In recent years, the rapid development of single-cell RNA-sequencing (scRNA-seq) techniques enables the quantitative characterization of cell types at a single-cell resolution. With the explosive growth of the number of cells profiled in individual scRNA-seq experiments, there is a demand for novel computational methods for classifying newly-generated scRNA-seq data onto annotated labels. Although several methods have recently been proposed for the cell-type classification of single-cell transcriptomic data, such limitations as inadequate accuracy, inferior robustness, and low stability greatly limit their wide applications.ensemble approach, named EnClaSC, for accurate and robust cell-type classification of single-cell transcriptomic data. Through comprehensive validation experiments, we demonstrate that EnClaSC can not only be applied to the self-projection within a specific dataset and the cell-type classification across different datasets, but also scale up well to various data dimensionality and different data sparsity. We further illustrate the ability of EnClaSC to effectively make cross-species classification, which may shed light on the studies in correlation of different species. EnClaSC is freely available at https://github.com/xy-chen16/EnClaSC.We propose a novel EnClaSC enables highly accurate and robust cell-type classification of single-cell transcriptomic data via an ensemble learning method. We expect to see wide applications of our method to not only transcriptome studies, but also the classification of more general data. Recent advances in single-cell RNA-sequencing (scRNA-seq) techniques make it possible to reveal previously unknown heterogeneity and functional diversity at a microscopic resolution –3. The eA variety of methods have recently been proposed for the cell-type classification of single-cell transcriptomes. For example, an unsupervised approach, named scmap , projectensemble approach for accurate and robust cell-type classification of single-cell transcriptomes. Through comprehensive experiments, we illustrate that our method is superior to existing methods in not only the self-projection within a specific dataset, but also the cell-type classification across datasets. With a few-sample learning strategy, EnClaSC can effectively characterize the cell types that have only a small number of samples. We further show the robustness of our method for various data dimensionality and different data sparsity. Through joint analysis of classification results with scRNA-seq datasets of different species, we demonstrate the ability of our method to make the cross-species cell-type classification.Motivated by the above understanding, we propose in this paper EnClaSC, a novel As illustrated in Fig. E be the value of gene expression in scRNA-seq, n the number of the cells in the dataset. The dropout rate of the j th feature is abbreviated as Dj. For the i th cell (sample), the expression of the j th feature (gene) of is abbreviated as Eij. For the j th feature, the level of gene expression F(j) is calculated by the logarithm-plus-one of its arithmetic mean value asLet For the estimation of gene expression, we first use the dropout rate to fit a linear model using the least square methodF(j) by the mean of the logarithm-plus-one of each feature, namely,We then estimate ∆F1(j) and ∆F2(j), respectively. Briefly, ∆F1(j) reflects the rate of gene expression, that is, whether the gene can be expressed or not. ∆F2(j) is the information entropy of each gene, which reflects the residual degree of each gene expression. We can use the integrated score G(j) = α∆F1(j) + (1 − α)∆F2(j) to consider the above two factors synthetically. Here, α is the control coefficient, which is responsible for regulating the influence of the two calculated factors. We set α to 0.5 in all the experiments of this work.The residuals of these two approaches between the true gene expression level are recorded as G(j) as the scores of features, and then sort the features according to the scores in descending order. We extract K sorted high-score features shared among the training and test sets. This feature selection approach can effectively maximize the common high-score features of different scRNA-seq datasets, and thus contribute to the cell-type classification across datasets.For a specific scRNA-seq dataset, we compute N be the number of samples in the training set, n the number of samples of the few-sample classes in the training set, M the number of samples in the test set, and D the number of features after feature selection. We pair the samples of these few-sample classes one by one to form a few-sample training set with n × n samples and 2 × D features. If the two cells of a paired sample belong to the same cell type, we mark the label of the paired sample as 1; otherwise, we mark it as 0. We then pair the samples of the few-sample classes in the original training set with each sample in the original test set to form a few-sample test set with n × M samples and 2 × D features.We define few-sample classes as the cell types whose number of samples does not exceed 0.5% of the total number of training samples. In order to fully extract features of the few-sample classes, we perform data augmentation and pre-train a few-sample classification model using samples of the few-sample classes in the training set. In more detailed, let Pearson correlation coefficients between the expression values of the two cells in corresponding few-sample test samples drawing on the idea of ensemble learning, namely,x is one of the classes in the original training set, Nx the number of samples of class x in the original training set, j the j th sample of class x in the original training set, i the i th sample in the original test set, and rij the Pearson correlation coefficients between i and j, cij the prediction result of LightGBM for the paired sample of i and j.We adopt LightGBM, a gradient boosting framework that uses tree-based learning algorithms, to perform the few-sample training . We use γ, then we assign the sample as the class that has the maximum score, otherwise, the sample is predicted as “unassigned” and should be further classified using the subsequent artificial neural networks.If the predicted maximum score of an original test sample is greater than γ, the sample is assigned as the class that has the maximum score, otherwise, it is classified as “unassigned”. We set γ to 0.7 in all the experiments of this work. For each test sample, we classify it as “unassigned” unless there are more than half of the 10 neural networks predict the test sample as a specific same class, and we thus classify the sample based on the voting result of the 10 neural networks.The neural network module uses artificial neural networks with ensemble learning strategy to classify the test samples which are predicted as “unassigned” by the few-sample learning module. We first design an artificial neural network framework with parameters as shown in Table kappa value and assigned rate value to assess the classification performance of the models. Briefly, we calculate the kappa values of the classification results of assigned samples and the real classes of corresponding samples using the following formulap0 is the sum of the number of samples correctly classified divided by the number of assigned samples in the test set, that is, the overall classification accuracy of assigned samples in the test set. Assuming that the number of real samples which are assigned by the model in each class of test set is a1, a2…ac respectively, the number of predicted samples in each class of the test set is b1, b2…bC respectively, and the number of assigned samples in the test set is n. We haveWe adopt the widely used N and use assigned rate to represent the proportion of the number of samples that can be assigned to a cell type by the classifier, namely,For the assessment of the ability of a model to recognize the cells in the test set, we assume that the number of samples in the test set is assigned rate should be maintained at a relatively high level, but not the higher the better, and thus the classification performance is primarily related to kappa values.Because the classes of test sets and training sets may not be completely consistent, the We downloaded three humans pancreatic scRNA-seq dataset provided by Baron, M. et al., Muraro, MJ et al., and Xin, Y. et al.  from NCBI Gene Expression Omnibus via accession GSE84133, GSE85241, and GSE81608, respectively . The humWe also downloaded two mouse retina scRNA-seq datasets provided by Macosko, EZ et al. and Shekhar, K. et al.,  from NCBI Gene Expression Omnibus via accession GSE63473 and GSE81904 , 19. MacTwo mouse brain cell scRNA-seq datasets provided by Romanov, RA et al. and Zeisel, A. et al.  and one human brain cell scRNA-seq dataset collected by Darmanis, S. et al. (hereinafter is abbreviated as Darmanis Dataset) were downloaded from the NCBI Gene Expression Omnibus via accession GSE74672, GSE60361 and GSE67835, respectively , 20, 21.In order to illustrate that our method can effectively perform self-projection within a scRNA-seq dataset, we conducted a series of self-projection experiments using 6 datasets, including Baron Dataset, Muraro Dataset, Xin Dataset, Segerstolpe Dataset, Macosko Dataset and Shekhar Dataset. We compared the performance of our method with scmap, a similarity-based method, and SuperCT, an ANN-based method (we self-implemented the method according to the paper because SuperCT is not open source) , 14. Usikappa value of EnClaSC is 71.09% lower than SuperCT, and the variance of the assigned rate of EnClaSC is 68.03% lower than SuperCT.Both EnClaSC and scmap provide a feature selection method, while SuperCT uses all features. When running EnClaSC and scmap, we selected 100 features, which is considered to be able to better demonstrate the advantages of scmap in their paper. We performed 5-fold cross-validation within each of the six datasets. As shown in Fig. We use six scRNA-seq datasets to demonstrate the superior cell-type classification performance of EnClaSC. Using Baron Dataset, Muraro Dataset, Xin Dataset, and Segerstolpe Dataset these four human pancreatic datasets, we selected each one of them as the test set, while the integration of remaining three datasets serves as the training set, respectively. In addition, we used two mouse retina datasets, namely, Macosko Dataset and Shekhar Dataset, to form two symmetric training-test sets. We used 100 selected features in EnClaSC and scmap, while all features in SuperCT, and repeated each experiment five times.kappa value of EnClaSC is higher than that of scmap given comparable assigned rates, except for the group where Macosko Dataset serve as the test set and scmap achieves much lower assigned rates. Compared with SuperCT, which also uses a neural network architecture, assigned rates of EnClaSC has little difference except for Baron Dataset as the test set, while most kappa values of EnClaSC outperform SuperCT. When Baron Dataset serves as the test set, the assigned rates of SuperCT is much lower than that of scmap and EnClaSC. In addition, it can be seen from the figure that the classification performance of EnClaSC is much more stable than that of SuperCT. Therefore, even though EnClaSC only uses 100 features, the classification performance of EnClaSC is still far better than that of SuperCT. In general, EnClaSC has a more outstanding performance in cell-type classification compared with other two base-line methods.As shown in Fig. We validated the contribution of the feature selection module using the four human pancreatic datasets including Baron Dataset, Muraro Dataset, Xin Dataset and Segerstolpe Dataset, and two mouse retina datasets including Macosko Dataset and Shekhar Dataset. We performed six experiments as the above section. We first took each one of the four human pancreas datasets as a test set and integrated the remaining three datasets as a training set. Then we took each one of the two mouse retina datasets as a test set, and the remaining one was served as a training set. We replaced the feature selection method of our feature selection module with PCA, which is commonly used for scRNA-seq dimensionality