RNA velocity is a novel and powerful concept which enables the inference of dynamical cell state changes from seemingly static single-cell RNA sequencing (scRNA-seq) data. However, accurate estimation of RNA velocity is still a challenging problem, and the underlying kinetic mechanisms of transcriptional and splicing regulations are not fully clear. Moreover, scRNA-seq data tend to be sparse compared with possible cell states, and a given dataset of estimated RNA velocities needs imputation for some cell states not yet covered.We formulate RNA velocity prediction as a supervised learning problem of classification for the first time, where a cell state space is divided into equal-sized segments by directions as classes, and the estimated RNA velocity vectors are considered as ground truth. We propose Velo-Predictor, an ensemble learning pipeline for predicting RNA velocities from scRNA-seq data. We test different models on two real datasets, Velo-Predictor exhibits good performance, especially when XGBoost was used as the base predictor. Parameter analysis and visualization also show that the method is robust and able to make biologically meaningful predictions.The accurate result shows that Velo-Predictor can effectively simplify the procedure by learning a predictive model from gene expression data, which could help to construct a continous landscape and give biologists an intuitive picture about the trend of cellular dynamics. Recent advances in high-throughput RNA sequencing technologies  have enaA major limitation of most trajectory inference methods  is they The original steady-state model for RNA velocity proposed by La Manno et al. assumes that transcriptional phases endure long enough to reach a steady state equilibrium, and the equilibrium mRNA levels can be approximated with a linear regression by simplification with a common splicing rate. Recently, to relax this assumption, Volker Bergen et al. proposed an algorithm called “scVelo” , which iThe concept of RNA velocity and its associated algorithms and models have become very popular in single-cell biology. However, this technique needs the support of RNA sequencing protocols. Moreover, to get splicing information we need to run a complex preprocessing pipeline which involves the issues of file format and is time consuming. More importantly, the data of estimated RNA velocities are still sparse compared with the size of the uncovered cell state space. Here we propose an ensemble learning pipeline for the prediction of RNA velocities, which can skip the complex procedures for splicing analysis, etc. When we have a new data sample from the same biological context, we can predict the direction of RNA velocity from a state unkown in the traning data. This is similar to the pedestrian prediction  in a driS(t) represents mature mRNA abundance over time, U(t) represents pre-mRNA abundance over time, k and t are cell-specific latent variables, where k represents discrete transcriptional state, and t represents latent time.Velocyto CLI or loompy/kallisto was used to obtain spliced/unspliced reads annotations. We filter the genes with counts number (both spliced and unspliced) smaller than the threshold, keep the top high variability genes. Then normalize in cell level and did logarithm transform. On Euclidean distances PCA space of counts matrix, a nearest neighbor graph was computed, first and second moments were obtained for each cell. According to the basic reaction kinetics:V for each transcriptional state of a single cell. Combining this information we can further inference cell future state of an individual cell. The movements can be UMAP projected into a lower dimensional embedding D to visualize.RNA velocity is termed as the time derivative of mature spliced mRNA Our goal is to predict the RNA velocity vector of each cell based on its gene expression data. As illustrated in a 2D space, we formulate it as a classification problem through an equal division of a 2D circle into d equal-sized segments, as shown in Fig. k genes from each cluster as the model features. We use the lower-dimensional embedding Figure After the division, the distribution of labels is not balanced. Therefore, we provide several ways to rescue: over-sampling, down-sampling and combine-sampling. We first test different sampling ways on different base models. For oversampling, we test adaptive synthetic (ADASYN) , synthetK subsets, where We divide the sample data into a training set and test set. The training set is for model training and test set is for model evaluation. For training, the parameters are saved and can be directly used for prediction in testing part. We adapt a stacking structure model. Figure k, the number of top genes selected from each cluster as features) are shown in Table We train and test the models on two single-cell RNA-seq datasets. One is the Mouse hippocampal dentate gyrus neurogenesis (DGN) dataset  availablTo test the generalization ability of our models, we randomly divide the cell samples into two disjoint sets with ratio of 7:3, 7 for training and 3 for testing. Figure To illustrate how to address the class imbalance issue, we take the DG dataset as an example. Figure Table k the number of top genes and d the number classes is shown in Fig. k controls the feature selection part. The curve rises first and after k reaches 20, it starts to oscillate. In the previous experiment we set k to 3, although we can increase k to get better performance. Parameter d controls the granularity of prediction, and the result shows that the number of divisions is 8. When we continue to increase d, the task becomes more difficult so that the score will decay. Figure After parameters fine tuning through grid search, Fig. We use the UMAP toolkit of scVelo  to map cOne limitation of Velo-Predictor is that its performance may depend on data. Although we have used the ensemble learning framework to balance the results from different baseline models for different sample-feature ratios, it is still an empirical approach. When the data distribution is not so complete and balanced, the prediction may be less accurate. The lowest input data size depends on the properties of data (e.g. in terms of samples and features) and biological scenarios (e.g. types of data). We have tested the case (Our work provides a prediction-based approach for study of cell differentiation mechanisms. Such predictions can also help impute the state space not yet covered by the scRNA-seq data, and the interpolation can help construct a continuous landscape surface. In the future, we can use single-cell multi-omic data to learn the bifucation point of cell differentiation more precisely. With the imputed direction information we can further do trajectory inference. Combined with an energy function such as that in the Hopfield network model used in our previous work , the preIn this paper, we described Velo-Predictor, an ensemble learning pipeline for RNA velocity prediction. While RNA velocity estimation is not straightforward, our pipeline can simplify the procedure by learning a predictive model from gene expression data. The results showed that our pipeline can predict the directions of cell state transitions accurately.


However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI , which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at:  Genes are among the most important building blocks of all organisms. Their transcription and translation are essential for maintaining fundamental cellular mechanisms. Genes are continuously and precisely regulated by a wide variety of mechanisms, including transcription factors, miRNAs, methylation, and mutations, which are often cumulatively referred to as multi-omics. When investigating a biological mechanism, each omics can only provide a single perspective. By matching multi-omics data sampled from a common subject, a multiple-perspective view can be generated for an enhanced understanding of the complex dynamics of biology in the subject. For each additionally integrated omics data type, a new relationship can be mined between a gene and the newly added, which increases the ability to represent complex relationships across multi-omics data types, as shown in A number of initiative projects have made great effort to collect and publicly provide large amounts of multi-omics data, such as TCGA . In addition, the majority of such studies focus on identifying genes showing significant correlation with a certain omics type using statistical methods, such as Pearson's correlation or cosine similarity. Furthermore, such approaches tend to focus on finding a matching omics relation for a single gene with each iteration of the analysis rather than analyzing all genes and omics data in a combined manner. This is mainly due to the heavy computation load and requirements of multiple testing, which makes statistical analysis difficult.A number of studies have reviewed multi-omics integration methods. A recent study  indices i and m, respectively. Such gene-level multi-omics integration can be advantageous in assessing the flow of information from omics to genes. For example, gene-level analysis of mRNA, methylation, and miRNA omics data can discover strong relationships across the three omics layers in means to explain the dynamics of gene expression  that learns hidden features through tensor decomposition for the integration of multi-omics data. MONTI is based on the gene-level integration method, which we find to be more helpful in understanding the results. The objective of MONTI is to extract feature genes that well explain some clinical attribute of interest in large multi-omics data. Being able to extract such a genes list with significant relation to clinical attributes can serve as a source that can naturally be used for simpler downstream analysis, such as, gene set enrichment of pathway analysis. Also, MONTI constraints the multi-omics data to be subject matched, where each omics data are collected from a common subject . Such design may avoid omics variance within a same group, thus, amplifying the signals of hidden features.In experiments with TCGA multi-omics data sets from breast, colon and stomach cancer samples, MONTI achieved significantly higher cancer subtype classification accuracy than existing multi-omics analysis methods. For the downstream analysis, genes associated with subtype-specific features were identified for biological interpretation.The MONTI workflow operates in two phases. In the first phase, the multi-omics data are integrated and decomposed using non-negative tensor decomposition. In the second phase, subtype-specific features and genes associated with them are selected using