Comparison of gene expression algorithms may be beneficial for obtaining disease pattern or grouping patients based on the gene expression profile. The current study aimed to investigate whether the knowledge within these data is able to group the ovarian cancer patients with similar disease pattern. Four different clustering methods were applied on 20 genes expression data of 37 women with ovarian cancer. All selected genes in this study had prominent roles in the control of the activity of the immune system, as well as the chemotaxis, angiogenesis, apoptosis, and etc. Comparison of different clustering methods such as K-means, Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Expectation-Maximization (EM) algorithm was the other aim of the present study. In addition, the percentage of correct prediction, Robustness-Performance Trade-off (RPT), and Silhouette criteria were used to evaluate the performance of clustering methods. Six out of 20 genes  selected by the Laplacian score showed key roles in the development of ovarian cancer and their prognostic values were clinically and statistically confirmed. The results indicated proper capability of the expression pattern of these genes in grouping the patients with similar prognosis, i.e. patients alive after 5 years or dead (62.12%). The results revealed the better performance for k-means and hierarchical clustering methods, and confirmed the fact that by using the expression profile of these genes, patients with similar behavior can be grouped in the same cluster with acceptable accuracy level. Certainly, the useful information from these data may contribute to the prediction of prognosis in ovarian cancer patients along with other features of patients. According to the statistical reports, ovarian cancer leads to the highest level of fatality among the women with malignant tumors and is recognized as the fifth cause of death due to cancer . Due to the lack of specific features, most of the ovarian cancers are diagnosed when they are at their final stage. Therefore, this type of disease has a high treatment cost as well as low prognosis. Nowadays, by understanding the molecular basis of this disease, a new treatment has been proposed to manage this disease . In fact, various types of cancer could be diagnosed through the analysis of genetic data. All cells of an organism have similar genes, but these genes have different interpretations at different instances and circumstances . Biologists measure the gene expression level in different experimental conditions in order to analyze the genes performance and the mechanism regulating them in cancer patients . One of the main issues in analysis of the gene expression data is to distinguish the gene groups which show a similar expression pattern . Therefore, using the gene expression analysis, genes or samples with similar behavior or function are identified . However, as the genes expression in the body affect each other, it seems essential to use the methods which take into account the simultaneous effects of different genes at different diseases, especially in cancers. Furthermore, due to special complexity in distinguishing a disease generating agents, individual gene examination may not lead to accurate conclusions . Accordingly, utilizing multivariate statistical analysis methods in examination of gene performance in different diseases has received attention by the researchers interested in this field . Among these, clustering methods are the most important instruments to study the gene microarray data . Clustering can figure out the structure of biomedical data which is helpful for diagnosis or treatment . The main objective of this research was to cluster the patients using their data expression of 20 genes which, based on the clinical literature, are the most effective genes in prediction, diagnosis and treatment of the ovarian cancer . Since one cannot exactly determine which method is always the most appropriate clustering method for a data type , comparison of different methods on a data set is of great importance. Accordingly, comparison of different clustering methods such as K-means, Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Expectation-Maximization (EM) algorithm was the other aim of the present study. In order to evaluate the performance of clustering methods, we utilized the patients’ prognosis  as a criterion. Therefore, the ability of the gene expression data in grouping the patients with similar prognosis was assessed.MaterialsStudy Design and ParticipantsA prospective study was performed on 37 women diagnosed with ovarian cancer during the year 2007 to 2010. Patients were recruited from a hospital affiliated with Shiraz University of Medical Sciences (south of Iran) and followed for 5 years. Only newly diagnosed patients were enrolled in this study, and definite diagnosis was made after surgical removal of the suspicious ovarian mass and pathologic examination. The type and grade of tumors were determined by an experienced pathologist. All participants provided written informed consent prior to enrollment in the study. Blood samples, RNA Extraction and Reverse TranscriptionPeripheral blood samples were drawn from ovarian cancer patients who did not receive any radiotherapy, chemotherapy, or immunotherapy prior to sampling. The total RNA of blood cells was extracted by lysis with ammonium chloride and Trizol reagent treatment . Then, the quality and quantity of RNA samples were measured by spectrometry at 260 and 280 nm. Contaminated DNA was removed from RNA by DNase I treatment  before cDNA synthesis. cDNA was synthesized from 0.5-μg of total RNA with the RevertAid First Strand cDNA Synthesis Kit , using both oligo(dt)18 and random hexamer primers.MethodsQuantitative Real-Time RT-PCR (qRT-PCR)-ΔCt formulas.Specific primers were designed to determine the expression of IFN-γ, Foxp3, IL-4, IL-6, IL-10, IL-12, IL-17, IL-23, IL-27, BCL-2, Oct4, survivin, SDF-1, CTLA-4, TGF-β, Fas, FasL, Her2, MDM2, P53 , and BetStatistical AnalysisClustering is a data mining technique that divides different data in separate clusters. In this method, the observations of heterogeneous population are divided into homogenous subsets named clusters. The purpose is to find the groups much different while their members are matched . Most of the clustering methods are based on the criteria of dissimilarity such as distance function. According to the most famous classification, clustering methods are divided into four categories , including: 1) Partition-based clustering, 2) Graph-based clustering, 3) Density-based clustering, and 4) Model-based clustering. In the present study, from each category of clustering techniques, a method was applied consisting of K-means, Agglomerative Hierarchical, DBSCAN and EM algorithm, respectively. In the following part, we will briefly introduce the methods used in the present study:1)K-means clustering method: In this method, each observation is assigned to the cluster with the least distance from the average of that cluster . The number of clusters is determined by the user as an input parameter, and the final result is sensitive to the selection of the initial centers of the clusters. The k points as the primary centers of the clusters are randomly determined and the distance between each data and the center of the clusters is calculated. Each data is then assigned to a cluster that has the shortest distance to the center of that cluster. The center of the new cluster is recalculated by averaging the data contained in each cluster. The distance between each data and the new center of the clusters is recalculated and the data are placed in the new clusters according to the minimum distance. The two previous steps are repeated until the centers of the clusters do not change and convergence is achieved.2) Hierarchical clustering method: This method is introduced as one of the most common methods of data clustering in microarray technology due to the method of displaying and placing individual elements in clusters. Using different proximity criteria for clustering in this algorithm creates different techniques . The number of clusters is also determined by the user. This method is also sensitive to outliers. Hierarchical clustering can be divided into two main types: Agglomerative (which starts with n clusters and ends with one) and Divisive (which starts with one cluster and ends with n ones), so that the results of both methods are definitive and irreversible. The results of the hierarchical clustering are demonstrative and based on a tree of objects, also known as dendrograms. Also, the Kofentic correlation coefficient is an indicator for evaluating the efficiency and goodness of the hierarchical clustering method; the higher this criterion, the more desirable data clustering.3) DBscan clustering method: Density-based spatial clustering of applications with noise (DBscan) is a density-based, simple, and effective clustering algorithm as it separates high-density areas from low-density areas . This method was proposed by Ester et al. in 1996 and can be used to identify the clusters of different shapes in a data set, even including noise and outlier data . This method can find clusters that k-mean method is unable to find. On the other hand, it faces problems such as optimal selection of ε and μ parameters in different conditions and heavy computational load. Two important parameters required for DBscan clustering are epsilon (eps) and minimum points (Minpts); the eps parameter defines the neighborhood radius around point x, and the Minpts parameter specifies the minimum number of neighbors in the “eps” radius. Then, for each main point, if it is not already assigned to a cluster, it creates a new cluster and finds all its connected density points and assigns them to the same cluster as the main cluster. This process is repeated for the remaining points in the data set. Finally, it considers the points that do not belong to a cluster as outlier or noise data.4) Clustering method based on EM algorithm: Expectation-Maximization (EM) algorithm is an unsupervised clustering method which does not need training and is based on complex models. It tries to find parameters of a probability distribution with an iterative approach, so that it maximizes the likelihood function. In general, the input of this algorithm is data set (x), total number of clusters (M), accepted error (e), and maximum number of iterations. For each iteration, it first executes step E (Expectation), in which it obtains the probability of each point belonging to each cluster, and then goes to step M (Maximization), which re-estimates the probability distribution parameter vector of each cluster. This algorithm terminates when the parameters of distribution converge, or a maximum number of iterations is reached .For reducing space dimension and removing the extra genes whose information is available in the others, two feature selection methods are utilized leading to three subset of genes: two subsets including the genes with Pearson