Untargeted metabolomics seeks to identify and quantify most metabolites in a biological system. In general, metabolomics results are represented by numerical matrices containing data that represent the intensities of the detected variables. These matrices are subsequently analyzed by methods that seek to extract significant biological information from the data. In mass spectrometry-based metabolomics, if mass is detected with sufficient accuracy, below 1 ppm, it is possible to derive mass-difference networks, which have spectral features as nodes and chemical changes as edges. These networks have previously been used as means to assist formula annotation and to rank the importance of chemical transformations. In this work, we propose a novel role for such networks in untargeted metabolomics data analysis: we demonstrate that their properties as graphs can also be used as signatures for metabolic profiling and class discrimination. For several benchmark examples, we computed six graph properties and we found that the degree profile was consistently the property that allowed for the best performance of several clustering and classification methods, reaching levels that are competitive with the performance using intensity data matrices and traditional pretreatment procedures. Furthermore, we propose two new metrics for the ranking of chemical transformations derived from network properties, which can be applied to sample comparison or clustering. These metrics illustrate how the graph properties of mass-difference networks can highlight the aspects of the information contained in data that are complementary to the information extracted from intensity-based data analysis. The number of features is usually much higher than the number of samples, and consequently, many are highly correlated, making the subsequent statistical analysis challenging  is a common technique used in metabolomics that allows thousands of metabolites to be detected in a single run. MS-based metabolomics data require several steps of preprocessing that may comprise normalization, transformations, and scaling. These steps aim to highlight the biologically important information in the signal intensities within the data while reducing the impact of undesired variation . For insllenging . Furtherllenging . Even ifllenging , dependillenging .In general, untargeted metabolomics’ data analysis focuses on two-dimensional numerical data matrices derived from intensity data to obtain a chemical global characterization of a biological system  or more 2 after substitution of –H, hydrogen atom, by a –CH3 group) would be represented by a mass-difference of 14.01565 Da , together with the ability to resolve isotope ratios, an elemental composition formula can often be assigned to the detected mass features based on possible formulas computed from specific rules for heuristic filtering such as the seven golden rules . These f01565 Da . MDBs caassigned . Ultrahinetworks  to reprenetworks , such asnetworks .MDiNs are one of the major types of experimental networks used for metabolomics data analysis  and haveDespite being originally aimed at high-resolution metabolomics data obtained in instruments such as FT-ICR or Orbitraps, MDiN-based molecular formula assignment was used for lower resolution LC-MS data if the right steps were taken, such as starting formula propagation from high-confidence annotated metabolites .via a mass-difference enrichment analysis to test MDB over- or under-representation in the edges connected to a set of nodes of interest compared to their overall prevalence in the MDiN , as the basis for class discrimination and feature importance assignment. Our fundamental assumption is that the set of detected metabolites and their possible chemical connections are a characteristic of each sample in a study. As a consequence, the graph properties of MDiNs must also be a characteristic. sMDiNs are built based on the occurrence of spectral features, disregarding intensity information prominently used in common data analysis workflows, taking advantage that the former is less prone to variation. Graph analysis methods fulfill the same role as the data pretreatments applied to two-dimensional numerical data matrices. However, instead of focusing on maximizing the contribution of significant information in the signal intensities, these methods can focus on different aspects of the network such as node centrality, network topology, and edge properties, and thus, they may provide different and complementary results and insights. Furthermore, feature importance assignment from sMDiNs can be based on this network characterization with the potential to be very versatile.To demonstrate the use of sMDiNs for class characterization and discrimination, we performed an empirical comparison of the performance of unsupervised and supervised analysis methods applied to profiles associated with the graph metrics of each sMDiN. The performance of these methods is compared with the performance obtained with the same datasets by applying a traditional intensity-based pretreatment, representing the typical metabolomics data analysis preprocessing pipelines. We conclude that sMDiNs keep essential information for class discrimination and do not degrade the performance of statistical methods, especially when node centrality analysis was applied, for both low and high sample number per class, and two-class and multiclass datasets. Furthermore, we show how further feature importance assignment, such as weighted MDB impact, can provide extra insights regarding our data and can complement the discrimination analysis. On the basis of this work, we believe that network analysis methods have the potential to extract complementary meaningful information from ultrahigh-resolution metabolomics datasets.Five data matrices were built from experimental untargeted metabolomics data with different numbers of features and missing-value abundance. These matrices (datasets) were based on three ultrahigh-resolution metabolomics studies: two obtained from Fourier transform ion-cyclotron resonance mass spectrometry (FT-ICR-MS) instruments, with different mass accuracy, resolution, and sensitivity, and one obtained from an Orbitrap instrument after hydrophilic interaction chromatography (HILIC).Vitis varieties susceptible or resistant to oomycete/fungal infections  were retained. Furthermore, the GD types matrix was generated for the assessment of the performance of supervised methods in two-class problems by changing the dataset class labels in GDc2 according to whether grapevine is from Vitis vinifera or wild Vitis (non-vinifera) species.The “grapevine dataset” (GD) was built from published data related to a study of the metabolome differences observed in fections  and openfections . Metabolfections . Data alfections , as prevfections . From thYD) was built from data related to a study to discriminate single-gene deletion yeast mutants  with FT-ICR-MS . The Senior and Lewis MetaboScape filter and the heuristic element count probability were applied. When the same formula was assigned to two different bucket labels, these were joined together. Data matrix YD was constructed by retaining features that occurred in at least two samples.The “yeast dataset” (T-ICR-MS  and openT-ICR-MS . In brieT-ICR-MS  – wild tT-ICR-MS . From thT-ICR-MS  and YeasT-ICR-MS  databaseHD) was constructed from data obtained from a study of the preoperative metabolic signatures related with prostate cancer recurrence or remission after a radical prostatectomy, obtained in an Orbitrap instrument  were treated as having the same mass value.The “human dataset”  characteristics is described in An overview of the five datasets  whereas the HD dataset was normalized by probabilistic quotient normalization  was applied independently to all datasets. Missing values were imputed either by replacing them with one-fifth of the minimum of nonmissing values in each sample (1/5 min) or by random forest (RF) missing-value imputation . The numlization  using thIn all of the results of this study obtained with the IDT-treated datasets, we report only the best value of the downstream performance methods obtained with the IDT using either one of the two of the missing-value imputation procedures.a priori, and it is indicated in 3H and corresponds to the addition of a –PO32− group plus 2 H+  while replacing an H atom. All common elements in metabolites  are represented in at least one of the selected MDBs. For the YD dataset, two extra MDBs were considered, representing either direct or indirect reaction mechanisms associated with glycation, leading to carboxymethylation and carboxyethylation  were excluded.The generated sample MDiNs were analyzed using six different metrics. For each combination of dataset and metric, a data matrix could be derived for profiling each sample with that particular graph metric. These data matrices are amenable to be analyzed by conventional statistical methods in a fashion identical to the methods employed with intensity-based data.Three node centrality metrics were used:• Degree: the number of connections with neighboring nodes.• Betweenness centrality.• Closeness centrality.In d node u .m/z peaks (network nodes) as features, with their values in each sample being the respective value of the centrality metric in the corresponding sMDiN.The resulting data matrices maintained the Two metrics focused on quantifying the influence of each MDB in the construction of the sMDiNs were also employed: the MDB impact (MDBI) and weighted MDB impact (WMDBI). These metrics were inspired by a similar concept introduced by Moritz and coworkers to identify metabolome differences and over-representation of reactions between two gray poplar genotypes . MDBI isThe last analysis method was the graphlet correlation distance, which includes the 11 nonredundant orbits of up to four-node graphlets (GCD-11) to express the network topology . GraphleVitis versus Vitis vinifera plants in GD types, by samples from patients with cancer recurrence versus remission in HD, and by the replicates in the remaining datasets, are the “ground truth” of correct clusters and allow the use of ground-truth-related metrics of clustering performance.The discriminatory power based on the differences between sMDiNs was assessed by comparing the effects of the performance of clustering and classification methods on the data matrices obtained from the network analysis methods on sMDiNs and on the intensity-based pretreated data. Two typical types of unsupervised methods were applied: agglomerative hierarchical clustering (HCA) and K-means clustering. Clustering performance was assessed by the methods’ ability in correctly clustering samples belonging to the same class. These classes, as defined by wild Agglomerative hierarchical clustering analysis with unweighted pair group method with arithmetic mean (UPGMA) linkage method and Euclidean distance was performed on each IDT-treated dataset and the matrices obtained from each sMDiN network analysis metric. Three metrics were used to express clustering performance. The “correct clustering” percentage is defined as the percentage of the classes whose samples all clustered together before clustering with other samples or already-formed clusters in the agglomerative procedure. The “discrimination distance” is defined based on the average of “class discrimination distance.” For each class, if it is “correctly clustered” (defined as in the metric before), the discrimination distance is the distance between the node that includes all samples of the class and the next closest node (including those samples) in the agglomerative procedure, normalized by the maximum distance of any pair of nodes in the clustering; if not, it is 0. The “correct first cluster” percentage is defined as the percentage of samples whose first time that they clustered was only with sample(s) from its own class.GD, 5 in YD, and 2 in GD types and HD). Owing to the randomness of the initial