reduction, feature selection methods of Seurat v3.0 and that of scmap, and compared them with our original method .kappa and the assigned rate of EnClaSC is much better than the EnClaSC framework using the feature selection method of Seurat v3.0 . EnClaSC has slightly higher kappa values with comparable assigned rates compared with the EnClaSC framework using the feature selection method of scmap. In summary, the feature selection module of EnClaSC is superior to other widely used feature selection approaches in the EnClaSC framework, and thus serves as the effective feature selection approach for the cell-type classification.The results are shown in Fig. Feature selection module” section. We replaced the neural networks in EnClaSC with a single neural network that does not use the ensemble learning strategy. We compared the performance of the above approach with that of our complete model, and show the results in Fig. kappa and 71.97% reduced variance of assigned rate on average, the main contribution of ensemble learning strategy with several neural networks is significantly improving the stability of classification performance. Besides, on most datasets, the ensemble learning strategy slightly improves the classification performance.We validated the contribution of the neural network module using the four human pancreatic datasets and the two mouse retina datasets, and we again performed six experiments as the “To demonstrate the contribution of the few-sample learning module, we compare the performance of EnClaSC with or without the few-sample learning module. We first used Shekhar Dataset as the training set, Macosko Dataset as the test set to evaluate the contribution of the few-sample learning module. This training-test set has a very distinct characteristic compared to other sets we have used, because there are massive samples of few-sample classes of the training set in the test set, which makes the classification task much more challenging. According to the definition in the Methods Section, we consider “rods” and “cones” as the few-sample classes in the training set. These two cell types account for 0.34 and 0.18% respectively in the training set, and account for 65.61 and 4.17% respectively in the test set.As shown in Fig. We also evaluated the contribution of the few-sample learning module using training and test sets whose cell types have similar sample distributions. We integrated Baron Dataset, Muraro Dataset, and Xin Dataset as the training set, and used Segerstolpe Dataset as the test set, and Segerstolpe Dataset, Muraro Dataset and Xin Dataset as training sets, Baron Dataset as the test set to form two training-test sets for the experiment.As shown in Fig. In addition, we found that in the Segerstolpe Dataset, which contains “MHC class II” cell type but not ‘macrophage’ cell type. In the case that Baron Dataset or Segerstolpe Dataset serves as the test set, the few-sample learning module can associate the ‘macrophage’ class with the “MHC class II” class as shown in Fig. To illustrate the robustness of our method for various data dimensionality, we selected different numbers of features among the four human pancreatic datasets including Muraro Dataset, Baron Dataset, Segerstolpe Dataset, and Xin Dataset. We took each one of the four human pancreas datasets as a test set and integrated the remaining three datasets as a training set, and repeated each experiment five times. In order to illustrate the superior data-dimension adaptability of our method, we selected 100, 300, 500, 700 and 900 as the feature numbers of our method and scmap. It is worth noting that instead of designing a feature selection module, SuperCT directly binarizes all features, and thus uses all features in these experiments.kappa value of scmap rises to the peak and remains basically unchanged, while the assigned rates of scmap drops dramatically with the increase of data dimensionality. At the same time, we can see that when the data dimensionality reaches 500 or becomes higher, the kappa values and the assigned rate of EnClaSC are consistently higher than that of scmap and SuperCT. In summary, EnClaSC scales up well with various data dimensionality, and the higher the data dimensionality, the better the performance of EnClaSC.As shown in Fig. kappa values hardly change with the increasing of the dropout rate, even though the assigned rate decrease slightly. Nevertheless, the performance of scmap becomes worse because the randomly zeroing of the feature matrix could cause significant changes in cell-to-cell similarity. The performance of SuperCT fluctuates irregularly with the increasing dropout rate. In some cases, the kappa value may decrease, which indicating that the increasing dropout rate could cause further deterioration of the stability of SuperCT. The results demonstrate that EnClaSC can account for the sparsity of single-cell gene expression data, and thus benefits the cell-type classification tasks with the exponential growth of the Drop-seq based scRNA-seq data.Considering that scRNA-seq data suffer from the dropout phenomenon, we further demonstrated that our method can scale up well with different data sparsity using the four human pancreatic datasets to perform four experiments. We randomly set 0, 10, 20, 30, 40, and 50% of the non-zero elements in the raw expression matrix, and repeated each experiment five times. As illustrated in Fig. cross-species classification, we used datasets of mouse brain cells provided by Romanov, R. A. et al. (Romanov Dataset for short) and Zeisel, A. et al. (Zeisel Dataset for short) as the training set, and a dataset of human brain cells provided by Darmanis, S. et al. (Darmanis Dataset for short) as the test set for the cross-species classification. Because there are only 466 samples on Darmanis Dataset, we did not use the Darmanis Dataset as the training set. As shown in Fig. assigned rate , but also has the highest kappa (79.13% higher than scmap and 80.06% higher than SuperCT) in this cross-species classification task. Similarly, using our feature selection module in EnClaSC also provides the highest kappa value , and assigned rates . The results demonstrate that EnClaSC has superior classification performance both in the classification of homologous cells of the same species and in the classification of homologous cells of different species.To illustrate that EnClaSC can effectively make scRNA-Seq techniques have advanced rapidly in recent years and enable the quantitative characterization of cell types at a single-cell resolution. EnClaSC is proposed to classify cell types using supervised learning. With the contribution of the tailored feature selection, neural network and few-sample learning modules, our method is superior to other baseline methods, such as scmap and SuperCT, with regard to not only accuracy and robustness, but also the performance of classifying few-sample classes.Our method can certainly be improved in some aspects. First, a more well-crafted module, such as modules considering dropout evens, can be introduced to better characterize the scRNA-seq data, and thus further improves the performance of our method. Second, our method can be extended to incorporate other types of functional genomics data such as chromatin accessibility , 24. FinWe have proposed a supervised learning method, named EnClaSC, for accurate and robust cell-type classification of single-cell transcriptomes. Each of the well-crafted feature selection, neural network and few-sample learning modules draws on the idea of ensemble learning, which makes EnClaSC superior to existing methods in the self-projection within a specific scRNA-seq dataset, the cell-type classification across different scRNA-seq datasets, various data dimensionality, and different data sparsity. We have further demonstrated the ability of EnClaSC to effectively make cross-species classification, which may shed light on the studies in the correlation of different species. Eventually, we expect that such a supervised learning approach will be widely applicable for the cell-type classification with the explosive growth of scRNA-seq data.


Multi-omic studies combine measurements at different molecular levels to build comprehensive models of cellular systems. The success of a multi-omic data analysis strategy depends largely on the adoption of adequate experimental designs, and on the quality of the measurements provided by the different omic platforms. However, the field lacks a comparative description of performance parameters across omic technologies and a formulation for experimental design in multi-omic data scenarios. Here, we propose a set of harmonized Figures of Merit (FoM) as quality descriptors applicable to different omic data types. Employing this information, we formulate the MultiPower method to estimate and assess the optimal sample size in a multi-omics experiment. MultiPower supports different experimental settings, data types and sample sizes, and includes graphical for experimental design decision-making. MultiPower is complemented with MultiML, an algorithm to estimate sample size for machine learning classification problems based on multi-omic data. Multi-omics studies are popular but lack rigorous criteria for experimental design. We define Figures of Merit across omics to comparatively describe their performance, and present new algorithms for sample size calculation in multi-omics experiments aiming either at feature selection or sample classification. Encouraged by constant cost reduction, data-sharing initiatives, and availability of data (pre)processing methods13, the so-called multi-platform or multi-omics studies are becoming popular. However, the success of a multi-omic project in revealing complex molecular interconnections strongly depends on the quality of the omic measurement and on the synergy between a carefully designed experimental setup and a suitable data integration strategy. For example, multi-omic measurements should derive from the same samples, observations should be many, and variance distributions similar if the planned approach for data integration relies on correlation networks. Frequently, these issues are overlooked, and analysis expectations are frustrated by underpowered experimental design, noisy measurements, and the lack of a realistic integration method.The genomics research community has been increasingly proposing the parallel measurement of diverse molecular layers profiled by different omic assays as a strategy to obtain comprehensive insights into biological systems14, Qualimap16 and SAMstat17 for mapping output, and MultiQC18 that combines many different tools in a single report. However, these tools do not apply to non-sequencing omics  and are not conceived to compare platform performance nor support multi-omic experimental design choices. Figures of Merit (FoM) are performance metrics typically used in analytical chemistry to describe devices and methods. FoM include accuracy, reproducibility, sensitivity, and dynamic range; descriptors also applied to omic technologies. However, the definition of each FoM acquires a slightly different specification depending on the omic technology considered, and each omic platform possesses different critical FoM. For example, RNA-seq usually provides unbiased, comprehensive coverage of the targeted space , while this is not the case for shotgun proteomics, which is strongly biased toward abundant proteins. Importantly, we currently lack both a systematic description of FoM discrepancies across omic assays and a definition of a common performance language to support discussions on the multi-omic experimental design.A thorough understanding of individual omic platform properties and their influence on data integration efforts represents an important, but usually ignored, aspect of multi-omic experiment planning. Several tools are available to assess omic data quality, even cross-platform, such as FastQC for raw sequencing (seq) reads19. These features may have different within-condition variability and the significance level must be adapted to account for the multiple testing scenario. Deciding on the effect size to detect may also prove difficult, especially when the natural dynamic range