Translational efficiency change is an important mechanism for regulating protein synthesis. Experiments with paired ribosome profiling (Ribo-seq) and mRNA-sequencing (RNA-seq) allow the study of translational efficiency by simultaneously quantifying the abundances of total transcripts and those that are being actively translated. Existing methods for Ribo-seq data analysis either ignore the pairing structure in the experimental design or treat the paired samples as fixed effects instead of random effects. To address these issues, we propose a hierarchical Bayesian generalized linear mixed effects model which incorporates a random effect for the paired samples according to the experimental design. We provide an analytical software tool, “riboVI,” that uses a novel variational Bayesian algorithm to fit our model in an efficient way. Simulation studies demonstrate that “riboVI” outperforms existing methods in terms of both ranking differentially translated genes and controlling false discovery rate. We also analyzed data from a real ribosome profiling experiment, which provided new biological insight into virus-host interactions by revealing changes in hormone signaling and regulation of signal transduction not detected by other Ribo-seq data analysis tools. Translational control is a key component in the regulation of gene expression. It is critical for growth, maintaining homeostasis, and responding to stress . AberranHigh-throughput techniques such as RNA sequencing (RNA-seq) have been widely used to assess gene expression. These techniques use total mRNA levels as a proxy for protein abundance. However, protein levels do not always correlate closely with total mRNA  because Ribo-seq entails sequencing of ribosome-protected fragments (RPFs): short tracts of mRNA inside the translating ribosome and thus protected from ribonuclease digestion. RPFs are sequenced in parallel with total mRNA from the same samples to compare levels of translation between different treatments after controlling for transcript abundance. More specifically, both Ribo-seq and RNA-seq are performed on the same initial translation-arrested lysate; part of the lysate is digested with RNase to degrade all mRNA not protected by a translating ribosome and is then sequenced to produce the RPF counts; another part of the lysate is sequenced without being digested to produce the total mRNA transcript counts. Any sample-specific biological or technical effects incurred prior to RNase digestion will affect both RPF and mRNA. It is therefore desirable to model a shared random effect for RPF and mRNA counts from the same sample.A common goal in the analysis of Ribo-seq data is to identify genes that exhibit differential translational efficiency across conditions, i.e., differentially translated genes (DTGs). The data matrix to be analyzed consists of both RPF counts and transcript counts for all genes in each sample . SeveralDESeq2 and edgeR use a conceptually similar model: a generalized linear model (GLM) based on the negative binomial distribution with a log link function, where the differential translational efficiency is represented by an interaction term in their model. These methods do not accommodate random effects. Hence, the pairing structure is handled by having a fixed effect for each sample. However, it is more natural to think of the sample effects as random because we are interested in generalizing the conclusion to the whole target population, not just the specific samples in the study. Also, the random effects model tends to produce better estimates when the number of observations per sample is small , which iBaySeq uses an empirical Bayes method for paired RNA-seq experiments that models counts from paired samples as beta-binomial distributed and estimates prior distributions for mean and dispersion parameters from the data using the maximum likelihood method. Under this model, DTGs are identified based on the posterior probability that there is a difference in ratio for paired counts between treatment conditions. While able to explicitly model the paired structure of Ribo-seq, baySeq does not produce effect size estimates and makes it difficult to represent and interpret results for complex designs.RiboDiff implements a method similar to edgeR or DESeq2, but separately estimates the dispersion trend with respect to the mean count for total mRNA and RPF, respectively. Xtail uses an ad-hoc approach to adapt DESeq2 to Ribo-seq: rather than fitting a GLM with a shared parameter for mRNA and RPF from the same sample, it fits models to the mRNA and RPF counts separately, and then compares the estimated log fold change parameters using a simulation technique motivated by empirical Bayes. Both RiboDiff and xtail cannot accommodate designs with more than two treatment conditions or a block effect for paired counts.p-value for each pair of mRNA and RPF counts, i.e., one p-value per gene per sample, which is intended to test the null hypothesis that the RPF count is as expected given the mRNA count. Babel then uses an ad-hoc approach to combine the set of p-values across samples into a single p-value for each gene for the null hypothesis that the gene does not exhibit differential translational efficiency. Babel cannot represent experimental designs with more than two treatment conditions and does not estimate an effect size for translational efficiency.Babel constructs a We propose a fully Bayesian hierarchical GLMM  for detecting differential translational efficiency in Ribo-seq experiments and present a variational Bayesian algorithm and software tool, riboVI, to fit the model. A variational Bayesian algorithm is a computationally efficient method for approximating Bayesian posteriors in order to conduct inference, and can be particularly beneficial in high-dimensional applications like the analysis of Ribo-seq. Variational Bayesian approaches have been used successfully in other applications to high-throughput sequencing data .Our proposed variational Bayesian method improves upon available methods for detecting DTGs by addressing the limitations described above. First, our modeling approach is tailored to Ribo-seq experiments, explicitly models the parameter of interest regarding translational efficiency, and uses the posterior distribution to detect DTGs. Second, our Bayesian method naturally borrows information across all genes and improves the performance of statistical inference in settings with a small number of replicates but a large number of variables. Information borrowing across genes occurs through the hierarchical Bayesian model structure, where gene-specific parameters share appropriate prior distributions. More specifically, the posterior estimation of the gene-specific translational efficiency utilizes information both from data on this specific gene and the prior distribution shared by all genes. Third, our model can represent the pairing between RPF and total transcript counts as a shared random effect. Fourth, our method more accurately classifies genes that exhibit differential translational efficiency and exhibits superior false discovery rate control over other methods.In the remaining sections of this paper, we specify our model and outline our variational Bayesian algorithm to fit the model to Ribo-seq data. We then describe the simulation studies and real data analysis that we conducted to compare the performance of our method to existing methods. Finally, we discuss remaining areas for improvement and future research.Ribo-seq data can be visualized as a table of counts, with rows for genes and columns for samples . RPF reaBecause paired RPF and mRNA counts are derived from the same biological sample and undergo several technical preparation steps together before being processed in parallel, they are expected to share some random biological and technical effects in common. Hence, incorporating such random effects is important to appropriately analyze Ribo-seq data from paired experimental designs.Arabidopsis plants.In this section, we first describe our GLMM model that incorporates a random effect for the pairing, present our variational Bayesian algorithm for obtaining posterior distributions, and then describe how to control multiple testing error. Finally, we describe the experimental procedures for a Ribo-seq experiment with g = 1, … , G, i = 1, … , N, and j = 1, 2, let ygij represent the read count for gene g, sample i, and preparation j, where j = 1 corresponds to RNA-seq count, and j = 2 corresponds to Ribo-seq count (RPF). We use the following hierarchical model for the data:For λgij is modeled by the fixed effects βg due to treatment and preparation (RPF or RNA-seq) and random effects ugi, where ugi explicitly represents the sample-specific random effects shared by each pair of RNA-seq and RPF counts. Through ugi, our model handles variation due to biological replicates and accommodates overdispersion compared to a simple Poisson distribution. For an experiment with two treatment conditions, the parameter vector βg has four elements and we denote them as . We let βg0 be the mean expression level  for RNA-seq data under the control condition; βg0 + βg1 be the mean expression level for RNA-seq data under treatment; βg0 + βg2 be the mean level for RPF under control condition; and βg0 + βg1 + βg2 + βg3 be the mean level for RPF under treatment. Accordingly, we have More specifically, the Poisson mean βg2 = (βg0 + βg2) − βg0 for the control group and βg2 + βg3 = (βg0 + βg1 + βg2 + βg3) − (βg0 + βg1) for the treatment group. The log-scale difference in translational efficiency between treatment and control is therefore βg3. In other words, the parameter βg3 corresponds to the interaction effect between treatment and preparation (Ribo-seq and RNA-seq), and hence it represents differential translational efficiency between treatments for gene g. With this parameterization, to determine differential translational efficiency amounts to testing HA: βg3 ≠ 0 for each gene g.With this parameterization, translational efficiency is modeled by βg3 can be modeled by a mixture of a continuous distribution and a point-mass at zero across all genes. We use such a mixture distribution as the prior distribution for βg3:π0) represents a Bernoulli distribution with parameter (π0). The null hypothesis that βg3 = 0 is then equivalent to the following hypothesis for each gene.