centroid assignments (GD types and HD) that have a larger number of samples per class, only the correct first cluster percentage and Rand Index were considered. This choice is justified by the higher likelihood that clustering percentage and discrimination distance will be close to zero because a single sample behaving as an outlier can make all other samples of a class being considered not correctly clustered.K-means clustering analysis was applied using the Euclidean distance. The cluster number was equal to the total number of classes in each dataset  were chosen as classifiers for the comparison between the IDT and several sMDiN graph properties. The classes for prediction were defined as identical to the ground-truth groups of the clustering analysis. The performance of the classifiers was assessed by their predictive accuracy. lidation .HD dataset, the results presented are the average results of the 20 iterations.For each classifier, 20 iterations of different cross-validation folds were performed. To avoid data leakage, for models based on the IDT, for each cross-validation fold and each iteration, the train and test data were treated independently by the same transformation pipeline. For the treatment that included RF missing-value imputation, the training and test sets were filtered to only include features appearing in at least two samples of the training set. Features of the test set occurring in only one sample were imputed by one-fifth of the minimum, instead of RF imputation. Features that did not appear in the test set were added to this one with the minimum value appearing in the training set. In the case of WMDBI, as it requires previous node degree importance assignment, these were computed from RF models using only the training set of that iteration/fold combination. The remaining sMDiNs metrics are a characteristic of each sMDiN, so these procedures to avoid data leakage do not need to be applied. Except for the GD types and HD), receiver operating characteristic (ROC) curves were computed for the IDT transformed data and for every sMDiN analysis with fivefold stratified cross-validation.After the optimization of the number of trees to 100, RF classifiers were built with the scikit-learn RandomForestClassifier object constructor, leaving other parameters as their default values. The gini importance of each feature was calculated for each model . For theGD and HD datasets and 6 for the YD and GD types datasets after optimization based on maximizing Q2, that is, minimizing the predictive residual sum of squares . For the matrices obtained from MDBI and WMDBI sMDiN analysis, four and six components were used, respectively. The data matrices obtained from the network analysis of sMDiNs were auto-scaled during the development of the PLS-DA models, whereas IDT-treated data were already scaled. For multiclass problems, one-hot encoding was used to encode class membership and the decision rule for class prediction was employed to assign to samples the class corresponding to the maximum value in ypred of the PLS output. For two-class problems, class membership was coded as 0 or 1, with 0.5 threshold for decision. For both classifier methods, permutation tests (with 500 iterations each) were performed to further assess model significance.PLS-DA classifiers were built using the PLS2–NIPALS algorithm implemented in PLSRegression of scikit-learn . The numhttps://github.com/ftraquete/paper_sMDiN.Data pretreatments, analysis of the sMDiNs, clustering methods, and classifiers were implemented in Python language version 3.9.7 using the following packages: pandas version 1.4.1 , numpy vOne MDiN was built from each benchmark dataset using the MDBs indicated in YD dataset, with a close-up on its most populated area, is depicted in The characteristics of the MDiNs derived from the five benchmark datasets are shown in HD sMDiNs were denser because of the high number of features (2 (methylation), H2 (hydrogenation), and O (oxygenation and hydroxylation)  , which aGDg2, GDc2, and YD) and two-class problems with a high number of samples per class (GD types and HD) and also obtained in different high-resolution instruments in order to empirically test the use of sMDiNs against an array of different types of metabolomics data, easier or harder to discriminate.To assess the viability of using sMDiNs as the information basis for class discrimination, the performances of two clustering methods (HCA and K-means) and two classifiers (RF and PLS-DA) trained on data obtained from the application of different network analysis and with data obtained with IDT were compared. The benchmark datasets included three examples of low sample number per class and more than two classes  for all methods. Thus, despite the better performance of IDT, K-means clustering for these two larger datasets with two groups cannot cluster well the samples into the two expected clusters.For HCA, in the case of Focusing on the other node-centric network methods, closeness centrality led to results only slightly below or equal to those of degree analysis, slightly outperforming it on occasion, whereas betweenness centrality resulted in considerable worse performance. Considering all node-centric analyses employed, degree stands out as the best and the less computationally expensive to calculate.YD) in MDBI and 60 in GCD-11 that might not be enough to discriminate the 30+ samples of GD data. Nevertheless, the fact that GCD-11, which focuses on network topology (60 features), performs poorly in comparison to MDBI (15 features) indicates that the overall topology of the sMDiNs might not be sufficiently distinctive for group discrimination. In contrast, WMDBI, a modified MDB impact metric that weights the edges based on the importance of the nodes it links, performed considerably better than MDBI and often similar to degree or closeness. It is worth noticing that the importance of the nodes was estimated by their gini importance in a RF (supervised) model based on the data of the degree analysis. We followed the assumption that the most important nodes from the degree analysis will be those that are more characteristic of the different classes. Thus, the edges of these nodes will also be more important and will then be given a higher weight. Degree profiles led to the best results, showing that they contain significant information for profiling. Clustering based on WMDBI is thus not the result of a purely unsupervised analysis, which might explain the very good performance. Despite the encouraging results with clustering, supervised methods are expected to be more appropriate to assess the relative merits of WMDBI.Considering global network characteristics, both MDBI and GCD-11 resulted in subpar clustering discrimination in comparison to the degree profiles, although, in general, MDBI leads to better results compared to GCD-11. A reason for this might be the aggressive truncation of features to 15  analysis of the sMDiNs sometimes even slightly outperformed the intensity-based pretreatments, being the best of the six graph properties compared here.GD types and HD illustrated the use of sMDiNs as a basis for class discrimination with two-class problems, a common scenario in metabolomics data analysis, and the remaining examples were multiclass. Performance was assessed by the model’s predictive accuracy. The classifiers chosen were RF and projection in latent structures–discriminant analysis (PLS-DA) due to their popularity in metabolomics data analysis. These classifiers were applied to data matrices obtained from IDT and the six network analysis methods. Average accuracy results are shown in HD dataset, owing to its large sample size, results shown are based on the model’s prediction accuracy on a test set (30% of samples).We compared the predictive performance of different classifiers applied to the benchmark datasets, considering the classes as target labels. GDg2 or HD), and followed trends alike those observed in clustering analysis. Classifiers developed for node centrality sMDiN methods performed better than those for global network characteristics (MDBI or GCD-11). RF classifiers from betweenness centrality profiles had comparable performances to the other centrality measures, but the corresponding PLS-DA classifiers show a sharp drop in performance . Degree profiles are just slightly better than closeness profiles in general, while having sometimes marginally lower accuracies than closeness centrality.The RF and PLS-DA classifier performance results were similar, with sometimes PLS-DA models leading to noticeable better accuracies , and HD. In fact, for GD types and GDc2, most models based on network metrics outperformed those based on IDT-treated data. For PLS-DA, however, IDT and degree analysis led to more similar accuracies, being nearly identical for most datasets. Interestingly, only the PLS-DA model built from closeness centrality achieved perfect accuracy for GD types.The RF accuracies of IDT-based models are lower than those of PLS-DA, except for the GD types and HD datasets showed the good performance of the network analysis, with degree slightly outperforming the IDT for GD types and IDT having slightly higher area under the curve (AUC) than the degree analysis . This also shows the potential of articulating multiple sMDiN metrics that look at different data aspects to improve our findings showcasing the versatility of this analysis. Furthermore, despite underperforming IDT-based and node centrality-based models, the decent performance achieved using WMDBI with only 15 or 17 features revealed that discriminatory information is being compiled into these features. Because sMDiNs have discriminatory information and these results showed that this information content is embedded in WMDBI analysis, we believe that it is viable to use this metric as a means to perform feature importance analysis to rank chemical transformations for the purpose of using their importance profile in sample discrimination.p-values were equal or below 0.05.Permutation tests were made to assess the significance of the accuracy of the models . In all GD datasets , easy problems as in YD, and two-class problems such as GD types and HD datasets. Therefore, there is useful and discriminatory information carried over and highlighted in sMDiNs, which makes its use in this context viable when compared to the workflows that start with two-dimensional numerical intensity matrices.The main conclusion is that sMDiNs can successfully be used as a basis for class discrimination not impairing and sometimes improving the performance of clustering or PLS-DA model classification and improving the classification by RF for all benchmark datasets. This was observed for hard problems as in the 2) is expected. For simplicity, so that the differences in the classes may be easily observable, the YD dataset was used as an example due to the smaller number of samples and classes. The importance of each of the MDBs estimated by their gini importance  and CO (formylation) also ranking high. On the other hand, with the least important MDBs for both methods (CHOH or O(–NH) for example), there is no stratification of values per class and a lack of consistency among samples of the same class. MDB impacts can be interpreted as reactions being over- or under-represented in some strains, that is, for example, phosphorylation-related reactions being under-represented in the yeast mutant strains from the WT, especially in ΔENO1 and ΔGLO1. In contrast, the prevalence of reactions represented by the least important MDBs was not altered significantly by the single-gene deletions. This helps the characterization of the strains and may be used to guide future studies, for example, in this case, to justify the under-representation of phosphorylation reaction in the single-gene deletion mutants. The use of this approach in metabolomics data analysis can become useful in two-class problems where differential representation can be more easily observable.The MDB