The pathway analysis is a de facto standard downstream analysis procedure in most genomic and transcriptomic studies. Here, we present MOPA , which is a multi-omics integrative method that scores individual pathways in a sample wise manner in terms of enriched multi-omics regulatory activity, which we refer to mES (multi-omics Enrichment Score). The mES score reflects the strength of regulatory relations between multi-omics in units of pathways. In addition, MOPA is able to measure how much each omics contribute to mES that may be used to observe what kind of omics are active in a pathway within a sample group , which we refer to OCR (Omics Contribution Rate). Using nine different cancer types, 93 clinical features and three types of omics , MOPA was used to search for clinical features that were explainable in context of multi-omics. By evaluating the performance of MOPA, we showed that it yielded higher or at least equal performance compared to previous single and multi-omics pathway analysis tools. We find that the advantage of MOPA is the ability to explain pathways in terms of omics relation using mES and OCR. As one of the results, the TGF-beta signaling pathway was captured as an important pathway that showed distinct mES and OCR values specific to the CMS4 subtype in colon adenocarcinoma. The mES and OCR metrics suggested that the mRNA and miRNA expressions were significantly different from the other subtypes, which was concordant with previous studies. The MOPA software is available at  With the growing interest in finding meaningful correlations across different omics data types, the size of multi-omics data is growing exponentially. Such multi-omics data are sampled and analyzed in means to explain certain clinical outcomes or biological phenomena of interest.Advancement in methods for measuring such omics data is outpacing the development of analytical methods that are able to embrace the many omics types in an integrative manner. The multi-omics research has now become a routine that is rapidly extending into various domains, such as, metagenomics and single-cell . While tRecently, a number of methods has been proposed that perform integrative analysis using multi-omics data. For example SNF , MOFA 66 and iClInstead of a list of omics features, it would be more helpful if the output was in units of pathways, since a pathway itself represent some biological mechanism, and thus is self explainable. Currently, three multi-omics methods were found whose output is in units of pathways, where one was able to compute pathway scores for each sample. The three methods are ActivePathway , multiGSMoreover, since pathway results are used to understand the biological mechanism of a disease or phenotype of interest, pathway scores or p-values are expected to associate with underlying biological semantics of a given experimental condition or clinically available features, such as cancer subtype, gender or age. However, the majority of tools are based on unsupervised methods that aim to discover new groups that exhibit group specific multi-omics profiles.Here, we propose MOPA , which is a tool that takes multi-omics data as input and outputs a score of a pathway per sample in association to a phenotype or clinical feature. A high scored pathway implies that the multi-omics profiles of genes within the pathway are different between the clinical feature groups of interest, which therefore may explain their biological difference in units of pathway and in context of multi-omics relation. The performance of MOPA was evaluated using multi-omics data from nine cancer types and 93 clinical features, followed by two detailed use case studies on colon and stomach adenocarcinoma cohorts. Compared to pathway based analysis tools, MOPA showed equal or improved classification performance. Distinct from other methods, MOPA is able to rank pathways by their association strength to a given target label. Most importantly, it provides mES (multi-omics Enrichment Score) and OCR (Omics Contribution Rate) metrics as output that represent the enrichment of multi-omics activity and the contribution of each omics towards the activity in each pathway. The mES and OCR metrics were developed to be used in means to enhance the comprehension of the pathway analysis result.The mES score is a single value that integrates all three omics. The mES score is computed for each pathway in a single sample. A high mES score indicates that a certain regulatory relationship between the three omics is significantly enriched in a pathway. For example, if genes are strongly suppressed by miRNAs within a pathway, a regulatory relation between mRNA and miRNA omics is present and the pathway will have a high mES. If this was true for the omics relation between mRNA and methylation, a high mES will be computed. Hence, mES indicates the strength of any combination of omics. To supplement the mES, we defined the second metric OCR, which is able to show specifically what type of omics relations are taking part in the mES score.Using mES and OCR, we illustrated that MOPA was able to detect pathways that were concordant with previously reported results in colon and stomach adenocarcinoma studies in context of multi-omics.https://www.cancer.gov/tcga. All the data satisfy the freedom-to-public criteria stated at https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/using-tcga/citing-tcga.The multi-omics data of patients from various cohorts in the TCGA data portal was used. The type of cohorts, number of patients and the clinical features used in this study are provided in the The multi-omics data was collected in a case, or patient, matched manner. Thus, cases with matching omics of interest were selected and processed as shown in Each omics layer is composed of different number of features, scale and data type, where the unit of features correspond to the omics specific elements, such as genes, miRNAs or methylation probes. MOPA focuses on detecting gene-regulatory cis-relations across the multi-omics layers. Hence, it is advantageous to convert each omics data into gene-level data, which is also natural for pathway analysis where each node in a pathway network corresponds to a protein or gene. This will result in a transformed data where each omics layer are now in unit of genes and have the same dimensions . Due to the massive number of possible combinations of trans-regulatory elements and their targets, it is difficult to create an informative gene × sample × omics tensor, since only a small portion will be meaningful. Hence in this study, the MOPA tool focuses on cis-regulatory elements.Such property allows us to combine the omics slices into a tensor, or cube, which can be decomposed using a non-negative tensor decomposition method to find latent features. For searching latent features, we used MONTI  , which wMOPA was applied to nine cancer types using mRNA, miRNA and methylation data to predict the outcomes of 93 clinical features using mES. We also aimed to show that the interpretation of the result was advantageous over other methods using OCR. Thus, we selected methods that were able to score pathways in a sample wise manner since the scores reflect the omics activity in a pathway. Collectively, the performance of MOPA was evaluated by comparing the classification accuracy on clinical target outcomes to four pathway scoring methods, of which three are based on single-omics and one multi-omics data. While our interest lies within multi-omics, we included the single-omics pathway analysis tools to show the strength of multi-omics utilization. Among the multi-omics pathway tools, only MOGSA provided a scoring matrix as output. At last, case studies were performed on the colon and stomach adenocarcinoma cohorts data.Gene expression, miRNA expression and methylation level omics data of nine cancer types were collected from the TCGA . Cases wFor validation and presentation of the utility of MOPA, two use case studies were performed on the molecular subtypes in colon and stomach adenocarcinoma cohorts. For each study, we showed that MOPA was able reproduce important biological results specific to the cancer type and its clinical feature groups reported in previous studies. Here, the clinical feature groups refer to attributes from the medical health records, such as cancer subtype, age, gender and cancer stage.The human KEGG  pathway The analysis workflow of MOPA consists of three steps as shown in T, or a tensor, with genes xijk, that is factorized into three loading matrices, S, G, and O with elements sif, gjf, and okf. Here, matrices S, G, and O refer to the decomposed sample, gene, and omics components, respectively. Here, i, j, k and f refer to sample, gene, omics and rank feature indices, respectively. The tensor T is factorized using a predefined number of ranks R, which are referred to as the latent gene-level omics features. An optimal R for each dataset was predetermined based on the residual error of the decomposition and classification accuracy. If sample labels are available, MONTI selects informative features f among the R features, where f ⊂ R. Otherwise, all features are considered for further analysis, where f = R.Pathway scores are computed based on the latent features that are learned by the gene-level multi-omics integration method MONTI. MONTI takes a set of gene-level converted multi-omics data as input and outputs a list of latent gene features. In case when sample labels, or clinical feature labels, are available, MONTI will further select features that are highly associated to them. MONTI showed to be successful in selecting such features in various cancer types and clinical features. Otherwise, if labels are not available, MOPA skips the feature selection procedure in MONTI and proceeds the analysis in an unsupervised manner. The latent features are computed from the multi-omics tensor using the non-negative tensor factorization method PARAFAC , which mf to samples based on their values in the sample component S. A value sif indicates the association, or weight, of feature f to sample i. Here, the task is to assign features to samples with high association and discard features with weak or no association to any sample. Thus, each sample may be assigned with a different set of features. However, samples within the same clinical feature group or sample cluster are expected to share a common set of features if their multi-omics profile share some common characteristics. To define a threshold for assigning features to samples, first, for each sample i, the vector si is min-max scaled to both normalize the feature values and prevent