Modern applications such as bioinformatics collecting data in various ways can easily result in heterogeneous data. Traditional variable selection methods assume samples are independent and identically distributed, which however is not suitable for these applications. Some existing statistical models capable of taking care of unwanted variation were developed for gene identification involving heterogeneous data, but they lack model predictability and suffer from variable redundancy.By accounting for the unwanted heterogeneity effectively, our method have shown its superiority over several state-of-the art methods, which is validated by the experimental results in both unsupervised and supervised gene identification problems. Moreover, we also applied our method to a pan-cancer study where our method can identify the most discriminative genes best distinguishing different cancer types.This article provides an alternative gene identification method that can accounting for unwanted data heterogeneity. It is a promising method to provide new insights into the complex cancer biology and clues for understanding tumorigenesis and tumor progression. Many variable selection methods have been proven to be successful in handling high-dimensional data in the past few decades. However, most of these methods assume the data are independent and identically distributed (i.i.d.), whose applications are severely limited. In most real-world applications, we are quite often facing the heterogeneous data violating the assumptions . For insExisting methods proposed for feature selection on heterogeneous data deal with those data generated from multiple views or sources that describe the same set of samples –12. The X, which contains n samples and p variables. The group information causing the heterogeneity is represented by a binary indicator matrix Gij with value ‘1’ indicates that the i-th sample belongs to the j-th group and vice versa. In different kinds of variable selection problems, the group factor can play different roles contingent on the corresponding interested factors. For instance, we have the unsupervised variable selection problems aiming to select the variables that can best determine the data group structure. In this problem, our factor of interest is G and we model X by: ε∈N represents the i.i.d. noise and Y are introduced and affected by the variables of X. The factors of interest is Y here and the association between Y and the variables of X is what we want to study. In this case, we model X by : We denote the gene expression data by γ reflects the effect of Y on each variable.in which F={Fk:1≤k≤K} denote the K unknown factors. Certainly, they should be regarded as uninterested factors and included in the data models. Thus, we correct the model for unsupervised problems as: In both types of the above problems, there may also involves some other unaware factors causing unknown heterogeneity sometimes. The unknown heterogeneity refers to the variation patterns caused by the unknown factors that are not explicitly included in the data model. Let and the model for supervised problems as: F. While in supervised problems, those uninterested factors refer to {G,F}.Apparently, the analysis of true relations between the factors of interest and each variable should take the unwanted heterogeneity into account. In unsupervised problems, the unwanted heterogeneity is caused by those uninterested factors referring to Xa. The effects of the factors of interest are thus represented by the variables of the new data set; (2) based on the adjusted data set Xa, select the variables associated with the factors of interest.Our variable selection strategy contains two main stages: (1) adjust the unwanted heterogeneity and create a new data set F for the first stage. We take the procedure similar to SVA . Figure B. We found that the top-ranked 16 genes matched the ground-truth exactly.We compared three methods accounting for the heterogeneity or not. Robust Multi-array Average (RMA) is the most popular method for gene expression analysis but taking no heterogeneity into account. It summarizes the replicate measurements to a gene expression value , which hIn this experiment, we study a supervised gene identification problem which is first conducted by Vawter et al. . It triep-values in an increasing order. In contrast, our SOSA ranked the genes based on the l2 norm of their corresponding row vectors of the estimated B. The parameter λ in SOSA was chosen by leave-one-out cross validation. Figure λ falling in the range between 0.0001 and 0.05. The best λ was chosen as 0.004 which corresponds to the minimum average objective value across all the folds. We treat all the 488 genes from the X and Y chromosomes as candidate positive controls considering most genes differentially expressed with respect to the gender will be located on the X and Y chromosomes. For the negative controls, we use the 799 housekeeping genes claimed by [X/Y genes or less housekeeping genes than the unadjusted ones, which demonstrates their significant improvement on the gene selection performance. Benefiting from the model stability accomplished by the regularization of the gene coefficients, our SOSA achieved better performance than SVA approach. We also observed that SOSA is superior to Lm _batch since it can adjust both the known batch effects and the unknown heterogeneous factors. For those methods without adjustment, SOS and IndAss achieved higher sensitivity than PLR by correctly detecting more X/Y genes, but IndAss has lower specificity since it wrongly identified much more housekeeping genes than the other two methods.We compared six representative methods: individual gene association analysis (IndAss), penalized logistic regression (PLR), the standard sparse optimal scoring (SOS), linear model adjusting batch effects (Lm _batch), SVA and our SOSA, to identify those genes that can best differentiate the samples from different genders. Their sensitivity and specificity in gene selection were investigated. PLR, IndAss and SOS run directly on the gene expression data without accounting for the unwanted heterogeneity. The ‘glmnet’ package was employed to implement PLR with the parameter chosen by cross validation. We ranked the genes based on the absolute values of the regression coefficients in a decreasing order. Linear model was also employed here to adjust the known batch effects, which however is not able to adjust the unknown heterogeneous factors. We also considered two alternative methods accounting for the heterogeneity: SVA and our SOSA. We considered three unknown factors here. By taking the lab/chip factor and the estimated unknown factors as covariates, SVA did the association analysis for each individual gene and then ranked the genes based on their aimed by . The ROChttps://archive.ics.uci.edu/ml/datasets/gene+expression+cancer+RNA-Seq##), which is a random extraction of RNA-Seq gene expressions of patients having five types of cancer: Breast (BRCA), Colon (COAD), Kidney (KIRC), Lung (LUAD) and Prostate (PRAD) from the TCGA data portal. This extracted and preprocessed data set contains 801 patients with 20,531 genes measured in total. Among them, 300, 78, 146, 141 and 136 patients have BRCA, COAD, KIRC, LUAD and PRAD respectively, which implies that the samples are relatively balanced across different types of cancer.The two studies above investigated the gene identification performance of our method on heterogeneous gene expression benchmark data with ground-truth genes known. In this experiment, we explore the gene identification performance of our method as well as the interpretation of those identified genes in the application to a pan-cancer study. This experiment aims to identify a set of genes differentially expressed across five common types of cancer that are clinically attractive as diagnostic biomarkers or therapeutic targets based on The Cancer Genome Atlas (TCGA) gene expression data. Particularly, we use the publicly available RNA-Seq (HiSeq) PANCAN data set from the UCI machine learning repository  analysis of the top 200 genes using WebGestalt tool . The topThe results from our experiments on three unsupervised and supervised gene identification problems have shown the superiority of our method in handling unwanted data heterogeneity issue. The first problem studied a representative application of finding the differentially expressed genes from gene expression data with batch effects in unsupervised circumstance. Our method performs better than RMA and SVA since it is capable of taking care of the unwanted heterogeneity as well as variable redundancy. The second problem studied a typical application of identifying differentially expressed genes from gene expression data with batch effects in supervised circumstance. Our method performs better in the sensitivity and specificity in variable selection than other state-of-the-art methods including penalized logistic regression model, simple statistical model for individual gene association analysis, sophisticated statistical model capable of accounting for data heterogeneity and linear model accounting for batch effects. This benefit from our two-stage embedded variable selection strategy. The third problem studied a general case of supervised gene expression analysis where the batch effects are unknown. Our method can achieve the highest classification accuracy comparing to other methods using the same number of discriminative genes. In summary, our method has wide applications in various kinds of gene identification problems.We proposed sparse optimal scoring with adjustment (SOSA) for gene identification on heterogeneous data involving unwanted heterogeneity. Particularly, our method is able to account for the unknown and unwanted heterogeneity that blurs the true signals of genes. The results from the first two studies on the batched gene expression data demonstrate that those methods accounting for the unwanted heterogeneity can significantly improve the gene identification performance. Moreover, our method is also superior to the statistical models capable of adjusting data heterogeneity because of the model stability and the ability of prediction on new samples. The results from pan-cancer study further validated the superiority of our method in identifying the genes discriminative in different cancer types to other state-of-the-art methods. The biological interpretation of the results provides new insights into complex cancer biology and clues for understanding tumorigenesis. In view of the commonly existing data heterogeneity issue such as batch effects in bioinformatics, our SOSA can also be adapted and serves as a promising tool to remove the unwanted heterogeneity prior to downstream analysis for many other biomedical applications such as cell population diversity in single-cell landscapes and so on.