L1 regularization, and these features are then used to generate a subtype classifier using the multi-layer perceptron (MLP) neural network. The overall workflow is depicted in Samples with matched gene expression, methylation, and miRNA expression data sets were collected for three case studies from TCGA: (1) 597 breast cancer samples, (2) 314 colon cancer, and (3) 305 stomach cancer samples. Only primary tumor samples with all three matching omics data sets were selected for the analysis. The pre-quantified gene and miRNA expression values from TCGA were used as provided. For the methylation data, we used the HumanMethylation450 BeadChip-based data and further selected probes located within the gene promoter regions . Subtype information were acquired from the original studies. The partially missing subtype information of the breast cancer case study was taken from Lim et al. , which wgene-centric two-dimensional sample(patient)-gene matrix. The values in each omics matrix are computed and assigned with respect to each gene. The tensor structure requires all slices to be of the same size. Thus, while each omics matrix is independently processed, they share the same set of genes and samples.Because we aim to discover gene regulatory multi-omics features, each omics data is individually processed to form a log2 quantile normalization across samples. For miRNA, they were first bundled per target gene, such that the number of bundles matched the number of genes. The geometric mean of miRNA expression per bundle was assigned to each corresponding gene. The expression values were then log2 quantile normalized. For methylation data, probes located within the transcription start site and 2 Kb upstream of gene promoter regions were grouped per gene. The average methylation level per gene was further quantile normalized.The gene expression values were preprocessed according to the provided TCGA level 3 gene expression data, which were subject to Due to the nature of tensor decomposition, the omics value in each matrix need to be scaled within a common range. If not, an omics matrix with comparably large values, such as gene expression, would have a diminishing effect on other omics matrices with relatively lower values. Hence, normalized matrices are further scaled within the range of 0–1. Finally, the omics matrices were stacked on an orthogonal axis to form a three dimensional tensor structure.There are several ways to decompose a tensor. PARAFAC  refers to the genes, ok indicates the type of omics and pm refers to patient samples. N, M and O indicate the number of genes, samples, and omics types, respectively. Three omics types are used in this illustration; thus, K = 2.An illustration of the PARAFAC model using gene expression, methylation level and miRNA expression data is in Cp) component  with the following equation,Subtype-associated tensor features, a subset of features selected from the tensor decomposition result, significantly improved subtype classification accuracy. To select such subtype-specific features, L1 regularization was used for each subtype and applied to the  refers to the weight of each feature to be inferred. The α value is the weight of the penalty term. Larger α values yields greater penalty, which will result in more features having zero weight and causing fewer features to be selected. We found that the L1 regularization achieved greater performance compared to the L2 regularization , where the weight is maximum at the corresponding feature index f.Based on the L1 selected features from Cg were given as input to build the MLP classifier.The significance of the selected feature genes was measured by their power of subtype classification accuracy. The classification accuracy was measured using a multi-layer perceptron (MLP) classifier with 10-fold cross validation. Here, values of the feature genes from MONTI was applied to three cancer types: breast cancer (BRCA), colorectal cancer (COAD), and stomach cancer (STAD). The cancer types were chosen based on the number of samples that had matched multi-omics data from the same patient. There were 597, 314, and 305 matched omics data for BRCA, COAD, and STAD, respectively. To avoid an overly sparse tensor, genes that do not have any methylation probes located within their promoter and 2 Kb upstream of transcription start site (TSS) regions were discarded, which resulted in 14,513 genes with 60,707 methylation probes in total. The average methylation beta values were taken and assigned per gene. Similarly, miRNA expression values were grouped per target gene and the arithmetic mean of miRNA expression values in a group was assigned to its target gene. The multi-omics data items were used to produce gene centric omics matrices, which were then combined to form a three dimensional tensor of each cancer type, i.e., genes × multi-omics × patient samples.R value for decomposing the tensor were needed to be chosen. In addition, a penalty strength, α value needed to be set for L1 regularization. Both were empirically chosen over a range of values by testing the subtype classification accuracy.Before deriving cancer subtype-specific features through tensor decomposition, a pre-defined rank Cp over different ranks. The subtype classification accuracy for BRCA, COAD, and STAD was the highest with ranks 450, 150, and 100, respectively. The α value for L1 regularization determines the strength of the penalty for the features. The larger the α is the smaller number of features and genes be selected. Subtype classification performance was further investigated using α values ranging from 0 to 0.1. To further select informative features, the non-zero weight features were ranked by their absolute coefficient value from which top 20% features were chosen.First, we evaluated the subtype classification accuracy using the feature in Cp were selected for subtype classification from the BRCA, COAD, and STAD tensors, respectively.The subtype classification accuracy was the highest when α = 0.01 . As a reCg) made to the improvement in subtype classification accuracy.The multi-omics tensors for the three cancer case studies were decomposed with the optimal rank numbers and α values that were chosen as explained above. We then investigated how much contributions feature genes . Improvement in classification accuracy was dramatic for COAD where use of single omics resulted in poor performance. Interestingly, methylation showed to be more influential in STAD, where ME alone achieved high classification accuracy. The CpG island methylator phenotype (CIMP) information can be used to characterize distinct subtypes of gastric cancer well and it is known that specific methylation patterns and clinicopathological features are associated  parameters were set as , , and  for BRCA, COAD, and STAD data sets, respectively. The output of SNF is the sample clusters, which was used to measure the F1 score.SNF (Similarity Network Fusion) integrates multi-omics data by constructing networks for each omics data in terms of the sample similarity using the omics data and then fusing the networks iteratively using the message-passing method. The principle is to keep edges between samples that are consistent across the different omics networks and to remove that are inconsistent and of low similarity. The optimal hyper parameters MOFA2 utilizes matrix decomposition with the purpose of identifying sources of heterogeneity in multi-omics data sets. It decomposes multiple two-dimensional matrices, where each matrix represents an omics data type comprised of genes and samples. The decomposition yields feature matrices, each associated to one of the input omics matrices, and an additional factor matrix, which represents the activation values of each feature per sample. Thus, if three omics data are given as input, they will be decomposed into four matrices . MOFA2 allows to chose the number of factors or features from the decomposed factor matrix, where we utilized as many as possible for each dataset. The maximum features that could be used was 10 for BRCA, COAD, and STAD, respectively. The output of MOFA was the Z sample factor matrix, which was used for measuring the F1 score.iCluster adopts a joint latent variable model for integrative clustering of multi-omics data. iCluster aims to data mine significant associations between different omics data types through likelihood-inference using the Expectation-Maximization algorithm. iCluster supports a omics optimal weight estimation function, which we used for each data set for clustering. The output of iCluster is the sample clusters, which was used to measure the F1 score.At last, sample PCA features were extracted and used for classifying the cancer subtypes. For each cancer and omics data, optimal number of PCA features were selected based on the classification accuracy via a parameter grid search. For BRCA, 10, 6, and 10 PCs were selected from gene, methylation, and miRNA data, respectively. Similarly, 8, 5, and 2 PCs for COAD and 20, 2, and 18 PCs for STAD were selected from gene, methylation, and miRNA data, respectively. The selected PCs were stacked and given as input to the random forest classifier to measure the F1 score.The average F1 score was measured via 10-cross validation for each tool with configurations described above. The train and test data were split before any normalization or feature selection in each BRCA, COAD, and STAD data set. The same train and test data sets were used to measure the F1 score in each method. Furthermore, the input data were both prepared in gene-level  and omics-level  format to observe the difference between the two integration methods. Thus, each method, except MONTI, was subject to two types of input data and were tested for classification accuracy accordingly. The tools measured with gene-level input data are labeled as SNF_g, MOFA2_g, iCluster_g, and PCA_g.The comparison results are shown in The relatively high classification accuracy of the cancer subtypes above implies that they may be explained using the feature extracted genes in terms of multi-omics. Thus, we further investigated whether clinical attributes, other than cancer subtypes, such as gender, mutation groups or metastasis can be explained using multi-omics data. Among the many clinical attributes, categorical attributes with <5 groups were used. Also, clinical attributes with high sample bias were excluded. As a result, a total of nine cancer types and 95 clinical attributes were analyzed using mRNA, methylation and miRNA data. For example, the “Pathologic M” feature of STAD, which is the TNM staging of metastasis, has three classes, which are M0, M1, and MX. If the cancer has spread, the