with the greatest impact for class classification in both metrics is “POrylation . Thus, trylation . Other MYD dataset because they are related to protein and phospholipid glycation, specific for phosphatidylethanolamines  or indirectly after a set of different reactions (m/z peaks), whereas global analysis will focus on the general and overall characteristics of the system.The “carboxymethylation” and “carboxyethylation” MDBs were added specifically for the olamines . Glycatiolamines . Despiteeactions . It is eeactions . Althougeactions . This anab initio by considering the detected metabolites as nodes with edges established if the difference in mass between nodes corresponds to one of the MDBs representing types of chemical transformations, generating networks akin to metabolic networks without a priori information regarding the system. The relevant characteristics of data here are how each detected feature might relate to other detected features. Therefore, unconnected nodes are uninformative.The use of MDiNs as a basis for class discrimination and identification of important features relies on the commonly accepted concept that the detected metabolites identified with high-resolution methods are characteristic of a biological system. Here, the set of spectral features is used to build a mass-difference network. This representation, where each sample has a different MDiN, is a basis for downstream data analysis. This approach is suitable for untargeted metabolomics, although it requires high-resolution and high-accuracy mass-spectrometry—based data. As stated, sMDiNs are built Several high-resolution metabolomics datasets were used as benchmarks to test the hypothesis that data representing profiles of graph properties can be used for clustering and classification. As these datasets come from different sources and different instrumental platforms and workflows and have different levels of class overlap, number of samples, and classes, they are real-life scenarios of data quality levels in current high-resolution metabolomics.m/z alignment or formula assignment by falling back on comparing the global sMDiN characteristics such as MDBI that, although less efficient, may still provide meaningful insight for sample and class characterization. As discussed later, this versatility comes at the cost of some additional methodological complexity.This study indicates that using sample MDiNs as a data basis for class characterization and discrimination, instead of the usual two-dimensional numerical data matrices, is viable in metabolomics data analysis and capable of offering and highlighting complementary information to traditional workflows. Node-centric network metrics generated profiles that resulted in similar or better class discrimination in downstream statistical analysis methods in comparison to IDT, which represents traditional methods in this study. The network analyses of the sMDiNs are an alternative to the data pretreatments of numerical matrices. Perhaps the greatest advantage of sMDiNs is the versatility that it provides. Although all traditional pretreatments are alternatives with a common objective of highlighting relevant biological information contained within the data intensity values while reducing the effect of undesired variation , networka priori choice of MDBs, they are not restricted by prior knowledge of the different metabolic pathways that make up the metabolic networks, knowledge of which may still be very incomplete in less studied biological systems . It is, therefore, robust to data leakage in training–testing procedures: the possible introduction of feature distribution information, a pitfall of the traditional preprocessing procedures, does not happen with sMDiNs. Thus, for model validation, the care to apply the preprocessing pipeline to the train and test sets independently is not necessary.Another advantage of sMDiNs is that they are already used for formula assignment . Thus, fIn this study, sMDiN application was benchmarked with examples of datasets obtained in different high-resolution MS instruments and with different number of samples, classes, and features, leading to good performances in the application of the clustering and classifier methods.GDc2, features that did not appear in at least two samples of the same class were excluded (a stricter criterion).The derivation of sMDiNs from spectral data is difficult if these data are not obtained with high-resolution metabolomics instruments and methodologies. High resolution is usually associated with high mass accuracy, an instrumental capability that is paramount to establish accurate mass differences to reliably find the connections that correspond to a given MDB . In thisThe construction of each sMDiN from direct infusion data is not able to incorporate differences in isobaric compounds or functionally different isomers, and each edge in the network could arise from any of the multiple possible specific reactions. Chromatographic separation data can, in principle, inform on the chemical structure behind a given mass value and can help in curating the network, allowing for the separation of nodes with the same mass. This cannot, however, be performed in general for most nodes of the sMDiNs, and the structural changes in the networks are not expected to be significant after the incorporation of retention time information. Even without using a finer network structure in our direct benchmark datasets, we were able to demonstrate that the graph metrics carry enough information to make clustering or classification procedures successful.a priori from a list of predefined masses or by the most common mass differences present in the dataset capable of being assigned a specific chemical transformation (2 change) leads to the same change of hydroxymethylation (change of CHOH). Furthermore, specific sets of chemical transformations may be added based on the biological systems analyzed and the purpose of the experiment. Thus, the number of MDBs and the tolerance for the mass-difference error allowed in MDiN construction should also be set based on the particular datasets and instrumental capabilities to prevent the sMDiNs to become too dense (their topology becomes too similar among the different samples) or too sparse (where not enough connections are established to differentiate graph properties). However, the choice of MDBs for a specific purpose must take into account the possibility of in-source dissociation of some compounds, leading, for example, to water loss, which corresponds to a dehydration MDB. In this work, we chose to restrict to only 15 MDBs  and should have some basis on observed chemical reactions. They can be chosen ormation . MDBs ma 15 MDBs  as a pro2 edge) and would represent metabolites with the same elemental formula. This could be the result of errors in the peak selection or alignment stages or because the mass error tolerance for building the MDiN was too high, for example. A possible solution would be to eliminate the edge with a higher associated error or to reduce error tolerance. Here, we demonstrate that it is possible for sMDiNs to be useful for class discrimination with high-quality and resolution metabolomics datasets with a simplistic application using a restricted list of MDBs, even without taking measures to increase sMDiN quality, showing the base potential of the methodology.With the parameter-tuning of mass error tolerance and choice of MDBs, primary MDiNs, like the ones in this study, can be derived. Extra steps may be necessary to increase the quality of the sMDiNs. For example, false positive edges can be established between two masses whose MDB does not correspond to their actual differences in elemental compositions. This is more likely to happen with higher masses, where the combination of possible formulas within a 1 ppm error margin increases exponentially . This caHD, some network analyses of a high number of samples might be computationally prohibitive.The next step is to carefully choose and apply different network analysis. Careful deliberation by the researcher regarding the network analysis methods that will be employed should be made because the information that they provide can be very specific, whether focused on node characteristics, local clusters, and edge properties such as MDBI or node properties like compound taxonomy. Thus, this methodology keeps class discrimination information while remaining versatile in the types of information that it can provide, which can be specific to the intention and information desired from the experimental work. Nevertheless, for larger datasets such as MDiNs lead to a very simple type of feature selection: rejection of unconnected, uninformative nodes in the MDiN. These tend to be approximately half to two-thirds of the dataset, resulting in a hefty filter process. This rejection retains features that (based on the MDBs selected) connect to at least another detected metabolite in the dataset. The possible side effect is the removal of some rarer and unorthodox metabolites that may exist and be informative. Further feature selection should not be made because every remaining node has an impact on the full dataset because it affects the network property values of other nodes. Thus, there are no counterparts to the types of feature selection normally used . Other feature selection procedures prior to building MDiNs should be carefully applied as to not exclude too many features as well as to not exclude informative features with plenty of missing values.m/z peaks) as nodes, establishing edges between nodes if their difference in mass corresponds to a chosen list of allowed chemical transformations that can occur in biological systems. Thus, they depict the chemical diversity of a sample building a network akin to a metabolic network, without the need for metabolite identification and mapping to the  known metabolic pathways. Sample MDiNs were built based on the features detected for each sample. Thus, differences between samples come from the difference in the set of features detected that dictate the nodes, edges, and other network properties to be analyzed, contrasting with the use of signal intensities by the two-dimensional numerical matrices. Graph property network analysis of the sMDiNs is a novel idea that we present as an alternative to data pretreatments but with the added benefit of being able to focus on different characteristics of the networks.The adoption of MDiNs at a sample level (sMDiNs) in the metabolomics data analysis framework was envisioned to be a viable alternative to the analysis of two-dimensional numerical matrices for biological system characterization and discrimination. MDiNs are built using the list of detected features (The four statistical methods employed performed consistently as well or slightly better with data from the degree analysis of sMDiNs than with data treated with common intensity-based pretreatments, showing that discriminatory information is retained in the sMDiNs. The use of a different network analysis for important feature assignment was also shown by highlighting weighted MDBI analysis on yeast data sMDiNs that revealed an under-representation of phosphorylation-like reactions in the single-gene deletion yeast mutants compared to the WT as an example of the possible versatility of the methodology.The procedure to build sMDiNs requires a high number of features and high-resolution data as well as a balanced missing-value occurrence. Thus, it is not generalizable to all scenarios for metabolomics data analysis. It can be efficiently coupled with formula assignment and feature selection methodologies based on the MDiN concept, which offers extra information for class discrimination and biological interpretation.On the basis of the demonstrated viability of the procedure as well as the additional information that can be gained from it, we propose the use of sMDiNs as a basis for the analysis of high-resolution untargeted data in metabolomics.