the case where a sample is not assigned with any feature by si.MOPA then assigns the features Pkde function means kernel density estimation.Then, the probability of features from the cumulative distribution function (CDF) was computed based on the scaled vector i have cumulative probabilities, which is denoted as CDFi. The probability of a feature in CDFi reflects its association strength to sample i. By testing a range of CDF thresholds for selecting informative features, 0.6 showed robust results across several different datasets as shown in i, where Now, rank features in sample Here, the multi-omics Enrichment Score (mES) and Omics Contribution Rate (OCR) are computed. mES reflects the multi-omics signal strength of a pathway in each sample, whereas OCR indicates how much each omics contribute to mES.S and G, 2) a Gene Matrix Transposed (GMT) file in binary format indicating the gene memberships to pathways and 3) the sample assigned features i. Here, j, the sample feature values dijt, up to the j-th ordered gene between the two distributions of pathway t in sample i as shown in pt)( ∈ pt) the indicator function that outputs 1 if the l-th gene is a member of pathway t and 0 otherwise. q refers to the number of genes in the dataset. dijt in ril value starts calculation and dijt value shows the difference between genes belonging to the pathway and genes not. The smallest dijt is subtracted from the largest dijt, which is the mES of pathway t in sample i  the decomposed sample and gene components, sample i ,mESit=mS, G and O components and 2) the sample assigned features S′, which are expected to represent the m group’s specific multi-omics profiles and denoted as pt)(. The omics profiles of features L (or number of omics types), using Second, to compute the omics contribution rate OCR, two types of input data are required: 1) the t of group m for each omics type k is obtained byThe OCR for pathway mES embeds each gene’s multi-omics profile per pathway and per sample. Thus, mES is computed without any knowledge of hidden groups in the sample dimension, such as subtypes, gender or cluster. A high mES implies that genes related to the selected features are enriched in the pathway of interest. Each selected feature also has an associated multi-omics profile. Collectively, in biological terms, a high mES implies that genes with a specific multi-omics profile are enriched within the pathway of interest. In the other hand, OCR aggregates group specific features to represent the ratio of their multi-omics profiles per group. Thus, the mES and OCR reflect different biological meanings. While mES reflects the enrichment of a certain multi-omics profile of genes within a pathway in a single sample, OCR shows the activation ratio of each omics for the enriched multi-omics profile in a sample group.j in all the samples. The rj is tested whether it is significantly different between the clinical feature group samples . Each gene is tested with Bonferroni corrected p-value. The genes are by the adjusted p-values, which may serve as source for further downstream analysis. The rank of genes implies the multi-omics activity related to the sample i associated features, which is expected to be similar across samples of the same clinical feature group.MOPA provides a number of features to aid interpreting mES and OCR in respect to the biological context of interest. First, survival analysis using mES is supported to further select survival related pathways. For each pathway, the top and bottom 20% samples are labeled and output as high and low mES. Second, using Cytoscape , a pathwTo validate the results of MOPA, the association between mES, OCR and clinical features was investigated. Three types of omics were used: gene expression (Ge), methylation level (Me) and miRNA expression (Mi). Here, we tested if the results were coherent with previous biological findings and also made effort to observe what types of clinical features were explainable using mES and OCR.The results were compared to other pathway enrichment tools, which were GSVA, ssGSEA, z-score and MOGSA. While MOPA is able to utilize clinical label information to compute mES and OCR, the supervised feature selection procedure was skipped for fair comparison since the comparing tools are all unsupervised methods. The unsupervised version of MOPA is referred to as MOPA_UN, which was used for every performance evaluation task involving comparison to other tools. The input data for GSVA, ssGSEA and z-score was the gene expression count matrix of the same samples used by MOPA. For MOGSA, the same data used by MOPA was used as input data, which is the gene-level data.For each cancer type, MOPA analysis was performed on each clinical feature group. A total of 93 clinical features were tested where MOPA was executed for each feature. By training a Multi-Layer Perceptron (MLP) model on the acquired mES matrix for each clinical feature target label, the 10-cross validated average F1 score of sample classification results was measured. The train and test data were split by 9:1 ratio. The pan-cancer clinical feature group classification results as radar charts in From the result, we observed that not all clinical feature groups were well classified. Among the clinical features, subtype, mutation clusters showed high F1 scores in all the tools. MOPA showed the highest F1 scores in COAD, STAD subtypes, mutation and hypermethylation clusters. Single-omics tools also performed well on subtype and hypermutated categories using gene expression data. While MOGSA also utilizes multi-omics data, the performance on the hypermethylation category in COAD was lower than the single-omics tools. In the STAD hypermethylation case, MOGSA performed better than single-omics tools but was below MOPA. A clear advantage of using multi-omics data was observed in classifying the hypermethylation groups in COAD, STAD, LUAD and PRAD, where single-omics tools showed poor performance, which may be due to the difficulty of predicting methylation clusters through the gene expression data. Especially, gender groups were well classified in the HNSC and KIRC datasets by MOPA compared to the other tools. From the results, we learned that noise is accumulated when incorporating more omics types that may hinder learning group specific features. Thus, how to integrate and analyze the heterogeneous multi-omics type is important in terms of not to loose structural information of each individual omics and further learn relations among them. For performance evaluation, we used four different classification methods as shown in While the performance of MOPA is not superior, its performance was at least equal or higher than the comparing methods. Furthermore, the mES and OCR metrics are able to interpret the pathways with richer multi-omics context, which will be discussed later.We further investigated how the classification performance was effected by different combinations of omics. For the same pan-cancer dataset, all possible combinations of omics, including single-omics, were generated and tested, which is provided in To compute mES and OCR, a number of hyperparameters are involved, which were tested for possible fine tuning.R value that determines the number of features in the decomposed components. By observing the changing residual of the decomposed matrices using a range of different R values, an optimal R was chosen based on the elbow method for each cancer type. As shown in R differed in the COAD, STAD and LUAD cohorts. Interestingly, a large R was expected to yield better performance since more features are learned, which was not the case. R values of 160 and 200 showed good performance in the three cancer types.mES depends on two hyperparameters. The first is the number of features in the input data. The input to MOPA is the decomposed loading matrices output by MONTI, which requires a user provided R, a range of CDF thresholds were tested. A higher threshold causes less number of features to be selected, which will cause loss of information when computing mES. In the other hand, a low threshold will select many of features that will weaken clinical feature group specific features. A threshold of 0.6 showed the most robust results in the three cancer cohorts, as shown in the The second is the set of selected features OCR is computed based on the features shared among the samples within the same clinical feature group. Such criteria is based on the assumption that each group share a common multi-omics profile. To test if this is true, instead of using the shared set of group features Such pattern was more evident using the group shared features. The Venn diagram in mES and OCR are computed using the preselected features by MONTI with clinical label information. Such supervised approach limits the analysis to association studies and cannot be used to detect novel sample subgroups. Thus, MOPA was further tested if it was able to detect group specific pathways in unsupervised manner. For such purpose, the feature selection process within MONTI was skipped, so that MOPA only used the CDF threshold for filtering low informative features. The samples were clustered using the mES matrix using the K-means algorithm to detect multi-omics sample clusters. The cluster numbers were then used as labels to measure the classification performance. Similarly, the pathway scores of the comparing tools were used for clustering in Similarly, the UMAP plot indicates that the subtype group samples were successfully identified by MOPA_UN. Meanwhile, ssGSEA also showed a high ARI score and well discrimination of subtype groups in COAD . Also inIn case feature labels are available, MOPA performs feature selection in a semi-supervised manner, which involves the feature selection process of MONTI in addition to the feature filtering by CDF. However, MOPA is able to compute mES and OCR without labels in case clinical feature label information is not available. While mES does not require any label information, OCR does. Thus, by performing simple K-means clustering on the mES matrix, sample clusters can be identified, which will serve as sample group labels that are required to compute OCR. In From two case studies, COAD and STAD, we present a detailed analysis procedure based on mES and OCR to show how MOPA was able to reproduce previously reported biologically important results.From the COAD data, a total of 106 pathways with significant survival p-values were detected to be associated with COAD molecular subtypes. For the survival analysis, two group of samples were defined in terms of the mES score. The top 20% and bottom 20% samples