sample is labeled as M0, and if not it is labeled as M1. If metastasis cannot be measured, it is labeled as MX. Thus, similar to the cancer subtype classification, we measured the classification accuracy of each of the categorical clinical attributes that were selected by the criteria described above. The details of the data set and clinical attributes are provided in MONTI was executed on each cancer type and each clinical feature as described in section 2. The classification accuracy of the cancer clinical attributes are shown in All cancer subtypes showed relatively high accuracy in BRCA, COAD, STAD, and PRAD (Prostate adenocarcinoma), which hints that the multi-omics profile is highly correlated with cancer molecular subtypes. Also, while mutation data was not utilized, the BRAF and RAS mutation classes were well distinguished in THCA (Thyroid carcinoma). From such result, we may infer that at least mRNA, methylation and miRNA omics have causal relationship with BRAF and RAS mutations, which was also reported in Agrawal et al. . In caseThe Pan-cancer analysis results show that some clinical attributes are able to be explained using mRNA, methylation and miRNA data while others need further investigation using other omics or clinical data. Collectively, we find that such results may help selecting omics when performing research on clinical features in a cancer cohort.While not shown in this study, the subtype classification accuracy decreased when involving certain omics types, particularly with the use of mutation profile data. For BRCA data, the accuracy dropped below 0.75 when SNP data were included in the tensor. The first short-coming of the SNP data was its extreme sparseness . We further attempted to impute the remaining missing values using the network-based stratification method for tumor mutations  method  funded by the Korean government (MSIT) (2019M3E5D3073365), the Collaborative Genome Program for Fostering New Post-Genome Industry of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (MSIT) (No. NRF-2014M3C9A3063541), and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020M3C9A5085604).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. 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Metabolomics aims to perform a comprehensive identification and quantification of the small molecules present in a biological system. Due to metabolite diversity in concentration, structure, and chemical characteristics, the use of high-resolution methodologies, such as mass spectrometry (MS) or nuclear magnetic resonance (NMR), is required. In metabolomics data analysis, suitable data pre-processing, and pre-treatment procedures are fundamental, with subsequent steps aiming at highlighting the significant biological variation between samples over background noise. Traditional data analysis focuses primarily on the comparison of the features’ intensity values. However, intensity data are highly variable between experimental batches, instruments, and pre-processing methods or parameters. The aim of this work was to develop a new pre-treatment method for MS-based metabolomics data, in the context of sample profiling and discrimination, considering only the occurrence of spectral features, encoding feature presence as 1 and absence as 0. This “Binary Simplification” encoding (BinSim) was used to transform several benchmark datasets before the application of clustering and classification methods. The performance of these methods after the BinSim pre-treatment was consistently as good as and often better than after different combinations of traditional, intensity-based, pre-treatments. Binary Simplification is, therefore, a viable pre-treatment procedure that effectively simplifies metabolomics data-analysis pipelines. Untargeted metabolomics experiments are focused on obtaining a global picture of a system with the objectives of identifying and characterizing all its metabolites or identifying key features, characteristics, and trends in the data that can help define and discriminate the systems under study ,2,3. At m/z values, also known as “features”, in comparison with the number of samples. A characteristic of these datasets is that a lot of these variables can be highly correlated due to physiological reasons, for example, for belonging to the same metabolic pathways or due to the sheer number of variables detected—the curse of dimensionality—leading to model overfitting when analyzed [Given its higher resolution, sensitivity, accuracy, and dynamic range, mass spectrometry (MS) is one of the selected analytical methodologies in untargeted metabolomics, leading to the detection of hundreds to thousands of variables and generating very complex data, which require robust and scalable computational and statistical tools to treat and extract meaningful information from them. An MS-based metabolomics dataset usually has a high number of variables, analyzed ,6. The vanalyzed .Robust computational and statistical tools have been developed and applied to treat and extract information from metabolomics data ,5. Howevm/z intervals), [After metabolite analysis, metabolomics data must be pre-processed, resulting in a 2D data matrix with variables represented in one dimension and samples in the other. Mass-spectrometry raw data processing includes spectra deconvolution, correction of the baseline and noise filtering, peak detection, or peak picking, peak alignment, and gap filling (if needed). In cases where MS is coupled with liquid or gas chromatography, retention time correction can also be employed ,9. Correervals), ,13. Afteervals), ,15. The ervals), . Data prervals), ,11. Tranervals), ,8. Mean ervals), . Each caervals), , exponenervals), ,8.Traditionally, data pre-treatments focus on the signal intensity values present in the dataset and are all intensity transformations, from the mathematical point of view. Despite maximizing the contribution of biologically important information contained in the intensities and, indirectly, in the metabolite abundances, these methods usually ignore (or diminish the relevance of) another important aspect of metabolomics data: the presence/absence of metabolites in a sample. This predominance of the signal intensity data in relation to the occurrence of spectral features is also supported by the traditional peak filtering/picking methods (exclude features that appear in a low number of samples) , followeTo make up for this gap in metabolomics and as an effort to make metabolomics data analysis more reproducible, we propose in this work a pre-treatment method based on the occurrence of features that forgoes the use of the more variable intensity data. In this method, named Binary Simplification (BinSim), feature occurrence in samples is encoded by 1 while feature absence is encoded by 0 in an already constructed data matrix .This approach discards the variation in intensity data between different metabolomics experiments, even within the same batch, due to differences in sample preparation efficiency, dilution, or ionization efficiency. Lin et al.  reportedBinSim is a pre-treatment procedure that replaces most of the peak filtering, missing value imputation and pre-treatment methods of normalization, transformation and scaling used in the metabolomics data analysis pipelines and provides a single faster and simpler step, where no parameter-method choice or tuning needs to be made. Given its simplicity and aggressive truncation of signal intensities, one might wonder that information contained in data will be lost to an extent where the performance of downstream statistical methods will degrade. To demonstrate otherwise and establish BinSim as a viable procedure, we carried out an empirical comparison of the performance of clustering and classification methods applied to several experimental datasets transformed with BinSim or with six combinations of intensity-based transformations that can be considered representative of typical pre-treatment steps employed in metabolomics data-analysis pipelines. We conclude that BinSim does not degrade and often improves the performance of these methods.The BinSim pre-treatment was envisioned as a reliable and simpler alternative to more established intensity-based pre-treatments. It focuses on the presence or absence of features from the different samples instead of intensity-oriented pre-treatments. Furthermore, it was specifically created with ultra-high-resolution metabolomics data analysis in mind, where the occurrence of missing values is expected to be high. It consists of encoding intensity values as one for features present in a sample and zero for missing values, resulting in a binary data matrix comprised of 0 s and 1 s  or Random Forest imputation (RF imputation) ; normaliYD 6/15 was specifically created as an example of a scenario where the presence of missing values is low, which, a priori, is expected to reduce the performance of methods applied to BinSim treated data. Moreover, the datasets encompass different levels of class overlap as seen in GDg2+ and GDc2+ showing a high proximity of samples belonging to different classes in the PCA scores plots, GDg2− and GDc2− still problematic but with classes more spread out, YD 2/15 and YD 6/15 showing well-defined and separated classes, and the GD types dataset, used in the two-class classifiers, with classes also well separated. Class overlap is expected to dictate poor performance in both clustering and classification methods, since discrimination is expected to be hard. Furthermore, the differences in data characteristics also relate to instrumental performance since YD data were obtained in a more recent platform of Fourier Transform Ion Cyclotron Resonance (FT-ICR) instruments.Dataset Therefore, the benchmark datasets represent several real-life scenarios of data quality levels in current high-resolution metabolomics.The human dataset, also used in the two-class classifiers, represents a large LC-MS dataset with many samples per class but with comparatively low abundance of missing values and a high degree of class overlap.To test the viability of the BinSim method in highlighting relevant information from metabolomics data, in the context of sample discrimination, the performance of two unsupervised, Hierarchical (HCA) and K-means Clustering, and two supervised statistical methods, Projection in Latent Structures Discriminant Analysis (PLS-DA) and Random Forest (RF), was compared. The ability to cluster together samples that are replicates was the criterion used to assess the performance of the unsupervised methods by defining “ground-truth” clusters, whereas predictive accuracy was the criterion used for classifiers. Furthermore, as