Cancer is a complex, heterogeneous collection of diseases with hundred of different subtypes. Genomic aberrations that are primarily thought to be the root causes of different cancers have been clinically used as evidence for both the diagnosis and also matching individual patients to proper treatment options. However, the complexity of cancer manifests itself differently in each patient when inspected at the molecular level. Even patients with the same cancer type rarely have identical root causes for the same disease. Without an extensive molecular profile of a patient, it has been challenging to match the patients to the best treatment options. To remedy this, comprehensive genomic profiling panels have been developed to monitor hundreds of genes for a given patient, which has helped broaden the treatment options for patients. However, genomic aberrations detected in such panels still do not reflect the full complexity of how a tumour responds to cancer drugs. In this study, we demonstrate that using an additional layer of molecular information  on top of genomic aberrations that can be detected with cancer gene panels can provide significant improvements in predicting the cancer drug response in pre-clinical cancer models. Thus, this study serves as a push towards incorporating the transcriptome measurements more routinely in (pre-)clinical practice.Comprehensive genomic profiling using cancer gene panels has been shown to improve treatment options for a variety of cancer types. However, genomic aberrations detected via such gene panels do not necessarily serve as strong predictors of drug sensitivity. In this study, using pharmacogenomics datasets of cell lines, patient-derived xenografts, and ex vivo treated fresh tumor specimens, we demonstrate that utilizing the transcriptome on top of gene panel features substantially improves drug response prediction performance in cancer. Cancer is a collection of diseases characterized by abnormal cellular growth and the invasion of other body parts. It affected 19 million people in 2020 and was the cause of 9.5 million deaths that year alone , promisiAlthough comprehensive genomic profiling using cancer gene panels has demonstrated value in broadening the treatment options for patients based on matching a patient’s genomic lesions to cancer driver gene aberrations associated with FDA-approved treatment indications ,9, the pHere, we set out to quantify the extent to which the usage of the transcriptome as an additional data modality improves the drug response prediction performance compared to only the genetic features restricted to the cancer gene panels (such as mutations and copy number variations). We leveraged publicly available pharmacogenomics datasets, including genomic and transcriptomic profiles and drug sensitivity measurements in three types of datasets: cancer cell lines  between the percentage of gene expression features among the top 100 features and an increase in MO’s predictive power over PS (p < 0.05)  . These r < 0.05) .2 value of 0.03, and the MO features yielded an R2 value of 0.49. Hence, the top predictive features for Venetoclax consisted solely of cell type and cancer hallmark signatures (2 between MO and PS). To an extent, this difference could be explained by a drug’s mechanism of action (MOA), as some perturb larger shares of cellular machinery than others. We selected the CCLE dataset as the most representative to test this (n drugs = 396)  . Among tIn this study, we set out to demonstrate two points. First, genomic features derived with comprehensive genomic profiling methods (panel sequencing) and used during clinical/pre-clinical drug development often have limited predictive power for drug response in cancer pre-clinical models. Second, we sought to elucidate how the drug response prediction could be improved using additional transcriptomic features. We showed that using cell type and cancer hallmark gene signature scores derived from the transcriptome on top of panel-derived features improves predictive power across different pre-clinical models , irrespective of the modelling method used. Our main aim in this study was not to argue that panel sequencing should be replaced by transcriptome profiling, but rather to demonstrate that using the transcriptomic features could have added benefit for treatment response prediction. The logic we follow is that if modelling drug responses using only panel features has limited power in pre-clinical models, then it would be even more limited for actual patients who would receive such treatments based on few marker genotypes, as the pre-clinical models cannot perfectly represent the complexity of the tumor or its microenvironment. Our second aim in this study was to look for general trends across drugs and datasets using off-the-shelf methods with a fair comparison of feature sets used in the modelling. We are mainly interested in the general trends; therefore, we refrain from making strong conclusions about individual drugs. However, we have quantified the predictive importance of all the studied features for each drug as Although the pre-clinical models we studied here do not perfectly reflect the complexity of the actual tumor microenvironment in a human patient, this kind of a large-scale data analysis of drug responses would not be feasible to carry out on actual patients due to logistical and ethical limitations. While replicating this kind of a large-scale analysis could not be extended to actual human samples, follow-up studies could address some questions that we have not addressed here. First of all, there could be many more alternatives in regard to how to preprocess the genomic and transcriptomic features in terms of converting the input data into less noisy and more information-dense latent features, optionally with added prior knowledge. For instance, mutation data could be converted into cancer mutational signatures, or transcriptome data could be integrated with prior knowledge networks to derive causal subnetwork features. Moreover, different layers of omics data modalities could be integrated using multi-omics integration methods. More sophisticated deep learning-based drug response modelling tools could be used, including pre-training and transfer learning approaches. Finally, the robustness and generalization power of the prediction models could be compared in a cross-dataset setting, where the models are built and validated in independently acquired resources or cross-tissue settings, where the models could be evaluated on cell lines derived from a tissue of interest unrelated to the tissues that are considered during the training procedure. However, our purpose here was not to benchmark the potential data processing or modelling algorithms, but rather to have a fair comparison of distinct feature sets in terms of their predictive power for drug response.In this study, the following publicly available pharmacogenomics datasets were used:https://depmap.org/portal/download/, accessed on 11 April 2022. The drug response measurements from the PRISM project [Cancer Cell Line Encyclopedia (CCLE)  download project  were usePatient-Derived Xenografts (PDX) .BeatAML: ex vivo drug sensitivity screening of acute myeloid leukemia patient tumor specimens  downloadAnnotation of drug classes was downloaded from Drug Repurposing Hub project .https://github.com/BIMSBbioinfo/multiomics_vs_panelseq, accessed on 11 April 2022.All codes to download, process, analyze the datasets, and reproduce the figures in this manuscript can be found here: https://www.oncokb.org/cancerGenes, accessed on 15 April 2022.). Mutation data was available in all three datasets.Mutations: The mutation data were converted into a matrix of mutation counts per gene per sample. The resulting matrix was further filtered to only keep mutation data for genes in the OncoKB cancer gene list : To reduce the dimensionality and obtain less noisy features, the gene expression datasets were converted into gene-set activity scores using single-sample gene set scoring (singscore R package ). The geDrug Sensitivity Measures: For the CCLE and PDX datasets, AUC (area under the curve) scores derived from dose–response curves as published in the respective resources were used in the prediction models. For the BeatAML dataset, recomputed AAC (area above the curve) scores were used as downloaded via the PharmacoGx R package .For drug response modelling, we considered two main scenarios based on the availability of datasets. In the first set, we considered mutation and/or copy number variation data for genes found in the OncoKB cancer gene list, which aims to simulate panel-sequencing. In the second set, we considered the features as in the first set along with the whole transcriptome profiling as an additional data modality, which is further converted into gene-set activity scores. The second set represents the multi-omics condition, in which the panel features are concatenated with the transcriptome features (gene-set scores).Only drugs that were treated on at least 100 samples were considered.For each drug, the samples were split into training (70% of samples) and testing groups (30% of samples). See Caret R package  was usedThe final model performance was evaluated on the testing data. Spearman rank correlation, R-square, and root mean squared error (RMSE) metrics were computed, and the R-square metric was used to report the results in the final figures.Feature importance rankings were obtained using ‘caret::varImp’ function and are provided in Drugs were summarized according to their mechanism of action and were evaluated by mean multi-omics improvement.We believe that in order to better understand cancer and develop better drugs and diagnostics, we need to make use of all the molecular features by integrating different omics datasets. In this manuscript, using multi-omics and machine learning techniques, we showed that multi-omics has indeed superior performance for drug response prediction in cancer.