In RNA-sequencing studies a large number of hypothesis tests are performed to compare the differential expression of genes between several conditions. Filtering has been proposed to remove candidate genes with a low expression level which may not be relevant and have little or no chance of showing a difference between conditions. This step may reduce the multiple testing burden and increase power.We show in a simulation study that filtering can lead to some increase in power for RNA-sequencing data, too aggressive filtering, however, can lead to a decline. No uniformly optimal filter in terms of power exists. Depending on the scenario different filters may be optimal. We propose an adaptive filtering strategy which selects one of several filters to maximise the number of rejections. No additional adjustment for multiplicity has to be included, but a rule has to be considered if the number of rejections is too small.For a large range of simulation scenarios, the adaptive filter maximises the power while the simulated False Discovery Rate is bounded by the pre-defined significance level. Using the adaptive filter, it is not necessary to pre-specify a single individual filtering method optimised for a specific scenario.The online version contains supplementary material available at 10.1186/s12859-022-04928-z. In next generation RNA-sequencing experiments (RNA-seq), thousands of genomic features  are investigated to study differential expression levels among several experimental conditions. Often the number of replicates per condition is small. When testing each gene individually, multiple testing procedures have to be applied to avoid an increase in false positive results and the power to detect truly differentially expressed (DE) genes between conditions is often low.It has been shown that data filtering can increase the number of rejections or the power, respectively, of high-throughput experiments –3. FilteSeveral methods for filtering were proposed for RNA-seq data, e.g., filtering based on the maximum or the mean of the observed counts across all groups  for each gene, with varying thresholds for the selection. It has been shown that filtering should be based on blinded data, i.e., independent of the groups, to avoid inflation of the Type I error rate . HoweveIn this paper, we propose an adaptive filter for RNA-seq data which selects one out of several filtering methods aimed at maximising the number of rejections. In a large range of simulation scenarios, practically no impact on the False Discovery Rate (FDR) is observed, if several filters are considered and the filter leading to the largest number of rejections is selected. No additional adjustment for multiplicity for the number of considered filters has to be included. The only additional rule that has to be considered is that if the number of rejections is too small (and lies below a specified filter parameter), a previously defined reference filter has to be chosen for the final analysis. Otherwise the FDR may be inflated. In the next section, we show that no uniformly optimal filter in terms of multiple power exists and that the proposed adaptive filter leads to the largest power without inflation of the FDR for many different simulation scenarios. The merits of the new adaptive filter procedure are illustrated using simulated and real data. In the Methods section, we review the data processing steps for RNA-seq data and define the adaptive filter.We first perform simulation studies to compare filtering methods from the literature with regard to multiple power, defined as the proportion of correctly rejected false null hypotheses under all false null hypotheses, i.e. the proportion of correctly identified truly DE genes under all DE genes (hereinafter denoted as power). Since there is no consensus rule on the choice of the filtering process, we propose the new adaptive filter approach which searches for the best filter in terms of the number of identified genes (as described in more detail in the Methods section). We then analyse the impact of the adaptive filter on the multiple power and the FDR.In the simulation study, we consider experiments comparing two groups of independent samples with mentclass2pt{minimmentclass2pt{minimFiltering was performed in two steps: We first filtered the raw counts and removed genes with only zero counts in both groups. This filter is denoted as the basic filter. Based on the remaining genes, the data were normalised and for each gene a p-value for group comparison was calculated (see Methods section). Next, one of the filters listed in Table 2 was applied to the raw counts. The unadjusted p-values of the hypotheses selected by the two filters  were then adjusted for multiplicity. In our simulations we examined and discussed two methods for multiplicity adjustment, the local False Discovery Rates (lfdr) , 15 and All simulations were performed with R version 3.6.0 or higher . At leasWe first compare the individual filtering methods with regard to the power. A comparison with the adaptive filtering approach is reported in the next section.m, the proportion of no filtering is always 0.For several simulation settings, the power of each filter as a function of the proportions  of filtered genes among the set of genes selected by the basic filter is shown in Figs. entclass1pt{minimaIt can be seen that in many scenarios mean and max-based filters maximise the power, however, the optimal percentile percentage depends very sensitively on entclass1pt{minimam, 10m, and 50m for the lfdr method. As expected, power increases with sequencing depth. Optimal power values can be found for the zero and the max-based filter, the optimal percentile percentage decreases for an increasing sequencing depth from 0.24 to 0.12.The PROPER simulations show rather flat power curves near the optimum, the power is increased by 0.5 to 2 percentage points in comparison to the basic filter. As this simulation generates many zero genes, the advantage of the basic filter compared to no filtering is very pronounced. The optimal power is observed with the max-based and the zero filter and lies between the 10% ) as described in the Methods section), can be found in the Additional file l was introduced and a reference filter was defined in advance. l was set to 5, i.e. in case of 5 or less rejections, the reference filter and not the adaptive filter is applied. Otherwise an inflation of the FDR under the global null hypothesis may be observed. In our simulation study, the Jaccard filter was specified as the reference filter.Since none of the considered filters is uniformly optimal, the adaptive filter may be a useful option to avoid the risk of choosing an inefficient filter in terms of power. In this section, the simulation studies presented above are extended and the performance of the adaptive filter is investigated. The adaptive filter incorporates all filters as described in Table m, or m, or Figures entclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimam and It can be seen that the improvement in power for the adaptive filter is only moderate compared to some of the individual filtering methods with a large number of rejections. However, the adaptive filter gives a larger power in all scenarios while individual filters perform well in tailored settings only. For instance, the Jaccard filter shows a good performance in a lot of scenarios, while for PROPER simulations with sequencing depth = 5l is 5 or larger. This holds also under the global null hypothesis  and a rIn the reanalyses first all genes with only zero counts were removed (basic filter). Second, the filtering strategies as described in Table entclass1pt{minimam genes, for other filters based on the non-zero genes) for multiplicity adjustment with lfdr . In. In22]).It can be seen that the improvement in power for the adaptive filter is only moderate compared to some of the individual filtering methods with a large number of rejections. However, which filtering methods and thresholds lead to large power values is in practice unknown and compared to any particular filtering method the increase in power by the adaptive filter can be very large. The adaptive filtering method selects the best filtering method (in terms of rejections) without the need to pre-specify a single individual filtering method and therefore avoids the choice of an inappropriate filter leading to a large loss in power. We suggest to consider each filtering method with several thresholds covering a broad range in the adaptive filter. For example, in the simulation study we included the thresholds given in Table 2.R as the criterion for the adaptive filter. This criterion may be replaced by, e.g., a post-hoc power estimator . m we have no theoretical proof that FDR control of the procedure can be guaranteed. We show by simulations, that for finite l, FDR control can be achieved by choosing a larger threshold l if m is small. Exceptions were observed for some scenarios of the SimSeq, PROPER and NB distributed simulations. However, for these, some of the (non-adaptive) filters do not control the FDR, and thus also the adaptive filter inflates the level (as explained in the Discussion). Note that choosing a larger threshold l comes at the cost that the procedure becomes less adaptive, because only filters can be chosen, where more than l hypotheses are rejected. While we cannot provide analytical formulas to guide the choice of l, we show by simulations that, in the considered scenarios, for The proposed adaptive filtering procedure has practically no impact on the FDR in many simulation scenarios, if the filter leading to the largest number of rejections is selected and no additional adjustment for multiplicity for the number of considered filters is performed. If, however, the proposed adaptive procedure rejects only a small number of hypotheses, the researcher has to stick to a previously defined reference filter to avoid FDR inflation. For small number of hypotheses n independent samples with m null hypotheses i in sample j, g, i-th gene. For all simulated data sets, the trimmed mean of M-values normalisation method (TMM) was applied where reads are scaled by weighted log-fold-change values of a reference sample [i is defined as the posterior probability in a Bayesian mixture model that for hypothesis i the null hypothesis holds . Foons, see  was applons, see . Second,The order of data processing  is not definite. Normalisation is performed before the data analysis, yet, there are different approaches on when to apply the filtering step in the analysis pipeline , 28. In F different filters, l is specified.First, a set of F filters are applied. Each filter generates a set of candidate genes of different magnitude and for each set the data analysis is performed  has been chosen). Then, multiple testing procedures are applied for each of the F sets of p-values and the resulting numbers of rejected hypotheses f.For a given data set, the k is chosen where the largest number of hypotheses are rejected, To determine the definite final analysis, the filtering method The adaptive filter chooses the filtering strategy with the largest number of rejections; however, the multiple testing procedure - in our case lfdr or BH procedure, adjusts only for the number of genes but not for the number of filtering strategies. Thus, in principle, the FDR may be increased when the filtering method leading to the largest number of rejections is selected. However, we showed in the simulations, that the observed FDR applying the adaptive filter is below l rejections: Choosing We propose a new filtering strategy which combines several filtering methods:  the FDR , 37, focns apply , the BH ns apply ). Thus, rem 1 in ). In therem 2 in ).It has been shown  that forAdditional file 1. Additional information on simulation strategies and on real data is presented.Additional file 2. Extension of the simulation studies from the manuscript .Additional file 3. Data example for the R-code from Additional file Additional file 4. Example code for real data example for programming language R.


