are used for the classification of molecular subtypes of specific cancer types see .The identification of novel disease subtypes can be improved by the application of these integrative methodologies. They can lead to the identification of improved targets for anti-cancer treatment or they could contribute additional knowledge to existing cancer subtypes from a multi-view perspective.The previous research aim categories are sometimes very closely related . Therefore, several tools can be employed in multiple of the selected research aims see . Most ofResearch aim: Firstly, the most important aspect is the research question: what is the purpose of the specific study, or which biological insights are aimed for regarding a specific cancer type or cohort? This can significantly inform the selection of the most suitable tools that match the specific research goal, as no single tool or pipeline covered above can address a complex disease like cancer in its entirety. In addition, except for the initial selection , benchmark studies can be further utilized and considered as guidelines for narrowing the candidate tools .Another crucial criterion is the experimental design and the interrogated datasets: which is the relative sample size? For example, usually ML-based approaches require a higher number of samples for model training and validation in comparison to unsupervised methodologies. Also, can the relative tool cope with the percentage of missing values and/or the nature of omics layers (continuous vs. sparse genetic data)? When a large percentage of missing values is present in both omic layers and clinical data, researchers should consider various published studies covering different methodologies for missing value imputation .Furthermore, the third selection criterion that is often underestimated is the presence of extensive documentation that accompanies an available tool. Despite the fact that an approach might be well suited for a specific research scenario, the absence of a rich vignette and detailed reproducible examples poses a significant constraint on the utilization of the respective methodology .Generally, we propose three main criteria that could guide the appropriate selection of all the above catalogued methodologies:https://bioconductor.org). It is comprised of a core team of more than 1200 researchers to support continuous development. It is widely used with around ¾ million distinct IP downloads annually, and well respected . Moreover, it provides detailed documentation and extensive vignettes based on high quality standards and a broad scientific community that can provide support (https://support.bioconductor.org). Additionally, each package is thoroughly tested in different computational systems for scalable and performant analysis. Furthermore, the majority of the above selected tools are based on the R language.Overall, as already pinpointed in the literature mining process above, there is a large amount of computational tools and pipelines that can be utilized for different research goals. However, few studies or reviews provide also comprehensive examples or tutorials on how to utilize public cancer genomics repositories, and perform multi-omics data integration. On this premise, we selected two tools that can be utilized in two distinct scientific scenarios: the MOFA R package for unsupervised methodologies, and the netDx R package for the supervised ones. Initially, the main rationale of selecting R language tools is that the Bioconductor project is the largest consortium for the statistical analysis and comprehension of genomics data  with specific factors. In detail, visualization of the samples in the latent space showed that the expression subtype had an association with Factor 1, clearly separating the terminal respiratory unit (TRU) subtype from other two . Collectively, GSEA analysis shows an overrepresentation of MAPK and AKT on the protein level in factor 1 see A. The MAAs the resulting MOFA factors can be utilized to predict discrete clusters of samples, we used all the inferred factors to cluster the patients in the latent factor space, implementing collectively all information from the multi-omics layers and their differential contributions. Here, as described in the Materials and Methods section, k-means clustering resulted in three discrete groups of patients. Visualization of the three resulting clusters from the integrated analysis showed a significant overlap (using Pearson chi-squared test) with various clinicopathological parameters, such as AKAP9 gene mutational status, expression subtypes and gender . Of notep-value = 0.0358), Factor 4  and Factor 9 (0.04750) were statistically significantly associated with overall survival as the response variable, using p-values derived from Wald statistic . From the identified 15 MOFA factors, Factor 1 (tatistic .The performed supervised multi-omics data integration on CLL data using netDx resulted in a performance accuracy of 93% (±1.5%). The model was able to clearly discriminate the samples into the binary classes defined by the IGHV mutation status  see . RunningEach network has been manually selected as input for annotation with AutoAnnotate . The titShared enrichment between the classes show general cancer associated signals. Enrichment of adipogenesis indicates cancer-induced changes to the regulation of adipose tissue, which promotes cancer cell survival during therapy . Also a Additionally to the described pathway enrichment analysis, we also performed clustering of patients based on multi-omics profiles. The following analysis is based on more strict selection of features with a minimum feature score of 9 for at least 70% of splits. In the last decade, new massively parallel sequencing technologies have yielded new biological insights at the RNA, DNA, cellular, and spatial resolution, resulting in the accumulation of massive amounts of genomics data. Recently, there is a growing interest in integrating diverse data from such distinct molecular layers, in order to shed light on the biology of various complex phenomena. The simultaneous examination of multi-layer views can paint in-depth molecular pictures that provide comprehensive insights into the way our “omes” interact in the manifestation of diseases like cancer. Indeed, multimodal data integration approaches are redefining precision oncology through the exploitation of different molecular entities, which characterize holistically the molecular landscape of distinct tumors and facilitate the identification of actionable targets with clinical utility. However, despite the fact that multi-omics integration is an active area of translational cancer research, it lacks established performance benchmarks and assessment standards.https://github.com/Jasonmbg/CaseStudy_MAE_TCGA_LUAD_Review and https://github.com/jonasboh/Case_Study_netDx_CLL). Altogether, these two reproducible pipelines can serve as a complement to the literature mining process, on how to address two different research scenarios based on the general categorization of model nature orientation (Supervised vs. unsupervised methodologies).While it lay beyond the scope of our current review to critically analyze and provide methodological insights for each mentioned tool, we addressed the main pros and cons of the unsupervised and supervised methodologies utilized in our case studies.On this premise, in this review we sought to create a detailed catalogue of all the available computational tools, which a researcher could utilize for the integration of heterogeneous cancer genomics data in the context of translational cancer research. Our main goal was not only to provide a rich resource of cutting edge technologies, but also to implement two reproducible case studies, that illustrate the analysis of heterogeneous cancer multi-omics data using state of the art tools, focusing on two typical research scenarios: the supervised