in respect to the mES score were defined as high mES and low mES groups. As shown in To reason why these three pathways were selected, we investigated how the high mES and low mES groups were associated with the COAD CMS1, CMS2, CMS3 and CMS4 molecular subtypes. The number of samples and percentage per subtype were counted in each high and low mES groups as show in From the result, we observed that the survival probability of high mES group (or CMS4 samples) was significantly lower than other subtype samples, which was also reported in . While eWe further examined the ‘Salivary secretion’ pathway to observe what genes contributed to the mES score the most. Each gene of the pathway were subject to ANOVA testing using n cancer .According to the OCR of the ‘TGF-beta signaling pathway’ in To compare the complete set of pathways with significant survival p-values, a pathway network specific to each subtype was constructed using Cytoscape as shown in Figs In the STAD molecular subtype  data, 34 pathways showed significant survival p-values . SimilarCollectively, through the two case studies, we showed that the mES and OCR metrics were useful to interpret the biological functions of the pathways in relation to survival. Both mES and OCR output a result in units of pathways. However, mES takes into account the feature values of each gene with membership to a pathway. Thus, the mES score reflects the multi-omics activity of a certain set of genes. In the other hand, OCR reflects the multi-omics activity in a more broader aspect. If the each different omics are similarly activated in a pathway, the OCR will show equal ratio of each. However, this does not imply that the same genes in the pathway have similar multi-omics activity. Instead, OCR represents the global activity of each omics in a pathway from which we can quickly see what types of omics are effecting the pathway.MOPA aims to provide a view that aids in interpreting a pathway in terms of omics activation. By comparing two or more conditions, it is difficult to determine how the multi-omics activity differ between them. To provide such insight, the mES and OCR metrics were developed, which reflects the activity of each omics in unit of pathway. Such approach is easier to understand compared to an output that is a list of genes, which requires further enrichment analysis. Using a nine different cancer multi-omics data sets MOPA showed equal or higher performance to competing tools. Through the two case studies, the mES and OCR metrics showed to well reflect previous biological findings of colon and gastric cancer types.While only three omics types was used in this study, MOPA does not limit the type of and also is not tailored to cancer studies. However, due to the nature of the tensor decomposition technique, MOPA may not perform well in cases where the dataset is small since number of ranks is constrained to the minimum dimension. In case of a small sample dataset, only a few features will be available to work with, which may not be enough to embed the complex multi-omics relations. By rigorous testing with different number of ranks in As a drawback, we found that the running time of MOPA was relatively longer than the compared methods. MOPA took approximately 13 minutes when using the COAD data, which was comprised of 14,464 genes, three omics types and 234 patients. ActivePathways came second with approximately nine minutes. The other tools completed under a minute. The longer execution time in MOPA was mainly due to the tensor decomposition task and the mES score computation.The multi-omics domain is quickly expanding to other domains such as single-cell COVID studies, we expect that the concept of MOPA can also be applied to such large and more complicated datasets. We find that an easy interpretation of high dimensional and complex omics data will be helpful for interpreting the underlying biology in a multi-perspective view.S1 TableThe datasets used in this study.(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 TableAccording to mES, the top 20 percent and the bottom 20 percent of samples were chosen and labels as mES and and mES low respectively. They were used for the survival analysis in COAD. The p-value is calculated according to each pathway, and the lower the value, the greater the difference in people’s survival according to mES. It shows how many samples belong to the top or bottom according to each clinical data type.(XLSX)Click here for additional data file.S5 TableAccording to mES, the top 20 percent and the bottom 20 percent of samples were chosen and labels as mES and and mES low respectively. They were used for the survival analysis in STAD. The p-value is calculated according to each pathway, and the lower the value, the greater the difference in people’s survival according to mES. It shows how many samples belong to the top or bottom according to each clinical data type.(XLSX)Click here for additional data file.S1 Fig(A) compares multi-omics to methylation, and (B) compares multi-omics to miRNA single-omics data. (C) compares multi-omics to methylation, and (D) compares multi-omics to miRNA single-omics data.(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 FigDepending on the number of samples and cancer, the number of ranks affects performance. Performance was compared according to the number of ranks.(DOCX)Click here for additional data file.S4 FigWhen CDF threshold uses MOPA, it becomes a cut-off criterion for each sample. Performance is significantly affected when it is determined how many rank features are selected for each sample according to this threshold.(DOCX)Click here for additional data file.S5 FigThe cancer subtype classification accuracy of COAD, and STAD was measured using features selected by the L1 and L2 method with different ranks in MONTI.(DOCX)Click here for additional data file.


Liquid biopsies are a non-invasive way to diagnose and monitor cancer using blood tests. Machine learning can help understand the genetic data from these tests, but it is challenging to validate clinical applications. In our study, we first compiled a large-scale dataset for cancer classification. Then, we extracted relevant features from the data and performed a binary classification, with the prediction outcome of either a sample collected from a cancer patient or a sample collected from an asymptomatic control. We used different convolutional neural networks (CNNs) and boosting methods to evaluate the classification performance. We have obtained an impressive result of 0.96 area under the curve. Finally, we tested the robustness of the models using test data from novel hospitals and performed data inspection to find the most relevant features for the prediction. Our work proves the great potential of using liquid biopsies for cancer patient classification.Liquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability to the model. In this work, we have used RNA sequencing data of tumor-educated platelets (TEPs) and performed a binary classification (cancer vs. no-cancer). First, we compiled a large-scale dataset with more than a thousand donors. Further, we used different convolutional neural networks (CNNs) and boosting methods to evaluate the classifier performance. We have obtained an impressive result of 0.96 area under the curve. We then identified different clusters of splice variants using expert knowledge from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Employing boosting algorithms, we identified the features with the highest predictive power. Finally, we tested the robustness of the models using test data from novel hospitals. Notably, we did not observe any decrease in model performance. Our work proves the great potential of using TEP data for cancer patient classification and opens the avenue for profound cancer diagnostics. In recent years, medicine has made numerous attempts to involve machine learning (ML) algorithms to improve patient outcomes and facilitate the work of clinicians. In addition to improving patients’ quality of life, machine learning can be used to reduce doctors’ professional burnout . PrimaryPrecision medicine has long sought to leverage molecular information about the disease to improve patient outcomes . Widely As liquid biopsies introduce a high level of data complexity, applying machine learning to data processing becomes a natural direction. The number of studies combining liquid biopsy analysis and machine learning is continuously expanding. The most recent applications of machine learning are related to circulating tumor cell (CTC) enumeration and CTC imaging ,11 or smApplying machine learning methods in clinical applications is still very challenging . For exaPreviously, we established that the platelet transcriptome retains remarkable stability in healthy individuals but changes dramatically under the influence of disease . Since tWe present a comparative study of various machine learning algorithms , boosting) on the task of liquid biopsy cancer classification using a recently introduced novel feature vector extraction.We show that using knowledge from the KEGG database works as efficient feature preselection.We study the robustness of the presented algorithms when presented with the samples collected at hospital locations that were not used in the training process.We identify the most important features for classification.However, before the full potential of utilizing data from the RNA sequencing of platelets is explored, we need to thoroughly test the stability and performance of the proposed methods. In this work, we capitalize on the ability of machine learning to unearth unapparent signals hidden in liquid biopsy data. Moreover, we take further advantage of the introduction of biological structures: the representation of various types of biological data as networks rather than as single features . An overWe gathered data from several datasets. However, it is essential to emphasize that all platelet samples were processed strictly according to the guidelines published by Best et al. . The datHealthy controls, NSCLC, and sarcoma samples were collected at VU University Medical Center , Netherlands Cancer Institute , and Massachusetts General Hospital .Samples of endometrial and ovarian cancer patients were collected at the Department of Gynecology, Gynecological Oncology, and Gynecological Endocrinology at the Medical University of Gdansk (MUG). The study was approved by the Independent Ethics Committee of the Medical University of Gdansk (NKBBN/434/2017). All patients from all included hospitals signed informed consent forms. Procedures involving human subjects