SPRR1B). Thus, using multi-omics data integration, we developed a promising cancer prediction algorithm.Early diagnosis of lung cancer to increase the survival rate, which is currently at a low range of mid-30%, remains a critical need. Despite this, multi-omics data have rarely been applied to non-small-cell lung cancer (NSCLC) diagnosis. We developed a multi-omics data-affinitive artificial intelligence algorithm based on the graph convolutional network that integrates mRNA expression, DNA methylation, and DNA sequencing data. This NSCLC prediction model achieved a 93.7% macro F1-score, indicating that values for false positives and negatives were substantially low, which is desirable for accurate classification. Gene ontology enrichment and pathway analysis of features revealed that two major subtypes of NSCLC, lung adenocarcinoma and lung squamous cell carcinoma, have both specific and common GO biological processes. Numerous biomarkers  were newly identified, whereas some biomarkers were consistent with previous findings in NSCLC (e.g.,  EGFR, BRAF, and KRAS) and fusion/rearrangements  [Lung cancer is the leading cause of cancer mortality, with the second-highest incidence rate worldwide as of 2020 . Lung caT, NTRK) ,6,7,8,9.Particularly in lung cancer, radiological images and histopathological phenotypes are more adequately considered as gold standards in tumor diagnosis and staging, but are also crucial predictors of response to therapies and prognosis. Further, artificial intelligence, primarily machine learning (ML) and deep learning (DL), has been successfully implemented in oncology in collaboration with radiology or histopathology to maximize sensitivity, specificity, and precision, especially in lung cancer ,11,12. IAmong such attempts, graph convolutional network (GCN)-based models have proven useful in multi-omics data stratification, correlation learning, weighted value order, and biomarker feature identification ,19. ThusImplementing a screening program to diagnose patients at an early stage is a critical factor to decrease lung cancer-related deaths and improve survival. This multimodal model may provide a solid basis for early diagnosis of NSCLC.The lung cancer patient data comprised DNA sequencing, gene expression, and DNA methylation . BaselinThe Gene Expression Omnibus (GEO) database is used as an authorized array- and sequence-based database in cancer-related studies. Gene expression and DNA methylation data were retrieved to build the study dataset from June to August 2021.The RNA-sequencing data  of lung cancer were obtained from The Cancer Genome Atlas (TCGA) data repository in June–August 2021. RNA-sequencing data of patients with LUAD derived from TCGA contained 504 samples: stage I (n = 274), stage II (n = 120), stage III (n = 84), and stage IV (n = 26). Patients with LUSC were classified as stage I (n = 242), stage II (n = 160), stage III (n = 84), and stage IV (n = 7).https://portal.gdc.cancer.gov (accessed on 14 May 2021) included 502 patients: stage I (n = 279), stage II (n = 120), stage III (n = 81), and stage IV (n = 22). For LUSC patients, the data comprised stage I (n = 199), stage II (n = 142), stage III (n = 62), stage IV (n = 5). Genomic data, including single nucleotide variants (SNV), indels, fusions, and rearrangements, from 818 patients with NSCLC were obtained from the same GDC portal. GTEx was developed in an ongoing effort to establish a comprehensive public resource for studying tissue-specific gene expression and regulation. In this study, 578 lung samples from nondiseased GTEx v8 tissues were used.DNA methylation data of LUAD retrieved from the GDC Portal  structure. In accordance with the DAG structure, which represents the data flow from one activity to another and has a defined direction, we can smoothly parallelize the evaluation with each set of multi-omics data and can be free of the total number of features . PreprocAfter applying a weighted sample similarity computed from the preprocessing module, each modified weight from the single-omics data is concatenated to an FC layer, and these vectors are then integrated as a multimodal weight vector in the same layer. In the Mutation FC layer, it collects all multimodal weight vectors and employs those vectors to generate the prediction of a binary label. Consequently, an NSCLC prediction model with a GCN–FC–FC architecture was established using multiple modalities b.−7, 5 × 10−7, 1 × 10−6, 5 × 10−6, 1 × 10−5, 5 × 10−5, 1 × 10−4, 5 × 10−4, 0.001, 0.005}. We selected a dropout rate of 0.5 and a sum method for node neighbor aggregations. The model was implemented using Python 3.8.10 and PyTorch 1.8.1 on an Intel(R) Core(TM) CPU i7-9800X, with 126 GB of memory, and a TITAN RTX 24 GB GPU with CUDA 10.2.We selected the cosine function as a similarity function and Leaky ReLU with a slope of 0.25 for the negative x-axis as an activation function. We employed the Adam optimizer and cross-entropy loss, and Xavier weight initialization. We also pretrained our model for 500 epochs and then fine-tuned it for 2500 epochs. The number of hidden units in our model was 400 and the dimension of all FC layers was 200. The search space for all three learning rates  was {1 × 10To optimize the weights of this GCN-based model, weight vectors of samples were obtained from the feature data of each sample, whose components were the feature values of the samples. Furthermore, we evaluated the similarities between two nodes in each sample–sample graph, especially within the 1-hop network of the graphs. With these similarities, the preprocessing module can restrict protruded components of the weight vector by adjusting the marginal value from the mean value of each feature, and hence smooth links, or directed edges, from each node to a binary label. For example, if one node with label 0 is located among nodes, and most nodes have label 1, then the weight of the link to label 0 decreases, and that of the link to label 1 increases at the same time. In this process, the similarity threshold 2 fold change and p value criteria were adopted for the selection of features or genes that were hypermethylated or hypomethylated and up- or down-regulated, respectively.To find the most relevant features while avoiding redundant ones, logp value < 0.05 and absolute value of log2 fold change >0.5, thousands of features or genes were initially identified. To curtail the total number of biomarker candidates, we used Welch’s t-test for every feature or gene, on the assumption that the probability distribution is independent, displaying normality, and having unequal variances, according to the cancer positivity and negativity of the samples. The data were randomly divided into training, validation, and testing sets in 55%, 15%, and 30% ratios, respectively. The training set was utilized to establish the DL model, the validation set was used to determine the parameters of the model, and the testing set was applied to evaluate the performance of the DL algorithm. Five-fold cross-validation was performed to determine the optimal parameters for each training and validation model. We conducted the influence test for features by observing the increment of the F1-score between the inclusion and exclusion of each differentially methylated and expressed genes. Among the features, the feature for More specifically, after filtering for biomarkers with analysis . The diaTo compare our model with common ML/DL models, we implemented convolutional neural network (CNN), logistic regression, and naïve Bayes models, and set these models as the baseline. Although the original Naïve Bayes is an ML model, we built this model as a DL-structured model. We used an activation function, optimizer, and loss function for all baseline models, similar to our model, namely, Leaky ReLU, Adam, and cross-entropy loss. Moreover, we set the number of baseline hidden units to 400 and the dimension of the baseline FC layer to 200, based on the construction of our model. Furthermore, we attached baselines with hidden FC layers, whose numbers were the same as the hidden layers in our model.For the CNN model, we chose a one-dimensional filter for convolution layers, max pooling with a kernel size of two, stride of one, and zero padding as the pooling method. Since we obtained better metric results when the dropout method was used, we used it at a rate of 0.5, instead of batch normalization. For the logistic regression model, we applied the batch normalizations for each batch and used a dropout method at a rate of 0.5. For the naïve Bayes model, we implemented each layer in a manner similar to the FC layer, using a standard normal distribution as a prior probability distribution.The three baseline algorithms were compared for their prediction performance on this study’s NSCLC multimodal data. Seven metrics were adopted to compute the classification performances of the competitor methods. By applying the same preprocessed data, the performances of the implemented algorithms were compared pairwise via five-fold cross-validation.p values corrected with Bonferroni less than 0.05 were considered significant. These analyses were performed and accessed between November and December 2021.To further investigate the etiologic and molecular biological characteristics of NSCLC biomarkers revealed by this study, we conducted enrichment and pathway analysis implementing Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and REACTOME, which were performed using DAVID 6.8 or analyzed using Cytoscape 3.9.0. GO terms and pathways derived from enrichment were visualized using the ClueGO v2.5.8 and CluePedia v1.5.8 plug-ins. The enrichment library used for GO Biological Process terms is “GO_BiologicalProcess-EBI-UniProt-GOA-ACAP-ARAP_13.05.2021_00h00.” GO selection criteria for representative pathways included three–eight GO levels, a minimum of three genes/term, and mapped genes representing at least 4% of the total associated genes. The Kappa score was used to define term–term interactions and associate terms and pathways into functional groups based on the shared genes between the terms. Ensemble gene ID was used as a gene identifier for all analyses. FDR and All statistical data were confirmed using SPSS Statistics  and MedCalc Statistical Software version 14.8.1 . Model performance comparisons were visualized using GraphPad Prism version 8.0.1 . ROC curve analysis was performed to measure the area under the ROC curve (AUC), sensitivity, specificity, accuracy, and precision.In the algorithm training step, we used the TCGA-LUAD, TCGA-LUSC, and Korean lung carcinoma datasets (GSE40419). These sets contain three types of data, including mRNA gene expression, DNA methylation, and genomic data, which are suitable for disease classification. We primarily focused on transcriptomics and DNA methylation in the identification of lung cancer prediction signatures. Genomic data, including SNVs, indels, and fusions/rearrangements, are expected to discriminate between the predictors that match the most suitable chemo- and/or immune-therapeutic agents.t-test (p < 0.05) on reliable assumptions. Then, we employed the influence test for features by observing the increment of the F1-score between the inclusion and exclusion of each feature. Using these approaches, we were able to specify 200 RNA-sequencing and 300 methylation features.The datasets were split into 777 training datasets (55.7%), 195 validation datasets (14.0%), and 423 test datasets (30.3%). These data included more than 10,000 features, which may affect the classification performance and undermine the algorithm training. To remove noise and redundant features from each multi-omics dataset, we first employed Welch’s We used the macro F1-score, weighted F1-score, and accuracy as performance metrics, and the AUC-ROC score as sub-metrics. For efficient training and validation, we used five-fold cross-validation. Training was performed to carry out initial class prediction using omics features and the corresponding sample similarity generated from omics data.Our GCN-based algorithm with DAG structure can diagnose lung cancer, more specifically NSCLC, based on two single omics data, transcriptomics, and DNA methylation.Major RNA expression and DNA methylation features identified by this GCN-based prediction algorithm were parallel to the GO enrichment and pathway analysis .Regarding accuracy and specificity, five-fold cross-validation of this NSCLC prediction model  showed tThe prediction performance of this model was compared with that of existing DL and ML algorithms: CNN, logistic regression, and naïve Bayes. All four methods were compared using the same preprocessed data and the mean ± standard deviation of the evaluation metrics . Actual To compare the model