βgp parameters  because we are not concerned with testing other sharp nulls. This also simplifies the algorithm required to fit the model. If testing for differential expression based on RNA-seq is also of interest, we could use a mixture distribution of Gaussian and point mass at zero as the prior distribution for the parameter βg1.In an experiment, some genes exhibit differential translational efficiency while other genes do not. Hence We use conjugate prior distributions for the hyperparameters:βg3. In this subsection, we give a brief introduction to variational inference in general, then describe the algorithm we use to fit our model specified in Since the Bayesian posterior for our model is not analytically tractable, we propose a novel variational inference algorithm to approximate the posterior in order to carry out inference. Our algorithm extends an existing algorithm for variational inference with Bayesian generalized linear mixed models to include our model’s hierarchical structure and the mixture prior for q(θ) will be a member and then minimizing the Kullbeck-Liebler divergence to the true posterior p(θ): KL(q(θ)‖p (θ|y)), where θ represents the set of model parameters and y represents the data.Variational inference is an alternative to sampling-based inference like Markov chain Monte Carlo (MCMC) for approximating an intractable posterior in Bayesian methods. Variational inference finds a close approximation to the true posterior by numerical optimization. This is achieved by first defining a family of distributions of which the variational posterior Often the family of the variational posterior is defined by imposing what is called a mean-field assumption: the variational posterior must be factorizable. This enables the derivation of closed-form expressions for the optimal values of the individual factors given the other factors. These expressions form the basis of an iterative algorithm. During every iteration, each factor is updated to its optimal value given the current values of the other factors before recalculating the Kullbeck-Liebler divergence. The algorithm terminates when the Kullbeck-Liebler divergence converges to a stable value.Our algorithm follows this basic structure, and is an extension and synthesis of work done by q(θ) must satisfy Eq. D represents the set ϕ represents the set of all hierarchical mean and variance parameters.We define the distributional family of the variational posterior with the following mean-field assumption: the distribution q(ϕ) instead of qϕ(ϕ). We use qm as shorthand for the variational factor associated with parameter m, and For readability we will suppress the subscripts on the factors of the variational distribution wherever context makes it clear which variational factor is referred to; e.g., We will first describe the method for updating the variational distribution for parameters where the mean-field assumption alone implies that the optimal value of the variational distribution has the same functional form as the prior for that parameter, which we call conjugate updates. Next, we describe the method of updating the variational distribution for parameters where this is not the case, called non-conjugate updates.q(D) and q(ϕ) in the factorized distribution in Eq. q(D) or q(ϕ), m can represent either D or ϕ, and m in Eq. q(D) and q(ϕ) are given in the supplementary material.t  using posterior probabilities that each gene is not differentially translated. If we have a collection of α by findingThen Bayesian FDR can be controlled at level Arabidopsis plants  were rub inoculated with sap from uninfected (mock) or RCNMV-infected Nicotiana benthamiana plants (Arabidopsis reference genome (TAIR10) using STAR v.2.5  using four sets of simulation studies. Simulations A, B, and C are model-based simulations representing progressively larger deviations from our assumed model. Simulation D is a real data-based simulation where the actual distribution of data is unknown.λgij) modeled as in Eq. βg and the sample effects ugi for each gene (edgeR assumes both are fixed). Then we used the empirical means and variances of the estimated βgp  as the means and variances of normal distributions from which to draw βgp parameters. For approximately 80% of genes in each simulated data set, we set the βg3 parameter to 0 so that there was no difference in translational efficiency between treatments for those genes. The remaining 20% of genes retained the non-zero value simulated from the normal distribution for βg3 and were considered to be true positives for the evaluation of method performance. We used the variance of the estimated ugi as the variance of a normal distribution centered at 0 from which to draw ugi . After βg and the ugi were drawn, we calculated the mean for each combination of sample and preparation (RPF or RNA-seq) according to Model 1) for each of G = 10,000 genes. Based on these means, count data were simulated from Poisson distributions in Simulation A and from negative binomial distributions in Simulation B. The dispersion parameters for the negative binomial distributions in Simulation B were drawn from the empirical distribution of the gene-specific dispersion values that edgeR estimated for the same real dataset. For both simulation studies, we have two settings with sample sizes n = 2 and n = 4.Model parameters for simulation studies A and B are based on estimates from the real dataset described in G = 10,000 genes from a negative binomial distribution. We then simulated fold changes from a gamma distribution with parameter values estimated from a real data set, as given in n = 2 or n = 4 per condition.Simulation C is inspired by the simulation studies described in the supplemental materials of the RiboDiff paper . First, G = 10,000 genes each.Simulation D is based on resampling from the real data set described in We evaluated each method’s ability to classify DTGs using ROC curves (receiver operating characteristic curves) by plotting the true positive rate (TPR) against the false positive rate (FPR). The ROC curves were averaged over 100 datasets for each simulation setting and presented in We also evaluated each method’s ability to control multiple testing errors by comparing the actual FDR to nominal levels , 2, wherIn addition to our simulation studies, we used riboVI, edgeR, and xtail to analyze a data set from a real Ribo-seq study on Arabidopsis plants, investigating the effect of infection by red clover necrotic mosaic virus (RCNMV). We collected leaves from the mock-group and infected plants at 5 and 8 days post inoculation (DPI) and performed Ribo-seq experiments as described in G = 17,388 genes. After analyzing this dataset using riboVI, edgeR, and xtail, we first compared the number of genes classified by each method as exhibiting differential translational efficiency at nominal FDR levels 0.05 and 0.1. RiboVI identified substantially more genes than the other two methods at the same nominal FDR level, as shown in βg3 in Model (2.1), as a way to identify genes whose change in translation is more likely to be associated with substantial effects on the biological system. Hence, we retained only the genes with an estimated translational efficiency change with an absolute value greater than 0.5  in addition to the FDR threshold for the functional analysis next. Overlap in genes detected by the three methods using this additional criterion is shown in Prior to analysis, we filtered out genes with a large proportion of 0 counts across all samples, retaining http://www.pantherdb.org/)   where geThe larger number of overrepresented GO terms found by riboVI facilitates the interpretation of results, in part because the increased specificity of GO terms from riboVI helps provide a detailed explanation of the biological system under study. For example, GO analysis of xtail-identified genes revealed an overrepresentation of signal transduction (GO:0007165), when analysis of riboVI-identified genes revealed not only signal transduction (GO:0007165) but also hormone-mediated signaling pathway (GO:0009755) and regulation of signal transduction (GO:0009966) thereby providing a more detailed understanding of the system. GO analysis of riboVI identified genes at 5 DPI also revealed terms not identified at all by analysis of xtail identified genes. Specific examples include terms such as cytoplasmic translational initiation (GO:0002183) and rRNA processing (GO:0006364). These findings, unique to riboVI, make biological sense considering single-stranded positive sense RNA viruses first co-opt host translation factors upon infection to produce viral proteins for replication. The results for genes identified at 8 DPI were similar in the sense that riboVI returned more numerous and specific terms which are biologically meaningful. More specific biological insights will be presented in a separate paper.not generated based on our model, our method riboVI outperforms the other methods with higher power for detecting genes that exhibit differential translational efficiency and better control of FDR.In this paper we propose a Bayesian hierarchical model for Ribo-seq data anaysis that incorporates a random sample effect to accommodate the experimental design factors. We derive a computational algorithm to fit our model and compare the performance of our method to the other methods available for analyzing Ribo-seq. Based on a variety of simulation studies where data were In an analysis of real data, riboVI identifies more genes as exhibiting differential translational efficiency than xtail or edgeR at the same nominal FDR. This is not surprising based on simulation results that show riboVI has better power and controls FDR to the desired level. The biological inferences for the detected DTGs need to be validated using complementary evidence. In this paper, we apply functional analysis of the detected DTGs through GO enrichment analyses, which show that riboVI identifies a larger number of more specific GO terms than the other two methods. The additional GO terms that riboVI identifies are associated with functions likely to be affected by viral infection, which makes biological sense. The increased information derived from the GO enrichment analysis of the riboVI gene set facilitates the interpretation of results and helps provide a more detailed understanding of the biological system. In future research, benchmark Ribo-seq data sets may be generated and further validate our method’s performance.A barrier to applying current Ribo-seq analysis methods such as xtail and RiboDiff is their inability to represent experimental designs with more than a single treatment with two conditions. As the complexity of Ribo-seq experiments grows, these methods will not be able to perform the requisite analysis. Our method riboVI is based on a GLMM framework and can easily incorporate different design structures. Note that our current code implementation is limited to 2-treatment comparisons, and we are developing this extension to our codebase, which we hope to release in a follow-up version of the riboVI package.βg3 from our model. This effect is more pronounced when only a small proportion of genes in a data set exhibit substantial changes in translational efficiency. One area for future research is to improve the accuracy of βg3 estimates, and this might be done by modifying the mixture prior for βg3 in Model 2.1. Also, with Bayesian posterior inference, we can modify the hypothesis to test and aim to only identify those genes with a large enough translational efficiency change.While riboVI has higher power than other methods for detecting DTGs, we notice that its estimated translational efficiency changes tend to be smaller than other methods. This is likely due to the shrinkage effect on the parameter https://github.com/dcannonwalker/riboVI.In summary, our method provides a useful advancement over available methods for differential translational efficiency analysis of Ribo-seq data. It demonstrates superior power and FDR control to detect DTGs in comparison with other methods in both simulation studies and in applications to real data. We provide an R package called riboVI which is freely available at


