We confirmed significant alterations with reverse transcription-quantitative PCR in the expression of hsa-miR-16-5p, hsa-miR-21-5p, hsa-miR-92a-3p, hsa-piR-33151, TRV-AAC4-1.1 and TRA-AGC6-1.1. Furthermore, hsa-miR-206, a previously identified amyotrophic lateral sclerosis biomarker, showed a binary-like pattern of expression in our samples. Using the expression of these non-coding RNA, we were able to discriminate amyotrophic lateral sclerosis samples from healthy controls in our discovery cohort using a random forest analysis with 93.7% accuracy with promise in predicting progression rate of patients. Importantly, cross-validation of this novel signature using a new geographically distinct cohort of samples from the United Kingdom and Germany with both amyotrophic lateral sclerosis and control samples (n = 156) yielded an accuracy of 73.9%. The high prediction accuracy of this non-coding RNA-based biomarker signature, even across heterogeneous cohorts, demonstrates the strength of our approach as a novel platform to identify and stratify amyotrophic lateral sclerosis patients.Objective biomarkers for the clinically heterogeneous adult-onset neurodegenerative disorder amyotrophic lateral sclerosis are crucial to facilitate assessing emerging therapeutics and improve the diagnostic pathway in what is a clinically heterogeneous syndrome. With non-coding RNA transcripts including microRNA, piwi-RNA and transfer RNA present in human biofluids, we sought to identify whether non-coding RNA in serum could be biomarkers for amyotrophic lateral sclerosis. Serum samples from our Oxford Study for Biomarkers in motor neurone disease/amyotrophic lateral sclerosis discovery cohort of amyotrophic lateral sclerosis patients ( A set of small non-coding RNA were found to be differentially expressed in the serum of patients with amyotrophic lateral sclerosis compared to healthy controls. Using their dysregulation in a random forest model, we could classify the samples’ disease state with 73.9% accuracy in our confirmation cohort.  The majority of those with the adult-onset neurodegeneration amyotrophic lateral sclerosis (ALS) typically develop a syndrome of rapidly progressive muscle weakness secondary to motor neuronal loss, resulting in death within 3 years of first symptoms, typically from ventilatory insufficiency . MicroRNA (miRNA) are common candidates as they have relatively simple structures, increased stability from RNase degradation and freeze-thaw cycles, and are present in blood (n = 16) were admitted to the Department of Neurology at Ulm University Hospital, Germany and were diagnosed by an MND neurologist (A.L.), and included neurological controls and ALS patients. ALS disease mimics in the confirmation cohort were similar to the BioMOx cohort, while neurological controls included multiple sclerosis, migraine, amnesia and spinal ischaemia.The discovery cohort of samples were patients recruited as part of the Oxford Study for Biomarkers in MND/ALS (BioMOx) from the Oxford MND Centre. Ethical approval for extraction and use of the biofluid samples and associated clinical data were obtained from South Central Oxford Ethics Committee B (08/H0605/85) and NRES Central Committee South Central—Berkshire (14/SC/0083). All participants provided written consent . The study included patients with ALS, patients with ALS mimic conditions  and healthy control (HC) subjects. Patients with ALS and mimic disorders were diagnosed according to standard criteria by two experienced neurologists  and clinically re-assessed on the day of sampling . For the confirmation cohort of samples, these were obtained from the ALS Biomarker Study from Queen Mary, University of London, and the Ulm Neurological Biobank from the University of Ulm to form the confirmation cohort. Participants from the ALS Biomarkers Study (London—City & East Research Ethics Committee 09/H0703/27) were consented in the study and baseline samples taken at or shortly after diagnosis by MND neurologist (A.M.), based on established criteria. This cohort of samples included healthy controls, disease mimics, neurological controls and ALS patients. Samples from the Ulm Neurological Biobank . For both cohorts, patients were classified as having fast- or slow-progressing ALS based on their rate of change in the ALSFRS-R clinical rating, with a threshold of 0.6 points per month selected based on the median value seen clinically to divide the samples . The numBlood was collected from patients into BD Vacutainer SST tubes, left to clot at room temperature and centrifuged at 3000 rpm for 10 min at 4˚C. The serum supernatant was then removed and aliquoted into 1.8 ml aliquots and stored at −80˚C. Minimal red blood cell lysis was checked using a haemoglobin ELISA  with a threshold of 0.6 g/l  following the recommended parameters for serum RNA. These libraries then underwent 75 bp PE sequencing on an Illumina MiSeq machine at the Oxford Genomics Centre, United Kingdom.Data were pre-processed to remove 5′ and 3′ adaptors and then underwent two analysis pipelines. The automated Qiagen QIASeq miRNA pipeline read the unique molecular indexes (UMI), sequentially aligned the RNA-seq data to a database of ncRNA transcripts using Bowtie. The second method used the Galaxy web servers using a range of open access script packages . Distribution of the data was determined using a Shapiro–Wilks normality test. One-way ANOVA was carried out across the four groups with Tukey’s multiple comparison for parametric data and a Welch’s one-way ANOVA with Gomes–Howell multiple comparison when non-parametric. Correlations were analysed using Pearson’s and Spearman’s rank correlation tests if the data were parametric and nonparametric, respectively. All statistics were two-tailed and significance was set at Modelling of the data to predict sample classification was carried out using Orange. Three BioMOx samples that underwent RT-qPCR expression profiling were excluded from this analysis as they did not have expression data across the six ncRNA biomarkers showing differential expression. Hsa-miR-206 was not included as it does not have a normally distributed expression pattern. Normalized ΔCq data for the remaining samples were run through a random forest model, generating 10 replicable trees with no subset smaller than 5. Disease state was the grouping variable, with the ncRNA as independent variables entered together.The data that support the findings of this study are available from the corresponding author, upon reasonable request.To obtain an overview of small ncRNA expression dysregulation in ALS, we profiled serum samples from slow- and fast-progressing ALS patients, and healthy controls from the BioMOx discovery cohort. Small ncRNA extracted from 200 µL of serum were pooled together, with libraries created from these pools to improve the signal-to-noise ratio as we have shown previously (s (2369) = 0.9724, P < 0.001], and consistent with previous studies showing minimal clonal amplification bias  = 0.2653, P < 0.001].Therefore, we complemented our analysis by using an open-sourced software hosted on the Galaxy web servers (GA) for concurrent alignment with a custom-made annotation file of all ncRNA transcripts including miRNA, rRNA, tRNA, piRNA, small nucleolar RNA (snoRNA) and snRNA. As piRNA is annotated to multiple loci on the genome and may be discarded for multiple alignments, we used the transcript aligner Salmon which generated an average alignment of 6.4% to annotated sequences, allowing for multiple mapping due to the short nature of the reads and target sequences . While lFig. 1C;P < 0.05) (s(489) = 0.3961, P < 0.0001; P < 0.0001; These differences persisted into those ncRNA significantly dysregulated between ALS patients and healthy controls. QA showed 32 differentially expressed miRNA or piRNA in ALS-FP and 103 in ALS-SP   with sim < 0.05) . Further < 0.05) , with onOf those significantly differentially expressed, QA showed miRNA were the dominant dysregulated species when compared only to piRNA . Howevers (81) = 0.7404, P < 0.0001].To confirm the dysregulation of the most promising ncRNA identified in our pooled RNA-seq analyses, we used RT-qPCR on individual samples from the BioMOx discovery cohort. Using NormFinder on our RNA-seq data, we identified two stably expressed normalizers, hsa-miR-718 and the highly expressed piRNA, hsa-piR-31068. We confirmed this using RT-qPCR analysis on individual samples from the BioMOx discovery cohort, with an average Cq value of 28.2, a coefficient of variations of 7.2% and 8.7% across all samples respectively, with their average 7.4%, and their expression correlated with each other  with a coefficient of variation of 7.8% and 9.3% for hsa-miR-718 and hsa-piR-31068, respectively. Using RT-qPCR, we profiled the expression of the six ncRNA that showed differential expression in the discovery cohort on these new samples, and found that while there was a trend for consistent regulation, these did not reach significance  compared to the ALS-FP (66.7%) and ALS-SP (79.2%) samples, and disease mimics 81.3%;  from theWith any biomarker discovery study, it is important to determine if the targets identified are able to help assign samples to their specific diagnostic criteria. Therefore, we compared the sensitivity and specificity of the seven ncRNA biomarkers to each other to determine their ability to distinguish control samples (including the disease mimics) to that of the ALS patients in the BioMOx discovery cohort. Hsa-miR-16-5p (AUC = 0.7033), hsa-miR-21-5p (AUC = 0.7071), hsa-miR-92a-3p (AUC = 0.6429), hsa-miR-206 (AUC = 0.6864) and TRA-AGC6-1.1 (AUC = 0.7015) all showed a significant ability to distinguish the two groups apart. Combining the expression of the seven biomarkers in a logistic regression model however did not provide improved discrimination (AUC = 0.6162).To determine whether this could be improved, we investigated using predictive classification modelling to increase the predictive performance of our biomarker signature. We excluded hsa-miR-206 from the modelling as many require the data to be normally distributed, which the binary nature of hsa-miR-206 expression does not demonstrate. We used a random forest model, which creates a number of decision trees, and uses a majority vote approach to determine the classification of the sample. This random forest model classified our samples between ALS and non-ALS samples 93.7% of the time in our discovery cohort , using 20% of all the samples to create the model and to test the classification with the other 80%. Undertaking this analysis, we found that after an initial high classification , in the partition cross-validation, we continued to see an accuracy of 69.4% (AUC = 0.755). While this was less overall than the previous model, we did find there was a minor increase in the number of ALS samples that were classified in comparison (59.6%). Therefore, through using the expression of these biomarkers, this is a first step in establishing a biomarker signature to identify patients with ALS.However, as the model was tested with the samples that underpinned it, we then utilized our samples from the confirmation cohort to test it. Interestingly, despite the differences in differential expression in our selected biomarkers in the confirmation cohort as described above, when we used the existing random forest model generated from the BioMOx samples to classify these new samples, we found that the model classified them with an accuracy of 73.9% (AUC = 0.656; RNA-seq analysis enabled us to identify and confirm, using RT-qPCR, dysregulation of four miRNA, one piRNA and two tRNA across our sample groups. Together, this ncRNA biomarker signature can categorize over 90% of individual samples into whether they come from ALS and non-ALS patients in our discovery cohort and over 73% in a distinctly separate cohort of samples. As such, this shows the strength of using next generation RNA-seq to look at small ncRNA, but it is important to note that there are challenges at investigating ncRNA in the serum, chief among them are those caused by the low abundance of intact small RNA and increased levels of degraded longer RNA transcripts. While not new, they continue to be an issue as these degraded reads are able to soak up reagents and reads, which in part could result in low alignment scores we observed. This has been observed in other studies using the same RNA-seq kit and target tissue, including a study that also used 200 µl serum to find an average alignment of around 18% (Indeed, our RNA-seq identified non-miRNA ncRNA dysregulated in ALS patients. While the precise function of piRNA outside of gene regulation in germline cells is uncertain, their role in other tissues is starting to become evident. Indeed, for our ncRNA biomarker hsa-piR-33151, one of our most consistently changed biomarker in both cohorts, it has been reported to increase in the exponentially growing breast cancer cell line MCF-7 compared to those that are quiescent . While oImportantly, the expression of none of our biomarkers were shown to correlate with the levels of haemolysis present in our samples, including hsa-miR-16-5p and hsa-miR-92a-3p which have been shown to be enriched in red blood cells (Using six of our ncRNA biomarkers in combination as a signature to classify the disease state of our samples, we observed a 93.7% classification accuracy between ALS and non-ALS samples, which cross-validated with a second cohort of samples to 73.9% accuracy. This is significant as it demonstrates that these biomarkers may prove to be useful in diagnosis of the disease, but further work is required to test whether these changes are present before symptom onset, something which has been remarked upon as being crucial, including for example in non-symptomatic people with disease-causing mutations . Howeverfcaa053_Supplementary_DataClick here for additional data file.