performances of the baseline and the proposed model, we adopted sensitivity, specificity, accuracy, precision, AUC-ROC, macro F1-score, and weighted F1-score for the binary classification of NSCLC. The best model varied depending on the performance metric, but the proposed model was at least equal to or higher than that of the other multi-omics integrative algorithms on six of the seven metrics we analyzed. In other words, the proposed model outperformed the other algorithms on most performance metrics.sensitivity, macro F1-score, and weighted F1-score. Precision, which is the ratio of correctly labeled samples to all labeled samples, can vary depending on the classifier threshold. Meanwhile, the sensitivity may remain unchanged.true positives indicate the number of NSCLC samples that have been correctly predicted, false positives denote the number of non-cancer samples that are misclassified as cancer samples, and false negatives signify the number of NSCLC samples that are misclassified as non-cancer samples. The F1-score, the harmonic mean of precision and sensitivity, is a useful measure in binary classification and was calculated according to Equation (8):More specifically, when comparing the models with the same number of hidden layers and the same activation function, we observed that our model outperformed the competitors in terms of accuracy, Macro F1 can be computed using the sum of the F1-score for actual positives and actual negatives, and was calculated according to Equation (9):macro F1-score. The highest macro F1-score indicated that the values for false positives and false negatives were sufficiently low to prove that this algorithm is best for classifying the correct class :To calculate the metrics for each label and adjust their Weighted F1, considering the number of actual occurrences per label, was 98.3% and it outperformed the competitor model, with an imbalanced dataset, that is, DNA methylation in this study. The proposed NSCLC prediction model demonstrated that it outperforms three other models applied using identical frameworks that are desirable for multi-omics data classification capability on identical datasets.Because the biomarkers identified by the NSCLC prediction model were quite diverse, further analysis of their molecular functions and biological processes was performed. LUAD and LUSC not only shared identical biological processes but also exhibited specific processes. GO term enrichment, KEGG, and REACTOME pathway analyses were performed on the identified biomarkers using Cytoscape software 3.9.0. and the DAVID database. In the NSCLC multimodal features, the GO term of cornification (GO:0070268) originated from LUAD features, whereas epidermis development (GO:0008544) was derived from LUSC features .Genes associated with the two GO terms cornification and epidermis development showed distinct downregulation in NSCLC . GO termKRT13, KRT14, KRT15, KRT16, and KRT17 were enriched genes, which allowed us to predict that these genes contribute to the downregulation of the estrogen signaling pathway (Cellular responses to retinoic acid (GO:0071300) and antimicrobial humoral responses (GO:0019730) were shared by both LUAD and LUSC . REACTOM pathway .10 p value <2.4; fold change +5–−8) of NSCLC multimodal features, analyzed from the DAVID database, are summarized in All the details of the GO terms, KEGG, and REACTOME pathway analyses are displayed in Owing to the exponential growth of omics technologies and NGS-based big consortium studies, labeled omics datasets with comprehensive annotations are continuously being developed. Therefore, it is increasingly important to employ labeled omics data to highly accurately predict disease diagnosis, tumor grading, and subtyping. Considering approximately 85% of lung cancers, NSCLC has two of the most common subtypes, LUAD and LUSC, which have considerably different biological signatures, although they are usually treated in the same way and classified identically into NSCLC. There are few biomarkers of NSCLC based on the implementation of multimodal feature selection methods. One of the main reasons for this is histologic and molecular heterogeneity, even within the same histological subtype. Hence, to accurately and robustly predict NSCLC, this study focused on the multi-omics integrative classification of NSCLC.Although multi-omics data integration usually leads to better outcomes, some studies have suggested that this is not always the case. Existing reviews comprehensively cover this challenging issue ,22. PoorA supervised omics integrative model has been reported to take advantage of a GCN for multi-omics data learning to perform effective disease classification and biomarker identification. Wang et al. utilized both omics features and correlations among samples represented by similarity networks for better classification capability in cancers and Alzheimer’s disease . RamirezNumerous multi-omics integration studies have proposed the applicability of DL for cancer diagnosis and prognosis. However, there are a few challenges and limitations to the extensive implementation of multi-omics DL in clinical practice. First, the fundamental problem encountered is the difficulty in resolving the relationship between the compressed features and biological meanings. Therefore, to verify the explainable performance in real-world applications, a clinical study must be carried out with external cohorts, followed by complete functional analysis. Second, owing to the large number of parameters, DL algorithms are difficult to train, must be tuned accurately, have unclear methods concerning handling data variability derived from data transformation and normalization, and often experience overfitting. In addition, their performance depends heavily on the size of the samples, which is limited ,24. StriAnother issue likely to be encountered when incorporating ML models trained using multi-omics data is the expense. It might not be practical to perform multimodal sequencing or microarray analyses routinely for all new patients. To implement cost-effective and high-throughput analysis for NSCLC diagnosis and/or prognosis, we further differentiate central or core biomarkers (approximately 30–50) out of a few hundreds of multi-omics feature pools, while maintaining comparable performance in NSCLC prediction.The superior performance of the macro F1 metric of this NSCLC prediction model can be explained by a previous DL cancer classification study. The macro F1-score has been applied as a useful measure of the effectiveness of individual algorithms on the rare classes because macro F1 is more heavily influenced by performance on the minority classes, that is, cancers vs. non-cancers . There iSPRR1A expression in LUAD indicates a more advanced stage and unfavorable prognosis [SPRR1B expression in LUAD may predict poor prognosis [SPRR1B expression disrupts or alters the cornification process, resulting in irreversible malignant transformation [Gene Ontology (GO) annotates genes  using ontology. GO analysis is widely used for specifying cellular location, molecular function, and biological process. One of main purpose of implementing GO is to perform enrichment analysis on a given feature list . GO annorognosis . Furtherrognosis . Additioormation . We inteKRT6A (log2FC −5.47), KRT14 (log2FC −3.71), S100A2 (log2FC −3.23), and KRT17 (log2FC −2.56) have significant diagnostic potential for NSCLC . This algorithm effectively identified salient biomarkers from integrated multi-omics data, which exhibited superior performance. There are downsides and limitations regarding the implementation of multi-omics DL. This study demonstrated that employing the proposed model to handle multi-omics data can help eliminate them. The future direction of the research is to achieve a more applicable DL algorithm and multi-omics-based diagnostic device, prove its clinical utility in large-scale human clinical trials, and ultimately support the improvement of the NSCLC survival rate, which remains drastically low.


We present telescoping bimodal LDA, a method to identify complex associations across high-dimensional count data, and we show TBLDA’s behavior for expression QTL mapping for bulk RNA-seq data from 10 tissues in the GTEx data. https://github.com/gewirtz/TBLDA.Expression quantitative trait loci (eQTLs), or single-nucleotide polymorphisms that affect average gene expression levels, provide important insights into context-specific gene regulation. Classic eQTL analyses use one-to-one association tests, which test gene–variant pairs individually and ignore correlations induced by gene regulatory networks and linkage disequilibrium. Probabilistic topic models, such as latent Dirichlet allocation, estimate latent topics for a collection of count observations. Prior multimodal frameworks that bridge genotype and expression data assume matched sample numbers between modalities. However, many data sets have a nested structure where one individual has several associated gene expression samples and a single germline genotype vector. Here, we build a telescoping bimodal latent Dirichlet allocation (TBLDA) framework to learn shared topics across gene expression and genotype data that allows multiple RNA sequencing samples to correspond to a single individual’s genotype. By using raw count data, our model avoids possible adulteration via normalization procedures. Ancestral structure is captured in a genotype-specific latent space, effectively removing it from shared components. Using GTEx v8 expression data across 10 tissues and genotype data, we show that the estimated topics capture meaningful and robust biological signal in both modalities and identify associations within and across tissue types. We identify 4,645 cis-eQTLs and 995 trans-eQTLs by conducting eQTL mapping between the most informative features in each topic. Our TBLDA model is able to identify associations using raw sequencing count data when the samples in two separate data modalities are matched one-to-many, as is often the case in biological data. Our code is freely available at  Genomic differences, such as single-nucleotide polymorphisms (SNPs), among individuals are important drivers of gene expression variability. Much previous work has focused on discovering expression quantitative trait loci (eQTLs), which capture associations between the number of copies of a minor allele present at a given genomic locus and the expression level of a single gene . HoweverFrom a statistical perspective, performing genome-wide one-to-one association tests yields an astronomical multiple-testing burden for trans-eQTLs, where the agnostic approach examines every interchromosomal gene and SNP combination. Statistical power is further reduced because trans-eQTLs, or eQTLs where the regulatory SNP is on a different chromosome than the gene that it regulates, often have smaller effect sizes than cis-eQTLs or eQTLs where the regulatory SNP is local to the gene that it regulates . One metProbabilistic topic models, such as latent Dirichlet allocation (LDA), are unsupervised machine learning methods that were initially introduced in natural language processing  and in sWe hypothesized that multimodal topic modeling could identify clusters of covarying genes and SNPs. Existing methods have used Dirichlet process mixture models to integrate two data modalities , but nonIn this work, we create a probabilistic model to find shared structure between gene expression and genotype data. Our model uses raw sequencing read counts and is designed for a nested data structure where although samples are paired, modalities may have different numbers of samples from each subject. This is often the case when we have many samples of gene expression from a particular donor—as in the GTEx data with multiple tissue samples per donor and also for single-cell RNA sequencing samples with multiple cells per subject—but a single germline genotype vector. We apply our unsupervised model to GTEx v8 data and use known sample tissue labels and cell type enrichment scores post hoc to interpret the biological context of the estimated components . To demoWe applied our telescoping bimodal LDA (TBLDA) model to gene expression data for the 10 tissues with the highest number of samples from the v8 GTEx data release and to the genotypes from all individuals who contributed to at least one of the samples . We tookFirst, we checked that ancestral structure, using reported ancestry as a proxy, was not associated with the estimated shared factors. As expected, ancestral structure was largely controlled for because it is captured in the genotype-specific portion of the model . This contrasts with the genotype-specific factors, each of which had a point biserial correlation