Developing machine learning models to support health analytics requires increased understanding about statistical properties of self-rated expression statements used in health-related communication and decision making. To address this, our current research analyzes self-rated expression statements concerning the coronavirus COVID-19 epidemic and with a new methodology identifies how statistically significant differences between groups of respondents can be linked to machine learning results.A quantitative cross-sectional study gathering the “need for help” ratings for twenty health-related expression statements concerning the coronavirus epidemic on an 11-point Likert scale, and nine answers about the person’s health and wellbeing, sex and age. The study involved online respondents between 30 May and 3 August 2020 recruited from Finnish patient and disabled people’s organizations, other health-related organizations and professionals, and educational institutions (n = 673). We propose and experimentally motivate a new methodology of influence analysis concerning machine learning to be applied for evaluating how machine learning results depend on and are influenced by various properties of the data which are identified with traditional statistical methods.We found statistically significant Kendall rank-correlations and high cosine similarity values between various health-related expression statement pairs concerning the “need for help” ratings and a background question pair. With tests of Wilcoxon rank-sum, Kruskal-Wallis and one-way analysis of variance (ANOVA) between groups we identified statistically significant rating differences for several health-related expression statements in respect to groupings based on the answer values of background questions, such as the ratings of suspecting to have the coronavirus infection and having it depending on the estimated health condition, quality of life and sex. Our new methodology enabled us to identify how statistically significant rating differences were linked to machine learning results thus helping to develop better human-understandable machine learning models.The self-rated “need for help” concerning health-related expression statements differs statistically significantly depending on the person’s background information, such as his/her estimated health condition, quality of life and sex. With our new methodology statistically significant rating differences can be linked to machine learning results thus enabling to develop better machine learning to identify, interpret and address the patient’s needs for well-personalized care.The online version contains supplementary material available at 10.1186/s12874-021-01502-8. A self-rated health condition answered to a single question has shown a strong validity and reliability for measuring and predicting multiple dimensions of the person’s health , 2. HoweFurthermore, besides using predefined questionnaire structures there is a great interest for developing adaptive methods that can identify the patient’s needs from any kind of free text passages, such as from healthcare chatbots, patient diaries, online guidance and screening for care, or their derivatives, for example emergency phone calls that are immediately annotated with a speech recognition . HowThink-aloud studies about self-rated health have identified sex- and age-dependent variations in the diversity and complexity of conceptualizations in interpretations and reasoning  and coreinfluence analysis concerning machine learning. The methodology can be used to measure the patient’s “need for help” ratings of expression statements in respect to groupings based on the answer values of background questions. Furthermore, the methodology enables to evaluate the applicability of training and validation of a machine learning model to learn the groupings concerning the ratings. The methodology enables to compare the validation accuracies of the machine learning model with the probabilities of pure chance of classifying the rating profiles correctly. In addition, the methodology enables to contrast the validation accuracies of the machine learning model with the occurrence of statistically significant rating differences for expression statements in respect to groupings based on the answer values of background questions. Table These current challenges motivate us now to propose, develop and define a new methodology that we refer to as In this research article, we now focus on introducing general principles of the new methodology and describe an illustrative empirical application of the methodology with our gathered experimental data.two main research questions (RQ):In accordance with the methodology presented in Table RQ1) How do different people rate the “need for help” for a set of health-related expression statements and how this rating depends on the background information about the person ? This main research question RQ1 emphasizes especially the steps 1-2 of Table RQ2) What kinds of results can be gained when training a convolutional neural network model based on the “need for help” ratings to classify persons into groups based on their background information? This main research question RQ2 emphasizes especially the steps 3-6 of Table Relying on the methods and results developed in our previous research , 22, we Resembling the previous research in the context of artificial intelligence –15, we wSince we aimed at identifying how the “need for help” ratings of expression statements can be used to classify persons into groups based on their background information, it was natural for us to focus now on experimenting with the supervised learning approach. To implement supervised learning, various alternative types of functions can be chosen to relate predicted values to the features that are present in the data, and these functions typically offer more flexibility for modeling than for example logistic regression models of traditional statistics . These fIn respect to the coronavirus COVID-19 epidemic, artificial neural networks have been applied to classify coronavirus-related online discussions and then to supply them with an emotional labeling based on a pre-existing emotion vocabulary and rules .In accordance with Table https://ilmaisu.cs.aalto.fi/research/welcome), the person was informed that only persons who are at least 16 years old are allowed to participate. Furthermore, to address the General Data Protection Regulation of the European Union a privacy notice about the research was shown to the person and he/she was asked to give an approval for handling his/her data.We carried out a quantitative cross-sectional study that gathered online questionnaire answers from 673 unique persons that we recruited consecutively from various Finnish patient and disabled people’s organizations, other health and wellness organizations, and educational institutions as well as organizations of healthcare professionals in the time period ranging from 30 May to 3 August 2020 based on a consecutive sampling approach. When accessing the online questionnaire at the Finnish web server of our DIHEML research project )/(max(x)-min(x)) and then these new values were shifted so that the mean value was positioned to the zero by the formula (x - mean(x)). To simplify practical calculations in the data analysis, the original “need for help” rating answer values in the range 0-10 were transformed linearly to a new range 0.0-1.0. To address our main research question RQ1, we use traditional statistical tests to evaluate overall answer distributions. We computed Kendall rank-correlation and cosine similarity measures for each comparable pair of parameter values of the “need for help” ratings of expression statements ES1-ES20 and the answers of the background questions BQ1 and BQ5-BQ7. Motivated by a recommendation of  we consiWe computed Wilcoxon rank-sum test  between two groups and Kruskal-Wallis test between three groups to identify statistically significant rating differences for each expression statement in respect to groupings based on the answer values of each background question  that we had extracted with the method we developed and reported in our previous research  from theFurthermore, the person was asked to answer to nine background questions . Our apAlthough our decision to use an image classifier requires now an additional transformation step for our initially character-encoded questionnaire data and thus can potentially introduce imprecision for the results, we motivate its use here as a general baseline architecture that can be fed with various alternative input data formats for comparison purposes. Thus by using this currently popular and openly available model we aim to facilitate building comparability of machine learning results across various biomedical data classification tasks containing diverse data formulations, and also enabling a possibility to involve humanly intuitive evaluation about the emerging data patterns from the intermediary raster image representations of labeled data sets.Since the convolutional neural network model required labeled input data in the form of images, we transformed with a self-made R language script our originally character-encoded questionnaire data into a set of grayscale raster images before feeding it to the model.First the original rating answer values in the range 0-10 were transformed linearly into the range 0.0-1.0. Each entity of twenty rating answers (in the range 0.0-1.0) of expression statements ES1-ES20 given by a certain person were transformed into an individual raster image so that each single rating answer value was represented by a region of 25 pixels (width 5 pixels and height 5 pixels) having a brightness value in the range 0-255 directly proportional to the greatness of the transformed answer value in the range 0.0-1.0. All the twenty separate 25-pixel-sized regions were then joined as a 5 × 4 matrix to form a combined