Nebula, a novel Bayesian integrative clustering analysis for high dimensional multi-modal molecular data to identify directly interpretable clusters and associated biomarkers in a unified and biologically plausible framework. To facilitate computational efficiency, a variational Bayes approach is developed to approximate the joint posterior distribution to achieve model inference in high-dimensional settings. We describe a pan-cancer data analysis of genomic, epigenomic, and transcriptomic alterations in close to 9000 tumor samples across canonical oncogenic signaling pathways, immune and stemness phenotype, with comparisons to state-of-the-art clustering methods. We demonstrate that Nebula has the unique advantage of revealing patterns on the basis of shared pathway alterations, offering biological and clinical insights beyond tumor type and histology in the pan-cancer analysis setting. We also illustrate the utility of Nebula in single cell data for immune cell decomposition in peripheral blood samples.Multi-modal molecular profiling data in bulk tumors or single cells are accumulating at a fast pace. There is a great need for developing statistical and computational methods to reveal molecular structures in complex data types toward biological discoveries. Here, we introduce  There is a great need for advanced statistical and computational methods and algorithms to facilitate scientific discoveries from mining these accumulating data sources. In this study, we focus on cross-modality integration and clustering analysis with two major applications: tumor subtype stratification and deconvolution of single cell subpopulation.Recent technological advances have allowed the collection of high-dimensional, multiscale molecular data using genome-wide platforms that assayed DNA  and RNA (mRNA and microRNA sequencing) expression in large tumor cohorts5. The recent pan-cancer integrative molecular classification study has revealed groupings of tumor samples primarily organized by histology, tissue type, or anatomic origin1. Nevertheless, recent oncology drug development landscape is dominated by efforts on targeting genomic alterations independent of tumor histology7. Examples include PARP inhibitors targeting HR-deficiency and immunotherapies approved for pan-cancer indication for MSI subtype. Therefore, a statistical and computational tool that allows discovery of subgroups of patient population on the basis of shared pathway alterations will enrich novel designs of modern clinical trials. In single cell studies, clustering analysis has been developed for spatial reconstruction, cell identity classification, and clonal decomposition11. With recent advances in multi-modality profiling of single cells13, integrative clustering algorithms with the incorporation of network priors will allow more in-depth understanding of cell heterogeneities and interactions.In tumor profiling studies, integrative clustering analysis allows patient stratification into subtypes that bring great potential to personalize cancer diagnosis and treatment15, kernel- or graph-based integration17 and Bayesian parametric or nonparametric mixture models18. Although most of them are capable to characterize a certain degree of concordance and heterogeneity across data types, there are still several main challenges. First, it is computationally imperative as well as biologically important to simultaneously identify subgroups and pinpoint the core sets of biomarkers that characterize the subgroups with probabilistic model inference. Secondly, there is a need to incorporate knowledge on biological functions (e.g. molecular pathways and regulatory networks) within and across molecular modalities for a more interpretable and useful classification of tumor and cell populations. It is particularly important to delineate molecular heterogeneity and biological understanding beyond cell of origin. Finally, scalable implementation is crucial for clustering analysis of modern multi-model data sets across tens of thousands of patient samples or single cells. To address these challenges, we aim to develop a high-dimensional clustering method that incorporates biological network information across different data modalities. We present Nebula , a novel Bayesian network-based clustering analysis for multi-modal integration and clustering with feature selection, and compare its performance in pan-cancer tumor profiling and single cell transcriptome sequencing data to state-of-the-art clustering algorithms including iCluster14 and Suerat8.Clustering models that incorporate multiple data types have been recently developed under joint latent variable approachesM-modality data z) under a mixture of null and alternative distributions, and it follows a distribution G, which samples from a DPM with base measure The main analytical objective is to jointly cluster patients based on molecular similarity within and across modalities, and identify associated biomarkers that capture the key characteristics for each subgroup. Under a Bayesian nonparametric Dirichlet process mixture (DPM) model, Nebula is capable to interactively learn group label and molecular signature through the guidance of biological network information. Specifically, as shown in Fig. https://github.com/nebula-group/nebula).Intensive computation is always a challenging issue for Bayesian modeling particularly for high-dimensional integrative analysis. To allow feasible implementation of Nebula, besides Markov chain Monte Carlo (MCMC), we also develop a computationally efficient variational Bayes (VB) algorithm to approximate posterior distribution. We have done extensive experiments to test the model performance under both MCMC and VB, and conclude that compared with MCMC, our proposed VB algorithm manages to speed-up posterior computation in almost a hundred-fold in the scale of our numerical experiments with minimal sacrifice in clustering and feature identification performance. Technical details of the model and the VB algorithm implementation can be found in Online Method. An open-source software implementation of Nebula is available on GitHub  associated with oncogenes and inactivating events  associated with tumor suppressor genes. For the DNA data matrix, we focus on a total of 187 cancer genes curated for canonical signaling pathways reported in Sanchez-Vega19 which are altered recurrently in tumor samples. We choose to focus on this gene set as genetic alterations beyond these genes are either rare or the functional impact is unclear. The gene expression data matrix includes upper quartile normalized RSEM data1 from mRNA-seq for 1239 genes, the union of 1000 most variable genes from the transcriptome  plus 141 immune-related genes and 103 stemness marker genes which will be described shortly. This dataset is assembled this way to reflect the fact that the molecular phenotype of a tumor is complex and influenced by a multitude of factors including cell-of-origin, histology , tumor microenvironment , dedifferentiation states, and oncogenic pathway activation. It allows a direct comparison of the Nebula analysis with the existing unsupervised integrative clustering approach.Our analyses include 8855 solid tumor samples across 31 solid tumor types in the TCGA PanCancer Atlas collection. DNA alterations  detected from whole-exome sequencing and 450k DNA methylation array, mRNA gene expression from mRNA-seq platforms, along with clinical annotation are available for each sample. We format the data into two separate gene-by-sample matrices for DNA alteration and mRNA expression respectively. The DNA matrix annotates individual genetic alterations for each gene by integrating across different data types at the DNA level as described in Sanchez-Vega19. An edge is drawn between two genes if they belong in the same oncogenic pathway. Members of these pathways and their interactions have been captured in a number of pathway databases, such as Pathway Commons20, REACTOME21 and KEGG22.To construct the biological graph, ten canonical oncogenic pathways are considered: cell cycle, Hippo, Myc, Notch, oxidative stress response/Nrf2, PI-3-Kinase/Akt, receptor-tyrosine kinase/RTK-RAS, TGF23 and a stemness index defined in Malta et al.24. Immune cell infiltration characterizes tumor microenvironment and can be quantified by the immune pathway gene expressions using RNA-seq data. It has been shown as a good prognostic index in several tumor types and can inform the design of immunotherapy treatment strategies26. The stemness index measures oncogenic dedifferentiation from the cell of origin and acquisition of progenitor-like, stem-cell-like features. It has been associated with distant metastasis, disease progression and poor prognosis. The signaling networks and pathways are converted to undirected graphs with vertices representing genes in the networks and edges corresponding to biological interactions within and between modalities.At the mRNA expression level, we included the immune marker set as defined in Yoshihara et al., (2013)p values for these tests to select the optimal Nebula solution that show maximal differences across the biological pathway activities. We note this model selection procedure can be extended to optimize other metrics of interest such as survival difference across the