While multiple replications of random sampling remain the favored method of determining the power, when the estimated sample size required to achieve a certain power exceeds the sample number, then simulated samples based on DMM can be used. We introduce an R-Package, MPrESS, to assist in power calculation and sample size estimation for a 16S rRNA gene microbiome dataset to detect a difference between populations. MPrESS can be downloaded from GitHub.Deep sequencing has revealed that the 16S rRNA gene composition of the human microbiome can vary between populations. However, when existing data are insufficient to address the desired study questions due to limited sample sizes, Dirichlet mixture modeling (DMM) can simulate 16S rRNA gene predictions from experimental microbiome data. We examined the extent to which simulated 16S rRNA gene microbiome data can accurately reflect the diversity within that identified from experimental data and calculate the power. Even when experimental and simulated datasets differed by less than 10%, simulation by DMM consistently overestimates power, except when using only highly discriminating taxa. Admixtures of DMM with experimental data performed poorly compared to pure simulation and did not show the same correlation with experimental data  The human microbiome is composed of communities of microorganisms, including bacteria, that live on and in the human body. These bacteria form distinct ecologies that can be explored through targeted deep sequencing and subsequent grouping of its community members through operational taxonomic units (OTUs). By cataloging the OTUs, through counts of mapped sequencing reads, microbial communities can be correlated with personal identity ,2, geogrThere are several analysis techniques that can be used to differentiate microbiomes. One involves an examination of the total OTU table, such as with permutational multivariate analysis of variance (PERMANOVA). Alternatively, the distance between sets of taxa, either with a phylogenetically-dependent metric such as UniFrac  or a phyBeyond either sampling or using simulated data from user-supplied estimations, OTU tables can be approximated by simulation from the initial dataset. Dirichlet mixture modeling (DMM) is one of the most widely utilized methods ,24,25,26With the availability of these models and programs, we examined the capacity of simulated OTU tables based on models to accurately reflect the diversity within the OTU tables and thus the accuracy of the resulting power calculations. The results of our investigations in combination with R-scripts written to generate and analyze the simulation and sampling datasets led us to create an R-package named MPrESS that calculates the power for pre-existing microbiome sets with distinct metadata values using the best practices discussed below, while also allowing for some user-based specification of the parameters as necessary. Creating an R-package both enables the use of pre-existing microbiome analysis tools, such PERMANOVA and DESeq2, and enables users to use a program that is platform agnostic. Additionally, the use of an R-package allows for a diversity in user inputs which can be used to best model the power. Potential user-specified changes include using only the most discriminating taxa in the provided OTUs; the techniques used to expand the provided dataset; and the cut-offs used to determine the minimal power. The MPrESS package also provides the summary and plotting of the results to enable additional researcher engagement with the data.We created MPrESS, an R-package that predicts the minimum number of samples required to discriminate individuals or groups having different metadata variables with sufficient power and is based on our analyses for the optimal calculations for the datasets, as discussed in the results section. A flowchart showing the MPrESS usage is shown in To calculate the necessary power, MPrESS either samples or simulates tables from the initial OTU data . The samp-value cutoff specified or the number of taxa to be used as given by the user to identify the discriminating taxa. The OTUs raw counts are normalized and compared using the negative binomial distributions according to the DESeq2 default parameters [Additionally, power calculations can also be directed to be performed only using the OTUs that are the most discriminating between a given metadata variable. In these instances, DESeq2 is used by MPRESS with the either the FDR-corrected rameters . The OTUp-value and the estimated discriminatory power at each run as a smoothed line with the individual means shown as distinct points [After the power values are calculated, the package returns a MPrESS class object that contains all the power calculation information including the sample number required to perform the power calculation and the final estimation type. Additional information about the estimations and the tests are shown in a table with OTUs profile information at each sample number, which can be accessed by printing or plotting the MPrESS object. The default MPrESS object plot is created with ggplot and shows the mean t points . An examhttp://hmpdacc.org/HM16STR/ (accessed on 6 March 2019) [Publicly available 16S rRNA gene microbiome datasets which incorporate samples from various locations, populations, and health status were used to guide and analyze MPrESS power calculations  . Metadatch 2019) . These sch 2019) . As the ch 2019) . Additioch 2019) , a syndrch 2019) ,37. Sequch 2019)  using PrThe samples downloaded from each study were processed using an in-house 16S rRNA gene data analysis pipeline. OTUs were generated using the default parameters in UPARSE  and taxot-tests  and by simulating based on the DMM (“simulated”). For the HMP dataset, samples from two states in the United States, Missouri and Texas, were used; for the China data, samples from two regions in China, Yunnan and Guangxi, were compared; and for the IBS dataset, samples marked as healthy and diagnosed with IBS were analyzed. We used the default alpha and beta values of 0.05 and 0.95 which are traditional values used to both detect differences and to be confident that these differences are repeatable. These microbiome datasets had significantly different OTUs from each other when they were compared by PERMANOVA analysis using both UniFrac and Bray–Curtis distances. The distances between sampled and simulated datasets (“extra-”) were significantly increased when compared to the distances between OTU tables generated only from sampling and between OTUs only generated from simulations (“intra-”), as calculated with two-sample t-tests . This wat-tests . DespiteGiven that the sampled and simulated derived OTU tables differed by less than 10%, we investigated how the power calculations using PERMANOVA compared between the two approaches, and, if they were different, b = both how consistent the differences were and what some potential influential factors might have been. Since sampling with replacement is used in other available power calculation packages , we alsoAs microbiome variation attributed to an individual’s traits, lifestyle, health status, and environment can often be concentrated in only a few taxa , it is pp-value for differences between the samples from the two metadata of <0.001. We wanted to quantify the extent to which simulated data gave a close approximation of the sampled data to verify 7 imulation simultion can accurately be used for power prediction, as seen in Figure 3. We used the Pearson correlation which calculates the extent to which the simulated data match the direction and level of sampled data versus the extended data. We found that simulated data, however, give a lower p-value for the power but do mirror the sampled dataset more closely than the extended sampling .Last, we investigated whether extenIg the OTU data with a dataset containing only a small sample size with simulated data would give a more accurate result as compared to a dataset that was constructed only from simulated data. We compared tests of datasets with samples of different starting sizes and then appending data either simulated from the starting samples or using completely sampled and simulated data . While ap-value (extension: 15% vs. simulation: 85%) and in power , though the difference was not as pronounced.It is possible that extending the data by sampling performed poorly solely due to the smaller number of samples used to calculate the DMM model, and that it was not an inherent problem with mixing sampled and simulated data within a dataset. To verify that the decreased PERMANOVA similarity was not driven by sample size, we compared sampled and simulated datasets only derived from a small sample sized dataset. We analyzed 60 randomly sampled HMP stool samples, 30 from Missouri and 30 from Texas. We compared the initial 60 samples, 60 simulated samples, and 60 samples extended by appending 20 simulated OTU profiles to 40 sampled using 100 replicates. For each replicate, a new set of 60 randomized samples was selected. We again observed that the simulated dataset constantly represented the sampled dataset more closely in PERMANOVA significance, as measured by the percent difference in the PERMANOVA MPrESS is a novel R software package that enables researchers to determine the minimum number of samples required to address a given study hypothesis using 16S rRNA gene microbiome data with sufficient power. MPrESS expands upon existing power calculation programs by integrating both sampling and simulations into the power estimation. Additionally, the user can compute power calculations based only on a subset of DESeq2 identified taxa. We observed that even when sampled and simulated datasets are different by less than 10%, simulation by DMM consistently overestimates the power necessary, though this is to a lesser extent than either admixed or sampling with replacement. This is especially observable when it is necessary to greatly exceed the initial sample number to calculate the power. Therefore, MPrESS prioritizes the useI existing OTU tables, and only when insufficient numbers of samples are available does it stop using the sampling method and instead performs simulations Iestimate the OTU tables. MPrESS does provide statistics for the power calculation for to the simulation estimates as well as the power calculations after the change in estimation method. This can allow for a visual estimate of the degree of power overestimation.MicrobiomDeSim, require parameter estimation, preferably calculated from existing datasets [The MPrESS package is designed to be used Ih 16S rRNA gene OTU data as they are the most often studied in microbiome research. However, the modular format of MPrESS allows for the easy addition of metagenomic data for the power calculation. The user can specify any taxonomy rank, i.e., species, genus, family, etc., to perform the power calculations. Likewise, the package requires seed data for the simulation; therefore, some preliminary data are required at this time for all power calculations derived from MPrESS. Even those power estimation programs which are fully simulated, such as datasets . While M