Cancers are genetically heterogeneous, so anticancer drugs show varying degrees of effectiveness on patients due to their differing genetic profiles. Knowing patient’s responses to numerous cancer drugs are needed for personalized treatment for cancer. By using molecular profiles of cancer cell lines available from Cancer Cell Line Encyclopedia (CCLE) and anticancer drug responses available in the Genomics of Drug Sensitivity in Cancer (GDSC), we will build computational models to predict anticancer drug responses from molecular features.We propose a novel deep neural network model that integrates multi-omics data available as gene expressions, copy number variations, gene mutations, reverse phase protein array expressions, and metabolomics expressions, in order to predict cellular responses to known anti-cancer drugs. We employ a novel graph embedding layer that incorporates interactome data as prior information for prediction. Moreover, we propose a novel attention layer that effectively combines different omics features, taking their interactions into account. The network outperformed feedforward neural networks and reported 0.90 for The outstanding results of our experiments demonstrate that the proposed method is capable of capturing the interactions of genes and proteins, and integrating multi-omics features effectively. Furthermore, both the results of ablation studies and the investigations of the attention layer imply that gene mutation has a greater influence on the prediction of drug responses than other omics data types. Therefore, we conclude that our approach can not only predict the anti-cancer drug response precisely but also provides insights into reaction mechanisms of cancer cell lines and drugs as well. Inherent complexity and heterogeneity of cancers make patients with same diagnosis respond differently to anticancer drugs, making cancer treatment difficult and intractable. In order to personalize cancer treatment, it is crucial to know drug responses of cancer patients, based on their molecular and clinical profiles. To address this, extensive patient drug screening is required to discover specific patterns of drug responses. Infeasibility of treating large populations of cancer patients has motivated researchers to collect large scale drug screening data on cancer cell lines. For example, Cancer Cell Line Encyclopedia (CCLE)  containsin silico from cancer cell lines: machine learning based approaches and network-based approaches. Machine learning approaches first extract features from multiple molecular measures  and then use classifiers or regressors such as support vector machines [There are mainly two types of methods for predicting drug responses machines , 4, elasmachines , and ranmachines  to make machines , 8. Netwmachines , 10. HowDeep learning methods such as neural networks have the ability to build complex and accurate models by learning from training data and been successful in many application domains . Lately,CCLE cell lines initially characterized by expression and genomic data have now been expanded to include genetic, RNA splicing, DNA methylation, histone modification, and mRNA expression data . IntegraOne of the contributions in our work is the integration of the interactome data while processing genomic features. The interactome provides prior knowledge of gene/proteins regulatory and physical interactions. Inspired by , we intrmRNA expression, mutations, CNV, RPPA expression, and metabolite expressions were used as input to the deep neural network (DNN). Data standardization and noise removal were performed to optimize the training process. For mRNA expression, RPPA and metabolite expressions, if certain expression had zero values across over 95% of the cell lines, it was removed from the dataset. Similarly, for gene mutations and CNV, if certain mutation type had no effect on over 95% of cell lines, it was also eliminated. For omics data using graph embeddings, the embedding layer size was same as that of the input layer. The size of the dense embedding layer for metabolites was 200. For mRNA expression and metabolomics datasets, the dense layer had size 64. For gene mutation and CNV datasets, the hidden sizes were 32. As for RPPA dataset, the hidden size was 128. The attention layer dimension was 110 and output layer dimension was 22. The layer sizes were empirically determined for best performance. We used Adams optimizer  to minimi and j and i and drug j, and We first experimented with individual omics datasets and processed them in feedforward DNN. Table ument}R2 . MSE is j across all cell lines. R2 indicates better model performance, and perfect prediction is achieved when R2 equals to one. When the model predicts all the outputs to be the average of true labels indiscriminately, R2 is zero. Moreover, it could also be negative occasionally, when the model performs even worse than producing averages all the time.MSE is a straightforward metrics for regression model evaluation, whereas its assessment can be significantly influenced by the scale of the target value which is drug response level measured by he model . \documeFor mRNA expression, mutations, and CNV data, we compared the results with graph embeddings and dense embeddings. Graph embeddings allow the use of functional relationships of biomolecules from interactome data as prior information. As seen from the table, use of graph embedding clearly improved the MSE and After measuring the individual models, we trained the models on whole dataset consisting of multi-omics types. During training the integrated model, the parameters in the embedding layer and dense layer were remained invariable, and only the attention layer’s parameters were learned. After removing the missing data points and keeping only the cell lines possessing all the omics features, our input dataset involved 522 cell lines in total. A three-fold cross-validation was implemented and the results of drug response prediction with fusion of all five-omics data are shown in Table To further explore the effectiveness of integrating different omics data, we performed ablation experiments by testing the performance of integrating all possible omics combinations. The results are displayed in Table m denotes the number of omics and Additionally, we preserved and analysed the attention weights of our models to demonstrate our findings above. Initially, the attention weights are in format of mentclass2pt{minimTable In order to evaluate the accuracy of our network further, we computed the rate at which the most effective drug predicted by our model in terms that the predicted drug response was in fact the recommended drug for that cancer cell type. To this end, we defined Top-1 and Top-3 candidates of drug predictions as the rates at which the true drug was predicted by our method as the topmost drug and within the top 3 drugs, respectively. Top-1 and Top-3 drug prediction accuracies of the network are given in Table To demonstrate the effectiveness of our approach, we compared it with Bayesian multitask multi-kernel learning (BEMKL), proposed by Ali et al. . BEMKL wWe compared our approach with DeepDR , a deep In this work, we investigated prediction of cancer cell lines’ responses to already available anti-cancer drugs by integrating deep learning and multi-omics approaches. The ability to predict drug response of a drug from a patient’s omics data enable drug repurposing and personalized treatment against cancer. We developed a DNN model capable of handling large and complex multi-omics data and integrate heterogeneous multi-omics information for drug response prediction. Our model was able to integrate heterogeneous multiple omics data effectively and to predict the most effective drugs and their activity against the patient’s specific cancer. Using our DNN model, we were able to achieve an There are numerous challenges in the implementation of DNN for drug response prediction. These include huge dimensionality of inputs, heterogeneity of inputs as data come from different omics platform, limited number of samples, and a requirement for large number of network parameters. This culminates in a need for a large number of samples for adequate training of deep neural networks. Previous attempts with DNN were limited to only two types of omics data and our model offer a general strategy to integrate any number of omics data types. In order to handle high dimensionality of omics data types, we proposed two embedding strategies - graph embedding and dense embedding. Furthermore, we demonstrated how graph embeddings enabled incorporation of interactome data. The attention layer offered an efficient mechanism for combining information from different omics data type. We employed attention mechanism only at the final layer. One can explore how attention mechanism can be employed at hidden layers as well. It is also noteworthy to explore how the number of trainable parameters can be further reduced. With decreasing cost of collecting omics data, there is need for novel computational techniques that can effectively integrate multi-omics data for downstream tasks such as personalized diagnosis and treatment.In a recent survey done to compare methods for drug prediction in cancer line few deep neural networks were featured in the comparative study . In thisThere were two novelties of our DNN model: (i) incorporation of functional interactions for processing mRNA expressions, gene mutations and CNV data, and (ii) use of an attention layer for combining embedding learned by networks of individual omics data. Our experiments show that the interactome data in the form of protein-protein interactions (PPI) improves drug response predictions. PPI data represents prior information among genes/proteins and explicit embedding of PPI significantly improves drug response prediction. The attention mechanism learns the weights for different omics data and improves the prediction accuracy. Our network outperforms