Background: Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data.Methods: High-throughput sequencing transcriptomic data processing and gene annotation were performed using R software. Machine learning models were constructed, and model performance was evaluated by machine learning methods in Python. The models were visualized and interpreted using the Shapley Additive explanation (SHAP) method.Results: Based on the preset parameters and using recursive feature elimination implemented via machine learning, the top 10 optimal genes were screened for the establishment of the machine learning models. In a comparison of model performance, CatBoost was selected as the optimal model. We explored the significance of each gene in the model and the interaction between each gene through SHAP analysis.Conclusion: The combination of CatBoost and SHAP may serve as the best-performing machine learning model for predicting transcriptomic and sepsis risks. The workflow outlined may provide a new approach and direction in exploring the mechanisms associated with genes and sepsis risk. Advances in gene sequencing have generated a large number of omics datasets, including that for the genome, transcriptome, proteome, metabolome, and others . GenomicMost of the current methods for screening target genes utilize differential gene expression analysis, dimensionality reduction, or correlation. Evidence indicates that a crucial proportion of phenotypic variation cannot be explained by analyzing significance , and thiMachine learning has garnered considerable attention owing to its excellent ability to deal with predictive analysis. Models of risk scores, built using machine learning methods via quantitative analysis of gene expression, can differentiate disease states from controls across transcriptomic datasets . CompareAlthough machine learning has shown promise in assisting humans in the analysis of a wide variety of datasets related to genomics and genetics, the data obtained must be analyzed, interpreted, and acted upon . A few cThe data was obtained from the Gene Expression Omnibus (GEO) database at the National Center for Biotechnology Information. The expression dataset GSE185263 was obtained from the GPL16791 platform  contributed by Baghela et al. . DiversePatients were recruited within 2 h of emergency admission if sepsis was suspected. The enrolled patients required the prevalence of at least two Systemic Inflammatory Response Syndrome/sepsis-1 criteria  and suspA total of 348 samples related to sepsis were recruited from different hospitals in different countries, including 44 healthy control samples from presurgical controls or healthy volunteers. For retrospective association with transcriptomic data, metadata with clinical and demographic parameters were collected at triage and within 72 h following ER and ICU admission.Preparation, processing, and analysis of the transcriptomic data were performed using the R software (version 3.6.3). Preparation, construction, evaluation, and visualization of machine learning data were performed using Python software (version 3.7).The complete flow chart for statistical analysis is shown in The R software was used for bioinformatics analysis including downloading, annotating, and organizing high-throughput data. The org. Hs.eg.db package was used for gene annotation.Machine learning algorithms were implemented in the Python software. The data were organized in the format required for implementing the machine learning algorithm. Samples were classified into healthy or sepsis groups, defined based on the outcome indicators for the classification prediction model. The KNN algorithm  was usedFeatures that do not follow a standard normal distribution may cause the estimators in machine learning to deliver faulty outcomes. In our study, we used the StandardScaler utility class from the preprocessing module to perform data standardization. Recursive feature elimination (RFE) can be used for feature selection to improve estimators’ accuracy scores or to boost their performance on very high-dimensional datasets. In our study, logistic regression  was used as an external estimator, which assigns weights to features. The goal of RFE was to select features by recursively considering increasingly smaller sets of features. As the desired number of features was set to 10, the procedure was recursively repeated on the pruned set until the 10 best features were selected.The sklearn (scikit-learn) package , a simplShapley Additive explanation (SHAP) package , a methoGSE185263)  . The dowThe gene expression matrix was inverted using the annotated variables with SYMBOL as the column name and the sample name as the row name. Variables with missing values greater than a proportion of 30% in the data annotated as SYMBOL was removed. The resulting dataset was used for implementing the machine learning algorithms.The data was organized in the format required for implementing the machine learning algorithm. The KNN algorithm  was usedTraditional methods of linear regression and Lasso regression were used first. The scores of the training and test sets were 0.66 and 0.53 in linear regression, respectively. Similarly, the scores of the training and test sets were 0.66 an 0.53 in Lasso regression, respectively. .The logistic model was constructed using the LogisticRegression classifier in the linear_model module of the scikit-learn machine learning library (sklearn). The score of the training and test sets were 0.98 and 0.94, respectively. The results of the model trained on the training and test datasets are shown in The Decision Tree model used was constructed using the DecisionTreeClassifier model from the sklearn. tree module. The scores of the training and test sets were 1.0 and 0.95, respectively. The results of the model for the test set are shown in The Random Forest model was constructed using the RandomForestClassifier method from the sklearn. ensemble module. The scores of the training and test datasets were 1.0 and 0.96, respectively. The results of the model entrained on the training set for the test set are shown in Next, the CatBoost model was constructed using the CatBoostClassifier model from the CatBoost algorithm. The scores of the training and test datasets were 1.0 and 0.97, respectively. The results of the model entrained on the training set for the test set are shown in Compared with the traditional methods, models built by machine learning had better scores. Similarly, on comparing the different models, the CatBoost model showed the best results for the test set and the f1-score .The calibration curves for models are shown in The comparison of receiver operating characteristic (ROC) curves of the logistic, Decision Tree, Random Forest, and CatBoost models based on the evaluation of ten-fold cross-validation results is shown in For the genomic prediction models, the k-fold cross-validation method was applied and it is a statistically powerful method to assess differences in model accuracies . The resx-axis at a net benefit of zero). The strategy with the highest net benefit at a particular threshold probability is optimal, irrespective of the size of the difference  had a positive impact on predictive power (ATP6V1D (blue) had a negative impact. The results were similar to those for CLIC1. Other genes with high gene expression had a negative impact (red) on prediction, whereas low gene expression (blue) had a positive impact on prediction.After sorting features, SHAP was implemented to distinguish the feature values for the selected genes. The SHAP summary plot was used to explain the model built using CatBoost. The results showed that high gene expression of ve power , whereasATP6V1D, the vertical axis indicating the SHAP value of the feature ATP6V1D, and the color indicating the eigenvalue of the feature CLIC1. Samples with higher CLIC1 eigenvalues (red) showed a greater influence of the feature, ATP6V1D, i.e., the vertical axis SHAP value was high.Additionally, the SHAP dependence plot was used to visualize the interaction between features. In The SHAP force plot was used to illustrate the model at the individual level. The horizontal axis is the SHAP value. Blue indicates that the feature negatively impacts the prediction ; red indicates that the feature has a positive impact on the prediction . As shown on the number axis, the base value is marked above the horizontal axis as the average f(10) value for all samples, and f(10) is marked above the horizontal axis as the average SHAP value after the sample was aggregated, that is, the model predicts the mean value. As shown in Evans et al. and Purcell et al. have proposed methods for aggregating information related to single nucleotide polymorphisms associated with a trait. These models generate polygenic risk scores from thousands of genes to predict an individual’s genetic risk of developing disease . By provIn our study, we obtained RNA expression data for sepsis at the first clinical presentation and used machine learning methods to infer disease risk from the transcriptomic data. Cheng et al. demonstrated that the integration of transcriptomic analysis and machine learning methods enhances the predictive modeling of genes affecting phenotypes . We evalp, small n” paradigm of limited biological samples and the prevalence of high-dimensional data, the selection of genes is a challenging problem  to be screened according to specific requirements to enable RFE to identify the optimal number of features using a cross-validation cycle.Gene expression data have been increasingly analyzed using machine learning. Due to the “large  problem . Heurist problem . In our Katsaouni et al. have described commonly used metrics in machine learning (they include metrics used in at least two studies), including logistic regression, random forests, support vector machines, deep neural networks, and convolutional neural networks . Due to Schrauf et al.  showed tThe traditional biostatistical approach to evaluating models focuses on the results for sensitivity, specificity, and AUC. These methods are mathematically simple to implement but have low clinical relevance. Studies have shown that discrimination by AUC is insufficient ; a modelTP6V1D had a positive impact on prediction, whereas low gene expression of ATP6V1D negatively impacted prediction, similar to CLIC1. Other genes with high expression had a negative impact on prediction, whereas low gene expression had a positive impact on prediction. The results of the interpretability analysis demonstrate the excellent applicability and generalizability of the findings obtained using the CatBoost model.Artificial intelligence methods based on machine learning yield black-box models. Compared with traditional models, these black-box models have shown overwhelming advantages in obtaining accurate predictions . In prinThe SHAP force plot illustrated the model at the individual level. Through SHAP analysis of individual samples, we screened the high-risk samples and identified the high-risk patients. If these analyses are extended to identify transcriptomic biomarkers associated with disease severity within the first few hours of ER or ICU admission, timely and aggressive interventions to prevent further development of severe sepsis may be prevented.Our study had several innovations. It was the first to implement the CatBoost machine learning approach applied to a polygenic risk scoring model. In comparisons using the same dataset, we showed that CatBoost was more efficient than other machine learning methods and showed better accuracy and validity. This study was also the first where SHAP was applied to a polygenic risk scoring model constructed based on machine learning to explain the model. This study was also the first wherein SHAP was applied in a machine learning model for patients with sepsis. The visualization of SHAP values helped explain the constructed risk prediction model. This study was the first attempt at constructing a polygenic risk assessment model using machine learning combined with SHAP scores in critically ill patients. Our analysis is by far the most integrated polygenic risk assessment process for transcriptomic data and can be extended to the analysis of other omics datasets.This study has a few limitations. First, the data was obtained from a public database, and the number of patients in the control group was relatively small. Second, our results have been evaluated using machine learning methods alone; further validation using molecular and clinical analyses is required. Our study can be used as a model for exploring genes related to disease occurrence or prognosis risk in transcriptomics or genomics, by expanding the RFE value in the second step if necessary to expand the screening scope of candidate genes. In later research, we will adopt a prospective multi-center randomized control design, and further expand the sample size. Combining this strategy with cohort studies, we will explore the relationship between the early expression levels of the above-mentioned candidate genes and the clinical prognosis.We describe herein an application based on bioinformatics, machine learning, and SHAP for risk prediction among patients with risk of sepsis at the first clinical presentation using transcriptomic data analysis. The process included preprocessing of sequencing data, screening of several genes, construction of the machine learning prediction model, its validation, visualization, and interpretation. The combination of CatBoost and SHAP may serve as an optimal machine learning model for transcriptomics and predicting disease risk. Extending these analyses to identify transcriptomic biomarkers associated with disease severity within the first few hours may enable timely and aggressive interventions to prevent progression to severe sepsis. The workflow may provide a new approach and direction for elucidating mechanisms associated with genes and phenotypes correlated with diseases.