grayscale raster image (width 25 pixels and height 20 pixels).We performed machine learning experiments with labeled images so that their labeling matched the groupings that we have just previously analyzed with Wilcoxon rank-sum test  between two groups and Kruskal-Wallis test between three groups to identify statistically significant rating differences . We gained a diverse distribution of answer values for the background questions n = 673). Table 73. Table Figures As shown in Table A significant correlation  linked The highest cosine similarity measure values emerging among the same value pairs seemed to support the clusters just identified by the correlation. This same highest cosine similarity measure value range (>=0.80) was reached also by the following pairs: having a sudden rise of fever and suspecting to have the coronavirus infection , having a sudden rise of fever and having the coronavirus infection , having a shortness of breath and a weakening health condition , having the coronavirus infection and having an infectious disease with a doctor’s verification ES10&ES17 (0.82), suspecting to have the coronavirus infection and having an infectious disease with a doctor’s verification , having the coronavirus infection and having an infectious disease  and the quality of life and the satisfaction about health .Wilcoxon rank-sum test  between two groups and Kruskal-Wallis test between three groups indicated statistically significant rating differences for expression statements ES1-ES20 in respect to groupings based on the answer values of each background question (BQ), as shown in Table Supplementing tests of one-way analysis of variance (ANOVA) between two groups and between three groups indicated statistically significant rating differences largely for the same expression statements as Wilcoxon rank-sum test and Kruskal-Wallis test. However, this statistical significance did not reappear with ANOVA tests between groups for ES5 in respect to BQ1 for three groups, ES14 in respect to BQ2 for two groups, ES19 in respect to BQ9 for three groups, and ES20 in respect to BQ5 for three groups. ANOVA tests between groups indicated also some additional statistically significant rating differences, such as for ES9-ES10 and E17 in respect to BQ2 for two groups, ES9-ES10 in respect to BQ9 for two groups, and ES4 in respect to BQ7 for two groups.A complete listing of means, medians and standard deviations of the “need for help” ratings for the groupings is provided in Data analysis supplement  by the number of all respondents (n = 673). Please see in Table In accordance with Table As Table .To describe our proposed new methodology in accordance with Table For the grouping “BQ1, two groups” statistically significant rating differences emerge for eight expression statements which are ES6-ES10 and ES16-ES18. Among them ES6 has a statistical significance with the highest level that is p = 0.001, ES8-ES10 have a statistical significance with the second highest level p < 0.01 and the remaining ES7 and ES16-ES18 have a statistical significance with the third highest level p < 0.05. Already these notions enable to identify rankings and distributions of the rating differences for expression statements in respect to the grouping “BQ1, two groups” based on the decreasing order of statistical significance  and the pattern of semantic topics of the expression statements that belong to the subset of eight expression statements that now reached statistical significance among all the 20 expression statements. Further rankings and distributions can be identified based on the values of rating differences for expression statements, for example ES6 having the highest positive rating difference value (0.07) and ES10 having the lowest negative rating difference value (-0.09), and for example the absolute values of each of the eight statistically significant rating differences being in the range of  in a specific decreasing order.In accordance with Table In accordance with the steps 1-6 of Table For different expression statements the “need for help” ratings have varied distributions as illustrated in Fig. We identified statistically significant rating differences for expression statements in respect to groupings based on the answer values of each background question, between two groups and between three groups , as shown in Table In groupings of two groups, the highest number of statistically significant rating differences (p < 0.05) emerged for the expression statements ES11  and ES6 . The rating for ES11 differed statistically significantly for all the background questions, except BQ1 , between two groups . The mean rating of ES11 was higher when getting lower answer values to BQ5-BQ7  than when getting higher answer values to BQ5-BQ7 (“group 2”). In contrast, the mean rating of ES11 was lower when getting lower answer values to BQ2, BQ4, BQ8 and BQ9  than when getting higher answer values to BQ2, BQ4, BQ8 and BQ9 (“group 2”).Since ES11 refers to an essential coronavirus-related situation (to be quarantined from meeting other people to prevent spreading an infectious disease), this emerging high differentiation of the “need for help” ratings can be considered as an important new finding that should be addressed when interpreting a person’s need for help during an epidemic (such as the coronavirus COVID-19 epidemic). Further research is needed to better confirm this our new finding but meanwhile we provide some initial illustration about the statistically significant rating differences for ES11 in respect to groupings based on the answer values of background questions. For example, the respondents who indicated a lower quality of life  gave for ES11 a mean rating of 0.47, whereas the respondents who indicated a higher quality of life gave a mean rating of 0.41. On the other hand, the respondents who indicated a lower age  gave for ES11 a mean rating of 0.41, whereas the respondents who indicated a higher age gave a mean rating of 0.46.Besides groupings of two groups, ES11 (to be quarantined from meeting other people to prevent spreading an infectious disease) and ES6 (having muscular ache) gained the highest number of statistically significant rating differences (p < 0.05) also in respect to groupings of three groups (4 groupings for both ES11 and ES6). Other expression statements having a high number of statistically significant rating differences in groupings of two or three groups include ES8-ES10 . Since ES8-ES10 refer to an essential coronavirus-related situation, also this emerging high differentiation of the “need for help” ratings can be considered as an important new finding that should be addressed when interpreting a person’s need for help, for example to support personalized screening, diagnosis and care planning. These three expression statements ES8-ES10 gained lower mean ratings from respondent groups who indicated a lower estimated health condition (BQ1), a lower quality of life (BQ5) and being a man (BQ8), and higher mean ratings from the opposite groups, respectively.Statistically significant rating differences (p < 0.05) in groupings of two groups emerged the most for the background question BQ8 , then followed by BQ9 , BQ1 , BQ5 , BQ2 , BQ7 , BQ4 , and BQ6 . Relatively similarly, in groupings of three groups, statistically significant rating differences (p < 0.05) emerged the most for the background question BQ9 (13 expression statements), then followed by BQ5 (7), BQ1 (5), BQ6 (2), and BQ7 (2).Figure 5 illustrates the emergence of two different ranking orders for the “need for help” ratings of expression statements ES1-ES20 in respect to the grouping based on the answer values of the background question BQ1  for two groups. Already these kinds of rankings can assist in addressing the needs of the patient depending on his/her background information. For example based on our results, for ES4  the younger respondents  gave a mean rating value 0.66 that was 0.10 greater than the mean rating value 0.56 given by the older respondents. This our finding can indicate that when seeking admission to care a representative of the younger people may interpret the need for help concerning this expression statement differently than a representative of the older people. To prevent misunderstandings and malpractices it is important to be aware of such possible interpretational differences in communication and decision making about care.The “need for help” ratings can be exploited also in many other ways to create rankings that can support personalizing the care. Each background question is linked to a specific set of expression statements (if any) that show statistically significant rating differences for this background question. Based on the rating differences and their strengths (levels) of statistical significance, a ranking order can be identified for those expression statements that are linked to by the same background question. On the other hand, an expression statement can get different rating differences and strengths (levels) of statistical significance for different background questions (if any). This enables to identify for each expression statement a ranking order of background questions that link to it.These various ranking orders offer an opportunity to find some distinctive link patterns between the person’s “need for help” ratings for expression statements and his/her answer values to background questions, and vice versa. For example, in groupings of two groups, ES14 (a lack of coping independently in everyday life) and ES15 (a lack of coping independently at home) show statistically significant rating differences for BQ2  but not for BQ5 , and on the other hand ES16 (having an infectious disease) and ES17 (having an infectious disease with a doctor’s verification) show statistically significant rating differences for BQ5  but not for BQ2. This emerging differentiation may enable a conclusion that the “need for