it is a frequently performed step of metabolomics data analysis, feature importance evaluation in the classifier models was also analyzed. The goal was to investigate whether BinSim treated data offers a new perspective on the same original data (discrimination achieved by highlighting at a different set of features).GDc2+ after BinSim and the three pre-treatments based on 1/2 min imputation are also shown in The overall results of applying Hierarchical Clustering and K-means Clustering after each of the four pre-treatments are shown in GDg2 datasets, which are characterized for having samples from different ground-truth clusters very close together and might be considered low quality datasets. In fact, for GDg2+, both clustering methods perform poorly regardless of the pre-treatment procedure, with the correct clustering percentage below 50% in all cases. Even for these datasets, BinSim is not the worst pre-treatment. It is worth noticing that BinSim was the only pre-treatment to allow 100% correct clustering for the two GDc2 datasets using HCA and for GDc2− using K-means. The results on the correct clustering percentage are qualitatively the same in HCA and K-means, even with the slight difference in definition: For HCA, “correct clustering” was defined as all the samples in a replicate group clustering together before clustering with a sample from any other group, whereas in K-means, the number of clusters in the result is prescribed as the number of replicate groups, and the definition requires homogeneity and completeness in the clusters obtained when the algorithm finally converges. Given these definitions, it is expected that correct clustering values will be lower for K-means when compared to HCA and that is, in fact, observed.The overall trend in these results is that the clustering methods applied to BinSim-treated data perform, in most cases, better than when applied to data subjected to the other pre-treatments. Considering the correct clustering percentage, which requires both completeness  and homogeneity (each cluster contains only replicates) both methods achieve higher values with BinSim, except in the case of K-means Clustering for the two GDg2+ and being the only pre-treatment to achieve close to top values for GDc2 datasets.As the correct clustering percentage might be considered to have course granularity, since its values can only be multiples of 100/number of clusters, the correct first clustering percentage for HCA and the Rand Index for K-means were also calculated as other measures of clustering performance with finer granularity. Looking at these measures, the same overall trend holds, with BinSim being the best or second best except for the Rand Index for the low-quality dataset It is also important to note that, using these measures of clustering accuracy, perfect clustering was obtained with the two YD datasets in all cases, which is consistent with the fact that these are high-reproducibility datasets with replicate groups well separated from each other. This indicates that, also for data of high quality, BinSim does not degrade clustering performance.Another measure for comparison, the Discrimination Distance, was also calculated. This measure represents the robustness of clustering performance to the negative impact of noise as it is an average of the normalized distance thresholds that, if overcome by outlier samples, would result in clustering errors. Datasets treated with BinSim consistently result in the best or close to the best value of Discrimination Distances when the K-means algorithm is used. As for HCA, BinSim only leads to lower Discrimination Distances for the two high-quality YD datasets. This qualitative discrepancy between HCA and K-means datasets might be related to the fact that in HCA the Jaccard dissimilarity was used as the distance metric for the BinSim-treated data and Euclidean distance in the other pre-treatments, whereas K-means uses Euclidean distance regardless of the pre-treatment. This is because K-means uses the projection of the samples in the n-dimensional space with n equal to the number of features, and therefore, binary distance metrics cannot be used to cluster the samples. So, the distance metric used was Euclidean for all the different datasets, including those treated with BinSim. Another reason might stem from the fact that the Discrimination Distance is defined differently in both methods, given their operating characteristics.It is worth noting that the correct clustering percentage and Discrimination Distance are very sensitive to outliers with the “correct clustering” definition used, since just one stray sample from a group can lead to that entire group being labelled as not “well clustered”. However, this is not a problem for the results obtained since each dataset used here has only three replicates. Hence, one sample being an outlier in the group corresponds to a hefty part of the group and should be considered. It would be remiss to not say that these methods are not suited to be directly applied to test the clustering efficiency for datasets with higher number of samples per group. In these cases, they should be adapted. On the other hand, the correct first cluster percentage should be resistant to outliers as is.GD datasets  and were also usually positively correlated with the dendrograms treated with the other treatments (computed using the Euclidean distance). From these results, we reached the conclusion that data subjected to the Binary Simplification treatment revealed the same trends and information, as far as the metabolic similarity of the defined distinct groups are concerned, and that this can be extended to higher-level clusters in HCA.Both correlation coefficients between the different dendrograms were, with few exceptions, higher than 0, and in fact, most of them indicate strong positive correlations. Furthermore, we could not establish the rule that intensity-oriented pre-treatments lead to clustering patterns quite different from those obtained from BinSim-based procedures: Correlations are not consistently higher between the three intensity-based pre-treatments than between these and BinSim with different distances and vice versa. For instance, NGP pre-treatment correlates higher with BinSim with all three distances in GD types was added to this benchmark, featuring a two-class problem targeting Vitis vinifera versus wild Vitis samples, based on GDc2− but with these different labels as targets. HD was similarly added to this benchmark, as another two-class problem with many samples per class but a rather low missing-value abundance and significant class overlap. These problems were created to benchmark BinSim’s effect on a common scenario in metabolomics data analysis: the use of two-class classifiers for discrimination and assignment of importance to features. As for the clustering methods, the seven pre-treatments were applied, and classification models were then built. Performance was evaluated by internal stratified cross-validation. The rationale for the inclusion of Random Forest was that the binary choices made during the operation of this algorithm would be able to extract the information present in a binary encoded dataset such as those resulting from the BinSim pre-treatment by choosing features where the two-levels would relate to class separation. This type of methods seems to be tailored to use BinSim treated data. On the other hand, PLS-DA is a dimension reduction classifier that was chosen due to its popularity in the metabolomics data analysis workflow [The next step was to compare the predictive performance of two types of classifiers, Projection in Latent Structures Discriminant Analysis (PLS-DA) and Random Forest (RF), considering the classes defined in the same way that they were defined for the comparison of the unsupervised methods. workflow ,22.The results of the accuracy achieved by the two methods for data subjected to the seven pre-treatments are shown in YD problems, close or equal to 1 in the 2-class GD types problem, and only leads to poor accuracy in the case of the two GDg2 examples, which are 11-class and difficult to discriminate problems, although still above 0.8 for GDg2−. It is worth noticing that the classifiers fitted on BinSim-treated data outperform the classifiers for the other treatments for the 11-class GDc2 problems. With RF, it is the only treatment leading to perfect classification. Furthermore, regardless of the fundamental algorithmic differences, the results of PLS-DA are qualitatively consistent with the results of RF. The example of the large dataset HD suggests that BinSim does not degrade the performance of classifiers in large and complex datasets. Here, the type of classifier made a difference, with PLS-DA classifiers performing better than the corresponding RF classifiers. However, even for this example, classifiers for BinSim-treated data achieve an accuracy very close to the most accurate combination in the remaining six pre-treatments. For the GD types two-class dataset, ROC curves were also computed for the RF models built for BinSim and the three combinations based on 1/2 min imputation. These curves are shown in The results show that the BinSim pre-treatment does not compromise the performance of the classifiers developed for the eight problems included in this benchmark. For most examples, the classifiers perform better with BinSim than with the other pre-treatments, and in the cases where another pre-treatment results in better method accuracy, BinSim follows very closely, and the performance difference is only very slight. In absolute terms, the accuracy with BinSim-treated data follows the general trend of the clustering methods and leads to perfect classification in the case of the 5-class p-values < 0.02), which means that the classifiers’ accuracy resulted from significant information present in the data and not from random noise. This could also be concluded for the BinSim pre-treatment.The significance of the accuracy of the models was assessed by permutation tests, where the predictive accuracy with randomly permuted labels, estimated by cross-validation, was compared to the predictive accuracy of the corresponding non-permuted model . In all GD datasets, BinSim models seemed to perform slightly better than the other pre-treatments. It was also more consistent since it was the best or close to the best of all pre-treatments with RF, PLS-DA, HCA, and K-means in all the datasets studied. The information extracted from these datasets led to consistently good discrimination power of the numerous classes. These datasets work as a good proof-of-concept for the viability of BinSim with