Most malignant brain gliomas (MBGs) are associated with dismal outcomes, mainly due to their late diagnosis. Current diagnostic methods for MBGs are based on imaging and histological examination, which limits their early detection. Here, we aimed to identify reliable plasma lipid biomarkers for non-invasive diagnosis for MBGs.Untargeted lipidomic analysis was firstly performed using a discovery cohort (n=107). The data were processed by a support vector machine (SVM)-based discriminating model to retrieve a panel of candidate biomarkers. Then, a targeted quantification method was developed, and the SVM-based diagnostic model was constructed using a training cohort (n=750) and tested using a test cohort (n=225). Finally, the performance of the diagnostic model was further evaluated in an independent validation cohort (n=920) enrolled from multiple medical centers.A panel of 11 plasma lipids was identified as candidate biomarkers with an accuracy of 0.999. The diagnostic model developed achieved a high performance in distinguishing MBGs patients from normal controls with an area under the receiver-operating characteristic curve (AUC) of 0.9877 and 0.9869 in the training and test cohorts, respectively. In the validation cohort, the 11 lipid panel still achieved an accuracy of 0.9641 and an AUC of 0.9866.The present study demonstrates the applicability and robustness of utilizing a machine learning algorithm to analyze lipidomic data for efficient and reliable biomarker screening. The 11 lipid biomarkers show great potential for the non-invasive diagnosis of MBGs with high throughput.A full list of funding bodies that contributed to this study can be found in the Acknowledgments section. Computed tomography (CT) and magnetic resonance imaging (MRI) are routinely utilized for detection of malignant brain gliomas, but the cost, side effect of radiation and relative inaccessibility dampen their application. Tumor biopsy-based strategies, such as genetic profiling of specimens obtained by biopsy, or sequencing of circulating tumor DNA (ctDNA) from cerebrospinal fluid, are too invasive for repeated sampling. Liquid biopsy-based strategies have also been explored for the diagnosis of gliomas, such as detecting ctDNA in blood, but the sensitivity is not high enough for clinical practice. A novel non-invasive strategy based on highly accurate and sensitive biomarkers is still urgently needed.In the current study, we proposed a strategy combining lipidomics and an SVM-based machine learning algorithm to screen plasma biomarkers for malignant brain gliomas. Lipidomics analysis was firstly performed to explore lipid expression profile in plasma and then SVM modeling was performed to retrieve lipid markers. Finally, we established a targeted liquid chromatography-multiple reaction monitoring-mass spectrometry (LC-MRM-MS) assay method and an SVM-based diagnostic model using a panel of 11 lipids. The performance of this diagnostic model is evaluated in large cohorts deriving for different medical centers.Our findings reveal that the combination of lipidomics and machine learning algorithm is a powerful and efficient strategy for biomarker screening. The marker panel and diagnostic model we developed in this present study showed prospect in future clinical applications with advantages including non-invasive sample collection, quick analysis and high accuracy.Alt-text: Unlabelled box,Malignant brain gliomas (MBGs) are the most common primary brain malignancies in adults. Increasing evidences have already suggested that early detection of cancer can be attributed to better prognosis or even cure in some malignancies.,Recently, with the rapid development of second-generation sequencing and mass spectrometry, liquid biopsy-based methods have emerged as promising non-invasive diagnostic modalities for cancer diagnosis. Several kinds of biomolecules in blood have been found as potential biomarkers for cancers, such as ctDNA, RNA, antigen and proteins,,Lipidomics is a prevailing analytical strategy that targets the entire lipid species present in a given biological sample.,,Machine learning (ML) refers to the use of data analysis to establish effective detection or verification models, and is an important part of artificial intelligence (AI).Four sample cohorts  comprising a total of 2,002 participants including MBGs patients (group name MBGs) and healthy controls (group name NC) were enrolled between June 2018 and September 2019. Baseline characteristics of the participants are summarized in For enrolled MBGs patients, 4 mL of arterial or peripheral blood was collected in tubes containing EDTA at the time of surgery. For NC group, peripheral blood was collected on the morning of the medical examination. All participants, including patients and healthy controls had fasted at least 8 hours before blood collection. Whole blood was centrifuged at 2500 g for 15 min and aliquots of supernatant (plasma) were transferred into cryovials and stored at -80°C.Ammonium acetate was purchased from Sigma-Aldrich . Formic acid, HPLC grade isopropanol (IPA), acetonitrile (ACN) and methanol were purchased from Fisher Scientific (USA). Deionized water was produced by a Milli-Q system. All standard chemicals were of analytical grade with typical purity of >99%. Phosphatidylcholine , lysophosphatidylcholine  and triglyceride (TG 18:1-18:2-18:3) were purchased from Avanti Polar Lipids (USA).Lipids were extracted from plasma samples using liquid-liquid extraction. For untargeted lipidomics, 50 μl of plasma were mixed with 200 μl of chloroform/methanol . For targeted quantification, lipids were extracted from 25 μl of samples with 100 μl of chloroform/methanol  containing LPC 13:0 and glyceryl trioctanoate (TG 8:0-8:0-8:0) at 10 mg/ml as internal standards. For untargeted lipidomics, an Ultimate 3000 liquid chromatography coupled with a Q-Exactive MS was used in data dependent acquisition mode. Chromatographic separation was performed on a X-select CSH C18 column . Two mobile phases were used for gradient elution: (A) ACN/water  and (B) IPA/ACN . Both mobile phase A and B contained 10 mM ammonium acetate and 0.1% formic acid. The gradient program was as follows: 0 min - 40% B; 0.5 min - 40% B; 0.6 min - 50% B; 6.6 min - 60% B; 6.7 min 75% B; 9.7 min – 99% B; 14 min - 99% B; 14.5 min - 40% B; and 19 min - 40% B. A pooled plasma sample was prepared as QC to assess the stability of the LC-MS instrument and ensure the reliability of the data. A QC sample was run before and after the sequence and after every 15 sample runs in the sequence. For targeted quantification, a Nexera UHPLC system coupled with a QTRAP 6500 MS was used in multiple reaction monitoring (MRM) mode. Chromatographic separation was performed on a X-select CSH C18 column  using the same mobile phases as for the untargeted lipidomics. Optimized MRM parameters were included in Table S1.The acquired lipidomic data (.raw) were processed using MS-DIAL softwareNo processing procedure was performed for missing values. L2 normalization was used to normalize the data according to the following equation.N samples ), we employed SVM to infer the weight W∈R(1×1304(758)) for all features. Once the weight W was obtained, we treated the square value of Wi as the importance weight of the To target selected features with information that is significant for classifying the MBGs versus NC, we computed the importance weight for each feature. Data in positive-ion (ESI+) and negative-ion (ESI-) modes were analyzed separately. Given all the normalized training data, denoted as Three pairs of glioblastoma samples and the corresponding adjacent normal tissues were taken during brain tumor resections and immediately frozen with liquid nitrogen. RNA was extracted using TRIzol. Sequencing libraries were then established using NEBNext UltraTM RNA Library Prep Kit for Illumina , and the library preparations were sequenced on an Illumina HiseqX10 instrument  and 125 bp/150 bp paired-end reads were generated. For data analysis, we used GRCh38 (hg38) as the reference genome. Hisat2 v2.0.5 was used for genome mapping, and featureCounts v1.5.0 was used to count the read numbers of mapping. We then calculated the number of Fragments Per Kilobase of transcript sequence per Millions base pairs (FPKM) to estimate gene expression levels.tsne function. Principle component analysis (PCA) and hierarchy cluster analysis were performed using the MetaboAnalyst web service . Transcriptomic data were test for enrichment in curated KEGG pathways of glycerophospholipid metabolism (hsa00564) and glycerolipid metabolism (hsa00561) using GSEA softwareMATLAB R2018a and Prism Graphpad v 8.0 software were used for statistical analysis. For data from the targeted assay, t-SNE analysis