subgroups which we describe later in the analysis of melanoma patient stratification.We ran Nebula across a set of parameter settings (3. Using the t-Distributed Stochastic Neighbor Embedding (t-SNE)27 approach to project the data onto a two-dimensional map clearly shows that tissue site explains the major variation in the data , stomach (STAD), colorectal ), or of the same histology  and head and neck (HNSC)) are further positioned close to each other in molecular distances . Biological and regulatory networks are embedded into indirect graphs with a multi-modal Ising model to induce coupling effect among selection status of connected genes. Supplementary Fig. 28. Here we perform a Nebula analysis using the same biological graphs as used in the full pan-cancer analysis on the 363 TCGA melanoma samples and eventually identify 4 subtypes with distinct prognostic outcomes, Clusters 2 and 3 show higher lymphocyte fraction and association with good prognosis  , 32% (MITF-low) and 15% (Keratin subtype). Here we show that by incorporating biological knowledge as prior, Nebula can enhance patient stratification toward identifying clinically relevant subtypes.The clinical role of tumor-infiltrating lymphocytes has been established in several cancer types29 profiling 68k peripheral blood mononuclear cells (PBMCs) from a healthy donor to dissect immune cell populations. Our analyses include a total of 12,039 cells after filtering as in Cole et al.30 and data pre-processing as described in Lopez et al.31. To map cell subpopulations, state-of-the-art single cell pipelines  typically use k-means or hierarchical clustering of the most variable genes29. Nebula offers several advantages over deterministic algorithms through a Bayesian probabilistic inference framework and the selection of features that characterize each subpopulation. In addition, we incorporated prior information in graph structures on 22 functionally defined human immune subsets across 547 genes that distinguish hematopoietic cell phenotypes as describe in Newman et al.32. We further restricted to marker genes with standardized reference expression value Finally, we describe Nebula analysis of a single-cell RNA-seq datasetIn this paper we present Nebula, a powerful clustering approach for high-dimensional multi-modal molecular data. The method constructs a unified framework to identify subgroups along with their associated molecular features under the guidance of biological networks within and across different data types. To facilitate computational efficiency. we also develop a variational Bayes algorithm to achieve scalable implementation in high-dimensional settings. We apply the method to both pan-cancer and a single cell data. In pan-cancer, using a set of canonical oncogenic signaling pathways, an immune and a stemness index across point mutations, copy number alterations, DNA methylation and mRNA expression data we show that current clustering methods performed on most variable features inevitably lead to subgroups primarily driven by cell-of-origin. By contrast, Nebula allows targeted identification of subgroups driven by shared pathway activities of interest, and can be readily extended to include any number of additional pathways and genesets of interest for discovering shared biology across cancer types.For the single cell data, using a scRNA data of peripheral blood mononuclear cells from a single healthy donor, we show as a proof-of-principal that Nebula can be readily applied to deconvolute heterogeneous cell subpopulations in single cell data. In this analysis, we demonstrate that Nebula can accurately identify major immune cell subsets. Unsupervised clustering performs equally well in this setting, but will be considerably less effective for mixtures with more complicated compositions with content unrelated to immune phenotypes and noise, and for discriminating closely related cell types . It is anticipated that Nebula has the potential to dissect the signals of interest with more precision. Furthermore, it can be applied to multi-modal single cell data to dissect heterogeneous cell populations. The Nebula method and software tool we developed here will speed up exciting biological discoveries potentially relevant to clinical applications with the increasing amount of multi-model datasets generated from patient samples in both bulk tumor and single cell settings.n subjects with M data modalities generated from different sources, e.g. biological procedures including gene expression, DNA alternation, etc. We denote the overall observed data as m across all the subjects, and i. The collected features are dependent with each other within and between each data modality. For example, the pathway membership within gene expression data, the regulation between gene expression and DNA alternation. Such information essentially can be transformed into undirected graphs with nodes representing features and edges indicating the existence of their relationship. Here, we use graph m, and m and g.We formulate the model for general multi-modal data structure. Suppose there are j within modality m for subject i. Vector m into informative and non-informative components. Accordingly, the informative piece is modeled under parameter set G and we assign G to be a nonparametric Dirichlet process (DP) distribution with base measure G is an infinite discrete distribution, and realizations of To study data structure and subject similarity in the presence of high dimensional data, clustering embedded with feature selection should be performed to group observations with respect to the identified features that define each group. However, there is a lack of unified approach to achieve so. To conduct clustering while simultaneously distinguish the contribution of features, we introduce a dummy indicator mentclass2pt{minimWe assign priors for the unknown parameters in models , 3). In. In3). Ij and k are adjacent in graph The underlying molecular dependency within and cross modalities is often omitted in existing clustering analysis, but provides crucial biological guidance to define patient subtypes. Here, based on the extract molecular network information, we assume the biologically graph within gene expression as 33 algorithm, which approximates the posterior distribution by a simpler distribution, called the variational distribution. We have done extensive numerical studies to test the performance of both algorithms, and observed the VB algorithm reduces computational cost for around 100 folds compared to the MCMC approach. We describe below the construction details for the VB algorithm.We develop posterior inference algorithms to estimate model parameters for Nebula. To estimate posterior distributions, we develop the Markov Chain Monte Carlo (MCMC) algorithm via Gibbs sampler by drawing from the conditional probability distribution of each parameter. However, due to the high dimensional feature space, MCMC algorithms are computationally intensive and difficult to scale up in real practice. Therefore, we develop a variational Bayes (VB) inferencen-dimensional vector, P is an P as P are unused. The posterior loglikelihood deduced from the model is given in Supplementary Note.To fix the ideas, note that the model parameters are now P for the variational measure q,H by setting H, although our model does not restrict the number of clusters.In variational inference, the variational distribution is often assigned a product (independent) measure of the individual model parameters, as it facilitates computation and leads to scalability. Thus, we take a product measure on nonnegative. This implies (1) maximizing ELBO is equivalent to minimizing the Kullback–Leibler divergence and therefore it is fair to say that the posterior distribution is approximated by the variational measure and (2) ELBO being maximized gets close to the evidence We approximate the posterior distribution by maximizing the evidence lower bound (ELBO), which is defined ase is the number of edges in the graphs The formula of ELBO is analytically available and we optimize ELBO by the (blockwise) coordinate descent algorithm. The complete algorithm for the Nebula under VB is presented in online Appendix. It is worth noting the time complexity of the algorithm is H to make it much larger than the cluster number.To implement the Nebula, we will need to pre-specify hyper-parameter values for F-statistics for the immune and stemness index measured by the transcriptomic changes in the TCGA pan-cancer analysis, and the p values for these tests to select the optimal solution that explains maximal differences across the biological pathway activities. In the single cell data analysis, we used a similar strategy to compute an integrated F statistic across the CIBERSORT32 immune marker subsets. In a homogeneous cohort setting, other calculations may be appropriate to refine parameter selection and evaluate results. For example, calculating log-rank statistic to maximize survival differences between groups.In the analyses, our goal is to achieve clustering with biological variability across the pathways we are interested in. To find the most desirable solution, We run the Nebula under a range of parameter values including Supplementary Figures.Supplementary Information 1.Supplementary Information 2.