approach, for when a researcher for example tries to find the features that characterize the taxonomy between known molecular cancer subtypes; and the unsupervised one, that tries to unravel the heterogeneity and stratify the cancer patients into new disease subgroups. These two case study examples can serve as start-to-end workflows, which a user can utilize to analyze from scratch public multi-layered cancer data. Both studies cover important parts from multi-omics data acquisition, preprocessing of individual omics layers, integration, model training and functional enrichment analysis, along with extensive documentation of the R code and can be directly obtained from github  illustrates a typical use case for supervised multi-omics data integration. The challenge here lies in applying multi-omics data integration on a sparse cohort of less than 1000 patients, with missing values, unequal feature sizes per layer and unequal class sizes . The used cohort includes clinical, methylation, drug response, and gene expression data. Unfortunately, we were not able to integrate mutation data, likely because of the sparsity of this data layer which comprised of the binary mutation status for 69 genes. The aim of this case study was to show the challenges and possibilities when working with multi-omics data for a specific research purpose. Supervised analysis in the multi-omics field is based on prior knowledge of the data and its biomedical context, which impacts both hypothesis and primary feature selection. In our study we classified CLL patients based on their IGHV mutation status in order to separate patients with better treatment response prognosis (IGHV-mutated) from those with a worse prognosis (no IGHV mutation). The majority of findings in the netDx study is based on the gene expression layer, which was used for pathway enrichment analysis see .The classification of CLL patients based on IGHV status resulted in multiple clusters see B. The imThe limitations of this study are not only based on the aforementioned data related issues but also due to the nature of supervised methodologies. Supervised approaches need to be validated on an external dataset in order to evaluate potential overfitting and the generalizability of predictions. Therefore, the good performance of netDx needs to be validated on an independent larger dataset using the same features as in the applied model. Observed differences in performance between netDx v. 1.2.2 and 1.3.1 led us to apply netDx v1.3.1 as an application under development in a Docker container. Increasing the number of performed cross validations could identify more clearly generalizable patterns in a small or very heterogeneous dataset, but it would further increase the calculation time. Fortunately, there are more enriched pathways for non IGHV mutated patients, which have a worse prognosis, than for IGHV mutated patients. Targeted therapy of these patients based on their enriched pathways could lead to better prognosis for these patients. In summary, we could identify enriched pathways which are known to be involved in the pathophysiology of CLL. Furthermore, we could highlight interconnected pathways in the mTOR and the TP63 network in non-IGHV mutated samples. After validation of these results, they could help lead to the refinement of existing treatment combinations for targeting the aforementioned enriched networks for IGHV-mutated and non-IGHV mutated patients.Furthermore, it is essential to highlight some putative limitations of our literature review. The literature research has been performed in an automated framework by using the Entrez Direct (EDirect) tool. General limitations of automated annotations concern the challenge to classify literature without evaluating its context. For example, publications of tools such as SIMMS  failed tInitially, one important aspect that influences the integration part includes the pre-processing steps prior to integrating any multimodal data: initially, appropriate normalization or transformation is essential to remove any technical confounders related to each omic data layer. For example, for count based data like RNA-Seq, size factor normalization and variance stabilization are generally recommended. Also, significant differences in size in at least one of the interrogated omics layers could inflate the data integration model to capture non-biological variation associated with this specific data layer, while downweighting more subtle sources of variation. In addition, it is well known that most genomic studies suffer from the “curse of dimensionality”, that is the number of features being substantially higher than the number of samples. Hence, a feature selection step like selecting the top most variable features per omic modality is essential, both in supervised and unsupervised approaches. However, filtering is not trivial especially when dealing with somatic mutations or copy number alterations, where a more “sophisticated” filtering is needed. Somatic mutations can be very sparse with the vast majority of cancer genes being of low prevalence, cancer-specific and not shared among all patients of the same cancer. Intratumoral diversification adds further complexity to the application of a simple reduction based on the frequency of no events . InsteadAdditionally, another crucial part lies in the biological interpretation of the integrative analysis: it is vital to associate any findings to molecular mechanisms and perturbed pathways, in order to identify any causal regulatory relationships between the profiled entities. For this purpose there are various recent tools and databases that perform pathway analysis and provide prior knowledge on molecular biology to construct intracellular communication networks well-suited for multi-omics functional annotation. These include but are not limited to SignaLink 2.0 , OmniPatFinally, a researcher should strongly consider to follow specific protocols such as the FAIR guiding principles   when pubFinally, in addition to the main aforementioned challenges that govern the integration, sharing and utilization of distinct omics sources, we would like to summarize three major aspects, which facilitate the robust and successful amalgamation of heterogeneous cancer data layers:A systematic and automated literature search in the PubMed database was performed with the aim to collect publications of interest and classify them into distinct meaningful classes without manual configuration. Based on fast rising numbers of publications in the field of multi-omics data integration, the here portrayed results may change heavily in the future. For literature mining we employed the Entrez Direct (EDirect) tool, which allows systematic filtering of publications when accessing the NCBI publication databases, and query for multiple molecular data types in a command-line frame . EDirectThe retrieved publications are listed in http://gco.iarc.fr/today/fact-sheets-cancers) epidemiological data for 2020. This can be largely attributed to its propensity to metastasize to the brain, and its high lineage plasticity resulting in poor prognosis and treatment relapse [Despite the significant advances in targeted treatments with receptor tyrosine kinase inhibitors like Sunitinib or immune checkpoint inhibitors, lung cancer remains the first leading cause of cancer-related deaths and the second most commonly diagnosed cancer worldwide in both sexes, based on the WHO GLOBOCAN database  gene list . This reAfter data preprocessing we used the R package MOFA+ (1.0.1) , an unsuChronic lymphocytic leukemia (CLL) is the most common type of adult leukemia in the western world. CLL is known as a chronic disease affecting B lymphocyte activity. B cells are activated by different stimuli of the B cell receptors (BCR) coming from cytogenetic abnormalities as well as different genetic alterations. Most of CLL patients carry at least one of four common