was conducted with MATLAB using the This study was approved by the ethics committee at Beijing Tiantan Hospital (reference number: KY2014-021-02) as a part of the Neurosurgical Clinical Information and Biobanking Project (Brain Tumor Section) and conformed to the ethical guidelines of the 1975 Declaration of Helsinki. Written informed consent was obtained from each participant.The funding sources listed at the end of the manuscript did not have a role in the study design, sample collection, data analysis, result interpretation, or manuscript writing. The decision to submit the paper for publication was made only by the authors listed.In the present study we aimed to identify plasma biomarkers for diagnosis of MBGs. An overview of the study workflow is presented in For the discovery cohort, a total of 1304 plasma lipids belonging to 16 lipid species, and 758 plasma lipids belonging to 27 lipid species were identified at MS2 level in ESI+ and ESI- ion modes, respectively . First, PCA score plots were used for data quality evaluation in which QC samples clustered well in both ESI+ A and ESIAfter the construction of the SVM discriminating model, we performed feature selection to retrieve candidate biomarkers. After 100*500 times of iterations, mean accuracies on the test set for each model are shown in To better adapt the application of this lipid marker panel to a clinical setting, we first constructed an SVM based diagnostic model using the MRM assay result of the training cohort. As shown in To further evaluate the diagnostic performance of the marker panel in an actual clinical setting, we tested it on a freshly collected validation cohort containing 920 participants from three medical centers. As shown in To investigate the potential mechanism(s) linking the plasma lipid markers to MBGs, we profiled the RNA expression levels in samples from MBGs patients as well as in normal brain tissues, and analyzed the gene expression levels in two relevant metabolic pathways  using the GSEA algorithm. As shown in Table S2 and Figure S6, these two pathways were significantly dysregulated in MBGs tissues .In this study, we combined LC-MS based lipidomic analysis with an SVM-based ML algorithm to screen for plasma lipid biomarkers for MBGs diagnosis. A panel of 11 lipids was ultimately identified as biomarkers and a diagnostic model was built and evaluated with independent patient cohorts. Our findings provide a powerful and non-invasive diagnostic method for malignant brain gliomas, and at the same time they provide a study workflow that combines lipidomics and ML that can be used to efficiently and reliably screen for biomarkers of diseases.,,P-value, fold change between groups and VIP values in PLS-DA/OPLS-DA models), the SVM-based ML algorithm we used can not only rank all the features according to their relative contribution for discriminating different groups, but can also provide overall accuracy in terms of numbers of selected features, thus making it practical to achieve a high accuracy with the least number of biomarkers. What is more, as reported by Mahadevan and colleagues, SVM is able to give a better predictive model with a fewer number of features when compared to PLS-DA.Because of its extensive metabolite coverage and high degree of sensitivity, untargeted metabolomics and lipidomics have become powerful strategies for plasma metabolite/lipid profiling, and have been widely used in biological studies and biomarker screening research.To validate the panel of markers and ultimately apply it in a clinical setting, we aimed to develop a high throughput assay which could accurately quantify the 11 lipid markers in a large sample cohort. Thus, an LC-MRM-MS based assay was developed with optimized analytical parameters with the assistance of commercial chemical standards. All 11 lipid targets were uniformly analyzed in ESI+ ion mode to take advantage of the higher signal response for LPCs and PCs. Finally, this optimized method reliable quantified the markers in 19 min. This MRM-based high throughput assay method could analyze one sample in about two hours including sample preparation and data processing time, and batch processing of samples could further shorten the average analyzing time for each sample to less than 30 min. This advantage makes it applicable and practical to be used in clinically related diagnostic applications involving a large number of samples. With the MRM assay result of the training cohort, we established the SVM based diagnostic model and tested it with a test cohort. To better evaluate the model performance in an actual clinical setting, we enrolled a newly collected validation cohort comprised of participants from multiple medical centers, and results from this analysis provide further validation of the robustness of this diagnostic model. We also evaluated the systematic biases and batch effects relevant to our study workflow. Systematic bias commonly exists in sample conditions, we compared patient plasma samples of different origin (artery or vein) and anesthesia status, but no differences were observed. Batch effects are often introduced during analytical procedures such as lipid extraction and LC-MS analysis, so we included internal standards for normalization, so that possible batch effects could be excluded. As shown in ,,An increasing number of studies have employed ML in medically related applications to take advantage of its high sensitivity and powerful diagnostic potential.The candidate lipid markers we found belonged to three lipid species . In cells, LPC, PC and TG are involved in pathways of glycerophospholipid metabolism and glycerolipid metabolism, and our RNA sequencing results showed that these two pathways were perturbed in plasma from MBGs patients, suggesting that changes in the plasma levels of these lipids may reflect a perturbed lipid metabolism in MBGs tissue. Compared with normal cells, tumor cells usually consume more saturated and monounsaturated LPCs which are carriers of fatty acids.,The approach described here has several important limitations. First, given that samples collected from different hospitals may vary in a manner by which they were collected and/or stored, the validation cohort from multiple centers would be better choice for biomarker performance evaluation. Because of the practical challenges of enrolling MBGs patients in this study, all 851 patients in MBGs groups came from the same medical center . However, NC enrolled in this study came from three medical centers including Tiantan Hospital itself, and the result showed that even though a partial bias exists in samples collected from different medical centers, our panel of proposed lipid biomarkers could still perform well enough to discriminate MBGs patients from NC. The second limitation of our study relates to the MRM assay method developed for biomarker analysis of the training, test and validation cohorts. An assay method able to absolutely quantify the 11 lipids would be the ideal choice because it can provide concentrations of the plasma biomarkers. However, no ideal blank matrix  is commercially available. Even though a kind of simulated blank plasma or charcoal-treated plasma samples have been used for absolute quantification of endogenous molecules in biological samples,The present study integrated SVM based ML algorithm into conventional lipidomic biomarker screening workflow, revealing a panel of 11 potential lipid markers for MBGs. These lipid markers and related algorithm have shown high accuracy in identifying MBGs blood samples, demonstrating their high value in disease diagnosis. Advantage of this diagnostic strategy includes non-invasive sample collection, quick analysis and high accuracy. What is more, our study shows the applicability, advantages and broad prospects of a strategy using ML algorithms and metabolomics in disease related biomarker screening.Y.Y., L.Z., N.J. and J.Z. conceived the study and designed major experiments. H.S. and Y.Yuan. made sample preparation. N.J. performed sample collection and clinical diagnosis. J.Z. performed the lipidomic profiling experiments, analyzed the data and established the targeted lipid profiling. G.W. performed machine learning and analyzed the data. C.Y. performed the RNA sequencing experiments and analyzed the data. Y.Z. Y.J. and Z.Z. helped interpret the results. The manuscript was written by J.Z., N.J. L.Z. and Y.Y.The authors declare that they have no competing interests.Data are available from the authors upon reasonable request. Raw mass spectrometry data was deposited in MassIVE database with the ID: MSV000085693. RNA-Seq data was deposited in NCBI SRA with the ID: PRJNA649346. SVM code and example data was supplied in the supplementary materials.