of the data has changed due to normalization procedures. Moreover, different omic platforms present distinct noise levels and dynamic ranges, and hence analysis methods might not be equally applicable to all of them. As a consequence, independently computing the statistical power for each omic might not represent the best approach for a multi-omic experiment, if the different measurements are to be analyzed in an integrative fashion and a joint power study for all platforms seems more appropriate. Although several methods have been proposed to optimize sample size and evaluate statistical power in single-omic experiments20, no such tool exists in the context of multi-omics data. Similarly, multi-omic datasets are increasingly collected to develop sample class predictors applying machine learning (ML) methods. In this case, the classification error rate (ER), rather than the significance value, is used to assess performance. In the field of ML, the estimation of the number of samples required to achieve an established prediction error is still an open question22 and there are not yet methods that answer this question for multi-omics applications.FoM are relevant to statistical analyses that aim to detect differential features, due to their impact on the number of replicates required to achieve a given statistical power. The statistical power of an analysis method, which is the ability of the method to detect true changes between experimental groups, is determined by the within-group variability, the size of the effect to be detected, the significance level to be achieved, and the number of replicates (or observations) per experimental group, also known as sample size. All these parameters are highly related to FoM. Estimating power in omic experiments is challenging because many features are assessed simultaneouslyIn summary, the multi-omics field currently lacks a comparative description of performance metrics across omic technologies and methods to estimate the number of samples required for their multiple applications. In this work, we propose a formal definition of FoM applicable across several omics and provide a common language to describe the performance of high-throughput methods frequently combined in multi-omic studies. We leverage this harmonized quality control vocabulary to develop MultiPower, an approach for power calculations in multi-omic experiments applicable to across omics platforms and types of data. Additionally, we present MultiML, an R method to obtain the optimal sample size required by ML approaches to achieve a target classification ER. The FoM definitions, together with the MultiPower and MultiML calculations proposed here constitute a framework for quality control and precision in the design of multi-omic experiments.We selected seven commonly used FoM that cover different quality aspects of molecular high-throughput platforms , no separation takes place and a low number of nuclei change energy status, leading the detection of only abundant metabolites and lower sensitivity than liquid chromatography (LC)–MS or gas chromatography (GC)–MS. NMR is capable of detecting ~50–75 compounds in human biofluid, with a lower sensitivity limit of 5 µmolar (ref. 25), while MS platforms are able to measure hundreds to thousands of metabolites in a single sample. Sensitivity in sequencing platforms depends on the number of reads associated with the feature, a parameter influenced by sequencing depth. Features with an elevated number of reads are more accurately measured, and hence smaller relative changes can be detected. For region-based omics, where the goal is to identify genomic regions where a certain event occurs, the above definition is difficult to apply, and sensitivity is described in terms of true positive rate or recall, i.e., the proportion of true sites or regions identified as such, given the number of reads in the seq output.Sensitivity is defined as the slope of the calibration line that compares the measured value of an analyte with the true level of that analyte Fig. . For a g27. Generally, reproducibility in sequencing platforms improves at high signal levels, such as highly expressed genes or frequent chromatin-related events. RSD is roughly constant in relation to signal in MS platforms, although in LC–MS the lifetime of the chromatographic column strongly influences reproducibility, leading to small datasets being more reproducible than experiments with many samples. This constraint imposes the utilization of internal standards, quality control samples, and retention time alignment algorithms in these technologies28. Moreover, untargeted LC–MS proteomics quantifies protein levels with multiple and randomly detected peptides, each with a different ER, a factor that compromises the reproducibility of this technique. On the contrary, NMR is a highly linear and reproducible technique29, and reproducibility issues are associated with slight differences in sample preparation procedures among laboratories. Sequencing methods normally achieve high reproducibility for technical replicates5 and are further improved as sequencing depth increases. Reproducibility at the library preparation level depends on how reproducible the involved biochemical reactions are. Critical factors affecting reproducibility include RNA stability and purification protocols in RNA-seq30, antibody affinity in ChIP-seq6, quenching efficiency in metabolomics31, and proteolytic digestion and on-line separation of peptides in proteomics33. Methyl-seq experiments based on the robust MspI enzymatic digestion and bisulfite conversion usually are more reproducible data than enrichment-based methods, such as methylated DNA immunoprecipitation (MeDIP)34. Finally, reproducibility for DNA variant calling is associated with the balance between read coverage at each genome position and the technology sequencing errors.Reproducibility measures how well a repeated experiment provides the same level for a specific feature or, when referring to technical replicates, the magnitude of dispersion of measured values for a given true signal. Traditionally, the relative standard deviation (RSD) is used as a measure of reproducibility, with RSD normally differing over the concentration range in high-throughput platforms35. Both limits affect the final number of detected and quantified features, which in turn impacts the number of tested features and the significance level when correcting for multiple testing. For MS-based methods, LOD and LOQ depend on the platform, can be very different for each compound, and normally require changes of instrument or sample preparation protocol for different chemicals. Additionally, sample complexity strongly affects LOD, as this reduces the chance of detecting low-abundance peptides, while pre-fractionation can reduce this effect at the cost of longer MS analysis time. NMR has usually higher LOD than MS-based methods. Conversely, LOD depends fundamentally on sequencing depth in seq-based technologies, where more features are easily detected by simply increasing the number of reads. However, there also exist differences in LOD across features in sequencing assays. Shorter transcripts and regions usually have higher LODs and are more affected by sequencing depth choices. For DNA-seq, the ability to detect a genomic variant is strongly dependent on the read coverage. MS-based and seq-based methods also differ in the way features under LOD are typically treated. MS methods either apply imputation to estimate values below the LOD 12, or exclude features when repeatedly falling under the LOD. In sequencing methods, LOD is assumed to be zero and data do not contain missing values, although, also in this case, features with few counts in many samples risk exclusion from downstream analyses.The limit of detection (LOD) of a given platform is the lowest detectable true signal level for a specific feature, while the limit of quantitation (LOQ) represents the minimum measurement value considered reliable by predefined standards of accuracyThe dynamic range of an omic feature indicates the interval of true signal levels that can be measured by the platform, while the linear range represents the interval of true signal levels with a linear relationship between the measured signal value and the true signal value Fig. . These F37. In metabolomics, linear ranges usually span 3–4 orders of magnitude, while dynamic ranges increase to 4–5 orders and can be extended using the isotopic peak of the analytes. A combination of analytical methods can increase the dynamic range, as different instruments may better capture either high or low concentration metabolites. NMR has a high dynamic range and can measure highly abundant metabolites with precision, although it is constrained by a high detection limit. For feature-based sequencing platforms, the dynamic range strongly depends on sequencing depth, and values can range from zero counts to up to hundreds of thousands, or even millions . However, due to technical biases, linear range boundaries are difficult to establish and may require the use of calibration RNAs (spike-ins). Moreover, linear ranges may be feature dependent and affected by sequence GC content, which then requires specific normalization. For region-based sequencing platforms, linear and dynamic ranges coincide, and vary from zero to one.In proteomics, molecule fragmentation by data-independent acquisition approaches increases the dynamic range by at least two orders of magnitude. A typical proteomic sample covers protein abundance over 3–4 to four orders of magnitude, a value that increases for targeted approaches38, is used to improve selectivity, while in proteomics selectivity strongly depends on sample complexity, which determines whether a given feature is detected or not. Similarly, in sequencing experiments, selectivity relates to competition of fragments to undergo sequencing. Highly abundant features or regions  may outcompete low-abundant elements. Generally, this problem is difficult to address by means other than increasing the sequencing depth. In the case of transcriptomics, the application of normalized libraries can partially alleviate selectivity problems; however, at the cost of compromising expression level estimations. Particularly for DNA-seq, variant detection is compromised at repetitive regions and can be alleviated with strategies that increase read length.A platform displays good selectivity if the analysis of a feature is not disturbed by the presence of other features. Selectivity influences the number and quantification of features and, consequently, statistical power. For MS platforms, targeted rather than untargeted methods obtain the best selectivity. In metabolomics, selected reaction monitoring, a procedure that couples two sequential MS reactions to obtain unique compound fragments and control quantitation bias using isobaric compounds41. Compound fragments with similar masses and chemical properties often compromise identification, while database incompleteness also contributes to identification failures. A typical identification issue in lipidomics is that many compounds are reported with the same identification label , but slightly different carbon composition and unclear biological significance. Conversely, NMR is highly specific. Each metabolite has a unique pattern in the NMR spectrum, which is also often used for identification of unknown compounds. For untargeted proteomics data, either spectral or sequence databases are used to determine the sequence of the measured peptide. Current identification rates lie at ~40–65% of all acquired spectra with a false discovery rate (FDR) of 1–5%. In targeted approaches, identification is greatly improved by the utilization of isotopically labeled standards. Proteomics suffers from the additional complexity of combining peptides to identify proteins, which is not straightforward. In fact, a recent study highlighted identification as one of the major problems in MS-based proteomics due to differences in search engines and databases42 and to high false-positive rates43. A common identification problem in seq-based methods is the difficulty to allocate reads with sequencing errors or multiple mapping