Among multiple subtypes of tissue or cell, subtype-specific differentially-expressed genes (SDEGs) are defined as being most-upregulated in only one subtype but not in any other. Detecting SDEGs plays a critical role in the molecular characterization and deconvolution of multicellular complex tissues. Classic differential analysis assumes a null hypothesis whose test statistic is not subtype-specific, thus can produce a high false positive rate and/or lower detection power. Here we first introduce a One-Versus-Everyone Fold Change (OVE-FC) test for detecting SDEGs. We then propose a scaled test statistic (OVE-sFC) for assessing the statistical significance of SDEGs that applies a mixture null distribution model and a tailored permutation test. The OVE-FC/sFC test was validated on both type 1 error rate and detection power using extensive simulation data sets generated from real gene expression profiles of purified subtype samples. The OVE-FC/sFC test was then applied to two benchmark gene expression data sets of purified subtype samples and detected many known or previously unknown SDEGs. Subsequent supervised deconvolution results on synthesized bulk expression data, obtained using the SDEGs detected from the independent purified expression data by the OVE-FC/sFC test, showed superior performance in deconvolution accuracy when compared with popular peer methods. An important but frequently underappreciated issue is how best to define a cell or tissue subtype-specific expression pattern. Ideally, a subtype-specific expression pattern would be composed of individual features that are most-upregulated in the cell or tissue subtype of interest while in no others 8.Molecular characterization often applies gene expression profiling to a complex biologic system that includes some molecular features that are expressed by all cell or tissue types in the system (such as housekeeping genes)9, the expression patterns of detected SDEGs could serve as the supervising information. However, detecting SDEGs using molecular expression profiles of purified/isolated tissue or cell subtypes remains a challenging task10. For example, the most frequently used methods rely on the extension of an ANOVA model where the null hypothesis states that samples in all subtypes are drawn from the same population. Consequently, ANOVA detects genes differentially expressed across any of the subtypes and can identify many false positive SDEGs (subtype-nonspecific classic DEGs) that may not conform to the SDEG definition (Supplementary Information). One-Versus-Rest Fold Change (OVR-FC) is another popular method based on the ratio of the average expression in a particular subtype to that of the average expression in all other samples (rest)12, and OVR t-test is occasionally used to assess the statistical significance of the detected genes13. However, a gene with a low average expression value in the rest is not necessarily expressed at a low level in every subtype in the rest. Expectedly, simulation studies show that Marker Gene Finder in Microarray data (MGFM) outperforms OVR t-test14. Alternative strategies include One-Versus-One (OVO) t-test and Multiple Comparisons with the Best (MCB)15 that use additional pairwise significance testing or the confidence intervals of OVO statistics16.SDEGs play a critical role in molecularly characterizing and identifying tissue or cell subtypes. For example, to support supervised deconvolution of complex tissues6. To assess the statistical significance of such a test, we propose a scaled test statistic (OVE-sFC) together with a mixture null distribution model. Because the expression patterns of non-SDEGs can be highly complex, a tailored permutation test is used to estimate the corresponding distribution under the null hypothesis.To address the critical problem of the absence of a detection method explicitly matched to the definition of SDEGs, here we introduce One-Versus-Everyone Fold Change (OVE-FC) test to detect SDEGs among many subtypes. Previously, the OVE-FC test was proposed as a means to detect SDEGs and improve multiclass classification, where the selection is based on whether the mean of one subtype is significantly higher or lower than the mean from each of the other subtypesj is expressed at the highest and second highest levels, respectively14,14, and matches well the definition of SDEGs18. Conceptually, the null hypothesis for non-SDEGs, and the alternative hypothesis for SDEGs, can be described as  method for fully unsupervised deconvolution19 (marker genes that reside near the vertices of the scatter simplex). To assess the statistical significance of OVE-FC tests and to leverage the information across subtypes or genes, we assume that Consider the measured expression level mentclass2pt{minimpes see “” sectionWe first validate the performance of OVE-sFC test on extensive simulation data, in terms of type 1 error rate and False Discovery Rate (FDR) control. We then demonstrate the detection power of OVE-FC/sFC in a comprehensive set of scenarios and in comparison with top peer methods using the partial area under the receiver operating characteristic curve (pAUC) as the performance measure. We show the utility of OVE-FC/sFC using benchmark public data, and then assess performance both by comparing with known SDEGs and by the accuracy of supervised deconvolution that uses the expression patterns of de novo SDEGs detected by OVE-FC/sFC.This work aims solely to detect SDEGs among multiple subtypes of interest, no bulk experiment or dataset is involved. All tests use the molecular expression data derived from purified/isolated subtypes, where the subtype can be a tissue subtype, cell subtype, or biological process. The supervised deconvolution case study provides one application that uses SDEGs detected as explicitly defined. The main objective of the proposed OVE test is to reduce the high false positive rates of existing methods while ensuring high sensitivity, particularly when involving a large number of multiple tests.p values. If the type 1 error is either too conservative or too liberal, the p value is inflated by either too many false positive or false negative estimates, the test loses its intended meaning, and the data become difficult to interpret correctly.To test whether our OVE-sFC test can detect SDEGs at appropriate significance levels, we assessed the type 1 error using simulation studies under the null hypothesis “” section8). Using the simulation data sets with various parameter settings, we show that in all scenarios the empirical type 1 error produced by OVE-sFC test closely approximates the expected type 1 error , we simulated a comprehensive set of scenarios to compare the power of OVE-sFC and peer methods to detect SDEGs. Simulation data are again generated by modifying the expression levels of real gene expression data, where a portion of the genes are designated as SDEGs that are upregulated specifically in one of the participating subtypes, with fold change drawn in certain ranges. To recapitulate the characteristics of real expression data, we used parameter values that are close to that estimated from real data, such as proportions of various non-SDEG expression patterns. To retain the mean–variance trend in RNAseq data, we sampled variance directly from the real RNAseq data 21.False Discovery Rate (FDR) control is an important issue when assessing detection power in large-scale multiple testing. For a well-designed significance test, the objective is to maximize power while maintaining the FDR below an acceptable level. To test whether the q-value reflects the actual FDR, ‘fdrtool’ was used to estimate the q-value for each geneExperimental results show that both overall and subtype-specific OVE-sFC tests achieve a well-controlled FDR that matches the q-value cutoff Figure , S6. In 17, Fig. For pAUC, the OVE strategy in OVE-FC/sFC achieved the highest power in detecting SDEGs Figs. –S3, as dWhile the OVE test aims to reduce high false positive rates produced by the existing methods, it shall also ensure high detection power (sensitivity). While the percentage of ideal SDEGs is expected to be small when compared with that of non-SDEGs, a high TPR is required to ensure sufficient accuracy of a supervised deconvolution and subtype enrichment analysis (two major utilities of SDEGs). The experimental results shown that, with the desired > 0.9 sensitivity as seen in Fig. When the sample size is small, the OVE-sFC test statistic leverages information across genes by estimating a priori variance via the limma method. This approach stabilizes the variance estimate for each gene. Furthermore, the OVE-sFC test statistic estimates the parameters of the limma model from all subtypes, producing better results than by applying a t-test independently with the limma model for each subtype pair. For small sample size cases, our results show that OVE-sFC clearly outperforms OVO t-test Figs. , S3. Not22. The constituent subtypes are composed of seven different human immune cells that were isolated from healthy human blood: B cells, CD4 + T cells, CD8 + T cells, NK cells, monocytes, neutrophils, and eosinophils. Because Roche and HUG used the same protocols for cell isolation and sample processing from two independent panels of donors, the derived gene expression profiles allow the use of a cross-validation strategy.To detect SDEGs associated with human immune cells, we applied the OVE-sFC test to two gene expression microarray data sets acquired from isolated/purified subtypes, GSE28490 (Roche) and GSE28491 (HUG)23. We also applied a Bonferroni multiple testing correction and a more stringent p value < 0.001; the number of SDEGs account for 10.7% and 2.7% of all probesets in the Roche and HUG data sets, respectively  score calculated from ideal SDEGs-guided supervised deconvolution to quantify the proportional abundance of each subtype (Supplementary Information). The correlation coefficient between the estimated scores and the true proportions was used to assess the accuracy of several SDEGs selection methods.Accurate and reliable detection of ideal SDEGs has a significant impact on the performance of many supervised deconvolution methods that use the expression patterns of ideal SDEGs to score constituent subtypes in heterogeneous samplesBoth OVE-FC and OVE-sFC were applied to three independent data sets acquired from the purified subtype expression profiles (GSE28490 Roche), purified subtype RNAseq profiles (GSE60424), and classified single-cell RNAseq profiles (GSE72056), respectively. Ideal SDEGs were detected by six different methods including OVE-FC, OVE-sFC, OVR-FC, OVR t-stat, OVR t-test, and OVO t-test, and then used to supervise the deconvolution of realistically synthesized mixtures with ground truth.The proportions of constituent subtypes were estimated by the CAM scores derived from expression levels of top-ranked SDEGs for each subtype. Supervised deconvolution results show that OVE-sFC, OVE-FC and OVO t-test achieved the highest correlation coefficients between the CAM score and the true proportions when compared with other methods Figs. a, S10.FiTo create a more biologically realistic case involving higher between-sample variations, we synthesized a set of n = 50 in silico mixtures by combining the subtype expression profiles from bootstrapped samples in the RNAseq data set according to pre-determined proportions. Again, supervised deconvolution results show that the ideal SDEGs detected by OVE-FC or OVE-sFC or OVO t-test achieved superior deconvolution performance Figs. b, S11.Using the more challenging case of RNAseq data , we repeated the simulations where in silico mixtures were synthesized by combining subtype mean expressions (GSE28491 HUG); ideal SDEGs were detected from the downsampled RNAseq profiles in GSE60424 (n = 3). Three purified samples were randomly selected for each subtype and analyzed by the six methods. In terms of ideal SDEG-guided deconvolution, OVE-sFC strongly outperforms OVO t-test. OVE-sFC also outperforms OVE-FC for phenotypically closer cell types (CD4 + T and CD8 + T cell types)  selected, Fig. on Table , is a go18, motivated by the need to obtain such SDEGs to supervise in silico tissue deconvolution19 and/or tissue subtype characterization9. This is a particulary important goal where the measured data are mixtures of the genes expressed by many of the subtypes present in the samples and the SDEGs are used to estimate both the proportions of each subtype in individual heterogeneous samples and the averaged subtype-specific expression profiles.Interpreting an expression profile of complex tissues requires knowledge of both the relative abundance of the different cell or tissue subtypes and their unique molecular characteristics. Understanding the relative contribution of individual cell or tissue subtypes in complex