Gentiana rigescens Franchet, which is famous for its bitter properties, is a traditional drug of chronic hepatitis and important raw materials for the pharmaceutical industry in China. In the study, high-performance liquid chromatography (HPLC), coupled with diode array detector (DAD) and chemometrics, were used to investigate the chemical geographical variation of G. rigescens and to classify medicinal materials, according to their grown latitudes. The chromatographic fingerprints of 280 individuals and 840 samples from rhizomes, stems, and leaves of four different latitude areas were recorded and analyzed for tracing the geographical origin of medicinal materials. At first, HPLC fingerprints of underground and aerial parts were generated while using reversed-phase liquid chromatography. After the preliminary data exploration, two supervised pattern recognition techniques, random forest (RF) and orthogonal partial least-squares discriminant analysis (OPLS-DA), were applied to the three HPLC fingerprint data sets of rhizomes, stems, and leaves, respectively. Furthermore, fingerprint data sets of aerial and underground parts were separately processed and joined while using two data fusion strategies (“low-level” and “mid-level”). The results showed that classification models that are based OPLS-DA were more efficient than RF models. The classification models using low-level data fusion method built showed considerably good recognition and prediction abilities . Low-level data fusion strategy combined with OPLS-DA could provide the best discrimination result. In summary, this study explored the latitude variation of phytochemical of G. rigescens and developed a reliable and accurate identification method for G. rigescens that were grown at different latitudes based on untargeted HPLC fingerprint, data fusion, and chemometrics. The study results are meaningful for authentication and the quality control of Chinese medicinal materials. Gentiana rigescens Franchet (Dian long dan) is a herbaceous species that grows in mountainous regions of Yunnan-Guizhou Plateau in the southwest of China [G. lutea L), G. rigescens is famous for its bitter properties that are due to the bitter active principles  [G. rigescens, which could be used as raw material for the preparation of functional food and a therapeutic drug for Alzheimer’s disease [G. rigescens have been the official drug of Chinese pharmacopoeia (2015 edition) for chronic hepatitis and important raw materials for the pharmaceutical industry in China [of China . Like Eue, etc.) ,3,4. Thoe, etc.) . Additioe, etc.) ,6,7. In  disease ,9,10,11.in China .G. rigescens were usually collected from different regions of Yunnan-Guizhou Plateau in order to provide satisfaction of continuously increasing industrial demands for raw materials. However, some of the researchers had reported that chemical constitutions of underground part of G. rigescens were extremely variable and diverse according to plant grown location or producing area [ing area ,14,15. Qing area ,14,15,16ing area .G. rigescens, Yunnan-Guizhou Plateau is characterized by very complex topography and it displays a wide variety of micro-climates [G. rigescens that were grown in different latitudes and build a classification model for tracing producing areas of medicinal materials.Latitude has a strong impact on the local climate environment in southwest China ,19. As tclimates ,19,20,21G. rigescens and other medicinal plants in the worldwide [As we know, the contents of bioactive compounds and quality of medicinal materials have a close relationship with the environment of producing area ,23,24,25orldwide ,32,33,34orldwide . Althougorldwide ,37,38,39G. rigescens by applying the chromatographic fingerprint methodology.When compared with chemical marker or chromatographic profile (targeted), chromatographic fingerprint (untargeted) contains unspecific and non-evident information and chemometric tools should extract chemical information . RecentlPanax notoginseng, Paris Polyphylla var. yunnanensis and other herb materials also showed the huge potential of this strategy in the discrimination of medicinal materials producing areas [P. Polyphylla var. yunnanensis, Macrohyporia cocos, and other species indicated that reliable classification results were also available by the fusion analysis of chemical fingerprint data collected from different medicinal parts of herbs [In the progression of improving geographical authentication of food and drugs, one of the important goals is building discrimination models with a less error rate and reducing the uncertainty of the prediction results ,44. Datang areas ,47,48. Tng areas ,43. Furtng areas ,43, whilof herbs ,49. Accuof herbs ,50. TherG. rigescens along the latitude gradients and to use chemometrics to mine fingerprint chemical information, and to investigate the potential of the untargeted chromatographic fingerprint to trace herbs grown at different latitudes. For this purpose, we developed fingerprint of rhizomes, stems, and leaves of G. rigescens by high-performance liquid chromatography with diode array detection (HPLC-DAD) technology. Subsequently, classification models for the identification of different producing areas were built by HPLC fingerprint combined with RF  and OPLS-DA . At last, two types of data fusion strategies, “low- level” and “mid-level” data fusion, were studied in order to improve the model performances.The aim of this study is to explore the variation of chromatographic fingerprints of t/min) of loganin (1), 6′-O-β-d-glucopyranosylgentiopicroside (2), swertiamarine (3), gentiopicroside (4), and sweroside (5) were 7.279, 9.213, 9.573, 11.376, and 11.622 min, respectively. Loganin and gentiopicroside were mainly accumulation in the underground part and sweroside accumulated more in the overground parts. Furthermore, differences in the chemical composition of rhizome, stem, and leaf can also be visually observed through chromatographic fingerprints. For facilitating subsequent data exploration and modeling analysis, the retention time of fingerprints signal was replaced by variables  and two-dimensional score plots visualized the differences and variation trends of three medicinal parts. Although the fingerprints between the aboveground and underground medicinal parts were obvious differences, an interesting result is that a trend of separation according to product region latitude was observed from the PCA and score plots of samples of three medicinal parts. For example, two-dimensional score plots of chromatographic fingerprint of rhizomes showed that the samples separation trend increases with an increase in geographical distance and a clear separation between samples that were collected from lower latitude and higher latitude regions . In contG. rigescens were different among rhizomes, stems, and leaves, and were affected by latitude gradients of the production regions. Especially between lower latitudes and higher latitudes, the samples seem to be clearly distinguishable. Based on PCA exploratory analysis (unsupervised methods), supervised pattern recognition (OPLS-DA) should be applied to gain better classification results for samples that were grown in different latitudes  score plots and VIP analysis showed a difference of phytochemical geographic variation for overground and underground parts. Those differences might affect the result of geographical origin traceability of samples.In a word, current research indicated that the chemical composition of G. rigescens according to their grown latitude.In recent years, literature had already reported satisfying classification results that were obtained by RF or OPLS-DA models ,52,53,54ntree (needs to be optimized) was defined as 2000, the initial value of mtry was defined as the square root of the number of variables, and the rest of the parameters were defined as the default value. Subsequently, OOB errors were calculated and the value of the best ntree was obtained according to the lowest OOB error. mtry was re-selected by searching the values ranged from 50 to 75. The calculation results found that the mtry value should be defined as 61, because of the model had the lowest OOB classification error. Finally, a final classification model was established based on optimum ntree and mtry values.In the beginning, samples from the data set of rhizomes (280 samples and 3839 variables) were separated into a calibration set (186 samples) and a validation set (94 samples) by the Kennard-Stone algorithm. Subsequently, 186 rhizome samples that were collected from four latitude gradients were used to establish the calibration model (R_RF). During the modeling process, the initial value of ntree and mtry could be found in Like previous investigations of the rhizome model, the data set of stems (280 samples and 4140 variables) and leaves (280 samples and 4140 variables) were separated into calibration sets and validation sets, respectively. Subsequently, RF calibration modes of stems (S_RF) and leaves (L_RF) were built. The optimum For the RF model of the stem, the accuracies of samples of calibration set of 92.47%, 94.62%, 93.01%, and 93.01% were achieved for low latitudes, mid-latitudes, mid-high latitudes, and high latitudes. Additionally, the accuracies of samples of validation set were 98.94%, 97.87%, 96.81%, and 97.87%, respectively .For RF model of the leaf, accuracies