chromosomal alterations, namely deletion 13q14, deletion 11q22-23, deletion 17p12, and trisomy 12. Frequently, mutations include genes that can be integrated into the NOTCH signaling, inflammatory receptor, MAPK, NFKB, DNA damage and cell cycle control, chromatin modification, transcription, and ribosomal processing pathways . HoweverA multi-omics drug perturbation study  using CNSomatic mutations of the IGHV gene are known to be prognostic clinical markers for chemoimmunotherapy outcome and therefore crucial factors for patient survival . AnalysiIn the analysis the IGHV status was linked to the differentiation of cancer cells and the activation of B-cell receptors, and is the main factor driving the variance in the gene expression layer of the used CLL cohort.We chose this subcohort for supervised multi-omics classification on the IGHV mutation status using netDx . One mothttps://hub.docker.com/repository/docker/shraddhapai/netdx), using the gene expression, the drug response, and the methylation data layer. The input was defined by the binary IGHV mutation status . Having to remove 28 samples based on the missing mutation status of the IGHV gene, the final input dataset contained 172 samples (98 with and 74 without IGHV mutation). No further data filtering based on missingness was applied, as netDx can handle missing values in different omics layers. The design of the features for patient classification was grouped by pathways for gene expression data and one feature per layer for the others. We compiled 728 pathways containing 10–200 genes from several curated pathway databases [We applied netDx v. 1.3.1 with R-4.0.3 by using one CPU on a MacBook Pro with macOS Big Sur 11.1, 3.1 GHz Quad-Core Intel Core i7 and 16 GB memory. We used a Docker container with preinstalled R and netDx as well as all dependencies (atabases . We usedFor one, we conclude that automated literature search, although not a guarantee for accurate or comprehensive results, gives a good overview and classification of published knowledge in a specific field, in this case the integration of multi-omics data in oncology. Secondly, the findings of our case studies demonstrate that we can retrieve both known results and novel findings using predominantly the core tools with minimal tuning. Furthermore, in the future the definition and improvement of data sharing and biomedical meta-data to enhance clinical decision support will be of critical importance. Finally, bringing together interdisciplinary computational teams and researchers will help promote the development of cutting edge techniques for multi-omics integration and analysis, increasing the necessity for multi-platform analysis with common datasets to benchmark the performance of various methodologies.


To assess the capacity of support vector machine (SVM) algorithms that are developed based on platelet RNA-seq data in identifying thyroid neoplasm patients and differentiating patients with thyroid adenomas, papillary thyroid cancer and metastasized papillary thyroid cancer.Platelets were collected and isolated from 109 patients and 63 healthy controls. RNA-seq was performed to find transcripts with differential levels. Genes corresponding to these altered transcripts were identified using R packages. All samples were subsampled into a training set and a validation set. Two SVM algorithms were developed and trained with the training set, using the genes with differential transcript levels (GDTLs) as classifiers, and validated with the validation set. GO and KEGG pathway enrichment analysis were performed using the R package clusterProfiler.We detected 765 GDTLs (442 up-regulated and 323 down-regulated) in platelets of patients and healthy controls. The algorithm identifying thyroid neoplasm patients achieved an accuracy of 97%, with an AUC (area under curve) of 0.998. The other algorithm differentiating patients with multiclass thyroid neoplasms had an average accuracy of 80.5%. GO analysis showed that GDTLs were strongly involved in biological processes such as neutrophil degranulation, neutrophil activation, autophagy and regulation of multi-organism process. KEGG pathway enrichment analysis revealed that GDTLs were mainly enriched in NOD-like receptor signaling pathway and pathways in endocytosis, osteoclast differentiation, human cytomegalovirus infection and tuberculosis.Our results indicated that the combination of SVM algorithms and platelet RNA-seq data allowed for thyroid neoplasm diagnostics and multiclass thyroid neoplasm classification. Thyroid cancer is the most common endocrine cancer, with an increasing incidence of about two fold in the last 25 years and accounting for 2% of all cancers . This ovIt has been reported that blood platelets respond to changes in the host systemic environment during tumorigenesis and cancer metastasis , thus reHealthy individuals and patients with biopsy-confirmed TA, PTC and MPTC were recruited from Renji Hospital, Shanghai, China. MPTC is defined as PTC that spreads to lymph nodes. Healthy individuals are voluntary blood donors who undertook thyroid ultrasound to rule out thyroid diseases. This experiment was approved by the Ethical Committee of the Renji Hospital. Written informed consent was obtained from all subjects. Patients with thyroid nodules and following complications were excluded: diabetes, hypertension, cardiovascular diseases, pregnancy and active infection. 2 ml peripheral whole blood samples were collected from all subjects and stored in EDTA-containing tubes. A patient class and a control class were established to classify samples.Platelets were isolated from the samples within 24 h after collection to minimize loss of platelet RNA quality and quantity. To isolate platelets, platelet rich plasma (PRP) was separated by a 20-min 1000 rpm centrifugation step at 4 °C. To reduce the risk of contamination of platelet preparation with hemocytes, only 9/10th PRP was drawn and transferred to 1.5 ml Eppendorf tubes, after which platelets were pelleted by a 20-min 3000 rpm centrifugation step. The supernatant was subsequently removed, and platelets were observed as white precipitate. Phosphate buffered saline (PBS) solution was used to wash the platelets and then removed after a 15-min 3000 rpm centrifugation step, after which an instantaneous centrifugation was performed, and a 10-μl pipette was used to remove residual PBS solution. Precipitated platelets were carefully resuspended in RNAlater (Thermo Scientific) and frozen at −20 °C. Contamination of hemocytes in platelet preparations was quantified by a hematology analyzer.Total platelet RNA of each sample was extracted using RNeasy Micro Kit  per the manufacturer’s instructions. The total RNA was quantified and qualified by Agilent 2100 Bioanalyzer, NanoDrop (Thermo Scientific) and 1% agrose gel. Reverse transcription was achieved with 1 ng of total RNA, oligo (dT) primer and Superscript II reverse transcriptase. To have sufficient platelet cDNA for robust RNA-seq library preparation, each sample was subjected to 20-cycle enrichment PCR, using the KAPA HiFi HotStart ReadyMix PCR kit , yielding ~200 ng of cDNA. AMPure XP beads were used to purify double-stranded cDNA, which was then treated with End Prep Enzyme Mix to repair both ends and add a dA-tailing in one reaction, followed by a T-A ligation to add adapters to both ends. Size selection of Adapter-ligated DNA was then performed using beads, and fragments of ~420 bp (with the approximate insert size of 300 bp) were recovered. Each sample was then amplified by PCR for 13 cycles using P5 and P7 primers, with both primers carrying sequences which can anneal with flow cell to perform bridge PCR and P7 primer carrying a six-base index allowing for multiplexing. The PCR products were cleaned up using beads, validated using an Qsep100 , and quantified by Qubit 3.0 Fluorometer .Libraries with different indices were multiplexed and loaded on an Illumina