samples can illuminate pathophysiologic mechanisms, biologic responses to various stimuli, or transitions in phenotype—especially when cell–cell and cell–matrix interactions in a complex system are necessary conditions for appropriate cell or tissue function. The SDEG expression patterns of relevant cells or tissues can be used to support supervised deconvolution to estimate the relative prevalence of these cell or tissue subtypes. Our present work on SDEGs is restricted to the widely adopted SDEG definitionp values can accurately estimate both subtype proportions and expression profiles; thus, these SDEGs can serve as effective molecular markers , sample size, and differential expression (fold change) between two subtypes expressed at the highest levels. Our experimental results show that OVE-sFC test maintains type 1 error rates closely matched to the expectations with varied effects Figure , and tenp values of OVE-sFC is assumed. This assumption holds when all genes are non-SDEGs and the estimate of the null distribution is sufficiently accurate. OVE-sFC works best when all assumptions in the model are valid. For example, while the proposed permutation scheme does not require the data to be normally distributed under the null hypothesis, OVE-sFC assumes that samples are drawn from a distribution with the same ‘shape’ for different genes. This assumption ensures that the null distributions across genes can be combined with variance-based standardization. When data distributions deviate significantly from a common shape, limma-voom/vooma/voomaByGroup variance models can be used to accommodate unequal variances by appropriate observational-level weights26. When data distributions deviate significantly from normality, a permutation ANOVA can be used to estimate the null hypothesis components of the mixture distribution. Figure 27 and DESeq228 can be adopted when needed.In our study, most of the assumptions applied are widely accepted because they are close to reality. In the simulation study to validate the type 1 error rate produced by OVE-sFC under the null hypothesis, a uniform distribution of the empirical p values are estimated) while OVE-sFC provides a significance assessment and can improve FDR control. Secondly, OVE-sFC improves detection power in some of the more challenging experimental conditions. Detecting SDEGs with accurate p values is an attractive feature of OVE-sFC that can help restrain the FDR to its expected level. Indeed, our experimental results show that OVE-sFC test outperforms OVE-FC in the more challenging cases involving nonideal SDEGs .ANOVA has been the most commonly used method to test differences among the means of multiple subtypes, often in conjunction with a post-hoc Tukey HSD test to compare all possible pairs of means5. OVE-FC/sFC can detect down-regulated SDEGs by reversing the comparison rule5. There are alternative definitions of ‘informative genes’ for different analytical purposes, such as when the goal is sample classification. In our earlier work on multiclass classification6, we have shown that upregulated SDEGs selected by OVE-FC are sufficient to achieve multiclass classification and can often improve classifier performance over alternative informative gene subsets of the same size.In addition to the SDEGs discussed here (genes uniquely up-regulated in a specific subtype), the counterpart of subtype-specific down-regulated genes (genes uniquely down-regulated in a specific subtype) are also of biological interest30.In the present study, we have chosen to introduce a method focused on univariate analysis. Our method does not consider the network structure among the genes or gene sets. For the future work, we will explore the possibility of networked SDEG detection, laveraging the latest advances in gene set analysis approaches based on multivariate tests19 are required. An advantage of unsupervised deconvolution is that it can identify both the cell/tissue subtype proportions and their specific expression patterns, albeit with potentially less fidelity, when neither is known a priori or measured from the same sample.Lastly, when subtype-specific expression patterns are unknown, unsupervised deconvolution techniques such as CAMmth component of the mixture null hypothesis We propose the following mixture distribution of the OVE-sFC test statistic sis Fig. 4\documenThis mixture null distribution model is proposed to model unknown but potentially complex expression patterns of non-SDEGs under the null hypothesis. The permutation scheme(s) estimates such a mixture null distribution. The main advantage of the proposed permutation scheme(s) is its flexibility and comprehensiveness, which closely match the mixture null distribution of various types and combinations. With varying proportions of different non-SDEG types, the OVE-sFC test can maintain the type 1 error rate close to the expected level with the help of the proposed permutation scheme(s) and the conditional probability of each non-SDEG type .The null distribution of OVESEG-test statistics under p values of candidate SDEGs are estimated using the learned ‘mixture’ null distributionP is the number of permutations, J is the number of participating genes, p value associated with gene j is calculated by:p values when considering all subtypes together . In contrast, OVE-sFC exploits all subtypes in estimating the variances. The benefit of using all subtypes for modeling is significant in challenging cases with higher variance, smaller sample size, and more subtypes (Supplementary Information).The OVR-FC uses a simple test defined bym values, mimicking all types of non-SDEGs under the participating null hypotheses Among the 10,000 simulated genes, a portion are housekeeping genes that take the baseline expression levels across all subtypes under Among the 10,000 simulated genes, ratios of non-SDEG patterns were consistent with the estimation from the base real dataset; microarray data GSE28490 or RNAseq data GSE60424. 100 SDEGs were mimicked with their upregulations sampled from a fold change range , or . Variances were sampled from base real dataset according to gene expression levels, preserving the potential mean–variance trend (Supplementary Information).In these data sets, each cell subtype consists of at least five samples, excluding a few outliers Table . FollowiFive subtypes  were included in synthesizing n = 50 in silico mixtures, where purified subtype mean expression data from the GSE28491 HUG dataset were combined according to pre-determined proportions with additive noise, simulating heterogeneous biological samples (Supplementary Information).Supplementary Infomations.


Mip6 is an RNA-binding protein that contributes to RNA export during environmental stress and is informative of the contribution of post-transcriptional regulation to control cellular adaptations to environmental changes. The experiment was performed in quadruplicate, and the different omics measurements were obtained from the same biological samples, which facilitates the integration and analysis of data using covariance-based methods. We validate our dataset by showing that ChIP-seq, RNA-seq and metabolomics signals recapitulate existing knowledge about the response of ribosomal genes and the contribution of trehalose metabolism to heat stress. Raw data, processed data and preprocessing scripts are made available.Gene expression is a biological process regulated at different molecular levels, including chromatin accessibility, transcription, and RNA maturation and transport. In addition, these regulatory mechanisms have strong links with cellular metabolism. Here we present a multi-omics dataset that captures different aspects of this multi-layered process in yeast. We obtained RNA-seq, metabolomics, and H4K12ac ChIP-seq data for wild-type and  Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11836743 One of the earlier steps consists of setting up the appropriate epigenetic modifications to allow the expression or repression of specific gene programs4. These modifications take place mostly on DNA and histones, ensuring access to the proper transcriptional machinery. The specific set of modifications across the genome regulates the final synthesis of the mRNA5. These newly synthetized RNA molecules are extensively modified prior to their export to the cytoplasm, where they can be degraded by the mRNA decay machinery, stored in specific foci or translated into proteins7. Throughout this journey, RNA molecules are guided by RNA-binding proteins that control their fate9. Finally, the encoded protein products participate in numerous processes, including cellular metabolism where organic compounds are transformed and/or stored. A number of these compounds, such as Acetyl-CoA, glucose or methyl groups, participate, in turn, in chromatin modifications that regulate gene expression.Eukaryotic gene expression is a complex process in which the information coded in genes is transformed into functions that support living cells. This process comprises different interconnected steps, which occur in separate compartments and are performed by specific molecular components15, these studies usually target aspects of the transcriptional regulation and produce separate results that contribute to the overall transcriptional model. Moreover, different strains, experimental conditions and batches are used by different labs and there are few examples of experimental datasets where all these layers have been measured on exactly the same samples. This poses challenges to the mathematical integration of the data, as methods that rely on the analysis of co-variation patterns will have application restrictions. When the multi-layered data are obtained on the same samples, additional analysis opportunities arise that facilitate the establishment of relationships across regulatory mechanisms.Our current understanding of transcriptional regulation was largely established using yeast as a model organism. Yeast is an ideal research model for transcriptome research because they exhibit most of the cellular complexity present in eukaryotes and have relatively compact, accessible genomes. However, while the interconnected transcriptional circuit has been studied and accepted by many yeast labsmip6Δ mutant, in control and heat-shock induced conditions. Mip6 is an RNA-binding protein that participates in RNA export under stress16 and consequently is informative of the contribution of post-transcriptional regulation to the adaptation of RNA levels to environmental changes. Taken together, the selection of yeast strains, growth conditions and omics experiments, creates a unique dataset to study the cross-talk between epigenetic, transcriptional and metabolic regulation in response to environmental cues. The availability of multi-omics data on the same set of samples will facilitate the application of powerful statistical approaches that fully leverage paired measurements to propose quantitative regulatory models. A subset of this collection, namely gene expression data for 20 genes that are regulated by stress transcriptional factors Msn2/4, has been published elsewhere16.In this Data Descriptor, we present a yeast multi-omics dataset that features three basic layers of the transcriptional circuit, measured in the same set of samples. These include one epigenetic modification - H4K12ac, a mark for active promoters-, gene expression -RNA-seq- and targeted metabolomics. Moreover, data are obtained for both WT and a mip6Δ strains) was grown at 30 °C until the exponential phase (OD = 0.7), and this culture was subsequently split across three flasks. One flask was maintained at 30 °C and labeled as time point 0. The other two flasks were incubated at 39 °C for 20 minutes and 120 minutes, respectively, by adding preheated media to rapidly increase the temperature to 39 °C. These last two flasks capture the heat-shock response, while the 30 °C flask serves as a control representing non-stress conditions. The rationale for having two flasks for the 39 °C temperature instead of a single flask sampled at two different time points was to avoid introducing effects related to culture volume. Panel B describes how samples were obtained for omics measuments. Basically, for each of the flasks described above three aliquotes were extracted for RNA-seq, NMR metabolomics, and ChIP-seq analyses. Therefore, the three omics assays were performed on the same cell culture. RNA-seq and NMR aliquotes were obtained first, and the remaining culture was treated to induce cross-linking before collecting the ChIP-seq aliquote. After aliquoting, each tube was inmediatelly frozen and stored at −80 °C.Figure The process described in Fig. 17 and genes were quantified with HTSEQ18, intersection-option. Supplementary Table Total RNA was isolated by hot acid phenol extraction. RNA