correlations less than 0.85 in each subset and one subset selected by Laplacian scores (a method based on Graph theory and a distance index) . In addition, the percentage of correct prediction, Robustness-Performance Trade-off (RPT), and Silhouette criteria were applied to evaluate the performance of clustering methods ). Since most of the analyses were based on the Euclidian distance among the gene expression values, data were standardized  before any analysis.Findings were based on information of 20 different genes obtained form 37 women who were suffering from ovarian cancer. Most of the patients were at the first stage of the disease (59.5%) and aged 20 to 30 (27.1%) or 51 to 60 years (24.3%). Twelve patients were unmarried (32.4%) and in most married ones the marriage age was in the age of 16 to 20 years (35.2%). Moreover, 54.1% of them had a favorable prognosis, i.e. they were alive for at least 5 years after diagnosis. Four selected subsets of genes are represented in For clustering the patients, four different clustering methods named hierarchical clustering, k-mean, DBSCAN, and EM algorithm were applied on four data sets. Silhouette mean value analysis determined two or four clusters as the optimal number of clusters in different clustering methods. Since cancer is a complex disease, it is influenced by various factors, such as upregulation of predisposing factors and downregulation of growth-inhibiting factors which ultimately lead to tumor growth and metastasis. Meanwhile, helpful information about genes or disease pattern could be obtained by comparison of gene expression algorithms under different conditions such as diverse tissues, blood specimens and different growing environments . All the genes selected in this study had prominent roles in the control of the activity of the immune system, as well as the chemotaxis, angiogenesis, apoptosis, and so forth. Also, the key roles of the genes selected by the Laplacian score including IFN-γ, Foxp3, IL-4, BCL-2, Oct4 and survivin in the development of various cancers and their prognostic value have been clinically confirmed. For instance, Shang et al. reported an association between high FoxP3+ Tregs infiltration and diverse array of solid tumors including cervical, ovarian, renal, and breast cancers . Another report implies that Foxp3+ regulatory cells are recruited to the tumor microenvironment of high grade serous ovarian cancer and act through immunomodulatory cytokines such as IL-10 and TGF-β . Elevated levels of IFN-γ are associated with inducing antitumor immune responses and anti-proliferative activity in the ovarian cancer cells which may mediate through modulating genes involved in cell proliferation or apoptosis . Furthermore, this cytokine is capable of stimulating the class II tumor suppressor gene H-REV107-1 in the ovarian cancer cells . Accordingly, treatment of ovarian cancer patients with IFN-γ may improve the survival of these patients . Induction of apoptosis can be applied through targeting the genes related to apoptosis such as BCL-2 and survivin which were selected by the Laplacian score in our study. Yokoyama et al.  suggested a novel combination therapy targeting Bcl-2/Bcl-xL and PARP, leading to greater cytotoxicity against high-grade serous ovarian cancer cells by inducing apoptosis. Also, targeting survivin, as a well-characterized tumor antigen, caused T cell activation, expansion and differentiation in high grade ovarian cancer patients . Other studied genes such as IL-4 and Oct4 which were selected by the Laplacian score are also of great importance in cancer development. For instance, Oct4 can regulate the mitosis and retinoblastoma tumor suppressor pathway. Thus, targeting this pathway may be considered as a potential therapeutic strategy for ovarian cancer . Besides, IL-4 and IL-4R pathway induces proliferation, survival and migration of the epithelial cancer cells and thus initiates pro-metastatic related mechanisms which can be prevented by targeting this cytokine axis . Collectively, all the genes selected by the Laplacian score in our study were individually effective in the development of ovarian cancer. Thus, we speculate that among the 20 genes studied, these 6 genes may play more important roles in the development and prognosis of ovarian cancer. In present study, we made an attempt to investigate whether the knowledge on gene expression data is able to identify the patients with similar disease pattern and categorize them in one group without considering other characteristics of the patients. Among different statistical techniques, clustering methods are of particular interest for this purpose. In gene expression data analysis, clustering can be done for both the genes and patients . Therefore, the patients or genes with more similar disease pattern or expression, respectively, are clustered on the same cluster. Four clustering methods applied on the present study were the known method of four clustering categories . The results showed better performance for k-means and hierarchical clustering methods based on Silhouette and RPT criteria . In addiIn order to investigate the genes with similar pattern of expression in the present research, hierarchical clustering method was used to cluster the genes. As a result, TGF-β1 was placed in a cluster and the rest in another cluster . It is nAt the end, it should be noted that the better performance for k-means and hierarchical methods may be attributed to lack of noisy data or low sample size . In addition, most of the women whose information was used in the current study were on stages 1 or 2 , and thiLow sample size and unavailability of the other gene expression data can be pointed out as the limitations of the study. Furthermore, simultaneous analysis of this data in both patient and healthy groups and applying other clustering methods  are recommended for future studies.SP was a major contributor to writing the manuscript; she also interpreted the results regarding the statistical methods used. SF and MN were the data analyzer. AH performed data gathering and preparation for the analysis. MR interpreted the patient data and the results of the current study regarding clinical view and also contributed to writing the manuscript. All authors read and approved the final manuscript.


Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches. Here, we present cyCombine, a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques, such as CITE-seq, flow cytometry, and mass cytometry. We demonstrate that cyCombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets. cyCombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. Robust, accurate, and scalable integration of cytometry data enables integration of multiple datasets for primary data analyses and the validation of results using public datasets. Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. Here, the authors present a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques. More recently, spectral flow cytometry enables the measurement of 40 features or more without compromising throughput5. Sequence barcoding-based cytometry, such as CITE-seq, has even further increased the number of markers to the hundreds by almost completely eliminating signal spillover6, and single-cell mass spectrometry is promising to increase feature counts even further9. Common to all these technologies is the desire to integrate data from different experiments, whether seeking to validate results using external datasets or aiming to increase the breadth and/or depth of the dataset used for a given study. This is rarely directly possible due to technical variance arising from data being generated with different antibody panels, reagent lots, or instruments; at different times; by different operators; etc.10. The resulting technical variance is commonly referred to as batch effects, and removing this undesired variance has remained a major unsolved challenge.Protein expression-based single-cell cytometry has evolved immensely over the past decades. While flow cytometry remains a staple of both basic cell biology research and clinical diagnosticsWhile many proposed methods offer means to alleviate the problem, the majority are designed for very specific applications, requiring technical replicates to be included across all batches, only enabling correction of batch effects in samples belonging to specific conditions, or being designed to work only on a specific type of cytometry data. These limitations preclude large-scale integration of data from different experiments, a feature that has become increasingly desired as more and more data is being published.In this work, we have developed the cyCombine method for integration of cytometry data to overcome these challenges. We show that cyCombine enables quantifiably accurate harmonization of cytometry datasets, by removing the technical noise between batches, while maintaining the biological signal. We developed cyCombine to be independent of technical replicates across batches, as well as robust enough to harmonize cytometry data generated with different technologies.11. ComBat was first introduced in 2007 as a tool to address batch effects in DNA microarray data, but the empirical Bayes model has since proven useful for different types of bulk expression data. However, ComBat is not directly applicable to single-cell data, as it is designed to detect and remove technical variance between samples from different batches, while preserving biological variance between samples belonging to homogeneous conditions. However, in single-cell cytometry data, each sample is often characterized by vast heterogeneity in the expression patterns of the different cell types, thus prohibiting explicit modeling of technical and biological variance between samples.The main engine of the cyCombine batch correction module is the tried and true empirical Bayes method for removal of batch effects, ComBat12, with an 8 × 8 node grid. This means that the cells will initially be clustered into 64 categories. This will typically be enough to capture the diversity of peripheral blood mononuclear cells, while ensuring that enough cells to capture the biological variance among cells from the same batches, as well as the technical variance between batches are assigned to each cluster. The grid size can be adjusted if less or greater heterogeneity is anticipated. Generally speaking, we would advise to err on the side of overclustering, as long as the data set is of sufficient size. This will not negatively affect the performance of cyCombine, but will increase runtimes (for full discussion and examples see https://biosurf.org/cyCombine). In order to ensure that phenotypically similar cells cluster together across different batches, the expression of each marker is initially standardized within each batch. This is done either by transforming the expression values to Z-scores, which works well for fairly low-variance batches , or ranks, which works well for high-variance batches . The transformed data are then used to cluster the cells