Microbial communities are known to be closely related to many diseases, such as obesity and HIV, and it is of interest to identify differentially abundant microbial species between two or more environments. Since the abundances or counts of microbial species usually have different scales and suffer from zero-inflation or over-dispersion, normalization is a critical step before conducting differential abundance analysis. Several normalization approaches have been proposed, but it is difficult to optimize the characterization of the true relationship between taxa and interesting outcomes. p values. We also consider a truncated test statistic to prevent substantial power loss. We experiment with a basic linear regression model as well as recently proposed powerful association tests for microbiome data and compare the performance of the omnibus approach with individual normalization approaches. Experimental results show that, regardless of simulation settings, the new approach exhibits power that is close to the best normalization strategy, while controling the type I error well. To avoid the challenge of picking an optimal normalization and accommodate the advantages of several normalization strategies, we propose an omnibus approach. Our approach is based on a Cauchy combination test, which is flexible and powerful by aggregating individual The proposed omnibus test releases researchers from choosing among various normalization methods and it is an aggregated method that provides the powerful result to the underlying optimal normalization, which requires tedious trial and error. While the power may not exceed the best normalization, it is always much better than using a poor choice of normalization.The online version contains supplementary material available at 10.1186/s12859-023-05147-w. Microbial communities have been revealed to be closely related to many conditions, such as obesity –3, diabep value for the association of each taxon is generated and statistical significance is determined after controlling for multiple testing. On the other hand, global association tests have also been proposed to accommodate general dependency patterns between overall microbiome composition and profiles of other types of genomic data, including kernel-based tests [The most common approach for identifying individual taxa related to variables of interest is to test the association between the variable and the abundance of each taxon, one by one. Many differential abundance analysis approaches have been proposed based on the linear model , 11, phyed tests –20 and ded tests –23.Unfortunately, intrinsic challenges of microbiome data often make it difficult to identify differentially abundant taxa. For example, a central challenge of microbiome profiling studies is the issue of differential library size , which is difficult to control  and doesNormalization, broadly defined, is a strategy for overcoming differences in library size . Failurep-hacking. Our approach is based on a Cauchy Combination Test (CCT) [p values . Simulations show that our approach often leads to power similar to, or exceeds, the best individual normalization strategy, while still protecting the false discovery rate.To address the challenge of picking a single, optimal normalization, we develop an omnibus approach wherein we consider analyzing the data under several different normalization strategies. We then aggregate the results from different normalization approaches while adjusting for the fact that different normalizations have been used, in order to prevent st (CCT)  which aln samples on which p taxa are measured. Let the raw vector of abundances for the J different normalizations that can be considered such that we have We assume a study in which there are The fundamental challenge that we hope to address is that it is unclear which normalization to use. Different normalizations correspond to different interpretations and implications of different bacterial taxa. Consequently, we propose a strategy in which we consider multiple potential normalizations. In this section, we first describe different, commonly used normalization strategies before outlining the specific approach for implicating taxa across different normalization approaches. We further describe simulation settings for evaluating the power and type I error of our strategy.(i)None(ii)Rarefaction Each sample’s observed counts are sub-sampled such that the total count is the same for all samples.(iii)Total sum scaling (TSS) Observed counts are scaled by the sample’s library size (sum of counts).(iv)Cumulative sum scaling (CSS) Observed counts are scaled by the sum of counts up to a cutoff quantile(v)Center Log Ratio (CLR) transform Observed counts are divided by the geometric mean of the sample’s counts, then log-transformed.Even though these normalization strategies work well under many settings, some concerns are discussed in the literature: artificial uncertainty in the sub-sampling step (rarefaction) [Normalization is an important step for reducing variability in the data due to differential library size. Some easily applicable normalization strategies include: faction) , a bias faction) , and uncp value for the association between each taxon and the variable of interest. In the next section, we describe how we combine the p values to get a single omnibus p value for each taxon.Given the inherent challenge of selecting a single optimal normalization strategy, we propose to simply apply multiple normalization approaches. Specifically, for each choice of normalization, we apply a valid statistical test to get the p values p value for the Assume that after applying each normalization, we apply a valid test to assess the association between each taxon and the variable of interest such that we have a p value. It is well-known that the Cauchy combination test is useful for dealing with sparse alternatives, high-dimensional large-scale datasets, and small p values, which are common situations in GWAS. In particular, the analytic p value approximation by the Cauchy distribution is very accurate under arbitrary dependency structures. Hence, in practice, the test only requires the individual p values as input, so this omnibus testing procedure is very fast. Specifically, for p value for the kth taxon and Given st (CCT)  for eachp values, it suffers the drawback of sensitivity to p values at or near 1. Specifically, the Cauchy combination p value, p values is mis-specified. To address this, we propose to use a truncated Cauchy combination test proposed by [Though CCT is convenient and exact for any number of posed by :2\documeTo check the performance of the method, we first follow the simulation setup in  using daSetting 1 : We select ‘Streptococcus’ taxon, which is differentially abundant with strong differences in the mean abundance by HBP status, and test the association between the selected taxon and HBP without adjustment for other covariates. To assess the type I error rate, we generate 600 samples from the empirical distribution functions (edf) of the normalized abundance in subjects without HBP. To assess the power of the test, we generate 300 samples each from the edf of HBP and non-HBP groups. We also examine cases where the two groups are mixed by n samples by resampling each of the real covariates with replacement independently and simulate D with the constraint Setting 2 : We create the starting dataset by rarefying the CARDIA dataset 10 times and averaging the resulting datasets. With this starting data, we fit each of the genera by the two-part quantile regression model –(v) are considered and we compare the omnibus p value with the p values based on each normalization strategy. To obtain the p values, we apply the simple linear regression, a zero-inflated quantile approach (ZINQ) proposed by [n) are chosen so that the results can be compared well between normalization strategies.We follow the preprocessing of  and focuposed by , a quantposed by . In addin/2 genotype data of haplotypes from African and European ancestry by randomly pairing 2 haplotypes, respectively, over a 1 MB chromosome according to coalescent theory using the cosi2 program [n samples of microbiome OTU counts from the Dirichlet-multinomial distribution. We first estimate the parameters of the Dirichlet-multinomial distribution using a real upper-respiratory-tract microbiome dataset [Setting 3 : To assess the type I error rate, we use the above setting without introducing any genetic effect on the microbiome. To assess the power of the test, we introduce the association between genetics and microbiome. For each individual For each test, we use a kernel RV coefficient (KRV) test to evaluate the overall association between genetic expression and microbiome composition. [We also consider the simulation setup in  to exami program . We also dataset . This daosition. , 40. We Table Table As shown in Table In this section, we illustrate the omnibus approach on the HIV dataset analyzed in . The HIVAs a result, this dataset consists of colorectal biopsies from a total of 40 HIV-infected, antiretroviral therapy (ART) suppressed individuals and 35 HIV-uninfected individuals with relevant participant demographic information, such as age, sex, sexual orientation, and race/ethnicity. The authors characterized the intestinal microbiome of colorectal biopsies using 16S rRNA sequencing and studied the association between the mucosal microbiome composition of colorectal biopsies and some relevant factors. For example, based on the fact that men who have sex with men (MSM) have an increased abundance of Prevotella, independent of HIV status, and this may result in the dysbiosis previously attributed to HIV infection, the authors observed the significant association between the overall microbial composition at the genus level and sexual orientation (MSM or non-MSM). They also showed that this association remained when adjusted for age, race, and HIV status.We utilize this dataset to illustrate how the omnibus approach accommodates several normalization strategies. Specifically, we conduct association tests between the mucosal microbiome composition of colorectal biopsies and the sexual orientation. According to the results in , we testTable p values are considered false positive signals. We evaluate type I error control by the proportion of taxa with p values less than 0.1. We repeat this procedure 50 times and the results are presented via boxplots . By using the omnibus approach, we can avoid the problem of choosing the optimal normalization strategy and instead test across a range of different normalization approaches. Numerical experiments and the real data application demonstrate that the omnibus test not only avoids the possibility of choosing the worst-performing normalization method but also exhibits power nearly as high as the power of the best normalization strategy.Other factors, such as sequencing methods, amplicon bias, and target gene copy number could impact the differential abundance analysis results. However, they generate bias in microbiome sequencing, making measured relative abundances systematically different from their underlying truth. In this case, bias-resistant modeling or bias-insensitive analytical methods are needed. However, the impact of bias is usually not handled via normalization approaches, and thus goes beyond the scope of this paper. This paper mainly handles the differences caused by differential library sizes that affect all taxa more or less evenly. The focus of this paper is to study whether the omnibus approach helps bypass the normalization issue in microbiome differential abundance testing.Certain transformations, in principle, can reduce the impact of compositional effects. However, without extrinsic information, there ultimately remains a particular denominator for normalization. Thus, our approach does not seek to overcome the issue, but rather we note that the statistical analysis is “valid” under any choice of normalization and compositionality is an issue of subsequent interpretation. That is, interpretation of significant findings is affected by the compositionality of the data and potentially by the normalization approaches considered, but fully assessing the impact of compositionality on the omnibus test lies outside the scope of the present work.In this paper, we focus on the linear regression, ZINQ, QRank, and KRV. We acknowledge that a wide range of alternative methods for differential abundance analysis could also be used –44; howeIn addition, we consider four normalization strategies, including the rarefaction, TSS, CSS, and CLR transformations. Other normalization methods are also commonly used, such as an additive log-ratio transformation (ALR) or an isometric log-ratio transformation (ILR) . HoweverAdditional file 1 Figure on histograms of some simulated abundances vs. true abundances.