help” ratings about coping independently (ES14-ES15) are more closely linked to having a health problem that reduces ability (BQ2) than to the quality of life (BQ5). Similarly, it may be concluded that the “need for help” ratings about an infectious disease (ES16-ES17) are more closely linked to the quality of life (BQ5) than to having a health problem that reduces ability (BQ2).After just discussing about the steps 1-2 of Table not meant to introduce a model that can actually learn the groupings very well. Instead, we aim now to propose and experimentally motivate a new methodology that can be used for analyzing how the machine learning models are influenced by the properties of the data so that these notions can be exploited in the future research to develop better machine learning models. We have chosen the specific openly available implementation of a convolutional neural network  as a baSince our essential goal is to ensure generating and evaluating comparable measures concerning the machine learning experiments, we do not want to rely just on the value of validation accuracy but instead we preferably want to observe especially the difference of the mean validation accuracy and the probability of pure chance of classifying the rating profiles correctly corresponding to groupings relying on the answer values of each background question , BQ1  and BQ6  it appears that the machine learning results may be well-influenced by the statistically significant rating differences that we identified for certain specific expression statements, as shown in Table Therefore with our current data set in accordance with the step 6 of Table When iteratively evaluating and fitting the machine learning model it is important to seek such a balance that avoids both overfitting and underfitting. For example convolutional neural network models with full connectivity can be prone to overfitting. With our current convolutional neural network model we have aimed to prevent both overfitting and underfitting by stopping the training and validation process at such an epoch step when the model has reached the lowest value for the validation loss and by applying a patience procedure that inspects still some further steps to prevent premature stopping at a local minimum, and also by averaging results from a large amount of separate training and validation sequences. In further experiments we suggest to avoid overfitting also by considering augmenting the original training data set with its random transformations, by dropping out a certain proportion of output units from layers of the model during the training, and by regularization of the input data formulation.Furthermore, with our current data set the fitting of the machine learning model may benefit from emphasizing especially those expression statements which reached the highest statistically significant rating differences in the four thematic subentities that we identified among them, as discussed above in the chapter “Limitations” (expression sets of possible influence). Thus it may be beneficial to aim at fitting the model to learn the groupings in respect to background questions so that for each grouping the adjustments can address especially those thematic subentities of expression statements that have the highest statistically significant rating differences for this grouping. Thus in the fitting of the baseline model it can be possible to emphasize the following thematic subentities: having respiratory symptoms or a weakening health condition (ES1-ES5) concerning groupings in respect to the age (BQ9); a lack of coping independently (ES14-ES15) concerning groupings in respect to the age (BQ9); having muscular ache (ES6) concerning groupings in respect to an estimated health condition (BQ1); and being quarantined due to an infectious disease (ES11) concerning groupings in respect to the satisfaction about health (BQ6).Our just mentioned notions about the applicability of our proposed new methodology are motivated by the previous research that has shown the applicability of an artificial neural network model in identifying the affectivity of online messages about the coronavirus  by reach1, n2 or n3) that reaches varying values for different groupings. This variability in turn has given the motivation that to enable comparability of groupings we want to observe especially the difference of the mean validation accuracy and the probability of pure chance of classifying the rating profiles correctly corresponding to groupings  and BQ9 (the age), respectively, which also reached the highest number of statistically significant rating differences (p < 0.05) for expression statements in respect to the same groupings with Wilcoxon rank-sum test and Kruskal-Wallis test  and thus representing a diversity of health conditions, abilities and attitudes. In addition, our results enable to compare the statistically significant rating differences in groupings in respect to the person’s background information and to further contrast them with the training and validation metrics gained in machine learning experiments based on the same groupings (see Table Future research should continue exploring and analyzing how different people interpret and evaluate health-related expression statements and how this possibly depends on the person’s background information. A specific emphasis should be given for developing adaptive modular methods that can be flexibly applied for various purposes of health analytics and also enhance fertile standardized practices that ensure comparability. Furthermore, the emerging new models, methods and algorithms should be well human-understandable for everyone and provided with open access, accompanied with appropriately and sufficiently anonymized data sets. In this spirit, we suggest that also our current findings and results can be used as a part of a greater reasoning entity to develop computational methods to identify, interpret and address the needs of the patient in diverse knowledge processes of healthcare to support personalized care.Additional file 1.Additional file 2.


R, for discovering differential alternative polyadenylation events in standard RNA-seq data.We present flexible Modeling of Alternative PolyAdenylation (flexiMAP), a new beta-regression-based method implemented in We show, using both simulated and real data, that flexiMAP exhibits a good balance between specificity and sensitivity and compares favourably to existing methods, especially at low fold changes. In addition, the tests on simulated data reveal some hitherto unrecognized caveats of existing methods. Importantly, flexiMAP allows modeling of multiple known covariates that often confound the results of RNA-seq data analysis.R package is available at: https://github.com/kszkop/flexiMAP. Scripts and data to reproduce the analysis in this paper are available at: https://doi.org/10.5281/zenodo.3689788.The flexiMAP Bioinformatics online. Alternative polyadenylation (APA) is the selection of alternative cleavage and polyadenylation sites during transcription of eukaryotic genes, resulting in isoforms with distinct lengths. APA has been shown to be prevalent in mammalian transcripts and alternative isoforms are linked to different stages of development, cell types and disease status . For exaInspired by the use of Generalized Linear Models (GLMs) in differential gene expression  and one is ‘proximal’ . Given aNlongij and Nshortij, respectively), and define the ratio, R, for gene i in sample j as:We count RNA-seq reads falling in the ‘long’ and ‘short’ regions of the 3′ UTR  showed that this approach was not sensitive enough for small numbers of samples or small fold changes. To allow more flexibility in modeling errors, we adopted instead a model where the response variable is assumed to be beta-distributed. This beta-regression model was implemented using the Finally, our method incorporates two filtering steps to improve accuracy, employing TIN (Transcript Integrity Number) values  to filtepolyester R package (We compared flexiMAP to three existing methods for APA analysis [DaPars ( package . For lar package . In thes package .All methods, including flexiMAP, were sensitive to the expression level of the transcript tested for differential polyadenylation . APA eveAlthough simulated datasets are important for benchmarking tests, eventually methods are only useful if they can be applied to real data. The dataset we used here is the same used by both DaPars and APAtrap in their respective publications and contains RNA-seq data from the Human Brain Reference and the Universal Human Reference MAQC samples  demonstrThe development of flexiMAP was primarily driven by the need to model multiple known covariates in APA analysis. Indeed, flexiMAP successfully discriminates between the effect of the condition of interest and that of an additional covariate in a simple simulated scenario of imbalanced datasets, where APA originates from the sex attribute of the samples rather than the condition of interest . SimilarWe presented here flexiMAP, a beta-regression-based method for detecting APA events in RNA-seq data, given a list of putative polyadenylation sites. Our method is both sensitive and specific, even when small numbers of samples are used, and has the distinct advantage of being able to model contributions from known covariates that would otherwise confound the results of APA analysis. FlexiMAP compares favourably with existing alternatives in tests involving simulated datasets. Importantly, these tests have highlighted some hitherto overlooked caveats of existing methods. Real datasets remain a challenge for all methods, not least because it is difficult to define objectively the ground truth, but flexiMAP is still outperforming other methods, when both specificity and sensitivity are taken into account using the Matthews Correlation Coefficient.btaa854_Supplementary_DataClick here for additional data file.