positions, which is addressed either by discarding multi-mapped reads or by estimating correct assignments using advanced statistical tools45. In ChIP-seq, an identification-related issue is the specificity of the antibody targeting the protein or epigenetic modification. Low antibody specificity may result in reads mapping to nonspecific DNA sequences, leaving true binding regions unidentified. The ENCODE consortium has developed working standards to validate antibodies for different types of ChIP assays6.Identification refers to the relative difficulty faced when determining the identity of the measured feature, a critical issue in proteomics and metabolomics. In principle, identification does not affect power calculations, but influences downstream integration and interpretation. Metabolite identification in untargeted MS-based methods requires comparisons with databases that collect spectra from known compoundsThe coverage of a platform is defined here as the proportion of detected features in the space defined by the type of biomolecule (aka feature space). Targeted MS platforms measure a small subset of compounds with high accuracy; hence the coverage is restricted and lower than for untargeted approaches. As sample complexity is frequently much larger than the sampling capacity of current instruments, even in untargeted methods, identification is limited to compounds with the highest abundances. Repeating sample measurement excluding the features identified in the first run is an efficient strategy to improve coverage, although this requires increased instrument runtime and sample amounts. Coverage in seq-based methods strongly depends on sequencing depth and can potentially reach the complete feature space associated with each library preparation protocol. In RNA-seq, oligodT-based methods recover polyA RNAs, whereas total RNA requires ribo-depletion, capture of antisense transcripts imposes strand-specific protocols, and microRNAs require specific small RNA protocols. In Methyl-seq, coverage also relates to the applied protocol. Whole-genome bisulfite sequencing has greater genome-wide coverage of CpGs when compared to Reduced Representation by Bisulfite Sequencing (RRBS), while RRBS and MeDIP provide greater coverage at CpG islands. In general, region-based sequencing approaches can cover the whole reference genome, with coverage depending on the efficiency of the protocol employed to enrich the targeted regions.The FoM analysis across omics revealed that each omic data type possesses different critical performance metrics. In MS, FoM mainly depend on the choice of instrument and approach—targeted vs. untargeted—, while FoM in sequencing methods rely on sequencing depth, library preparation, and eventual bioinformatics post-processing. Although FoM are not a property of data but of the analytical platform, they directly impact data characteristics relevant to experimental design. Overall, FoM strongly relate to the variability in the measurements, which changes in a feature-dependent manner. FoM also determine the final number of measured features and the magnitude of detectable change. Hence, reproducibility influences measurement variability; sensitivity, linear, and dynamic range determine the magnitude and precision of the measurements, and are associated to effect size; the number of features measured by the omic platform is given by the LOD, selectivity, identification, and coverage. These three parameters, variability, effect size, and number of variables, are the key components of power calculations in high-throughput data used by MultiPower to estimate sample size in multi-omic experiments. Figure 46 (Supplementary Note m), expected percentage of features with a significant signal change (p1), and variability measured as pooled standard deviation (PSD). Note that the number of features measured by each omic platform varies by several orders of magnitude, from 60 metabolites to 52,788 DNase-seq regions  with a Cohen’s d of 0.8, and at a sample size (n = 4) with a larger Cohen’s d. Results showed that a reduction in the number of replicates can counteract the loss of power if accepting an increase in the magnitude of change to be detected. Finally, MultiPower estimates were further validated by calculating power and Cohen’s d with the published replicate numbers in STATegra (n = 3), and verifying the agreement in magnitude and direction of change between RNA-seq and RT-PCR for six B-cell differentiation marker genes46 , while only six and nine replicates per group were required by metabolomics and RNA-seq, respectively  usually collect data from a large number of subjects, where biological variability is naturally higher than in controlled experiments. In such studies, not all subjects may have measurements at all omic platforms and when integrating data decisions should be made to either select individuals profiled by all omic assays—to keep a complete multi-omic design—or to allow a different number of individuals per platform. We illustrate the utility of our method in cohort studies by using MultiPower to estimate power for the integrative analysis of four omic platforms available for the TCGA Glioblastoma datasetMulti-omics datasets may be used in cohort studies to classify biological samples into, for instance, disease subtypes or to predict drug response. In these cases, the analysis goal is not to detect a size effect but to achieve a specified prediction accuracy. MultiML computes the optimal sample size for this type of problem. Briefly, MultiML uses a pilot multi-omics dataset to estimate the relationship between sample size and prediction error, which is then used to infer the sample size required to reach a target classification ER  was set to 0.01. Sample sizes were calculated for combinations of two to five omics platforms. We found that the number of samples required to obtain ERtarget decreased as the number of omics data platforms increased, suggesting that complementary information was captured by the multi-omics approach resulting in a more efficient predictor  or greater. Then, for the predicted sample sizes, we obtained their actual ER from the data and compared them to ERtarget to evaluate the accuracy of the MultiML estimate, and if the magnitude of the input dataset affected this accuracy. As expected, MultiML accuracy increased with the size of the input data and the number of included omics types  or at sample classification using ML (MultiML).As multi-omic studies become more common, guidelines have been proposed for dealing with experimental issues  associated with specific omics data typesFoM have traditionally been used in analytical chemistry to describe the performance of instruments and methods. These terms can also be intuitively applied to sequencing platforms, but we noticed that the meaning and relevance of FoM slightly differ for both types of technologies. Here, we explain FoM definitions across omic platforms and discuss which of these metrics are critical to each data type. Detection limit, selectivity, coverage, and identification are FoM with critical influence on the number of features comprising the omics dataset, which in turn affects the power of the technology to identify features with true signal changes. Power diminishes as the number of features increases due to the application of multiple testing corrections to control false positives. Reproducibility and dynamic range may also be very different across platforms, and these have a direct impact on the within-condition variability and across-condition differences of the study. Moreover, while sequencing depth critically affects many of the described FoM of sequencing platforms, in MS-based methods, the choice of a targeted or untargeted method strongly influences FoM values. The number of features detected by the omic platform together with the different measurement variabilities across features and the magnitude of change to be detected, represent major components of power calculations for omics data. The highly heterogeneous nature of these factors across omics platforms calls for specific methods for power calculations in multi-omic experiments, which is addressed by MultiPower.MultiPower solves the optimization problem of obtaining the sample size that minimizes the cost of the multi-omic experiment, while ensuring both a required power per omic and a global power. As any power computation approach, MultiPower indicates the sample size required to detect a targeted effect size given a significance threshold. MultiPower graphically represents the relationship between sample size, dispersion, and statistical power of each omic, and facilitates exploration of alternative experimental design choices. Estimates for MultiPower parameters are optimally calculated from pilot data, although they can also be manually provided. The tool accepts normally distributed, count and binary data to facilitate the integration of omic technologies of different analytical nature. Data should have been properly preprocessed and eventual batch effects, removed. In this study, we showcase MultiPower functionalities using sequencing, microarray, and MS data, although the method could be applied to other technologies, such as NMR. Importantly, the optimal sample size can be computed under two different requirements: an equal sample size for all platforms ensuring a common minimal power, or different sample size per omic to achieve the same power. This is relevant for the choice of downstream statistical analysis. Methods that rely on co-variance analysis typically require uniform sample sizes and MultiPower will provide this while revealing the differences in power across data modalities. Methods that combine data based on effect estimates allow sample size differences and can benefit from the equally powered effect estimation. We illustrate the MultiPower method in three scenarios, where different parameterizations are assessed and include both controlled laboratory experiments and cohort data to highlight the general applicability of the method. By discussing interpretations of power plots and the factors that contribute to sample size results, we provide a means to make informed decisions on experimental design and to control the quality of their integrative analysis.The MultiPower approach is not directly applicable to ML methods used for sample classification, as in this case the basic parameters of the power calculation—significance threshold and effect size—are not applicable. Still, sample size estimation is relevant as multi-omic approaches are frequently used to build classifiers of biological samples. This is not a trivial problem because, aside from FoM, feature relationships within the multivariate space are instrumental in ML algorithms. The MultiML strategy calculates samples size for multi-omic applications, where the classification ER can be used as a measure of performance. MultiML is itself a learning algorithm that learns the relationship between sample size and classification error, and uses this to estimate the number of observations required to achieve the desired classification performance. Hence, pilot data are a requisite for MultiML. Additionally, MultiML has been designed to be flexible for the ML algorithm and to evaluate multiple combinations of omics types in order to identify optimized multi-omic predictors.Altogether this work establishes, for the first time, a uniform description of performance parameters across omic technologies, and offers computational tools to calculate power and sample size for the diversity of multi-omics applications. We anticipate MultiPower and MultiML will be useful resources to boost powered multi-omic studies by the genomics community.In this study, we discuss seven FoM, which we broadly classified into two groups: quantitative or analytical FoM include sensitivity, reproducibility, detection and quantification limit, and linear or dynamic range, and qualitative FoM, which are selectivity, identification, and coverage. To describe how they apply to omic technologies, we distinguish between MS and seq-based platforms.49) is also a relevant platform in metabolomics and is incidentally discussed.MS platforms refer to metabolomics and