Whole genome sequencing (WGS) has helped to revolutionize biology, but the computational challenge remains for extracting valuable inferences from this information. Here, we present the cancer-associated variants from the Cancer Genome Atlas (TCGA) WGS dataset. This set of data will allow cancer researchers to further expand their analysis beyond the exomic regions of the genome to the entire genome. A total of 1342 WGS alignments available from the consortium were processed with VarScan2 and deposited to the NCI Cancer Cloud. The sample set covers 18 different cancers and reveals 157,313,519 pooled (non-unique) cancer-associated single-nucleotide variations (SNVs) across all samples. There was an average of 117,223 SNVs per sample, with a range from 1111 to 775,470 and a standard deviation of 163,273. The dataset was incorporated into BigQuery, which allows for fast access and cross-mapping, which will allow researchers to enrich their current studies with a plethora of newly available genomic data. Large quantities of whole genome sequences for paired tumor and normal tissue samples exist, but downstream processed data from these data are often not made available easily for further research. Our aim is to take 1342 of the whole genome sequence data generated by The Cancer Genome Atlas project and make them available as variant calls for downstream research, covering an average of 117,223 cancer-associated variants per sample pair. This set covers 18 different cancer types. We also show several different use cases for these data using the BigQuery tool. In brief, this family of technologies allows the genome to be broken into small components and sequenced in a massively parallel way; an entire genome can be sequenced in a day instead of the decade it took to generate the first draft . ScientiThe true cost of high-throughput sequencing includes both the raw material costs and the bioinformatics costs. Bioinformatics costs cover computational resources needed to store, analyze, and perform specialized tasks such as quality control and annotation on the data . The cosIn this spirit, we generated 1342 variant call datasets based on The Cancer Genome Atlas (TCGA) whole genome sequence (WGS) and alignment data. The dataset was constructed after analyzing the TCGA dataset for suitable whole genome sequencing experiments. The entries varied in their composition, so we focused on the unambiguous normal–tumor pairs, which left 2207 potential samples to analyze as shown in 2.TCGA WGS data are available in alignment form for cancer-related studies but are not always further processed into variant calls. We used the pipeline shown in After generation of the processed data, we performed an ingestion pipeline as shown in 2.1.The NCI CRDC cloud-enabled data ecosystem, through three different cloud resources, provides access to large data in a colocalized way to facilitate faster analysis. ISB-CGC was the chosen cloud resource for this analysis since it provides direct access to the data outside of a set framework. Due to the large amount of data being analyzed, it was necessary to use a custom framework to both exploit the highly parallelizable nature of the work and to manage computational resources appropriately. Data access was facilitated through virtual machine instances on Google Cloud through the ISB-CGC cancer cloud.2.1.1.The goal of the original study  was to lAll possible samples were examined for inclusion in the expanded dataset. Samples that were not typical normal–tumor pairs were removed from the data generation, leaving behind a set of 2207 unambiguous normal–tumor pairs from the TCGA data . For var2.1.2.The alignment procedure from raw sequence data to BAM files was conducted by the TCGA network. The methods for this process are outlined online. Variant calling on normal–tumor pairs was performed with VarScan2 software, as outlined in the pipeline in 2.2.Utilizing Google Cloud Engine, we constructed a management system that retrieved appropriate data, ran the VarScan2 pipeline in parallel, and then captured the relevant data. SLURM  was chosA custom Python interface was written for the SLURM master node in order to easily facilitate submission of jobs based on TCGA ID codes see  below. A2.2.1.The first job run was the download and split job performed by a custom bash shell script. This would download all appropriate files for the job through the Google-provided gsutils tool, copy custom scripts to the compute node, and then split the alignment data by chromosome. Progress was reported to the analytics server through a custom script. Split alignment files were then staged on the compute node for both the normal and tumor samples.2.2.2.The variant calling computations were performed next through the VarScan2 pipeline with individual chromosome references. After the first job was completed, which fetched the data, these chromosome-specific jobs were run in parallel. Progress was reported during each step to the analytics server. In short, the supporting scripts performed the following:SAMtools mpileupsamtools mpileup -f ${REFERENCE} -q 1 -B ${SORTED_NORMAL} ${SORTED_TUMOR}1> ${MPILEUP_OUTPUT}where ${REFERENCE} is the appropriate chromosome reference file, ${SORTED_NORMAL} is the sorted normal tissue alignment (BAM) file, ${SORTED_TUMOR} is the sorted tumor tissue alignment (BAM) file, and ${MPILEUP_OUTPUT} is the output file for this intermediate pileup file.Base Somatic Mutation Calling through VarScan2java -jar VarScan.jar somatic ${MPILEUP_OUTPUT} ${BASE_OUTPUT} –mpileup 1 –min-coverage 8 –min-coverage-normal 8 –min-coverage-tumor 6 –min-var-freq 0.10–min-freq-for-hom 0.75 –normal-purity 1.0 –tumor-purity 1.00 –p-value 0.99–somatic-p-value −.05 –strand-filter 0 –output-vcfwhere ${MPILEUP_OUTPUT} is the output file from the SAMtools mpileup step, and ${BASE_ OUTPUT} is an arbitrary output file name.Somatic SNV callingjava –jar VarScan.jar processSomatic ${OUTPUT}.snv.vcf –min-tumor-freq 0.10 –max-normal-freq 0.05 –p-value 0.07where ${OUTPUT} is the output file name from the base somatic mutation calling.Somatic indels callingjava –jar VarScan.jar processSomatic ${OUTPUT}.indel.vcf –min-tumor-freq 0.10 –max-normal-freq 0.05 –p-value 0.07where ${OUTPUT} is the output file name from the base somatic mutation calling.2.2.3.Once all variant calling jobs were complete, a cleanup and storage script was run. This program performed various housekeeping functions such as compressing the output files, moving them to the storage bucket, copying error output and other logging to storage, and posting progress to the analytics server. This job then removed the staging directory on the compute node, freeing it up to begin the next sample in its queue.2.3.The ISB-CGC VCF to BigQuery (VCF2BQ) pipeline provides a method to extract, transform, and load (ETL) VCF files stored in Google Cloud Storage buckets to Google BigQuery tables. This script was provided by ISB-CGC in order to automate some of the effort required to upload data to BigQuery within ISB-CGC. The primary purpose of the script is to preserve the integrity of the VCF file format most researchers are familiar with while splitting some of the columns to simplify queries and leverage the power of BigQuery data analytics. The script, implemented in Python, uses various Python data structures and libraries to transform and load VCF files into BigQuery tables. The script is optimized to handle VCF files derived from tumor–normal variant calling algorithms, but can also be used for more general VCFs as well.Each VCF file is converted into a pandas data frame that contains columns that preserve the variant record information as well as columns of metadata about the files, including TCGA Genomic Data Commons (GDC) identifiers  along with information about variant caller . These columns serve as unique identifiers that can be used by researchers to locate more information about the files in the GDC data repository to join with other data types in BigQuery (see example notebook). The script can execute on single files or on bulk VCFs at once. All generated pandas data frames are concatenated into a large dataframe, which is loaded into BigQuery. The final VCF BigQuery table can be used for downstream analysis using either Google BigQuery’s standard Structured Query Language (SQL) or can be further interrogated in R or Jupyter Notebooks. The script is maintained in the ISB-CGC ETL GitHub repository.The VCF2BQ Script was used to extract, transform, and load controlled-access TCGA WGS VCF generated using the Varscan2 pipeline. dbGAP authorization for TCGA data is required to access the VCF files.The script is optimized to run on the Google Cloud Platform, leveraging Google’s Compute Engine, Google BigQuery, and Google Cloud Storage Buckets. Optimally, the script is executed on Google Compute Engine VM instance because of the fast transfer speeds between virtual machines and cloud storage.3.In the original analysis , five diFinal data generation revealed 157,313,519 pooled (non-unique) cancer-associated single-nucleotide variations (SNVs) across all samples. This was an average of 117,223 SNVs per sample with a range from 1111 to 775,470 and a standard deviation of 163,273, illustrating the variation in sample preparation and experimental design decisions made by the participating laboratories. The makeup of the dataset ranges from Acute Myeloid Leukemia (LAML) at 0.89% of the samples to Uterine Corpus Endometrial Carcinoma (UCEC) at 9.17%. While this range is large, the average of each of these 18 cancer types was 5.56%, the median value was 6.04%, and the standard deviation for the ratios was 2.54. The sample distribution by cancer type is relatively balanced, as seen in When looking at the distribution of number of variants across cancers, a slightly different picture emerges, as there are a few outliers. Looking next at the non-coding region of the genome, we see in https://datacommons.cancer.gov/repository/cancer-data-service, accessed on 1 June 2022). Searchable tables for metadata related to the samples are available via the Institute for Systems Biology (ISB)’s Cancer Gateway in the Cloud (ISB-CGC) , one of NCI’s Cloud Resources  [The SNV calls provide valuable information on cancer-associated