using the SOM, and the node labels are assigned to the original expression value cells lls Fig. .Fig. 1cyTo integrate data from experiments designed with multiple panels of antibodies for increased feature breadth, cyCombine includes a module for panel integration. This module is likewise based on SOM clustering of cells from the different panels using the overlapping markers, followed by probability-based imputation of missing channels by drawing expression values from multi-dimensional kernel density estimates calculated on the cells from the opposing panel Fig. . The cluIn order to demonstrate that cyCombine enables co-analysis of data from different experimental batches, we generated a CyTOF dataset consisting of 128 samples, run in seven batches. The experiment contained two conditions: 20 healthy donor (HD) samples and 108 chronic lymphocytic leukemia (CLL) samples, collected from 56 patients at two different time points. Samples were depleted of B cells in order to isolate and study the phenotypes of the non-malignant immune cells. Each sample was split in two and stained with two different antibody panels, overlapping by 15 markers and differing by 40 markers  population when comparing close-to-treatment CLL samples to HDs , respectively. While the raw data from the three data types assume distinct groupings in UMAP space  samples generated by CyTOF (HIMC dataset), CITE-seq (Illumina dataset), and spectral flow cytometry , each tool was tested only on datasets for which it was explicitly designed and tested by the authors. cyCombine was the only tool in the test that could handle every single dataset in full and showed superior performance for all of them when comparing the EMD reduction and MAD score Fig. . SelecteDeeper cytometric characterization of cell populations can have great implications, such as better diagnostics, development of novel therapeutics, and identification of important markers of immunity. However, a robust batch correction method is needed in order to fully realize the potential of single-cell cytometry. Correction of batch effects is often necessary to detect subtle biological variance in multi-batch experiments, and it is almost certainly a necessity for large-scale integration of data from different experiments.In cyCombine, we handle cellular heterogeneity by applying careful overclustering of the data using a SOM. Co-clustering of data from all batches is enabled by an intermediary transformation of the expression values. The subsequent batch correction is performed using an empirical Bayes model, designed to reduce technical noise, while maintaining the biological signal. While others have previously used the EMD as a metric to measure the reduction in technical variance, we additionally describe the use of the MAD for quantifying the conservation of biological variance, which is a feature that has been overlooked in the majority of previously published methods.11. The SOM overclustering step ensures that both population abundances and cell phenotypes are retained, such that if batch effects are not present in a dataset, running the algorithm will not affect the expression values.Using these metrics, we demonstrate that cyCombine batch correction is quantifiably more accurate than existing tools, and through analysis of three different biologically relevant datasets, we highlight the high degree of flexibility and robustness of our method: cyCombine is independent of technical replicates across batches and makes no assumptions about homology of marker expression distributions. It is largely insensitive to sample and batch sizes, as it handles batch correction for as few as eight cells in each SOM partitionThe primary limitations of cyCombine are inherited from ComBat, namely that batches and experimental conditions cannot be confounded. This means that at least one condition from each batch must be present in at least one other batch. Additionally, it is important to note that, while the cyCombine panel merging module enables imputation of non-overlapping features, batch correction is only possible for features present in all batches.p values. Please refer to the panel merging vignette at https://biosurf.org/cyCombine for deeper discussion and thorough performance evaluation of cyCombine and other panel merging tools.The accuracy of the imputations depends on the information content of the overlapping markers. Imputation is based on draws from multidimensional (kernel density estimated) distributions of the marker(s) to be imputed in the panel where their expressions were measured. In other words, the imputation is essentially a copy of the expression of the given marker(s) from highly similar cells from the marker-containing panel. This means that cell frequencies, marker distributions, and co-expressions are completely preserved. However, if the overlapping panel of markers is not able to accurately co-cluster cells expressing the markers to be imputed, the imputations will not be meaningful. As such, imputed marker expressions should generally only be used for visualization purposes, and we do not recommend basing differential expression analyses directly on imputed values as this can lead to inflated https://biosurf.org/cyCombine.Both the challenge and the possibilities presented here become no less relevant when both the rate of growth and heterogeneity of cytometry data increases as new technologies become more prevalent. cyCombine scales linearly with the number of cells, and we envision that cyCombine will catalyze an increase of large-scale analyses of cytometry data. Of particular interest are applications such as harmonization of clinical cytometry data, which may enable better application of machine learning algorithms for diagnostics, for example by enabling faster detection of minimal residual disease in hematological cancers. A range of use cases, including code and in-depth discussions, are available in the cyCombine vignettes: 17. However, the modules of cyCombine are not limited to data in FCS format, but are designed to work on any expression matrix that can be represented in an R data.frame—including CITE-seq protein expression data etc. cyCombine contains functions for importing FCS files, detection and correction of batch effects, plotting, evaluating batch correction, as well as performing panel merging. All functions are described in detail in the reference manual and the use case vignettes (https://biosurf.org/cyCombine).cyCombine was designed with protein expression-based cytometry data in mind, and the functions for data preparation are made to handle FCS files. cyCombine assumes that the data has already been pre-gated . When using the built-in functions, the data will be ArcSinh-transformed with a cofactor of choice . For CyTOF data, if counts are randomized, de-randomization is recommendedZ-score normalized or converted to ranks, individually for each batch. Z-scoring is appropriate for similar datasets , whereas ranking tends to perform better for less similar datasets . A SOM12 is applied to the full normalized dataset. The grid size of the SOM should reflect the expected heterogeneity and result in a slight overclustering of the data. In cyCombine, the grid size defaults to 8 × 8, partitioning cells into 64 clusters. Then, the SOM node labels are assigned to the original expression value cells, a per cluster batch correction is applied using ComBat11, and values are capped per-marker to the range of the input. The batch correction step can be performed with or without the use of a non-batch cofactor, e.g., phenotype or sample treatment. The cyCombine approach consequently allows for complex study designs, where not all conditions may be present in each batch, and where technical replicates were not included. It is possible to perform batch correction in studies with more than two conditions, and one may integrate different datasets with only one overlapping condition while accounting for this imbalance. The only requirement is that at least one condition from each batch is present in at least one other batch.cyCombine’s batch correction module involves three separate steps: First, the expression of every marker is either 18. The EMD has previously been suggested to be a good metric for comparing protein expression distributions19. Briefly, the EMD was used to compare the distribution of each marker within SOM nodes across batches. Generally, the SOM nodes were determined post-batch correction using 8 × 8 grids, and the labels were transferred to the uncorrected data so each cell had the same label in both the uncorrected and corrected data. For an in-depth discussion, see the performance benchmarking vignette at https://biosurf.org/cyCombine. The distributions were binned with bin size = 0.1, and the EMDs for every marker for each pairwise batch comparison were computed. These scores were determined for both the uncorrected and corrected data, removing those values where both had an EMD < 2. The EMD reduction is given as:n is the total number of comparisons (number of SOM nodes times the number of markers times the number of pairwise batch comparisons). Furthermore, we have developed a score that reflects the amount of variance removed during a batch correction process. The score is based on the MAD and quantifies the variability of each marker in the dataset before and after correction. In practice, it is calculated very similarly to the EMD reduction: The MAD is calculated for the dataset after performing a SOM-based clustering, and is calculated per cluster, per marker, and per batch. So, the MAD is calculated per batch, whereas the EMD calculations are performed for each pairwise batch–batch comparison. This means that the MAD score quantifies intra-batch effects of the correction, and the EMD reduction quantifies inter-batch effects. After calculating the MADs for both the corrected and uncorrected datasets, the MAD score is calculated as the median of the absolute difference in MAD per value:n is the total number of comparisons (number of SOM nodes times the number of markers times the number of batches). For an introduction to EMD reduction and MAD score, please see the performance benchmarking vignette at https://biosurf.org/cyCombine.In order to evaluate the performance of the methods, we primarily applied an approach based on the EMD strongly inspired by Van Gassen et al.cyCombine also contains two functions for marker imputation. One function is designed with panel merging in mind and imputes the expression values of non-overlapping markers across two datasets. It works by first doing a SOM-based (defaults to an 8 × 8 grid) clustering of the datasets based on all of the overlapping markers. Then, for each cell in one of the datasets, the values for the missing markers are imputed by using the values from cells in the other dataset that fall within the same SOM node. The imputations are made by simulating a multi-dimensional kernel density estimate: Each cell’s missing values are imputed by randomly drawing a cell from the other dataset and adding a