of 92.47%, 96.24%, 93.01%, and 94.62% were achieved for the calibration set. Additionally, accuracies of 85.11%, 93.62%, 89.36%, and 93.62% for the validation set .The OPLS-DA models of rhizomes (R_OPLS-DA), stems (S_OPLS-DA), and leaves (L_OPLS-DA) were constructed based on the same calibration and validation sets that were used in RF models. All of the models were constructed based on the internal seven-fold cross-validation and permutation plot could be found in R2 of models ranged from 0.77 to 0.82 and the Q2 of models were larger than 0.50, which indicated that the OPLS-DA models were well fitted and better predictive. The permutation test results could be found in The classification results of R_OPLS-DA model showed  accuraciFinally, we made a comprehensive comparison to the six models’ classification performance superiority on the basis of the above analysis. For the RF model, the order of calibration total accuracy was as follows: R_RF (96.24%) > L_RF (94.09%) > S_RF (93.28%). The order of validation total accuracy was as follows: S_RF (97.87%) > R_RF (95.21%) > L_RF (90.43%). For the OPL-DA model, the order of calibration total accuracy was as follows: R_OPL-DA (98.92%) and S_OPLS-DA (98.92%) > L_OPLS-DA (98.12%). The order of validation total accuracy was as follows: R_OPL-DA (96.81%) > S_OPLS-DA (93.62%) > L_OPLS-DA (92.55%). Classification models that were built by using leaf data set presented the worst performance from the accuracy point of view. Additionally, validation sets of the L_RF and L_OPL-DA model had lower Matthews correlation coefficient (MCC) values. By contrast, all of the models based on rhizome data set presented a better classification performance . The best total accuracy occurred when rhizome data combined with the OPLS algorithm. We could find that phenomenon of imbalance category recognition in R_OPLS-DA model was better than other models from SE values, SP values, MCC values, and EFF value.Although the classification performance for OPLS-DA and RF models on the basis of rhizome data set was good, the model classification ability, accuracy, sensitivity (SE), specificity (SP), MCC, and efficiency (EFF), need to be enhanced. In a further step, the feasibility of combining the information from rhizome, stem, and leaf fingerprint data for samples geographical traceability was investigated by low-level and mid-level data fusion strategies.According to the method that was described in data preprocessing , fingerpntree and mtry values were selected at first  of  Sichuan . Four prO-β-d-glucopyranosylgentiopicroside (purity: ≥98%), swertiamarine (purity: ≥98%), gentiopicroside (purity: ≥98%), and sweroside (purity: ≥98%) were purchased from the Chinese National Institute for Food and Drug Control , Shanghai Shifeng Biological Technology , respectively.HPLC-grade acetonitrile, methanol (MeOH) were supplied by Thermo Fisher Scientific . HPLC-grade formic acid was purchased from Sigma-Aldrich . Deionized water was obtained from Wahaha Group Co., Ltd. . The primary grade reference standards loganin (purity: ≥98%), 6′-The dried samples  were ground and then passed through a 100 mesh sieves. Each sample powder (25 mg) was accurately weighed and extracted while using 1.5 mL 80% methanol-water solution, at 25 °C. The samples were extracted while using an Ultrasonic extractor for 40 min. The final extract was filtered with a 0.22 μm syringe filter into an HPLC vial and then subjected to HPLC analysis ,58.Chromatographic analyses were performed with an Agilent 1260 Infinity LC system , which was equipped with a G1315D diode-array detector, a G1329B ALS autosampler, and a thermostated column compartment. The HPLC fingerprint was recorded by Chemstation software .The analytical separation was adopted from a published method for chemical fingerprinting analysis . The sepwww.models.kvl.dk. The preprocessing fingerprint was analyzed in the following work [HPLC fingerprints from the 280 rhizome samples, 280 stem samples, and 280 leaf samples, a total of 840 fingerprint data was exported in CSV format and imported to MATLAB R2018b , which was used for correlation optimized warping (COW) alignment preprocessing of chromatographic fingerprint. MATLAB code of COW is freely available from ing work .Exploratory data analysis (EDA) is necessary for building predictive models ,61. It cUnlike PCA, orthogonal partial least squares discriminant analysis (OPLS-DA) is a supervised pattern recognition technique. As an extension of PLS, an inbuilt orthogonal signal correction filter was incorporated in the OPLS-DA model . This alRandom forest (RF) is another supervised pattern recognition technique utilized in the study. RF is an ensemble learning method . A largeG. rigescens grown in four different latitudes was finished with PCA. Two supervised pattern recognition techniques, OPLS-DA and RF, were applied to build classification models for G. rigescens producing areas. SIMCA 14.1 software managed PCA and OPLS-DA . RF classification models were established with R 3.5.1 program and package randomForest (Version 4.6-14) [In this work, exploratory data analysis of HPLC fingerprints of  4.6-14) .In the case of low-level fusion strategy , differeIn the case of mid-level fusion , the firIn the research, relevant variables of RF classification models were determined by the R software package Boruta , and VIP2), cumulative interpretation ability (R2), root mean square error of estimation (RMSEE), root mean square error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP) were important evaluation indexes for the predictive power of OPLS-DA model [Five parameters, including accuracy (ACC), sensitivity (SE), specificity (SP), efficiency (EFF), and Matthews correlation coefficient (MCC) were applied to evaluate the identification ability of RF and OPLS-DA models. The ruggedness of OPLS-DA model was investigated through 200 times permutation tests. Furthermore, cumulative prediction ability , TN (correctly identified samples of negative class), FN (incorrectly identified samples of positive class), and FP (incorrectly identified samples of negative class) were calculated according to confusion matrixes of classification models. Subsequently, ACC, SE, SP, EFF, and MCC were calculated while using Equations (1)–(5) and values of Q2, R2 are to 1, the more well performance the model is.For model performance, lower values of RMSEE, RMSECV, and RMSEP mean better predictive ability for the models. Conversely, the closer that values of ACC, SE, SP, EFF, MCC, and QG. rigescens chemical profiles were influenced by the latitude gradients of producing areas and lower latitudes and higher latitudes samples seemed to be clearly distinguishable. According to the score plots of PCA and OPLS-DA, the phytochemical geographic variation of the overground and underground part along the latitude gradients was visualized. Subsequently, the potential of fingerprint data obtained while using HPLC-DAD to discriminate and classify G. rigescens grown in four different latitudes was investigated. Additionally, RF and OPLS-DA models were used to develop an effective way for geographical traceability of the G. rigescens that were grown in four different latitudes. When using independent data sets to build models, rhizomes data set combined with OPLS-DA presented the best performance with a classification accuracy of calibration and validation set varied from 94.68% to 98.94%. In a further step, the feasibility of combining the chromatographic fingerprint data from overground and underground organs was investigated based on two kinds of data fusion strategies in order to improve the performance of classification models: low-level and mid-level. Notably, classification performances of OPLS-DA models were efficiently improved by low-level data fusion strategy and better performances of RF models appeared to be achieved by mid-level data fusion strategy. Although satisfactory results were obtained with both RF and OPLS-DA based on two kinds of data fusion strategies, OPLS-DA combined with rhizome-stem fusion data set was the optimum model for discriminating G. rigescens samples according to their grown latitudes, with an accuracy of (97.87–100.00%), SE of (0.96–1.00), SP of (0.98–1.00), MCC of (0.95–1.00), and EFF of (0.97–1.00).The findings in this study showed that