Characterizing the topology of gene regulatory networks (GRNs) is a fundamental problem in systems biology. The advent of single cell technologies has made it possible to construct GRNs at finer resolutions than bulk and microarray datasets. However, cellular heterogeneity and sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing GRNs. Additionally, most GRN reconstruction approaches estimate a single network for the entire data. This could cause potential loss of information when single cell datasets are generated from multiple treatment conditions/disease states.To better characterize single cell GRNs under different but related conditions, we propose the joint estimation of multiple networks using multiple signed graph learning (scMSGL). The proposed method is based on recently developed graph signal processing (GSP) based graph learning, where GRNs and gene expressions are modeled as signed graphs and graph signals, respectively. scMSGL learns multiple GRNs by optimizing the total variation of gene expressions with respect to GRNs while ensuring that the learned GRNs are similar to each other through regularization with respect to a learned signed consensus graph. We further kernelize scMSGL with the kernel selected to suit the structure of single cell data.scMSGL is shown to have superior performance over existing state of the art methods in GRN recovery on simulated datasets. Furthermore, scMSGL successfully identifies well-established regulators in a mouse embryonic stem cell differentiation study and a cancer clinical study of medulloblastoma. Gene expression arises from a network of regulatory interactions between transcription factors, co-factors and signaling molecules , 2. ElucA major focus area in clinical research lies in studying the changes in gene coexpression networks across different tissues, cell types/states, and conditions. For example, in the extensively studied breast cancer datasets from the cancer genome atlas, there are four main subtypes of breast cancer . The varRecent advances in next generation sequencing technologies have made it possible to profile the transcriptomes of individual cells, hence capturing expressions of thousands of genes at a cellular resolution. Dozens of algorithms have been proposed for the reconstruction of gene regulatory networks from single cell RNA sequencing (scRNA-seq) datasets , 10. ThiMultiple algorithms have been proposed for joint estimation of networks from high dimensional data. Most of these methods assume that the data has a Gaussian distribution. Seminal papers by , 13 paveg et al.  proposedg et al. , 23, (b)g et al.  and eveng et al. , the lasgraph signals [Recent work in graph signal processing (GSP) extends classical signal processing concepts to data defined on nodes of a graph, i.e.  signals . GSP bas signals . Since g signals –30 have In this paper, we present a multiple signed graph learning algorithm (scMSGL) for joint inference of GRNs from multiple classes (conditions/disease states). Based on the method developed in , scMSGL V is the node set with E is the edge set and i and j. G is an unsigned graph, if edge weights are constrained to only positive values. Combinatorial Laplacian matrix of the unsigned graph G is A weighted undirected graph can be denoted as G is a signed graph. A signed graph G can be decomposed into two unsigned graphs, If the edge weights are allowed to take on negative values, G is a function i. Graph Fourier transform (GFT) of G can then be quantified as:G.A graph signal over an unsigned graph quencies , 33. GFTG, total variation can be quantified based on the decomposition of G into For a graph signal G, Dong et al. [G with the assumption that signals admit low-frequency representation in the graph Fourier domain of G. Thus, G can be learned by minimizing  low-frequency (smooth) representation over i and j are connected by an activating edge, their gene expressions should be similar, i.e low-frequency. On the other hand, if i and j are connected by an inhibitory edge, their expressions should be dissimilar, i.e., high frequency. These assumptions are biologically reasonable and have been validated in [G is learned by minimizing In , 2) is  is 2) isdated in . Based or), proportionality measure   and zeroent}τzi) . These kasure in  and \docN matrices. G, which is also learned by combining information from G reflects the common connections shared across Let G is then:G with G with larger values of Let me as in . Algoritmentclass2pt{minimG, respectively. G to be scMSGL requires the selection of six hyperparameters, three of which control the properties of the positive parts of the learned graphs while the remaining control the negative parts. As mentioned above, The performance of scMSGL is evaluated on both simulated and two real scRNA-seq datasets. For simulated data, learned graphs are compared to ground truth networks to quantify the performance of scMSGL. Signed version of area under precision recall curve (AUPRC) is used as the performance metric during this analysis. We report AUPRC ratio, which is the ratio of AUPRC value of scMSGL to that of a random predictor. More details on how AUPRC is calculated can be found in Additional file ) method  algorith) method , 19 haveHyperparameters of scMSGL are set as described in “ BEELINE  pipelinementclass2pt{minimmentclass2pt{minimd and c.For all datasets, we use mentclass2pt{minimData generation: To validate the performance of scMSGL, we simulate gene expression data from a multivariate zero-inflated negative binomial (ZINB) distribution. The ZINB distribution has been shown to accurately capture the characteristics of single cell datasets in several published studies [G with G to a signed graph by randomly selecting half of the edges and assigning a negative sign to them while assigning a positive sign to the other half. Next, we generate G. Half of the added edges are set as negative edges, while the other half are set as positive. The ZINB simulator is then used to generate datasets k and  studies , 37. Giv studies  and illu studies , 31. Two dataset . Each siSensitivity to the number of cells: We first study the performance of the methods with varying number of cells when the dropout ratio is set to 0.26, mentclass2pt{minimSensitivity to dropout ratio: In the second analysis, we evaluate the performance of the different methods with increasing dropout ratio while fixing the number of cells to 400 and mentclass2pt{minimSensitivity to view similarity: Next, we study the effect of view similarity on the performance of algorithms. Datasets are generated with varying mentclass2pt{minimKernel comparison: Formulation of scMSGL allows us to use various kernels. Therefore, we study how the performance changes with respect to the kernel type. Datasets are created using the BA model and results are shown in Fig. mentclass2pt{minimTime complexity comparison: We compare the different methods based on their run time complexity. We generated datasets using BA model with varying number of cells and number of genes. Table In the first dataset, number of genes, dropout ratio and In the second dataset with increasing number of genes, the number of cells, dropout ration and Central to the differentiation process and many other cellular processes is the expression of right combination of genes or modules of genes. Accurate characterization of the co-expression networks for progenitor and multiple cell types can help in understanding the cascade of cellular state transitions . In this3We first estimated the subgroup specific and the consensus graphs. Based on the results of simulated data, we employ the zero-inflated Kendall’s tau kernel. Next, we calculate the signed node degrees of each gene, i.e., Medulloblastoma (MB) is a highly malignant cerebellar tumor mostly affecting young children . Severalg et al.  used theg et al. .4 Using database . Bulk prdatabase , 49. BasSubgroup specific networks along with the consensus graph were estimated with zero-inflated Kendall’s tau kernel. Table In this paper, we presented scMSGL for joint inference of multiple GRNs from scRNA-seq datasets having multiple classes. scMSGL learns functional relationships between genes across multiple related classes of single cell gene expression datasets under the assumption that there exists a shared structure across classes. The main novelty of our paper lies in the formulation of a highly efficient optimization framework that extends the signed graph learning  approachThere are various aspects of the proposed method that can be considered for improvement as future work. One challenge in implementing scMSGL is how to select the kernel function. This challenge can be addressed by combining information from multiple kernels during learning. An open problem in graph learning literature is hyperparameter selection, which is also a limitation of the proposed method. Current work selects the hyperparameters by searching the values that would result in graphs with desired properties. Future work can improve the accuracy of the learned graphs through better hyperparameter selection and multi-kernel strategies. Computational complexity of scMSGL is quadratic with respect to the number of genes (similar to scSGL) and linear in number of views. Therefore, its application to datasets with very large number of genes is not feasible. However, recent developments in GSP to scale GL to large-scale problems  can be eAdditional file 1. Additional file includes optimization process for scMSGL, definition of Signed AUPRC, simulated data generation process, results on sensitivity of scMSGL to hyperparameter selection and details about selected hyperparameters values of all methods.