Gaussian error, which is based on a draw from a Normal distribution with mean 0 and standard deviation corresponding to the bandwidth of each marker in the training population. However, if there are less than 50 cells from the other dataset within the SOM node, the values for the missing channels are set to NA as imputation would be unreliable.The other function was made for salvaging a single channel within a dataset in selected batches. This can be useful in cases where one has a completely mis-stained marker in a single batch. It relies on the same principles, but instead of transferring information in one dataset to another, it utilizes intra-dataset batches.CLL samples were obtained from the CLL Research Consortium (CRC) based at the University of California, San Diego, from patients who provided informed consent and as part of an institutional review board approved protocol. All samples were anonymized by the CRC. The dataset was generated at the DFCI and contained PBMC samples from 20 healthy donors (5 from DFCI and 15 from HemaCare) and samples from 56 patients with CLL. The latter were sampled at two distinct time points (T1 and T2), the mean time between T1 and T2 was 58.7 months (sd = 47.4 months), and T2 was obtained close to first treatment   supplemented with 10% heat-inactivated FBS, sodium heparin (20 UI/mL) and 25 units/mL benzonase nuclease (Life Technologies and Sigma-Aldrich). Samples were subjected to B cell depletion using EasySep Human CD19 positive selection kit II (Stem Cell Technologies) before resuspension in RMPI and 10% FBS.The samples were spun down and aspirated. Five micromolar of cisplatin viability staining reagent (Fluidigm) was added for two minutes and then diluted with culture media. After centrifugation, Human TruStain FcX Fc receptor blocking reagent (BioLegend) was used at a 1:100 dilution final in cell staining buffer (CSB)  for 10 min followed by incubation with cell surface CyTOF antibody panels for 30 min  dissolved in PBS was used at a final concentration of 4% formaldehyde for 10 min in order to fix the samples before permeabilization with the FoxP3/Transcription Factor Staining Buffer Set (ThermoFisher Scientific). The samples were incubated with SCN-EDTA coupled palladium 20-sample barcoding reagents (Fluidigm) for 15 min, washed 3× in CSB, and then combined into a single 20 PBMC sample for subsequent staining. Conjugated intracellular CyTOF antibodies (Supplementary Data 6 cells/mL. Samples were acquired on a Helios CyTOF Mass Cytometer (Fluidigm).The samples were processed in seven batches per antibody panel, each batch containing both control and patient samples. During sample processing, some samples were excluded due to dead cells or having too few cells to apply both panels. The final dataset has measurements for a total of 128 samples, all of which were included in the staining with panel 1, and 112 that were also stained with panel 2. The 20 healthy donors were all stained with both panels. The CLL samples stained with panel 1 consisted of 52 samples at T1 and 56  at T2. For panel 2, the numbers were 45 and 47, respectively. To identify single cell events, DNA was labeled for 20 min with an 18.75 μM iridium intercalator solution prior to acquisition. Samples were subsequently washed and reconstituted in cell acquisition solution in the presence of EQ Four Element Calibration beads (Fluidigm) at a final concentration of 1 × 1020 as implemented in the premessa R package21. The normalized files were then compensated with a panel-specific spillover matrix to subtract cross-contaminating signals, utilizing the CyTOF-based compensation method established by Chevrier et al.22 as implemented in CATALYST v. 1.12.2. These compensated files were then deconvoluted into individual sample files using a single-cell based debarcoding algorithm established by Zunder et al.23 available in premessa v. 0.2.6. This was followed by pre-gating to live intact singlet cells using FlowJo version 10 (Tree Star Inc) as shown in Supplementary Fig. The raw FCS files were normalized to reduce signal deviation between samples over the course of multi-day batch acquisitions, utilizing the bead standard normalization method established by Finck et al.24 using the cyCombine prepare_data function, using de-randomization and ArcSinh-transformation with cofactor = 5. The two panels consisted of a total of 6,027,290 and 6,831,388 cells. Subsequently, each panel was batch corrected using cyCombine with scaling and an 8 × 8 SOM grid using CLL/HD status as cofactor. After correction, all cells were clustered using an 8 × 8 SOM grid and the labels were transferred to the uncorrected data. The EMD was calculated for each marker comparing the batches and the EMD reductions and MAD scores between corrected and uncorrected data were determined for each panel. The data from the two panels was then co-batch corrected using the 15 overlapping markers with scaling and an 8 × 8 SOM grid maintaining CLL/HD status as cofactor but using panel as batch. After co-correction, the 40 (19 + 21) non-overlapping markers were imputed using an 8 × 8 SOM grid and the resulting datasets were combined to a single 55-marker dataset.The pre-gated FCS files for each panel were read into R v. 4.0.012 and ConsensusClusterPlus v. 1.54.013 using 23 markers: CD3, CD4, CD8, CD45RA, CD45RO, CD197, CD127, CD25, CD5, CD19, CD20, CD56, CD16, CD33, CD14, HLA-DR, CD123, CD1c, CD1d, CD11c, CD11b, FCER1A, and CD34. The result was extracted for 45 meta-clusters, and each of these was manually annotated based on its marker expression. Among these clusters, there were eight pairs of clusters, which displayed highly similar expression patterns. Consequently, each of these sets were merged to a single final cluster, as previously described25, leaving 37 clusters. Four of those clusters were labeled as either B cells (CD19+ CD20+) or CLL cells (CD19-lo CD20-lo CD5+), but because these populations can be considered cells that escaped the applied depletion, we removed those clusters from downstream analysis. Furthermore, four clusters displayed abnormal expression patterns, e.g., lack of lineage markers. When considering the mean viability stain for the clusters, it was observed that these four clusters all fell within the top-six highest values. This, together with the abnormal expression patterns, indicated that these clusters were composed of poor-quality cells, which we also excluded from further analysis. This left a final set of 29 populations and 10,719,711 cells to study.The 55-marker data was then clustered using a 10 × 10 SOM grid26 (testDA_voom). Each test included individual false discovery rate (FDR)-correction for the populations included, but no correction was performed between tests. Instead, a FDR-threshold of 0.01 was used for significance. When relevant, the paired nature of the data was considered by using random effects. For differential expression testing within clusters, we analyzed the cell originating from each panel separately, meaning that no imputed values were included. The methodology for differential expression testing was also derived from the work by Weber et al.26 (testDS_limma), in which medians serve as the foundation of the tests. Only markers not used for clustering were included in testing. Again, pairedness was considered when appropriate, and an FDR-threshold of 0.01 was used.Differential abundance testing was carried out using an approach presented by Weber et al.n = 615) were discarded. For the three-datatype integration, the pre-gating was followed by clustering to 20 meta-clusters using a 6 × 6 SOM12 grid and ConsensusClusterPlus13 based on expression of 11 markers overlapping with the healthy donor spectral flow cytometry (SFC) and CITE-seq sets . These clusters were annotated manually based on protein expression levels, and 8932 cells were removed due to ambiguous expression patterns.A single healthy donor PBMC sample (Human Immune Monitoring Center (HIMC) healthy donor, ctrls-001, MATLAB-normalized) was downloaded from FlowRepository (ID: FR-FCM-ZYAJ) and pre-gated to live intact singlets in FlowJo version 10 (Tree Star Inc). The 174,601 cells were processed in R using cyCombine with de-randomization and ArcSinh-transformation with a cofactor = 5. For the integration with the CLL dataset, this was followed by manual gating to 10 cell types based on the lineage markers, CD3, CD4, CD8, CD14, CD19, CD20, CD33, CD45RA, CD56, CD161, CD197, and HLA-DR. Unlabeled cells (5 was downloaded from FlowRepository (ID: FR-FCM-Z2QV). The dataset consists of samples from four healthy donor PBMCs, which were frozen and thawed, stained with 40 different antibodies in one panel, and analyzed using a 5-laser full spectrum flow cytometer (Cytek Biosciences Aurora).The SFC dataset from Park et al.12 grid and ConsensusClusterPlus13 based on expression of 11 markers overlapping with the healthy donor CyTOF and CITE-seq sets. The clusters were annotated manually based on protein expression levels, and 21,307 cells were removed due to ambiguous expression patterns.Pre-processing was carried out in FlowJo version 10 (Tree Star Inc). The dataset was gated on lymphocytes, and singlets and non-debris were identified using forward and side-scatter. Dead cells were excluded using live/dead stains. Data from these gates were then exported in FCS format before further analysis in R: Using cyCombine, the data was loaded and transformed using ArcSinh with a cofactor = 6000. A single sample (donor 303444) with 582,005 cells was selected and clustered to 20 meta-clusters using a 6 × 6 SOMhttps://support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0/pbmc_10k_protein_v3). This data was generated on the PBMCs of a single healthy donor stained with TotalSeq-B antibodies. It was sequenced on an Illumina NovaSeq and processed by Cell Ranger v. 3.0.0.The filtered feature/cell matrix from the “10k PBMCs from a Healthy Donor—Gene Expression and Cell Surface Protein” dataset was obtained from the 10× website  were considered to be representative of the low-quality cells, and were discarded along with any cells labeled as B cells, since these were residual cells resulting from incomplete depletion. The HIMC sample was likewise gated to ten populations using the same 12 lineage markers. This resulted in a total of 352,210 cells, with 17 overlapping markers between the datasets . Datasets were batch corrected using cyCombine with an 8 × 8 SOM grid with the rank normalization method (and average ties method). Each set was considered a batch, and the HD/CLL status was used as a cofactor. The result of the batch correction was evaluated with the EMD reduction and MAD score as well as visual inspection of UMAP plots comparing the location of each cell type (which was assigned separately) across datasets.For the integration with the HIMC healthy donor sample, two samples from the DFCI set (one CLL and one HD) from panel 1, batch 5 were selected (before any batch correction was applied) and manually gated to 10 cell types based on 12 lineage markers: CD3, CD4, CD8, CD14, CD19, CD20, CD33, CD45RA, CD56, CD161, CD197, and HLA-DR. Unlabeled cells . Each dataset was considered a batch and no cofactors were considered. The result of the batch correction was evaluated with the EMD reduction and MAD score as well as UMAP plots comparing the location of each cell type (which was assigned separately) across datasets.18, CytofRUV28, iMUBAC29, and CytofBatchAdjust30. Other tools exist, both developed for flow and mass cytometry, including gaussNorm and fdaNorm32, which the authors state are no longer supported, and the tools cydar33, BatchEffectRemoval34, BatchEffectRemoval201835, SAUCIE36, and swiftReg37, which are not included due to either not being peer-reviewed, not being maintained, requiring a license, or being designed to work only on very specific cases, such as harmonizing two technical replicates. We tested each included tool on the datasets from the original publications and the set of datasets from other publications deemed to be suitable by the authors of each tool; i.e., some tools require technical replicates and not all datasets include these. Furthermore, we only tested each tool on datasets from platforms for which the use is demonstrated in the original publication. For tools with multiple tested settings, the setting with the best overall performance based on both the EMD reduction and MAD score was recorded.We compared the performance of the cyCombine batch correction module with four batch correction algorithms designed to work with mass cytometry data: CytoNormCyTOF and OgishiSFC. An overview is presented in Table All five included tools were run on the CyTOF datasets originally presented in the CytoNorm and CytofRUV papers, as well as the DFCI samples from batch 3 of both panels 1 and 2, where each panel was considered a batch. We will refer to these sets as the Van Gassen, Trussart, and DFCIb3 data, respectively. Additionally, we batch corrected six CyTOF datasets and one SFC set without technical replicates using cyCombine and iMUBAC. These datasets are the DFCI panel 1 and panel 2 sets, and five datasets presented in the iMUBAC article: Each of the three panels of the Krieg dataset, as well as a CyTOF and a SFC set originally generated for iMUBAC, which we refer to as Ogishi18 consists of 40 samples from two healthy controls. They comprise unstimulated and stimulated samples each run ten times (ten batches). Thirty-seven protein markers were measured. The Trussart dataset28 consists of 24 samples from nine healthy controls (HCs) and three CLL patients, each run twice (two batches). Thirty-one protein markers were measured. The FCS files were pre-processed with bead normalization and debarcoding according to the script from the CytofRUV supplementary files (using CATALYST). The Krieg1, Krieg2, and Krieg3 datasets38 comprise 30, 26, and 25 markers, and each contain 60 samples. They were, according to the original publication, processed as four experimental batches. Three conditions are considered: Healthy donors (n = 20), responders (n = 22), and non-responders (n = 18) to anti-PD-1 immunotherapy. Each condition is included in each of the four batches. The dataset was pre-processed according to the instructions in the iMUBAC article: DNA and viability intercalators were used to exclude dead cells, doublets, and debris with the prepSCE function from iMUBAC. The OgishiCyTOF dataset29 contains measurements on 38 protein markers and consists of 57 samples in seven batches. A total of three conditions were included: Healthy (n = 50), MSMD (n = 5), and Salmonellosis (n = 2). Some of the healthy samples are biological replicates. The dataset was pre-processed according to the instructions in the iMUBAC article: DNA and viability intercalators were used to exclude dead cells, doublets, and debris. The OgishiSFC dataset29 measured 18 protein markers across 14 samples in two batches. A total of three conditions were included: Healthy donors (n = 11) and two types of autoimmune disease (n = 1 and n = 2). The dataset was pre-processed according to the instructions in the iMUBAC article: The viability stain was used to exclude dead cells and logicle transformation was used. The DFCI sets comprised two conditions: Healthy donors and CLL samples. As mentioned, the panel 1 data (DFCI1) had 36 measured markers, and the panel 2 data (DFCI2) had 34 markers. The DFCIb3 set consisted of the samples originating from batch 3 in each of the two panels, which had 15 overlapping markers. The DFCI samples were pre-processed as described above.The Van Gassen datasetWhen running CytoNorm, we used FlowSOM clustering with a 10 × 10 grid and 25 final clusters (no downsampling). The batch effects were modeled using 101 quantiles. All protein markers were included. For the Van Gassen set, the 20 samples from healthy control 1 were used to model batch effects and the 20 samples from healthy control 2 were normalized. Evaluation of batch effect reduction was carried out using only the samples from healthy control 2. For the Trussart dataset, the CLL2 and HC1 samples were used as the technical replicates (training data). The remaining 20 samples were used as validation data and the evaluation of batch effect reduction was carried out using only the HC2-9, CLL1, and CLL3 samples. For the DFCIb3 set, the CLL_08_T1 and HD_05 samples were used as technical replicates, and the remaining 35 samples were used for evaluation. Corrected values were capped at 300 to avoid problems with very large values during evaluation.For running CytofRUV, we used clustering with 20 clusters on lineage markers only . All markers were corrected at varying values of k = {5, 10, 15, 20}. For the Van Gassen set, all healthy control 1 samples were used as technical replicates (two sets of ten samples each). For the Trussart set, the CLL2 and HC1 samples were used as the technical replicates, and for the DFCIb3 set, the CLL_08_T1 and HD_05 samples were used. All samples were included in the evaluation.For running CytofBatchAdjust, all files were renamed according to the tool requirements. For Van Gassen, PTLG021 was used as the reference batch and the unstimulated healthy control 1 samples were used as anchors. We tested CytofBatchAdjust with method = {95p, SD, quantile} and transformation = {TRUE, FALSE}. For the Trussart set, HC1 was used as the anchor sample and RUV1b samples as reference batch, whereas DFCIb3 correction used HD_05 as the anchor and panel 1 as the reference batch. All markers were used for correction and all samples were used in evaluation. Corrected values were capped at 300 to avoid problems with very large values during evaluation.SFC set, for which 500,000 cells per batch were included. For the OgishiCyTOF set, only 47 local healthy donor samples were included as in the original publication (travel/family controls excluded). All evaluations were based solely on the downsampled datasets using all markers.iMUBAC was run largely according to the details in the original article. For all datasets, only healthy donors were included in correction, and downsampling to 200,000 cells for each batch was applied for all datasets, except for the Krieg3 dataset, for which we downsampled to 50,000 cells per batch, and the OgishicyCombine was generally run on all available samples using the conditions stated in the presentation of each dataset. We ran cyCombine with norm_method = {scale, rank} on the full datasets with all markers.CyTOF dataset comprising seven batches and 38 protein markers for testing the runtime and memory usage of the different tools. Several of the evaluated tools ran directly on FCS files; therefore, running these tools on a range of different sizes required storing downsampled versions of the original FCS files in new ones. This was done by loading the original FCS files, disregarding non-overlapping columns, sampling to the predefined sample sizes, and storing the resulting data in respective folders. By storing the data this way, it was ensured that all tools were run on the same data at each data size. The runtime and memory usage were measured for each tool for every sample size using the UNIX command time -v. The Maximum resident set size and the elapsed parameters in the output defined the memory usage and runtime, respectively. The test was performed on 40 cores  on an HPE Apollo 2000 system with up to 192 GB PC4 2933 RAM. The standard laptop was a 2018 MacBook Pro with 16 GB 2400 MHz DDR4 memory and a 2.6 GHz 6-Core Intel Core i7 processor.We used the Ogishi39 on no more than approximately 500,000 cells (to avoid overcrowding the plots). Samples were downsampled if more cells were present, whereas all statistical analyses and clustering were done on the full datasets unless otherwise specified. Plots were generated using ggridges v. 0.5.240 and ggplot2 v. 3.3.341, and patchwork v. 1.1.142 was used for combining plots.UMAPs were generated using uwot v. 0.1.9Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1


We conducted a screening analysis to assess the presence of a characteristic extracellular circulating microRNAs (ci-miRNAs) profile in Behçet's syndrome (BS).Total RNA was extracted from platelets-free plasma (PFP) samples obtained from 16 BS patients and 18 healthy controls. Ci-miRNAs profiling was conducted by using dedicated Agilent microarray hybridization and data extraction technology. Statistical analysis of data extracted from microarray scanning revealed the deregulation of 36 ci-miRNAs, which turned out be differentially expressed between BS patients and healthy controls. Detailed experimental methods and data analysis were described here.The raw and normalized microarray data were deposited into Gene Expression Omnibus (GEO) under accession number GSE145191. For each sample, the overall miRNA profile is available both as raw and normalized mean fluorescence intensity values (contained respectively in the GEO Supplementary file and Sample table) associated to each microarray probe.Samples characteristics and GEO data description are provided in 22.1Sixteen BS patients, fulfilling the International Criteria for Behçet's Disease (ICBD) All subjects were free from any laboratory or clinical sign of malignancies, infections or other immune-mediated diseases at blood sampling.2.2Eight ml of peripheral blood were collected from each subject in BD Vacutainer K2 EDTA tubes  by standard venipuncture. Among all commonly used anticoagulants, EDTA was selected due to its reported minimal effect on the circulating microRNA (ci-miRNA) profile during sample preparation step, as well as for the absence of interference with downstream applications 2.3Total RNA was extracted starting from a volume of 0, 25 ml of plasma. Trizol-LS reagent was used, following the manufacturer's protocol. RNA concentration and purity were assessed using the NanoDrop ND-2000 spectrophotometer . Qualitative sample analysis was performed using the Agilent 2100 Bioanalyzer system and Small RNA Assay Chips . Only Samples showing acceptable concentration and quality were included in the analysis.2.4Circulating miRNome profiling