Stage I epithelial ovarian cancer (EOC) represents about 10% of all EOCs. It is characterized by a complex histopathological and molecular heterogeneity, and it is composed of five main histological subtypes , which have peculiar genetic, molecular, and clinical characteristics. As it occurs less frequently than advanced-stage EOC, its molecular features have not been thoroughly investigated. In this study, using in silico approaches and gene expression data, on a multicentric cohort composed of 208 snap-frozen tumor biopsies, we explored the subtype-specific molecular alterations that regulate tumor aggressiveness in stage I EOC. We found that single genes rather than pathways are responsible for histotype specificities and that a cAMP-PKA-CREB1 signaling axis seems to play a central role in histotype differentiation. Moreover, our results indicate that immune response seems to be, at least in part, involved in histotype differences, as a higher immune-reactive behavior of serous and mucinous samples was observed with respect to other histotypes. Epithelial ovarian cancer (EOC) is a highly lethal and heterogeneous gynecological disease. It is characterized by a complex histopathological and molecular heterogeneity, and it is composed of five main histological subtypes , which have peculiar genetic lesions, different biomarkers, response, and outcomes to standard treatments ,2,3.Studies have provided evidence that most ovarian cancers arise from tissues that are not normally present in the ovary such as fallopian tubes, endometrial tissue, or tubal-mesothelial junction, thus determining differences among the various histologic types ,5,6,7.In recent years, many efforts have been dedicated to provide a catalogue of molecular alterations in advanced EOC with the goals of developing and deploying therapies to improve patient survival. Indeed, transcriptome alterations are the proxy of tumor molecular activities and their investigations may suggest which strategies the tumor cells adopt to escape immune system recognition and death, to proliferate and to spread.Currently, a large amount of data are available on advanced EOC, especially on the high-grade serous histotype , but, giMoreover, very recently, tumor microenvironment composition, tumor-immune cell interaction, and the association genotypes-immunophenotypes have been recognized as key aspects in cancer progression and have been used successfully for patients’ stratification ,10. FromIn the last years, using transcriptome data, our group addressed two complementary issues on stage I EOC: (i) the identification of a prognostic signature shared across the different histotypes, and (ii) the identification of histotype-specific biomarkers.β and PIK3/AKT pathways [Regarding the first aim, using a pathway-based integrative method  on an Itpathways .Regarding the second aim, we identified small but robust miRNA signatures for clear cell (miR-30a and miR-30a*) and mucinous (miR-192 and miR-194) subtypes  showing Thus, our recent studies suggest that some mechanisms, possibly related to the genetic patient background or to the common anatomical site of growth, are playing a key role in driving tumor prognosis and that the different molecular specificities of the different histotypes act as satellite and synergic events in modulating tumor aggressiveness and relapse.With this hypothesis, using in silico approaches and gene expression data, we tried to describe the subtypes-specific molecular alterations that modulate the intrinsic mechanisms that regulate tumor aggressiveness in stage I EOC. We analyzed the transcriptome of a large multicentric cohort of stage I EOC patient and provide a global overview of the transcriptome alterations focusing on different questions: (i) known signatures proposed for high-grade serous ovarian carcinoma by The Cancer Genome Atlas (TCGA) consortium  can be uWe retrospectively collected tumor samples from patients diagnosed at stage I EOC from three independent Italian cohorts, for a total of 208 snap-frozen tumor biopsies. (i) 135 samples were collected at Gynecology Department of San Gerardo Hospital  and conserved in the tissue bank of the Department of Oncology of IRCCS Mario Negri Institute , (ii) 26 samples at the A. Nocivelli Institute for Molecular Medicine, Division of Obstetrics and Gynecology  and (iii) 47 samples at the Department of Gynecology-Oncology . Primary tumor tissues were collected at the time of diagnosis and first surgery prior to any chemotherapy treatment. Samples were submitted for a complete staging procedure according to the International Federation of Gynecological and Obstetrics (FIGO) criteria with a diagnosis of stage I EOC. Tumor histological types were determined following World Health Organization (WHO) standards.n = 76) included 16 clear cell (Cc), 19 endometroid (End), 17 mucinous (Muc) and 16 high grade serous (SerHigh) and eight low grade serous histotypes, while the validation sets (n = 132) were 22 Cc, 55 End, 21 Muc, 26 SerHigh, and 8 SerLow histotypes. The training set underwent genome-wide characterisation, while the validation set was used for RT-qPCR experiments.Samples were randomly stratified into a training set and validation set to have sizable histotype groups inside each set; 76 samples of the Milano cohort were dedicated to the training set and 59 samples of Milano together with Brescia and Torino cohorts for the validation set. Histopathological and clinical annotations are described in Training and validation sets have an average follow-up of nine and six years respectively, comparable mean age at the time of diagnosis, similar percentage of relapse, and comparable histo-pathological and clinical characteristics, such as grade and FIGO substage.Based on gene expression profiles, Tothill et al.  identifip-value = 0.002—Here, we wonder whether these signatures can explain, at least in part, the transcriptome variability of stage I EOC samples and if specific histotypes are associated with one or more of these signatures. p-value = 0.02—In agreement with this last result, we observed a significant association with grade  in different stage I EOC grades and histotypes. To better explore these hypotheses, we decided to (i) study the immune system involvement of our stage I samples using dedicated computational approaches and (ii) provide a characterization of cell signaling pathways across the different histotypes.The levels of infiltrating immune cells in tumors are often associated with growth, cancer progression, and poor patient outcome ,20. To uThe deconvolution model by Chen et al.  uses genp = 0.06), macrophages M2 are significantly more abundant in Cc (p = 0.006), while Treg are highly abundant in SerLow (p = 0.04) . Finally, abundances of immune cell types do not show significant association with survival (OS or PFS) using multivariate models adjusted for histotypes and grades . Few weak but significant differences in the amount of cell types can be appreciated across histotypes. In particular, NK cells resting are significantly more abundant in Muc (.04) see . Moreoved grades .The immunoscore proposed by Charoentong et al.  evaluateIn agreement with the the cell deconvolution approach, all samples, independently by histotype and grade, showed low fractions of infiltrated immune cells. Many genes of the CP class often showed expression values under the detection threshold (gray slices) in both activating and suppressing molecules B.p = 0.07) and interestingly patients in cluster 2 have a significantly longer survival than the other patients when adjusted for Grade  (p = 0.01), while effector cells are more abundant in SerHigh histotype and low abundant in Muc (p = 0.03) (p < 0.001).Considering the global immune-landscape, we found two main clusters of patients characterized by different levels of expression of MHC, EC, SC, and CP expressed biomarkers C. The cl08–0.92) E. Going  = 0.03) . Moreove = 0.03) , we founMHC genes are highly upregulated compared with the other group of immuno-related genes B,D. HighMoreover, although not significant, we observed a moderate increase of IPS levels for Muc and Ser samples (both high and low grades) compared to Cc and End C.p = 0.02; Tregs and Myeloid-derived suppressor cells (MDSCs); G3 vs. G2 p = 0.08 Finally, as expected, different grades have slightly different values of IPS . This seIn conclusion, both approaches indicate that Ser samples seem to be more immuno-reactive with respect to other subtypes having activated effector cells, small abundances of NK cell resting, and altered checkpoints.FDR < 0.05 as threshold), not all the subtype comparisons show significant results; in particular, the analysis using Molecular Function categories gives poor results  enrichment analyses . However results . On the  results . As expeThe GO functional analyses reported above give a general description of the biological processes characterizing subtypes without taking into account (i) the links among these processes and (ii) the impact that the non-coding part of the transcriptome has on expression alterations. To fill these gaps, we compared integrated transcriptomes (mRNA and miRNA) using a combined network-based approach as described in Materials and Methods. Briefly, each histotype is compared with all the others, and the results are combined into a unified network in which our previously identified prognostic circuit  was inteThe outcome of our analysis is a network composed of 166 genes and 70 miRNAs broadly covering around 13 pathways . The netThe first result that emerges from the structure of the network is that transcriptional differences across histotypes are found at the coding gene level, while at the pathway level all histotypes showed a high degree of overlap. This indicates that every histotype shows alterations on specific coding transcripts and gene-family members, but that the same biological processes are involved. Moreover, we found that histotype-specificity seems to be principally guided by small non-coding RNAs as most of them modulate the cross-talk between different areas of the network with a histotype-specific expression.Secondly, our results indicate a central role of cAMP-PKA-CREB1 signaling axis as the main pathway associated with histotype specificity. This pathway promotes the expression of cAMP-dependent target genes and requires the activation of CREB1 by cAMP-dependent protein kinase A (PKA). This signal has been already linked