were in accordance with the Helsinki Declaration, as revised in 1983.The final dataset contained the data of patients with 6 different cancer types, patients with brain metastasis from different primary sites, and 422 non-cancer patients (asymptomatic controls and multiple sclerosis patients). An overview of the data is presented in The DESeq2 package  with a vAs each RNA-sequenced platelet sample consisted of reads belonging to 39,843 different splice variant types, we decided to experiment with CNNs and convert these transcripts to a two-dimensional array. The value in each position represents the number of transcript counts detected; hence, the higher the gene expression of each splice variant, the higher its value. Values in the array were arranged according to their biological significance. As our primary classifier was developed to recognize the tumor-educated platelets (TEPs) of cancer patients, we decided to focus especially on pathways that might be deregulated due to tumor development. Hence, we searched the KEGG database  and seleThe full vector obtained from each platelet sample contained 267 rows corresponding to the 267 signaling pathways. In general, the rows can be divided into the following groups: metabolism, genetic information processing, environmental information processing, cellular processes, immune systems, other organismal systems, cancer, and other human diseases.Machine learning models are known to be vulnerable to various confounding factors. They exhibit reduced classification performance when presented with data from novel hospitals . Thus, wIn this section, three models used for the classification of cancer versus not-cancer are briefly presented. The code and data will be made available to the public upon paper acceptance.The first model used for the experiments was the classifier that was previously shown to obtain very high accuracy in the discrimination between healthy donors and ovarian cancer . A deep We also experimented with standard CNN models. We choose ResNet architecture  with 18-Another algorithm tested on our dataset was the eXtreme (XGBoost) classifier . We condThe test set included 30% of the stratified random samples. The remaining 70% of the samples were used for stratified 5-fold cross-validation. Class balance was preserved in each subset, including the split of controls into healthy donors used in ovarian cancer classification. Each model was then tested using the test set. Unless stated otherwise, each experiment was repeated three times. For data augmentation, we also experimented with MixUp , but no Based on our previous experiments, we decided it would be interesting to compare the accuracy of DNNs and CNNs as CNNs utilize local information. To verify whether the local information is useful in this task , we conducted experiments on random permutations of rows and columns . We demoHence comes the question of whether using the KEGG database is useful at all for this task. Using the KEGG pathway database allowed us to remove a number of features, from over 39,000 variables to roughly 23,000, eliminating noise and focusing on domain knowledge; however, it was not known what the impact of such filtering on classifier performance would be. As such, we additionally trained a boosting algorithm on all of the features  and observed that the balanced accuracy and AUC dropped to 0.819 and 0.922, respectively (compared to 0.889 and 0.96 when using KEGG feature preselection).Finally, we performed a robustness test by testing data from samples collected from a different location than the data used for the training process . For each experimental setting, the models were trained for 100 training rounds, each with a different hyperparameter set. The top 10 splice variants implicated in class prediction included genes associated with cell signaling and gene expression regulation , ribosome formation , immune system , and cell energetics (NDUFB11). Platelets are known to play a significant role in inflammation and immune responses. Thrombocytosis, an increased platelet count, is very common among cancer patients. Platelet-cancer crosstalk generates a vicious feedback loop: tumor cells secrete molecules that activate platelets, promoting, in turn, cancer-associated inflammation, cell proliferation, dissemination, and immune system evasion. Hence, the decrease in RNA expression in platelets collected from cancer patients stems from the intense translation of the mentioned genes to proteins in response to different cues associated with disease progression .According to a gene expression profiling study published by Gnatenko et al., NCOA4 encodes cDNA for RFG (RETproto-oncogene RET/PTC3) and belongs to the top 50 human platelet-expressed genes . Its rolContrary to what we have published previously  and contPlatelets demonstrate certain benefits over other liquid biopsy sources: convenient isolation, abundance, high quality of extracted nucleic acids, and the ability to process RNA information in response to external cellular cues . Hence, In this work, we have presented a meaningful, large-scale comparison of different methods for liquid biopsy classification in the form of RNA-sequenced platelets collected from a cohort of cancer patients and non-cancer donors.We have shown that using the information from the KEGG database as a feature preselection allows us to improve classifier performance. The features belonging to various KEGG pathways have high predictive power, and even separate groups of pathways could provide high accuracy for classification on their own.Further, we have validated that the models work well when using data from novel hospitals, which is of great importance for clinical trials. We conclude that the boosting method seems to be the optimal selection; it achieved the same accuracy as the more computationally expensive CNN-based methods. Furthermore, it allowed us to add a level of interpretability to the model, extracting the most important features that affected the final prediction results. Using only the 20 top features was sufficient to obtain 0.924 in the AUC metric. Further development and implementation of machine learning applied to platelets in clinical settings will mandate solid interdisciplinary measures. We have established features (splice variants) that are crucial for robust classification, enabling the development of a highly sensitive assay that is time- and cost-effective.


Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to consistently analyze these states within an evolutionary context on genetic clones. Here we present CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles onto the latent space of copy number clones. CONGAS+ clusters cells into tumour subclones with similar ploidy, rendering straightforward to compare their expression and chromatin profiles. The framework, implemented on GPU and tested on real and simulated data, scales to analyse seamlessly thousands of cells, demonstrating better performance than single-molecule models, and supporting new multi-omics assays. In prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ successfully identifies complex subclonal architectures while providing a coherent mapping between ATAC and RNA, facilitating the study of genotype-phenotype maps and their connection to genomic instability. Cancer cells, compared to normal ones, often acquire or lose chromosomes and, in the context of cancer progression and treatment response, copy number alterations can drive subclonal cancer dynamics. Here, we seek to use single-cell sequencing data from both RNA and ATAC data to determine tumour subclones associated with copy number alterations. We develop a Bayesian model that links a latent copy number state per subclone with transcript abundance (RNA) and chromatin accessibility (ATAC), and can cluster cells into subclones with similar copy number profiles. Our model works when input cells are sequenced independently, or with modern multi-omics protocols. By linking aneuploidy to gene expression and chromatin conformation, our approach provides a novel way to map genotypes with phenotypes to study the molecular basis of cancer heterogeneity. Cancer is a disease where multiple sub-clones evolve under positive, neutral and negative selection forces, with the complex molecular configuration of each sub-clone determining selection dynamics –3. We caFrom a computational perspective, this type of analysis has sparkled a number number of important data integration challenges (see the review in ). ExtendCONGAS [CONGAS improved over competing methods that decoupled copy number inference from clustering and, in this work, we follow a similar rationale to develop CONGAS+, which augments scRNA-seq with scATAC-seq data in order to resist statistical confounders such as allele-specific expression and post-transcriptional regulation [CONGAS+ can integrate independent/multiomics scRNA-seq/scATAC-seq measurements using a biology-informed latent space model. Using probabilistic programming on GPUs [CONGAS, stochastic variational inference and gradient descent allow CONGAS+ to analyse tens of thousands of cells seamlessly. In this paper, we prove this by analysing a B-cell lymphoma (∼6400), a basal cell carcinoma (∼2400 cells), a prostate cancer cell line (∼16000 cells) and a gastric cell line (∼7000 cells) in various combinations of scRNA-seq and scATAC-seq assays.Copy number inference algorithms for scRNA-seq or scATAC-seq have been independently investigated for both molecules –22. For CONGAS , a probagulation –25. CONG on GPUs  as in COCONGAS+ is a Bayesian model to infer and cluster, from scRNA-seq and scATAC-seq of independents or multiomics assays, phylogenetically related clones with distinct Copy Number Alterations  separate tumours from normal cells, ii) or detect distinct tumour subclones. Importantly, this deconvolution unravels the composition of a sample  in the RNA/ATAC data distributions. One caveat to detect peaks from RNA compared to ATAC is overdispersion, as transcript counts might be more difficult to separate for peak detection  of X can be either discrete raw counts, i.e. CONGAS+ ), for the multiomics the z are shared across RNA and ATAC and have a unique dimension N. In the flat model it is however possible to share the mixing proportions of among the two types of data, so to ensure that the same proportion of cells in the two data types are assigned to each cluster.The model is a parametric Dirichlet mixture for segments . Input X CONGAS+ , and a fependent  reflectsX, i.e., if the latent number of DNA copies of a segment i ∈ {1, …, I} is c, we predict r > 0 RNA transcripts and