data difficult to discriminate. For the YD datasets, both RF and PLS-DA models had a perfect predictive accuracy (100%) in the discrimination of the five groups built on the models. This leads to the conclusion that the BinSim pre-treatment did not discard substantial and essential information by ignoring intensity data to hamper a perfect discrimination. Furthermore, comparing the results of YD 2/15 with YD 6/15, the reduction of missing value abundance in the latter did not compromise such discrimination. Similarly, the lower abundance of missing values in HD dataset did not compromise the discrimination of the two classes.The main conclusion of this benchmark is that BinSim does not negatively impair classifier performance and often leads to its improvement. Despite the similar performances of the different models using the negative mode After establishing that the discrimination power of different statistical methods did not degrade with BinSim-treated data and that it was often improved, the next step was to investigate if BinSim was, as hypothesized, “looking at the information differently”, that is, if BinSim is giving more weight to information/features that are not as important in the results of the application of classifiers to data subjected to the other pre-treatments. Different “feature importance” metrics were used, specifically, the Gini Importance in RF models  and the GDg2− and YD 2/15 as examples, and considering the top 2% important features, Using the datasets GDg2− with pre-treatment P where BinSim has the second highest number of unique features. Furthermore, the number of common features with the other treatments tends to be small.Considering the number of unique important features, BinSim-treated datasets have the most unique important features calculated from both classifier types, except for the case of the RF model applied to GDg2− after the NGP and NP treatments. These trends are also observed for the distribution by number of classes . This was observed when this filtering was performed in the two GDc2 datasets, leading to an increase in the performance of different methods for BinSim-treated datasets when compared to the GDg2 datasets, where the reproducibility filter was global, considering all samples. It is interesting to note that the abundance of features that appear in very few classes in RF models of BinSim-treated data is not echoed in features that are present in almost every group except one or two. Therefore, this leads to the conclusion that there are more features that act like “biomarkers” than the opposite (are only absent in one or two classes).Considering the distribution of the important features with the number of samples where they occur, both RF and PLS-DA models chose features for BinSim-treated datasets that appeared in a lower number of samples, except for the PLS-DA models applied to YD 2/15 dataset, where a significant number of important features occurred in only three samples, the number of samples in each class , this happens when the contribution of a feature to a component separates approximately half of the classes between each other, that is, two classes from the other three; therefore, features that appear in two or three classes only (appear between six and nine samples) are prioritized.The distribution of the feature occurrence by number of samples was very different in the RF and PLS-DA models. In the RF models, they mostly appeared in a very small number of samples ranging between three and six, that is, features that were exclusive to one class or sometimes two classes. This was more apparent in the ch class B. The ch more from the low-intensity features that count as present in the data. By attributing to these separations a slightly greater importance, when compared to other intensity-based pre-treatments, NGP acts closer to BinSim. In fact, in the Venn diagrams , or, conversely, when key features are absent from just one or two classes. However, this kind of information tends to be overshadowed by the intensity data in the usual metabolomics workflow due to the extensive peak filtering, missing value imputation, and the intensity-oriented nature of the traditional pre-treatments. Peak filtering tends to exclude features with higher amounts of missing values. Depending on the filtering method, those might include features exclusive to one or a few classes, overlooking the importance of these features in discrimination. The estimates of the loss of BinSim-RF important features for the example R values , their pR values , they arThe good performance observed with BinSim-treated datasets might be a consequence of the big inherent variability of the intensity data of FT-ICR-MS that reduces the efficiency of the other treatments. With a lower sample/class ratio, trying to discern intensity patterns that reliably discriminate the different classes is challenging for classifiers and, therefore, more prone to errors, since an incidental pattern found in a very limited number of training samples might not be replicated in the corresponding test set, leading to misclassifications. We hypothesize that the BinSim’s robustness is higher (in comparison to other treatments) on low- to medium-sized datasets, where the amount of training samples to build a classifier is limited and relying on intensity patterns may be more prone to errors because of their variance. This could possibly be explored in future studies.m/z in MS-based metabolomics, chemical shifts in NMR-based metabolomics) and the tolerance prescribed for the deviations between spectral feature values between the different samples. However, this spectral alignment will influence the results of all pre-processing pipelines and will not specifically affect BinSim in any distinctive way. The signal-to-noise ratio is another instrumental parameter that will affect the results of BinSim since it will directly dictate whether a feature is a missing value by thresholding its intensity. In addition to affecting all other pre-treatments, the signal-to-noise ratio impact is not expected to be of great concern because it leads to relative, not absolute, thresholds. Since BinSim substitutes the normalization step, the overall intensity biases in samples, which are corrected by normalizations, could, in principle, cause differences in missing/non-missing value assignments of spectral features. However, constant signal-noise-ratio thresholds mitigate the effect of these biases since the baseline noise level variations tend to follow the general signal intensity variations of a sample. Therefore, it is expected that features be classified as missing/non-missing almost independently of overall intensity differences between samples.Another advantage of BinSim is that the results of the binary encoding are less dependent on instrumental settings and data analysis stages upstream pre-treatments. There are few operational settings that determine how a data point will be binarily encoded. One of them is the alignment of spectral features between the ensemble of samples, which constructs the data matrix, and is, in turn, dependent on instrumental measurement accuracy  were discarded. The effect of this type of filtering is apparent when GDg2+ and GDg2− are compared with GDc2+ and GDc2−: All methods improved performance, especially after the BinSim transformation. This was expected, since it improves the likelihood of variables becoming characteristic of classes and effectively “spikes” their occurrence in those classes and samples of the same class are artificially made more similar to each other, something that benefits methods used on BinSim data. This form of filtering, that is, justified by performance improvement arguments, should be used with care, since it may lead to a form of data leakage: When using internal validation methods, “test samples” used to validate the models were made artificially closer to the training samples. The use of this type of filter in training data in a discriminatory analysis context requires the model to be validated by an external metric so that testing samples are always treated as truly “unseen samples”.In this study we performed a form of feature selection: The rejection of variables with a very low number of sample occurrence  was performed prior to the application of all pre-treatments. This was justified by a reproducibility criterion, as such variables may be considered the result of noise or excessive variability but close to the limit of detection. This form of selection affected all pre-treatments in our study, and it is a procedure that was not applied with the argument of performance improvement in mind. Above all, it had the effect of significantly reducing the number of variables, most of which would not carry any information for further analysis whether in occurrence or intensity. However, a more stringent form of this selection was also employed: In datasets BinSim, as a pre-treatment method, might prevent some types of analysis that rely heavily on intensity-based data. Univariate methods are no longer possible and value predictions from regression methods might also require data that are not binarized. We showed that BinSim works well for clustering and classification, which are common in metabolomics data analysis but its effect on the performance of regression methods needs to be established by a systematic comparison, conceptually similar to the one performed in this study.As proposed in this study, BinSim behaves as a binary encoding of feature occurrence in the data matrix and is akin to the binary encoding of categorical variables. However, issues resulting from the introduction of such encodings in the explanatory data matrix have been pointed out for some situations. For instance, when the explanatory data matrix combines variables of different measurement scales, such as categorical and interval or ratio scales, PLS based methods, including PLS-DA, must be adapted to ensure optimal scaling . To avoiThe main goal of this study was the comparison of the effects of Binary Simplification and traditional pre-treatments of the data matrix in downstream metabolomics data analysis. For that purpose, we constructed eight data matrices from experimental data which encompass different levels in terms of number of features and abundance of missing values. Seven of the data matrices (datasets) were based on three metabolomics datasets obtained from two different Fourier transform ion-cyclotron resonance mass spectrometry (FT-ICR-MS) instruments, with different mass accuracies and resolutions. The eighth dataset was obtained in an Orbitrap instrument coupled with a hydrophilic interaction chromatography (HILIC).Vitis varieties that are susceptible or resistant to oomycete/fungal infections [Vitis plants were analyzed by direct infusion in a 7T-Apex FT-ICR-MS, by electrospray ionization in positive (ESI+) and negative (ESI−) modes [m/z distance tolerance, generating a data matrix with 5821 peaks in the negative mode and a data matrix with 30,660 peaks in the positive mode. From these datasets, several matrices were constructed by performing different levels of a variable selection related to spectral feature reproducibility  at least twice in all 33 samples, for each of the acquisition modes; matrices GDc2+ and GDc2− were generated after retaining only features that occur at least twice in the three replicates of at least one Vitis variety (class), for each of the acquisition modes. In the assessment of the performance of supervised methods, an additional dataset was created, suitable for fitting two-class classifiers. This is just dataset GDc2− with class labels “vinifera” and “wild”, corresponding to grapevine varieties of Vitis vinifera and to wild Vitis (non-vinifera) species, respectively. This dataset is referred to as GD types.