to the platinum resistance in advanced ovarian cancer by Dimitrova et al.  showing cAMP-PKA-CREB1 signaling is linked to a large range of other biological signals such as metabolic pathways, BAD oncogene, and, exclusively in Cc, the regulation of intracellular availability of Calcium. It is clear from the network topology that miRNAs elements play central role in histotype-specificities modulating the interplay between different areas of the network such as RAC1, MAPK signaling pathway, cell cycle, and growing factor receptors.We confirm the role of Cc miRNA biomarkers miR-30a-3p and miR-30a-5p  in cell Apart from their role in physiological events, integrins are also involved in many pathological conditions such as inflammation and tumor progression. Here, different histotypes are characterized by the deregulations of specific integrins: ITGA1, ITGA11, ITGB1 are downregulated in Ser samples while ITGA10, ITGB4 in Cc, ITGB8 in Muc and ITGB3 in End. The involvement of ITGA1 at least in serous histotype suggests an MYC dependent regulation. Very recently, a new competing enhancer regulatory mechanism between PVT1 and MYC has been demonstrated . PVT1 prThe only link to the immune system is reported through the presence of SOCS2 that is a target of miR-192 and is downregulated in Ser samples. SOCS2 is a negative regulator of the IFN-gamma signaling pathway, which, in turn, is a key regulator of balance between immune activation and immune suppression . SpecifiTo validate our network results, a list of 53 (30 miRNAs and 23 genes) transcripts were selected and their expression values were assessed using qRT-PCR in the training set. The selection was made according to literature evidence, expression differences across histotypes, and topological position within the network.In the training set, a total of 20 molecular elements (38%) were confirmed as histotype-specific (7 over 23 genes and 13 over 30 miRNAs) . Apart fThese 20 elements were further tested in an independent validation set . Ten outPrimary tumor tissue of patients with a diagnosis of stage I EOC was included in the study. Analysis protocols and sample collection procedures were carried out in accordance with the recommendations of local scientific ethical committees. A written informed consent was obtained from all the patients enrolled in the study, which has been conducted following the Declaration of Helsinki set of principles.At the time of surgery, tumor tissue samples were sharp dissected and snap frozen in liquid nitrogen within 15 min from restriction, and then stored at −80 °C.Training set was composed of 76 frozen biopsies conserved in the tissue bank collected since September 1992 and located at the Department of Oncology, IRCCS Mario Negri Institute, Milano, Italy . Biopsies were collected from patients who underwent surgery for EOC at the Obstetrics and Gynaecology Department, San Gerardo Hospital, Monza, Italy, as described by Marchini et al. .A validation set was composed of 132 frozen samples: (i) 26 tumors’ tissues from the tissue bank collected since March 2003 at the A. Nocivelli Institute for Molecular Medicine, Division of Gynecologic Oncology, University of Brescia, Italy. Samples derived from patients who underwent primary surgery for EOC at the Obstetrics and Gynecology Department, Spedali Civili, Brescia, Italy ; (ii) 47 tissue sample belonging to a tissues collection that were collected since January 1992 and available at the Department of Gynaecology-Oncology, University of Torino, Torino, Italy (ii) and finally 59 additional samples from the tissue bank at the Department of Oncology, IRCCS Mario Negri Institute, Milano, Italy.Histopathological and clinical characteristics of the 208 enrolled patients are summarized in Frozen tissues specimens (30 mg) were homogenized in an TissueLyser LT  and total ribonucleicacid (RNA) purified using RNeasy Mini Kit isolation system (Qiagen), following manufacturers’ protocols. Total RNA concentration and proteins contamination were determined by a Nanodrop spectrophotometer . Only samples with a RNA integrity number (RIN) larger than six and a Nanodrop A260:280 ratio between 1.8 and 2.1 were further processed and aliquots stored at −80 °C until use. Array experiments were performed using standard procedures as previously published by Calura et al. . BrieflyMetaGxOvarian data set using a random forest approach. The trained classifier was used on the samples, selecting for each the molecular signature with the highest probability. The computation was performed with the consensusOV R package available on Bioconductor.The samples were classified according to a number of previously-published signatures in ovarian cancer as described by Helland et al. , BentinkThe absolute quantification of immune system cell fractions from bulk tissue gene expression profiles has been provided using CIBERSORT . NormaliThe immunophenograms and immunophenoscores were constructed as proposed by Charoentong et al. using the R code distributed by authors . The immt-test was used to assess differences among the z-score of the four sets of determinants. A Kruskal–Wallis rank sum test was used to assess differences of IPS across histotypes. We used a pheatmap CRAN R package to provide cluster analyses [analyses . We usedanalyses . Kaplan–Pct ≥ 0.8) and predicted miRNA-mRNA couples with a Pearson correlation coefficient |r| ≥ 0.4 and q-value ≤ 0.05. Four pathway tests have been performed through pairwise comparison between each histotype and all the others, then resulting networks were merged together. The network proxy of survival identified in Calura et al. [Gene and microRNA integrated topological pathway analysis has been performed using Micrographite pipeline  and Clipa et al.  was alsot-test were used to verify between groups’ mean differences, then the p-value underwent Benjamini–Hochberg FDR-controlling procedure, and only comparison with adjustedp-value ≤ 0.1 was considered statistically significant and reported in Gene and miRNAs expression levels were validated by qRT-PCR on both training (array set) and validation sets of patients. qRT-PCR have been performed using Sybr Green protocol  on an Applied Biosystems 7900HT instrument . Experiments were run in triplicate, using 384-well reaction plates in an automatic liquid handling station . Raw data was generated with Sodium dodecylsulphate (SDS) Relative Quantification software , data were normalized using the geometric mean of the four independent housekeeping controls . Two-sided Student’s In this work, using in silico analyses, we describe the molecular signatures and biological processes engaged in stage I EOC, with the aim to give a global view of the tumor heterogeneity with a focus on histotype specificity.Using gene expression data on a sizeble stage I EOC multicentric cohort, we found that a SerHigh subtype shows more aggressive phenotypes with respect to the other subtypes as many genes involved in cell proliferation, migration, and DNA repair are transcriptionally altered. Moreover, our results indicate that single genes rather than pathways are responsible for histotypes’ specificities and that miRNAs seem to be key modulators of these common pathways. The cAMP-PKA-CREB1 signaling axis plays a central role in histotype differentiation together with metabolic pathway, cell cycle regulation MAPK signaling, and apoptosis.Interestingly, immune response seems to be, at least in part, involved in histotype differences, as a high immune-reactive behavior in low grade serous and mucinous samples was observed compared to other histotypes. In addition, we found differences between low and high grade serous samples as the latter seems to be characterized by a lower amount of infiltrated immune cells with a less immune-reactive phenotype.Similar findings but using a standardised haematoxylin and eosin staining approach on advanced EOC samples were obtained by . The autIn general, we found high levels of major histocompatibility complex (MHC) genes in all samples confirming that these tumors are characterized by an intact antigen processing apparatus and by abundant cytotoxic T lymphocyte (CTL) infiltration. These features, however, do not appear to translate in beneficial effects on patients survival as already reported by Gooden and collegues (2011).Taken together, our results suggest that the immune system, in combination with other signaling cascades, is one of the molecular players in early stage ovarian cancer supporting the idea that different subtypes are variably immunogenic.If our results might contribute to define molecular specificities and similarities of stage I EOC subtypes, they certainly need further validations (i) increasing the sample size especially for low grade serous subtype that in our cohort is poorly represented and (ii) with additional immune-specific assays to confirm cell type composition. Moreover it is clear that, although computational methods for the study of tumor microenvironment from bulk data are becoming increasingly efficient, the interpretation of their results is still challenging. In this context, the emerging technologies for single-cell and spatially resolved transcriptomics are boosting cancer genomic research towards new perspectives allowing a better understanding of the cellular composition and structure of tumors and how these contribute to the molecular subtype specificities.