a > 0 chromatin peaks in the segment as t = R or A) is a segment-specific rate to observe RNA transcripts and ATAC peaks. The generic CONGAS+ likelihood, for either RNA or ATAC after marginalizing the local latent variable z, is defined asK-dimensional mixing proportions, xn,i the counts for the n-th cell and the i-th segment, and Φ is a K × I × H tensor for the probability distribution over latent CNAs, which here have h = {1, 2, …, H} possible states (H maximum copies). This assumption is justified by the resolution of current single-cell technologies. To map independent scRNA-seq/scATAC-seq on the same set of clusters, Φ is shared across data types, and f is a generic observational model that depends on X.As in other methods , 18, thexn,i mapped to the i-th segment of the n-th cell, the function f associated with the k-th mixture component is a Negative Binomial parameterized by mean and overdispersion, i.e.,For scRNA-seq/scATAC-seq integer counts θi, learnt from data), the library size factor of the cell  the joint RNA/ATAC CONGAS+ log-likelihood has a shrinkage formR and ΩA are the set of parameters for RNA/ ATAC. Please see Φ with a Gumbel-Softmax  their entropy [The mean depends on the expected counts per allele  [K ≤ 10 clones, adding n ≤ 3 new segments per clone following the tree structure. We generated clusters mixing proportions from a uniform Dirichlet, and RNA and ATAC counts mapped to CNAs were corrected assuming a linear model. With 10 replicas for each K, we created 90 datasets with 1500 scRNA-seq and 1500 scATAC-seq cells each. Please see CONGAS+ with K ≤ 10, BIC for model selection and a shrinkage parameter λ = 0.5, since the same multimodal signals were simulated for ATAC and RNA. We measured i) the Adjusted Rand Index (ARI), i.e. the similarity between the known and inferred cluster memberships, and ii) the mean absolute error (MAE) between simulated and inferred CNAs. We reported in K = 3. For the same cells, CONGAS+ did identify the simulated clones, with density plots mimicking the trends in the data perfectly (We tested the plain s (PBMC) , 31. Afts (PBMC) , and obts (PBMC) ) and ATAs (PBMC) ) profileerfectly .CONGAS+ on GPUs is extremely fast.Across all tests we observed a very good performance (median ARI > 0.7 and MAE < 1) suggesting that we can retrieve clones assignments and their CNAs . We alsoThe same test has been carried out against alternative tools for scRNA-sq or scATAC-seq data . In partCONGAS+ (RNA/ATAC), CONGAS (RNA) and copyKAT (RNA). Using a joint ATAC-RNA assay, CONGAS with λ = 0.1—i.e., weighting 90% the ATAC more than the RNA—detected CNAs that distinguish tumour from normal cells, obtaining a median ARI ∼0.7 on ATAC but a lower ARI on RNA , we performed 10 runs to compare the ARI for cluster assignments against tumour/normal labels [CONGAS+ did not fit the ATAC bimodality, merging 63% of tumour and 90% of normal cells together. Instead, for λ < 0.25—more weight assigned to ATAC—assignments retrieved are perfect. As expected, the model is never able to separate tumour and normal cells from RNA, due to its unimodal distribution. Overall, these tests show that if the quality of ATAC/ RNA are different λ can indeed act as a shrinkage effect, and CONGAS+ offers a principled approach based on likelihoods to inspect the optimal λ, which is set by default to 0.5. We provide a guideline to tune its value based on the results of the default run, that considers how well the the inferred distributions fit the data in both modalities  because it directly accesses DNA. This technology is however expensive and laborious, and it is therefore interesting to assess how much we can retrieve from scRNA/scATAC sequencing of a sample with matched scDNA-seq because, as alternative technologies, they have the advantage of measuring cellular phenotypes.CONGAS+ on scATAC-seq and scRNA-seq using the segmentation and CNA-priors determined from the most abundant scDNA-seq subclone in [CONGAS+ (i) maps RNA/ATAC cells to the corresponding scDNA-seq clones and (ii) infers the correct integer copy number values for each clone.For the gastric cancer cell line SNU601, we collected scDNA-seq and scRNA-seq from , and scAclone in   among subclone , such assubclone , identifCONGAS+, as cells are phased against the input measurements. While this type of assay are yet to become common practice, we sought to test our model against B-cell lymphoma data [CONGAS+ which shares the latent variables across RNA and ATAC, and the other exploits the plain version where the variables are independent. We aimed at measuring, besides the composition at the subclonal level with the multiomics CONGAS+, if the plain model splits cells consistently with the joint assay.Multiomics ATAC/RNA assays represent the ideal test-bed for oma data  sequenceoma data . We carroma data . One tesFirst, we observed that cell types were distinguishable in a joint ATAC/RNA low-dimensional representation  Fig 7A)Fig 7A). CONGAS+  splits tumour and normal cells  identified 5 clusters, with parental and ENZ-48 cell lines clustered together, and the two resistant cell lines split in 4 clusters CONGAS+ inferred CNAs private to each resistant line, such as an amplification on the p-arm of chromosome 1 Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 Text(PDF)Click here for additional data file.S4 Text(PDF)Click here for additional data file. 14 Aug 2023AttachmentResponse to reviewers.pdfSubmitted filename: Click here for additional data file. 30 Sep 2023Dear Dr Caravagna,We are pleased to inform you that your manuscript 'A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing' has been provisionally accepted for publication in PLOS Computational Biology.Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. 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Long-read transcriptome sequencing (LRTS) has the potential to enhance our understanding of alternative splicing and the complexity of this process requires the use of versatile computational tools, with the ability to accommodate various stages of the workflow with maximum flexibility.We introduce IsoTools, a Python-based LRTS analysis framework that offers a wide range of functionality for transcriptome reconstruction and quantification of transcripts. Furthermore, we integrate a graph-based method for identifying alternative splicing events and a statistical approach based on the beta-binomial distribution for detecting differential events. To demonstrate the effectiveness of our methods, we applied IsoTools to PacBio LRTS data of human hepatocytes treated with the histone deacetylase inhibitor valproic acid. Our results indicate that LRTS can provide valuable insights into alternative splicing, particularly in terms of complex and differential splicing patterns, in comparison to short-read RNA-seq.https://isotools.readthedocs.io/.IsoTools is available on GitHub and PyPI, and its documentation, including tutorials, CLI, and API references, can be found at   Long-read transcriptome sequencing (LRTS) allows for full-length sequencing of expressed transcripts. In contrast to short-read RNA-seq, this technology does not require fragmentation of transcripts and thereby avoids introduction of errors due to alignment ambiguity and other technical artifacts. This is particularly relevant for the analysis of complex alternative splicing, since short reads cannot be assigned reliably to transcripts over longer genomic distances, and thus facilitates the identification of novel transcripts  and quantification (RNA-seq) of transcripts  inhibitor valproic acid (VPA). We compared the quantification of LRTS with RNA-seq on the same samples both on the recovered transcript level as well as on the splice event level. Our analysis revealed known and novel splicing events with The preparation of the samples processed here is described in IsoSeq subreads were processed using Iso-Seq v3.4 pipeline with recommended parameters , resultiSTAR aligner version 2.7.6a  with the following identifiers: ENCFF089AFK, ENCFF220OWX, ENCFF241CGD, ENCFF248QKX, ENCFF373BNI, ENCFF419FNU, ENCFF875ILB, and ENCFF885VJU.We downloaded CAGE TSS peaks for HepG2 cells from the ENCODE data portal  and polyadenylation sites (PASs), IsoTools employs a gene-wise peak calling approach to identify the most prominent start and end positions of reads. For each transcript, we assign the TSS and PAS by identifying the peak closest to the most read start and end positions, respectively. By using this method, IsoTools standardizes the positions of TSSs and PASs across all transcripts of a given gene, without being biased by the reference annotation. Finally, IsoTools computes quality control (QC) values for each transcript based on various features, to facilitate the detection and subsequent filtering of technical artifacts see .Transcript filtering is implemented as a query syntax, based on logical combinations of named “tags,” by convention, a single word in capital letters. These tags are defined Python expressions, which are evaluated in the context of the transcript dictionary, so it may depend on all metrics and properties of the transcript. IsoTools provides predefined tags, covering technical artifacts, but also the novelty categories, and properties of the reference annotation. In In analogy to the commonly used definition on splice graphs , we defiA supporting one of the alternatives and B supporting the primary. This definition results in a finite set of classified binary ASEs for each gene. They can be quantified by the percent spliced-in index (PSI), defined as the number of long reads supporting transcripts from A over the total number of reads, supporting transcripts from A or B.Bubbles are structures in the segment graph, with two paths starting in a common segment d xe cf. . We defiz-test, apply if two individual samples are compared.We implemented three statistical tests for differential splicing. The first two, likelihood ratio test with binomial model and two-proportions i, and For the “binomial likelihood ratio test,” the number of supporting reads is modeled with a binomial distribution. The likelihood ratio test is specified by the statisticwhere z-test” can be used as an approximation. The test is specified by the statisticwhere A) and primary (set B) variants, respectively. Both tests yield very similar results .In addition to the splicing structure, IsoTools extracts a range of information from the alignments, including the number of reads per transcript, several QC metrics, and mutation information, and integrates this information with data from the reference genome and annotation. The resulting tree-based