coefficient of at least 0.41 (ranging to 0.99) with at least one reported race.P < 0.01), which suggests that certain tissues may have underpowered downstream analyses. Overall runs, whole blood, and skeletal muscle had the most associated factors , whereas the tibial nerve had the fewest (905). The skin (sun-exposed), skin (not sun-exposed), lung, subcutaneous adipose, and thyroid had the weakest associations (median inner products between 60.7 and 69.7); whole blood, tibial nerve, and esophagus mucosa had the strongest (median inner products between 88.6 and 109.6). Accordingly, whole blood and skeletal muscle samples allocate most of their topic membership into tissue-specific factors . Taken together, the two skin tissues had the largest group with 6,515 genes, whereas whole blood had the largest number of unique genes considering the other sets . All tissue-associated robust gene sets were enriched for functionally relevant biological process Gene Ontology (GO) sets . Furthermore, all relevant robust tissue gene sets  contained most of the tissue-specific transcription factors (TFs) present in the overall analysis , whereas only one gene set included another tissue’s TF (subcutaneous adipose contained one TF from whole blood) . We noteGene set enrichment analysis of tissue-associated robust gene sets.Table S1 −3) and active enhancer-associated H3K27ac Hnisz (BH FDR ≤ 9.8 × 10−5), and H3K4me1 Trynka (BH FDR ≤ 2.6 × 10−5). In particular, several single tissue-associated SNP sets are associated with DGF ENCODE, DHS_Trynka.extend.500, H3K4me1 Trynka, H3K27ac Hnisz, and Enhancer Hoffman .Table S3 Although the GTEx data provide the ground truth of each sample’s origin tissue, this is not the case across all data sets. Thus, we next evaluated whether our model could recover robust components across relevant runs in an unsupervised manner. To do this, we ran our model 484 times, once for each pair of chromosomes in the GTEx v8 data, and identified shared components across these runs (see the Extended Methods section). Across all runs, we recovered 197 clusters of robust genotype factors and 1,799 groups of robust gene expression factors. Loadings that were well-correlated with each other across runs tended to cluster by tissue; 81 of the robust genotype clusters and 468 of the robust gene expression clusters included factors that were associated with the same tissue . Only 14Because of the nature of bulk RNA-seq expression data, the GTEx samples average expression over heterogeneous tissue samples containing various cell types. We computed the Kendall correlation between cell type enrichment scores and factor values to determine whether factors capture sample cell type composition . We use To test whether traditional eQTLs ascertained using univariate tests are captured by TBLDA, we ran a linear model for association between the top 10% most informative SNPs and genes on common factors for each tissue separately using MatrixEQTL  on held-Cis- and trans-expression quantitative trait loci mapped using the multivariate testing approach (BH FDR < 0.1).Table S4 We also discovered 995 trans-eQTLs at BH FDR < 0.1, which include 901 unique trans-eGenes and 979 unique trans-eVariants (Table S4). In contrast to both the data, which consist of 86% protein-coding genes, and the cis-eQTLs, trans-eQTLs have an approximately equal number of lincRNA- and protein-coding eGenes  and 939 trans-eQTLs (853 unique trans-eGenes and 933 unique trans-eVariants) at BH FDR < 0.1 (Table S5). Whole blood had the most cis-eQTLs and the most trans-eQTLs. Similarly, to the full analysis above, the trans-eQTLs have an approximately equal split of lincRNA and protein-coding eGenes, whereas the cis-eGenes are mostly protein-coding . Of the Cis- and trans-expression quantitative trait loci mapped using only tissue-associated topics (BH FDR < 0.1).Table S5 Thus, to increase power to find trans-eQTLs shared across tissues, we next limited association tests to features in general factors that were not linked to any tissue. This approach yielded 964 trans-eQTLs (863 unique trans-eGenes and 945 unique transeVariants) and 2,901 cis-eQTLs .Table S6 Inferred covariates such as PEER factors are known to capture and thus inadvertently control for broad regulatory effects that may have a true genetic basis, potentially removing broad trans-eQTL signals . To testCis- and trans-expression quantitative trait loci mapped using the multivariate testing approach, without including PEER factors as covariates (BH FDR < 0.1Table S7 ALDH3B2). Although we fail to capture this extended signal because we use an LD-pruned SNP set, our model still groups the gene together with its genomic hotspot. Furthermore, although the eVariant is not shared, 37/439 (8.4%) of the GTEx skeletal muscle transeQTLs include RP11-65J3.3, which we identify as a trans-eGene in that tissue. Taken together, these results suggest that our approach finds overlapping cis-eQTL signals but expands our ability to identify broad-acting trans-eQTLs in these bulk data.Next, we explored the overlap of our eQTLs and the GTEx consortium cis- and trans-eQTL list, produced by the consortium through an exhaustive tissue-specific testing approach . A majorMAPRE3  and ARFGEF3  in the thyroid and sits in the 9q22 locus .One interesting example from the model fit using all GTEx samples is the trans eVariant rs4297160, which is associated with both 22 locus . Specifid cancer . The 9q2h PTCSC2 . Further thyroid . Notably thyroid ; in linefactor 1 ), COLGALitopathy ), and FMtabolism ; Fig 5).tabolism  adjustedP-values with BH FDR < 0.1 from our method have a different distribution from the GTEx cis-eQTLs found via exhaustive search . Our cis-eQTLs found via TBLDA are a subset of all true associations, and they tend to have more moderate associations than the set of GTEx cis-eQTLs  model that uncovers shared latent factors between bulk RNA-seq expression and genotype data when there is no one-to-one mapping among the samples for each data modality. The model takes raw counts as input, which avoids any potential data skewing because of normalization. We fit the model in an unsupervised manner using gene expression data from 10 tissues in the GTEx v8 release and matched donor genotypes. We intentionally exclude hard-coding tissue labels into the TBLDA because different tissues have a range of overlapping cell types, meaning that samples from certain tissues will share varying proportions of underlying processes and eQTLs. In addition, in large data sets, some samples may be mislabeled. Using known GTEx covariates, we established that the recovered topics reflected meaningful biology such as sample cell type proportion . Robust Running linear association tests on top-ranked features from each factor using MatrixEQTL  on a helPreprint). Second, because we do not include a private subspace for gene expression, true latent components that reflect expression-specific variation such as batch effects will be forced to contribute to the modality-shared factors. We believe this is important to retain signal for broad regulatory effects that especially affect trans-eQTL discovery. Nevertheless, if the model is used in a context such as single-cell RNA sequencing, where there are known and strong expression-specific covariates such as batch effects, this design choice should be reconsidered. Furthermore, a natural question that arises for all parametric latent factor models is how to determine the number of topics. We stress that there is no “correct” topic number and the user will want to make a reasonable trade-off between computational speed for inference and the granularity of signal captured. In practice, we recommend anywhere from 20 to 150 factors depending on the size of the data set. Given these qualities, natural extensions to the model include adding latent or semi-supervised expression-specific topics and extending it to a nonparametric framework.There are several potential points of contention in our model. First, although the model’s probabilistic nature provides important measures of uncertainty for noisy genomic data, because of our inference procedure, the posterior should be interpreted with caution because variational inference is known to underestimate the posterior variance PreprintX ∈ RG×L for G genes across L samples and a genotype matrix Y ∈ RS×N in minor allele dosage format  for S SNPs across N individuals. We henceforth refer to genes and SNPs as features. Each individual i ∈ 1,..., N contributes at least one sample, and every sample ℓ ∈ 1,..., L comes from exactly one known individual; this is the telescoping property of the data.Given a genotype matrix and an RNA sequencing expression matrix, our goal is to find latent factors that capture groups of SNPs and genes that covary across samples. We have two input matrices: a RNA-seq count matrix K latent topics where (i) each sample ℓ has topic membership proportion ϕℓ ∈ SK and (ii) each individual i has topic membership proportion θi ∈ SK, where SK denotes the K-dimensional simplex where all values are positive and sum to 1  between the sample and individual spaces:The sample to individual mapping is encoded in ψk, genotype topics are modeled independently over SNPs and consist of independent λjk as in the structure model  subspace for genotype to control for ancestral structure in mixed-population samples, which consists of nonnegative factor and loadings matrices β and τ (see the Extended Methods section) given ependent , as is oependent . This fiWe use stochastic variational inference to compute posterior estimates for Φ, Λ, Ψ, and α (see the Extended Methods section for details).Preprint). The multinomial distribution allows us to separate out variation because of library-size effects from the underlying compositional variation, which is more biologically relevant.In this multimodal version of LDA, the same latent factors are shared across data modalities, allowing features of each modality to be directly linked together. Critically, because our framework directly models count data, we avoid spurious or distorted signals through data normalization , and becψk, λjk) have a larger relative contribution. However, the proportion of total counts for each gene varies widely. Genes with higher counts may dominate certain topics merely because of their high expression levels, overshadowing lower expressed genes that are actually more informative for that topic compared with others. Consequently, instead of using the raw expected loadings, we determine the importance of each feature across topics by ranking the average 2-Wasserstein distance between the posterior variational distributions. This allows us to control for both average feature counts and varying uncertainty in model estimation by using the full information provided in the posterior estimates. In particular, the 2-Wasserstein value, also known as the “Earth-movers distance,” is specifically designed as a distance metric between two empirical densities, unlike KL divergence, which is not a symmetric measure and often performs poorly when the two densities are defined on unequal basis measures. SNP minor allele frequency (MAF) is much less variable than gene total counts. To control for allele counts, we rank SNPs after regressing out the coded MAF in each loading (see the Extended Methods section for details).Features that have higher weights within a topic  for numerical stability, an Adam optimizer, and a learning rate of 0.05. ξ and σ were set to symmetric one vectors, ζj = γj = 1, δ to 0.05, and μ to 0.85. The model was fit separately for feature sets from each chromosome combination, for a total of 22 × 22 = 484 runs. Let x be the average ELBO over the latest 1,000 epochs and y be the average ELBO over the 1,000 epochs before those. Runs were terminated when https://github.com/gewirtz/TBLDA.We used Pyro v1.4.0 (i)ϕℓ. This vector should be kept symmetrical, and we recommend running the model with an uninformative prior where σ = 1. If the user wants to fit a model where samples comprise many topics, they should running the model with a more concentrated prior, setting σ > 1. Conversely, if the user wants each sample to be drawn from only a few topics, they can set a sparser prior where σ < 1.σ: This is the hyperparameter for the sample-topic proportion (ii)γ: These are the hyperparameters for the SNP loadings λjk. Although we set them equal to one for an uninformative prior, users could also set them to be less than one for a sparsity-inducing prior. In that case, a common choice would be setting the hyperparameters to 1/K, where K is the number of topics. We recommend trying an uninformative prior first to let the data lead, and if topic collapse is observed, next try the sparsity-inducing prior.