Emerging evidence suggests that host-microbe interaction in the cervicovaginal microenvironment contributes to cervical carcinogenesis, yet dissecting these complex interactions is challenging. Herein, we performed an integrated analysis of multiple “omics” datasets to develop predictive models of the cervicovaginal microenvironment and identify characteristic features of vaginal microbiome, genital inflammation and disease status. Microbiomes, vaginal pH, immunoproteomes and metabolomes were measured in cervicovaginal specimens collected from a cohort (n = 72) of Arizonan women with or without cervical neoplasm. Multi-omics integration methods, including neural networks (mmvec) and Random Forest supervised learning, were utilized to explore potential interactions and develop predictive models. Our integrated analyses revealed that immune and cancer biomarker concentrations were reliably predicted by Random Forest regressors trained on microbial and metabolic features, suggesting close correspondence between the vaginal microbiome, metabolome, and genital inflammation involved in cervical carcinogenesis. Furthermore, we show that features of the microbiome and host microenvironment, including metabolites, microbial taxa, and immune biomarkers are predictive of genital inflammation status, but only weakly to moderately predictive of cervical neoplastic disease status. Different feature classes were important for prediction of different phenotypes. Lipids (e.g. sphingolipids and long-chain unsaturated fatty acids) were strong predictors of genital inflammation, whereas predictions of vaginal microbiota and vaginal pH relied mostly on alterations in amino acid metabolism. Finally, we identified key immune biomarkers associated with the vaginal microbiota composition and vaginal pH (MIF), as well as genital inflammation . Lactobacillus spp. We established predictive models and identified key signatures related to vaginal microbiota, vaginal pH and genital inflammation. Integration of multiple different “omics” data types resulted in only modest increases in prediction accuracy compared to models trained on a single data type. Since the most predictive data type was not known a priori, this multi-omics approach yielded insights that would not have been possible with any single data type. Metabolomics data was predictive of different features of the cervicovaginal microenvironment and host response but integrating multi-omics data is likely to be essential for realizing the advances promised by microbiome research.This work was undertaken to improve our understanding of interactions between microbes, metabolites and the host in the cervicovaginal microenvironment. We employed a multi-omics approach to investigate relationships between microbiome, vaginal pH, metabolome, immunoproteome in women with and without cervical neoplasm identifying a tight link to abundance of  Despite the availability of preventive measures, such as human papillomavirus (HPV) vaccination and Pap smear screening, cervical cancer remains a major public health problem, particularly in low- and middle-income countries . InfectiLactobacillus species 17], metaFig 1). The vaginal microbiota compositions were determined by 16S rRNA gene sequencing revealing 763 amplicon sequencing variants (ASVs). Cervicovaginal metabolic fingerprints were profiled by liquid chromatography-mass spectrometry and identified 467 unique metabolites. Levels of immune mediators and other cancer-related proteins in cervicovaginal lavage (CVL) samples were evaluated using multiplex cytometric bead arrays for 68 targets. These data, which were previously analyzed independently, were integrated resulting in 72 samples with complete microbiome, metabolome and immunoproteome data for the bioinformatics analyses presented here. Seventy-two patients were classified into five disease groups: Ctrl HPV- (n = 18), Ctrl HPV+ (n = 9), LSIL (n = 10), HSIL (n = 27) and ICC (n = 8). Sixty-one women (85%) were Caucasian and 11 women (15%) were of other races. Thirty-five women (49%) identified themselves as Hispanic/Latina. The average age of participants was 38 years old (ranging from 22 to 58). Forty-nine women (68%) were overweight [body mass index (BMI) >25]. Age, race, ethnicity and BMI were not significantly different among the disease groups. Thirty-eight women (53%) exhibited high vaginal pH (>5.0). Vaginal pH significantly varied among the disease groups ranging from 17% women with high pH in Ctrl HPV+ group to 88% women in ICC group (P = 0.0002).In a previous multicenter study, we enrolled 100 pre-menopausal, non-pregnant participants, including HPV-negative (Ctrl HPV-) and HPV-positive women without cervical neoplasm (Ctrl HPV+), women with low-grade (LSIL) and high-grade squamous intraepithelial lesions (HSIL), and women newly diagnosed with invasive cervical carcinoma (ICC) . MicrobiFig 2A–2D). For the metabolome and immunoproteome features we performed a principal component analysis (PCA). The first two components accounted for 47.5% of the sample variance for metabolome samples  and for 44.3% of the variance for immunoproteome samples .We performed a principal coordinate analysis (PCoA) of the microbiome data using the Jaccard distance where the first two coordinates explained 20.8% of the observed sample variance  and by Lactobacillus dominance . For metabolome features a significant clustering was observed for all four patient covariates . For immunoproteome data a significant clustering was only found for genital inflammation .Evaluating the clustering of omics datasets based on defined patient covariates, we found that microbiome samples cluster significantly according to vaginal pH Prevotella (including Prevotella bivia), Peptoniphilus, Streptococcus anginosus, Atopobium vaginae, Sneathia sanguinegenes, Veillonellales, Finegoldia, and other taxonomic groups . Lactobacillus ASVs , some Prevotella (including Prevotella bivia), and other ASVs, were correlated with a range of metabolites including phenylalanylglycine, the anti-inflammatory nucleotide cytosine, glycerophosphoglycerol, glycerol, N-acetyl methionine sulfoxide, and maltopentaose . These separations roughly mirror genital inflammation and disease status categories, corresponding with our present findings (described below) and previous work showing association between many of these lipids, ICC, and high inflammation; and between these non-lipid metabolites, LD, and low inflammation [Streptococcus, Prevotella (including P. bivia), Megasphaera, Finegoldia, A. vaginae, Sneathia amnii, and S. sanguinegens ASVs. Interestingly, 3-hydroxybutyrate was also correlated to L. iners.Microbe-metabolite interactions were predicted using mmvec . Numerouammation ,32. ThreS1 Fig and S2 Table). This includes the inflammation- and ICC-associated lipids 1-palmitoyl-2-arachidonoyl-gpe (16:0/20:4), 1-palmitoyl-2-linoleoyl-gpc (16:0/18:2), 1,2-dilinoleoyl-gpc (18:2/18:2), 1-palmitoyl-2-docosahexaenoyl-gpc (16:0/22:6), several sphingomyelins, 1-stearoyl-2-docosahexaenoyl-gpc (18:0/22:6), 1-linoleoyl-2-arachidonoyl-gpc (18:2/20:4n6), 1-palmitoyl-2-arachidonoyl-gpc (16:0/20:4n6), and the bile acid glycochenodeoxycholate . Many of these associations are driven by high abundances of these lipids, sphingomyelins, and other metabolites in cancer cases: cancer biomarkers are the top predictive features for all of these metabolites , and when ICC cases are removed from the dataset microbial features  are included among the top predictive features, though high predictive strength remains for many  of these targets (S3 and ). The ability to accurately predict the abundance of these metabolites through cross-validation highlights the close correspondence between the metabolome, microbiome, and immunoproteome across patients, both respective and irrespective of cancer diagnosis.To further dissect relationships among the metabolite, microbiome, and immunoproteome, Random Forest regression with 10-fold cross-validation was used to determine the ability to predict the abundance of individual metabolites based on microbiome and immunoproteome profiles, revealing very strong predictive strength for a wide variety of targets . Metabolites (primarily inflammation-associated lipids) are the most predictive features for each of these targets, but microbial features occur among the top 25 predictive features for many of these, most notably Coriobacteriales bacterium DNF00809, S. amnii, Veillonellales, S. sanguingegens, P. bivia, Parvimonas, A. vaginae dominating the top important features for predicting cervicovaginal CEA concentration, regardless of cancer diagnosis . Several of these biomarkers are clearly related to ICC, as indicated by reduced predictive strength after ICC cases are removed from the dataset; however, most of these markers exhibit similar performance and important feature associations after removing ICC cases (S7 and ).Random Forest regression was also performed to predict concentration of immunoproteomic biomarkers based on microbiome and metabolome profiles, demonstrating strong predictive strength for several targets, including proinflammatory cytokines and chemokines , the anti-inflammatory cytokine IL-10, growth factors  apoptosis-related proteins , the hormone prolactin, the cytokeratin CYFRA21-1, and other cancer biomarkers   and NLD groups (n = 40). Microbiome data were excluded from the predictive model, as these measurements are non-independent due to compositionality constraints, i.e., changing the relative abundance of one feature (such as a Lactobacillus ASV) will alter the relative abundance of other features.To evaluate the ability of metabolome and immunoproteome features to predict LD , we used Random Forest classification with 10-fold cross-validation. We define LD as any sample in which Fig 4A and 4B). In other words, cervicovaginal metabolome and immunoproteome profiles are tightly linked to the abundance of Lactobacillus spp., suggesting that host immunological response is associated with vaginal microbiome composition. The top predictive features consist primarily of non-lipid metabolites, consistent with the mmvec results , though the immunoproteomic biomarkers macrophage migration inhibitory factor (MIF) also rank among the top 25 most important predictive features . MIF is more abundant in NLD women , consistent with higher inflammation and ICC.Results demonstrate a very