integrity was checked with Bioanalyzer (Agilent) and then submitted to a commercial sequencing facility (Macrogen Corea). Sequencing was done with Illumina using the TruSeq protocol. Between 50–60 million reads of 100 bp paired data were obtained from each sample. Raw sequencing data quality was checked by fastQC and good overall quality Fig.  was obse19 R package was used to perform the quality control of count data. We observed most of reads mapped onto protein-coding genes (>80%), as expected . Principal Component Analysis (PCA) indicated a slight batch effect for the day of culture growth . Metabolite identification and assignment were performed with the help of the Human Metabolome Database and 2D NMR experiments. Signal peaks of spectra were normalized considering that the sum of peak areas across all metabolites was constant for every sample, and values for each metabolite were given as a fraction of the total area. A total of 45 compounds were detected, that included 5 sugars, 17 amino-acids, 4 alcohols, 3 vitamin-derivated compounds, 5 carboxylic acids, and other compounds , plus 3 unidentified metabolites , they were quenched with 125 mM glycine for 15 minutes. Subsequently, the cells were collected by centrifugation, split in two aliquots (one for each ChIP) and washed with 25 mL of cold Tris-saline buffer  three times. The pellets were frozen in liquid nitrogen and stored at −80 °C until further processing. Cells were disrupted by adding 300 µL of lysis buffer  and 200 µL of glass beads and vortexing for 13 minutes at 4 °C. The cell extracts were sonicated for 30 minutes in a Bioruptor sonicator (Diagenode) at high intensity using 30 seconds on/30 second off cycles in a 4 °C water bath. The cellular lysate was clarified by centrifugation at 12,000 rpm for 10 minutes at 4 °C and the whole supernatant was used for immunoprecipitation by incubating with magnetic beads  bound to anti-histone H4 (Abcam) or anti-histone H4K12ac (Active Motif) antibody for 2 hours at 4 °C. Beads were subsequently washed twice with lysis buffer, twice with lysis buffer supplemented with 360 mM NaCl, twice with wash buffer , and once with TE buffer. Samples were eluted by adding 50 µL of elution buffer  to the beads and incubating for 10 min at 65 °C. This step was repeated twice. The samples were incubated overnight at 65 °C to reverse the cross-linking and then incubated with 100 µg/250 µl of proteinase K (Ambion) for 1.5 h at 45 °C. DNA was isolated by phenol extraction. This DNA was sent to Macrogen Corea for sequencing.For ChIP-seq samples, chromatin immunoprecipitation was performed as previously described24. Reads were mapped to the yeast saccer3 genome with Bowtie225. Supplementary Tables Sequencing was done following the Illumina TruSeq protocol. Around 20 million 50 bp reads were obtained for each sample. Note that two ChIP-seq data files were obtained for each sample: H4 and H4K12ac. H4 files contain the reads after purification of total H4 histone and H4K12ac files contain the data associated to acetylation of Lysine 12. Raw sequencing data quality was checked by fastQC and good overall quality Fig.  was obse26 was used to call Histone 4 acetylation peaks on the H4K12ac samples alone. Next, a consensus file was generated by merging peaks across all samples using the merge command from bedtools software27 with default parameters. These consensus regions were used to map back reads of all samples, including H4 samples. Peaks were quantified with HTSEQ18, intersection-option. NOISeq19, R package was used to perform a quality control of count data. Moreover, coverage per base was obtained for both, H4 and H4K12ac samples, using the genomecov command from bedtools26.H4 sample of mip6.39.0 was discarded as it showed poor sequencing performance. Macs2 softwaremip6Δ heat-shock multi-omics data have been deposited in different public repositories29. Table The yeast WT and 30.Additionally, processed data are also accessible from the Figshare repository, with 10.6084/m9.figshare.c.4716677 mip6Δ strain data behaved similarly. Replicates were highly and equally correlated with each other, and no experimental outliers were detected.In order to assess replicability, pairwise scatter plots were obtained for RNA-seq data Fig. , metabol32, and it is well known that ribosomal genes rapidly shut down after heat treatment. Moreover, previous studies assessing the impact of heat-shock on yeast cells revealed a protective effect for trehalose34. We evaluated whether this effect was corroborated by our data.Translational repression upon heat-shock occurs in most eukaryotesmip6Δ strains. The strongest effect was observed after 20 minutes of heat (blue bars), and recovery was observed for all genes after 120 minutes (orange bars). The drastic downregulation of several RP genes after 20 minutes at 39 °C was further validated in independent experiments by q-PCR  of the comparison between consecutive time-points. We compared this distribution to the Log2FC of gene expression. This analysis showed that the gene expression response measured by RNA-seq (grey boxes) has a larger dynamic range than H4K12 acetylation measured by ChIP-seq (white boxes), as the distributions are broader in the former Fig. . Additiomip6Δ cells were larger than in WT. We therefore investigated further this pathway. We confirmed a general -although with different magnitudes- upregulation for genes members of the trehalose metabolism pathway  and product (TPS2), suggesting a direct regulation of trehalose levels by these gene products (Fig. Figure way Fig. . Interesset Fig. . We foun416 Fig. . Finallycts Fig. .Fig. 8Trmip6 in the metabolic control of the heat-shock response, further supporting the biological interest of the dataset.Taken together, this section shows a biologically consistent and coordinated signal of our RNA-seq, ChIP-seq and metabolomics datasets that agrees with previous findings. Our analysis also suggests a specific role for Supplementary table S1Supplementary table S2Supplementary table S3


Screening agrochemicals and pharmaceuticals for potential liver toxicity is required for regulatory approval and is an expensive and time-consuming process. The identification and utilization of early exposure gene signatures and robust predictive models in regulatory toxicity testing has the potential to reduce time and costs substantially. In this study, comparative supervised machine learning approaches were applied to the rat liver TG-GATEs dataset to develop feature selection and predictive testing. We identified ten gene biomarkers using three different feature selection methods that predicted liver necrosis with high specificity and selectivity in an independent validation dataset from the Microarray Quality Control (MAQC)-II study. Nine of the ten genes that were selected with the supervised methods are involved in metabolism and detoxification  and transcriptional regulation (Ablim3). Several of these genes are also implicated in liver carcinogenesis, including Crat, Car3 and Slc23a1. Our biomarker gene signature provides high statistical accuracy and a manageable number of genes to study as indicators to potentially accelerate toxicity testing based on their ability to induce liver necrosis and, eventually, liver cancer. Toxicogenomics databases have been designed and, through rigorous experiments on rat and human cell models, provide an avenue to understand the molecular basis of adverse conditions due to chemical toxicant exposures. Computational methods provide an opportunity to develop this much-desired capability2. These methods are relatively low cost to develop and test, can expedite data analysis, can reduce cost by reducing the scale of animal studies, and can reduce time to market for a safe product.Pathological and biochemical data in non-human mammals are used extensively by the agrochemical and pharmaceutical sectors for assessing mammalian toxicity and effects on human health of molecular innovations. This effort is extensive; in addition to other cost and effort, required mammalian toxicity assessment packages can use ~ 6000 animals per molecule studied. Despite such careful screening, major setbacks to pharmaceutical product development pipelines still result where human toxicity is detected during late stages. When toxicity is not determined in this testing, a danger to public health arises if adverse effects on humans are only observed in the population after years of deployment. These risks can be greatly mitigated if early biomarkers of eventual toxicity can be found. Toxicogenomics, or the application of genomics methods to predict adverse effects of exogenous molecule exposure6. Feature selection and classification can both be achieved by supervised methods for classification or unsupervised learning methods 5 that are primarily used for discovery.Toxicogenomics analyses are commonly categorized in the big data paradigm because of the large number of gene profiles that arise from the small number of samples, thus the need for data reduction tools. Classical statistical methods of identifying differentially expressed genes from microarray or RNA sequencing data results in lists comprising thousands of genes, which is not ideal for laboratory testing. Machine learning approaches such as feature selection and classification often use robust statistical modeling to reduce the number of features or variables used in the models6. Previously, several studies have used machine learning methods for prediction of biological end points9. Despite many attempts in the field, however, predictive ability remains relatively poor due to systematic noise associated with design of gene expression experiments10, high number of features in the signature, low predictive performance14, or poor performance of identified biomarkers at validation stage15. Innovations in data analysis pipeline design and modeling are still sorely needed.Studies have shown that the use of supervised classification predictive models can help to find discriminative gene signatures across multiple platforms of microarray data16. After determining these features, a set of predictive models were optimized. Finally, the model was applied to untrained MAQC-II data to evaluate liver toxicity predictions18. The targeted conclusion of our study was to determine a small set of genes that successfully predicted liver necrosis and could be used for predictive testing in animals.The goal of this study was to construct a suitable modeling framework based on machine learning for feature selection, feature ranking, and predictive analysis applicable to liver toxicity. The developed framework was applied to the TG-GATES data set to select and rank the gene expression features that can serve as biomarkers for liver toxicity in ratshttps://dbarchive.biosciencedbc.jp/en/open-tggates/data-2.html. The in vivo models were categorized by whole organism outcomes of exposure related to cellular injury20. The treatments included 42 chemical compounds (Table 16). Livers were harvested after indicated time points. RNA was isolated, and gene expression patterns were analyzed using the common array platform, Affymetrix Rat 230 2.0 microarray that contained probes for 31,099 genes.Gene expression data were obtained from TG-GATES database for male rat, in vivo experimental models utilizing Affymetrix Microarray Chip from the TG-GATES database ds Table A at contds Table B 16. Liv17. From the six datasets, we focused on the National Institute of Environmental Health Sciences (NIEHS) data set for validation since it pertains to toxic effect of chemicals on liver. The study was similar to TG-GATES, which used microarray gene expression data acquired from the liver of rats exposed to various hepatotoxicants. Gene expression data, collected from 418 rats exposed to one of eight compounds , were used to build classifiers for prediction of liver necrosis. Each of the eight compounds were studied and analyzed using the common array platform (Affymetrix Rat 230 2.0 microarray), data retrieving and analysis processes. Similar to TG-GATES studies, four to six male, 12 week old F344 rats were treated with low-, mid-, and high-dose of the toxicant and sacrificed at 6, 24 and 48 h later. At necropsy, liver was harvested for RNA extraction, histopathology, and clinical chemistry assessments17.Data from the Microarray Quality Control Project (MAQC II) was used for validation and assessing classification performance of the top selected features23. Briefly, EE treatment data from the common array platform, Affymetrix Rat 230 2.0 microarray, which reported expression value of 31,099 genes were obtained from TG-GATES database. Data were normalized using the robust multi-array (RMA) average expression measure (Affy (v 1.57.0) package from Bioconductor)25. RMA was calculated on raw microarray gene expression