was performed using the Agilent Human miRNA 8 × 15k Microarray kit v3.0 and the miRNA Complete Labeling and Hyb Kit  following the manufacturer instructions.Briefly, For each sample, a total amount of 100 ng of total RNA was dephosphorylated, 3′ end-labelled with Cy3-pCp and dried. Cy3-labeled RNA in hybridization buffer was hybridized overnight  to Agilent Human microRNA Microarray Chips using the recommended hybridization chamber and oven. Following hybridization, the microarrays were washed with the Agilent Gene Expression Wash Buffer 1 for 5 min at room temperature followed by a second wash with preheated Agilent Gene Expression Wash Buffer 2 (37°C) for 5 min before scanning.2.5https://bioconductor.org/packages/release/bioc/html/AgiMicroRna.html) Following microarray chips scanning procedure expression data were extracted from the TIFF images obtained from the G2565 Agilent's Microarray Scanning System (using a scan protocol with a resolution of 3 µm and a dynamic range of 20 bits) using the integrated Agilent Feature Extraction (AFE) v9.1 software . In order to perform data pre-processing and differential expression analysis, the processed information contained in the data source file were the following: gTotalGeneSignal, gMeanSignal , gIsGeneDetected, ControlType, ProbeName, and GeneName. Files were then processed using the R library AgiMicroRna v3.12 package, available on Bioconductor repository  algorithm that has been specifically implemented for Agilent miRNA microarrays.This pre-processing method has been shown to have better precision than the one recommended by Agilent Firstly, probe summarization was based on the median expression value for the replicated probes.Median background values were then subtracted from the median expression values obtained from the AFE software. After obtaining the normalized total gene signal, probe signals were filtered based on the quality flags that AFE algorithm attaches to each feature. The quantile method was then applied in order to perform inter-array normalization  in order to compensate for systematic technical differences between probe affinity and arrays.vs HC) was assessed comparing mean microarray fluorescence intensity values using the Limma (Linear models for Microarray Data) differential expression t-test. A 2.0 times fold expression cutoff was applied to minimize the effect of probe background signal.After background correction, statistical significance of differences between study groups  miRNAs were identified from the microarray analysis on the basis of a http://www.mirbase.org).To account for discrepancies in miRNA nomenclature due to the different miRBase releases, array features names were converted to MIMATIDs (unique mature miRNA accession number) using the mapping file available in miRBase  or median with IQR (interquartile range). All statistical tests were two tailed with a significance level of 0.05.This study was conducted according to the Helsinki Declaration and approved by the local ethical committee of Azienda Ospedaliero-Universitaria Careggi, Florence, Italy (protocol number CE 13972). A signed written informed consent was obtained from each participant enrolled in the study.Giacomo Bagni: Investigation, Formal analysis, Methodology, Software, Data curation, Validation, Visualization, Writing – original draft, Writing – review & editing. Giacomo Emmi: Conceptualization, Supervision, Project administration, Funding acquisition, Writing – original draft, Writing – review & editing. Elena Lastraioli: Methodology, Data curation, Visualization, Investigation. Francesca Di Patti: Investigation, Formal analysis, Methodology, Software, Data curation, Validation, Writing – original draft, Writing – review & editing. Elena Silvestri: Investigation, Formal analysis, Methodology, Software, Data curation, Validation. Angela Guerriero: Investigation, Formal analysis, Methodology, Software, Data curation, Validation, Visualization, Writing – original draft. Serena Pillozzi: . Elena Niccolai: Methodology, Data curation, Visualization, Investigation. Amedeo Amedei: Conceptualization, Supervision, Project administration, Funding acquisition. Lorenzo Emmi: Conceptualization, Supervision, Project administration, Funding acquisition, Writing – original draft. Domenico Prisco: Conceptualization, Supervision, Project administration, Funding acquisition, Writing – original draft. Annarosa Arcangeli: Conceptualization, Supervision, Project administration, Funding acquisition, Writing – original draft, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article.


Extra virgin olive oil (EVOO) is a key component of the Mediterranean diet, with several health benefits derived from its consumption. Moreover, due to its eminent market position, EVOO has been thoroughly studied over the last several years, aiming at its authentication, but also to reveal the chemical profile inherent to its beneficial properties. In the present work, a comparative study was conducted to assess Greek EVOOs’ quality and authentication utilizing different analytical approaches, both targeted and untargeted. 173 monovarietal EVOOs from three emblematic Greek cultivars , obtained during the harvesting years of 2018–2020, were analyzed and quantified as per their fatty acids methyl esters (FAMEs) composition via the official method (EEC) No 2568/91, as well as their bioactive content through liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) methodology. In addition to FAMEs analysis, EVOO samples were also analyzed via HRMS-untargeted metabolomics and optical spectroscopy techniques . The data retrieved from all applied techniques were analyzed with Machine Learning methods for the authentication of the EVOOs’ variety. The models’ predictive performance was calculated through test samples, while for further evaluation 30 commercially available EVOO samples were also examined in terms of variety. To the best of our knowledge, this is the first study where different techniques from the fields of standard analysis, spectrometry and optical spectroscopy are applied to the same EVOO samples, providing strong insight into EVOOs chemical profile and a comparative evaluation through the different platforms. Procedural blank subtraction was also performed in the software interface to subtract false positive peaks and avoid potential contamination. Only features of 3-fold higher intensity compared to the respective intensity of the blank were taken into account and included in the final peak-list.Regarding the un-targeted approach, a complete data pre-processing workflow, including peak-picking, mass calibration and time alignment was performed using Time aligned Region complete eXtraction (T-ReX) algorithm, embedded in Bruker’s MetaboScape software. In particular T-ReX 3D was used, compatible with LC-HRMS data acquired, which extracted features according to 3 particular parameters . It provides simplified models as well as a more intuitive interpretation of the model results. Among different feature selection techniques provided by scikit-learn, model-based feature selection was applied. Specifically, some learning algorithms perform feature selection as part of their overall operation ,39. For Feature Selection was performed on the spectroscopic data as well as the HRMS data, which due to their nature might contain redundant information. For the FAMEs data, no feature selection was performed, as the data contain targeted information.The classification procedure was performed to the data of the different techniques . Specifically, four classification methods were examined: K Nearest Neighbors (KNN) , SupportKNN assumes that the samples of each class are “close” to each other. Having the train data and their corresponding labels, each new sample is classified according to the majority class of its k-nearest neighbors.Random forest is an ensemble model that fits multiple decision trees on different sub-samples of the initial train data. For a new sample, each tree in the Random Forest outputs a class prediction. The model finally predicts the most voted class. Moreover, the Random Forest model outputs the importance of each feature. The most important features are the ones that contribute the most to the classification results. Random Forest could also be used as a feature selector prior to the classification procedure, by selecting the most important features according to a threshold.Logistic Regression is a generalized linear model used for classification purposes. Specifically, it fits a logistic function  to the training data, that takes values in . For each new sample, the model outputs the probability that it belongs to the positive class based on the fitted logistic function. If the probability is over 0.5 the model predicts that the sample belongs to the positive class, or else to the negative class. The multiclass case is handled according to a one-vs-rest scheme.Support Vector Machines for binary classification tasks aim to find a hyperplane in the p-dimensional space (p features in the data) that discriminates the samples of different classes. The support vectors are defined as the closest samples to the hyperplane and specify its position and orientation. SVMs can handle data that are not linearly separable by mapping them into a high-dimensional feature space, where they become linearly separable (kernel trick) . The mulBoth feature selection and classification methods require some user-defined parameters (hyperparameters). For example, KNN’s hyperparameter is the number of the nearest neighbors; the hyperparameters of SVM are the type of kernel  and the regularization parameter C. The hyperparameter tuning was performed during the cross-validation procedure. The different values of the hyperparameters of each method are shown in The data were randomly split into train (70%) and test set (30%) in a stratified manner such that the percentage of samples for each class is preserved as observed in the original data. The train set, total number 121 samples  was used for the model construction and the test set, total number 52 samples  was used for testing purposes.In order to determine the model with the best hyperparameter set, stratified k-fold cross validation was performed in the train set. During the stratified k-fold cross validation the train samples are randomly partitioned into k equal sized folds. Each fold contains approximately the same percentage of samples of each class as in the train set. Each model is trained in the k-1 folds and the left-out fold is used for testing. The procedure is repeated k times, where every fold is used exactly one time as the test fold. The final estimation originates from the mean performance of all repetitions. In our case k was equal to 5.After the model with the best hyperparameters was determined from cross-validation, it was trained to the entire train set. The model’s performance was then evaluated on the test set. The results were compared to the baseline (“DummyClassifier” of scikit-learn), which is a model that always predicts the most frequent