EP300, CXCR4, WNT4, ZIC4, MEIS1, SLC8A1, NFASC, ASCL2, KIF5C, SYNGAP1, SEMA4F, ROR1, DPYSL4, ARTN, RTN4RL1, and TLX2. Our findings contribute to enhanced survival outcomes for patients with medulloblastoma. Continued research and validation efforts are needed to further refine and expand the utility of our approach in other cancer types, advancing personalized medicine in pediatric oncology.Childhood medulloblastoma is a malignant form of brain tumor that is widely classified into four subgroups based on molecular and genetic characteristics. Accurate classification of these subgroups is crucial for appropriate treatment, monitoring plans, and targeted therapies. However, misclassification between groups 3 and 4 is common. To address this issue, an AI-based R package called MBMethPred was developed based on DNA methylation and gene expression profiles of 763 medulloblastoma samples to classify subgroups using machine learning and neural network models. The developed prediction models achieved a classification accuracy of over 96% for subgroup classification by using 399 CpGs as prediction biomarkers. We also assessed the prognostic relevance of prediction biomarkers using survival analysis. Furthermore, we identified subgroup-specific drivers of medulloblastoma using functional enrichment analysis, Shapley values, and gene network analysis. In particular, the genes involved in the nervous system development process have the potential to separate medulloblastoma subgroups with 99% accuracy. Notably, our analysis identified 16 genes that were specifically significant for subgroup classification, including  Medulloblastoma (MB) is the most prevalent malignant form of brain tumor among children, accounting for approximately 20% of all central nervous system (CNS) malignancies. The pathological features of MB are heterogeneous, and its emergence in the cerebellum is attributed to genetic and epigenetic alterations that disrupt critical pathways in cerebellar development . Accordi. Transcriptomic analysis can identify medulloblastoma subgroups, but it has limitations in capturing the microenvironment and impact of modifications on gene expression, as well as dealing with technical variations, noisy data, and incomplete transcriptome coverage. DNA methylation profiling is more reliable in accurately classifying medulloblastoma subgroups  datasets, including GSE85212 (N = 763), GSE130051 (N = 1390), GSE90496 (N = 390), GSE54880 (N = 276), GSE109379 (N = 128), and GSE75153 (N = 91) . All thep-value >0.05 in over 5% of samples were removed from subsequent analysis.a) We assessed the signal quality using the detectionP function from the Bioconductor minfi package. We then calculated the p-values for each CpG probe across all samples. Probes with a b) As all samples used in the current study were from the cerebellum, we used the preprocessQuantile function from the minfi package to normalize the data. We excluded CpG probes related to sex chromosomes and probes associated with single nucleotide polymorphisms (SNPs). On average, the total number of remaining probes was 420,000.c) The methylation beta values ranging between 0 and 1 were calculated using the getBeta function from the Bioconductor minfi package. Briefly, such values were obtained based on the methylated and unmethylated probe intensities using formula M/(M + U + 100) ; M and Ud) To deduce missing demographic information, including age and sex, we employed the methyAge algorithm and the predictedSex function from the Enmix  and minfWe downloaded raw data files in “idat” format for all the aforementioned GEO datasets and assessed their quality using the minfi Bioconductor package . Subsequet al. to integrate the DNA methylation dataset with gene expression data and to further generate new labels. SNF allows for the identification of similarity networks, enabling the creation of the most appropriate labels for the methylation dataset using spectral clustering. To this end, we combined 763 samples from the methylation dataset (GSE85212) with the same number of samples from the gene expression dataset (GSE85217). The data integration was performed using the following parameters: 51 nearest neighbors, sigma = 0.85, and 120 iterations. As our study focused on medulloblastoma, which is characterized by the four subgroups, we set the cluster number to four and used the result of spectral clustering as the ground truth labels. We converted the cluster numbers into subgroups by comparing the sample number from the fused dataset and actual labels. Next, we evaluated the performance of the fused network by calculating the normalized mutual information (NMI) score, ranging from 0 to 1. An NMI score of 1 indicates that the fused network leads to the same labels as the actual labels, while a score of 0 indicates the opposite.In our study, we utilized the similarity network fusion (SNF) technique  proposedFeature selection is a critical step in machine learning, as it allows for the identification of the most relevant features, resulting in decreased prediction model error rates and computational time. In this study, we utilized a random forest model (RF) to train the top 5,000 most variable CpG probes obtained from Median Absolute Deviation (MAD) through the mad function in the stats package. To this end, we grew 300 trees using the RF model and determined the importance of each probe across all subgroups using the varImp function from the caret package.To evaluate the prognostic potential of prediction biomarkers, we conducted an overall survival analysis by adapting the MethSurv webtool pipeline . We utilTo overcome the challenge posed by imbalanced sample sizes for each MB subgroup in the methylome data, we implemented a technique called synthetic minority oversampling (SMOTE)  using thWe utilized t-distributed stochastic neighbor embedding (t-SNE), a non-linear dimensionality reduction technique using the Rtsne package , to reduOur aim was to address the multiclassification challenge of accurately classifying medulloblastoma (MB) subgroups by leveraging the DNA methylation levels as a key feature. To do this, we used a diverse set of machine learning algorithms. The six algorithms employed were random forest (RF), naive Bayes (NB), K-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGB), and linear discriminant analysis (LDA). Furthermore, to capture the intricate nonlinear relationships, we incorporated an artificial neural network (ANN) model. Since the ensemble-based algorithms RF and XGB combine the predictions of multiple weak models to improve overall performance, we included those models in our study. On the other hand, NB operates as a probabilistic model, employing Bayes’ theorem to calculate the likelihood of class membership based on the independent features. KNN is classified as a nonparametric supervised learning algorithm, meaning that it does not make explicit assumptions about the underlying data distribution and defers computations until prediction. SVM can function either as a linear or as a nonlinear model, using a hyperplane or kernel trick to separate classes in the feature space. LDA is a linear model that projects data onto a lower-dimensional space to maximize class separation, aiding classification . The utiTo train the abovementioned machine learning prediction models, we split the data into the training and test sets with a ratio of 0.8 for machine learning models using the sample. split function from the caTools package. Furthermore, we performed cross-validation in ten random folds (k = 10) using the createFolds function from the caret package .The RF model was trained using the Random Forest package  with 300We implemented ANN models using the Keras package in R with TensorFlow 2.10 . The datTo prevent overfitting, we applied regularization techniques, including dropout , L2 regularization on the second layer (regularizer_l2 = 0.009), and early stopping after five patients. The model was optimized using the categorical cross-entropy loss, stochastic gradient descent (SGD) optimizer, 200 epochs, batch size of 16, learning rate of 0.03, decay of 0.00006, momentum of 0.05, and Nesterov momentum.To optimize the computational training time, we utilized the mclapply function from the parallel package to run the machine learning models in parallel on available CPUs. The training was performed on an Ubuntu machine equipped with an Intel Core i5-6200U processor and 16 GB RAM.In our study, we evaluated the performance of each classification model using standard metrics, which included accuracy, sensitivity, specificity, precision, F1-score, and area under the curve (AUC) as described by similar studies . BrieflyHere, true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) indicate whether the model predicted correctly or incorrectly. We also computed the AUC from the pROC package . The AUCTo plot the training and testing results of a classifier, we designed a custom R script. Initially, the dataset was partitioned into a training set and a test set. Subsequently, principal component analysis (PCA) was conducted on the training and testing sets separately using the preProcess function from the caret package, enabling the extraction of two primary components that captured the most significant variability in the data. Following this, the training and test sets were transformed using the derived PCA outcomes. A grid structure was then constructed, encompassing values pertaining to the two principal components. Utilizing the trained classifier, labels are predicted for the grid set. Moreover, a color mapping scheme was employed to associate colors with the predicted and actual subgroups, enhancing the interpretability of the resulting plot.p-value <0.05.To investigate the molecular function of the predicted CpG biomarkers and their relevance to the MB subgroups, we performed gene enrichment analysis. To annotate the CpGs with the genes, we utilized the minfi and IlluminaHumanMethylation450kanno.ilmn12.hg19 packages. The resulting genes were used as the input for the gprofiler2 package  to identTo interpret the contribution of each identified biomarker to the MB subgroup prediction, we used the Shapley value, which is a local interpretation method in IML (Interpretable Machine Learning). Since the machine learning models employed in this study cannot directly elucidate the relationship between CpG probes and their target class, we employed the Shapley value to provide human-understandable explanations of the models’ results. The Shapley value is computed as the average marginal contribution of a CpG probe or gene beta value across all possible coalitions. For a single prediction of each MB subgroup, it randomly changed the value of each beta value from zero to the actual value of the sample and calculated the prediction for all patterns of changes due to the addition of each CpG. We used the iml package  to calcuIn this study, we utilized the igraph package  to perfo(i) Collection of data from Gene Expression Omnibus (GEO), followed by pre-processing and processing steps;(ii) Implementation of similarity network fusion (SNF) to establish new class labels by integrating DNA methylation and gene expression data;(iii) Median Absolute Deviation (MAD) analysis was applied to select informative prediction biomarkers, followed by random forest (RF) analysis for feature selection. Furthermore, survival analysis was performed based on the prediction biomarkers.