“Grapevine datasets” were constructed from already published and openly available data  related fections . Detailefections ,29. Brie−) modes , and dat−) modes  at 1 ppmcibility : matricefigshare public repository [Saccharomyces cerevisiae: the reference strain BY4741 , 3 single-gene deletion mutants of this strain, related to methylglyoxal metabolism—ΔGLO1, ΔGLO2, ΔGRE3, and the single-gene-deletion control ΔENO1. The raw data from the 15 samples were aligned using the MetaboScape 4.0 software  using the T-ReX  algorithm with the following parameters: m/z delta = 1.10, Intensity Threshold = 0.00, Maximum Charge = +1. From the resulting “bucket table”, two datasets were constructed by selecting features based on its reproducibility with different thresholds: In dataset YD 2/15, features that occurred in at least 2 samples were retained, and in dataset YD 6/15, features that occurred in at least 6 samples were retained.“Yeast datasets” were built from data obtained from direct infusion analysis by electrospray ionization in a 7T-Solarix XR FT-ICR-MS, operating in positive (ESI+) ionization mode ,32 and apository . The datHD, was built from data obtained from a study of the preoperative metabolic signatures associated with prostate cancer recurrence versus remission after radical prostatectomy [“Human dataset”, here referred to as atectomy . These datectomy . The avaatectomy . The aveGDg2−, GDg2+, GDc2−, GDc2+, YD 2/15, YD 6/15, GD types, and HD) is found in A general characterization of all eight datasets . Dataset HD was normalized by Probabilistic Quotient Normalization [NP: samples were normalized by the reference feature  considers the occurrence of spectral features to construct a binary data matrix encoding feature presence  as 1 and absence  as 0. No further transformation was applied.In total, seven different combinations were applied to each dataset: P, NP, and NGP applied to either 1/2 min- or RF-imputed data and BinSim.GDg2+, GDg2−, GDc2+, GDc2+, YD 2/15, and YD 6/15. For classifiers, these replicates of the same type, species, or strain also define the target classes to be predicted, except for the GD types dataset where two classes were defined as described above, considering wild Vitis plants versus Vitis vinifera plants and the HD dataset where the two classes represent patients with cancer recurrence versus cancer remission (binary classifier problems). For classifiers, predictive accuracy was the main performance goal to be assessed. Clustering methods assessed were Agglomerative Hierarchical Clustering (HCA) and K-means Clustering, and classifiers methods were Random Forest (RF) and Projection in Latent Structures Discriminant Analysis (PLS-DA).BinSim was compared with the other pre-treatments regarding the effect on the performance of selected clustering and classification methods representing the two general types of unsupervised and supervised methods used in metabolomics data analysis. For clustering, performance was assessed by the ability of the methods to correctly cluster replicates of the same type, species or strain of the organisms that contribute to the different datasets. These groups, defined by the replicates, are the “ground truth” of correct clusters allowing the use of ground-truth related metrics of clustering performance. Clustering was applied after each pre-treatment for datasets Agglomerative Hierarchical Clustering Analysis with UPGMA (average) linkage method was performed on each dataset. Euclidean distance was used for the datasets treated with intensity-based pre-treatments, whereas, for the BinSim-treated datasets, three dissimilarity metrics were chosen due to their binary natures: Jaccard and Yule dissimilarities and Hamming distance.-Jaccard dissimilarity :(1)DJacc-Yule dissimilarity :(2)DYul-Hamming distance :(3)DHamFor two samples, S1 and S2, if Clustering performance was expressed by three metrics. The “correct clustering” percentage is defined as the percentage of the groups whose samples all clustered together before any other clustering with other samples or already formed clusters in the agglomerative procedure. The global “Discrimination Distance” (DD) is defined as the average of “group discrimination distance”. For each group, the discrimination distance is 0 if the group is not “correctly clustered” (in the sense used for the correct clustering metric) or it is the distance between the node that includes all the samples of the group and the next closest node (including those samples) in the agglomerative procedure, normalized by the maximum distance of any pair of nodes in the final resulting clustering. The “correct first cluster” percentage is defined as the percentage of samples whose first clustering was only with a sample(s) from its group. The similarity between the dendrograms obtained for the same dataset with different pre-treatments was evaluated by the cophenetic correlation coefficient  and the GD and 5 in YD) and using Euclidian distance (including for the BinSim-treated datasets). As K-means Clustering results can vary with the randomness of initial centroid assignments [K-means Clustering Analysis was applied per dataset and treatment parameterized with a cluster number equal to the total number of classes/groups , especially with low sample size in each class, this process was iterated 20 times and the mean accuracy of all repetitions was taken as a global metric for the cross-validation evaluation.The classifiers chosen to use as a comparison test between the different pre-treatments were Random Forests and PLS-DA. Except for the lidation  and 5-foThe number of trees used for the Random Forest classifiers was optimized to 100. Other parameters were left as the default values used in the scikit-learn RandomForestClassifier object constructor. For each model, the Gini Importance of each feature was calculated . The topGD types classification problem, a ROC curve for each of the three pre-treatments starting with 1/2 min imputation and for BinSim was also computed.For the two-class 2) computed from cross-validation. Moreover, 11 components were chosen for all GD datasets, 6 components for the YD datasets and GD types, and 15 components for the HD dataset. Class membership was encoded by the one-hot encoding method, and the prediction decision samples were assigned to the class corresponding to the maximum value in ypred of the PLS output. In the GD types and HD problems, class membership was encoded as 0 or 1, with a 0.5 threshold for decision. As it was conducted for the Random Forest, the top 2% of the features considered as most important were taken to build the models, and their sample and class occurrence was calculated. Here, the Variable Importance in Projection (VIP) was used to estimate the importance of each feature to build each model [Projection in Latent Structures Discriminant Analysis (PLS-DA) classifiers were built for each dataset and each pre-treatment using the PLS2—NIPALS algorithm implemented in PLSRegression module of scikit-learn . The defch model .For both RF and PLS-DA methods, permutation tests (with 500 iterations each) were performed to further assess if the models were significant.https://github.com/aeferreira/binsim_paper since 16 November 2021.Data transformations, clustering methods, and classifiers were implemented in the Python language version 3.8.5 using pandas version 1.2.4  and scikThe Binary Simplification (BinSim) pre-treatment was specifically created with metabolomics’ data analysis in mind as a simpler and reliable alternative to traditional pre-treatments. It focuses on the presence or absence of features instead of the intensity-based nature of the other pre-treatments. The occurrence of features leads to the construction of binary data matrices encoding feature presence as 1 and absence as 0. Despite this binarization and aggressive truncation of intensity information, the performance of both clustering and classification methods was not compromised. HCA, K-means Clustering, PLS-DA, and RF did perform consistently as well or, often, better with BinSim-transformed data than with data subjected to combinations of other intensity-based pre-treatments. Furthermore, the general trends observed in the comparison of performances were also consistent between the different methods.With RF models, the characteristics of important features are distinctive: with BinSim-treated data, classifiers tend to attribute higher importance scores to features with sample occurrences characteristic of indicators of one or very few classes/groups, while, with data subjected to intensity-based pre-treatments, the distribution of sample occurrence of important features tends to be more uniform or less related to class occurrence. After BinSim, classifiers score features according to a “different perspective” on data.BinSim, as pre-treatment, substitutes several steps in a metabolomics data analysis pipeline, in particular, missing-value imputation, normalization, and scaling. The pre-treatment stage of data analysis becomes straightforward, as the need for choices of methods and method parameter vanishes.While not generalizable to all scenarios in metabolomics data analysis, BinSim is a viable alternative to intensity-based pre-treatments for high-resolution, high-mass-accuracy untargeted data, where missing-value occurrence carries a significant information content. Since metabolomics studies that generate data with these characteristics are currently becoming common, we propose the adoption of BinSim as pre-treatment step in metabolomics data-analysis.