HiSeq instrument per the manufacturer’s instructions . Sequencing was carried out using a 2 × 150 bp paired-end (PE) configuration; image analysis and base calling were conducted by the HiSeq Control Software (HCS) + OLB + GAPipeline-1.6 (Illumina) on the HiSeq instrument. The sequences were processed and analyzed by Fengneng Pharmaceutical Technology (Shanghai) Co., Ltd.https://bioinfogp.cnb.csic.es/tools/venny/) was used to screen class-specific GDTLs as classifiers for the multiclass algorithm.In order to purify raw data of fastq format, Cutadapt (v1.9.1) was used to remove adapters and ploymerase chain reaction (PCR) primers, and FASTP was used to filter reads with the following standards: (1) reads shorter than 50 bp were filtered; (2) reads with more than 5N bases were filtered; (3) reads with an average quality per base below Q15 were removed. For quality checking, a sample will be excluded if its clean reads account for <80% of its raw reads or its clean reads do not meet quality standards (Q20 > 90% and Q30 > 80%). Clean reads were aligned to human reference genome GRCh38 (ftp://ftp.ensembl.org/pub/release-91/fasta/homo_sapiens/dna/) via STAR (v2.4.2a), yielding BAM files, which were subsequently processed by HTSeq (ve0.6.1) to estimate differential transcript levels of genes. Here, STAR was also used to remove multi-mapping and unmapped reads, and a sample will be excluded if its uniquely mapped reads cannot account for >85% of its total reads before removal. Gene-level differential analysis at transcript-level resolution was performed using Bioconductor package DESeq2. Basemean > 100, |log2(FC)| > 1 and Padj < 0.05 were set to detect GDTLs. VENNY 2.1 (Gene Ontology (GO) is a structured, controlled vocabulary for the classification of gene function at the molecular and cellular level. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a collection of databases that can help to understand large-scale datasets generated by transcriptome sequencing. We performed GO and KEGG pathway enrichment analysis to functionally cluster GDTLs via the R package clusterProfiler.Prior to algorithm development, a train_test_split function implemented in Python 3.7.2 was used to randomly split samples into a training set and a validation set. The ratio of training:validation was set to be 60:40. Each time we executed the algorithms, samples in each sample class were extracted realtimely per this ratio according the total size of their classes to fill the training and validation sets, thus SVM outcomes were dynamic. To test the stability of performances of both algorithms, we execute both algorithms 1000 times and calculated their average accuracies. We also tried to adjust the ratio from 60:40 to 40:60 to increase the number of samples in the validation set relatively. For binary and multiclass sample classification, a SVM algorithm implemented in Python 3.7.2 was used. Particularly, we applied a One-Versus-One approach to the classification algorithm. The algorithm’s accuracy reflects the percentage of correct predictions. In principle, the algorithm was trained on the training set to fix SVM parameters before the validation set was predicted.Quantification of platelet purity of the used samples was not conducted initially when we carried out the study. To support the credibility of our isolation results, 10 newly collected blood samples were applied to platelet isolation following the protocol described above. The contamination of hemocytes was quantified using a hematology analyzer, yielding an average platelet purity of 93.7% Table .Per the ratio mentioned above, sizes of the training set and the validation set were fixed to be 103 and 69. Samples in each sample class were also extracted per this ratio according the total size of their classes to fill the two sets. For the binary algorithm, the training set contains 67 patient samples and 36 normal samples, and the validation set contains 42 patient samples and 27 normal samples. For the multiclass algorithm, the training set contains 20 TA, 26 PTC, 23 MPTC and 34 normal samples, and the validation set contains 12 TA, 15 PTC, 13 MPTC and 29 normal samples. Sample details were listed in Table n = 63) and patients with thyroid adenomas (n = 32), papillary thyroid cancer (n = 41) and metastasized papillary thyroid cancer (n = 36). Platelet RNA-seq yielded a mean read count of ~18.8 million reads per sample, and a total of 58302 RNAs were detected in the dataset. We identified 765 GDTLs  in platelets of healthy individuals (n = 63) and patients (n = 109), 442 with higher and 323 with lower expression . This algorithm employed the 765 GDTLs as classifier for sample classification, to which the contribution of each GDTL was evaluated at the meantime, yielding two top 50 weighted gene plots , yielding an accuracy of 97%, with an area under the curve (AUC) of 0.998 , and we subsequently determined whether it could correctly classify each individual sample in the validation set (n = 69) as healthy individual or patient with one of the three neoplasms. The validation resulted in an average accuracy of 80.5%  analyzed. Based on four datasets , Liang et al.  reported cancers –15.Our study has some limitations. The two algorithms we presented were developed with a quiet small number of samples. The performance of both was believed to improved by including more samples, especially the multiclass algorithm’s. The binary algorithm was good at proving the capacity of a SVM algorithm in identifying thyroid neoplasm patient but not clinically practical so far, unless other diseases that share similar clinical symptoms with thyroid neoplasms are included. Besides, the SVM hyper parameter Cost-C was not fine-tuned. In principle, a bigger or a smaller C parameter results in a hyperplane with larger minimum margin or a hyperplane that correctly separate more training points, respectively. Because we used a default C parameter, the accuracy, sensitivity and specificity that our algorithms achieved might not be optimal. Also, we did not estimate the contribution of variables, such as patient age, gender, blood sample storage time, alcohol, smoking and treatment history to the altered platelet profile.The capacity of SVM algorithms developed based on platelet RNA-seq data in identifying thyroid neoplasm patients and differentiating patients with multiclass thyroid neoplasms was proven in this study. Further algorithm development is warranted to include significantly more matched patient/healthy individual blood samples. Further validation is expected to determine the potential of our algorithms for therapy selection, disease recurrence monitoring, etc. Also, future studies should address causes and anticipated risks of samples misclassified in our study, such as cancer patients classified as healthy individuals.Supplementary Figure S1Supplementary Figure S2Supplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S6