(iv) Construction of AI-based prediction models following Synthetic Minority Oversampling Technique (SMOTE) application;(v) Evaluation of the models using multiple parameters, including accuracy, sensitivity, precision, AUC, and F1-score;(vi) Gene Ontology (GO) analysis was used to functionally annotate the selected genes.In this study, we used a combination of data integration and AI-based techniques to effectively classify subgroups of medulloblastoma. The methodology used in this study is presented in We further conducted gene network analysis and interpreted the classifier decision by utilizing Shapley values. The subsequent sections provide detailed results from each of the steps mentioned above.In this study, using similarity network fusion (SNF), we identified four distinct clusters in both the gene expression and methylation datasets . We thenThe t-SNE visualization revealed  only a mIn our study, we employed six robust machine-learning algorithms, namely, SVM, KNN, NB, RF, XGB, and LDA, along with an artificial neural network, to predict medulloblastoma subgroups based on DNA methylation samples using 399 predictive biomarkers. As a result of the fusion process, a subset of samples (n = 16) had their labels switched . These nThe overall performance of the classifiers based on the validation set (GSE90496) is presented in Since the focus of our study was the classification of MB subgroups, we evaluated the performance of each model, considering the different MB subgroups, across multiple validation datasets. Notably, all tested classifiers exhibited exceptional performance on the GSE90496 validation set, exceeding 0.92 in accuracy, precision, sensitivity, F1-Score, specificity, and AUC . We spec. Thus, the classifiers successfully captured the underlying variability and discriminating features among the different MB subgroups.Furthermore, we visualized the ability of the classifiers based on the training and test sets, as shown in Across the different validation sets, our models consistently displayed higher performance. For example, on the GSE130051 dataset, the NB model emerged as a top-performing classifier with accuracy exceeding 0.96, while other models achieved accuracy ranging from 0.91 to 0.95 . The ANNIn summary, our analysis revealed slight variability in the performance of different prediction models across a diverse range of validation sets, with an average accuracy exceeding 0.96.p-value <0.05). The top biomarkers with the lowest p values included CBFA2T3, PRDM16, TRIM65, KIAA0182, SEMA4F, OR6N1, RPTOR, KIAA0415, SAG, and TTC15  in which the selected genes were enriched  on the model performance, we computed the Shapley values for the trained neural network model. Each gene with its corresponding beta values and their contribution in terms of Shapley values on the ANN model across different subgroups are shown in ZIC4’s hypermethylation state  has a highly positive impact on the model’s ability to predict Group 3 but has a negative effect on the WNT subgroup. At the same time, ZIC4 has a low negative impact on the model’s ability to forecast SHH and Group 4 subgroups. Additionally, we identified other genes, such as ARTN and SLC8A1, which have a positive contribution to the model’s ability to predict Group 3, with beta values equal to 0.847 and 0.327, respectively.For example, we found that CXCR4 and MEIS1 genes and lower methylation levels of NFASC had a positive impact on the ANN model’s ability to predict the Group 4 subgroup. In the WNT subgroup, ASCL2, SYNGAP1, RTNR4L, and NFASC gene hypermethylation status, as well as KIDINS220 and S100A10 gene hypomethylation, had a highly positive impact on prediction. For the SHH subgroup, we found that higher methylation levels of SLC8A1 and lower methylation levels of ROR1, CXCR4, and RTN4RL1 had a high contribution to the prediction.Furthermore, we observed that higher methylation levels of the ARTN and WNT4 for Group 3 and WNT subgroups, respectively. These genes suggest possible associations with their respective subgroups, highlighting their importance in driving molecular characteristics and prognostic outcomes. Building upon these findings, we integrated ARTN, WNT4, EP300 and ROR1 into the gene list of cluster 4, resulting in improved performance for cluster 1.We conducted network analysis using the methylation beta values of 49 genes enriched in the nervous system development process identified based on the functional enrichment analysis . The resRTN4RL1, TLX2, ARTN, WNT4, EP300, and ROR1 into the existing gene list from cluster 5. Additionally, cluster 6 was improved by using the same gene list as cluster 5. However, for cluster 3, we included SEMA4F, SLC8A1, CXCR4, SYNGAP1, NFASC, and MEIS1 in the existing list of significant genes, thereby improving its predictive power.Furthermore, by adapting a similar procedure, we intended to improve cluster 5, which initially exhibited the lowest performance. To achieve this, we incorporated the additional genes Accurate classification of molecular subgroups in medulloblastoma (MB) is vital for initiating appropriate treatment plans. In our study, we utilized a comprehensive approach integrating data and AI-based methods and utilized synthetic sample generation using SMOTE to address limited data and maintain class balance. Our developed prediction framework, MBMethPred, was designed explicitly for medulloblastoma subgroup classification using DNA methylation data. MBMethpred incorporates multiple AI models to enhance accuracy, processing speed, ease of use, and user-friendliness.et al.'s (2018) study was the classification of central nervous system tumors, rather than focusing on medulloblastoma. Furthermore, it lacked an accuracy score specifically for medulloblastoma. Similarly, Sharma et al. exclusively concentrated on the classification of Groups 3 and 4 subgroups. Additionally, both . In contrast, Compared to the molecular-based MB subgroup classification methods  Supplem, MBMethPOur study comprehensively evaluated the models’ effectiveness in classifying MB subgroups using multiple validation datasets. Although slight variations were observed in the performance of prediction models across different datasets, the overall high performance observed in our study strengthens the reliability and generalizability of the models. Thus, incorporating multiple validation sets and prediction models is essential for robust evaluation of model reliability.CBFA2T3 (. Similarly, the study by RPTOR gene, along with other identified biomarkers, in classifying non-WNT/non-SHH medulloblastomas. These findings suggest that the identified biomarkers hold the potential for predicting patient prognosis and guiding treatment decisions.Gene-specific effects on model prediction were identified using Shapley values, offering insights into the contributions of specific genes to subgroup classification. Additionally, survival analysis identified significant associations between the identified biomarkers and survival outcomes in MB patients. Moreover, the biomarkers with significant survival outcomes correlated with previously reported oncogenes. For example, the CBFA complex, which includes CBFA2T3 , is suggEP300 encodes a histone acetyltransferase protein that activates the expression of genes critical for the development and progression of medulloblastoma (CXCR4 has been suggested to be the oncogenic driver of MB (SYNGAP1 is a GTPase-activating protein that is known to cause cognitive deficits by inducing alterations in glutamatergic neurotransmission (WNT4 is a member of the Wnt signaling pathway and has been associated with the pathogenesis of WNT and SHH subgroups (Our functional enrichment analysis highlighted the association between the model performance and biological relevance. For instance, blastoma . CXCR4 her of MB . In addismission . Finallyubgroups . Thus, tFinally, network analysis considered correlations among genes enriched in nervous system development and identified distinct clusters with potential relevance to medulloblastoma. Moreover, training a separate artificial neural network model for each cluster improved the classification accuracy by gradually incorporating genes from different clusters. Thus, our integrative approach enhances the understanding of the complex molecular heterogeneity underlying medulloblastoma and provides a basis for further research.It is important to acknowledge some limitations of our study. Although we utilized gene expression profiles for data integration and further implemented SNF to define the new labels, our prediction models exclusively rely on the DNA methylation datasets. However, it is worth highlighting that the availability and accessibility of additional datasets, especially those including diverse patient populations, are currently limited, potentially impacting the generalizability of our findings. Therefore, further research in this direction is highly warranted to explore the clinical applicability of our study.In conclusion, we developed a robust classifier for medulloblastoma subgroup classification. Moreover, our functional enrichment analysis offers valuable insights into the molecular pathogenesis of medulloblastoma. Survival analysis enables the evaluation of prognostic relevance for individual biomarkers. By identifying key genes in medulloblastoma subgroup classification and their functional relevance, our study provides insights into disease stratification. While our approach has the potential to be adapted for subgroup prediction in other cancer types, it requires careful validation and adaptation to specific datasets to ensure its reliability. Despite the underlying limitations, our findings contribute to the advancement of medulloblastoma research, with the potential to improve patient outcomes.