changes in the genome outside of the original research driven by them . In orde3.1.The pipeline as described in the These high-confidence somatic SNVs were generated for each of the processed samples as a standard VCF file and ingested into BigQuery as a single table across all samples. Raw VCF files can be regenerated from a BigQuery table if needed after ingestion (see Usage Case 3 in this paper).The SNV dataset shows individual positions in the genome where a nucleotide in the cancer tissue differs from the reference genome (hg19) but not the paired tumor tissue. Both the normal and tumor samples were also required to have adequate coverage. In the current pipeline, this translates to a read depth of 8 for normal tissue reads and 6 for tumor tissue reads at the location of the SNV call. VarScan2 calls SNVs by using a heuristic method and performing a statistical test which considers the number of aligned reads for each allele –22.3.2.We pursued several strategies to assess the quality of the data and to correct for errors that are common in highly parallelized pipelines. After the variant calling pipeline was finished, the output data were a compressed archive of different sets of information including SNVs, indels, germline mutations, error logs, etc. If a computation failed, the size of the output archive would be approximately 10 kb. This allowed for quick screening for failed computations that needed to be rerun or investigated further. This was a semi-frequent occurrence for several reasons. The data were controlled and sometimes required significantly more time to transfer than our access window, occasionally causing transfer issues.Additionally, as each step of the computation was run, we were reporting progress and command output values to a database being run in the cloud cluster. This database accepted reporting for a wide variety of values including error codes of interest and computational metrics for future regression analysis. Examining the database was useful for diagnosing computational problems as they occurred and allowed for on-the-spot corrections. These corrections were often re-running the sample due to I/O issues or file corruption.Variations were mapped to 10,000 base windows to bucket the variations for visualization purposes. These windows were plotted in a Manhattan plot-type graph with region number and chromosome on the x-axis, while the y-axis was the number of variations found in the region. Computations that failed to produce expected results across the genome would have entire chromosomes missing, which indicated a computational problem. Samples which failed this test were re-run and all were successfully recovered.To shield against outliers, a strategy was taken to limit the right-tailed variations to being within a standard deviation of the average variant counts across the samples. Since an alignment cannot produce fewer than zero variations, the data are right-skewed but the validation would allow the left side to be arbitrarily low and only check against the right side. This is because we would not necessarily expect an arbitrary low count of variations to be an outlier since it is possible that only a handful of variations could lead to cancer. On the other hand, it is difficult to imagine that millions of variations would be required for oncogenesis and more likely this related to mis-matched samples (or some other error). In any case, with the average total variation count (not restricted to high-confidence somatic to capture a broader section of outliers) being 1,651,974 and the standard deviation being 4,090,131, no left-side values would fail the test even if they were not excluded. All variant counts that then fell above 5,742,105 variations were discarded as outliers using this methodology and are reported in An additional strategy used for quality control was that all downloads were checked against their md5sum hashes  to verify there was not a download-related corruption issue. There were several samples that were only partially transferred during the download, and a simple retransfer was able to correct this.A final validation was performed after the BigQuery tables were ingested. The tables were validated to have the expected number of samples and verify that variant counts matched what was seen in the raw VCF files.4.Here, we present a dataset which covers 1342 WGS cancer-associated SNV calls across 18 different cancer types as defined by TCGA. These data cover an assortment of variants within the whole genome and offer an opportunity for researchers to deeply dive into differences between normal and cancerous tissue within the same patient.The pipeline itself, as illustrated in Currently available datasets through TCGA provide only whole exome sequencing (WXS)-level data. While there is a high number of exome sequences for normal–tumor paired samples as a result of the TCGA project, whole genome sequences have remained in raw format. This publication presents the cancer-associated SNVs for many of the WGS samples published by TCGA. With this dataset in hand, researchers can supplement their current research with these enriched data without bearing the high computational and financial cost of determining the variants themselves.From this study, SARC and LAML data within the TCGA project are interesting outliers. All the samples within these cancers have an unexpectedly high number of variants compared with all other cancers in the set. The fact that they appear to be outliers requires a closer look at the specifics of the data collection and sequencing for these cancers before using them in further studies as they may be inappropriate datasets for some contexts. Other cancer types have many samples with strikingly low numbers of variants. These samples tend to be ones collected early in progression. This matches expectations as younger cancers would have less time to accumulate mutations. An upper level or ceiling on variations would not be expected since these samples were taken at various stages of cancer growth and therefore could have had significant time to continue to mutate. This finding provides confidence on using those specific cancer-associated variations.These two cancer types, along with KIRP, also show some interesting differences compared to the other cancer groups with reference to non-coding vs. coding variation counts. Within this study, the samples in these cancer types have very close percentages of coding variants out of the entire variant set. They also have nearly the same ratio of non-coding to coding variations. This implies that the methodology used for these cancers in the TCGA project may have had some bias in it. Alternatively, it could imply that there is something particular about these types of cancers which weighs variations in this way. Whether or not non-coding variations have a special meaning for these cancers is an interesting research topic.The ability to easily use this dataset was one of the priorities of the authors during this study. Several exemplary ways to use the variant datasets are examined below.4.1.Cross-mapping between annotation and variant databases is a common use case to increase the value of the variant database. In addition to annotation and variant crossmappings, a similar approach is used to enrich a variant database with additional entries. The first use case was to take an existing cancer variant database and determine, quickly, how many variants are within the new dataset that are not represented in the existing dataset. We used the BioMuta database , which pWe first ingested BioMuta into BigQuery as a custom dataset, following instructions for BigQuery. From the BigQuery web interface, we constructed a SQL command to map the high-confidence somatic variations to the BioMuta database to determine how many variants found from this project are not yet represented in the BioMuta database. While constructing the SQL command can seem daunting, it uses a relatively easy-to-learn structured query language (SQL) with which many scientists are already familiar.An example SQL command is provided here, although there would need to be slight modifications depending on the details of the BioMuta ingestion.SELECT v4_0.*     FROM biomuta.v4_0          WHERE NOT EXISTS) AND POS = v4_0.chr_pos);This command retrieves from the high-confidence somatic variant BigQuery table all of the variants that exist there but do not exist in the BioMuta version 4 dataset. The output from this command will return a table with all entries in the somatic high-confidence variant table that are not found in BioMuta. The count of rows in this table is the number of variants that are not represented in the BioMuta dataset. We found that there were 7,630,735 (non-unique) variants which were not found in the BioMuta dataset. The duration of the BigQuery search creating this mapping was 18.3 s.4.2.Summary statistics of a dataset can easily be generated by using either the BigQuery API and generating via Python (or some other language) or generated through the BigQuery language itself. A simple example is to generate the counts of high-confidence somatic variations for each cancer type.SELECT COUNT(CHROM), project_short_name         FROM ‘isb-cgc-04–0026.fs_scratch.tcga_variants’         GROUP BY (project_short_name);This SQL command will generate a table of each of the cancer types as well as the variation counts found in the somatic high-confidence table. It performs this task by reading the project name and counting the number of hits for each, and then presenting the results as the output from the command. The project counts from this use case are included in 4.3.While the data as published in BigQuery tables are useful for cross-table investigation, it is often required to have a VCF-formatted file for a specific pipeline where the software is expecting that format. A standard formatted VCF file can be directly generated from the BigQuery tables, as needed.Unlike the other use cases, this case requires the output from the BigQuery table as the input and then processes it into a standard VCF-format file. The table output can be retrieved either through the BigQuery interface by running a general fetch command focused on the sample of interest (shown below) and saving the output table in comma-separated value (CSV) format through the BigQuery tools, or through the BigQuery API in Python or another language (not shown).SELECT * FROM ‘isb-cgc-04–0026.TCGA_WGS_HG19_VCF.somatic_hc_variants’ WHERE project_short_name) = ‘TCGA-44–2656’Once the input data have been generated, a simple script can be used to convert these data into a VCF file see  below by4.4.While this study presents a large, cancer-associated SNV dataset there are several limitations which should be noted. First is sample size. Even 1342 whole genome cancer variant sets are likely to be insufficient to untangle cancer comprehensively. Cancer is an umbrella term capturing diseases of many different tissues; further, even within these categories, each individual cancer can be caused by different mechanisms of cellular dysregulation. Therefore, it is unlikely that even a dataset of this size will have enough explanatory power to answer all biological questions related to even a single type of cancer. The hope is that this dataset, along with many others that are produced, can help drive understanding of this disease when supplementing ongoing research.The dataset is not a comprehensive processing of all of the TCGA sequencing data. There are around 2200 whole genome normal–cancer pairs within the consortium’s data, meaning this dataset includes 60% of the TCGA cancer-associated variants. For several different reasons including computational, logistical, and a conservative approach to the data that were in question, the entire set was outside of the scope of this experiment. This represents both a limitation of this dataset as well as an opportunity for additional data available to supplement more specific research questions.Additionally, TCGA data represent a single coherent study and may not represent all cancer data. The consortium study had various standards for data collection and analysis which are incredibly useful for comparing data between the different laboratories, but also run the risk of biasing the entire dataset in some way. Care will need to be taken when combining this dataset with others to make sure they are compatible.Any insights gleaned from the dataset would need to be validated against real-world samples. A purely data-driven approach can only point us in the right direction for research but cannot currently replace validation-level research that would be required in a clinical setting.The TCGA project utilized short-read-focused, next-generation sequencing which, while revolutionary, does not offer a comprehensive genetic profile of the genome. Longer read technologies help in examining copy number variation, different techniques are used for 3D mapping of the genome, non-coding rearrangement, and other block rearrangement of the genome, and many other techniques are under development to see beyond the nucleotide-level sequence. All of these techniques, and more, would be useful in a full examination of the cancer genome. This study reports on the short-read-focused results and is therefore limited to the information captured by these techniques.Finally, despite the high-confidence estimates used in the study, there are likely to remain false positive variants in this set. It is possible that many of these have no or very low impact, which may be difficult to deduce without a large quantity of cancer genomic data, far beyond this study.Even with these limitations, the dataset of 1342 whole genome, cancer-associated, high-confidence SNV calls provides an exciting opportunity for researchers to supplement their current and future studies.Supplementary


However, high dimension, extreme sparsity, and large scale of scATAC-seq data have posed great challenges to cell-type identification. Thus, there has been a growing interest in leveraging the well-annotated scRNA-seq data to help annotate scATAC-seq data. However, substantial computational obstacles remain to transfer information from scRNA-seq to scATAC-seq, especially for their heterogeneous features.We propose a new transfer learning method, scNCL, which utilizes prior knowledge and contrastive learning to tackle the problem of heterogeneous features. Briefly, scNCL transforms scATAC-seq features into gene activity matrix based on prior knowledge. Since feature transformation can cause information loss, scNCL introduces neighborhood contrastive learning to preserve the neighborhood structure of scATAC-seq cells in raw feature space. To learn transferable latent features, scNCL uses a feature projection loss and an alignment loss to harmonize embeddings between scRNA-seq and scATAC-seq. Experiments on various datasets demonstrated that scNCL not only realizes accurate and robust label transfer for common types, but also achieves reliable detection of novel types. scNCL is also computationally efficient and scalable to million-scale datasets. Moreover, we prove scNCL can help refine cell-type annotations in existing scATAC-seq atlases.https://github.com/CSUBioGroup/scNCL-release.The source code and data used in this paper can be found in  Recent advances in single-cell high-throughput sequencing technologies have enabled the emergence of diverse experimental methods that are capable of characterizing different properties of single cells. Single-cell RNA-sequencing (scRNA-seq) is the most widely used technique for the characterization of complex tissues and organisms at the single-cell level (k-nearest neighbors (kNNs) classifier or other classifiers can be applied to transfer labels between modalities. Current diagonal integration methods can be divided into two categories according to their strategies of processing raw omics features. The first category simplifies the diagonal integration into horizontal integration , thereby reducing the computational complexity. However, transforming scATAC-seq data may lose part of information of raw data, hence the transformed scATAC-seq can be inaccurate . GLUE C adopts tHere, we present a novel transfer learning method to transfer labels from scRNA-seq data to scATAC-seq data, scNCL, which achieves the state-of-the-art label transfer performance using a neural-network approach. We start from simplifying diagonal integration into horizontal integration by transforming the scATAC-seq feature to GAM using existing tools, such as Signac . To makef and a classifier network: g. The inputs consist of three parts: a gene expression matrix (GEM) from scRNA-seq, a GAM from scATAC-seq, and low-dimensional representations of raw scATAC-seq features . Before training, a kNN graph is constructed based on the low-dimensional representations of raw scATAC-seq data. At each training step, two minibatches of cells are sampled from GEM and GAM, respectively. The feature extractor projects these cells into a low-dimensional latent space and then the classifier infers the cell-type assignments for each cell.scNCL is a semi-supervised framework for cross-modal label transfer, which is motivated by scJoint. Specifically, scNCL learns a feature extractor network: To learn the encoder and classifier well, scNCL uses four loss functions: (i) to regularize the whole latent space, a new projection regularization (PR) loss is used; (ii) to explicitly harmonize embeddings between scRNA-seq and scATAC-seq, a FA loss is used; (iii) to learn discriminative features for various cell types, a cross-entropy (CE) loss is used for supervised learning on scRNA-seq data; (iv) to preserve the neighborhood structure of scATAC-seq cells in raw feature space, a NCL loss is used. The PR loss, FA loss, and NCL loss are used to optimize the encoder while the CE loss optimizes the whole network. The overall framework of scNCL is depicted in M denotes the number of shared genes between two datasets. K denotes the number of cell types in K types in Given two datasets: a labeled scRNA-seq dataset: f parameterized by d denotes the embedding dimensionality. Then, the classifier network g takes the feature embedding as input and outputs K-class probability vector after softmax transformation, k denotes the k-th class. The confidence that a cell is predicted to be a novel type is defined as e.For each cell’s gene expression/activity profile N cells, respectively.At each training step, a minibatch of cells is generated by sampling equal-sized subsets of cells from B denotes B; b and j-th dimension of embedding; B. Since Similar to scJoint, scNCL aims to build an orthogonal embedding space and to maximize the variability of embeddings. To achieve this goal, scNCL uses the PR loss, which is adapted from the NNDR loss  some scATAC-seq cells with the same type become more distant from each other in the transformed gene space compared to the raw feature space. These cells can be further separated in the latent space. After alignment with scRNA-seq data, these scATAC-seq cells may be classified as different types; (ii) some scATAC-seq cells with different types become closer in the transformed gene space compared to the raw feature space. These cells can stay closer in the latent space. After alignment with scRNA-seq data, these scATAC-seq cells with different types may be classified as the same type. Moreover, the cell-type supervision is only posed for known cell types (in scRNA-seq), which indicates the model learns discriminative representations faster on the known types compared to the novel types (in scATAC-seq data). This leads to smaller intra-class invariance of known types compared to novel types  Novel-type detection performance. Since identification of novel types is a binary classification problem, we reported the threshold-free area under the receiver-operator curve (AUROC) using the prediction confidence of novel types, We evaluated the performance of label transfer from two aspects: (i) prediction accuracy of common cell types. The overall accuracy rate was computed for the common cell types between scRNA-seq and scATAC-seq data. It is defined as:where We first evaluated scNCL’s performance in scenarios where scRNA-seq and scATAC-seq data have the same collections of cell types. We used the PBMC dataset, a subset of MCA dataset (referred as MCA-subset), and multiple subsets of HFA datasets. Specifically, we extracted 19 common cell types from