data structure  can be aTo validate the method, we generated datasets from human hepatocytes treated with the HDAC inhibitor VPA and untreated cells (CTL), yielding 2 615 181 and 4 200 885 aligned full-length non-chimeric poly-A HiFi reads, respectively. Our saturation model (Section 2) indicated that for both samples, the probability of observing even rare transcripts (one TPM) was over 70%, and for transcripts expressed at two TPM or more, the probability of observing at least two reads was close to 100%. These results demonstrate the saturation of transcript discovery at the given concentration and detection threshold .To minimize the impact of technical artifacts and improve the accuracy of transcript identification, IsoTools employs various QC measures, such as the transcript length distribution, to identify systematic depletion of shorter or longer transcripts in comparison to the reference . Additiopermissive, balanced, and strict, suitable in different analysis situations. Filter queries for the strategies can be found in Transcriptome reconstruction yields a high number of gene and transcript candidates. While a large fraction is supported by only few reads, or can be traced back to technical artifacts, the identified numbers by far exceed the number of annotated transcripts, with a great range of experimental evidence and biological properties. For any transcript-based downstream analysis, a well-tailored filtering strategy is crucial. To this end, we implemented a filtering query syntax that enables the user to efficiently implement arbitrarily complex filtering rules, to fit the specific requirements (Section 2). We demonstrate three different filtering strategies, here called permissive filtering, we request at least two reads per transcript, and removing potential technical artifacts. This reduces the numbers to 21 447 genes, with 157 134 transcripts, of which 77.8% are not annotated in GENCODE. Since novel transcripts may require additional evidence, the balanced filtering additionally requires at least 7 reads (about one TPM) for transcripts not annotated in the reference, yielding 15 315 genes, with 57 698 transcripts, 49% of which are novel. However, many of these transcripts contribute less than 5% to the total read count of the gene, yielding complex gene models with up to 134 transcripts per gene even after balanced filtering. To simplify transcript analysis on gene level for downstream analysis, we suggest keeping only transcripts with a “substantial” contribution of at least 5% to the genes total, and at least 7 reads. This strict strategy yields 12 267 genes, with 21 582 transcripts, of which 32% are not found in GENCODE. Generally, stricter filtering yields higher fraction of known transcripts , while novel combinations of existing splices sites  as well as transcripts using novel splice sites  are reduced . For  reduced .We compared IsoTools transcriptome reconstruction with four other recently published tools for transcriptome reconstruction  fully match known transcripts (FSMs) whereas the most prevalent category for novel transcripts are categorized as “novel combinations” of known splice junctions, affecting 9.9% and 9.3% of the reads, for CTL- and VPA-treated samples, respectively . Often tin vivo relevance.ES transcripts are among the next most frequent categories of novel transcripts; however, we found that a large part within this categories must be attributed to misalignment of short exons eq reads , a techneq reads . We usedeq reads . PATL1 ir = 0.75 and 0.76 for VPA and CTL), confirming the suitability of long reads for expression quantification. However, we observed a reduced correlation on the transcript level (r = 0.42 and 0.43), which may on the one hand be attributed to challenges in transcriptome reconstruction using long reads, or bias and ambiguity from short-read transcript assignment on the other. To provide a quantification approach that is independent of the transcriptome reconstruction step, we developed a method to detect ASEs as bubbles in the segment graphs of gene models .ASEs are subdivided and classified according to the underlying molecular principles as ES, IR, 5' and 3' alternative splicing (5AS and 3AS), and ME exons . Relativr = 0.84), 5AS (r = 0.8), 3AS (r = 0.74), and IR (r = 0.58), while quantification of more complex ME events was not correlated (r = −0.05)  . This rez-test to identify DSEs between treated (VPA) and untreated (CTL) hepatocytes, and found 777 DSEs in 538 different genes at an FDR of 1%. These events include 26 ME, 253 ES, 90 5AS, 59 3AS, and 349 IR events, featuring different levels of complexity. Also for the differential events, short-read quantification with rMATS was in good agreement, with a Spearman correlation of 0.68 , the most significant splicing event involves the ME exons 4A and 4B of SLC39A14 (Solute Carrier Family 39 Member 14), a metal cation transporter responsible for the uptake of trace elements such as zinc, iron, and manganese in the liver. Both versions, containing either exon 4A or 4B, yield functioning proteins, but the uptake kinetics vary substantially . A similq-value = 3.55E−111, PSI 9% in CTL and 15% in VPA). Again, rMATS analysis of short-read RNA-seq confirmed this differential event (PSI in CTL 12% and VPA 18%). FN1 is one of the first genes for which alternative splicing was described, and regulation and functional effects of splicing in FN1 have been studied extensively (ES events account for 32.6% of differential splicing. The most significant differential ES event affects increased inclusion of exon 32 of the FN1 gene (fibronectin 1; Here, we presented IsoTools, a flexible and powerful Python framework for the analysis of LRTS data. It provides data structures to search, access, and filter the transcripts, as well as functionality to compute QC metrics, to compare and annotate the transcripts with reference annotations, to integrate data from several LRTS experiments, to quantify transcript expression levels based solely on LRTS, to perform statistical analysis for differential splicing, and to export of the data in several output formats. In addition, the tool facilitates the depiction of summary statistics as well as complex splicing models of individual genes and transcripts. IsoTools stands out with its unparalleled flexibility, thanks to its modular design and interfaces for custom metrics and algorithms. This makes it a highly adaptable solution for LRTS data analysis, particularly for advanced users seeking to tackle novel use cases, such as single cell LRTS analysis. The tool’s ability to import and export transcriptomes in gtf format not only provides added convenience, but also ensures compatibility with other tools and facilitates integration into existing workflows. With the ability to import transcriptomes from different sources, users can also easily compare and validate the results from different pipelines to ensure the accuracy of their analysis. The modular design of IsoTools represents a significant departure from traditional, rigid data analysis tools. This innovative approach allows users to fully customize the tool and explore new applications, making IsoTools a truly unique and valuable resource for LRTS data analysis.To characterize novel transcripts, we introduce a fine grade biologically motivated classification scheme, refining the established technically defined classes. The categories facilitate direct interpretation of differences between samples, and may hint toward specific disturbed splicing mechanisms, such as introduced by SF3B1 hotspot mutations, which specifically result in shifted in 3' splice sites (We propose a graph-based approach to identify and classify alternative splicing, based on bubbles in the segment graph. This definition yields binary events, which are classified by event type and quantified with PSI values. These events provide the basis for statistical tests for the detection of differential splicing, either between two samples or groups of samples. This approach is fundamentally different from differential expression analysis on transcript level, for which the well-established framework based on negative binomial generalized linear models . Howeverz-test and the likelihood ratio test with binomial model implemented here, neglect this variability, making the analysis prone to false positive results. Thus, the third implemented test, the likelihood ratio test with beta-binomial model, facilitates the comparison of groups of samples. The biological variability within the groups is estimated from the data and taken into account. While this approach promises to be more robust, it depends on replicates and thus could not be demonstrated with our data; however, it is demonstrated with data from ENCODE in the differential splicing tutorial which is part of IsoTools online documentation. To date, LRTS datasets with biological replicates are the exception, but continuously falling sequencing cost, higher throughput in combination with sample multiplexing, as well as better software facilitating additional applications will improve the cost benefit ratio of biological replicates.Much like gene expression, alternative splicing is subject to biological variability within samples and groups of samples of the same condition. Statistical tests that compare individual samples, such as the two-proportions In primary human hepatocytes, IsoTools identified aberrant splicing events in different categories, caused by the HDAC inhibitor VPA. The role of HDACs in modulating alternative splicing has recently been emphasized by investigating the role of histone marks in the choice of splice sites and regulation of splicing . We obsein vivo.IsoTools is simple to install, flexible, versatile, and easy to use. It offers novel functionality, including expression quantification and differential splicing analysis, extending the range of potential applications for LRTS. Its main strength comes from the open and modular framework design, facilitating exploitative analysis and development of specific use cases. For starters, the extensive documentation contains several example workflows, covering relevant use cases which can simply be adapted by the user. To realize standardized workflows, there is also a command line interface, providing some of the functionality without the need to write python code. We demonstrated the utility of our tool by analyzing LRTS data from hepatocytes treated with VPA, and identified novel and DSEs, of which several are expressed also in human liver samples and are thus likely relevant btad364_Supplementary_DataClick here for additional data file.