Gliomas are a type of malignant central nervous system tumor with poor prognosis. Molecular biomarkers of gliomas can predict glioma patient’s clinical outcome, but their limitations are also emerging. C-X-C motif chemokine ligand family plays a critical role in shaping tumor immune landscape and modulating tumor progression, but its role in gliomas is elusive. In this work, samples of TCGA were treated as the training cohort, and as for validation cohort, two CGGA datasets, four datasets from GEO database, and our own clinical samples were enrolled. Consensus clustering analysis was first introduced to classify samples based on CXCL expression profile, and the support vector machine was applied to construct the cluster model in validation cohort based on training cohort. Next, the elastic net analysis was applied to calculate the risk score of each sample based on CXCL expression. High-risk samples associated with more malignant clinical features, worse survival outcome, and more complicated immune landscape than low-risk samples. Besides, higher immune checkpoint gene expression was also noticed in high-risk samples, suggesting CXCL may participate in tumor evasion from immune surveillance. Notably, high-risk samples also manifested higher chemotherapy resistance than low-risk samples. Therefore, we predicted potential compounds that target high-risk samples. Two novel drugs, LCL-161 and ADZ5582, were firstly identified as gliomas’ potential compounds, and five compounds from PubChem database were filtered out. Taken together, we constructed a prognostic model based on CXCL expression, and predicted that CXCL may affect tumor progression by modulating tumor immune landscape and tumor immune escape. Novel potential compounds were also proposed, which may improve malignant glioma prognosis. A glioma, accounting for 81% of malignant brain tumors, is a primary brain tumor originating from glial stem cells or progenitor cells . The malTumor microenvironment (TME) consists of tumor cells and non-tumor cells, such as microglia, peripheral macrophages, myeloid-derived suppressor cells, vascular endothelial cells, and others , 6. The The C-X-C motif chemokine ligand (CXCL) family widely participates in immunocyte recruitment and affecting tumor progression like tumor migration and angiogenesis , 9. For In our study, samples of TCGA were set as training cohort, while CGGA2, CGGA1, four datasets from GEO database, and our own clinical samples were treated as validation cohort , CXCL9 , CXCL10 , CXCL11 , CXCL13 , CXCL3 , CXCL1 , CXCL6  in the GBM group were significantly higher than that in the lower-grade gliomas (LGG) group, while CXCL12  had a higher expression in the LGG group , CXCL9 , CXCL10 , CXCL11 , CXCL6 , and CXCL1  were enriched in the IDH wide-type gliomas (The samples in the TCGA dataset were divided into cluster1 and cluster2 (The support vector machine (SVM) was used to learn the characteristics of the two cluster samples and reconstruct the cluster model in validation cohort  can be mapped, implying they may share similar regulator or exert similar function , age , IDH , cancer , and 1p19q  were considered as independent prognostic indexes for OS time  dataset, mRNAseq_325 (CGGA2) dataset, and mRNAseq_693 (CGGA668) dataset], and GEO database . All expression profiles were transformed into log2(TPM+1).For each glioma patient of the 50 samples, major exclusion criteria were incomplete follow-up data, poor quality of samples, and missing baseline clinicopathological features. Formalin-fixed paraffin-embedded tumor tissues were then collected for sequencing. One μg RNA per sample was used as input material for RNA sample preparations, and DNA was extracted and sheared followed by sequencing library preparation using NEBNext UltraTM RNA Library Prep Kit. Subsequently, PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers, and the Index (X) Primer. Biotin-labeled probe was applied to capture target regions after removing the PCR primer. The captured libraries were sequenced on an Illumina Hiseq platform, and 125 bp/150 bp paired-end reads were generated. Raw data (raw reads) of fastq format were first processed through in-house perlscripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter, ploy-N, and low-quality reads from raw data. Meanwhile, calculation of Q20, Q30, and GC content of the clean data were performed. All downstream analyses were based on clean data with high quality. Reference genome and gene model annotation files were downloaded from the genome website directly. The reference genome index was built using Hisat2 v2.0.5, and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5, and Hisat2 was selected as the mapping tool. FeatureCounts v1.5.0-p3 was then applied to count the reads’ numbers in order to map to each gene. The TPM of each gene was calculated based on the gene length and reads count mapped to this gene. Glioma sample collection was approved by the ethics committee of Xiangya Hospital.By using the R package “Consensus Cluster Plus” to perform consensus cluster analysis, the samples were divided into different groups to create a clustering model , 45. TheSupport vector machine was introduced to reconstruct the cluster model in the validation cohort based on the characteristic of the cluster model with R package “e1071.” Kernel of algorithm was set as radial. Self-validation was conducted with R package “caret,” and the sensitivity of the cluster model was 0.9888 and the specificity was 0.9795 (range of sensitivity and specificity is 0 to 1).The elastic net regression algorithm and their coefficient were calculated automatically . A risk Risk = 0.028 * CXCL1 + 0.027 * CXCL9 + 0.14 * CXCL10 + 0.04 * CXCL11 + (−0.12 * CXCL12) + 0.047 * CXCL14The GO and KEGG analyses were carried out based on GSVA analysis, and corresponding information was downloaded from the molecular signature database (MSigDB) , 49. ResThe ESTIMATE algorithm was applied to evaluate the composition of the tumor microenvironment . The immInformation about drug sensitivity was downloaded from the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) and the Cancer Therapeutics Response Portal (CTRP) database. Cell line expression matrix was obtained from Cancer Cell Line Encyclopedia. R package “pRRophetic” was introduced to predict sample sensitivity to certain compounds, and lower AUC values represented higher sensitivity. The knn imputation strategy was applied to imputed NA value in the expression matrix, and we discarded samples with more than 30% NA value. The “limma” package was performed to identify potential drugs, and correlation <−0.7 was set as threshold . SimilarKaplan-Meier analysis was used to generate survival curves, and the validity was evaluated by log-rank test. The receiver operating characteristic (ROC) curve and the area under the curve  were introduced to compare the predictive capabilities of different models. Univariate and multivariate Cox regression analyses were used to filter prognostic variables . Therefore, these variables have been verified by Schoenfeld’s test to construct a nomogram with the R package “survival” and “RMS,” respectively , 56–59. Statistical analysis was performed by R (version 3.6.2). Wilcoxon rank‐sum test was used to compare two groups. One‐way ANOVA was used to compare multiple groups. Spearman correlation analysis was used for correlation analysis . NS, notThe original contributions presented in the study are included in the article/ZW, YL, and YM: Writing—original draft, writing—review, editing, data curation, and formal analysis. HZ and ZD: Investigation and methodology. WY, HC, and XZ: Validation. ZL and QC: Conceptualization, supervision, project administration, and funding acquisition. All authors contributed to the article and approved the submitted version.This study is supported by the National Nature Science Foundation of China ; the China Postdoctoral Science Foundation (NO.2018M633002); the Natural Science Foundation of Hunan Province ; the Hunan Provincial Health and Health Committee Foundation of China (C2019186); and Xiangya Hospital Central South University postdoctoral foundation, and the Fundamental Research Funds for the Central Universities of Central South University (No. 2021zzts1027).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.


Alternative splicing contributes to the diversity of RNA found in biological samples. Current tools investigating patterns of alternative splicing check for coordinated changes in the expression or relative ratio of RNA isoforms where specific isoforms are up- or down-regulated in a condition. However, the molecular process of splicing is stochastic and changes in RNA isoform diversity for a gene might arise between samples or conditions. A specific condition can be dominated by a single isoform, while multiple isoforms with similar expression levels can be present in a different condition. These changes might be the result of mutations, drug treatments or differences in the cellular or tissue environment. Here, we present a tool for the characterization and analysis of RNA isoform diversity using isoform level expression measurements.SF3B1 mutations.We developed an R package called SplicingFactory, to calculate various RNA isoform diversity metrics, and compare them across conditions. Using the package, we tested the effect of RNA-seq quantification tools, quantification uncertainty, gene expression levels and isoform numbers on the isoform diversity calculation. We analyzed a set of CD34+ hematopoietic stem cells and myelodysplastic syndrome samples and found a set of genes whose isoform diversity change is associated with https://www.bioconductor.org/packages/release/bioc/html/SplicingFactory.html).The SplicingFactory package is freely available under the GPL-3.0 license from Bioconductor for the Windows, MacOS and Linux operating systems (Bioinformatics online. The mechanism of alternative splicing is well-known and described in most eukaryotic organisms . AlternaRNA-sequencing experiments regularly investigate consistent alternative splicing or mRNA isoform changes between conditions . SplicinA number of methods are already developed to detect changes in expression variance  we selected the 17 control samples from the SRP133442 dataset and calculated the normalized naive-entropy and Gini-index for all genes. For the three pairwise comparison  and each sample we selected all genes with non-NA diversity values and calculated their Spearman correlation.https://github.com/COMBINE-lab/salmon), and converted the estimated read counts to TPM values. Using the bootstrap data, we calculated all available diversity metrics for all genes from all samples and bootstraps. Finally, we selected the  and the bootstrap package (version 1.3-25) using 500 bootstrap replicates.