CUP patients typically have poor prognosis but therapy targeting the original cancer tissue can significantly improve patients’ prognosis. Thus, it’s critical to develop accurate computational methods to infer cancer TOO. While qPCR or microarray-based methods are effective in inferring TOO for most cancer types, the overall prediction accuracy is yet to be improved. In this study, we propose a cross-cohort computational framework to trace TOO of 32 cancer types based on RNA sequencing (RNA-seq). Specifically, we employed logistic regression models to select 80 genes for each cancer type to create a combined 1356-gene set, based on transcriptomic data from 9911 tissue samples covering the 32 cancer types with known TOO from the Cancer Genome Atlas (TCGA). The selected genes are enriched in both tissue-specific and tissue-general functions. The cross-validation accuracy of our framework reaches 97.50% across all cancer types. Furthermore, we tested the performance of our model on the TCGA metastatic dataset and International Cancer Genome Consortium (ICGC) dataset, achieving an accuracy of 91.09% and 82.67%, respectively, despite the differences in experiment procedures and pipelines. In conclusion, we developed an accurate yet robust computational framework for identifying TOO, which holds promise for clinical applications. Our code is available at  Although there is no drug specifically approved for CUP, multiple guidelines recommend treating this disease using multi-agent cytotoxic chemotherapy3. However, the responses of CUP patients to non-targeted chemotherapies are poor with a 5-year survival rate around 11%4.Carcinoma of unknown primary (CUP) is a type of metastatic cancers with unknown cancer origin. CUP accounts for 3–5% of all cancer incidences in the United States12. However, this method is labor-intensive, requires highly skilled physicians, and has varying accuracy rates in predicting TOO for different cancer types13. Although imaging techniques such as PET/CT and ultrasound have been utilized for assisting in clinical diagnosis of CUP18, their diagnostic accuracies vary from ~ 30 to ~ 90%, which is not high enough for safe clinical usage. Thus, novel diagnostic methods are needed to address this issue.In order to solve this problem of identifying the tissue-of-origin (TOO), several diagnostic methods have been proposed in the past decades. From 1980 to 2010s, immunohistochemistry is the mainstream method to identify cancer primary tissue27, mutation profile29, copy number profile and methylation profile were used in the diagnosis of CUP. The assumption for inferring TOO using the omics data is that the metastatic site retains the molecular characteristics of the primary site30. For example, Liu et al. achieved an accuracy of 81% using mutation profile across 13 cancer types28. Ma et al. achieved an accuracy of 84% across 39 cancer types using expression profile24. There are also methods combining multiple types of omics data. For example, He et al. inferred TOO by integrating the features from RNA expression and DNA somatic mutation31. Liu et al. evaluated the potential for identifying TOO using methylation, expression and mutation data, finding that methylation could achieve similar accuracy as expression data for inferring TOO32. However, since the methylation data is more expensive than other omics data such as expression profiles, inferring CUP using expression profile is currently the recommended approach.Recently, with the advancement of sequencing techniques, omics data including RNA expression profile24. Using micro-array, Bloom et al. combined the cDNA and oligonucleotide platform with artificial neural network (ANN) to trace the primary tumor origin26, obtaining an accuracy of 83–88% on different platforms. Xu et al. reported a multiple-platform 154-gene panel based on TCGA RNA-seq data to detect the primary origin of metastatic tumors. They selected the 154 genes by recursive feature selection and trained a classifier based on support vector machine (SVM), achieving an overall accuracy of 92%33. For TOO tracing using RNA-seq, Liang et al. developed a TOO classifier on TCGA data based on Naïve Bayes algorithm, achieving an accuracy of 91%34. Li et al. used TCGA RNA-seq data as the training set and achieved an accuracy of 96.1% for cross-validation, and an accuracy of 83.5% for an independent GEO dataset35. Deep learning-based methods were also used to infer TOO, such as Grewal et al.’s neural network achieving a 99% accuracy in a 126-sample dataset and an 86% accuracy in a 201-sample dataset36. He et al. developed a neural network for predicting TOO using 150 genes at a 94.87% accuracy27. Zhao et al. developed pipeline by log transformation followed by an 1D-inception structure for inferring TOO, achieving an accuracy of 98.54% in the cross-validation phase, surpassing most methods before37.For obtaining gene expression profile, RT-PCR, micro-array and RNA-seq were majorly used. For TOO tracing using RT-PCR, Ma et al. collected 578 labeled samples covering 39 tumor types, including 75% primary tumors and 25% metastatic tumors. The dataset was split to 466-sample dataset (frozen) and 112-sample test set (FFPE) according to the sample type. A 92-gene list was used for inferring TOO, and k-nearest neighbor algorithm (KNN) (k = 5) was applied to the problem, reaching an accuracy of 84% in the leave-one-out cross validation. The result also showed there was no difference in the accuracies on predictions of primary or metastatic tumorAlthough TOO inference methods usually perform well in cross-validation, they are often insufficient when tested on independent samples, particularly those with cancer metastasis. Furthermore, a comprehensive comparison on the effects of different gene normalization methods, feature selection techniques and classification algorithms is yet to be conducted. Here, we designed a computational framework to infer TOO based on machine learning integrating normalization, feature selection, training and testing processes. We also conducted a comprehensive analysis of different normalization, feature selection and classification methods. Based on the analysis, we proposed a model that employs the most effective combination of normalization, feature selection and classification method. Finally, we evaluated the performance of our trained model on independent datasets.38. We present the details for all datasets used in Table We collected RNA-seq data from two sources in this study. First, we collected a 10,304-sample data from The Cancer Genome Atlas (TCGA) and further split it into a 9911-sample primary dataset and 393-sample metastatic dataset, as described in Materials and Methods. For independent validation, We obtained a 1988-sample dataset from The International Cancer Genome Consortium (ICGC)39. As a result, we used 32 cancer types from TCGA. The TCGA dataset contains 9911 primary tumor samples covering all 32 cancers and 393-metastatic tumor samples covering 11 cancers. The ICGC dataset contains 1988 samples and covered 10 cancers. We used the TCGA primary dataset to train our model, and used the TCGA metastatic dataset and the ICGC dataset to access our model.The TCGA primary dataset covering 33 main cancers  and standardization after log2 transformationThe training data was used to test the combinations of preprocessing, feature selection and classification methods using tenfold cross validation. The best combination was used to train a model on the TCGA primary dataset. The trained model was tested on the independent test datasets. A schematic diagram of our approach work is shown in Fig. We evaluated the tenfold cross validation accuracy on the training dataset to assess the effectiveness of each step. We created a plot of the accuracies of all possible combinations in Fig. The gene number is a significant factor affecting the classification performance, while the random forest algorithm was relatively insensitive to the gene number, demonstrating the robustness of ensemble learning. Log2 transformation, followed by standardization, was superior to l1 normalization in most cases. This may be caused by that the optimizer perform better when data is normally distributed. Random selection for feature selection could also work well, except for the KNN method.It is worth noting that logistic regression was the feature selection method in six of the top 10 combinations  is comparable to that of selecting 100 genes in total by random forest and classifying by KNN (87.28%). In summary, the selection of methods and classification algorithms can significantly impact the accuracy of predictions.We conducted feature selection for all training samples by performing log2 transformation followed by standardization using logistic regression. 