ζ,(iii)ϕk. We follow the same guidance as for ζ and γ, except that ξ is the parameter vector for the Dirichlet, which generalizes the beta.ξ: This is the hyperparameter for the gene loadings (iv)μ,δ: These are the hyperparameters for α, which controls the mixture proportion for the genotype-specific versus shared space. The model will naturally put more weight on the separate genotype portion, and we want to restrict this so that TBLDA learns more shared structure. Thus, we want to restrain α from getting too close to zero or one. In practice, we recommend setting μ ≥ 0.1 and δ ≤ 0.8.As a general practice for downstream model analysis, we recommend that users identify factors associated with all covariates . For example, when given technical covariates such as batch, users may remove all batch-associated factors from downstream analysis.After regressing out allele frequency, we take the top 10% of SNPs from each loading with the highest absolute value residuals. The 10% of genes from each loading with the highest 2-Wassterstein distances are considered the top gene features. Because the feature numbers vary by chromosome, runs have differing numbers of top features associated with their factors.The tissue-specific TF list originated from Table S3 in For each tissue, the set of robust tissue-associated genes consists of the genes that are top-ranked in at least one tissue-associated factor across all TBLDA runs.For each tissue, we calculated the 75th percentile of the distribution of total tissue-associated factors across all runs that each top-ranked SNP is associated with. SNPs that are top-ranked in at least the 75th percentile of each tissue’s associated factors across all runs comprise the set of robust tissue-associated SNPs.We held out two randomly selected samples from all individuals who contributed four or more samples to use for the eQTL pipeline and used all remaining samples to fit TBLDA. We used MatrixEQTL v2.3  with modr2 > 0.95 for genes was flagged—along with the highly correlated factors—as a robust component. For each robust component, we averaged the constituent loadings to produce a representative factor loading. All components whose representative loadings exceeded r2 > 0.95 were further collapsed into a single robust component.We computed the correlation of each factor loading with all other loadings from runs on the same chromosome. Any factor with more than two loading Kendall τ > 0.15 for SNPs and three Pearson phs000424.v8.p2.All raw sequencing and genotype data from GTEx v8 used in this study can be found in dbGaP under accession number


Data-driven soft sensors have increasingly been applied for the quality measurement of industrial polymerization processes in recent years. However, owing to the costly assay process, the limited labeled data available still pose significant obstacles to the construction of accurate models. In this study, a novel soft sensor named the selective Wasserstein generative adversarial network, with gradient penalty-based support vector regression (SWGAN-SVR), is proposed to enhance quality prediction with limited training samples. Specifically, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is employed to capture the distribution of the available limited labeled data and to generate virtual candidates. Subsequently, an effective data-selection strategy is developed to alleviate the problem of varied-quality samples caused by the unstable training of the WGAN-GP. The selection strategy includes two parts: the centroid metric criterion and the statistical characteristic criterion. An SVR model is constructed based on the qualified augmented training data to evaluate the prediction performance. The superiority of SWGAN-SVR is demonstrated, using a numerical example and an industrial polyethylene process. Data-driven soft sensor models ,4,5,6,7 The reliability of training data for the efficient development of data-driven soft sensors is a key aspect ,23. HoweThe virtual sample generation (VSG) technique is effective in handling the problem of the insufficient construction of soft sensors with limited training data ,28,29,30Deep learning-based VSG methods have gained increasing attention in fields such as imaging and natural language processing . In receThis study aims to develop an enhanced data augmentation soft sensor framework to meet the challenge of limited labeled samples in the polyethylene process. The proposed soft sensor is named the selective Wasserstein GAN, with gradient penalty-based SVR (SWGAN-SVR). First, to expand the sample capacity and enrich the data information, virtual samples are generated using a Wasserstein GAN with a gradient penalty (WGAN-GP) network. Owing to the instability of the model training, both suitable and unsuitable samples are generated for the task simultaneously. Subsequently, a data selection strategy is adopted for sample filtering, which is composed of two parts: the centroid metric criterion and the statistical characteristic criterion. Moreover, the selected qualified samples serve as supplements for the original samples. Without the loss of generality, SVR is adopted as the base regression model. Consequently, using qualified augmented virtual samples, a more accurate and reliable prediction model can be constructed, compared to that using only the original data.The remainder of this paper is organized as follows. M samples, respectively. In contrast, data-augmentation-based soft sensors are constructed based on the augmented dataset. To address the problem of insufficient models that are established with limited training data, virtual samples, which are denoted as N generated samples.It has traditionally been assumed that the amount of available training data is sufficient for soft sensor modeling, and many studies have focused on the design and improvement of modeling methods. However, it is difficult to collect sufficient samples in many situations, such as for industrial processes with large measurement intervals or during the early stages of new working conditions . GANs, wUnfortunately, owing to the unstable training of GANs ,41,42,43r, r], where r is a constant. The discriminator attempts to distinguish between the real and generated samples and concentrates its parameter distribution on the two extremes of the maximum and minimum; that is, r and −r. Consequently, a WGAN often becomes stuck in a poor regime and fails to learn.GANs have recently attracted significant attention owing to their good distribution-learning capabilities. The vanilla GAN uses the Jensen–Shannon (JS) divergence to measure the distance between the generated and original data. However, this often causes problems, such as mode collapse and vanishing gradients . To addrTo solve the problem caused by the weight clipping of the WGAN, Gulrajani et al. proposed a WGAN with a gradient penalty (WGAN-GP) . SpecifiOwing to the unstable training of the WGAN-GP, the quality of the generated virtual samples varies significantly. A data selection strategy is proposed for sample filtering, to eliminate the negative effects of unqualified virtual samples that are generated by the WGAN-GP for model construction. The selection strategy includes a centroid metric criterion, which is denoted as S1, and a statistical characteristic criterion, which is denoted as S2. The distribution scatters of the original and rough virtual samples are plotted in First, the S1 criterion is developed to filter the virtual samples in region A. The samples in region A are too close to the centroid ith original sample and The Euclidean distance is commonly used to measure the distance between two samples. A large distance indicates that the samples are far from one another. The square of the distance between jth generated sample and the original samples is calculated as follows:jth generated sample.Similarly, the square of the distance between the According to the definitions of h that satisfies the following formula:β-fractile of the Chi-square distribution, with a degree of freedom, q.Subsequently, the S2 criterion is adopted to filter the unsuitable virtual samples in area B. The samples in region B are far away from the distribution of the original data and tend to be outliers. The samples can be screened according to the statistical characteristics of the original samples. Based on the probability density function us paper . In partk-qualified samples are selected from the rough generated data and are denoted as In summary, according to the aforementioned two-stage data selection strategy, n is the number of samples for the SVR model.In this case, SVR is adopted as the base soft-sensor model for nonlinear processes. SVR is a statistical learning method that uses the structural risk-minimization criterion instead of the empirical risk-minimization criterion for model construction . The tarThe constrained optimization can be solved using the Lagrange function by introducing Lagrange multipliers. Subsequently, Equation (8) is converted into a dual problem, as follows:Therefore, the SVR model can be described asN virtual samples are generated, which is denoted as k-qualified samples A flowchart of the SWGAN-SVR model is presented in tth observation, respectively, and m is the sample size.A numerical example and an industrial polyethylene process were adopted to validate the effectiveness of the proposed SWGAN-SVR modeling method. The commonly used root-mean-square error (RMSE), coefficient of determination  and WGAN-SVR(S2) were further enhanced, compared to the results of WGAN-SVR. By adopting only one data selection criterion, unsuitable virtual samples around the centroid or far-away outliers were screened out, which improved the quality of the augmented samples. This also demonstrates that unsuitable virtual samples result in the insufficient construction of reliable soft sensors. Furthermore, after simultaneously adopting the S1 and S2 criteria, SWGAN-SVR achieved the best prediction performance among the five methods. This indicates that a two-stage data selection strategy is beneficial for selecting qualified augmented samples and improving the performance of the base SVR soft sensor.The scatter distributions of the original samples and the 450 generated samples are presented in For a better illustration, the detailed prediction results and relative prediction errors of the five soft sensors on the test set are presented in An industrial polyethylene process  was util2 value, indicating the worst prediction accuracy among the five methods. This occurred because the initial training data were insufficient for the construction of reliable soft sensors. With this data augmentation strategy, the WGAN-SVR, WGAN-SVR(S1), WGAN-SVR(S2), and SWGAN-SVR methods can improve the prediction accuracy, compared to the SVR approach. The generated virtual samples fill the information gap in the initial data and increase the sample capacity. Moreover, by adopting the two-stage data selection criteria, the SWGAN-SVR method achieved the best prediction performance among the five methods. The SWGAN-SVR method attempts to select the qualified virtual samples and, subsequently, to improve the quantity and quality of the initial training data. Note that owing to the strong nonlinearity of this example, the R2 index was relatively smaller than that of the numerical example described in Using a 10-fold cross-validation method, a suitable number of virtual samples was first determined for this example. The complete RMSE indices for different numbers of virtual samples are presented in The scatter distribution of the rough generated samples and selected unsuitable samples are presented in The detailed prediction results of the five soft sensors on the test set are depicted in In this study, a reliable soft sensor framework is developed to enhance prediction performance by introducing augmented data. Because having limited training data will be insufficient for establishing a reliable soft sensor, rough virtual samples are generated using the WGAN-GP method to enrich the sample information. Subsequently, based on a two-stage data selection strategy, qualified augmented samples are gradually selected to eliminate the negative effects of unsuitable samples on the prediction performance. Based on the qualified augmented training samples, the SWGAN-SVR method is designed to capture the process characteristics, which is beneficial for regression. The prediction results for the two examples demonstrate the advantages of the proposed approach. Further investigations will aim to enhance the quality of the generated samples, using GANs. Additionally, the combination of the process characteristics to generate more informative samples for practical applications is an interesting topic.