high predictive accuracy (average AUC = 0.94), indicating a near-perfect ability to predict LD or NLD across subjects via cross-validation . We assessed the predictive relationship between pH and cervicovaginal metabolites, microbiota, and immunoproteome using cross-validated Random Forest classification models. Typically, women with LD microbiota have a vaginal pH of 4.5 or lower. However, for the purposes of this analysis, samples were grouped into “low”  and “high” pH groups . Vaginal pH level is closely related to demographic characteristics, and Hispanic women tend to have slightly higher average vaginal pH compared to NHW [Lactobacillus abundance). Thus, we defined pH ≤ 5.0 as “low” for the purposes of this study. Results indicate a weak to moderate predictive relationship (AUC = 0.72) . Predictive power was lost because a large proportion (26.4%) of women with low vaginal pH were predicted to belong to the high pH group . Results also indicate that this binary pH model, as expected, exhibits many of the same characteristics as the LD/NLD prediction model: many of the same top predictive features were identified . Notably, the top predictive features consist primarily of non-lipid metabolites, and MIF is again in the top 25 most important predictors, both associated with high pH as well as NLD (S9 and ). Hence, together these findings recapitulate the associations between LD, low vaginal pH, and low inflammation, and between NLD, high pH, higher inflammation, and carcinogenesis, as well as the microbial and metabolic context of these states, explored in more detail below.Vaginal pH is an important feature of the cervicovaginal microenvironment which relates to d to NHW ,17. We aFig 6A). Predictive accuracy is very good for high (AUC = 0.95) and no inflammation (AUC = 0.89), but lowest for low inflammation (AUC = 0.79), due to misclassification of some samples as either high or no inflammation . Similar to pH classification but to a lesser extent, this reflects the shortcoming of binning samples for classification into categorical groups, a necessary limitation due to the small sample size of the current study. Regression models predicting actual inflammation score demonstrate high accuracy at lower inflammation scores, but lower accuracy at the upper range due to sparsity of high-inflammation samples for cross-validation . Larger sample sizes in future studies will enable more accurate prediction of low-inflammation samples through prediction of actual inflammation scores, refining our current estimates of associations between genital inflammation and cervicovaginal microenvironment. As it stands, categorical classification performs moderately well, and can identify a range of features predictive of inflammation, primarily lipids, but also several immunoproteomic biomarkers including MIP-1α, IL-10 and IL-6 .Next, we tested the relationship between the cervicovaginal environment and genital inflammation, as a crucial characteristic of ICC progression. We have previously utilized a scoring system to quantify genital inflammation in our cohort . To assiS14 Fig). Although many of the same carcinogenesis-related metabolites and immune markers were top predictors in these models, accurate differentiation could not be achieved, primarily because of the low sample size and large class imbalances, but also due to the large number of classes with borderline differences . Given the low per-group sample sizes, approaches to mitigate class imbalances were not feasible in the current study, but larger sample sizes and pooled analyses will facilitate better estimates in future studies. However, it should be noted that ICC predictive accuracy was moderately high (AUC = 0.76), in spite of the low sample size and class imbalance . This indicates that ICC could be predicted with fairly high accuracy across subjects, but non-ICC groups could not be reliably distinguished due to the similarities between these groups. Combining LSIL and HSIL prior to classification increases accuracy, indicating ambiguity between these groups, as reflected in the imprecise distinction between these histological classifications. Hence, ICC elicits signature characteristics in the cervicovaginal microenvironment across subjects that can be used to identify these subjects, but intermediate stages of progression  cannot be fully distinguished . Larger sample sizes and longitudinal measurement in future studies may improve our ability to diagnose ICC or even predict cancer risk based on cervicovaginal microenvironment characteristics .Given the ability to predict genital inflammation, a crucial feature of ICC progression, based on features of the cervicovaginal microenvironment, we sought to determine if cervical neoplasm status could also be predicted based on these features using cross-validated Random Forest classification. Samples (n = 72) were grouped into control HPV- (n = 18), control HPV+ (n = 9), LSIL (n = 10), HSIL (n = 27), and ICC (n = 8). This yielded low predictive accuracy  . For LD, combining multiple datasets did not increase accuracy . Metabolites alone could predict LD status with high accuracy; immunoproteome data exhibited lower accuracy. For pH prediction, metabolites, immunoproteome and microbiome datasets on their own could predict pH with moderate accuracy; integrating all three omics datasets led to an overall increase in mean accuracy .To test whether integration of multiple omics dataset leads to increased predictive accuracy of our models, we evaluated the performance of each Random Forest classifier with different combinations of data types with the expectation that more data types could only yield better predictive accuracy. Results indicate that integrating data led to modest increases in accuracy for most classification tasks, but with mixed results . Both metabolome and immunoproteome datasets yielded nearly identical high predictive accuracy, whereas microbiome data exhibited poor predictive accuracy. Combining all three datasets led to a slight increase in predictive accuracy.Genital inflammation was the one measurement that showed little change in accuracy with integration of multiple omics datasets (Lactobacillus dominance (LD) in the vagina and cervix relates to HPV clearance and disease regression, whereas dysbiotic anaerobes contribute to HPV persistence and progression of cervical neoplasm [The vaginal microbiota, HPV infection and cervical neoplasm are related in ways that are still not fully understood. Emerging evidence suggests that neoplasm –31. Hostneoplasm ,33,40–43neoplasm .Fig 1). Using integrated multi-omics, we aimed to establish predictive models and identify key signatures related to vaginal microbiota structure, vaginal pH, genital inflammation and cervical neoplasm status. We identified specific metabolites that were predictive of Lactobacillus dominance, vaginal pH, and genital inflammation . These findings demonstrate that vaginal microbiota and host defense responses strongly influence cervicovaginal metabolic fingerprints [Fig 3), suggesting tight coupling of the microbiome, metabolome, and immunoproteome [We investigated relationships between multiple clinical “omics” datasets  collected from women who had not been vaccinated against HPV at different stages of cervical neoplasia . Indeed, clinical studies have shown contrasting results related to genital inflammation and cervical dysplasia. Some studies report that infection with high-risk HPV types or precancerous dysplasia has not been associated with increased levels of genital inflammation ,40,43. AStreptococcus), linking microbiota to inflammatory markers.Our integrated analyses revealed that different classes of metabolites are important for prediction of different phenotypes: lipids were strong predictors of genital inflammation, while amino acids, peptides and nucleotides were predictive of the vaginal microbiota composition. Sphingolipids and long-chain unsaturated fatty acids in particular ranked as top predictors of genital inflammation. Emerging studies have demonstrated that sphingolipids are implicated in multiple pathological processes, such as inflammatory diseases, diabetes, and cancer . In a prStreptococcus, Prevotella, Megasphaera, Atopobium and Sneathia, and unexpectedly with one ASV classified to the predominant vaginal Lactobacillus spp., L. iners. L. iners-dominant vaginal microbiota has been shown to more often transition to dysbiotic NLD microbiota compared to other Lactobacillus spp. [L. iners produces a different ratio of lactic acid isoforms [L. iners in the cervicovaginal microenvironment is still questionable [Predictions of vaginal microbiota and vaginal pH relied mostly on alterations in amino acid metabolism, which was in accordance with previous reports on cervicovaginal metabolomes ,34,45. Slus spp. . Furtherisoforms , which visoforms ; therefotionable . We havetionable . Severaltionable  and ovartionable ,59.Lactobacillus spp. (particularly L. crispatus) positively correlated with N-acetyl methionine sulfoxide, a reactive oxygen species. Production of hydrogen peroxide, another reactive oxygen species, by vaginal Lactobacillus spp. has been postulated to have a protective effect against invading pathogens [Other key metabolites that we identified to highly correlate with dysbiotic microbiota were pipecolate and deoxycarnitine. In a previous study on metabolomes of women with BV, these two metabolites positively associated with BV status and the presence of “clue cells” , a key cathogens ,61. SimiFigs 5), suggesting that Lactobacillus colonization may be closely involved in regulating markers of genital inflammation, including MIF. In accordance, several reports have demonstrated significantly increased levels of MIF in cervicovaginal fluids of women with vaginal dysbiosis or BV compared to women with healthy LD microbiota [Through our integrated multi-omics approach, we also identified key immune biomarkers associated with the vaginal microbiota composition and vaginal pH, for instance MIF, a pleiotropic cytokine regulating inflammatory reactions and stress responses . MIF wascrobiota ,63,64. Pcrobiota . Immunohcrobiota –67. MIF crobiota  and direcrobiota . Thus, ecrobiota . OverallFig 3). However, those classifiers do not achieve perfect accuracy even for this simple microbiome summary statistic of LD, therefore we expect that the microbiome provides context