proteomics, which often operate in combination with LC–MS or GC–MS. MS platforms can be used for “untargeted” (measuring a large number of features including novel compounds) and “targeted” assays. While MS is the platform used in most proteomics and metabolomics based studies, NMR , a genome annotation file defines the target features to be quantified, and hence these are known a priori. For region-based assays , the definition of the target feature to be measured  is part of the data analysis process. Applications of RNA-seq to annotate genomes could be considered a region-based assay. We consider here omic assays with a dynamic component regarding the genotype, such as RNA-seq, ChIP-seq, ATAC-seq, Methyl-seq, proteomics, and metabolomics, and also genome variation analyses. However, single-cell technologies are not included, as they require a separate discussion.Analytical FoM quantify the quality of an analytical measurement platform and are defined at the feature level . We describe FoM as properties of a calibration line that displays the relationship between the measured value and the true quantity of the feature in the sample Fig. . In our Lastly, while analytical FoM are defined at the feature level, omic platforms by nature measure many features simultaneously and consequently, the FoM may not be uniform for all of them. For example, accuracy might be different for low vs. highly expressed genes or polar vs. apolar metabolites. In this study, we consider FoM globally and discuss how technological or experimental factors affect the FoM of different ranges of features within the same platform.The MultiPower R method Fig.  performsPi) and a minimum average power for the whole experiment (A). Equation (xi):I is the number of omics, ci is the cost of generating a replicate for omic i, α is the significance level, and f represents the power function.MultiPower minimizes the total cost of the multi-omics experiment while ensuring a minimum power per omic (Equation  describexi) per condition and for a particular omic i, the significance level (α), and other parameters that depend on the nature of the omic data type. For normally distributed data, a t-test is applied and the power for omic i is expressed as i is the true difference of means in absolute value to be detected, and σi is the PSD considering two experimental groups. Count data obtained from sequencing platforms can be modeled as a negative binomial distribution (NB) and an exact test can be applied to perform differential analysis. In this case, the power of omics i is estimated as described in ref. 50, where power is expressed as ϕi the dispersion parameter of the NB that relates variance and mean (see Eq. (ωi) between both groups as well as the average counts (μi).We calculate statistical power under the assumption that the means of two populations are to be compared in the case of count or normally distributed data. Consequently, power is in these cases a function of the sample size  and the sample size, but variability is not considered.α*). We followed the strategy proposed in other studies53 given by Eq.  to the optimization problem in Eq. (xi) is calculated and the initial estimation of x is set to x = maxI{xi}. Next, the second constraint on the average power is evaluated for x. If true, the optimal sample size is xopt = x. Otherwise, x is increased until the constraint is met. Note that, under this sample size scenario, the cost per replicate does not influence the optimal solution xopt.Under the first scenario, adding the constraint of equal sample size for all omics  is the power for sample size n, given the parameters of omic i.Under the second scenario, allowing different sample sizes for each omic, the optimization problem in Eq.  becomes https://github.com/ConesaLab/MultiPower.The MultiPower R package implements the described method together with several functionalities to support both the selection of input parameters required by the method and the subsequent interpretation of the results. The R package and user’s manual are freely available at The MultiPower package requires different parameters to compute the optimal sample size. As the choice of parameters can be challenging, MultiPower can estimate them from pilot or similar existing data. The multi-omic pilot data must have two groups and at least two replicates per group. The algorithm assumes data are already preprocessed, normalized, and free of technical biases. Both normally distributed and count data are accepted. We recommend raw count data to be provided for sequencing technologies as MultiPower deals with the sequencing depth bias. However, when count data contains other sources of technical noise, we recommend previous transformations to meet normality  and indicating MultiPower that data are normally distributed. We also recommend removing low count features from sequencing data. For data containing missing values, these must be removed or imputed before running MultiPower. MultiPower also accepts binary data . In this case, values must be either 0–1 or TRUE/FALSE.power.t.test function from stats R package , while the sample_size and est_power functions from R Bioconductor RnaSeqSampleSize package54 are used for count data, where a NB test is applied. For binary data, the power.prop.test function from stats R package is used. Moreover, MultiPower transforms parameters provided by the user to fit specific arguments required by these functions, in such a way that magnitudes are maintained roughly comparable for all data types.Each data type requires a different sample size computation. For normally distributed data, MultiPower uses the d (d0) value to homogenize input parameters. Cohen’s d is defined as Δ/σ and does not depend on the scale of the data as occurs for the Δ value. Therefore, the same value can be chosen for all omic platforms. Cohen55 and Sawilowsky56 proposed the classification presented in Supplementary Table d value. MultiPower computes the Cohen’s d value for all omic features and applies this value to estimate the set M1 of DE features (those with d > d0), which are called pseudo-DE features. We recommend setting the same Cohen’s d initial value (d0) for all the omics although MultiPower allows different values for each one of them.The power function for normally distributed data depends on the effect size, i.e., the true difference of means to be detected . The delta parameter depends on the dynamic range of the omic and may be nonintuitive following data preprocessing. On the contrary, the fold change and mean counts are used in count data to estimate power. Therefore, MultiPower uses instead the Cohen’s d when comparing proportions in groups A and B is Cohen’s h, which can be defined as h = |φA − φB|, where φi = 2 arcsin √pi, and pi is the proportion of 1 or TRUE values in group i. Classification in Supplementary Table h.The equivalent to Cohen’s p1) for each omic. Given the number of features in a particular omic (m), the expected number of DE features can be simply represented as m1 = mp1. The RnaSeqSampleSize package50 estimates the expected number of true detections r1 for count data. For normal or binary data, r1 = m1 is assumed(as in ssize.fdr R/CRAN package57).The multiple testing correction is applied to each omic analysis (see Eq. ), takingσ) is computed as the percentile Pk of the PSDs for all the pseudo-DE features, where Pk is set by the user . Thus, the Δ estimation should equal the chosen PSD. However, to avoid dependence on a single value, MultiPower evaluates all the pseudo-DE features with PSD between percentiles Pk−5 and Pk+5 and the Pk100 −  of the corresponding Δ values is taken as conservative choice. For count data, MultiPower estimates the parameter w that considers the different sequencing depth between samples as w = DB/DA, where Di is the geometric mean of the sequencing depth of the samples in group i divided by median of the sequencing depth of all samples (MSD). To compute the rest of parameters, count values are normalized by dividing them by this ratio, that is, the sequencing depth of the corresponding sample divided by MSD. To be consistent with the previous choice for normally distributed data, the variance per condition (σ2) is also estimated as the percentile Pk of the variances for all pseudo-DE features and conditions. Mean counts (μ) are obtained as the percentile Pk100− of mean counts in the reference group (A) corresponding to pseudo-DE features with variance between percentiles Pk−5 and Pk+5. The dispersion parameter (ϕ) is then derived from Eq. (ω) is estimated as the percentile Pk100− of the fold changes corresponding to pseudo-DE features with a PSD between percentiles Pk−5 and Pk+5. For binary data, the proportions chosen to estimate power correspond to the Pk100− of pseudo-DE features for Cohen’s d, and are stored in the delta output parameter of MultiPower. As variability is not considered for this data type, dispersion power plots are not generated in this case.The intrinsic characteristics of each omic feature may result in different power values for each one of them. To have a unique power estimation per omic, an average power parameter from the distribution of such parameters across pseudo-DE features must be provided. For normally distributed data, the PSD parameter . In both plots, the power for the estimated optimal sample size or the fixed dispersion value is represented by a square dot.To provide answers to these and similar questions, MultiPower returns several diagnostic plots  to be detected and allowing a smaller sample size without modifying the required power. To perform this analysis, the postMultiPower function can be applied, which computes the optimal sample size for different values of Cohen’s d, from the initial value set by the user to dmax = mini in I{P90i(d)}, where P90i(d) is the 90th percentile of the Cohen’s d values for all the features in omic i. This choice ensures sufficient pseudo-DE features to estimate the rest of the parameters needed to compute power.If the optimal sample size estimated by MultiPower exceeds the available budget, researchers may opt for increasing the effect size  and returns both a summary of the power and optimal sample size for each comparison, and a numerical and graphical global summary for all the comparisons. In this global summary, the global optimal sample size is computed as the maximum of optimal sizes obtained for the individual comparisons. Therefore, the solution given by MultiPower method does not allow a different sample size for each group. Users must be aware that different optimal sample size could be obtained in this case.Although MultiPower algorithm is essentially defined for a two groups comparison, the MultiPower R package supports experimental designs with multiple groups. Assuming that a pilot dataset is available, the O omic data matrices of dimension N observations × P predictor variables, and a categorical response variable Y providing the class each observation belongs to  are selected to build a class predictor using the ML algorithm, sampling strategy (SS), and cross-validation (CV) method indicated by the user. The algorithm incorporates a LASSO variable selection step to reduce the number of predictors and computation time. LASSO regression is incorporated at the performance evaluation step to avoid overfitting. This process is repeated 15 times as default, and for each iteration the classification ER of the predictor is computed. These data are used to build a polynomial model that describes the relationship between ER and the number of observations in the multi-omics dataset, which can be then used to estimate the sample size required for a given ERtarget. Supplementary Fig. The MultiML method deals with a multi-omic sample size estimation problem, in which we have prior data consisting of a list of  to Fig. . The numER_calculator, which basically takes multi-omic pilot data and returns the estimated sample size. This function requires an ERtarget, which is the maximum classification ER that the user is willing to accept in the study. When not provided by the user, MultiML takes the ER value obtained from the pilot data. Users may select from two CV methods, tenfold and leave-one-out CV, and