Caldatribacterium sp., Acidaminococcaceae sp., Planctopirus sp., Desulfobulbaceae sp.,Nocardia farcinica as well as regulatory T cells (Tregs), resting mast cells, monocytes, M2 macrophases, neutrophils were identified as significantly different . Our open-source software is freely available from GitHub at https://github.com/gutmicrobes/PSO-SVR.git.Recent transcriptomics and metagenomics studies showed that tissue-infiltrating immune cells and bacteria interact with cancer cells to shape oncogenesis. This interaction and its effects remain to be elucidated. However, it is technically difficult to co-quantify immune cells and bacteria in their respective microenvironments. To address this challenge, we herein report the development of a complete a bioinformatics pipeline, which accurately estimates the number of infiltrating immune cells using a novel Particle Swarming Optimized Support Vector Regression (PSO-SVR) algorithm, and the number of infiltrating bacterial using foreign read remapping and the GRAMMy algorithm. It also performs systematic differential abundance analyses between tumor-normal pairs. We applied the pipeline to a collection of paired liver cancer tumor and normal samples, and we identified bacteria and immune cell species that were significantly different between tissues in terms of health status. Our analysis showed that this dual model of microbial and immune cell abundance had a better differentiation (84%) between healthy and diseased tissue.  Contrary to the intuition of most people, bacteria are present in almost every part of the human body, with over 1,000 species in the gut alone. Our commensal bacteria maintain a dynamic balance with each other, and their imbalance can lead to a variety of diseases in the human body, including many cancers . As a coPorphyromonas gingivalis promotes hepatocellular carcinoma by affecting host cell signaling and thus cytokine response, cell cycle, and apoptosis  and rectal fungi  while few have examined the bacteria present within hepatocellular carcinoma tissues. Tumor-infiltrating bacteria could be analyzed using sequencing reads of non-human source, however, these unmapped foreign reads are often overlooked. In fact, it is possible to accurately estimate microbial abundance in tissue biopsies using foreign reads remapping . In thisStudies have also shown that the infiltration of various immune cell populations, including monocytes/macrophages, natural killer cells (NK), NKT cells and T cells, is the main pathogenic feature for oncogenesis or other lesions in liver . Rohr-UdThus, researchers have just begun to understand the regulatory role of bacteria in the development of cancer, as well as oncogenesis at the interface of bacteria/immune cell interaction. Immune cells alone are a key component of the TME and play a critical role in cancer development and immunization. Therefore, in order to understand the dynamics of the TME, it is equally important to understand the presumed synergistic dynamics between bacteria and tissue-infiltrating immune cells. Computational deconvolution methods have become a convenient choice to assess tissue-infiltrating immune cells by formulating the problem as a system of equations used to describe the gene expression of a sample as a weighted sum of the expression profiles of mixed cell types. That is, once given the immune cell-type characteristic matrix and overall gene expression, by solving the deconvolution problem, immune cell types and levels can be reasonably co-quantified with bacteria using mRNA-seq data without resorting to additional experiments.The deconvolution problem could be solved in several ways, such as by Support Vector Regression (SVR)  , linear At present, the main method of optimizing the SVR deconvolution problem uses an heuristic algorithm. Particle swarm optimization (PSO) is simple and easy to operate algorithm, and the optimization search process of PSO take into account the local search ability and global search ability, which can greatly improve the accuracy of SVR solution. At present, PSO- SVR algorithm has been applied in many fields. For example, Mingcong Deng et al. applied PSO- SVR algorithm to robotics in 2014 to predict the ball receiving time of a robot player . In 2017Mohammadi et al. evaluated several methods for solving the deconvolution problem and found that combining the loss function with regularization can improve the solution performance in the presence of highly correlated cell types in the mixture . HoweverCaldatribacterium sp., Magnetobacteriaceae sp. as pathological markers and together they were powerfully predictive for the pathologic status. Based on results from the three cases, classification accuracy when using only a single input feature is lower - only bacteria: 0.70, only immune cell feature: 0.74, and both features: 0.84. Such preliminary results are useful in resolving standing low response issue of immune checkpoint inhibitor-based immunotherapy . The data set included 98 samples . The read sequences that did not map to the reference genome GRCH38 were extracted from the BAM files. These foreign reads were then mapped to the RefSeq (NCBI Reference Sequence Database) (https://www.ncbi.nlm.nih.gov/refseq/) - a large collection of bacterial genomes. The mapping results were then input to the GRAMMy pipeline for the relative abundance estimation of infiltrating bacteria. The obtained expression and microbial profiles were used for downstream analysis. The data processing flow is shown in The mRNA-seq raw sequence data in BAM format of liver cancer patients’ normal and tumor tissue biopsies for this study were downloaded from the Seven Bridges Cancer Genomics Cloud . Apply Fastp to remove low-quality reads and partial reads, and excise poor quality bases. Use the BWA tool to remap the unmapped reads to a comprehensive microbial reference library RefSeq, containing the full genome sequence of 23,790 bacteria and archaea. We then applied GRAMMy algorithm to estimate the bacteria relative abundance.The mRNA analysis pipeline begins with the Alignment Workflow, which is performed using a two-pass method with STAR. STAR aligns each read group separately and then merges the resulting alignments into one. Following alignment, BAM files are processed through the RNA Expression Workflow to determine RNA expression levels. The reads mapped to each gene are enumerated using HT-Seq-Count. Expression values are provided in a tab-delimited format.Three real data sets were used to validate the PSO-SVR algorithm as compared to the CIBERSORTX, EPIC, and MCP-counter algorithms. The first validation data set from the CIBERSORT article consists of the gene expression profiles of 20 peripheral blood mono nuclear cells (PBMC) samples and the ratio of immune cells determined through flow cytometry for the same samples. The second data set comes from the National Center for Biotechnology Information Search database (NCBI database) under the GEO Datasets GSE64385. It consists of the gene expression profile of 10 colorectal cancer (CRC) samples and the composition of immune cells as determined by immunohistochemistry of the same samples. The third data set from CIBERSORTX consists of the gene expression profiles of 19 melanoma samples and the composition of immune cells obtained through single-cell sequencing technique of the same samples. The full description of these data sets is shown in We first filtered the reads that did not matched to the human genome reference GRCH38 from the RNA-Seq samples in BAM format using SAMtools . The filExtracting unmapped single reads: we used the command “samtools view -u -f 4 -F 264” to extract single-end unmapped reads in the format of FASTQ .Extracting unmapped paired-end reads: we used the command “samtools view -u -f 12 -F 256” to extract paired-end unmapped reads with the format FASTQ.Sequencing quality issues like low-confidence bases and sequence-specific bias complicate mRNA-seq analyses . We applFor the extracted unmapped paired-end reads, we used the command “fastp -q 0 -u 100 -n 10 -l 36 -A -G -M 0 -i” to delete sequences with base quality lower than 40% of Q15, sequences with N greater than 5, sequences with length less than 36, and broke up sequences, respectively. For the extracted unmapped single reads, we used the command “fastp -q 0 -u 100 -n 10 -l 36 -A -G -M 0 -i” to perform the same QC process on single-ended sequenced sequences. To merge the extracted single-end and paired-end unmapped reads, we used the SMS2  softwareWe then used the BWA tool  to remapThe Mann Whitney U test, also known as “Mann Whitney rank sum test”, was proposed by H.B.mann and D.Rwhitney in 1947. It assumes that any two samples are from two populations that are exactly the same except the population mean, in order to test for significant difference between the mean of the two populations.μ1,μ2 respectively. With only one translation difference between f1 and f2 at most, we get: μ1 = μ2 – α. The assumptions to be tested are as follows:Assuming that the mean values of two populations exist, they are recorded as The steps of Mann Whitney U test are:AN and BN from two populations A and B, arrange (AN + BN) observations in order of size. If the same observations exist, the average of their bit order is used.Randomly select two independent random samples with capacity of AT and BT of two samples.Calculate the grade and AT and BT. The two calculated U values are not equal, but their sum is always equal to ANBN, that is, AU + BU = ANBN. If AN < 20 and BN < 20, the test statistics are:The formula of Mann Whitney U test can be given according to AU and BU is used as the test statistic.In the test, because the critical value table of Mann Whitney U test only gives a smaller critical value, the smaller U value in U > αU and root mean squared error (RMSE). Since MSE squares the error (y – y^predicted = e), if e > 1, the value of the error will increase a lot. If there is an outlier in our data, the value of e will be very high and will be much greater than │e│. This will make the model with MSE loss give a higher weight to outliers. In order to minimize this outlier data point, we use the RMSE value, namely root mean square error, but at the expense of the prediction effect of other normal data points, which will eventually reduce the overall performance of the model.Correlation analysis refers to the analysis of two or more variable elements with correlation, so as to measure the correlation degree of two variable factors Correlation analysis can be carried out only when there is a certain connection or probability between the elements of correlation.(1) Pearson correlation coefficientGiven two continuous variables x and y, the Pearson correlation coefficient is defined as:Where ρ close to 0 indicates that there is no correlation between the two variables; whereas close 1 or - 1 indicates that the two variables are strongly correlated.