Molecular subtyping and therapeutic target identification in osteosarcoma have been extremely challenging due to the high degree of heterogeneity in its genetic profile and the lack of specific oncogenic driver genes. Integrating multiplatform profiles of somatic copy-number alteration, gene expression and methylation, three molecularly distinct and clinically relevant subtypes of osteosarcoma were revealed. Additionally, several novel gene signatures were identified based on the subgrouping results. We demonstrated the pivotal role of the cross-platform analysis for investigating complex diseases such as osteosarcoma.Osteosarcoma (OS) is the most common primary bone malignancy that exhibits remarkable histologic diversity and genetic heterogeneity. The complex nature of osteosarcoma has confounded precise molecular categorization, prognosis, and prediction for this disease. In this study, we performed a comprehensive multiplatform analysis on 86 osteosarcoma tumors, including somatic copy-number alteration, gene expression and methylation, and identified three molecularly distinct and clinically relevant subtypes of osteosarcoma. The subgrouping criteria was validated on another cohort of osteosarcoma tumors. Previously unappreciated osteosarcoma-type-specific changes in specific genes’ copy number, expression and methylation were revealed based on the subgrouping. The subgrouping and novel gene signatures provide insights into refining osteosarcoma therapy and relationships to other types of cancer. Osteosarcoma (OS) is a primary bone malignancy, and the most common bone cancer in pediatric patients. The standard of care for all patients diagnosed with osteosarcoma remains MAP chemotherapy, the same treatment first introduced in the late 1980s . WhetherMolecular genetic studies of osteosarcoma have primarily focused on specific genes or pathways using single platform data. Single nucleotide polymorphism (SNP), structural variations (SV), somatic copy-number alterations (SCNAs), loss of heterozygosity (LOH), and RNA expression have all been investigated ,4. ThesePreviously, the potential prognostic biomarkers and subtyping in osteosarcoma was mainly identified from single-platform data and a clustering approach. For example, Southekal et al. , recentlIn summary, we used multi-platform profiling and SNF on osteosarcoma patient samples with diverse clinical outcomes. SCNAs, gene expression and methylation data were combined, seeking to bring novel insights into the biological process underlying osteosarcoma tumors that have distinctly different prognosis. The data information is listed in We downloaded relevant drug sensitivity data from the Genomics of Drug Sensitivity in Cancer . This data set includes drug response data and genomic marker genes of sensitivity.We used Birdsuite 1.5.5   and PennThe processed gene expression data from either the GeneChip™  Human Exon 1.0 ST Array or the Human Gene 1.0 ST Array was downloaded directly from TARGET dbGAP (accession phs000218.v22.p8.c1.) and ArrayExpress (A-AFFY-142). These gene expression data have been preprocessed as TPM value (transcript per million). To combine datasets from different resources, we further normalized all data using quantile normalization from R preprocessCore package. Batch effects were removed by R sva package with ComBat.The processed and normalized methylation data was downloaded directly from TARGET dbGAP (accession phs000218.v22.p8.c1). For the final matrix, the top one percent of variable probes were retained based on the variation among the samples for further downstream clustering analysis. Then R FDb.InfiniumMethylation.hg19 package was applied to find the closest genes of specific IlmnID for annotation purposes.Similarity network fusion (SNF)  firstly MAster Regulator INference algorithm (MARINa)  infers tp-value ≤ 0.05) for three subgroups identified by the SNF method, and the patients with different SCNA status on 17p13.1–17q11.2. The R tool, “survival” was used for survival analysis, and the Kaplan–Meier survival curve plots were generated. In addition, a log-rank or Mantel–Haenszel test was conducted to assess survival differences between patients with high or low expression (median as threshold) of the top ten master regulators. Univariate Cox hazard ratios (HRs) with 95% confidence intervals were calculated.We performed survival analysis using the log-rank test and Cox-regression analysis (KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis of a gene set was performed using R clusterProfiler package with Benjamini–Hochberg (BH) adjustment. Then, a dot plot was used for visualization.p-value or adjusted p-value < 0.05 was considered significant.Except when specified, all analyses were performed using R (4.0.3). Except for the specified statistical significance in The sample’s size has been pre-determined, and after the subgrouping we have three subgroups of the patients with sample sizes of 30, 27, and 34 for the training set. The statistical power was calculated from R package pwr using an alpha level of 0.05, and a moderate effect size of 0.5, which yields power greater than 0.9 for a comparison between any two groups. In the validation set, there were 16, 10, and 8 samples in three groups. Using the same type I error and effect size, the lowest power was 0.5 for subgroup 3 with sample size 8; whereas the other two powers reached 0.85, using the same parameters. This statistical potential has illustrated a high likelihood of detecting meaningful differences by SNF subgrouping.t-test was applied. The Pearson’s correlation coefficient was calculated for the association between gene expression and SCNA. The Pearson correlation measures the strength of the linear relationship between two variables, with a value of −1 meaning a total negative linear correlation, 0 being no correlation, and +1 meaning a total positive correlation.Unsupervised hierarchical clustering with Euclidean distance was used for the cluster analysis on single set of data, i.e., SCNA, gene expression, and methylation. For relatively moderate sample size or categorical variables, we used non-parametric tests. Specifically, Fisher’s exact test was applied to drug response and metastasis data to evaluate the performance of predictive classification. The Kruskal–Wallis test was applied to find the most varied genes among the three subtypes. When the sample size was large or the variables were continuous, the Eighty-six osteosarcoma tumor samples were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program with matched SCNA, gene expression and DNA methylation profiles. Another cohort of 38 osteosarcoma samples from Dr. Eberhard Korsching’s lab  with SCNConsidering the large total number of genes in the human genome, we firstly filtered the gene data based on the following criteria: genes with abnormal copy number representing at least 30% of samples; genes with either high expression (top 1000) or high variation (top 2000) in expression; genes with high variation in methylation (top 1%). p-values for survival, drug response and metastasis among these three subgroups are summarized in Similarity network fusion (SNF)  is desigp-values for survival, drug response and metastasis among three subgroups from SNF or unsupervised clustering  that have abnormal copy number represented in at least 30% of the samples to avoid the over-representation of normal copy number, which might influence the statistical significance. The Manhattan plots  for expression, with p-value < 1 × 10−5 in the training set and p-value < 0.01 in the validation set; (3) for methylation, with p-value < 1 × 10−6 for the training set. After subgrouping, we investigated the most varied genes among these three groups by calculating the Kruskal–Wallis test an plots  show thep-values smaller than 0.01 in SCNA, gene expression and methylation from the training and validation sets, which are highlighted in red in the Manhattan plots , we found that a large percentage of patients, ranging from 17% to 52%, are inclined to have abnormal copy number (either gain or loss) for these genes. An overall trend of higher percentage of patients with copy number gain, compared with copy number loss  among the three subgroups. The top 50 genes that significantly vary among the three subgroups are mostly located at 17p13.1–17q11.2 , in SNFgroup1 as compared with SNFgroup2, are shown in We used RNA-seq data to profile transcriptional gene expression in these samples from osteosarcoma patients. The algorithms of master regulator inference algorithm (MARINa)  is desigNotably, three transcriptional modules out of the top ten MRs, CDK6 , EGFR 5 and PANXp-values in survival and drug response  was then applied to the genes with p-values smaller than 0.05 from the ANOVA test  OVA test B. Hippo Presently, the exact role of methylation in gene expression is unknown. A recent pan-cancer study suggested conflicting methylation–expression correlations exist beyond the commonly accepted associations between the promoter region methylation and silencing of gene expression . We obsePrior studies in osteosarcoma, either using single- or multi-platform data, have focused on identifying driver or marker genes. Recent research shows that, even though high levels of structural variations and mutations cluster in osteosarcoma and contribute to the genomic heterogeneity, few of these recurrent alterations detected from single-platform data can be related to clinical outcome . On the Combining SCNA, gene expression and methylation through SNF, our integrated and multi-platform investigation reveals three molecularly distinct and clinically relevant subtypes in OS tumors which appear to exhibit differing degrees of aggressiveness or responsiveness to current standard-of-care treatments. The subgrouping criteria was validated using another dataset. Then, we investigated the differences among these three subgroups based on single-platform profiles, and identified several novel factors that potentially contribute to the variation in clinical outcomes at the genomic, transcriptomic and epigenetic level. There is moderate overlap at the individual gene level among these single-platform profiles (only 21 genes intersected among them). Among the top ten MRs identified by MARINa, only two genes (EGFR and NCAM1) have abnormal copy number for at least 30% of tumor samples. Moreover, our single-platform analysis based on the subgrouping reveals different potential marker genes that are associated with clinical outcomes. Among all the potential gene signatures, BRD4, CDK6 and EGFR have been tested in pre-clinical models and their corresponding inhibitors can serve as strong candidates for clinical testing in the future. Lastly, as discussed before, DNA expression and copy number are largely positively correlated, but with a varied degree of correlation from gene to gene. The methylation contributions to transcriptional regulation occur in a complex and dynamic manner. Cross-platform analysis is therefore crucial, especially for investigating complex diseases such as osteosarcoma, to capture multi-layered connections and complexity from different sources.The osteosarcoma risk assessments provided by this study have the potential to become clinically relevant classifiers. For example, patients with SNFgroup2 profiles experienced the worst clinical outcomes and could be stratified at diagnosis as high-risk patients that warrant more aggressive chemotherapy treatment or prioritization for trials evaluating novel therapies. Indeed, our analysis suggests several targetable pathways that may be important to the biology of some SNFgroup2 tumors. This “wide net” approach may overcome some of the challenges presented by the well-known intra-tumoral heterogeneity of osteosarcoma, which has historically made it challenging to find prognostic biomarker genes that are suitable for universal application. Determining the true utility of such strategies will require additional studies, including prospective analysis in a clinical trial setting.Going beyond this obvious clinical application, these integrated multi-omics findings in the clinical setting present opportunities to guide targeted drug discovery and validation using cell lines or animal models. Genome-wide and focused screening studies (in vitro or in vivo) facilitated by CRISPR and other techniques may be especially powerful next steps with potential to accelerate the process of novel target identification. It would be reasonable to expect that some of the marker genes identified in this analysis will prove to be genuine drivers of aggressive disease in osteosarcoma.Our integrated, multidimensional investigation into osteosarcoma divided osteosarcoma tumors into three subgroups with distinctive molecular features and clinical outcomes. These data provide a comprehensive genomic architecture for osteosarcoma and emphasize the need for data integration from different platforms. Going forward, developing clinically relevant classifiers, and discovering targeted drugs using cell lines or animal models will lead to more effective cancer treatments for osteosarcoma.