prediction performance results are averaged across iterations. MultiML can operate with any user-supplied ML algorithm, provided that this is a wrapped R function with input and output formats supported by MultiML. By default, MultiML includes RF and PLS-DA as ML options. For RF the R package randomForest58 is required. In this method, the classification ER is calculated after constraining all selected omics variables into a wide matrix. For PLS-DA the mixOmics R package59 is required. In this case, each omics data matrix is reduced to its significant variables and analysis is performed maintaining a three-way N × P × O structure, where N are individuals, P are the significant variables, and O are the different omics in the study. If PLS-DA is selected as ML method, the SSs may or not be balanced, returning either an overall classification ER or a balanced ER calculated on the left-out samples. For RF method, only ER is available as both methods give similar results. MultiML also provides different prediction distances for PLS-DA and RF used to assign a category to samples. For PLS-DA, maximum distance, distance to the centroid, and Mahalanobis distance were implemented. These prediction distances can be defined as a model with H components. Given NnewInds new individuals and their corresponding omic data matrix Xnewinds, the predicted response variable Ŷnewinds can be computed as follows:W is a p (variables) × H matrix containing the loading vectors associated with X; D is a p × H matrix containing the regression coefficients of X on its H latent components; and B is an H × n  matrix containing the regression coefficients of Y on the H latent components associated to X. The predicted scores (Tpred) are computed as:MultiML is implemented as a set of R functions that calculate the classification ERs at increasing number of observations, fit the sample size predictive model and graphically display results. Auxiliary functions are included to prepare multi-omic data and optimize computational requirements. The main function is 60. The RF classifier is trained using bootstrap aggregation, where each new tree is fit from a bootstrap sample of the training observations zi = . The OOB error is the average error for each zi calculated using predictions from the trees that do not contain zi in their respective bootstrap sample. The prediction distances are then applied to assign a category to each new sample. To reduce the number of omics variables and computational time, a generalized linear model via penalized maximum likelihood is applied. The regularization path is computed for the LASSO penalty using the glmnet R package61. This variable selection step is performed on a random selection of n observations and repeated as many times as required (15 by default) to retain the q variables that best explain the classification vector Y. The algorithm then takes a new set of n′i observations and, uses only the q variables to calculate the classification ER using the ML and CV method chosen by the user  number of ticks and iteratively increases them until the addition of new ticks does not improve the accuracy of the sample size prediction curve. The algorithm termination protocol implemented in MultiML, forces a stop when at least 12% of the range of values of three consecutive models (ticks addition steps) are equal. Finally, a first-order-smoothed penalized P-spline calculation is performed to model the relationship between the number of samples and the classification ER (refs. 63). The degrees of freedom of this model are the number of observation subsets evaluated (ticks) minus 1. The model is used to predict the sample size required to obtain a given classification error.In turn, for RF, out-of-bag (OOB) estimate was includedRequiredTimeTest allows users to estimate the time required to run the full predictive model at the local installation. For users who can benefit from parallelization options, we have implemented the slurm_creator R function that creates a. sh script to run MultiML calculations in a SLURM cluster. Moreover, MultiMIL can be run incrementally. The function Previous_CER allows the utilization of a previous MultiML result with N samples in a new MultiML calculation that expands the size of the prior dataset by M samples, thereby significantly accelerating the calculation of the new model.MultiML is a computationally intensive algorithm. The function ErrorRatePlot prints graphically the relationship between different sample sizes and their corresponding classification ERs. An example is shown in Fig. target not achievable with the pilot dataset. The user can also create a comparative plot of all omic combinations to determine the best contributing data modality to an accurate classification by using Comparative_ERPlot function.MultiML returns all numerical data of the ML models created to fit the sample size prediction model. This includes the evaluated subsets of observations and data types, the classification ER values obtained at each iteration, and the predicted sample size together with the margin of error of the prediction. Additionally, the function Further information on research design is available in the Supplementary InformationReporting Summary


The analysis of tumours using biomarkers in blood is transforming cancer diagnosis and therapy. Cancers are characterised by evolving genetic alterations, making it difficult to develop reliable and broadly applicable DNA-based biomarkers for liquid biopsy. In contrast to the variability in gene mutations, the methylation pattern remains generally constant during carcinogenesis. Thus, methylation more than mutation analysis may be exploited to recognise tumour features in the blood of patients. In this work, we investigated the possibility of using global CpG (CpG means a CG motif in the context of methylation. The p represents the phosphate. This is used to distinguish CG sites meant for methylation from other CG motifs or from mentions of CG content) island methylation profiles as a basis for the prediction of cancer state of patients utilising liquid biopsy samples. We retrieved existing GEO methylation datasets on hepatocellular carcinoma (HCC) and cell-free DNA (cfDNA) from HCC patients and healthy donors, as well as healthy whole blood and purified peripheral blood mononuclear cell (PBMC) samples, and used a random forest classifier as a predictor. Additionally, we tested three different feature selection techniques in combination. When using cfDNA samples together with solid tumour samples and healthy blood samples of different origin, we could achieve an average accuracy of 0.98 in a 10-fold cross-validation. In this setting, all the feature selection methods we tested in this work showed promising results. We could also show that it is possible to use solid tumour samples and purified PBMCs as a training set and correctly predict a cfDNA sample as cancerous or healthy. In contrast to the complete set of samples, the feature selections led to varying results of the respective random forests. ANOVA feature selection worked well with this training set, and the selected features allowed the random forest to predict all cfDNA samples correctly. Feature selection based on mutual information could also lead to better than random results, but LASSO feature selection would not lead to a confident prediction. Our results show the relevance of CpG islands as tumour markers in blood. Classifiers based on global methylation profiles of cancer tissue were established in recent years and are in the works for several more entities. An example for global methylation classifiers is the classifier for central nervous system (CNS) tumours of the German Cancer Research Center (DKFZ). It works with Illumina methylation assays of fresh or formalin-fixed, paraffin-embedded (FFPE) material. It can distinguish the approximately 100 known tumour types of the CNS and was even shown to be more precise than conservative histological classification for some tumour types . Both clLiquid biopsies are likewise of emerging interest in the medical field in the last few years and have even started to be established in clinical use. The first liquid biopsy diagnostic tool was approved by the U.S. Food and Drug Administration (FDA) in 2016 and would identify small-cell lung cancer with mutations in the cell-free DNA (cfDNA) .Liquid biopsies as a diagnostic or prognostic tool are a subject of research for many other tumour types and diseases. Not only genetic but also epigenetic biomarkers are currently under evaluation for cfDNA-based liquid biopsy. The methylation patterns of cfDNA have been shown to be useful for the identification and even localisation of different tumour types with a probabilistic method called CancerLocator, which uses self-computed CpG clusters .Panagopoulou et al. were concerned with the methylation of certain loci typical for breast cancer but also took into account the level and size of cfDNA fragments. They were able to identify breast cancer patients with their liquid biopsy, utilising a logistic regression model as classifier .In these works, CpG sites or clusters were used for classification. However, approximately 70% of annotated gene promoters are associated with CpG islands (CGI) which are, on average, 1000 base pairs (bp) long . ImportaThis work aims to address the question of whether CpG islands rather than CpG clusters or sites can be used as a marker for cancers in liquid biopsies. As a model, we used hepatocellular carcinoma (HCC), since there were methylation data of cell-free DNA (cfDNA) samples from tumour patients available online. Between 75% and 85% of liver cancer incidences are HCC. There are around 600,000 deaths from HCC per year worldwide, the incidence as well as mortality being around two to three times higher in men than in women . It is oAll datasets were publicly available and obtained from the Gene Expression Omnibus database. The authors providing the data to the Gene Expression Omnibus (GEO) database stated that informed consent was given by the patients as well as healthy individuals. The methylation and meta data cannot be used to identify or locate a tissue donor and should be fit for research purposes according to the declaration of Taipei.n = 22). No information about the tumour size and stage, fibrotic background, or Child–Pugh score were available. The MELD (Model of End Stage Liver Disease) score and AFP (Alpha-Fetoprotein) were available [Most of the sets were produced with an Illumina Infinium 450k bead chip, except for GSE130748 and GSE110185, which were produced using an Illumina Infinium EPIC bead chip. The cfDNA samples were extracted from plasma, ensuring that nearly only cell-free DNA is investigated. Both healthy cfDNA sample sets were taken from bigger sets in GEO and were used as controls in the corresponding experiments. The cancerous cfDNA was taken from an experiment that aimed to identify HCC liquid biopsies in the background of cirrhosis; we used the samples from patients with tumours  and quality control of the datasets, which was done for each set independently. The normalisation was done with all samples in one set after combining them and discarding sites that do not occur in all samples. RnBeads computes the methylation value of a CpG island as a mean of all associated sites and names the islands after their location on hg19 ,13.The CpG island methylation data was processed in python. We used a random forest as classifying algorithm and tuned its hyperparameters with a random search. The random forest was tuned and trained with features previously selected by three different feature selection methods. In this work, we will also discuss the performance of these three feature selection techniques in combination with a random forest classifier for the application on methylation data. We used the scikit learn library .Depending on the training dataset, the feature selection method has a strong impact on the preciseness of prediction of the used classifier. We tested selection using the ANOVA F-value, mutual information selection, and selection from a logistic regression with l1-penalty (LASSO). The first are both filtering methods, while the LASSO selection is a so-called