feedforward neural networks without using graph embeddings or the attention layer.In addition, the observations from the ablation experiments and the attention score distribution reveal that, among the multi-omics data, Gene mutation data distinctly contributes more to the prediction of drug responses than other omics data. When Gene mutation is excluded, RPPA dominates the weight of prediction. Therefore, our proposed approach provides a great predictive power of anti-cancer drug responses, together with an insight of the potential reactions between cell lines and drugs.We downloaded mRNA expressions, genomic mutations, reverse phase protein array (RPPA) expressions, and metabolomics data from CCLE, and copy number variations (CNV) and half maximal inhibitory concentration (IC50) of drug responses from GDSC, and interactome data from HINTmRNA expressions: mRNA expression profiles in cells are measured by RNA sequencing technique in transcripts per million (TPM), which indicate how many genes actively transcribed in the cell. Earlier, analysis of RNA-seq data has revealed a comprehensive portrait of gene expressions in these cell lines [ll lines  and contll lines  for neurGene mutations: Gene mutations involve numerous variations, such as silent, missense, nonsense, deletion, insertion, splice site, nonstop, etc. Cancer development usually involves the accumulation of multiple gene mutations [utations . For exautations . We downCopy number variations (CNV): Copy Number Variation (CNV) records the number of copies of a gene in a cell and are structurally variant regions in which copy number differences have been observed between two or more genes, which are highly characteristics of cancer [f cancer . CNV havf cancer . CNV datRPPA data: Reverse Phase Protein Array (RPPA) is a high-throughput proteomics method that, comparing to mass spectrometry proteomics, has higher sensitivity for low-abundance proteins [proteins  and provproteins . RPPA daMetabolomics data: Metabolomics involves study of small-molecule biochemicals (metabolites) within a biologic system [c system . Certainc system . MetabolInteractome data: Interactome data such as protein-protein interaction (PPI) lend us an overview of interactions among biological processes. HINT database [database  consistsDrug response data: In the experiments of this research, IC50 values  were adopted to evaluate drug response levels. IC50 values were measured in uM and represented in log scale where lower the IC50 value, more effective the drug [the drug . We downFigure In order to incorporate the prior information from the interactome, embedding layer for mRNA expressions, mutations, CNV, and RPPA expressions used graph embedding while the metabolomics data goes through a dense embedding. The output of dense embedding layer is given byng layer  is givenThe graph embedding layer allows incorporating prior information about interactions among genes/proteins, underlying fundamental biological mechanisms of cancer . From thThe embedding layer is followed by one dense layer. The output of the dense layer for m omics data is given byd, dataset m, and feature i. Output layer weights W are defined byd and i will be the same. Then the k can be designed to be 1 when The dense layer outputs of different omics data are concatenated as z:entclass1pt{minimay is given byThe output


The extraction of novel information from omics data is a challenging task, inparticular, since the number of features (e.g. genes) often far exceeds thenumber of samples. In such a setting, conventional parameter estimation leads toill-posed optimization problems, and regularization may be required. Inaddition, outliers can largely impact classification accuracy.Here we introduce ROSIE, an ensemble classification approach, which combinesthree sparse and robust classification methods for outlier detection and featureselection and further performs a bootstrap-based validity check. Outliers ofROSIE are determined by the rank product test using outlier rankings of allthree methods, and important features are selected as features commonly selectedby all methods.We apply ROSIE to RNA-Seq data from The Cancer Genome Atlas (TCGA) to classifyobservations into Triple-Negative Breast Cancer (TNBC) and non-TNBC tissuesamples. The pre-processed dataset consists of  The extraction of novel information from omics data is challenging. Inparticular, classification based on transcriptomics data is hampered by a largefeature space and a comparably low number of individuals  during the classification process. Anovel approach for outlier detection by Lopes et al. , for example, applies a consensus approach that combines the inherentresidual measures of several classification methods to obtain a consensus ranking ofsamples in terms of their outlierness. Since feature selection and also outlierdetection methods are based on different assumptions, their performance also variesdepending on the specific characteristics of the dataset which they are applied to.Likewise, a comparison of different methods in an in silico study also depends to aconsiderable extend on the model which has been used for data generation, sinceevery method has its strengths and weaknesses and there is not a single bestsolution. The idea of Ensemble approaches is to combine several methods which returnthe same kind of output in order to increase accuracy and reduce the number of falsepositively selected features. It has already been shown that the sparse Ensembleapproach of Lopes et al.  achieves high accuracy in feature selection compared to other sparse androbust classifiers in settings where the number of outliers is low . However, in datasets with a larger proportion of outliers, these might havean impact on the classification, and thus on the results of outlier detection andfeature selection. Therefore, important features can be missed in the selection.In addition, transcriptomics data frequently contain erroneous or noisy values.Independent of whether these values are caused by measurement errors or inherentoutlying behavior, they can influence the classification process of all theremaining patients Combining the idea of an Ensemble approach with the need for robustness againstoutliers, we propose to use an Ensemble of robust sparse methods, which we nameRObust Sparse ensemble for outlIEr detection and feature selection (ROSIE). Thegeneral workflow of ROSIE (i) combines sparse and robust classification methods foroutlier detection and feature selection and (ii) performs a validity check in termsof altered data.18 (SPRM-DA or SPRM), Robust andsparse K-means clustering 20 (RSK-means), and Robust and sparse logistic regression withelastic net penalty 22 (enetLTS). For each method, a ranking of outlierness for allfeatures is obtained and combined to a single consensus ranking by calculating theRank Product (RP). Outlierness is subsequently assessed using the RP test. Bootstrapsamples drawn from the original dataset are used to verify results.To build our Ensemble, we selected three sparse and robust methods with freelyavailable implementations in R packages, to perform supervised (classification) andunsupervised (clustering) learning tasks: Sparse robust discriminant analysis withsparse partial robust M regression . ROSIE is indeed able to select features in a robust way. Morover, several ofthe selected genes have been associated with TNBC in other experimental and machinelearning contexts, which corroborates the biological significance of the genesselected by ROSIE.This pipeline is evaluated on simulated data. Results show that the procedureidentifies outliers reliably in different settings. Subsequently, ROSIE is appliedto a transcriptomic breast cancer dataset to differentiate triple-negative breastcancer (TNBC) from other breast cancer types (non-TNBC). TNBC is an aggressivebreast cancer subtype, with a marked heterogeneity and a poor survival, for whichthe selection of new biomarkers for the development of new targeted therapies is ofclinical relevance The Ensemble procedure is illustrated in The second part . Outliers were created in two differentways. First, a subgroup of samples was randomly selected and their labelsswitched. This reflects errors in the a priori classification. This wasperformed for 5% and 15% of the samples, respectively, leading to two datasets.Second, in order to mirror outliers in gene expression in the third dataset, 15%of the features were randomly selected and their standard deviation computed.Then, 5% of the samples were randomly selected and the values corresponding tothe selected features increased by three times the respective standarddeviation. Breast Invasive Carcinoma data collection. The Cancer Genome Atlas comprises one of the largest collections of omics datasets for more than , corresponding to the Breast Invasive Carcinoma RNA-Seq Fragments PerKilobase per Million (FPKM), excluding the clinical variables subset. Thedataset was obtained using the brca.data R package , as described by the authors .We considered a dataset consisting of RNA sequencing (RNA-Seq) data of breastcancer patients from The Cancer Genome Atlas The dataset consists of suspect samples. For HER2 measurements based on three different readouts were available for aclassification of samples, HER2 ) level,HER2 (via IHC) status and the HER2 level measured by fluorescence in-situhybridization testing (FISH). Altogether, 28 patients showed non-concordancebetween two of the resulting HER2 labels, of which  and the Consensus Coding Sequence project . By additionally removing genes whose expression level remained constantacross all patients, a subset of , which performs class prediction for a series of feature subsets. Foreach subset sensitivity, specificity and subsequently the receiver operatingcharacteristic (ROC) curve are computed. The area under the ROC curve is thenused as the measure of variable importance. By sorting the features according totheir variable importance, we discarded those with