class, in our case Koroneiki. As metrics of evaluation the accuracy score, the sensitivity and specificity, as well as the confusion matrix were used. Accuracy is the percentage of correctly predicted samples. The sensitivity and specificity are separately measured for each class. For each class, sensitivity (True Positive Rate) is shown in the following equation:Specifically, it refers to the proportion of samples that are correctly predicted to belong to the class (True positives-TP) among all samples that actually belong to the class ). The specificity for each class refers to the proportion of samples that are correctly predicted that they do not belong to the class (True Negative-TN) among all samples that actually do not belong to the class ). It is shown in the following equation:The highest value for sensitivity and specificity is 100%. The mean sensitivity (specificity) is the average sensitivity (specificity) of all classes. The confusion matrix is also presented for further intuition of the results. It is a c-by-c matrix, where c is the number of classes. Each row represents the true label and each column the predicted one. The diagonal elements represent the correct predictions, while the non-diagonal elements represent the misclassifications. The highest performance is achieved when the diagonal elements are equal to the number of samples per class and all the non-diagonal elements are zero.In general, machine learning algorithms benefit from data pre-processing. In order to evaluate the data of each different technique, the most appropriate and required pre-processing methods were applied. Specifically, autoscale (z-score) was performed in the FAMEs data.In HRMS data, extracted features were subjected to normalization by internal standard  to reduce variation between samples that may attributed to sample procedure, instrumental conditions, or data-processing factors.For the Raman spectroscopic data, baseline subtraction was performed to correct the baseline offset caused by sample fluorescence on the Raman spectra, using the airPLS algorithm (Zhangfit) . SNV andTo avoid information leakage from the test set, both for the FAMEs and spectroscopic data, the autoscale procedure was performed independently on each feature of the samples in the train set. The statistics were stored in order to be used on the test data. In this work, a thorough study has been performed for the varietal identification of Greek EVOOs. Different analytical techniques have been implemented and compared, from more conventional ones such as gas chromatographic FAMEs’ determination, to more sophisticated ones, such as spectrometric  and spectroscopic techniques . The analytical results were combined with Machine Learning algorithms for the classification of the samples to Koroneiki, Kolovi and Adramytiani varieties. For each technique, the predictive model with the best hyperparameter set was chosen. The models’ predictive power was evaluated on test data. The results indicated that the FAMES and HRMS analysis achieve the highest predictive ability. In parallel, the spectroscopic techniques exhibited high performance with a small number of misclassifications. The models were furtherly tested using commercially available market EVOOs for the verification of their variety. The results demonstrated that both targeted and untargeted techniques correctly predicted the variety of all the commercially available market EVOOs, except from the visible absorption spectroscopy, where only 3 out of the 30 market EVOOs were misclassified. Based on these encouraging results, HRMS metabolomics as well as optical spectroscopic techniques combined with machine learning methods provide an alternative approach for the identification of olive oil variety, compared to the official methods already established . Through untargeted metabolomics a thorough investigation on EVOOs chemical profile may be accomplished, while that identification of compounds of interest is also enabled, exploiting HRMS full capabilities. Regarding optical techniques, they have prevailed in the field as they are facile, rapid and cost-effective compared to other analytical techniques. They also require no sample pre-treatment, while their predictive performance is comparable to the modern analytical procedures.Finally, to the best of the authors’ knowledge, this is the first time where different techniques from the fields of standard analysis, spectrometry and spectroscopy are applied to the same EVOO samples, and the results are compared and evaluated by machine learning techniques. The findings of this work are valuable for providing a novel methodology for EVOO authentication that might help in the field of production, packaging and trading of EVOO.


Classification is widely used in gene expression data analysis. Feature selection is usually performed before classification because of the large number of genes and the small sample size in gene expression data. In this article, a novel feature selection algorithm using approximate conditional entropy based on fuzzy information granule is proposed, and the correctness of the method is proved by the monotonicity of entropy. Firstly, the fuzzy relation matrix is established by Laplacian kernel. Secondly, the approximately equal relation on fuzzy sets is defined. And then, the approximate conditional entropy based on fuzzy information granule and the importance of internal attributes are defined. Approximate conditional entropy can measure the uncertainty of knowledge from two different perspectives of information and algebra theory. Finally, the greedy algorithm based on the approximate conditional entropy is designed for feature selection. Experimental results for six large-scale gene datasets show that our algorithm not only greatly reduces the dimension of the gene datasets, but also is superior to five state-of-the-art algorithms in terms of classification accuracy. The development of DNA microarray technology has brought about a large number of gene expression data. It is a hot topic in bioinformatics to analyze and mine the knowledge behind these data . As the Feature selection methods are generally divided into three categories: filter, wrapper, and embedded method . The filAttribute reduction is a fundamental research topic and an important application of granular computing . AttribuIn this article, a heuristic feature selection algorithm based on fuzzy information granules and approximate conditional entropy is designed to improve the classification performance of gene expression data sets. The experimental results for several gene expression data sets show that the proposed algorithm can find optimal reduction sets with few genes and high classification accuracy.The remainder of this article is organized as follows. Section “Materials and Methods” gives the gene expression datasets for the experiment and our feature selection algorithm. Section “Experimental Results and Analysis” shows and analyzes the experimental results. Section “Conclusion and Discussion” summarizes this study and discusses future research focus.The following six gene expression datasets are used in this article.(1)Leukemia1 dataset consists of 7129 genes and 72 samples with two subtypes: patients and healthy people .(2)Leukemia2 dataset consists of 5327 genes and 72 samples with three subtypes: ALL-T , ALL-B , and AML (acute myeloid leukemia) .(3)Brain Tumor dataset consists of 10,367 genes and 50 samples with four subtypes .(4)9_Tumors dataset consists of 5726 genes and 60 samples with nine subtypes: non-small cell lung cancer, colon cancer, breast cancer, ovarian cancer, leukemia, kidney cancer, melanoma, prostate cancer, and central nervous system cancer .(5)Robert dataset consists of 23,416 genes and 194 samples with two subtypes: Musculus CD8+T-cells and L1210 cells .(6)Ting dataset consists of 21,583 genes and 187 samples with seven subtypes: GMP cells, MEF cells, MP cells, nb508 cells, TuGMP cells, TuMP cells, and WBC cells .The six gene expression datasets are summarized in U = {x1, x2, …, xn} be a nonempty finite set and denote a universe, I = , UI denotes all fuzzy sets on U.Let Fuzzy sets are regarded as the extensions of classical sets .F is a fuzzy set on U, i.e., F: U → I, then F(xi) is the membership degree of xi to F.F ∈ UI is The cardinality of IU×U denotes all fuzzy-binary relations on U × U.Fuzzy-binary relation are fuzzy sets on two universes. R can be represented byFuzzy-binary relation rij = R ∈ I is the similarity of xi and xj.where Definition 2.1  is called an information system.tion 2.1 . Let UbeA = C⋃D, C is a condition attribute set and D is a decision attribute set, then the pair  is called a decision information system.Moreover, if U, A) is an information system and P ⊆ A, then an equivalence relation (or indiscernibility relation) ind(P) can be defined by  ∈ ind(P)⇔∀a ∈ P, a(x) = a(y).If  = {y| ∈ ind(P)} and U/ind(P) = {[x]ind(P)|x∈U}.For [x]ind(P) and U/ind(P) are briefly denoted by [x]P and U/P, respectively.Usually, P ⊆ A, X ⊆ U is characterized by According to the rough set theory, for X, respectively.X is crisp if X is rough if F,G ∈ UI. For x ∈ U, F(x) and G(x) are the membership degrees of x belonging to fuzzy sets F and G, respectively. F(x) and G(x) ∈ . Actually, it is very difficult to ensure that the equation F(x) = G(x) holds. For this reason, we propose the following approximately equal relation of fuzzy sets.Given Definition 2.2 Given A,B ∈ UI. If there exists k ∈ N(k≥2) such that for any x ∈ U, A(x),B(x) ∈ , then we say that A is approximately equal to B, and denote it by k is regarded as a threshold value.Definition 2.3 For each a ∈ U, define Rx:U→,Rx(a) = R(x ∈ U), Rx is referred to as a fuzzy set that means the membership degree of a to x.Definition 2.4[x]Ris referred to as the fuzzy equal class of x induced by the fuzzy relation R on U.xi]RDefinition 2.5 [ is named as the fuzzy information granule induced by the fuzzy relation R on U.Definition 2.6G(R) = {[x1]R,[x2]R,…,[xn]R} is referred to as the fuzzy-binary granular structure of the universe U induced by R.It is easy to prove: xi−xj|| is the Euclidean distance between two objects xi and xj. In general, σ is a given positive value.k satisfies:Obviously, (1)k∈k = k.(3)k = 1.R = )n×n, then R is called the fuzzy relation matrix induced by Laplacian kernel.Let Definition 2.7 Given a decision information system , ∀X ⊆ U, X ≠ ϕ (ϕ is an empty set), then the approximate accuracy of X is defined asa(X)≤1.where |.| denotes the cardinality of set. Obviously, 0≤Definition 2.8 Given a decision information system , ∀B ⊆ C, the fuzzy information granule of object x under B is [x]R_B, the partition of U derived from D is {X1,X2,…,Xk}, then the conditional entropy of D relative to B is defined asRB denotes the fuzzy relation based on attribute set B and log is a base-2 logarithm.where The approximate accuracy can effectively measure the imprecision of the set caused by the boundary region, while the conditional entropy can effectively measure the knowledge uncertainty caused by the information granularity. We combine the two to propose approximate conditional entropy.Definition 2.9 Let  be a decision information system, ∀B ⊆ C, the fuzzy information granule of object x under B is [x]R_B, the partition of U derived from D is {X1,X2,…,Xk}, aB(Xi) is the approximate accuracy of Xi under RB, then the approximate conditional entropy of D relative to B is defined asTheorem 2.1 Let  be a decision information system, ∀B ⊆ C, the fuzzy information granule of object x under B is [x]R_B, the partition of U derived from D is {X1,X2,…,Xk}.