about the cervicovaginal microenvironment that is not present in the other feature types used here. Second, our supervised classification approaches may need improvement for integrating data types. This is likely, given that integrating microbiome multi-omics data is currently a very active area of bioinformatics research. In this case, higher accuracy will be possible as feature extraction and normalization methods designed for microbiome multi-omics improve. Third, there may be more variance in microbiomes than metabolomes across individuals , requiring a larger training data set for microbiome-based classification than for metabolite-based classification. In this case it is possible that a larger training set would allow for accurate microbiome- or immunoproteome-based classification.Many of the predictive models used in this study integrate metabolome, immunoproteome, and microbiome data. We hypothesized that integrating multiple data types would lead to a cumulative increase in predictive accuracy, as accumulating more features should more completely model the host environment. We instead observed that our metabolomics data nearly always drove classifier accuracy, and inclusion of other data types resulted in modest, if any, increases in classifier performance accuracy. There are a few explanations for this that are not mutually exclusive. First, the metabolites profile might contain features that are proxy information for other feature types , and hence only gain minimal benefit for integration with those other data types and serve as a good predictor of those other features. This is supported by our finding that metabolome and immunoproteome can almost perfectly predict LD where most of the important features are metabolites . The information gained in this multi-omics survey can now be used to prioritize data to collect in future studies, with the caveat that larger sample sizes and additional populations are needed to fully resolve the predictive power of various omics types for cervicovaginal microenvironment across human populations. While the metabolome data in our study appears most predictive, and this finding has been presented in other recent studies [Given that we observed only a modest increase in classifier performance accuracy with the use of multiple “omics” data types, it may seem that the benefit of including these additional data does not justify their cost. We provide a few counterpoints to this idea. First, we cannot know,  studies –50, we sIn our previous work, we investigated pairwise associations between pH  and micrAlthough our study provided new insights into the multifaceted host-microbe interplay during cervical carcinogensis, there is much work to be done to improve our approaches for integrated multi-omics analyses. For example, developing machine learning classification tools for microbiome multi-omics data that can handle multiple observations per subject to make better use of longitudinal data, and interactive visualization tools that can assist with exploration and interpretation of multi-omics network data will facilitate work. Combining these approaches with novel methods  and dataThe research and related activities involving human subjects were approved by the Institutional Review Boards at University of Arizona (no. 1510171298), University of Arizona Cancer Center/Dignity Health St. Joseph’s Hospital and Medical Center (no. PHXB-15-0027-70-15) and Maricopa Integrated Health Systems (no. 2015–040). All participants provided informed written consent and all research was performed in accordance with the federal guidelines and regulations and the Declaration of Helsinki.Seventy-two premenopausal, non-pregnant women were recruited at three clinical sites located in Phoenix, Arizona: St. Joseph’s Hospital and Medical Center, University of Arizona Cancer Center and Maricopa Integrated Health Systems . The participants were grouped as follows: Ctrl HPV- (n = 18), Ctrl HPV+ (n = 9), LSIL (n = 10), HSIL (n = 27) and ICC (n = 8). Classification of patients into the five groups and detailed inclusion/exclusion criteria were described previously . CervicoImmunoproteome, metabolome and microbiome datasets used in this study were described previously ,32,37,38For immunoproteome analysis, levels of 68 proteins were determined in CVL samples using multiplex cytometric bead arrays: customized MILLIPLEX MAP Human Cytokine/Chemokine I (cat. no. HCYTOMAG-60K), Th17 (cat. no. HTH17MAG-14K), High Sensitivity T Cell (cat. no. HSTCMAG-28SK), Circulating Cancer Biomarker 1 (cat. no. HCCBP1MAG-58K) and Immuno-Oncology Checkpoint Protein 1 (cat. HCKP1-11K) Magnetic Bead Panels  or enzyme-linked immunosorbent assays: Human IL-1F9 (IL-36γ) ELISA kit  in accordance with the manufacturer’s protocols ,38. DataGlobal untargeted metabolome analysis of CVL samples was performed by Metabolon, Inc  using a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution . MetabolFor microbiome analysis, DNA was extracted from vaginal swabs using PowerSoil DNA Isolation Kit  following the manufacturer’s instructions . AmplicoMicrobial DNA sequence data were processed and analyzed using the QIIME 2 version 2019.7 . DADA2  was usedPrior to the application of supervised learning, samples were selected based on their availability of all omics features and defined targets, resulting in 72 samples. Additionally, features with the same value for all samples were discarded (69 features affected). Supervised learning was performed in q2-sample-classifier  via 10-fMicrobe-metabolite interactions were estimated using mmvec . This meS1 TableS1 Fig, for the top 20 most accurately predicted features.Low mean squared error and high r-squared values indicate close correspondence between predicted and true values. These values correspond to plots displayed in (XLS)Click here for additional data file.S2 TableS5 Fig, for the top 20 most accurately predicted features.Low mean squared error and high r-squared values indicate close correspondence between predicted and true values. These values correspond to plots displayed in (XLS)Click here for additional data file.S1 FigRandom Forest regressors with 10-fold cross-validation were used to predict the abundance of each selected metabolite in (PDF)Click here for additional data file.S2 FigS1 Fig. *Microbial features are displayed in red, with the first 6 characters of the ASV ID followed by the genus/species-level Greengenes taxonomy.Feature importance of top 15 features used in the final Random Forest regression model for each metabolite prediction displayed in (PDF)Click here for additional data file.S3 FigS1 Fig, but with cancer cases removed. Predictive accuracy remains high for most metabolites, indicating that cancer cases do not drive the associations observed for that metabolite.Plots display the predictive accuracy of the top 20 metabolites displayed in (PDF)Click here for additional data file.S4 FigS3 Fig. *Microbial features are displayed in red, with the first 6 characters of the ASV ID followed by the genus/species-level Greengenes taxonomy.Feature importance of top 15 features used in the final Random Forest regression model for each metabolite prediction displayed in (PDF)Click here for additional data file.S5 FigRandom Forest regressors with 10-fold cross-validation were used to predict the abundance of each selected biomarker in (PDF)Click here for additional data file.S6 FigS5 Fig. *Microbial features are displayed in red, with the first 6 characters of the ASV ID followed by the genus/species-level Greengenes taxonomy.Feature importance of top 15 features used in the final Random Forest regression model for each cancer biomarker prediction displayed in (PDF)Click here for additional data file.S7 FigS5 Fig, but with cancer cases removed. Predictive accuracy remains high for most metabolites, indicating that cancer cases do not drive the associations observed for that metabolite.Plots display the predictive accuracy of the top 20 biomarkers displayed in (PDF)Click here for additional data file.S8 FigS7 Fig. *Microbial features are displayed in red, with the first 6 characters of the ASV ID followed by the genus/species-level Greengenes taxonomy.Feature importance of top 20 features used in the final Random Forest regression model for each cancer biomarker prediction displayed in (PDF)Click here for additional data file.S9 FigBoxplots display quartile distributions, swarmplots display individual values of top important feature abundances in LD and NLD groups.(PDF)Click here for additional data file.S10 FigLeft, histogram displays number of samples per pH value, binned into increments of 0.5 . Right, scatterplot displays true vs. predicted log10 vaginal pH for each subject , indicating very poor regression results due to pH skew.(PDF)Click here for additional data file.S11 FigBoxplots display quartile distributions, swarmplots display individual values of top important feature abundances in “typical” (pH ≤ 5.0) and “high” (pH > 5.0) groups.(PDF)Click here for additional data file.S12 FigLeft, histogram displays number of samples per genital inflammation score. Right, scatterplot displays true vs. predicted inflammation scores for each subject .(PDF)Click here for additional data file.S13 FigBoxplots display quartile distributions, swarmplots display individual values of top important feature abundances in no (score = 0), low (0 < score < 5), and high inflammation (score ≥ 5) groups.(PDF)Click here for additional data file.S14 FigReceiver operating characteristics (ROC) analysis showing true and false positive rates for each group, using random forest classifiers with 10-fold cross-validation to test predictive accuracy across subjects. Higher area under the curve (AUC) indicates better accuracy. Micro-average is calculated across each sample, and hence impacted by class imbalances. Macro-average gives equal weight to the classification of each sample, eliminating the impact of class imbalances on average AUC. Notably, invasive cervical carcinoma (ICC) cases are predicted moderately well, indicating a characteristic signal associated with ICC but not with intermediate stages of progression. HSIL and LSIL = high- and low-grade squamous intraepithelial lesions, respectively.(PDF)Click here for additional data file.S15 FigColumn names depict selected targets and row names selected omics features.(PDF)Click here for additional data file.