We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis microarray expression set (meta-expression set). Measurements had to be obtained via microarray-technique from whole blood samples of adult or pediatric patients with sepsis diagnosed based on international consensus definition immediately after admission to the intensive care unit. We aggregate trauma patients, systemic inflammatory response syndrome (SIRS) patients, and healthy controls in a non-septic entity. Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). We evaluated classifier training and discrimination performance in 100 independent iterations. To test diagnostic resilience, we gradually degraded expression data in multiple levels. Clustering of expression values based on DE genes results in partial identification of sepsis samples. In contrast, RF, SVM, and DNN provide excellent diagnostic performance measured in terms of accuracy and area under the curve . We prove DNNs as the most resilient methodology, virtually unaffected by targeted removal of DE genes. By surpassing most other published solutions, the presented approach substantially augments current diagnostic capability in intensive care medicine. Understanding of the syndrome sepsis as a complicated disturbance of core immune functions has advanced in the last years, resulting in improved outcomes for affected patients. According to the latest Sepsis-3 definition, the syndrome is described as a life-threatening condition arising from the body’s response to an infection injuring its tissues and organs. Since sepsis is still related to high death rates and severe long-term effects , preciseTo reach a final decision on diagnosing complex diseases like sepsis, it is essential for physicians to precisely consider a vast collection of clinical information for each case. The utility of machine-learning-based methods to support physicians in the identification of sepsis patients has been evaluated and was demonstrated to show higher performance to predict sepsis 3–4 hours before its onset compared to existing scoring systems like modified early warning score (MEWS), sequential organ failure assessment (SOFA), and the simplified quickSOFA (qSOFA)  to focusConcurrently precision medicine emerges as a novel comprehensive approach to access information offered by molecular diagnostics. With sepsis, gene expression profiles could help identify and detect transcriptomic sub-phenotypes (endotypes) to create individually tailored therapy strategies, e.g., severe immune suppression . By offeConventional bioinformatic analyses on high-dimensional gene expression datasets aim to delineate differentially expressed genes by comparing predefined groups. By applying arbitrary thresholds, the most promising candidate genes are condensed to informative gene signatures. While those results yield important pathophysiological insights into the biological processes, the conversion of the result to a forward-oriented diagnostic classifier demands extensive steps: defining a minimal gene set of maximal informational content, incorporating individual weights for each gene and the interaction, iterative validation, and optimization of the tool. Overall, this approach possesses inherent pitfalls, not least, the inevitable loss of information and the risk of selecting “wrong” genes.Therefore, we asked if conventional machine-learning algorithms might be a superior straight-forward approach to establish a robust diagnostic framework, able to handle high-dimensional gene expression data without the requirement of preprocessing or selection and thus circumventing many drawbacks of conventional bioinformatics analysis.This publication implements automated sepsis prediction and sample classification on a molecular level for diagnosis using microarray-derived gene expression data. To assess feasibility and performance, we performed differential expression analysis and applied and compared different well-established machine-learning-based methodologies for the aim of accurate and reliable diagnosis.Machine learning has been demonstrated as most effective when used for analysis of high-dimensional datasets . To creaTo challenge our machine learning approach, we intentionally added trauma patients’ samples with no infection signs to the data collection. The differential diagnosis of sepsis and trauma from gene expression data is a challenging issue attributed to the indifferent immunological phenotype, based on the comparable underlying transcriptional response . This stConcerning 1,354 septic samples, a limited number of 520 samples with trauma are available. In general, if one class is represented by many examples, while only a few represent the other, class imbalance emerges and can lead to significant performance issues for classification . To avoiAll selected data series were merged into a meta-expression set to ensure compatibility between microarray platforms. The merging process is described in . We perfIt is essential to perform inter-study normalization of gene expression data between all 26 included data series as a prerequisite of differential expression analysis. Since GEO data series GSE25504 comprises samples originating from four different microarray platforms, we had to extend batch effect adjustment to 29 cohorts. Subsequently, ComBat  method wFor machine learning algorithms, in contrast to an elaborate batch effect adjustment routine, the preprocessing for the entire meta-expression dataset limited to a two-step procedure. First, gene-wise subtraction of the mean value from all individual expression values (centering) was applied. Subsequently, centered expression values were divided per gene by their standard deviation .Differential expression analysis between sepsis and non-sepsis patients was obtained by limma . The sofFor implementing automated sepsis diagnosis, we chose four machine-learning methods: Decision tree (DT) and random forest (RF), both generating tree-based models for class determination, and the more complex support vector machine (SVM) and deep-learning neural network (DNN) classifiers to consider recent developments in artificial intelligence (AI). Data analysis and processing with DT, RF, and SVM were based on the CRAN R packages “tree” (cran.r-project.org/web/packages/tree), “randomForest” (cran.r-project.org/web/packages/randomForest), and “e1071” (cran.r-project.org/web/packages/e1071) using the recommended parameters specific to the respective methodology. As the framework for DNN, we chose Google TensorFlow , which wWe performed all training, validation, and testing steps by TensorFlow’s graphics processing unit (GPU) implementation to shorten the run time for calculations. All other machine-learning procedures are computationally less extensive and could be entirely executed on the workstation’s central processing unit (CPU).We adapted the neural network’s architecture to enforce generalized classification for the samples provided and avoid over-fitting the classifier to idiosyncrasies of the underlying meta-expression set. Therefore, additional dropout layers  and an LThe best performing architecture (reported as the accuracy of classification) given the data provided is a five-layer neural network (multi-layer perceptron) with one hidden layer at its center and two adjacent dropout layers between the essential input and output layers.For predicting sepsis syndrome with established machine-learning-based methodologies, we gradually execute the following five-step procedure: (1) The complete meta-expression set is split into two parts to provide subsets for classifier training and testing. Regarding training data, approximately 85% of the available samples are evenly and randomly drawn from the included septic patients, and the composite group of non-septic samples. For classifier testing, 15% of the entire meta expression samples are assembled accordingly. (2) The classifiers are constructed in a training process based on information extracted from the combination of supplied samples, and their respective class labels. (3) Trained classifiers are utilized to predict the class affiliation of all samples taken from the previously separated testing subset (4) Results are compared to the correct class labels, and predictive metrics are reported. (5) Finally, constructed classifiers and randomized expression data subsets are discarded to enable unbiased replication of the procedure. For each method, we performed 100 independent iterations.For DNNs, we conduct an adapted and slightly more complex workflow: (1) The meta-expression set is split into three parts arranging subsets for classifier training, validation, and testing. 