(1)Hace(D/B) gets the maximum value |U|log|U| if and only if [xi]RB = U and |Xj| = 1.(2)Hace(D/B)gets the minimum value 0 if and only if [xi]RB ⊆ [xi]RD.Proof. (1) Due to [xi]RB = U and |Xj| = 1, we have aB(Xj) = 0 according to Definition 2.7.log(2−aB(Xj)) = 1.Thus, Clearly, Hace(D/B) = |U|log|U|.By Definition 2.9, we have The converse is also true.xi]RB ⊆ [xi]RD, we have aB(Xj) = 1 according to Definition 2.7. Thus log(2−aB(Xj)) = 0. Obviously, Hace(D/B) = 0 according to Definition 2.9.(2) Due to [The converse is also true.Theorem 2.2 Let  be a decision information system, ∀L,M ⊆ C, if M ⊆ L, then Hace(D/M)≥Hace(D/L).Proof. Due to M ⊆ L ⊆ C, we have aM(X)≤aL(X) according to Definition 2.7.Then M ⊆ L and U/D = {X1,X2,…,Xk}, we haveBy Hace(D/M)≥Hace(D/L) according to Definition 2.9.Consequently, Hace(D/B) decreases monotonically with the increase of the number of attributes in B, which is very important for constructing forward greedy algorithm of attributes reduction.Theorem 2.2 shows that Definition 2.10 Let  be a decision information system and B ⊆ C, if Hace(D/B) = Hace(D/C) and Hace(D/(B−{b})) > Hace(D/C)(∀b ∈ B), then B is called a reduction of C relative to D.The first condition guarantees that the selected attribute subset has the same amount of information as the whole attribute set. The second condition guarantees that there is no redundancy in the attribute reduction set.Definition 2.11 Assume that  be a decision information system, ∀c ∈ C, define the following indicator,IIA is called the importance of internal attribute of c in C relative to D.then Definition 2.12 Assume that  be a decision information system, ∀c ∈ C, if IIA > 0, then attribute c is called a core attribute of C relative to D.Definition 2.13 Assume that  be a decision information system, B ⊆ C, ∀d ∈ C−B, define the following indicator,IEA is called the importance of external attribute of d to B relative to D.then IEA shows the change of approximate conditional entropy after adding attribute d. The larger IEA is, the more important d is to B relative to D.In this article, a novel feature selection algorithm using approximate conditional entropy (FSACE) is proposed and described as follows.k=3 in KNN and Gaussian kernel function is selected in SVM. Other parameters of the three algorithms are the default values of the software.All experiments are performed on a personal computer running Windows 10 with an Intel(R) Core(TM) i7-4790 CPU operating at 3.60 GHz with 8 GB memory using MATLAB R2019a. The classifiers  are selected to verify the classification accuracy, where the parameter σ is tested. For gene expression data, feature selection aims to improve classification accuracy by eliminating redundant genes. The different values of σ influence the size of granulated gene data, which affects the classification accuracy of selected genes. Therefore, the different values of σ should be set in the process of feature selection of gene expression data sets. Moreover, the different values of σ also affect the composition of the selected gene subset. To obtain a suitable σ and a good gene subset, the classification accuracy of the selected gene subset for different values of σ should be discussed in detail.In this part, the classification accuracy of different Laplacian kernel parameters values of σ. The results are shown in The corresponding experiments are performed to graphically illustrate the classification accuracy of FSACE under different values of σ greatly influences the classification performance of FSACE. σ is usually set to make the classification accuracy highest. Thus, the appropriate parameter values of σ can be obtained for each data set from σ is 0.95, the classification accuracy is the highest. In σ is 0.55, the classification accuracy is the highest. In σ is 0.80, the classification accuracy is the highest. In σ is 0.75, the classification accuracy is the highest. In σ is 0.60, the classification accuracy is the highest. In σ is 0.75, the classification accuracy is the highest. Therefore, the appropriate values of σ for different data sets are determined.The classification results obtained from the three classifiers  with 10-fold cross-validation are shown in The results of feature genes selection from six gene expression data sets are shown in To evaluate the performance of FSACE in terms of classification accuracy, FSACE algorithm is compared with several state-of-the-art feature selection algorithms, including EGGS , EGGS-FSAs shown in As shown in Therefore, FSACE can not only effectively remove noise and redundant data from the original data, but also improve the classification accuracy of gene expression data sets.O(|U|2|C|2). Because the gene expression data sets usually contain a large number of genes, the time complexity of FSACE is high. In addition, FSACE does not consider the interaction between attributes. Therefore, reducing the time complexity of FSACE and seeking more efficient feature selection algorithm considering interaction between attributes are two issues that we will study in the future.Firstly, the concept of approximate conditional entropy is given and its monotonicity is proved in this article. Approximate conditional entropy can describe the uncertainty of knowledge from two aspects of boundary and information granule. And then, a novel feature selection algorithm FSACE is proposed based on the approximate conditional entropy. Finally, the effectiveness of the proposed algorithm is verified on several gene expression data sets. Experimental results show that compared with several state-of-the-art feature selection algorithms, the proposed feature selection algorithm not only can obtain compact features, but also improve classification performance. The time complexity of FSACE is http://portals.broadinstitute.org/cgi-bin/cancer/datasets.cgi (cancer Program Legacy Publication Resources).Publicly available datasets were analyzed in this study. This data can be found here: The author confirms being the sole contributor of this work and has approved it 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.


The big genomics data from various aspects  is now available in cancer research and clinic application. These (multi-)omics data come with a new feature of high dimension: much more features/predictors relative to the available sample size. Meanwhile, researchers are looking beyond individual omics study and exploring integrative analysis of (multi-)omics data. Accordingly, there are novel statistical and machine learning methods designed for the high-dimensional and/or integrative (multi-)omics data analysis. The present Research Topic collects the methodology development and application of statistical and machine-learning methods for high-dimensional clinical (multi-)omics, and integration analysis, mostly, in cancer research.Wang et al. did Cox regression analysis on combined immunohistochemical (IHC) markers and synthetic lethal gene pairs. New prognostic markers for Asian oral cancer were reported. Xu et al. used unsupervised cluster-of-clusters analysis to integrate subgroup classification from different omics and identify potential driver genes in cervical cancer. They found four statistically significant expression subtypes by clustering of tumor copy number variation (CNV) and methylation profiles.For multi-omics integration analysis, classical statistical and machine leaning approaches are widely used. Kim et al.). Ye et al. proposed a new meta-analysis method to integrate multiple transcriptomic studies and categorize biomarkers by concordant patterns with application to Pan-Cancer studies. Jeong et al. presented a kernel canonical correlation analysis (CCA) method to construct condition specific transcriptional networks. CCA with a positive definite kernel is a well-used method for multiple source data analysis. They employed kernel CCA to embed transcription factors (TFs) and target genes (TGs) into a new space where the correlation of TFs and TGs are reflected. Their approach successfully detected novel TF-TG relations in addition to replicated existing regulatory interactions.New approaches have been developed based on these classical methods as well. For example, the Mimi-Surv Model built on Cox regression was designed to identify miRNA-mRNA integration set associated with survival time , kernel-based methods, tree-based methods , and latest versatile deep learning models . In our collection, Källberg et al. compared 13 feature selection methods for their ability to identify a subset of genes that can be used to accurately classify cancer subtypes based on gene expression data. Each of the feature selection techniques was applied to four human cancer data sets with known subtypes, enabling accuracy assessment. Their findings demonstrated that the feature selection methods based on modality outperformed the most commonly used approach of selecting the genes with the highest variability.With many powerful tools in the field, it is always interesting to evaluate their strengths and appropriate usage. Zhou et al. used gene expression data and 6 machine learning methods to predict the 3-year survival risk for patients having heart failure with preserved ejection fraction (HFpEF). In their result, the kernel partial least squares with the genetic algorithm (GA-KPLS) outperformed penalized regression, random forest, support vector machine (SVM), and logistic regression. Jiang et al. employed mendelian randomization (MR) and meta-analysis to study the causal relationship between alcohol consumption and risk of autoimmune inflammatory diseases from totaling 1 million individuals' genetic data. With the enormous genetic data and comprehensive analysis, they noted an overall null association between alcohol consumption and common autoimmune inflammatory disorders.In addition to the applications in cancer, As summarized above, this collection of original research papers presents a significant amount of progress made in the integrative analysis of clinical and (multi-)omics cancer data, prediction in cancer diagnosis/survival/progression, statistical, and machine learning methods for high-dimensional data analysis. While the generation of data has far outpaced our ability to make sense of those data, further development and application of statistical and machine-learning methods are required for the analysis of contemporary genetics in cancer and other diseases.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it 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.