values under standard normalization options (https://web.mit.edu/~r/current/arch/i386_linux26/lib/R/library/affy/html/AffyBatch-class.html). After normalization, the data were centered and scaled for differentially expressed genes analysis. To identify differentially expressed genes upon EE treatment, statistical analyses were performed on normalized gene expressions from dose response and time course data using Limma (v 3.34.9) package from Bioconductor27. Design matrices, constructed in R, identified coefficients of interest specifically high dose treatments (denoted with a 1) and control dose treatments (denoted with − 1). Gene expression data were first fitted to a multiple linear model, based on the design matrix. The linear model was then fitted to an empirical Bayes model with the contrast matrix representing the differences between high and control doses for each molecule29. T statistics and F-statistics were computed from the model. Significant features were selected with p-value < 0.05 for further feature selection methods. Resulting differentially expressed gene list was used to perform hierarchal clustering using Cluster 3 software30. Clustered data was visualized using Treeview java (https://jtreeview.sourceforge.net/). Gene set enrichment analysis software was used to identify enriched functional gene groupings32. Principal component analysis was performed using StrandNGS . Graphs for biochemical analysis  and average gene expression values were plotted using Graphpad Prism8 software .To select best dose and earliest time point of liver toxicant exposure, EE data was used as described before25. RMA was calculated on raw microarray gene expression values under standard normalization options (https://web.mit.edu/~r/current/arch/i386_linux26/lib/R/library/affy/html/AffyBatch-class.html). After normalization, the data were centered and scaled for gene expression analysis.To prepare data for feature selection and classification using machine learning, microarray data (Affymetrix Rat 230 2.0) for compounds that induce necrosis were obtained from TG-GATES database and MAQ CII project. To avoid batch effects, data were normalized using the robust multi-array (RMA) average expression measure (Affy (v 1.57.0) package from Bioconductor)34 that combines traditional statistical modeling with machine learning methods to perform predictor selection and ranking. These selected biomarkers formed the inputs for an integrative modeling process to determine the performance of significant markers for classification.To assess the hypothesis that an early exposure gene signature is associated with liver toxicity, we applied a methodology38, as outlined below and summarized in Supplementary Fig. We integrated all analytical steps into a machine learning pipeline, similar to one used previously for patient classification39 and an empirical ranking score based on the feature importance measure40. Methods for feature selection included Mann–Whitney, t-test, DCor as marginal screening methods; Boruta, RFE with both RF and SVM as wrapper methods; and RF, Elastic Net, Lasso, Ridge Regression Cross Validation (RidgeCV) and SVM as embedded methods. For each approach, the top N features were noted and utilized in the outer cross-validation loop of the integrative modeling process. Most algorithms are part of scikit-learn, scipy, and BorutaPy packages.First, to determine a gene feature’s measure of importance in predicting the necrosis response we used a set of feature selection approaches  on all predictors (i.e. genes and liver phenotypes)42 with all compounds grouped together in the same fold and with a validation set43. Models were built for each feature selection approach and each predictive modeling approach. Predictive statistics were gathered as well as receiver operator characteristic (ROC) curves for each combination to visualize the classification performance  of the classifiers. Predictive modeling approaches include: logistic regression, RF, and support vector machine (SVM), Lasso and ElasticNet46. We built models incrementally from one feature to 100 features to understand and determine tradeoffs for identifying a cutoff for how many N features to select.Cross-validation (out-of-sampling-testing) is utilized for obtaining the rankings by assessing every feature’s predictive power on unseen data47 partitions the samples into training and testing sets and proceed by fitting the model on the training set and evaluating the AUC on the testing set. Repeatedly performing the procedure independently, we summarize AUCs of all iterations for comparison48. To compare the performances of the developed classification model using gene biomarkers and the traditional diagnostic model, we obtained the AUC measures from all models over all randomization runs, and perform a two-sample t-test to detect differences. For each feature selection and classification method combinations, we reported area under the curve (AUC), F-statistics and MCC49 (Table https://www.tableau.com/).Parameter tuning and performance evaluation were performed using the MAQCII-NIEHS (GSE16716) as the validation set, utilizing the area under the cross-validated ROC curve (AUC) as a quantitative performance metric. For parameter tuning, we tested tree depth of Boruta at 4, 5, 6, and no limit. We chose to focus on the depth of 4 to avoid overfitting. We experimented with alpha values for Elastic Net and Lasso using the Scikit learn GridSearchCV, which selects the best performing parameters. In addition, we experimented with the C value for SVC. For the rest of the algorithm default parameters were used. All parameters are listed in Table 49 Table . Results50. In the TG-GATES data set, high-dose EE treatment caused a statistically significant change in clinical pathology parameters such as alkaline phosphatase by day 4, and total bilirubin levels by week 2 , which showed that manifestation of clinical pathologic indicators of liver damage, metabolic changes, and liver necrosis by high-dose EE exposure at the earlier time point was consistent with gene expression. Many genes were up- or downregulated in the liver by the high-dose EE exposure at all-time points assayed  dataset Fig. A as prolk 2 Fig. B51. StatTo identify the earliest time point data to be used in feature selection, we utilized 3, 6, 9, and 24 h and the 4, 8, 15 and 29 days’ time-points. Hierarchical clustering of 1387 differentially expressed genes identified eight clusters with distinct gene expression kinetics and function  utilizing the TG-GATEs dataset as training set, and MAQC-II dataset as an independent validation set. With this extensive testing and independent assessment, the gene signature that results is more likely to be a generalizable predictor. Based on ROC values, filter and wrapper feature selection methods in combination with Logistic Regression, RF and SVM performed with high accuracy . To perform more detailed analysis, we focused on the four best performing feature selection methods  and five classification methods  Fig. B and unb52. We compared various feature selection and classification methods to identify early gene biomarkers of liver toxicity using an extensive gene expression database, TG-GATEs and an independent validation dataset, MAQC II. Initially, we focused on necrosis, which is a valid end point to predict liver cancer52 as necrotic cell death is a common feature in liver disease55. Given that necrosis is a fairly common end point for adverse processes, we anticipate that our methods are applicable to other apical end-points. Rather than depending solely on the parametric models, the methods utilized in the feature selection and predictive analysis are adaptive, and involve models requiring the optimization of a tuning or smoothing parameter to control the trade-off between model generality and complexity. Appropriate choice of tuning parameters is critical for feature selection stability and good performance of the resulting predictive model estimator. TG-GATEs microarray gene expression data contains few samples (n) and very large features or genes (p). In machine learning, this p ≫ n problem usually has major consequences for prediction modeling. For example, over fitting may occur, which can cause unreliability for the prediction model to be used on other data sets 56. Our study design with an extensive, independent validation and careful feature selection and curation, likely overcomes this hurdle.In this study, we built a ML-based predictive process composed of ten genes that should be regulated in rat liver after 24 h of toxicant exposure and accurately predicts a liver necrosis phenotype, an indicator of liver carcinogenicity after long-term molecule exposure58 and to deep belief networks60. These methods combined computational power with model building about the behavior of the error function in the parameter space, and they improve on manual parameter tuning. To improve the performance of our feature selection and predictive analysis steps we utilized MAQCII-NIEHS (GSE16716) dataset as the surrogate for pre tuning the parameters of these methods17. Since we used an independent validation set (MAQCII) to select prediction models with higher accuracy, we avoided overfitting issues that typically afflict studies that only employ cross-validation. We also utilized methods that dealt directly with binary classification rather than regressive methods to generally predict multiple apical end-points from the TG-GATEs database.Parameter tuning has traditionally been a manual task because of the limited number of trials. Recently, it has been shown automated pre-tuning surrogate-based parameter optimization was successfully applied in the learning for a wide variety of feature selector/classifiers61. The dataset we used contained much less features from a smaller population. Since, in our study we are dealing with many more features from larger number of experiment we used an expanded list of feature selection methods that fall into one of the three main categories: Mann–Whitney, t-test, DCor as filter methods; Boruta, RFE with both RF and SVM as wrapper methods; and RF, Elastic Net, Lasso, Ridge Regression Cross Validation (RidgeCV) and SVM as embedded methods. For assessing classification performance we used logistic regression, RF, and support vector machine (SVM), Lasso and ElasticNet. Instead of relying on one machine learning method9, we used an exhaustive approach wherein we have compared combinations of aforementioned feature selection and classification methods and tested their performance rigorously on a validation set. Our process addresses several limitations of traditional methods for multimodal signature studies in terms of data handling  as well as procedural (increased performance over a single approach and assessment of key features in the context of phenotype)36. The net outcomes were that we obtained a minimal descriptive set of 10 biomarkers (key star features) related to liver toxicity , a ranked list of biomarkers that describe a phenotype, a classifier useful for toxicity screening, a confidence measure for the classifier, and a classifier performance evaluated on MACQII data unseen during training63. Number of features used for classification is very low, which avoids the problem of overfitting. In addition, we used an iterative process where we selected features and tested their performance on the validation set. This exhaustive process ensured that only best predictors with minimum number of genes were used and that their performance was validated in an independent dataset (MAQCII) to avoid low reproducibility of identified biomarkers.We have previously used t-test and RF coupled with logistic regression to identify biomarkers of breast cancer risk64, Car365 and Slc23a166.To avoid overfitting while building our prediction models and to eventually utilize the biomarker genes in a practical laboratory test for unknown chemicals, we limited our gene list to 10 candidates. The genes that were selected with various methods are involved in metabolism and detoxification  and transcriptional regulation . Several of these genes were implicated in liver carcinogenesis including CratIn summary, using feature selection, modeling and validation with an independent data set, we found a robust set of genes that appeared to be broadly generalizable for prediction. We selected the top genes and the best models to predict whether a compound would cause liver necrosis. This selected pipeline provided predictions with high accuracy. Given the broad set of conditions and a manageable set of predictor genes, we anticipate that this signature can be used to predict future carcinogenic effects of long-term exposure to liver toxicants in rodent models and accelerate the predictability of toxic effects in humans.Supplementary Information.