80 genes were selected for each cancer  and DEFA in uveal melanoma (UVM), have low expression levels in some cancers, revealing how marker genes can provide more relevant information.The top gene, characteristic of each cancer, was identified and combined into a set for expression level visualization. The log2 transformed average expression value for selected genes in different cancers were represented on a heatmap, shown in Fig. Gene sets for each cancer were analyzed to look for similarities, and we found that several gene sets overlapped. For example, 53 genes were common to both the cholangiocarcinoma (CHOL) and liver hepatocellular carcinoma (LIHC) gene sets, which explained the misclassification between these two cancers.We further examined gene functions of all 80-gene sets using enrichment analysis. The results showed a high degree of enrichment in common human organ developmental processes, such as keratinocyte differentiation, epidermal cell differentiation, and epidermis development, as shown in Fig. We also performed the enrichment analysis on the top-5 genes from each cancer to find the functions of the core genes that take the major effect in the predictions. We demonstrated the most significant pathways from each cancer in Fig. To verify our framework in independent datasets, all 80-gene sets for 32 cancer types were combined to create a comprehensive 1356 gene set for further training and used logistic regression as the classification algorithm. We tested our model on 2 independent datasets: (1) the metastatic dataset from TCGA; (2) the non-TCGA ICGC dataset. The model trained from TCGA primary tumor dataset using 1356 genes achieves a 91.09% accuracy on the metastatic dataset from TCGA. We plotted the confusion matrix for the dataset in Fig. To account for the lack of full consistency in gene sets between the TCGA and ICGC datasets, we initially created an overlapping gene set of 9180 genomic features. Using this 9180-gene set, we conducted preprocessing, feature selection, and model training on the TCGA primary datasets resulting in the identification of 80 genes by feature selection. Feature selection was applied, allowing for the selection of 474 genes for the final model training. This set of 474 genes was integrated into a logistic regression model, which was used to test the ICGC dataset with 82.67% accuracy. The confusion matrix for the dataset is displayed in Fig. 37. The robustness of our framework was further indicated by the performance on two independent datasets, which achieved accuracy rates of 91.09% for the TCGA metastatic dataset and 82.67% for the ICGC dataset. These observations demonstrate the efficacy and importance of our computational framework for TOO inference.In this study, we designed a computational framework including the data preprocessing, feature selection and classification in one tool for TOO inference. Besides, we thoroughly investigated the impact of preprocessing methods, feature selection methods and classification methods on the predication accuracy of tissue-of-origin inference. Our study showed that log2 transformation and standardization provide an optimal starting point for preprocessing for RNA-seq data. Traditional machine learning methods, such as logistic regression yielded similar accuracies to deep learning approaches when using only 1000 genes27 or the minimum Redundancy-Maximum Relevance (mRMR) algorithm were not compared. Moreover, gradient boosting decision tree (GBDT)-based methods35 and deep learning algorithms42 might further enhance the prediction accuracy. Though we achieved a similar accuracy rate of 98.54% for cross-validation as Zhao’s study37, we failed to achieve the same level of accuracy (96.70%) for the same TCGA metastatic dataset for independent test. Therefore, we suggest utilizing neural networks like 1-D convolutional networks to improve our predictive result. Furthermore, we suggest exploring classification methods with complex structures, such as multi-layer neural networks, and integrating additional data types, such as histopathological image, which are regularly used in cancer diagnosis and prognosis prediction46. Although recently developed TOO-inferring medical image-tools show promise47, more work in this area is necessary to utilize multi-omics for a higher accuracy in TOO inferring.There are some limitations to our study. First, we did not explore all possible combinations of the steps. For instance, we did not use quantile normalization, which is a popular method in expression profile, because the method was conducted within one dataset instead of one sample. Additionally, feature selection methods, such as correlation-based methods37. Conventional machine learning algorithms have less parameters than neural networks. Hence, our framework might be suitable for inferring TOO subtype.Secondly, it is unclear if our framework could infer the subtype of cancer origin. As stated by Zhao et al., small sample number was a barrier for neural networks to learn more information24. Moreover, we did not compare the performance of our model between different tumor grade levels. Further tuning of our model may be necessary if sample preservation method or tumor grade level were taken into account.Thirdly, our work did not differentiate FFPE and frozen samples as distinct datasets, as pointed out by Ma et al.51.Finally, to make our work medically applicable, in-house RNA-seq data is necessary. Further efforts are required to adjust the parameters per our data. As mentioned above, logistic regression can predict TOO using an expression profile covering 1356 genes. We look forward to utilize sequencing techniques such as capture that sequence specific genes to reduce the costs for the experimentThis study implemented a machine learning framework to identify the primary origin of tumor tissue using RNA sequencing expression profiles. Comparing different methods for preprocessing, feature selection and classification, we determined that log2 transformation and standardization was superior than normalization methods that express values as a proportion. We found that logistic regression performs well in feature selection and classification for this task. Furthermore, we found that predicting with using 1356 genes as features resulted in a relatively high accuracy for predicting the origin of the primary cancer site. This work suggests the RNA-seq and machine learning algorithms might be used in clinical practice when other pathological methods fails to determine the primary origin site of certain cancers.https://portal.gdc.cancer.gov/). The ICGC RNA-seq data were downloaded from Data Portal (https://dcc.icgc.org/releases/release_28/Projects/) by searching the keyword “exp_seq”. To avoid information leakage, the ICGC samples that also showed up in TCGA were not included for ICGC dataset. For the TCGA data, we removed all the metastatic tumors in the TCGA dataset for test set by checking TCGA identifier, leaving the samples with primary tumors  as the training dataset and metastatic tumors  as the test dataset. For all datasets, the TPM value of each sample and each gene from were extracted, generating a M × N matrix where M is the number of the sample number and N is the number of the gene number. All the samples were labeled by its cancer type.The TCGA RNA-seq data were downloaded from TCGA Data Portal  error, 1e−6 was added to all expression values before log2. The expression values will all be scaled by this denominator, i.e.52, a random forest model was trained on all genes. The base estimator number was set to 2000 for the random forest classifier. For each decision tree, sub-samples are drawn with replacement by bootstrapping method. Each decision tree will use up to For selecting features using random forestFirst, a multinomial logistic regression model was trained using all data. The l2 penalty was used for regularization and regularization strength was set to 1e−4 . Then, for each cancer, the weights for all genes for sorted by absolute value. To select N genes for each cancer, the top-ranked N genes were first selected as the genes for classifying this cancer. The selected N genes for all cancers were combined as the final features. For logistic regression, we divided 10 for the feature selection for each cancer.54 was used as the database for the enrichment analysis. Genes were clustered by R package clusterProfiler55. The visualization was done by R package ggplot256.For the analysis of biological significance, the functions were annotated for the specific gene set. Gene ontologyN-fold cross validation (where N is an integer), all the samples were stratified into N subsets by different random seeds. And the algorithm was repeated N times. During each repeat, one of the N subsets was used as the test set and the other N − 1 subsets were consolidated to a training set. Features that were selected within the training set were used to train a model. The test set was then used to evaluate the model. Then the average error across all N trials was computed.In a We used the default parameters in random forest and logistic regression.For the multi-class classification based on SVM, the one-vs-all strategy and rbf kernel were used. For regularization, l2 penalty was used and the inverse regularization parameter C was set to 10,000 for scikit-learn implementation.57.All above mentioned feature selection and classification methods were implemented using scikit-learn package58.To plot the accuracies for all combinations, functions of FacetGrid from package seaborn version 0.9.0 was used60. Before plotting, the expression values were first added 1e−12 and transformed by log2.To plot the heatmaps, the R package pheatmap version 1.0.12 was usedSupplementary Figure 1.Supplementary Figure 2.Supplementary Table 1.Supplementary Table 2.Supplementary Table 3.Supplementary Table 4.