Alternative polyadenylation (APA) results in messenger RNA molecules with different 3′ untranslated regions (3’ UTRs), affecting the molecules’ stability, localization, and translation. APA is pervasive and implicated in cancer. Earlier reports on APA focused on 3’ UTR length modifications and commonly characterized APA events as 3’ UTR shortening or lengthening. However, such characterization oversimplifies the processing of 3′ ends of transcripts and fails to adequately describe the various scenarios we observe.FGF2 in multiple cancers. However, over half of the events we identified display complex changes to 3’ UTR length that resist simple classification like shortening or lengthening.We built a cloud-based targeted de novo transcript assembly and analysis pipeline that incorporates our previously developed cleavage site prediction tool, KLEAT. We applied this pipeline to elucidate the APA profiles of 114 genes in 9939 tumor and 729 tissue normal samples from The Cancer Genome Atlas (TCGA). The full set of 10,668 RNA-Seq samples from 33 cancer types has not been utilized by previous APA studies. By comparing the frequencies of predicted cleavage sites between normal and tumor sample groups, we identified 77 events (i.e. gene-cancer type pairs) of tumor-specific APA regulation in 13 cancer types; for 15 genes, such regulation is recurrent across multiple cancers. Our results also support a previous report showing the 3’ UTR shortening of Recurrent tumor-specific regulation of APA is widespread in cancer. However, the regulation pattern that we observed in TCGA RNA-seq data cannot be described as straightforward 3’ UTR shortening or lengthening. Continued investigation into this complex, nuanced regulatory landscape will provide further insight into its role in tumor formation and development.The online version of this article (10.1186/s12864-018-4903-7) contains supplementary material, which is available to authorized users. Alternative polyadenylation (APA) is a widespread regulatory mechanism that yields mRNAs with different 3′ untranslated regions (3’ UTRs) –5. APA aAPA is commonly characterized as length modulation of 3’ UTRs , 15–17. While specialized high-throughput 3′ end sequencing protocols have been developed for CS profiling, they are not as widely adopted as RNA-Seq  and provData analysis at the TCGA-scale is challenging. The download and storage alone can be a substantial undertaking. But with cloud computing, all data storage, transfer, and analysis could take place within a scalable cloud environment, avoiding most local storage cost and slow Internet communication. The cloud can provide thousands of CPUs in a short time for massively parallel processing, which could speed up large-scale analysis substantially.https://cloud.google.com/) [Here, we built a cloud-based, targeted de novo transcript assembly pipeline that incorporates KLEAT, which we developed previously . We execle.com/) , and prehttps://basespace.illumina.com/datacentral, dataset name: “HiSeq 2500: TruSeq Stranded mRNA LT (SEQC: UHR & Brain)”, sample ID: mRNA-UHRR-C1), and then compared their predictions to the CSs reported by PolyA-Seq, a data type also derived from the universal human reference and which served as the ground truth for tool evaluation [To select a cleavage site (CS) prediction tool, we benchmarked DaPars , KLEAT  and Contaluation   image for easy deployment and results reproducibility. We reached massive parallelism with a peak usage of over 3800 4-vCPU virtual machines running concurrently. In total, the samples consist of 1.6 trillion reads (53 bp average length) and amount to 67 TB of data after compression. We processed all samples in three days.The pipeline was designed for the GCP, a scalable cloud computing environment, and packaged as a Docker  < 0.002, Methods). Such analysis is applied to all gene-cancer type pairs available. In total, we identified 77 events that involve 33 genes across 13 cancer types. For 15 of these genes, the tumor-specific regulations are recurrent in multiple cancer types.We quantified the usage of each predicted CS by calculating its frequency within a group of samples, leveraging the availability of tens or hundreds of normal and tumor samples per cancer type within TCGA. The usage frequency is defined as the fraction of normal/tumor samples within a cancer type predicted to use a given CS (Methods). We refer to all CS frequencies within one gene in a sample group as a cleavage pattern. When comparing a tumor cleavage pattern to a normal one, we identify an event (gene-cancer type pair) of tumor-specific APA regulation if one CS has a significantly higher frequency in tumor while another CS has a significantly lower frequency  indicates an increase in 3’ UTR length. In four TCGA cohorts , the frequency of F2 increases in tumor samples, while that of F3 decreases, both significantly . We conclude that FGF2 undergoes 3’ UTR shortening in these cancers. The F1 site is over 2 kb from the closest annotated CS, and demonstrates the ability of our analysis to detect potential novel CSs. However, its usage frequency is low  from normal to tumor; hence it is ignored for interpreting the 3’ UTR length modulation.The ene Fig.  presentsCCNE1 . Among them, R2 and R3 undergo significant shifts in their frequencies from normal to tumor in both KIRC and UCEC. However, these shifts occur in opposite directions, indicating 3’ UTR shortening in KIRC, but 3’ UTR lengthening in UCEC. As for R4, which is associated with a different stop codon, its frequency decreases significantly in KIRC, indicating decreased expression of its corresponding isoforms in KIRC tumor.In contrast to ene Fig.  has two CDKN2A gene, like RNF43, also displays multiple disease-dependent frequency shifts in KIRC and HNSC , while others have a one-to-one relationship to separate stop codons   . Despite complex mappings between CSs and stop codons, this event can be characterized as 3’ UTR shortening. Ignoring CSs that were mapped either to a separate stop codon , or to multiple stop codons (P2), we are left with three CSs. Among these, P4 (shorter 3’ UTR) increases in frequency, while P1 and P3 (longer 3’ UTRs) decrease in frequency in tumor samples, implying 3’ UTR shortening.Finally, we show RCA Fig. . DespiteFor all identified 77 events of tumor-specific APA regulation, we identified equal numbers (16) of 3’ UTR shortening and lengthening events, and we labeled the remaining 45 events as having complex trends . While we also report 3’ UTR shortening and lengthening events, including the independent identification of a previously published observation (FGF2) [The current paradigm of APA characterization revolves around analyzing two CSs at a time, namely proximal and distal CSs, and emphasizes 3’ UTR length change, namely shortening or lengthening . Howevern (FGF2) , over han (FGF2) .CDKN2A). In addition, 16/33 of the reported genes have stop codons related to NMD transcripts; in some genes (e.g. EZH2), a stop codon can be used in both protein coding and NMD transcripts, which complicates the implications of regulation by APA even more. Describing APA regulation by 3’ UTR length change is mostly inadequate, and comprehensive characterization will require techniques that can disambiguate the relationship between a gene’s 3’ UTRs and CS repertoire. At a deeper level, APA is a partial view of the differential expression of isoforms at the 3′ end.While a CS is essentially a single point (after clustering) on the genome axis, and quantifying its frequency is relatively straightforward, the analysis quickly becomes complex once we considered their corresponding 3’ UTRs. The complexity of APA analysis is multifold. First, in the 77 events of tumor-specific APA regulation reported here, 33/297 (11%) CSs are mapped to more than one stop codon. In such a multi-mapping situation, CS frequency cannot represent the usage of any of the corresponding isoforms, neither can an expression-level type of quantification from a specialized 3′ end sequencing protocols , 32, 33.CD47 has only two CSs and one stop codon, and its two APA isoforms have identical upstream sequence compositions; thus, proximal and distal CSs, short and long 3’ UTRs all have concrete meanings. Remarkably, the CD47 protein translated from the long isoform is relocated to the cell membrane while the protein corresponding to the short isoform remains in the endoplasmic reticulum [We recognize that, despite its limitations, the old paradigm of 3’ UTR shortening and lengthening is sufficient within specific contexts. For example, eticulum .Our APA analysis on RNA-Seq data is analogous to that using PAS-Seq  and 3’REp < 0.01, Fisher’s exact test) and one significant decrease in the frequencies of two CSs of a given gene. This requirement was designed to minimize the influence of gene expression variation on the comparison of CS frequencies. Without this requirement, an increase or decrease in the CS frequency could simply be a result of gene up- or down-regulation in the normal or tumor samples. For example, if a gene is down-regulated in tumor, its corresponding CSs will be observed less frequently. Conversely, if there is an increase in the usage of at least one other CS, it implies the involvement of APA regulation mechanisms [CCND2 in COAD, HNSC, KIRC, KIRP, LUSC, and THCA; DICER1 in BRCA, LUSC, and STAD; RAB10 in BLCA, BRCA, COAD, LUSC, STAD, and UCEC, data shown at the aforementioned URL [To report an event (gene-cancer type pair) of tumor-specific APA regulation, we enforced a rather stringent requirement; the co-occurrence of at least one significant increase (chanisms . While schanisms  (e.g. CConed URL ).RNF43. Second, we expect the requirement of concurrent increase/decrease in CS frequencies to reduce our sensitivity in detecting APA regulation events. As a result, some tumor-specific APA regulation events may indeed be recurrent in other cancer types, but below the detection limit of our approach.Of the 33 genes reported herein, 18 show tumor-specific APA regulation in a single cancer. We propose two potential reasons for the lack of recurrence