Polycystic ovary syndrome (PCOS) is one of the most common metabolic and reproductive endocrinopathies. However, few studies have tried to develop a diagnostic model based on gene biomarkers. In this study, we applied a computational method by combining two machine learning algorithms, including random forest (RF) and artificial neural network (ANN), to identify gene biomarkers and construct diagnostic model. We collected gene expression data from Gene Expression Omnibus (GEO) database containing 76 PCOS samples and 57 normal samples; five datasets were utilized, including one dataset for screening differentially expressed genes (DEGs), two training datasets, and two validation datasets. Firstly, based on RF, 12 key genes in 264 DEGs were identified to be vital for classification of PCOS and normal samples. Moreover, the weights of these key genes were calculated using ANN with microarray and RNA-seq training dataset, respectively. Furthermore, the diagnostic models for two types of datasets were developed and named neuralPCOS. Finally, two validation datasets were used to test and compare the performance of neuralPCOS with other two set of marker genes by area under curve (AUC). Our model achieved an AUC of 0.7273 in microarray dataset, and 0.6488 in RNA-seq dataset. To conclude, we uncovered gene biomarkers and developed a novel diagnostic model of PCOS, which would be helpful for diagnosis. Polycystic ovary syndrome (PCOS), as a heterogeneous endocrine disorder, is closely associated with menstrual dysfunction, infertility, hirsutism, acne, obesity, and metabolic syndrome . The thrAlthough the identified genetic risk markers can be used as predictive and diagnostic tools for PCOS, they may not possess the strong power due to the complicated genetic architecture . CombinaIn recent years, the wide application of microarray technology and more advanced, accurate RNA-sequencing technology made the study of disease mechanism more convenient. In view of the differences between the two platforms, it is necessary to analyze the data of the two platforms separately.The main difficulty arisen in establishing a classification model using gene expression data was how to find the most meaningful index or feature for classification. To address this, various machine learning approaches such as random forest (RF) , 14 and In this work, we established a diagnosis model of PCOS using microarray and RNA-seq data from Gene Expression Omnibus (GEO) database with the combined utilization of RF and ANN. Firstly, the RF classifier was used to identify the key genes for classification, and then, the ANN was performed to calculate the weights of the key genes in microarray and RNA-seq data, respectively. Finally, a scoring model named neuralPCOS was developed with the integration of RF and ANN. To validate the accuracy and superiority of the diagnosis model we established, we evaluated the performance with microarray and RNA-seq data and compared them to other marker genes obtained in previous studies , 21.n = 29) was used for the differentially expressed genes (DEGs) screening (step 1). Gene ontology (GO) enrichment analysis (step 2) and the acquisition of key genes for classification by RF (step 3) were further performed. After computing the gene weight using ANN in two kinds of expression data (microarray and RNA-seq) (step 4), a classification model was developed (step 5). Finally, we used two independent dataset (the microarray ComBat dataset2 and the RNA-seq–based dataset GSE84958) for further validation (step 6).For establishment of the diagnostic model of PCOS, RF and ANN were adopted in this study. The study overview was schematically depicted in In the present study, a wide search through the National Center for Biotechnology Information Gene Expression Omnibus database (NCBIGEO) platform was conducted with the key words “PCOS, human”. As shown in P < 0.01 and |logFoldchange| >0.26 [The dataset GSE6798, based on Affymetrix Human Genome U133 Plus 2.0 Array  contained 16 cases of PCOS and 13 cases of control, was used for DEGs analysis. The boxplot was performed using R package stats (v 3.5.0). The R package limma was used to calculate the DEGs between the PCOS and control samples by the classical Bayesian method with e| >0.26  and was e| >0.26 .P < 0.01 after adjusted by the Benjamini and Hochberg method. To eliminate some redundancies, GO terms that intersects more than 75% of the genes contained in term were removed. GObubble and GOChord were performed with R package GOplot to illustrate the functional analysis data [To further reveal the biofunction of selected DEGs, GO enrichment analysis, including biological process (BP), cellular component (CC), and molecular function (MF), was performed using R package clusterProfiler . Signifisis data .We used random forest to classify the DEGs with the R package randomforest . Firstlyn = 26) and validation data (n = 27). The RNA-seq training data GSE84958 (n = 26) and microarray ComBat dataset1 (n = 28) were used to construct the neural network model. The R package neuralnet was used for neural network analysis [The GSE84958 dataset was randomly divided into training data n = 27) and microarray validation data (n = 23) with R package pROC, respectively [The AUCs of three kinds of scores  were calculated in GSE84958 RNA-seq validation data , phosphatase and tensin homolog (PTEN), and insulin-like growth factor-binding protein 1 (IGFBP1) in endometrial cells (ECs) of PCOS [EC-PCOS: three upregulated genes including insulin-like growth factor 1 ( of PCOS .HSD3B2), steroidogenic acute regulatory protein (STAR), inhibin subunit beta A (INHBA), and cytochrome P450 family 19 subfamily A member 1 (CYP19A1) in granulosa cells (GCs) of PCOS [GC-PCOS: upegulated genes including hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 2 ( of PCOS .Three kinds of score:n = 29)  were represented by volcano plot, including 134 upregulated genes and 210 downregulated genes  were obtained  (P = 7.49E − 03), myofibril (adjusted P = 5.55E − 04), and actin binding (adjusted P = 5.19E − 04) were the most significantly enriched GO terms in BP, CC, and MF, respectively  . Muscle ly Table . The 11 ly Table . The analy Table .In order to obtain more reliable PCOS-specific genes, we inputted the above 264 DEGs into the RF classifier. The lowest error rate occurred when the number of variables was 4 ; meanwhin = 26) and microarray ComBat dataset1 (n = 28). ANN topology of microarray ComBat dataset1 and RNA-seq data indicated 11 input layer, 3 hidden layer, and 2 output layer  and GSE84958 RNA-seq verification data (n = 27) were used to test the ability for classifying the samples in 3 classification models, including neuralPCOS constructed in this study and EC-PCOS and GC-PCOS from other researches. The performance of these models was examined using area under the receiver operating characteristic curve (ROC) (Microarray ComBat dataset2 (ve (ROC) . First, ve (ROC) . The resve (ROC) . From thhttp://www.pcosdb.net/) [http://pcoskb.bicnirrh.res.in) [In recent years, the development of machine learning algorithms and the availability of gene expression data in public databases provide approaches to infer biomarkers for disease diagnosis or prognosis in a wide range of fields –33. In tdb.net/)  and the .res.in) , 40. Ano.res.in) . CompariTMOD1) [BTBD9) [TECRL) [GSTO1) [AMPD3) [ALPK2) [RAPH1) [ALDH6A1) [ZNF385B) [ST3GAL2) [n = 23) and RNA-seq validation dataset . In the meanwhile, two sets of marker genes in other researches were also evaluated. One set of genes was the upregulated genes that involved in the insulin signaling pathway  [HSD3B2, STAR, INHBA, and CYP19A1 [In the present study, when identifying DEGs with GSE6798 dataset, we removed the DEGs with low expression level, which can obtain more authentic genes. GO enrichment analysis was performed and displayed by bar plot and bubble plot. Among the 11 enriched GO terms, 4 terms including actin binding , myofibrTMOD1) ; BTB dom [BTBD9) ; trans-2 [TECRL) , 45; glu [GSTO1) , 46, 47; [ALPK2) ; Ras ass [RAPH1) , 49; aldALDH6A1) –52; zincZNF385B) ; ST3 BetST3GAL2) . Given tST3GAL2) , the gen IGFBP1) ; the oth CYP19A1 . The resn = 76; normal: n = 57), the number of sample size in each dataset is small, and the individuals in integrated microarray training dataset are from different countries. To get microarray training and validation datasets with larger sample size, 3 and 2 small sample size datasets were combined, respectively. Although the batch effect was removed, it was still not the most suitable datasets. Another drawback of our study is that the expression data are from diverse tissues containing skeletal muscle, adipose, endometrium, and granulosa cells. Last but not least, we did not perform 10 fold cross-validation in ANN analyse due to the limited sample size. Although this is a compromising strategy in the case of limited sample size, our model has an excellent classification performance, a diagnostic model for single tissue type still needs to be constructed with more convincing datasets and machine learning algorithms in the future.Even so, our study still has some limitations. Although our total sample size is not too small (PCOS: A novel diagnostic model for PCOS was established based on machine learning algorithms using microarray and RNA-seq datasets, which showed better prediction performance in microarray data than using existing marker genes.