embedded feature selection method, which is a classification method itself and utilises the shrinkage of the penalty function to select the most important features. Filter methods take the dataset and compute values or thresholds for the features, by which the features are then selected. All selection methods were tested with a k of one to 10, with k being the number of features to select.Both ANOVA and LASSO were used for methylation data in similar works. We decided to additionally test mutual information, since it is a measurement related to the gain methods the random forest uses to choose the optimal split.The hyperparameters tuned included the number of estimators between 100 and 500, the max depth from 4 to 50 nodes, and if bootstrap aggregating should be used, or just conservative random selection. Additionally, the splitting criterion could be both information gain and Gini gain, and there were two splitting thresholds to choose: the minimum samples per leaf after a split between 2 and 100 and the minimum of samples per leaf before a split between 2 and 10. The area under curve of the receiver operating characteristic (AUROC) was used as a scoring function for optimising the random forest, and 500 iterations of randomised parameter settings in the given scales were done.The scripts are available in a Github repository linked in the x- and y-axis. The datapoints display all 229 samples used in this work. The PCs are computed from CpG sites (a) as well as CpG islands (b) as features of the respective input data to the PCA. It can be used to get an overview of the data and see if the liquid biopsy datasets are distinguishable from the blood datasets with unsupervised techniques. It can be observed that the liquid biopsies appear closer to the blood than to the solid tumour samples, which is to be expected. Nevertheless, the liquid biopsy samples can be distinguished clearly from the PBMC as well as the whole blood data. However, the differences between the healthy (green) and cancerous (red) cfDNA are not as clear if the whole dataset is used as input.It is also observable that the differences between the samples shrink if the CpG sites are computed to CpG island methylation: the difference between whole blood and PBMCs against the cfDNA from plasma samples is more distinct. Additionally, the scale of the scatterplot with the PCs computed from CpG sites is way larger. The greater the difference between two samples, the greater the difference in PC values.This indicates that is easier to classify a sample using its CpG sites, rather than its islands; therefore, the feature extraction is of primary concern if a sample has to be classified using its island methylation.Since the PCA shows that the correct distinction of healthy and cancerous cfDNA samples will not be trivial, we employed three different feature selection techniques to shrink the dimension of the features in our dataset. High-dimensional data in relatively low sample sizes are hard to manage for machine learning applications. Therefore, data processing and feature selection are imperative for an efficient learning algorithm. In this work, we tested the ANOVA F-value, mutual information (MI), and LASSO feature selection methods. Given a set composed of all the datasets shown above, a random forest trained with features selected by all three feature selections was able to identify cfDNA samples as healthy or cancerous. The dataset was split randomly to a training set of 60% of the sample size and a test set of 40%. Using ANOVA in combination with random forest yielded an average accuracy score of 0.98 +/– 0.009 in a 10-fold cross validation, in which specificity was 0.96 +/– 0.048 and sensitivity was 0.97 +/– 0.038. Combined with MI selection, the classifier achieved an average accuracy of 0.98 +/– 0.017, showing a sensitivity of 0.99 and a specificity of 0.96 +/– 0.018 representing the best results in classifying the cancerous samples in this composition of data and a randomly split training and test set. With LASSO, the average accuracy in a 10-fold cross-validation was 0.98 +/– 0.018. Sensitivity was 0.98 +/– 0.019 and specificity was 0.98 +/– 0.023. The AUROC and average precision score are nearly 1 in all three combinations, which is due to using AUROC as a scoring function for the hyperparameter tuning. The average accuracy and F1 scores for all three selection methods in combination with a random forest are given in All information was taken from the UCSC  genome browser hg 19. The features were selected from the complete dataset. cfDNA of healthy individuals showed the same methylation state as PBMC samples. Likewise, the cancerous cfDNA showed the same state as the solid tumor samples, except for chr1:2979276-2980758, which shows a mixture of both states in different cfDNA samples.When using all samples save the liquid biopsy samples as a training set and testing on the liquid biopsy samples, only mutual information feature selection led to correctly predicted samples; it was still able to predict some of the samples correctly, but often mispredicted the majority of cfDNA samples of either healthy or cancerous class, depending on the chosen features. The other two feature selection methods could not provide a selection that would lead to acceptable results, predicting almost all cancerous cfDNA samples as healthy (data not shown). After removing the whole blood samples and just training on the solid tumours and PBMC samples, which are the main contributors of DNA fragments in the liquid biopsy samples that we wanted to predict, the ANOVA feature selection outperformed the other two. Mutual information still leads to a better result than random classifier with an AUROC over 0.5, but the mispredicted samples are always the same, with all different numbers of features selected. LASSO could not select CpG islands that enabled the classifier to correctly predict the cfDNA samples. However, ANOVA would always choose features that led to almost 100% accuracy with the cfDNA samples. At its lowest, three features selected led to 100% accurate results, meaning accuracy, sensitivity, and specificity values were all 1. These are the three methylation islands shown in The results displayed were achieved with two PBMC samples removed . These were removed due to being strong outliers in the PCA. Interestingly, predictions from features selected by MI would increase their accuracy when these samples were not removed. The accuracy increased from the displayed 0.569 to 0.709 and the F1 score increased from 0.615 to 0.804. Combined with the other two selectors, the random forests performance increased when removing the two samples.All information was taken from the UCSC genome browser hg 19. The features were selected from solid tumour and PBMC samples. cfDNA of healthy individuals showed the same methylation state as PBMC samples. Likewise, the cancerous cfDNA showed the same state as the solid tumor samples.Circa 2% of the genome contains high CpG density (approximately 1 CpG per 10 bp) in so-called CpG islands located at transcription start sites (TSS) within 50–60% of gene promoters. Typically, 5–10% of the CGIs are hypermethylated in cancer cells, leading to the permanent repression of tumour suppressor genes [Several studies have reported the potential of investigating tumour-specific methylations in blood for the screening and diagnosis of cancer, mostly focusing on the analysis of specific promoters by quantitative Methylation-Specific PCR (qMSP); for example, the Epi proColon assay  that was approved by the FDA (US Food and Drug Administration) for screening colorectal cancer .Here, we were able to build a classifier using methylation data from purified PBMCs and solid HCC tumours as input that could distinguish the cfDNA of a HCC patient from healthy cfDNA with an accuracy, sensitivity, and specificity of 1 using a random forest and features selected by ANOVA. The use of PBMC and HCC alone to build the classifier, without the need to include cfDNA profiles, is particularly interesting, since the abundance of cfDNA samples from plasma is still quite rare. To our knowledge, this is the first time that a classifier for cfDNA has been built by using merely PBMC and solid tumour data. Since this kind of data is free available to the scientific community for a large number of patients with different tumour entities via GEO and TCGA (The Cancer Genome Atlas Program), our approach can be easily tested in other cancer types. Other studies identified cancer-specific methylation sites by comparing the methylation profile of tumour and normal adjacent tissues , by inclOur classifier worked by using the methylation of either CpG sites or island as input. It is difficult to make correct predictions based on methylation values when limited to using island methylation compared to the singular CpG sites, since information is lost when taking the mean of multiple CpG sites. This can already be observed using unsupervised methods such as PCA, where the difference in PC values shrinks significantly between the CpG sites as input and the CpG islands. However, singular CpG sites do not have a physiological impact that is yet known, while the islands in some cases can give insight on the physiology of the cancer from which the DNA originates.The best performing features shown in Thus, these features are interesting not only in their ability to lead a classifier to correctly predict the liquid biopsy samples, but they also could be used to identify functionally relevant players in HCC.When being able to use cfDNA samples and solid tumours as well as healthy liquid biopsies, all feature selections in combination with a random forest would lead to solid predictions of all sample types, cfDNA included, with an accuracy between 0.96 and 0.99 as well as specificity from 0.96 to 0.98 and sensitivity between 0.98 and 1. Thus, if it is possible to obtain cfDNA samples of patients and healthy individuals alike, a prediction for diagnosis could be implemented. This has been already shown in the work of Hlady et al., showing that HCC can be diagnosed in a liquid biopsy even in the background of liver cirrhosis but using CpG sites as markers. That study contained and provided the cfDNA of HCC patients that were used in this work . HoweverIt should be mentioned that the mutual information feature selection is more versatile and less susceptible to changes in the source of its samples; in comparison to ANOVA and LASSO, it would predict with the highest accuracy if whole blood samples were inserted in the training phase, and its accuracy even increased when the two outlying PBMCs were not removed from the set. The ANOVA feature selection would lead to the features that are capable of correctly classifying the cfDNA as cancerous and healthy only with the solid tumour and PBMC dataset. Adding whole blood data or other PBMC sets (data not shown) would lead to a close to random predictor, illustrating the dependence of supervised learning methods on the training set. Mutual information would keep its accuracy or even increase it with a broader training set.With both ANOVA and LASSO selections, the whole blood samples restricted a solid prediction of cfDNA samples when used as part of the training set. A possible explanation is that the whole blood samples contain relatively high amounts of non PBMC-stemming cfDNA, leading to the selection of features that are not sufficient for the prediction of cfDNA extracted from plasma.n = 22 and 10, respectively). Additionally, no pathological characteristics of the tumours were available. This information would be important to prove if our approach can identify only advanced tumours or also tumours at a very early stage. However, our algorithm was able to predict cfDNA samples correctly independently of the AFP value. Since the AFP value correlates with grade, progression, and survival [A limitation of this work is the limited number of cfDNA HCC and healthy samples used (Our data indicate that the development of biomarkers based on tumour-specific versus PBMC-specific island methylation is a promising approach that may be exploited to improve the early detection of cancer and disease monitoring of HCC and other tumours.