80% of all samples are prepared as a training subset following the description above. Subsets for validation and testing receive the remaining 5% and respectively 15% of all available samples. (2) The neural network is trained in multiple epochs using the corresponding independent, randomly composed subsets. While training, initially randomized values for the connection-assigned weights are gradually adapted to minimize error and maximize prediction accuracy by comparing results to the existing true class labels. Validation metrics calculated at the end of each epoch were exclusively used to guide initial model prototyping. During training for final classifier evaluation, no callback functions for automated assessment of validation data were employed. (3) Prediction of the previously separated testing subset is facilitated using the trained neural network. We again performed 100 independent iterations.Subsequent performance reporting and re-initialization steps are conducted according to the descriptions mentioned above.Since the meta-expression set is still slightly imbalanced regarding the number of samples in each class, by evenly drawing septic and non-septic samples, we realized a stratified sampling strategy to ensure the required equal representation of both classes for training, validation (for DNNs), and testing. With the iterative repetition of the entire approach, Stratified ShuffleSplit cross-validation is implemented to prove the reproducibility of classification results.After completion of training and prediction steps during each iteration, predictive metrics (area under the curve (AUC) and probability of correct classification (PCC)) are calculated based on the respective machine-learning classifier results, and receiver operating characteristic (ROC) plots are generated using R package “PresenceAbsence” . The difIn general, training  is facilitated with subsets of samples holding unaltered expression data. To test the resilience and flexibility of the four machine-learning-based classifiers, we introduced virtual obstacles. Therefore, testing data was altered to resemble real-world issues frequently emerging in gene expression data, e.g., arising from technical and methodological bias. We modified expression values in a sequence of three consecutive levels of signal degradation : (1) NoiSoftware components used for preprocessing of merged meta-datasets, batch effect adjustment, differential expression analysis, classifier training, analysis, and reporting are described in Within this study, we reanalyzed publicly accessible datasets from repositories. Responsibility for the necessary provision of ethics approval and participation consent is signed over to the original data authors.We applied differential expression analysis based on batch-effect adjusted data in a first experiment using the complete meta-expression set. The contrast was defined to compare differences between the first class of samples diagnosed with sepsis and the entity of non-septic patients as second class. In this comparison, 2,361 genes were reported to be differentially expressed (Log transformed fold change (LogFC) ±1; adjusted p-values <0.05). Agglomerative hierarchical clustering of all samples  based on these differentially expressed genes identifies three main subgroups of samples . The mosFour different methodologies were tested for their performance to generate a precise binary classifier between sepsis patients and non-septic samples, based on labeled, preprocessed expression data originating from the meta-expression set. We subsequently evaluated classifier performance on unknown samples by comparing the assignment results to the true class. To test the implementation’s resilience and reveal limitations between the machine-learning methods, we retried the last experiment under the same conditions but using virtual obstacles to simulate real-world bias. The testing samples’ expression data were incrementally degraded in three steps, while the training subset’s gene expression values were left unchanged.(I) At the first level of test data modification, random noise is added to all original gene expression values, imitating the occurrence of a sample- or batch-related variability. The effect of this procedure on classification performance is varying: DNNs were mostly unaffected by the added noise with mean AUC values of 0.990 (0.989–0.991) and mean PCC values of 0.953 (0.951–0.955) . SVM and(II) Cumulatively to signal modification with noise, the second step of test data degradation introduces random zero-replacement of expression values for 75% of all available genes. For DNNs, the calculated mean AUC and accuracy is 0.964 (0.961–0.967) and 0.906 (0.901–0.910), respectively . Albeit (III) The third step of modification adds randomized substitution of noise-interfered and zero-replaced gene expression data with artificial, yet plausible, expression values for 50% of all available genes. This last level of data degradation mimics the unstructured impact of secondary factors influencing gene expression. As a factual combination of the former two levels with additional artificial data, this randomized modification and replacement of test data could lead machine learning classifiers to misinterpret the remaining true expression values. With this level of severe data modification applied, SVM and RF methods further drop and thus now deliver poor and accordingly marginal performance (Mean AUC: 0.615 0.604–0.626) and 0.773 (0.764–0.781), respectively; mean PCC: 0.600 (0.592–0.609) and 0.709 (0.701–0.716), accordingly) . The per6 and 0.7In a third experiment, we examined the effect of gene identity on diagnostic performance. Therefore, we excluded 2,361 genes from the meta-expression set, which were identified by differential expression analysis. The residual 3,571 genes (approximately 60%) are processed with all investigated machine-learning-based methods independently in analogy to the second experiment. Classification is again repeated for the native testing data and all levels of data degeneration for 100 iterations while performance is reported. In a detailed comparison of experiments 2 and 3 , DNNs will most likely achieve perfect classification performance to distinguish septic patients from non-septic samples.The standard procedure of blood sample collection and laboratory analysis must be extended by microarray gene expression measurement and additional computation-based analysis to take the suggested method’s diagnostical advantage. Apart from microarray kits and equipment requirements on the lab-side, a regular server system for hosting neural network classification software would be necessary to facilitate in silico diagnoses. The actual diagnosis of a potentially new and unknown expression set could be executed in a four-step process by (1) limiting the available expression values of the new data to the defined standard set of 5,932 genes. Further, to ensure predictability, newly collected expression values had (2) to be centered gene-wise based on the mean values of the full meta-expression set, and (3) scaled by its standard deviation. After preparation, the expression values are required to be (4) processed inside a pre-trained neural network environment, resulting in an instant prediction of sepsis occurrence, even during the temporary availability of fractions of the complete gene expression data. The here proposed methodology could therefore offer precise predictions of sepsis in real-time.With “SeptiCyte LAB”, the first host response gene expression assay for diagnosis of sepsis, has recently passed the U.S. Food and Drug Administration (FDA) for validation studies with AUC results reported ranging between 0.82 to 0.89 depending on the degree of confidence in the clinical diagnosis . BesidesWe trained deep-learning artificial neural networks and other machine-learning-based classifiers with microarray gene expression data with the goal of sepsis classification for this publication. Referring to accuracy, the proposed approach using DNNs exceeds solutions interpreting EHR data based on a comparable number of samples  and RT-PS1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.


Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. The online version contains supplementary material available at (10.1186/s13059-021-02553-2). The identification of differentially expressed genes or transcripts, e.g., in diseases or in response to treatment, is a standard but important task in molecular systems medicine.Differential gene expression analysis compares the expression profiles of two or more groups of samples to reveal genes with significant differences between the groups. Technologies for high-throughput gene expression profiling include microarrays and RNA sequencing, the latter being more widely used in clinical research today. Both are intrinsically different, e.g., signal- vs. count-based measurement, and their results subject to platform-specific biases , 2. ManyOne major challenge of differential expression studies is the lack of robustness due to the high technical and biological variability of the data –15, whicp-values or effect sizes may not be realistic. Furthermore, meta-analysis largely ignores possible differences between cohorts  [To control the exchange of sensitive molecular profiling data from, e.g., next-generation sequencing experiments, databases, such as dbGaP  or EGA , restricbutions)  or data butions) , which mbutions) .privacy-aware [privacy-preserving  , differecy-aware  designate.g., DP ), if it FL has become increasingly popular in bioinformatics for GWAS , 43, surDP perturbs the data or results by adding noise to them. Although DP is privacy-preserving and complementary to FL, it might dramatically impact the accuracy of the results. HE performs computation on the encrypted data from the participants. It suffers from two practical disadvantages : it suppFL is a promising alternative to SMPC and HE in terms of performance and scalability. Unlike HE, it does not increase computational cost much compared to the centralized method, and unlike SMPC, it transfers only a small number of model parameters through the network. Similar to HE, SMPC, and meta-analyses, FL is not privacy-preserving like DP, i.e., the server might reconstruct the raw data using the model parameters obtained from the clients –51. HoweThe privacy of federated methods can be enhanced by applying HE or SMPC on the shared model parameters. In comparison to purely SMPC- or HE-based methods, hybrid approaches are computationally efficient because heavy computations are distributed across the clients. Additionally, they offer enhanced privacy compared to pure FL, because the original values of the local parameters remain hidden from the server, and only global parameters are revealed to the server and the clients.Flimma (federated limma), a novel federated privacy-aware tool for the identification of differentially expressed genes called Flimma. Our new tool represents a federated implementation of the popular differential expression analysis workflow limma voom [limma voom among other popular count-based methods, because it is comparably fast without sacrificing accuracy [Flimma could be easily modified for handling microarray data, since the limma method was originally designed for such data [voom [In this paper, we introduce mma voom , one of accuracy . Besidesuch data  and onlyta [voom .Flimma is based on HyFed [limma voom, Flimma enhances the privacy of the data in the cohorts since the expression profiles never leave the local execution sites and only aggregated parameters are revealed to the server and the other local sites. In contrast to meta-analysis approaches, Flimma is particularly robust against heterogeneous distributions of data (in particular of confounders and class labels) across the different cohorts, which makes it a powerful alternative for multi-center studies where patient privacy is a key concern.on HyFed , a hybriFlimma and four meta-analyses approaches on two real-world datasets: a breast cancer expression dataset from TCGA [Flimma’s power, we model the multi-party setting by randomly partitioning both datasets into virtual cohorts, while introducing different levels of imbalance w.r.t. target class labels and covariate distributions. For both datasets, we simulated three realistic scenarios leading to different levels of sample distribution heterogeneity between local cohorts. We split the breast cancer dataset such that three virtual cohorts yield different frequencies of the LumA subtype to simulate an imbalanced distribution of disease subtypes collected at different clinical centers  with the results obtained by running limma voom on the combined dataset. For the privacy-aware approaches, we computed the root mean square error (RMSE), the precision, the recall, the F1 score, the Pearson and the Spearman correlation w.r.t. the results of the aggregated analysis with limma voom, which we treated as ground truth.We compared negative log-transformed Flimma produces the same p-values as the aggregated analysis with limma voom in all scenarios, including the imbalanced ones. This implies that Flimma is robust against heterogeneous data distributions across the clients. However, this is not the case for the meta-analysis approaches. In general, their RMSEs increase (and Pearson correlations decrease) as the scenarios become more imbalanced, and they introduce false positives and false negatives even in the balanced scenario. In spite of the difference in p-values calculated by all meta-analysis methods, their gene rankings were quite similar to the ranking produced by the aggregated limma voom .As shown in Fig. p-value  and difs [edgeR , DESeq2  [DESeq2 , or sleu [sleuth ) exist aComBat or RUVSeq [Flimma effectively handles batch effects, other analyses of expression data such as clustering or classification might require transformed data.Another prospective direction for future work is the development of accessory tools for gene expression analysis. This includes for example, federated principle component Analysis (PCA), useful for quality control, or federated batch effect correction methods, such as r RUVSeq . Althouglimma has been initially developed for differential gene expression analysis, it is widely used for the analysis of various omics data types, e.g., proteomics [Flimma modifications suitable for the analysis of other omics data types in the future.Although oteomics , 70, metoteomics , and micoteomics . TherefoFlimma is a privacy-aware tool for the federated identification of differentially expressed genes. It is user-friendly and publicly available at https://exbio.wzw.tum.de/flimma/including tutorials and a video documentation on its principle and application to real data. While Flimma results are mathematically equivalent to limma voom, Flimma operates on distributed cohorts without the