However, to date, there is a lack of corresponding online resources to utilize these valuable data to study gene expression dysregulation and viral infection, two major causes for cancer development and progression. To address these unmet needs, we established OncoDB, an online database resource to explore abnormal patterns in gene expression as well as viral infection that are correlated to clinical features in cancer. Specifically, OncoDB integrated RNA-seq, DNA methylation, and related clinical data from over 10 000 cancer patients in the TCGA study as well as from normal tissues in the GTEx study. Another unique aspect of OncoDB is its focus on oncoviruses. By mining TCGA RNA-seq data, we have identified six major oncoviruses across cancer types and further correlated viral infection to changes in host gene expression and clinical outcomes. All the analysis results are integratively presented in OncoDB with a flexible web interface to search for data related to RNA expression, DNA methylation, viral infection, and clinical features of the cancer patients. OncoDB is freely accessible at  The advent of large-scale multi-omics datasets, such as those presented in The Cancer Genome Atlas (TCGA), have greatly helped us to understand the characteristics of a wide variety of tumors. With these cancer datasets, molecular aberrations at the DNA, RNA, protein and/or epigenetic levels can be systematically identified to better characterize human cancers. To date, TCGA is the most prominent cancer genomics program which offers >10 000 primary tumor and matched normal samples spanning 33 cancer types  are frequently identified as main drivers of cancer development . For exaAt present, multiple online resources have been developed for analysis of cancer genomic data by focusing on the TCGA datasets. One prominent tool is CBioPortal , which phttps://portal.gdc.cancer.gov/). There is only a limited number of normal control samples in TCGA. To address this limitation, we also included RNA-seq data from over 1600 normal samples in GTEx . The workflow of our pipeline is outlined in Figure We developed an RNA-seq data analysis pipeline, adopting standard features recommended by the GDC . A line graph is presented to summarize averaged methylation levels for each probe within the gene structure  for tumor or normal samples  test is performed to identify clinically significant genes by correlating RNA-seq or DNA methylation data to individual clinical parameters. When a user specifies a gene of interest for analysis, the result is displayed in a boxplot for RNA expression data or line graph for DNA methylation data  model which calculates the survival probability along with survival time. Based on the levels of RNA expression or DNA methylation, all selected cancer cases are stratified into either the high or low group according to a user-defined percentage cutoff. Additionally, summary statistics are presented, including the Viral infection is a major cause of cancer, leading to aberrant changes at various gene regulatory levels such as RNA expression and DNA methylation. Thus, differential expression / methylation analysis on virus-positive vs. virus-negative tumors can be a useful approach to identify viral targets in the human transcriptome, providing mechanistic insights into virus-induced cancers. In OncoDB, the oncovirus status of each tumor sample was determined with the sequencing analysis pipeline Figure , and theIn the Oncovirus Analysis module, a user can perform gene-focused analysis to evaluate potential viral association for user-specified genes in one or more cancer types. A boxplot for expression analysis or a line graph for methylation analysis is presented to visually compare the virus-positive and virus-negative groups  in the virus-positive group or virus-negative group separately, with a survival plot presented for each group. An example of survival plot based on RNA expression analysis of CDKN2A in HPV-positive cervical cancer is shown in Figure OncoDB is a comprehensive online database to analyze gene expression and methylation data from over 10 000 patients spanning 33 major cancer types. Researchers could conveniently retrieve and analyze a large quantity of cancer data without requiring advanced bioinformatics skills. In OncoDB, RNA-seq, DNA methylation, and related clinical data are integratively analyzed and then visually presented on the web portal. Another unique feature of OncoDB is its oncovirus analysis module, which was developed by integration of viral infection status with cancer genomic and clinical data; this new feature provides functional as well as clinical insights into the roles of oncoviruses in cancer development and progression.http://oncodb.org.All data in OncoDB are freely accessible at


Its vignettes walk the user through some analyses contained in this paper.While DNA methylation (DNAm) is the most-studied epigenetic mark, few recent studies probe the breadth of publicly available DNAm array samples. We collectively analyzed 35 360 Illumina Infinium HumanMethylation450K DNAm array samples published on the Gene Expression Omnibus. We learned a controlled vocabulary of sample labels by applying regular expressions to metadata and used existing models to predict various sample properties including epigenetic age. We found approximately two-thirds of samples were from blood, one-quarter were from brain and one-third were from cancer patients. About 19% of samples failed at least one of Illumina’s 17 prescribed quality assessments; signal distributions across samples suggest modifying manufacturer-recommended thresholds for failure would make these assessments more informative. We further analyzed DNAm variances in seven tissues  and characterized specific probes distinguishing them. Finally, we compiled DNAm array data and metadata, including our learned and predicted sample labels, into database files accessible via the  There are currently no R/Bioconductor  packagesThe vast majority of GEO DNAm array data is composed of samples using Illumina’s HumanMethylation 450K (HM450K) BeadArray platform. Restricting attention to HM450K samples with IDATs published on or before 31 March 2019, we identified 35 360 samples from 362 studies, over three times the number of samples studied by either , or 29)29). Fromrecountmethylation, a companion R/Bioconductor -based databases accessible using onductor  package esearch function of Entrez Programming Utilities v10.9 to search for every HM450K sample published to GEO as of 31 March 2019 for which two gzip-compressed IDAT download URLs were available. We ultimately downloaded IDATs for 35,360 sample records. Search and download were performed using the script https://github.com/metamaden/recountmethylation_server/blob/master/src/server.py. Note, the HM450K platform accession ID, GPL13534, is specified in the script https://github.com/metamaden/recountmethylation_server/blob/master/src/settings.py, and changing this will cause the server to target a different array platform.We used the minfi v1.29.3 . These  ggplot2 . The scrlog2 median methylated and log2 median unmethylated signals, as reproduced in the recountmethylation data analysis vignette at https://www.bioconductor.org/packages/release/bioc/vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.pdf.We subsequently computed array-wide https://github.com/metamaden/recountmethylation_server/blob/master/src/dl.py. From SOFT files, sample-level metadata were extracted as JSON-formatted files. Study-specific metadata fields were filtered prior to learning sample annotations (below). These steps were performed using the scripts at https://github.com/metamaden/recountmethylationManuscriptSupplement/tree/main/inst/scripts/metadata.Sample metadata was downloaded from GEO as study-level Simple Omnibus Format in Text (SOFT) files using the script Marmal-aid . To leahttps://github.com/deweylab/MetaSRA-pipeline . This ppipeline .minfi (v.1.29.3) and wateRmelon (v.1.28.0) R/Bioconductor packages in our script https://github.com/metamaden/recountmethylationManuscriptSupplement/blob/main/inst/scripts/metadata/metadata_model_predictions.R. We tested concordance of mined and predicted sex and age to inform the use of these predictions and reliability of learned annotations (Results).After noob normalization, we performed model-based predictions of sample age , sex 3333 and blstats (v.3.6.0) R package, using noob-normalized autosomal DNAm from all samples and a subset of filtered samples from seven non-cancer tissues  with the stats v..6.0 R pastats v..6.0 R paWe identified samples of seven distinct tissues , where each tissue included at least 100 samples across at least two study records . While wewastools .M-values, defined as logit(Beta), with the limma v3.39.12 package. We then converted the adjusted DNAm to Beta-value scale. To account for the impact of confounding variables, we removed probes whose DNAm variances showed significant (p-adjusted < 0.01) and substantial (percent variance ≥ 10%) contributions from model-based predictions of age, sex and cell type fractions, which removed 39 000 to 194 000 (8–40% of) probes across tissues  across seven distinct tissues. We also identified probes with high and tissue-specific variance. For each analysis we used a two-step probe selection process in each tissue where we selected (i) probes in the highest or lowest 10th quantile of variance (e.g. an absolute quantile variance filter), and (ii) probes in the highest or lowest 10th quantile variance