Smooth muscles are a specific muscle subtype that is widely identified in the tissues of internal passageways. This muscle subtype has the capacity for controlled or regulated contraction and relaxation. Airway smooth muscles are a unique type of smooth muscles that constitute the effective, adjustable, and reactive wall that covers most areas of the entire airway from the trachea to lung tissues. Infection with SARS-CoV-2, which caused the world-wide COVID-19 pandemic, involves airway smooth muscles and their surrounding inflammatory environment. Therefore, airway smooth muscles and related inflammatory factors may play an irreplaceable role in the initiation and progression of several severe diseases. Many previous studies have attempted to reveal the potential relationships between interleukins and airway smooth muscle cells only on the omics level, and the continued existence of numerous false-positive optimal genes/transcripts cannot reflect the actual effective biological mechanisms underlying interleukin-based activation effects on airway smooth muscles. Here, on the basis of newly presented machine learning-based computational approaches, we identified specific regulatory factors and a series of rules that contribute to the activation and stimulation of airway smooth muscles by IL-13, IL-17, or the combination of both interleukins on the epigenetic and/or transcriptional levels. The detected discriminative factors (genes) and rules can contribute to the identification of potential regulatory mechanisms linking airway smooth muscle tissues and inflammatory factors and help reveal specific pathological factors for diseases associated with airway smooth muscle inflammation on multiomics levels. Smooth muscles are a specific muscle subtype that is widely identified in the tissues of internal passageways, such as vessels, and internal organs, including the lungs and intestines. This type of muscle has the capacity for controlled or regulated contraction and relaxation. Various types of smooth muscles are distributed all over the human body. Airway smooth muscle is a unique smooth muscle type that constitutes the effective, adjustable, and reactive wall covering most of the entire airway from the trachea to lung tissues . SimilarAirway smooth muscle is regulated by various internal and external factors to maintain the balance required for pulmonary oxygen exchange . CytokinAs discussed above, interleukins, such as IL-13 and IL-17, are functionally correlated with the biological processes of ASMCs, and interactions between interleukins and ASMCs may also be correlated with various diseases. Asthma is a typical respiratory inflammatory disease that has been widely reported to be functionally correlated with airway smooth muscles in an inflammatory environment . For exaMany previous studies have attempted to reveal the potential relationships between interleukins and ASMCs at different omics levels. Recently, a specific study on the relationships between asthma-promoting cytokines (IL-13 and IL-17) and ASMCs tried to identify key regulatory factors on the transcriptomics and epigenetics levels. Researchers identified 225 genes around differentially methylated regions by using independent IL-13 and IL-17 and combined interleukins and 2014 differentially expressed transcripts by comparing different cytokine-stimulated groups . HoweverGSE146377. Only samples with methylation and gene expression data were analyzed. Each of the IL-13, IL-17, IL-13 + IL-17, and vehicle groups had 64 samples. Methylation data were generated with Infinium MethylationEPIC and included 786,326 probes. The expression levels of 18,279 genes were profiled with Illumina HumanHT-12 V4.0 expression beadchip. We aimed to investigate the responsive genes of ASMCs to IL-13, IL-17, and IL-13 + IL-17.In March 2020, researchers from the University of Chicago released the gene methylation and expression data of ASMCs under the stimulation of multiple inflammatory factors to the Gene Expression Omnibus database (GSE146377) with more than 500 samples (either transcriptomics or methylation data). All the transcriptomics and gene methylation data were generated from the primary cultured ASMCs. In this study, we aimed at interpreting the biological significance of lung smooth muscle and related inflammatory factors during the initiation and progression of multiple diseases like COVID-19 which has ravaged all over the world recently. Following the goal, we downloaded the methylation and gene expression profiles of primary cultured ASMCs exposed to IL-13, IL-17, IL-13 + IL-17, and vehicle from the Gene Expression Omnibus database under the accession number of The methylation and gene expression profiles of ASMCs have much more features than samples. The Monte Carlo feature selection (MCFS)  was deemm features from the original whole M features (m « M); (ii) the learning of a classification model on the bootstrap dataset for each feature subset, which generates p decision trees from classification model; (iii) the production of p × t decision trees by repeating the above steps t times; and (iv) the calculation of the relative importance score (RI) for each feature. Among the constructed p × t decision trees, a given feature may occur in some of them. The split on a node using such feature in each of these decision trees can reflect its importance, which can be measured by the information gain achieved by such split. Furthermore, the classification ability of the decision tree should also be included. Thus, the contribution of a feature in a decision tree can be the determined by the information gain achieved by the split, the number of samples in the split node and the classification ability of the tree. The RI value of a feature f can be the sum of contributions on all constructed decision trees, which is defined asTo evaluate the importance of features, MCFS generally includes the following steps: (i) the selection of random feature subsets with nf(τ) is a node of feature f in the decision tree τ. The information gain of nf(τ) is expressed as IG(nf(τ)), and (no.innf(τ)) is the number of training samples in nf(τ). u and v are two weighting factors, which is suggested to one.where wAcc is the weighted accuracy, and http://www.ipipan.eu/staff/m.draminski/mcfs.html. For convenience, default parameters were used.After all investigated features are assigned the RI values, a feature list is produced by the decreasing order of RI values of features. In this study, we adopted the MCFS program downloaded from Incremental feature selection (IFS)  is an itAs mentioned in section “Incremental Feature Selection,” a powerful classification algorithm is necessary for the IFS method. This study tried four classification algorithms: random forest (RF) , supportRandom forest  is an asC was set to 1.0.Support vector machine  is a stak training samples with the least distance to the query sample ; (4) the estimation of the label distribution of such k nearest training samples; and (5) the prediction of labels for the query sample by using the class label with the highest distribution frequency. In this work, the tool “IBk” in Weka  , a widelX is a 0–1 matrix indicating the predicted class of each sample, Y is a 0–1 matrix representing the actual classes of all samples, and cov represents the covariance of two matrixes.where In addition, the accuracy on each category and overall accuracy (ACC) were also calculated to fully indicate the performance of each model.In this study, we employed several computational methods to investigate the methylation and gene expression profiles of ASMCs. Samples were divided into four groups: the control group, IL-13 stimulation group, IL-17 stimulation group, and combined (IL-13 and IL-17) stimulation group. We organized the data into three types: the methylation data of the four groups, the expression data of the four groups, and the combined data of the four groups. For each type of data, we utilized a similar analytical pipeline. The entire procedures are illustrated in Y-axis and number of used features as X-axis was plotted for each classification algorithm, as shown in For methylation data, we first used MCFS to evaluate each feature, obtaining a feature list, which is available in The similar analytical pipeline was applied on the gene expression data. A feature list was first obtained according to the results of MCFS, which are provided in Finally, for combined data, we did the same test. The feature list yielded by the MCFS method is provided in The results for RIPPER indicated that datasets containing only epigenetic data with RIPPER and the MCC of 0.319 might be unacceptable for further analyses and that the use of methylation data might be ineffective for constructing reliable quantitative rule-based models for distinguishing the different statuses of ASMCs. Expression data and combined data could provide an optimal RIPPER MCC of approximately 0.900, validating the reliability and efficacy of the features and rules learned from the two datasets.topGO v2.38.1) on them. The results are provided in For all three datasets, the best optimum classifiers all used SVM as the classification algorithm. In detail, for methylation data, the optimum SVM classifier adopted top 4940 features, while the optimum SVM classifiers used top 3440 and 3103 features, respectively, for other two datasets. Their corresponding genes were called optimum signatures (genes) for the corresponding dataset. To reveal the potential biological functions that optimum genes are correlated with, we performed GO enrichment analyses using R package  that were remarkably correlated with the interactions between interleukins  and ASMCs, but also established specific rules to distinguish four ASMC statuses: quiescent, IL-13 activated, IL-17 activated, and IL-13–IL-17 combined activated. Similar signature analyses have been validated under three conditions, i.e., single transcriptomics level, single epigenetic level, and combined transcriptomics and epigenetic levels. All the identified signatures and rules were validated on the basis of recent publications, indicating the efficacy and accuracy of our prediction. Given the limitation of this manuscript’s length, we only chose several typical genes for introduction. The detailed discussion on the signatures and rules is given below.BEND6 with specific methylation alterations on the first exon (cg08811259). BEND6 has been widely reported to be functionally correlated with the Notch signaling pathway  targets the 5′ UTR of KCNC2. This gene is a core regulator of the voltage-gated potassium channel and has been confirmed to participate in the pathogenesis of multiple diseases, including extratemporal epilepsy  that contributes to distinguishing the different statuses of ASMCs. All such genes/transcripts have also been further validated to be effective in accordance with recent publications.MAP3K8, a member of the serine/threonine protein kinase family. MAP3K8 has been confirmed to be associated with typical differential expression levels in systematic neutrophilic inflammation involving airway tissues  for detailed discussion. For metal ion binding, correlated with ciliary base, calcium ion binding has been shown to regulate the ASMCs related inflammatory reactions via regulating the function of ciliary base  and (2) GO:0001666 (response to hypoxia). Early in 2014, a systematic network analyses on the transcriptomics profiling of airway smooth muscle tissue confirmed that focal adhesion associated pathways play irreplaceable role for physical or pathological inflammatory effects like asthma related inflammation  and GO:0017147 (Wnt-protein binding) for detailed analyses. For multi-omics data, the GO term seems to be more general. Cell division has been widely shown to be correlated with inflammatory responses in the airway related tissues , laying a foundation for fully demonstration on the inflammatory factor medicated regulations on ASMCs. Methodologically, we confirmed that the application of multi-omics biomarkers for GO enrichment analyses may improve the efficacy and accuracy for disease associated function exploration, providing an alternative approach for pathological studies.Via multiple machine learning models, we identified a group of signatures for the different statuses of ASMCs on the transcriptomics, epigenetic, or dual-omics level and established several quantitative rules on the multiomics level for the classification of cells with different biological/pathological statuses. All the qualitative signatures and quantitative rules have been validated by recent publications, confirming the efficacy and accuracy of our analyses. By summarizing the results, we conclude that the use of transcriptomics data may be more appropriate than that of epigenetic data to classify ASMCs under different activation conditions. Moreover, we conclude that the combined use of transcriptomics and epigenetic data is highly effective and accurate for cell classification.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146377.Publicly available datasets were analyzed in this study. This data can be found here: TH and Y-DC designed the study. Y-HZ, ZL, and TZ performed the experiments. Y-HZ, LC, and HL analyzed the results. Y-HZ, ZL, TZ, and HL wrote the manuscript. All authors contributed to the research and reviewed the manuscript.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.