® Xeon® Gold 4118 2.30 GHz CPU type, a total of 157 GB memory and Ubuntu 18.04.5 LTS operating system. We used the Salmon-based quantification for the benchmarks and tested both the diversity and difference calculation steps using the samples from the two MDS datasets. We calculated elapsed time in seconds and maximum memory used in megabytes for the diversity calculation step as follows. Using a fixed number of 60 669 starting genes, we increased the sample number from 10 to 130 using steps of 10, 20, 40, 60, 80, 100 and 130 for the same calculations. To test the difference calculation step, we used the same increasing sample numbers as in the diversity calculation performance benchmarks and tracked resource usage with the same bash shell command.The performance benchmark was done using a single core on a server with IntelWe used SpliceHetero (version 1.0), SEVA (available in the GSReg R library version 1.24.0) and Whippet (version 1.6.1) for comparison, analyzing the Salmon-based quantification results of the 17 control and 17 MDS samples from the SRP133442 dataset.We ran SpliceHetero with parameters -slb True and -prn 17. To prepare the input data, we first filtered the STAR splice junction files, keeping only those splice junctions that appear in the STAR index sjdbList.fromGTF.out.tab file and had at least one uniquely mapping read. Then, we converted the filtered STAR junction files to BED6 format, and sorted the output file. Finally, we ran SpliceHetero on these sorted BED files and we annotated the output splice junction level entropy values with the GENCODE v34 transcriptome annotation. Finally, we averaged these entropy values across genes and used these values for further comparison.We ran SEVA in RStudio Server (R version 4.0.2) using the GSReg SEVA function from the GSReg library with the parameter single.strand.genes.only set to FALSE and used the Bioconductor annotation database TxDb.Hsapiens.UCSC.hg38.knownGene (version 3.10.0) for gene annotation. To prepare the input data, we first performed RPM (Reads Per Million) normalization on the STAR splice junction files considering uniquely mapping reads only. We used the SEVA gene-level E1 and E2 variance estimates for further comparison.We ran Whippet with default parameters. To prepare the input data, we converted the raw fastq files to have only a single ‘+’ character on every third line. Then, using Whippet’s whippet-index.jl script, we created an index file based on the GENCODE v34 transcriptome annotation gtf file, and ran Whippet’s quantification algorithm with the whippet-quant.jl script on the modified fastq files with the additional -biascorrect parameter. Finally, we compared the MDS and control samples using Whippet’s whippet-delta.jl script. Finally, we averaged Whippet’s splicing event level entropy estimates across genes and used these values for further comparison.SF3B1 somatic mutations based on the original paper describing the dataset  separately with either a mean diversity increase or decrease between the SF3B1 mutant and SF3B1 wild-type groups. We used the bone marrow marker gene sets originating from  the package calculates a diversity value for each gene in each sample, using splicing isoform expression values and (ii) calculates differential diversity results between conditions. Diversity values from (i) can be used independently from step (ii) for custom downstream analyses or visualizations.The package can process R matrices and data frames with expression values, assay data from the SummarizedExperiment Bioconductor object, data from the DGEList object of edgeR  for a gene. As the maximum value of Shannon-entropy depends on the number of splicing isoforms for a gene, we implemented a normalized Shannon-entropy, that ranges between 0 and 1. This makes it possible to compare entropy values of genes with different number of isoforms. A 0 Shannon-entropy means a single splicing isoform is expressed from a gene, while 1 means all isoforms are evenly expressed, with no dominant isoform. The package can also calculate Laplace-entropy, a Bayesian estimate of the Shannon-entropy, where a pseudocount of 1 is added to the isoform categories for each sample.Shannon-entropy is a classic measure of uncertainty in information theory, in our case, ranging from 0 to logThe Gini-index is originally intended to represent income inequality in economics. It ranges between 0 and 1, where 0 means complete equality, i.e. all isoforms have the same expression, while 1 means complete inequality, with only a single isoform being expressed. The Simpson-index is a measure of diversity originally used in ecology to quantify species diversity. A 0 Simpson-index means low diversity, i.e. one dominant isoform, while 1 means high diversity, where all isoforms have the same expression. Similar to the nonnormalized Shannon- and Laplace-entropy, the maximal value of the Simpson-index depends on the isoform number. The inverse Simpson-index starts at value 1, and higher values mean greater isoform diversity, the maximal value also depending on isoform number.The calculate_diversity function returns a SummarizedExperiment object, that contains the gene-level splicing diversity values, together with gene names, sample ids and metadata information, including the method used, and if normalization was applied. The function removes genes with a single isoform and adds an NA value for genes where the expression of all isoforms is 0 in a specific sample, and a meaningful diversity value is impossible to calculate.2 fold changes in diversity can be calculated using the mean or median values across conditions. The function returns the mean or median for both conditions, the difference of means or medians, and their log2 fold change.Users can calculate splicing diversity changes between two conditions using the calculate_difference function. Accepted input formats are R data frames or a SummarizedExperiment object. In the case of a data frame, gene names must be present in the first column, and splicing diversity values in all additional columns. Differences and log2 scale ratio. However, in the case of values not bounded in  we suggest using the log2 fold change.Caution must be taken when choosing and interpreting a difference metric for a specific diversity type. The normalized Shannon-entropy, the normalized Laplace-entropy and the Simpson-index are all bounded in  and as transcriptomic diversity increases their value also increases. In contrast, while the Gini-index is also bounded in , its value decreases as the transcriptomic diversity increases. Finally, the inverse Simpson-index and the nonnormalized Shannon and Laplace-entropy are not bounded in . The inverse Simpson-index starts at 1, while the nonnormalized entropies start at 0, and their maximal value depends on the isoform number. Therefore, we recommend using the mean or median difference for values bounded in  not to over-emphasize relatively small changes magnified by the logP-values, together with the false discovery rate (FDR) corrected ones. It automatically excludes genes from the significance analysis, where some of the sample diversity values are missing and the total number of samples is insufficient for significance calculation.Statistical significance of the changes can be assessed using a Wilcoxon-test or sample label shuffling and the function returns the Using the reprocessed RNA-seq datasets, we investigated the effect of gene expression and isoform number on the various diversity metrics. Using the same datasets, we assessed the effect of different transcript quantification tools on diversity metrics. We calculated the naive normalized entropy and the Gini-index for all genes across all samples and calculated the Spearman-correlation of these values between methods for all samples. To further investigate how quantification tools, and transcript expression levels influence diversity calculation, we selected a set of CD34+ hematopoietic stem cell marker genes from the (Finally, we investigated the effect of removing an increasing number of transcripts with low expression from our analysis. Using the Salmon expression estimates from the reprocessed datasets, we carried out a benchmark, investigating the total runtime and memory consumption of SplicingFactory using a single CPU core. We also tested the total running time for the same setup, and results are shown in In addition to the performance benchmark, we compared the results of our tool to three additional tools characterizing splicing diversity. Using the Salmon expression estimates for the 17 control or 17 MDS samples from the SRP133442 dataset, we focused on comparing the actual gene and sample level diversity metrics of SpliceHetero, SEVA and Whippet, if available to our tool.We used the gene-level averaged entropy estimates of SpliceHetero of the MDS samples and calculated their correlation with our diversity metrics for the same samples. Results are shown in SF3B1 mutated and wild-type samples from the SRP149374 dataset and results are shown in Finally, using the Salmon expression estimate based results, we investigated differences in diversity between splicing factor mutated and nonmutated MDS samples. We compared the ABCC5, ACOT13, DCAF16, MPC2, MRPS21 and TMEM14C. ABCC5 is described as differentially spliced in SF3B1 mutated breast cancer (MRPS21 is described as a gene, whose upregulation is associated with poor response to azacytidine in MDS and related cancers (SF3B1 mutations, cancer or hematopoiesis for the other four genes.The six genes detected by all three methods as significantly changing were SF3B1 mutated samples compared to wild-type, and checked their overlap with the (SF3B1 mutations are known to be associated with impaired erythropoiesis (SF3B1 mutations. Mutations in the yeast ortholog of SF3B1 is known to alter the fidelity of branch site selection (SF3B1 in human cells might change the splicing regulation of specific genes, leading to changes in the otherwise coordinated expression of specific splicing isoforms.Finally, we chose the full list of differential diversity genes detected by the normalized naive-entropy metric, to investigate them further. We separated the 97 genes into two lists, with increased or decreased diversity in the In this study, we have developed a package called SplicingFactory that enables the analysis of splicing isoform diversity in biological samples and between different conditions. Our tool has several benefits. It integrates into the Bioconductor R package ecosystem, and uses standard data input and output structures, facilitating analysis and integration into bioinformatics pipelines. SplicingFactory is able to process expression quantification results from a full transcriptome annotation and a large number of samples using limited hardware resources and completes in a reasonable amount of time. The tool can process any kind of expression value, although values not normalized for transcript length are not recommended. It implements seven different diversity metrics, and methods to test differences between sample groups. As our tool provides gene and sample diversity values, it can be the basis of independent downstream analyses, not anticipated by us.We demonstrate the usefulness of our tool by analyzing a set of CD34+ control and MDS RNA-seq samples. Most MDS patients have at least one oncogenic mutation, with splicing factor mutations occurring in >50% of patients (SF3B1 mutations. Some of these genes are already known to be involved in the pathogenesis of MDS, and they are enriched for specific marker gene sets, involved in hematopoietic differentiation.Mutations in spliceosome genes might act as amplifiers of splicing noise, and lead to large fluctuations in complex GRNs, altering cell fate, pathogenicity or disease penetrance (Additional analyses are needed to understand the role splicing diversity in cellular differentiation, development and disease, and we hope our tool will facilitate them.This work was supported by the Semmelweis University Directorate of Innovation, Hungary [STIA_18_KF to E.S.] and the National Research, Development and Innovation Office, Hungary [FK-132666 to E.S.].P.S. is an employee of Turbine Simulated Cell Technologies Ltd. T.P. is an employee of Covance Inc.https://www.ncbi.nlm.nih.gov/sra, and can be accessed with the SRP133442 and SRP149374 identifiers.The data underlying this article are available in its online supplementary material or in NCBI SRA at btab648_Supplementary_DataClick here for additional data file.