lowest variable importance,such that The huge amount of raw data was reduced by considering for the analysis onlyprotein coding genes reported by the Ensembl genome browser In order to investigate the performance of our procedure in detecting outliers,we applied ROSIE to the three simulated datasets. Details about theclassification settings and the choice of the hyperparameters are given inSupporting Information S2 and S4. Results were compared with those of theindividual approaches by ROC analysis . For theIn summary, this analysis shows that the performance of the individual methodsvary significantly and strongly depend on the particularly dataset at hand andthe kind of outliers, while ROSIE is able to compensate for the failure of oneof the methods. Moreover, on average, ROSIE detects outliers more reliably interms of averaged AUC values. Since for real datasets the outlier percentage andnoise levels are usually unknown a priori, ROSIE can indeed provide robustresults in a situation of lack of detailed information.We examined the TNBC dataset with the three previously described methods SPRM,RSK-means and enetLTS. Details about the classification settings and parameterselections are given in the Supporting Information Section S2. Final parametercombinations for each method are listed in Table S3. After aggregating outlier rankings for all three methods and calculating the luential . All infFive bootstrap samples were used to validate results. The three classificationmethods were applied to each of these samples, and commonly selected featuresand a list of influential samples were identified. A summary of the individualbootstrap optimization runs is given in Table S5 in the supplementary material.All influential samples were repeatedly selected as influential in all bootstrapruns they are part of. Influential samples appear in one up to all five of thebootstrap blocks with a mean appearance of AGR2, TBC1D9 and TGFB3.The larger block comprises FOXC1, UGT8 andHORMAD1  to the dataset in order to identify differentiallyexpressed genes. In total, 7529 genes were found to be differentially expressedby this analysis. There is a very good agreement between the two methods. Inparticular, all genes found by ROSIE are among the differentially expressedgenes identified by edgeR, thus reassuring that these are indeed correlated withthe classification. Moreover, all those genes have a quite low false discoveryrate, as can be seen by a ROC analysis with the genes found by ROSIE as groundtruth . This analysis shows that ROSIE is able to identify DEGs asimportant features.Based on these results, we asked the question whether the selected genes areprimarily those which are differentially expressed between the two groups.Therefore, we applied edgeR  is one of the genesrepeatedly selected also in the bootstrap runs of the Ensemble procedure. AsHORMA domains play a role in chromatin binding, the protein encoded byHORMAD1 has been suggested to be involved in meiosis andits expression as a potential marker for cancer . In previous studies analyzing differentially expressed genes betweenTNBC and non-TNBC, HORMAD1 has already been highlighted as oneof the key upregulated genes differentiating TNBC and non-TNBC 38.Additionally, HORMAD1 overexpression, referring to the higherHORMAD1 levels of the second mode of TNBC samples, has alsobeen reported to contribute to Homologous Recombination Deficiency and to be apotential composite predictive biomarker for sensitivity to platinum-basedchemotherapy in TNBC patients .HORMA domain containing 1 . In our opinion, thisdoes not allow for any conclusions regarding similarity or dissimilarity betweennon-TNBC and outliers. If outliers were similar to the other class (here TNBC) inthis analysis, one could argue that this probably hints to just a wrong labeling ofthose samples. However, this is not the case and needs further investigation in thefuture.The workflow which we have presented can also be applied to other datasets with alarge feature space and a low number of samples. In particular, it can handleoutliers in the dataset. Overall, compared to the application of a single robust andsparse method, Ensemble approaches that combine inherently different methods mightbe superior in distinguishing spurious from true findings., a more flexible robust clustering approach which is particularly designed tofit clusters with different scatters and weights, it would for instance beinteresting to replace trimmed k-means by this more general approach blueor evenmore advanced versions 45 in future applications.In future work, it remains to be seen how much the results of our Ensemble approachdepend on the individual methods which are combined. In our application study, forexample, we have observed a large similarity between SPRM and enetLTS, whichovershadows the ranking results of RSK-means. As K-means can be seen as a specialcase of tclust Furthermore, ROSIE suffers from long run times, especially for RSK-means, which hasthe longest run time despite the smallest number of parameter combinations in theparameter selection step. This needs to be addressed to make ROSIE applicable tolarger datasets in future work.Click here for additional data file.Supplemental material, sj-pdf-1-smm-10.1177_09622802211072456 for ROSIE: RObustSparse ensemble for outlIEr detection and gene selection in cancer omics data byAntje Jensch, Marta B. Lopes, Susana Vinga and Nicole Radde in StatisticalMethods in Medical Research


Support vector machine (SVM), random forest (RF), and logistic regression (LR) were used to establish the prediction model, and microarray data from GEO was analyzed to identify differentially expressed genes (DEGs) associated with the dichotomous outcomes of clinical pregnancy (pregnant vs. non-pregnant). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis provided multi-dimensional validation for selected DMGs.A total of 338 differentially methylated CpG sites associated with 146 unique genes across the genome were identified. Among the identified pathways, the prominent ones were involved in the regulation of cell growth and oocyte development . The area under the curve (AUC) of machine learning classifiers was 0.94 (SVM) vs. 0.88 (RF) vs. 0.97 (LR). 196 DEGs were found in transcriptional microarray. Mapped genes were selected through overlapping enriched pathways in transcriptional profiles and methylated data of CCs, predictive of successful pregnancy.Methylated profiles of CCs were significantly different between women receiving ICSI-IVF procedures that conceived successfully and those that did not conceive. Machine learning approaches are powerful tools that may provide crucial information for prognostic assessment. Pathway analysis may be another way in multiomics analysis of cumulus cells. Therefore, more insights into non-invasive biomarkers for oocyte maturation and competence are required.There is a rising consciousness in the field of reproductive medicine that oocyte quality is a vital limiting factor in female fertility, especially given the steadily rising age of first conception in mothers. Poor oocyte quality results in either polyspermy, arrested embryonic development, or spontaneous abortion. At present, the evaluation of oocytes mainly relies on morphological features that focus little on these cells' quality and competence . In addiIn pre-antral follicles, the oocyte grew accompanied by relatively undifferentiated granulosa cells (GCs). The follicular antrum formed approximately in the end of the oocyte growth phase, when the GCs differentiate into two anatomically and functionally distinct lineages: mural GCs (MGCs) that line the wall of the follicle, playing principally a steroidogenic role, and cumulus cells (CCs), which establish bidirectional functional interaction with oocytes through gap junctions and paracrine factors , 3. HereStudies have shown the age-related genetic and epigenetic alterations in cumulus cells . Likewisin vitro fertilization (ICSI–IVF) remains unknown. As a result, more research into the relationships between the methylation state of CpG sites and associated gene expression in cumulus cells, as well as their relationship with pregnancy outcomes, is required.Although genetic and transcriptional factors reflecting the development and movement of cumulus GCs have been extensively studied –9, the rIn this study, bioinformatics analyses were initially performed to identify the methylation status and transcriptional profiles of CCs and their relationship with pregnancy outcomes in patients undergoing ICSI, as well as to analyze the predicted functions and pathways of the DMGs. We also applied multiple machine learning approaches that have been used for cancer genomic classification or subtyping to predict pregnancy outcomes based on selected DMGs. Furthermore, multiomics analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DMGs and differentially expressed genes (DEGs) was performed.The pipeline used in bioinformatic analysis and building the machine learning model was provided in http://www.ncbi.nlm.nih.gov/geo/). The methylation microarray (GSE144664) included 12 IVM-conceived CC samples and 12 IVM-unconceived CC samples based on the HumanMethylation450 BeadChip . For the same gene corresponding to multiple probes, probes with the largest median absolute deviation were selected as representatives. This study was a second analysis of GEO data; therefore, patient consent was not required.All microarray data were downloaded from the Gene Expression Omnibus (GEO) database  ≥ 2 were set as the significance thresholds. DMGs related to pregnancy outcomes were analyzed using the Gene Ontology (GO) resource via the ClusterProfiler package (https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) and KEGG pathway analysis. Only terms with a p < 0.05 were considered significant.In this study, the Q–Q graph was used to detect whether the sample data followed a normal distribution, and then Student's Machine learning