across mean DNAm bins , scipy (v1.1.0) and pandas v0.23.0 Python packages to manage jobs and downloads, perform data extraction and calculate summary statistics. We used the minfi v1.29.3 and limma v3.39.12 R/Bioconductor packages for downstream quality control, preprocessing and analyses. Plots were generated using base R functions, ggplot2 (v3.1.0) and ComplexHeatmap (v1.99.5)  ,42. To rhttps://github.com/metamaden/recountmethylationManuscriptSupplement. Large supplemental data files are accessible at https://recount.bio/data/recountmethylation_manuscript_supplement/.Supplemental Information, including methods, code, and scripts reproducing analyses, figures, and tables are accessible at recountmethylation R/Bioconductor package at http://bioconductor.org/packages/devel/bioc/html/recountmethylation.html.Databases of the samples compiled and analyzed in this manuscript are accessible, along with comprehensive instructions and analysis examples, in the We obtained sample IDATs and metadata for studies from the GEO. GEO is the largest public database for human DNAm array studies, and the majority of GEO’s DNAm array samples use one of three of Illumina’s BeadArray platforms: the HumanMethylation27K (HM27K), the HumanMethylation450K (HM450K) and EPIC, also known as the HumanMethylation850K (HM850K). On GEO, we identified 104 746 unique sample accession numbers (a.k.a. GSM IDs) from 1605 study accession numbers (a.k.a. GSE IDs) published using one of the three major Illumina DNAm array platforms Figures . Among 1Raw data for a DNAm array sample is comprised of two IDAT files, one for each of the red and green color channels. Accessible raw data is important for uniform normalization of samples across studies, yet not all samples on GEO come with these data. In total, 37 919 samples  included sample IDATs, where 93% were run on HM450K, 5% on EPIC, and 2% on HM27K. By platform, EPIC included the largest percentage of sample records with available IDATs at 63%, followed by HM450K at 43% and HM27K at just 3%. The more frequent availability of IDATs for newer arrays seems to reflect a significant shift in data submission norms well after the inception of the HM27K platform.There were enough study and sample metadata for us to annotate 27 027 samples, 76% of the 35 360 we analyzed. We annotated these samples by applying regular expressions to the mined metadata. Our vocabulary for annotations was composed of 72 distinct terms  strongly inspired by those used in the methylation array resource Marmal-aid . Tissue Prior work showed chronological age can be predicted with high accuracy from DNAm among non-cancer tissues ,6. We caP < 2.2e-16), followed by study , cancer status , and predicted sample type . Compared to variances attributed to the study variable, the relative low variances attributed to the cancer status and predicted sample type variables may be due to high study-specific variance in metadata completeness or availability. High age differences  and errors (R-squared = 0.76) likely resulted from either metadata inaccuracies, age label misattributions from our mining strategy, or inclusion of cancers and non-tissue samples . In the subset of 6019 likely non-cancer tissue samples across 37 study records with study-wise MADs ≤ 10 years, epigenetic age variance contribution from mined age increased to 93%  and contribution from study decreased to 2% , supporting the well-established finding that chronological age is accurately predicted from epigenetic age in non-cancer tissues  showed most epigenetic age variation was explained by chronological age .Illumina prescribes 17 quality assessments for its 450K array, each measuring the performance of a different step in a methylation assay such as extension or hybridization ,36. A giWe studied signals and outcomes to determine how best to use the BeadArray controls for sample quality assessments. Cross-sample signal distributions for five BeadArray controls were bimodal, with distinct low- and high-signal modes; minimum quality thresholds fell near low-signal modes . For theP < 1e-3), where ‘colorectal,’ ‘intestine,’ and ‘mucosa’ were enriched among FFPE, while ‘nasal,’ ‘pancreas,’ and ‘epithelial’ were enriched among FF samples.We investigated the impact of storage conditions on sample quality across 28 studies by comparing 3467 formalin-fixed paraffin embedded (FFPE) and 5729 fresh frozen (FF) samples . FFPE saAcross the 12 of 17 total BeadArray controls each with at least one failing sample, 228 FFPE samples  and 241 FF samples  failed more than one control. FFPE samples failed 10 of the 12 metrics between 0.1 and 3.2% more often than FF, including all three bisulfite conversion metrics Figure . FF sampfst of failed samples per study, defining a failed sample as one that either (i) fails at least one BeadArray control, or (ii) has log2 median methylated and log2 median unmethylated signals each <11 as described in , and the ratio of variance in PC2 for leukemia samples to variance in PC2 in blood samples was 6.18 (F-test P < 1e-2). This is consistent with how (i) leukemia samples have greater variance than blood samples at each of the majority of probes (311 127 or 66%) and (ii) leukemia samples have greater median variance than blood samples across probes , and the ratio of variance in PC2 for brain tumors to the variance in PC2 for non-cancer brain samples was 22.40 (F-test P < 1e-5). This is consistent with how (i) brain tumors have greater variance than non-cancer brain samples at each of the majority of probes , and (ii) brain tumors have greater median variance than non-cancer brain samples across probes , which we also used to study DNAm variability (below), showed clear clustering by tissue. The first two components explained 57% of total variance, with PC1’s contribution 38% and PC2’s contribution 19%. Sperm samples clustered far apart from the six somatic tissues Figure . After rP-values < 1e-3). It is likely the 4577 probes are of low utility for quantifying DNAm differences across tissues, and their removal prior to performing an EWAS across non-cancer tissues could help increase statistical power.CpG probes with low DNAm variation and low mean DNAm differences across experimental groups are less informative for quantifying group-specific DNAm differences. We analyzed autosomal DNAm variation in seven distinct tissues , as measured by noob-normalized, study-corrected Beta-values . We identified 4577 probes each with consistently low variance (≤10th quantile) in each tissue and low difference between highest and lowest mean Beta-value (<0.01) across tissues . Among pP < 1e-3). Of the 14 000 total high-variance probes, 10 016 (71%) mapped to a gene region, typically at the gene body  with high and tissue-specific variation in autosomal DNAm, as measured by noob-normalized, study-corrected Beta-values  and HDF5-SummarizedExperiment (119 GB) files and raw methylated and unmethylated signals and noob-normalized Beta-values are provided as HDF5-SummarizedExperiment files . The recountmethylation R/Bioconductor package facilitates database access as described in the user’s manual. It allows full database utilization with rapid queries on the provided sample metadata, including model-based estimates for sex, epigenetic age and blood cell types  and ll types . The pacWe conducted a cross-study analysis of methylation array samples comprising a large subset of available HM450K samples on GEO. While we omitted studies using the HM27K and EPIC platforms, our compilation strategy could also be generalized to these platforms . Further, while our results suggest BeadArray controls could be improved by applying different quantitative thresholds for failure, it remains unclear whether a single universal threshold or multiple experiment-specific thresholds is desirable for each  even when any one is the same across a sample set, since that attribute may vary in a cross-study analysis.Repeat study-level metadata in sample-level metadata. This includes sample types  and characteristics .Include units of numerical variables to ensure their proper interpretation.at diagnosis, tumor-adjacent normal tissue and blood from leukemia patient.Clarify circumstances under which attributes were obtained where appropriate. For example, age recountmethylation R/Bioconductor package. First, future compilations will add samples run on the newer EPIC/HM850K platform, allowing for novel cross-platform analyses. Further, our metadata handling pipeline will be revised to be fully automated by using regular expressions to recognize key metadata. This will replace the manual variable aggregation step . Finally, we will support regular compilation updates by enabling rapid setup and better dependency handling . These improvements will empower the researcher to maintain a comprehensive compilation of DNAm array IDATs from GEO in a wide array of computing environments.We have several methodological changes planned that will improve the DNAm array databases accessible with the recountmethylation including our new tissue and disease state labels, model-based predictions for age, sex and blood cell composition, as well as noob-normalized Beta-values for array samples. This resource should prove valuable for reusing publicly available methylation data.We performed extensive analyses of 35 360 HM450K samples with IDATs from 362 studies in GEO, approximately three times the number of samples considered in prior cross-study analyses . We furtlqab025_Supplemental_FilesClick here for additional data file.