(2) Spearman correlation coefficientSpearman correlation coefficient is defined as Pearson correlation coefficient ρ between hierarchical variables. For samples with a sample size of N, N original data are converted into hierarchical data. Compared with Pearson correlation coefficient, Spearman correlation coefficient is insensitive to data errors and extreme values, which is defined as.Where R and S are the grades of observed values i respectively, Logistic regression, also known as log probability regression, is a machine learning method used to solve the binary classification problem, which is used to estimate the possibility of something. It does not need to scale the input features, and the interpretability of the model is very good. The influence of different features on the final result can be seen from the weight of features. We fit the following regularization model to binary features as:Where β is the regression coefficient. Parameter α is the balanced lasso (L1) and ridge (L2) regularization, and λ determines their weights.Therefore, we use the logistic regression classifier to classify 98 samples. Their corresponding bacterial relative abundance data and immune cell proportion data were used as input characteristics, and the sample status  was used as the prediction variable 0 or 1. 75% of the data were used for training and 25% for testing. The association between hepatocarcinogenesis and tumor bacteria and invasive immune cells was analyzed through the classification results under different input characteristics.Support Vector Regression (SVR) is a commonly used technique for non-linear unsupervised learning. The generalization ability and prediction accuracy of SVR depend on the choice of kernel function coefficients and penalty factors. Due to the large feature size, small sample size and unknown sample distribution, in order to avoid over fitting as much as possible, we used a simple and effective linear kernel function in order to avoid overfitting as much as possible. We introduced the particle swarm optimization (PSO) technique, which uses a powerful algorithm to invoke an iterative approach to solve parameter optimization. It can improve the accuracy of SVR results, and increase the robustness of the results through multiple iterations. The overall flowchart is shown in a priori knowledge of purified leukocyte subpopulation expression profile data , the proportion of immune cells in tumor biopsies can be accurately estimated. The idea was first used in CIBERSORT. The gene expression profile data were first transformed into a linear combination of marker genes for immune cells to solve for f (representing the proportion of immune cells) in a linear combination equation. The linear combination equation was expressed as:We briefly introduce the SVR model for immune cell infiltration estimation. Based on a large amount of tissue gene expression profile data combined with n×L denotes the expression profile data of n genes from L samples for deconvolution. fm×L is the proportion of immune cells to be sought, specifically indicating the proportion of each immune cell in L samples among the m immune cell types. Sn×m is the signature matrix, indicating the expression profile data of n genes in each immune cell for m immune cells. The SVR algorithm solves the linear combinatorial equation using a deconvolution scheme.Where Xibest, gbest), where pibest denotes the individual optimal value of the particle and gbest is the global optimal value. In the computation, the expression data of each gene  was treated as a particle in the swarming iteration.The accuracy of the immune cell ratio calculated through the above SVR model is influenced by the model parameters such as the penalty factor C, sensitivity ϵ, and kernel function coefficient ϕ. These parameters are crucial to an SVR model’s accuracy and generalizability. However, in practice, these parameters are largely manually chosen without justification. Here we propose to optimize  parameters through iterations using the particle swarm algorithm. In each iteration, the swarming particles update their velocity and position vectors by tracking two “optimal values” . SVR models using optimal parameters are validated using benchmark data sets. We apply model with these parameters to the expression data of liver cancer patients’ tissues to solve for immune cell infiltration ratios.We apply this iterative optimization process of particle swarm to solve the SVR model, so that its optimal input parameters are optimized according to the swarm fitness metric. Upon initialization, we specify the approximate ranges of the parameters ϵ, C, and ϕ as: ϵ = , C = , ϕ = , and qThe major innovation of PSO-SVR over previous SVR deconvolution methods is that it applies the machine learning technique – particle swarm algorithm, to do SVR iterative optimization. In the process, the swarming algorithm searches for SVR hyperplane formed by support vectors that capture as many data points as possible while satisfying the given constraints and avoiding overfitting, using a linear “ω-insensitive” loss function. The function penalizes only those data points outside a specific error radius. The support vectors were selected from the signature matrix of genes, and the standard reference expression profile (signature matrix) was selected from a composition of 22 immune cells including CD4 T, and CD8 T immune cells in CIBERSORTX, but other signature gene sets can be applied as well.We gathered three data sets see . immunohAs shown in To further evaluate the PSO-SVR estimates using the PBMC-FC, CRC-IC and Melanoma-scRNA data sets, we used three metrics, namely root mean square error (RMSE), Pearson correlation coefficient, and Spearman’s correlation coefficient. The individual indicators are shown in We observe in Finally, the results calculated through PSO-SVR were compared with other algorithms such as CIBERSORTX, EPIC, MCP-counter, for calculating the proportion of immune cells in infiltrating tumors. From the results of PBMC-FC and Melanoma-scRNA . Immune cells with significant differences were identified as shown in As it can be seen in As known, neutrophils provide the first line of defense for the innate immune system by phagocytosing, killing, and digesting bacteria and fungi . CD8 T cCaldatribacterium sp. , Acidaminococcaceae sp. , among others. We used the nonparametric Mann-Whitney U test. The p-values were corrected for multiple testing through FDR.In Caldatribacterium sp., Acidaminococcaceae sp. (amino acid cocci), and Planctopirus sp. are significantly more abundant in infiltrating tumor tissues as compared to normal tissues and are likely associated with the pathological development of hepatocellular carcinoma.Noticeably, Caldatribacterium sp. leads to DNA damage in hepatic stellate cells, which then promotes the development of hepatocellular carcinoma through metabolites or toxins from intestinal bacteria  in the primary tumor tissue is significantly higher than that in normal tissue samples see .In order to further analyze the impact of tumor infiltrating immune cells and bacteria on the occurrence and development of liver cancer, we used the downloaded gene expression data and transcriptome data of 98 liver cancer samples, and calculated the corresponding bacterial abundance data by using the estimation method of microbial relative abundance in this paper. The PSO-SVR algorithm described in Section 3.1 was used to calculate the relative proportion of immune cells in liver cancer samples.Then the logistic regression classification method was applied to three cases: case 1, only bacterial abundance data were used as the input feature; In case 2, only the immune cell proportion data were used as the input feature; and in case 3, both bacterial abundance and immune cell ratio were used as input characteristics. From the classification results of the three cases , open project of the National Engineering Laboratory for Agri-product Quality Traceability (No.AQT-2020-YB6), and GuangDong Basic and Applied Basic Research Foundation (2022A1515-011426).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Serum microRNAs (miRNAs) are promising non-invasive biomarkers for diagnosing glioma. However, most reported predictive models are constructed without a large enough sample size, and quantitative expression levels of their constituent serum miRNAs are susceptible to batch effects, decreasing their clinical applicability.n = 15,460) based on the within-sample relative expression orderings of miRNAs.We propose a general method for detecting qualitative serum predictive biomarkers using a large cohort of miRNA-profiled serum samples . An additional validation set without glioma samples (non-cancers = 2611) showed a predictive accuracy of 95.9%. The second panel included 32 serum miRPairs (32-miRPairs), reaching 100% diagnostic performance in training set on specifically discriminating glioma from other cancer types , which was reproducible in five validation datasets . In other brain diseases, the 5-miRPairs classified all non-neoplastic samples as non-cancer, including stroke (n = 165), Alzheimer’s disease (n = 973), and healthy samples (n = 1820), and all neoplastic samples as cancer, including meningioma (n = 16), and primary central nervous system lymphoma samples (n = 39). The 32-miRPairs predicted 82.2 and 92.3% of the two kinds of neoplastic samples as positive, respectively. Based on the Human miRNA tissue atlas database, the glioma-specific 32-miRPairs were significantly enriched in the spinal cord (p = 0.013) and brain (p = 0.015).Two panels of miRNA pairs (miRPairs) were developed. The first was composed of five serum miRPairs (5-miRPairs), reaching 100% diagnostic accuracy in three validation sets for distinguishing glioma and non-cancer controls (The identified 5-miRPairs and 32-miRPairs provide potential population screening and cancer-specific biomarkers for glioma clinical practice.The online version contains supplementary material available at 10.1186/s12864-023-09203-w. Glioma is adults’ most common primary brain tumor, accounting for approximately 80% of all central nervous system malignant tumors . Due to miR-100 [miR-29b [miR-100 was 0.839, with a sensitivity and specificity of 83.33 and 77.89%, respectively [miR-29b was 0.866  [MicroRNAs (miRNAs) have been reported to play essential roles in different pathological and physiological processes, including cancer development . Many stmiR-100  and miR-[miR-29b  has been[miR-29b . Anotherectively . The aren = 15,460). The first model was used to differentiate between glioma and non-cancer control samples. The second model was used to discriminate gliomas from other cancers specifically. Prior to this, few studies have identified glioma-specific diagnostic biomarkers from serum. Considering the clinical ease of use of serum material and the stability and robustness of REO-based biomarkers, the two models we developed would have the potential to provide additional benefits for glioma screening and specific diagnosis in clinical practice.To overcome the limitations mentioned above and identify robust miRNA biomarkers for screening and specific glioma detection, we conducted an extensive case study containing 15,460 serum samples involving 13 cancer types and non-cancer control samples, and four