MCA dataset to focus on transferring common cell types, resulting in 19 726 cells for scRNA-seq and 57 563 cells for scATAC-seq. Also, we extracted 54 common types from HFA dataset, resulting in 433 695 cells for scRNA-seq and 656 074 cells for scATAC-seq (referred as HFA-subset-full). In addition, to evaluate the robustness to various dataset sizes, we subsampled the HFA-subset-full dataset with various number of cells: 20 000 for scRNA-seq and 30 000 for scATAC-seq, 40 000 for scRNA-seq and 60 000 for scATAC-seq, 80 000 for scRNA-seq and 120 000 for scATAC-seq, and 160 000 for scRNA-seq and 240 000 for scATAC-seq, all of which were referred as HFA-subset-50k/100k/200k/400k, respectively. We repeated the sampling five times for HFA-subset-50k/100k/200k and repeated three times for HFA-subset-400k. All these datasets have the same collections of cell types between scRNA-seq and scATAC-seq. Seven state-of-the-art data integration methods for single-cell data were used for comparison: Seurat 4 , Portal F1-scores for major cell types in scRNA-seq but also achieves high F1-scores for those minor types in scRNA-seq . The target data contains 19 common types and 10 novel types. Results show that scNCL delivers the highest overall accuracy rate and AUROC among all methods, leading to the best OSCR value . Portal Together, these results suggest that scNCL can achieve superior trade-off between inferring common cell types and detection of novel types.We showcased that scNCL can be used to refine annotations of already labeled scATAC-seq datasets. Taking MCA dataset as an example, we attempted to apply scNCL to transfer labels from scRNA-seq data to scATAC-seq data. The full scRNA-seq data contains 67 type annotations, and the full scATAC-seq data contains 29 original type annotations. The transferred labels and original labels are plotted in Col1a1, which has a high gene expression enrichment in “stromal cells” of lung FACS data  as “stromal cells” (719 cells) with high average confidence (>0.85) . These cACS data . Hence, ACS data , which ha (>0.6) . These cACS data . The GO ACS data .Cyth4, Spp1, and Olr1, all of which are markers of “microglia” cells in scRNA-seq data (As another example, we applied scNCL to transfer labels from scRNA-seq data in the HFA dataset to scATAC-seq data in the HFA dataset. The scRNA-seq data contains 77 cell-type annotations and scATAC-seq data contains 54 original type annotations. The transferred labels and original labels are shown in seq data . The GO seq data . The posIn this work, we present scNCL, an automated cell-type identification method that utilizes the well-annotated scRNA-seq data to annotate scATAC-seq data. To deal with the heterogeneous features between modalities, we propose to transform scATAC-seq features to gene activity scores with the prior knowledge and introduce contrastive learning to preserve the neighborhood structure of cells in raw scATAC-seq data. Multiple loss functions are used to achieve structure preservation and transferable latent features.Experiments on various datasets demonstrate that scNCL achieves higher transfer accuracy of common cell types and better detection of novel types compared to existing horizontal integration methods and diagonal integration methods. We also showcase that scNCL can be applied to refine annotations of labeled scATAC-seq data. In addition, benefiting from the efficient architecture, scNCL is fast and scalable to million-scale datasets. Finally, ablation studies demonstrate the rationality of our PR loss over original NNDR loss and the superiority of our proposed NCL loss . ParametIn this study, we only focus on transferring labels from scRNA-seq to scATAC-seq. However, in principle, scNCL can be extended to transfer information across other modalities if the input can be transformed into the same feature space, such as scRNA-seq to methylation data (btad505_Supplementary_DataClick here for additional data file.


Perineural invasion is a prevalent pathological finding in head and neck squamous cell carcinoma and a risk factor for unfavorable survival. An adequate diagnosis of perineural invasion by pathologic examination is limited due to the availability of tumor samples from surgical resection, which can arise in cases of definitive nonsurgical treatment. To address this medical need, we established a random forest prediction model for the risk assessment of perineural invasion, including occult perineural invasion, and characterized distinct cellular and molecular features based on our new and extended classification. RNA sequencing data of head and neck squamous cell carcinoma from The Cancer Genome Atlas were used as a training cohort to identify differentially expressed genes that are associated with perineural invasion. A random forest classification model was established based on these differentially expressed genes and was validated by inspection of H&E-stained whole image slides. Differences in epigenetic regulation and the mutational landscape were detected by an integrative analysis of multiomics data and single-cell RNA-sequencing data were analyzed. We identified a 44-gene expression signature related to perineural invasion and enriched for genes mainly expressed in cancer cells according to single-cell RNA-sequencing data. A machine learning model was trained based on the expression pattern of the 44-gene set with the unique feature to predict occult perineural invasion. This extended classification model enabled a more accurate analysis of alterations in the mutational landscape and epigenetic regulation by DNA methylation as well as quantitative and qualitative differences in the cellular composition in the tumor microenvironment between head and neck squamous cell carcinoma with or without perineural invasion. In conclusion, the newly established model could not only complement histopathologic examination as an additional diagnostic tool but also guide the identification of new drug targets for therapeutic intervention in future clinical trials with head and neck squamous cell carcinoma patients at a higher risk for treatment failure due to perineural invasion. Haematogenic and lymphatic spread as well as local and perineural invasion (PNI) encompass the main routes for solid tumor dissemination. PNI is a common threat in multiple cancers, including head and neck squamous cell carcinoma (HNSCC), and serves as a prognostic indicator of unfavorable survival due to increased local recurrence rates and a shorter time to tumor relapse ,2,3,4,5.Despite the emerging clinical relevance, the determination of PNI by the histopathological examination of entire histological slides remains a major challenge and is subject to a wide variance of reported incidence rates for HNSCC (between 6% and 90%). The high variance can be explained in part because PNI can involve both small and large nerves with sporadic distribution patterns in sometimes extremely large tissue samples, making accurate pathological assessment a time consuming and tedious task. To solve this problem, several recent studies focused on computational approaches to extract nerves and PNI from histologically stained whole-slide images, utilizing deep learning networks or artificial-intelligence-based classifiers . Though n = 357) that functioned in processes such as extracellular matrix remodeling, collagen catabolic processes, and cell adhesion . W. WCDKN2AThe “caret” package in R was used to train a machine learning (ML) model for clusters A and subcluster B2 based on the transcript values (FPKM) of the PNI-related 44-gene signature. To create a best performing model for the risk prediction of PNI, we used cluster A and subcluster B2 but excluded subcluster B1 with an almost balanced ratio of PNI− and PNI+ tumors. A cross-validation was used for parameter tuning with a tuning length of 150 . We computed a confusion matrix and plotted ROC curves for the three used classifiers . Finally, the random forest model was selected for further analysis, which also had the advantage of dealing with relatively small datasets and binary variables compared to neural networks. The trained random forest model was applied to other solid tumors from TCGA, and some cohorts  were excluded from the further analysis because of an unbalanced ratio (<10% in either ML A or ML B2).https://cancer.digitalslidearchive.org/#!/CDSA/hnsc). Selected tumors were classified as ML B by the random forest model and were either PNI− (n = 6) according to clinical data or not analyzed (PNI NA).Digital images of H&E-stained whole slides from 12 tumors of TCGA-HNSC were reviewed on 6 September 2022 in the Cancer Digital Slide Archive developed by researchers from the Emory University and Winship Cancer Institute  . The COnA somatic mutation analysis was performed using Oncomaps in cBioportal ,66. Signhttps://science.bostongene.com/kassandra/downloads (accessed on 21 December 2022). p-Values were computed between ML A and ML B2 using a t-test.Absolute immune cell scores including different subtypes of B cells , T cells , other immune cells , and stromal cells  of xCell, CIBERSORTx, and Kassandra were downloaded from Gene set variation analysis (GSVA) scores were computed using the R package “gsva”. The Hallmark gene sets were accessed from the Molecular Signatures Database (MSigDB)  on 21 SeIn conclusion, we trained a random forest classification model for the risk assessment of perineural invasion that could improve the diagnosis and treatment decision making for primary head and neck squamous cell carcinoma, independent of the quantity of available tumor samples. In contrast to similar approach from previous studies, our model enabled the detection of occult PNI and an extensive validation in different HNSCC cohorts as well as in other solid tumors. With the unprecedented analysis of multiomics data, we were able to provide new insights into the development of perineural invasion at the molecular level. Thus, our machine learning model can also assist future studies in gaining an in-depth knowledge on the basic principles of cellular and molecular alterations driving perineural invasion and to establish new drug targets for innovative therapeutic strategies aiming at the prevention of cancer cell dissemination.