disclosure of sensitive data. To enhance data privacy, Flimma uses a hybrid federated approach, where the local parameters of the clients are hidden from the server and only global parameters resulting from the aggregation are disclosed. In contrast to meta-analysis approaches, Flimma is robust against heterogeneous distribution of data across the different sites and to technical batch effects. In summary, Flimma is a promising alternative to meta-analysis methods for multi-center gene expression projects, as it enhances patient privacy while providing the same results as a centralized analysis.limma voom is the state-of-the-art method for differential expression analysis. Initially designed for microarrays [voom function, which removes the mean-variance trend from RNA-seq data and makes it suitable for analysis by limma [limma published an updated guideline on the recommended limma voom workflow [filterByExpr function from the edgeR package, conversion of raw read counts to log2-transformed counts per million (log-CPM), and normalization of gene expression distributions. We only differ from this workflow by using the upper-quartile (UQ) normalization [voom computes precision weights, which compensate for the mean-variance bias that is typical for RNA-seq data and thus makes them suitable for use in limma.roarrays , it was by limma . Recentlworkflow . Data prlization  instead lization , since tlization , 74. Sinlization , 76, we Flimma is based on HyFed (https://github.com/tum-aimed/hyfed) [HyFed comprises four software components: an aggregator server, a compensator server, a client app, and a web interface.d/hyfed) , a hybrid/hyfed) . HyFed cHyFed, clients mask their local parameters with noise before sending them to the aggregator to enhance the data privacy. The noise matrix has the same shape as the parameter matrix and contains random numbers. The approach to random number generation depends on the data type of the masked matrix and is described in detail in the next section. The noise matrix is sent to the compensator server, which aggregates the noise received from all clients and shares the global noise matrix with the aggregator. The aggregator calculates noisy global parameters and denoises them, by subtracting the global noise matrix provided by the compensator from the noisy global parameters. The proposed hybrid approach provides improved privacy, because a reconstruction attack would require compromising two servers in this case. The aggregator and compensator server components should run in separate machines at distant physical locations. Ideally, to minimize the risk of reconstruction attacks, they should be controlled by third-party organizations not connected to any of the study participants. Currently, the publicly available Flimma web tool is using the aggregator running at the Chair of Experimental Bioinformatics, Technical University of Munich (Germany), while the compensator is hosted at the Department of Mathematics and Computer Science at the University of Southern Denmark (Denmark).To start the project, the coordinating user signs into the web interface, creates the project, sets its parameters , and invites the participants. Each participant receives a token and a project ID from the coordinator and locally runs the client app to join the study and to select the local dataset. The computations are orchestrated by the aggregator server, which coordinates the clients, aggregates their local model parameters to global parameters, and returns global parameters to clients. Unlike in FL, with limma voom, each Flimma client accepts a matrix of read counts and a design matrix, specifying class labels and covariates for each sample. Flimma outputs a table with p-values, fold-changes, and moderated t statistics for each gene.As the original Flimma is publicly available at https://exbio.wzw.tum.de/flimma/. The “HowTo” page provides a quick-start guide for Flimma along with test data and describes input file formats.Flimma employs the local parameter masking approach of HyFed, which treats non-negative integer-valued parameters and real-valued parameters differently. For masking non-negative integers, it applies the standard additive secret sharing scheme based on modular arithmetic over the finite field p is a prime number [Ni are drawn from Mi using modular addition over M=(M′−N) mod p.e number . The eleReal-valued parameters are protected by the secret sharing approach based on Gaussian distribution , 79. NoiMi) given the knowledge of another random variable  [Flimma and the methodology of its evaluation are described in AIMe registry [https://aime-registry.org/report/v6v9dj.To avoid manual execution of /flimma)  and at Z/flimma)  under thregistry  at httpslog(FC)|>1, and BH-adjusted p-value < 0.05. For the results produced by each method, we computed the RMSE, the precision, the recall, the F1 score, the Pearson, and the Spearman correlation. Since only a small number of the most significantly differentially expressed genes is of interest for some research tasks, we have also investigated how the performance of the methods varies with the numbers of top-ranked genes selected.For each method, we considered a gene determined as differentially expressed, if it has |https://gdac.broadinstitute.org/runs/stddata__2016_01_28/data/BRCA/20160128. 850 expression profiles classified as luminal, or basal-like subtypes and annotated with the age of diagnosis and tumor stage were kept. Although breast cancer samples are classified into 4–6 subtypes [Unprocessed read counts summarized to gene-level and clinical annotations of samples were downloaded from subtypes –96, we fsubtypes , 94 . Expression profiles of sun-exposed and non-sun-exposed skin samples annotated with mean ischemic time and sex were kept. The resulting dataset comprises 1277 expression profiles of 677 sun-exposed and 600 non-sun-exposed skin samples, also annotated with sex and ischemic time. In contrast to the TCGA-BRCA dataset, a smaller fraction of genes was differentially expressed between sun-exposed and non-exposed skin samples . ARCHS4 collected raw reads from publicly available human and mouse GEO datasets and uniformly preprocessed them. Raw reads from each human-derived sample were pseudo-aligned against the GRCh38 human reference genome and quantified by kallisto [genefu R package [Raw read counts for three breast cancer cohorts from GSE129508 , GSE14924  (https:/kallisto . Since i package . Same asAdditional file 1 Table S1.Additional file 2 Table S2Additional file 3 Table S3Additional file 4 Supplementary Text and Figures S1-S3Additional file 5 Table S4Additional file 6 Table S5Additional file 7 Table S6Additional file 8 Table S7Additional file 9 Review history.