P4 medicine  is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data.We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models .Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients.The online version contains supplementary material available at 10.1186/s12859-023-05400-2. In the past, diseases were considered the result of alterations in the function of one or more genes, so the diagnosis and treatment of patients were based on reductionist approaches to correct these genetic alterations. However, a fundamental revolution in medicine is a change from this reductionist view to a more holistic (systems biology) approach to understanding the biology of disease –3. In syP4 medicine  is the latest approach to overcoming complex diseases like cancer. The development of computational models that can use omics data to predict disease and offer proper drugs to each person is very challenging , 7. One Many studies aimed to obtain genes expressed differently in tumors and normal. These genes are critical to understanding the function of the disease, but in these studies, two groups of individuals were compared –20. At tIn this study, we developed a model called DeeP4med to apply deep learning in P4 medicine. We used the datasets collected and preprocessed  which isAfter creating the model, we evaluated the model’s performance with two different approaches: (1) Performance analysis of the Transferor and Classifier of DeeP4med. (2) Investigating the validity and biological significance of the data generated by the model by Differentially expressed genes (DEGs) and enrichment of analysis.Transferor’s performance for changing the type of mRNA , we computed its F1-Score  and disease  beforehand. For this purpose, we report their F1-Score, Precision and Recall, for tissue and disease classification. These performance measures, along with their corresponding confusion matrices summarised in Fig. In order to show the To reduce the data dimensionality, we used principal component analysis (PCA) as a preprocessing step . When deSince our primary purpose was to develop a model that (i) can predict the disease state  on a patient-by-patient basis based on (RNAseq) healthy tissue information and (ii) predict the healthy state from known tumor information , we designed DeeP4med to produce two types of expression matrices for each tissue:(1) “transfer tumor” (TT). This data is generated by applying DeeP4med to RNAseq data form normal tissue samples .For notation clarity, we label this data set as ON_TT(2) “transfer normal” (TN). This data is generated by applying DeeP4med to RNAseq data from tumor tissue samples .For notation clarity, we label this data set as OT_TN.Half of the data are original in these two types of expression matrices; the remaining are transfers. To evaluate the model's performance of these two matrices in each tissue, 1) DEGs analysis and 2) ENRICHMENT analysis is performed. Then the results were compared against each other. The number of samples in each tissue is shown in Additional file  DEGs ana ENRICHMEDEG analysis between tumor and normal states was performed using the limma package  on the iWe predicted that if DeeP4med works accurately, there should be a significant overlap of DEGs identified in conditions (i) and (ii).p-value ≤ 0.05 and LFC  ≤ -1 (down-regulated) and LFC ≥ 1  were considered for further analysis. The result files from the idep.951, including DEGs and PCA plots for each tissue, are in the Additional file To test this, genes with adjusted Using Eqs.  and 2),,2), the The prostate had the highest true positive rate, so we chose it to evaluate the model's performance from a biological aspect. Figure By DEGs and using the Enrichment Analysis Visualization Appyter website, severalhttps://comphealth.ucsf.edu/app/tcga-110) website, the expression profiles of real prostate cancer samples in the TCGA database were compared with the expression profiles of different cell lines, as shown in the Additional file According to Table According to Table https://www.coremine.com/medical/#search) database revealed that there were approximately 20 articles related to prostate cancer and MAPK cascade (GO:0000165) and 12 articles related to actin cytoskeleton reorganization (GO:0031532) and prostate cancer. In 2019, Wu et al. [Based on enrichment results in Table u et al.  with an u et al.  for the u et al.  MAPK4 exAccording to Table To better evaluate the performance of our model, we compared the matrices produced in the model with the original matrix (ON/OT). We used two different approaches: a biological approach using the DEGs method and a statistical approach using the PCA and UMAP methods which show the distribution of samples by reducing the dimensions. The output of our model for each tissue is two types of matrices: OT/TN and ON/TT. Next, we separately compared the DEGs obtained from each of these matrices with the DEGs obtained from the true matrix by the Venn diagram. There are 13 tissues; for each tissue, there are two comparisons , and in each comparison, two states of up and down were analyzed separately. Therefore, 52 Venn diagrams were obtained . Transcriptomic data is readily available through different projects  and is also more dynamic than genomic data alone, as it also reveals changes in the epigenome of cells and how gene expression is modulated by different disease conditions but also, in the context of cancer cells, by the interaction of tumor cells and the tumor microenvironment. That means RNAseq captures the changes in disease cells by measuring the cell's gene expression profile. Because the changes in all genes are measured, RNAseq data is very comprehensive and suitable for applications in Deep Learning. Our fundamental goal in developing DeeP4med was to use deep learning to predict changes in gene expression profiles. In this regard, previous work has attempted to do this using different datasets. For example, DeepChrome uses histone modifications to predict gene expression profiles . HE2RNA Our deep learning model contains two separate deep models, Classifier and Transferor, based on their function. We first trained a neural network called Classifier to classify the type of gene expression profiles  and their corresponding tissues. Then, using Classifier as our discriminator, we trained Transferor, an autoencoder for transferring the type of gene expressions from the tumor to their nearest normal version and vice versa while keeping their tissue of origin unchanged. The Transferor is conditioned on the type of input sample  and simultaneously generates the normal and tumoral versions of the input. Classifier helps us accurately measure the performance of the Transferor in terms of concordance between the expected type and tissue and that of the first generated mRNA. Another performance measure that has been introduced to the loss function of the Transferor is the mean squared distance between the input sample and the second generated mRNA.The loss function of the Transferor is a weighted sum of three losses. The first two losses are computed based on the Classifier's output and measure the Transferor's performance as a classification task. The third loss computes the distance between the generated mRNA and the input and can be considered a regression task. We used mean squared error to measure the distance between the input and the generated mRNA. We use cosine similarity to measure the correspondence between the type and tissue of the input and generated mRNA . To asseThe classifier has an architecture similar to that of the model proposed in DeePathology , which iThis network can be symbolized as:Combination of autoencoder and classifier with weighted loss as below:Such that d before . We usedThe transferor consists of an encoder and two decoders that share parameters. In each forward pass of the model, an mRNA profile and its type are encoded and again concatenated to each type separately. The resulting vectors are encoded sequences of mRNA concatenated with a type that is opposite or the same as the input. Then, the type of augmented embeddings is fed to the decoders individually Fig. . FormallMLPφ shows our encoder is parametrized by φ and h is the embedding of an mRNA given its type. Equations  and 7)  7) show We want DeeP4med to keep the tissue unchanged but control the type of mRNA. Formally, it should satisfy Eq. :8\documeEach output of the Transferor contributes to the loss function: The first output, which should have the same tissue but the opposite type as the input, is evaluated by the Classifier. The second output, which should have the same tissue and the same type as the input, is used to measure the similarity between the input and output. Finally, the total loss for this network is a weighted sum of the cosine distance between the Classifier’s outputs and the expected tissue and type and the mean squared distance between the generated mRNA and the input. Mathematically, we have Eq. :9\documeAt this stage, only the Transferor parameters are updated, and the classifier parameters are frozen.Units: The number of neurons in layers is critical to finding the best architecture.Activation function: In Artificial neural network (ANN), an activation function is applied after a weighted sum of input for each neuron. ReLU (Rectified linear unit) [x))[ar unit) , definednit) [x)) and LineDropout rate: Dropout layer set to zero values with probability as defined rate. This is a widely used technique for preventing overfitting in recent years.In Neural Networks (NN), there are many hyperparameters, and tuning them is critical for finding the best model. Given that we used multilayer perceptron networks, the hyperparameters are:The hyperparameter search space is shown in the Additional file A general review of all the results shows that DeeP4med has been successful in terms of machine learning methods. Also, regarding biological results, DeeP4med performs relatively well depending on the tissue type. Of course, DeeP4medis still needs to complete and have considered all aspects. Indeed, the performance of the model can be improved in future studies.Additional file 1. Details of computational performance and hyperparameter space of the DeeP4med (Transferor and Classifier) as well as biological evaluation of the model.Additional file 2. Dataset (gene expression matrix of different tissues), DEGs and enrichment analysis results, common and important genes between different matrices.