for these events. First, a gene undergoing tumor-specific APA regulation in one cancer may not do so in another. This includes cases where APA regulation in multiple cancer types may be following different patterns, as we have highlighted for FGF2) to complex (e.g. CDKN2A); thus, they are suitable for studying tumor-specific APA and demonstrating the complexities of APA regulation in human cancers. We acknowledge their limitation in showing genome-wide tumor-specific APA regulations. Still, the genes inspected in our study reveal that APA regulation in cancer is more complex than previously thought. Despite our stringent requirements for reporting a tumor-specific APA regulation event, a sizable proportion of APA genes (33/114) were identified, and about half of them (15/33) show recurrent tumor-specific APA regulation across multiple cancers. Therefore, we think that the specific events reported herein represent a wider phenomena, and that many more additional events of tumor-specific APA regulation remain to be discovered.This work presents the results of a targeted analysis. The 114 genes we selected are not only cancer-related, but also display a range of 3′-end patterns from simple . In total, 10,668 samples were analyzed, with each sample identified by a unique analysis ID. Sample types are generalized as ‘normal’  and ‘tumor’ . A more detailed description of the protocols for data collection is provided in Additional file We used a copy of the TCGA RNA-Seq data hosted by the Institute for Systems Biology-Cancer Genomics Cloud (ISB-CGC) pilot on the Google Cloud Storage, part of the Google Cloud Platform (GCP), mirroring the repository hosted at the NCI Genomic Data Commons  . The resThe pipeline was implemented in Python with the Ruffus framework . The sofhttps://cloud.google.com/genomics/reference/rest/v1alpha2/pipelines) was used to orchestrate all VM instance tasks including VM creation, deletion, and data transfer, and it substantially reduced the administrative workload.We executed the pipeline on the ISB-CGC powered by the GCP. For each RNA-Seq sample, a virtual machine (VM) instance with four vCPUs, 20 GB of memory, and a sample size-dependent amount of persistent disk was used. For each instance, we requested a sufficient disk size for storing both input data and intermediate and final results, calculated as 30 x Size(sample) + 50 GB. The scaling factor of 30 is based on experience in pilot runs, and the extra 50 GB was reserved for storing reference data. Google Genomics Pipelines API  for all candidate genes. To calculate 3’ UTR lengths, we also extracted the mapping information between annotated CSs and stop codons from transcripts. A more detailed description of the extraction process can be found in Additional file The Ensembl annotation was downloaded from gene, transcript_strand, chromosome, cleavage_site, length_of_tail_in_contig, number_of_bridge_reads, and max_bridge_read_tail_length. In total, 67,544,140 CSs were predicted across 10,668 samples. First, we filtered out off-target CSs by only selecting those that were associated with the candidate genes, keeping 17% of the predictions. After initial filtering by genes, we reassigned each remaining CS to the closest clustered annotated CS (See Annotation pre-processing), and then calculated the signed distance between them. We also calculated the location of PAS hexamer motifs, if present, searching up to a 50 bp window upstream of a predicted CS. When multiple PAS hexamers existed in the window, the strongest one was picked [Its distance to the closest annotated CS was required to be 25 bp or less. The 25-bp threshold was chosen by plotting the distribution of distances, and taking a threshold at the plateau. This criterion was designed for selecting CSs that had already been annotated.One of the two strongest PAS hexamers AATAAA and ATTAAA  were reqThe CSs were predicted by KLEAT with all parameters set to default. We post-processed the KLEAT results before any CS usage frequency analysis . However, we verified that the clustering results were insensitive to different cutoff values, even though the number of clusters could vary.s is the number of samples predicted to use the CS, and g is the total number of samples with sufficient gene expression level of this gene available for this cancer type and sample type. For each sample, the gene expression level is considered sufficient if at least one CS was predicted within the gene; otherwise, the expression was considered insufficient, and the sample was excluded from the frequency calculation.For a given CS in each gene, each cancer type and each sample type , its frequency is calculated as the fraction of samples that were predicted to use it:p < 0.01) and one significant decrease in the frequencies of two CSs, respectively.For every predicted CS of every gene in each cancer type, we calculated its frequencies in both normal and tumor samples, and then evaluated the significance of the difference with a Fisher’s exact test. The input to the test included the number of normal and tumor samples with and without a CS predicted. The frequencies of multiple CSs within one gene collectively formed a cleavage pattern for that gene, and to report the difference in patterns between normal and tumor, we required the co-occurrence of at least one significant increase  increase and decrease in CS frequency. Thus, pair-level p-values are less than 0.0001 (0.01 × 0.01). For gene-cancer type pairs that are not reported, we assigned an arbitrary p-value of 1. In total 1596 hypothesis tests (114 genes × 14 cancer types) were conducted, and applying the Benjamini-Hochberg procedure [To estimate the false discovery rate (FDR), we obtained an upper bound for the rocedure , we obtaWe first mapped a predicted CS to the closest annotated one. If it was > 25 bp away, the predicted CS was considered potentially novel, and was ignored for length trend resolution because of the uncertainty of its corresponding stop codon. After trying a range of values, the 25-bp cutoff was selected when the number of unmapped CSs reached a plateau.After mapping we determined the associated stop codons for each CS, also based on annotation. We do not assume that a CS could be associated with all upstream stop codons, in accordance with the transcript annotations, which do not support an all-to-all type of relationship Supplementary figures. It includes all supplementary figures except Additional file 2:Table S1.List of 114 cancer-related genes. Table S2. Summary of the 33 cancer types. Table S3. Summary of the reported 33 genes involved in 77 events of tumor-specific cleavage patterns. Table S4. Details of all CSs involved in the reported 77 APA events. Table S5. Mapping relations between diseases in TCGA and those in COSMIC (Supplementary Methods). (XLSX 235 kb)Supplementary tables. It includes all supplementary tables. Additional file 3:Figure S4. Illustration of all 77 identified events of tumor-specific cleavage patterns. (PDF 5699 kb)Additional file 4:Supplementary methods. Description of additional methods. (PDF 157 kb)Additional file 5:Figures available for download. (PDF 86 kb)


Interferon-stimulated genes (ISGs) are a set of genes whose transcription is induced by interferon (IFN). The measure of the expression of ISGs enables calculating an IFN score, which gives an indirect estimate of the exposition of cells to IFN-mediated inflammation. The measure of the IFN score is proposed for the screening of monogenic interferonopathies, like the Aicardi-Goutières syndrome, or to stratify subjects with systemic lupus erythematosus to receive IFN-targeted treatments. Apart from these scenarios, there is no agreement on the diagnostic value of the score in distinguishing IFN-related disorders from diseases dominated by other types of cytokines. Since the IFN score is currently measured in several research hospitals, merging experiences could help define the potential of scoring IFN inflammation in clinical practice. However, the IFN score calculated at different laboratories may be hardly comparable due to the distinct sets of IFN-stimulated genes assessed and to different controls used for data normalization. We developed a reliable approach to minimize the inter-laboratory variability, thereby providing shared strategies for the IFN signature analysis and allowing different centers to compare data and merge their experiences. Type I interferon (IFN) production is part of the innate immune response to viruses or intracellular bacteria, which is triggered by the sensing of pathogen-associated nucleic acids . Even thType I interferonopathies are marked by the hyper-expression of a set of genes  in inflamed tissue and often in peripheral blood, leading to the definition of the so-called “IFN signature” ,6. The ISince then, the measure of expression of the ISGs  is increasingly used in biomedical research centers, as well as for the functional classification of other conditions characterized by a type I IFN dysregulation , to distDifferent ISG sets were identified to evaluate interferon-mediated autoinflammation and are frequently restricted to 5-6 targeted genes ,12,13, sEven though the IFN signature measure has become widely available at research hospitals, there is no consensus for the selection of calibration controls. Thus, it is hard to compare data among distinct centers and to estimate the potential of IFN signature testing for discriminating among inflammatory conditions in the clinical practice. For example, thousands of subjects with antiphospholipid syndrome have been described in multicenter studies , while tThe main problem hindering the use of IFN signature analysis for in vitro diagnostics is the expression of data relativized to independent healthy control(s) in each laboratory, leading to unpredictable inter-laboratory variability. It is logical to assume that the use of pooled cDNA from healthy donors can represent a convenient strategy for calibrating Real Time quantitative PCR (qPCR) for the assessment of the IFN score. However, it can be difficult to predict the optimal number of samples