other brain diseases and healthy samples. The relative expression orderings (REOs) of serum miRNAs were employed in developing predictive models, which belonged to the kind of single sample classifier . Comparehttp://www.ncbi.nlm.nih.gov/geo/), with a total of 12,447 samples, of which 8032 were non-cancer control samples, and 4415 were serum samples of 13 cancer types , Alzheimer’s disease , meningioma and PCNSL . These data were assayed by the 3D-Gene Human miRNA V21_1.0.0 platform, detecting a total of 2550 miRNAs. GSE113486 was used as the training set, and all other datasets were used as the validation set.We downloaded six cancer datasets of serum miRNA expression from the GEO database  between the control and case samples. The REO of the miRPair can be naturally used to predict to which group an unknown sample belongs. Based on this hypothesis, the flow of identifying REO-based biomarkers is as follows  miRPairs. Then, for a miRPair , the percentage P of its REO exhibiting as EmiRNAa > EmiRNAb in the control samples was calculated as follows,k represents the number of those samples with EmiRNAa > EmiRNAb in control samples, and m represents the total number of control samples.The miRNAs detected in the training set were paired to form P of a miRPair in the control samples is no less than a threshold, for example, 95%, which is adjustable as needed. Then, the REO of the miRPair is considered stable in the control samples and is called a stable miRPair.Suppose the percentage EmiRNAa > EmiRNAb and EmiRNAa ≤ EmiRNAb were calculated and denoted by n1 and n2, m1 and m2, respectively. Fisher’s exact test was used to test whether the distribution of REOs of the miRPair in the controls differed from that in the case samples. If the BH adjusted p-value is smaller than 5%, the REO of the miRPair was considered significantly reversed under the case condition and defined as a reversed miRPair.For each stable miRPair, the numbers of control and case samples showing the REOs of P was calculated, where △P=Pcontrol(EmiRNAa > EmiRNAb)-Pcase(EmiRNAa > EmiRNAb). The greater the △P value, the more significant the difference in the distributions of REOs between control and case samples. For a miRPair, △P = 1 indicates that the REOs of the miRPair in control samples are all EmiRNAa > EmiRNAb, while in case samples are all EmiRNAa ≤ EmiRNAb. Those miRPairs satisfying △P > Pt (Pt is a given threshold) were determined as candidate miRPairs.For a reversed miRPair, △We identified a combination with maximum sample coverage for each candidate miRPair. The steps were as follows.EmiRNAa > EmiRNAb under the control condition or EmiRNAa ≤ EmiRNAb under the case condition. The combination covered a sample if at least one miRPair it possessed exhibited the reference pattern on that sample. Sample coverage was then calculated as the percentage of covered samples to the total sample number in the training set.First, we defined the reference pattern of REO as showing miRPairi, at first, the combination is {miRPairi}. The search process is shown in Fig. miRPairi, miRPairj} with the most extensive sample coverage was selected. Then, the search process for the next miRPair is kept on from the remaining candidates until adding a miRPair into the combination cannot increase the sample coverage. The obtained combination is the maximum sample coverage combination identified for the candidate miRPairi.Then, we searched for a combination with the maximum sample coverage for each candidate miRPair. For a candidate After searching combinations with the maximum sample coverage, we can then define an activity score for each candidate miRPair, i.e., the number of occurrences in all combinations. The higher the active score, the greater the importance of the candidate miRPair.k miRPairs are taken as prediction biomarkers, respectively, where k = 1: n (n is the number of candidate miRPairs). Then, the prediction models are constructed based on the voting rules according to the REOs of miRPairs in the model. The geometric mean of negative predictive value (NPV) and positive predictive value (PPV) is calculated, and the top k miRPairs that reach the maximum first are used as the final prediction biomarker.The candidate miRPairs are sorted according to their activity scores, from the largest to the smallest. The top t-test and were considered significant at a false discovery rate (FDR) smaller than 5%.Differentially expressed miRNAs between phenotypes were identified using the Student’s The miRNA functional annotation was based on miEAA online miRNA functional enrichment and annotation tool , which cAll statistical analyses in this study were performed with R 3.6.1 software.P(EmiRNAa > EmiRNAb) ≥ 95% in non-cancer control samples, we identified a total of 1,337,295 stable miRPairs, and 94.63%  of them still maintained the REOs of EmiRNAa > EmiRNAb in 95% of the 4720 non-cancer samples in GSE122497. This result indicated that the within-sample REOs of miRNAs had high stability.The 97 non-cancer control and 38 glioma serum samples in GSE113486 were used as control and case samples for training. With EmiRNAa > EmiRNAb in non-cancer samples, while in glioma, all showed EmiRNAa ≤ EmiRNAb , , , , and , referred to as 5-miRPairs.Then, we assessed the REO alterations in glioma serum. Among the 1,265,546 stable miRPairs, 857,298 showed a significant reversal of REOs in the serum of glioma . We found that 34 reversed miRPairs showed an REO completely reversed between non-cancer and glioma, i.e., all showed EmiRNAa > EmiRNAb, and vice versa for glioma samples. In the three independent validation sets of GSE13901, GSE112264, and GSE113740, the prediction accuracy was 100%  ≥ 80%. Among them, 753,316 had significantly reversed REOs in the glioma group . Controlling ΔP ≥ 0.7 (here ΔP=Pnon-glioma(EmiRNAa > EmiRNAb)-Pglioma(EmiRNAa > EmiRNAb), see EmiRNAa > EmiRNAb, and vice versa for glioma samples.To further identify the glioma-specific biomarker, we constructed a model with glioma as the case group and the other cancers as the control (non-glioma) group. The training set GSE113486 contains 13 types of cancers, including 370 cases of bladder cancer and about 40 cases of other cancers. To maintain a balanced sample size, we randomly selected 40 cases of bladder cancer. A total of 1,208,616 stable miRPairs were identified in the non-glioma group when controlling n = 1252), GSE113740 (n = 306), GSE106817 (n = 1123), GSE139031 (n = 164), and GSE122497 (n = 542). The results showed that the prediction accuracy of 32-miRPairs was 98.74, 100, 98.31, 95.73, and 99.45% for the five independent datasets  and brain (p = 0.015). In the KEGG database, the target mRNAs of these miRNAs were significantly enriched in DNA replication (p = 0.021), Fatty acid degradation (p = 0.026), Glutathione metabolism (p = 0.037), Retrograde endocannabinoid signaling (p = 0.009) and PPAR signaling pathway (p = 0.035), as shown in Fig. We conducted the functional enrichment analysis of the 36 miRNAs in 32-miRPairs using the miEAA online miRNA functional enrichment tool, based on the Human miRNA tissue atlas database and the KEGG database. Figure Considering the enrichment of brain-related functions of the 32-miRPairs, we also collected serum miRNA data to evaluate whether the models can be applied to distinguish glioma from other brain diseases. Two non-neoplastic brain diseases (stroke and Alzheimer’s disease), one benign brain tumor (meningioma), and one malignant brain tumor (PCNSL) were collected. As shown in Table Glioma is highly infiltrative, difficult to remove surgically, and has a poor prognosis . BecauseThe REO-based biomarkers were developed by pairing two miRNAs. Compared to single miRNAs, they can resist fluctuations in expression levels . As qualWe included the non-cancer samples as controls in the study during the discovery phase to identify biomarkers suitable for early diagnosis. Unlike most studies that compare GBM patients with healthy serum populations, the identified 5-miRPairs are more suitable for clinical application scenarios of cancer detection, i.e., distinguishing whether one has cancer or not, as the early diagnosis population is relatively rarely wholly healthy. By applying to the serum data of stroke and Alzheimer’s diseases, the 5-miRPairs model achieved 100% diagnostic accuracy in these non-neoplastic brain disease samples. For healthy control samples, it still performed well, with none of them classified as glioma. Therefore, our setting of the control population would be suitable for clinical application scenarios of early detection.One of the data sets we used was derived from Ohno et al. . This stOne of the study’s limitations is the lack of stage or grade information in serum glioma samples; thus, we could not directly evaluate the performance of our models in early glioma detection. Another limitation is the lack of data from different platforms. The data used in this study were all from the 3D-Gene Human miRNA V21_1.0.0 platform. However, this should not hinder the potential clinical translation of the developed biomarkers. As discussed by Liu et al., different platforms only affect the number of marker gene pairs and do not affect the diagnostic efficacy of the biomarkers . Our resThe number of candidate miRPairs selected for the construction of biomarkers was based on the goals pursued in clinical practice: ease of use and better diagnostic performance. Theoretically, more candidate miRPairs are better. However, as the degrees of reversal of miRPairs (measured by △P) differed, more candidate miRPairs might not necessarily improve the predictive power. For example, the accuracy decreased when using 121 candidate miRPairs for constructing a model discriminating glioma and non-cancer controls. For the glioma-specific model, the accuracy decreased when using 144 candidate miRPairs. Therefore, we chose five candidate miRPairs for the first model, considering the clinical ease of use, as five pairs have already achieved 100% classification accuracy. For the second model, we chose 32 candidate miRPairs because they first reached the maximum of the geometric mean of negative and positive predictive values.has-miR-3162-3p and has-miR-7108-3p. Further analysis of the expression of the two miRNAs revealed that, in the training set, they were expressed at the highest levels in glioma samples, lower in the other 12 cancer types, and lowest in non-cancer controls  and IDH wild-type glioblastoma . This issue deserves further attention and will be our study topic when more serum miRNA data are available for different grades of glioma.In conclusion, the within-sample relative expression orderings are more suitable and robust than quantitative levels to serve as serum biomarkers for glioma. Upon 12,447 microRNA-profiled serum samples, we identified five and 32 serum microRNA pairs for diagnosis screening and cancer-specific glioma detection with high diagnostic performance in retrospective cohorts. These biomarkers will be prospectively validated to demonstrate their clinical applicability further.Additional file 1: Table S1. Glioma-specific 32-miRPairs.