methods have been widely applied to many types of pattern-recognition problems. First, principal component analysis (PCA) was applied to the methylated profiles of DMGs, showing a remarkable separating plane between different methylated patterns. Based on the methylation features of CCs, three types of machine learning classifiers, namely support vector machine (SVM), random forest (RF), and logistic regression (LR), were developed for prognosis assessment of pregnancy outcomes following ICSI-IVF procedures.An SVM is a non-probabilistic supervised learning approach that creates a multidimensional hyperplane to divide the covariate space into two groups that allow for classification. It has been widely used in the analysis of binary results. As originally developed, these models are based on a linear decision surface (hyperplane) that differentiates between two classes of observations and maximizes the distance between the hyperplane and single observations . If the An RF classifier contains a set of decision trees and is based on two modules: bagging and random feature selection. In the bagging step, each tree is trained with a round of bootstrapping from the training data. During the training process, the predictive effect of the RF classifier is tested in out-of-bag samples that were not selected in the bootstrap sample. When a tree is growing, the RF classifier randomly selects a subset of features in each split node. The RF classifier circumvents overfitting and stratifies samples by calculating the complex interactions between variables.https://thebiogrid.org/) for matching genes. Subsequently, 32 BioGRID-matched genes and DMGs were selected as the molecular bases for the machine learning classifiers.In this study, we applied a hybrid feature-selection method. The key proteins in oocyte–CC paracrine signaling and the sex-hormone-regulation axis were selected and used for searches in Biological General Repository for Interaction Datasets  that represent negative or positive prediction results. The receiver operating characteristic (ROC) curve was plotted to display the false-positive  values on the X-axis, and the true-positive  values were plotted on the Y-axis. Thus, the ROC plots display the direct association between the FP and TP rates. The total area under the curve (AUC) for the ROC plots was applied to evaluate the predictive performance of the classifiers.https://www.python.org/). A 50–50% train-test data split was used. In SVM, The RBF kernel (K) was selected since it is a Gaussian function that maps the original feature map to a non-linear space  were selected, and statistical analyses of differential gene expression was performed using the Python programming language and the scikit-learning package (Python v3.7). KEGG analyses were performed using the ClusterProfiler package in R.The transcription file GSE113239 (GPL18451) from the GEO database was used to provide multi-dimensional validation data for the selected DMGs. The prospective clinical trial included 10 women (age: 23–35 years) with idiopathic infertility. Over a period of 14 months, CC gene expression in 10 CC clusters associated with each oocyte was individually evaluated by microarrays using a NimbleGen human gene expression 12 × 135K microarray kit . Microarray data from the sequencing of 10 CCs were analyzed to identify DEGs associated with the dichotomous outcomes of clinical pregnancy (pregnant vs. non-pregnant). Data were preprocessed to generate a standard preprocessing structure. Significant genes from these analyses  were established as DMGs. Methylation capture bisulfite sequencing of CCs from women receiving ICSI–IVF identified 338 differentially methylated CpG sites that correlated with 146 unique genes across the genome. Among them, 164 CpG sites were hypomethylated, and 174 CpG sites were hypermethylated. A total of 71 genes matched with hypomethylated CpG sites  contained both hypermethylated and hypomethylated CpG sites. The volcano map . KEGG analysis identified prominent pathways that included Hedgehog signaling (hsa04340), ErbB signaling (hsa04012), progesterone-mediated oocyte maturation (hsa04914), the renin–angiotensin pathway (hsa04614), ovarian steroidogenesis (hsa04913), calcium signaling (hsa04020), and N-glycan biosynthesis (hsa00510) , 11 (7.5%), and 13 (8.9%) genes from the three respective groups could not be annotated. DMGs were mainly enriched in negative regulation of growth and organ morphogenesis . For patsa00510) .Literature review identified four regulatory proteins secreted by oocytes  and that act on CCs, with seven maternal upstream regulatory proteins  were selected as search keywords in BioGRID. The results returned a total of 2,962 related records and 851 non-repetitive interactive genes . The 32 p < 0.05) in signaling pathways that included ovarian steroidogenesis and Hedgehog signaling, which also overlapped with the methylation data  was significantly hypermethylated in pregnant women; thereby possibly influencing the activity of Ca2+/calmodulin kinase (CaMK). Ca2+ signaling pathways play a crucial role in the development and maturation of healthy oocytes and are amplified by the activation of the CaMK cascade through successive phosphorylation events  is a pivotal downstream molecule of the mitogen-activated protein kinase (MAPK) cascade, and its activation positively regulates the G2/M transition in mammalian cell lines . Severalout mice . Hence, Tumor necrosis factor-alpha (TNF-α) is present in the follicular fluid while its receptors are expressed by GCs. In a group of IVF-ICSI-treated women, TNF-α level in both follicular fluid and serum was found to be higher in good responders than in poor responders . AnotherACTA1 in CCs was significantly hypermethylated in women with positive pregnancy test results, thus it probably influences oocyte meiosis via the bipolar spindle formation pathway.Several cellular processes are crucial for oocyte meiosis I and II, including nuclear positioning, germinal vesicle breakdown, spindle migration, spindle rotation, chromosome segregation, and polar body extrusion. Several studies, mostly using the mouse oocyte model, have shown that actin filaments are pivotal for these processes, particularly for the formation of bipolar spindles –22. In oMGAT5 methylation, as discovered in this study, might contribute to alterations in COC expansion dynamics in women treated with ICSI-IVF.CCs produce extracellular matrix (ECM) molecules, resulting in cumulus expansion that is essential for ovulation and fertilization, and is predictive of oocyte quality . As a byPan et al. found changes in the protein and methylation levels of AGT in the renal tissue of adult IVM mice, suggesting an association between IVM manipulation and epigenetic regulation of the RAS system . In our Hydroxysteroid 17-beta dehydrogenase 7 (HSD17B7) belongs to the hydroxysteroid (17β) dehydrogenase superfamily and acts as a steroidogenic enzyme for the synthesis of arachidonic acid, a precursor of prostaglandins, as well as the conversion of androstenedione to biologically active testosterone in non-testicular tissues and the conversion of estrone to estradiol. The role of HSD17B7 in ovarian function was revealed in HSD17B12+/– ovaries in a female mouse model, in which meiotic spindle formation in immature follicles, polyovular follicles, and oocytes trapped inside the corpus luteum were observed . TherefoWNT9B/WNT10B/ Frizzled-7/ AXIN2, which are involved in the Wnt signal/β-catenin pathway, were significantly lower in human dysmature CCs than in control cells  is involved in generating the plasma membrane electrical potential, resulting in an intracellular pool of functional enzymes, which might be re-expressed during early development in response to physiological needs . Upregulost fish . The sigKEGG pathway analysis and extensive literature review revealed several candidate genes in CCs that can serve as markers of oocyte quality; however, a reliable method for selecting oocytes with pregnancy potential remains a challenge. In this study, we applied three machine learning approaches, namely SVM, RF, and LR, to predict pregnancy outcomes of ICSI procedures. The model applies a hybrid feature selection method that combines the biological process and filter method to find the suboptimal features for the original input features. In the ROC curve analysis, the areas under the ROC curve were 0.94 (SVM) vs. 0.88 (RF) vs. 0.97 (LR). Together, this study demonstrates that machine learning approaches can provide insights into pregnancy outcomes following ICSI-IVF procedures.EFNB2, RGS2, and VCAN) proposed as biomarkers of pregnancy could not be validated in their CC samples. Xu et al.  pathway analysis of differentially expressed genes (DEGs).Oocyte quality is a critical limiting factor in female fertility, and current evaluation of oocytes mainly relies on morphological features that focus little on cell quality and competence. Moreover, current techniques for  cumulus –7, the rhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144664; https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE113239.Publicly available datasets were analyzed in this study. This data can be found here: FC analyzed and interpreted the patient data regarding cumulus cells (CCs) of women undergoing ICSI–IVF and was a major contributor in building prediction model and writing the manuscript. YC participated in building the prediction model. QM revised the manuscript. All authors read and approved the final manuscript.This work was supported by Clinical Medical Research of China Medical Sciences—Stem Cell Basic Research Project (Grant No. 19020010780), the National Natural Science Found of China (Grant No. 81270750), and the Natural Science Found of Guangdong China (Grant No. 2019A1515011845). The sponsor was involved in study design, writing of the report, and the decision to submit the article for publication.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.