to be pooled, which requires minimizing variability between one pool and another prepared at distinct laboratories, as already pointed out by others ,24.This study aims to investigate, through laboratory, bioinformatic, and statistical analyses, a reliable approach to minimize the variability that can be observed in inter-laboratory assays. The final goal is providing shared recommendations for IFN signature analysis and interpretation of data in the clinical practice, thereby allowing data sharing among reference centers and improving knowledge on IFN-related disorders.The study is part of the IRCCS Burlo Garofolo project RC #24/2017, approved by the Institutional Review Board and by the Friuli Venezia Giulia Independent Ethical Committee . All investigations were performed after obtaining written informed consent from volunteers and patients or their parents/guardians. On wet IFN signature analysis was assessed by quantitative PCR (qPCR) in ten young-aged healthy subjects .To establish whether data from qPCR and RNAseq analysis were comparable, IFN signature on wet (by qPCR) and in silico (by RNAseq) analysis was performed in twenty subjects with inflammatory diseases, such as systemic lupus erythematosus (SLE), interferonopathies or inflammatory bowel diseases (IBD), and patients’ relatives, recruited at our center (Dataset E). A brief description of patients’ clinical diagnosis is displayed in RPS4Y1 and USP9Y. To increase healthy donors numerosity, in silico IFN signature investigation (by RNAseq) has been performed in twenty healthy individuals, collected at our center Dataset A , and selected from different whole blood RNA-sequencing (RNAseq) open-access web-based datasets: we sorted another three datasets, while considering exclusively healthy control samples, accessible at ArrayExpress  and at the Gene Expression Omnibus (GEO) . Specification about gender was not available for all the samples. However, sex has been easily inferred by expression analysis of the sex-specific genes The dataset composition is shown in Peripheral blood was collected in PAXgene Blood RNA Tubes  and, after two-hours incubation at room temperature, tubes were frozen at −20 °C until processing. Total RNA was extracted with PAXgene Blood RNA Kit , following the manufacturer’s instructions, and quantified with NanoDrop Spectrophotometer . RNA integrity was checked using an Agilent Technologies 2100 Bioanalyzer. Up to 1 μg of total RNA was retro-transcribed using SensiFAST cDNA Synthesis Kit . IFI27, IFI44L, IFIT1, ISG15, RSAD2, and SIGLEC1. Using AB 7500 Real Time PCR software, each target quantity was normalized with the expression level of HPRT1 and G6PD, and the relative quantification (RQ) was conducted relating to a “calibrator” sample  using the 2−ΔΔCt method [The expression of six IFN-stimulated genes was assessed by qPCR using AB 7500 Real Time PCR System , TaqMan Gene Expression Master Mix  and UPL Probes  for t method . The medTranscriptome sequencing was performed using the TruSeq Stranded mRNA Sample Preparation kit  and sequenced on a NovaSeq 6000 platform , generating 2X100 bp paired-end reads (30 million reads per sample) in twenty subjects from Dataset E (patients and patients’ relatives) and four out of eleven controls from Dataset A.https://www.bioinformatics.babraham.ac.uk/projects/fastqc/), quality filtering by Trim Galore , read alignment to hg38 using annotation from GENECODE v.31 (https://www.gencodegenes.org/) with STAR [RNAseq raw data (either our data and open-access web-based data) workflow was conducted as follows: quality control by FastQC  [To assess the ISGs expression variability within the group of twenty healthy subjects, shortlisted from the datasets described above, we determined the expression values for each gene, normalized by Fragments Per Kilobase per Million mapped reads (FPKM) method with edgeR (unction) ,30, usinPrincipal component analysis (PCA), useful for data visualization, was conducted with DESeq2, to define the overall variability between samples.Considering that each of the six genes measured was expressed on a different scale, we decided to calculate the sample size based on the coefficient of variation, instead than the mean and the standard deviation. We further hypothesized that different runs did not increase the variation in comparing the samples, assuming the only origin of variability to be represented by the subjects’ heterogeneity. t-test, given α = 0.05, β = 0.2, and degrees of freedom equal N-1. If the noncentrality parameter under these conditions  resulted in being lower than the one calculated on our samples, we considered the sample size as appropriate. To determine the statistical power for data obtained by qPCR and RNAseq, we computed the noncentrality parameter (λ) using GPower 3.1.9.2. software ,32, with2 contingency analysis; p-values <0.05 were considered significant. GraphPad Prism 6 software was employed for χTo identify the appropriate sample size for variability assessment, we computed λ for increasing numerosity (up to forty) using GPower 3.1.9.2. , and determined a “plateau value” by an exponential decay function . IFI27, IFI44L, IFIT1, ISG15, RSAD2, SIGLEC1) was assessed in ten healthy controls processed by on wet qPCR analysis. Five out of six genes showed low variability coefficients and noncentrality parameters (λ) that fulfilled the analysis criteria  (The variability of expression of the six ISGs (e (λref) Thus, ten healthy controls could not be considered an appropriate sample size to represent an ideal healthy population, in which the physiologically floating expression values of the ISGs present acceptable variability. For this reason, we should increase the numerosity of healthy controls to obtain a suitable pool in which the gene expression variability is minimized. To improve the power of the variability measurement, we decided to take advantage of RNAseq open-access web-based data, as an easy source to increase the number of healthy subjects to calculate ISGs interindividual differences. This choice came from comparisons between the relative ISGs fold change assessed in the same twenty subjects (Dataset E) by both qPCR and RNAseq analysis, selecting the same set of three out of eleven healthy controls from Dataset A, to normalize data for both techniques. 2 contingency analysis p-value = 0.405, not significant). The comparability of IFN scores is easily explained, considering that these values represent the median of the six relative ISGs quantifications, and they broadly reflect the overexpression status in the analyzed sample  were extremely consistent between the two techniques for each individual  on a numerosity up to forty individuals using GPower software. Then, we determine the plateau value of the exponential decay function of the previously computed λ values. The provided plateau (3.01) corresponds to λ for sample size n = 15 , leadingWe performed a principal component analysis (PCA), a data visualization analysis, to evaluate the total expression variation of the six ISGs and to define the most homogeneous set of twenty healthy individuals. This investigation allows the detection of possible rare outliers with the highest variance that might not be considered as suitable controls to study IFN signature.RNAseq records have been chosen considering the presence of similar features such as blood collection type, RNA extraction protocol and library selection, to reduce as much as possible the technical procedure variability . n = 4/11), Dataset B  and Dataset C , twenty-one specimens in total. n = 20), we examine Dataset D  and we ran the same analysis again, obtaining a satisfactory level of variation, without outliers, between datasets and among individuals. From these preliminary observations, we randomly chose two out of twelve samples from Dataset D, combining them with data previously selected. Again, we observed an acceptable gene expression variability among our final twenty-controls-sized group  using GPower software, and the variability of the ISGs expression in fifteen and twenty healthy controls processed by in silico RNAseq analysis. n = 15 and n = 20 subjects as already calculated, assuming that the variability coefficient does not change as the sample size increases  very close to λref (3.01). For this reason, we can consider more adequate to increase the numerosity to twenty subjects, a still easy-to-gather number of healthy donors. Thus, we could assert that twenty healthy controls could be considered a suitable sample size for IFN signature analysis performed by qPCR, as predicted by in silico RNAseq  and usable worldwide with the same reference values, not only in the most advanced research areas. Conversely, only the analysis and comparison of data available at various centers can tell industries whether the development of such diagnostic kits is worthwhile or not. Thus, we focused on a strategy that could be immediately applied in biomedical laboratories to facilitate the sharing of experience and minimize the inter-laboratory variability. We investigated if using a pool of biological samples from healthy controls could solve the data normalization issue, which is the main source of inter-laboratory variability. We proposed that this strategy could “equalize” the differences in gene expression that physiologically occur among individuals.n of subjects could be suitable to level differences, through an approach based on laboratory (qPCR), bioinformatic (RNAseq), and statistical data integration analyses. For this purpose, we evaluated if any pool of healthy controls more than a given number Starting from the ISGs expression assessment in peripheral blood from ten healthy subjects by on wet qPCR analysis, we found that this sample size is not suitable for equalizing the variable expression levels of all the six ISGs in healthy volunteers. To find the appropriate number of samples to be pooled together with low enough variability, we further investigated if public data from RNAseq can be exploited to expand our analysis. We thus compared relative ISGs fold change values calculated by qPCR and RNAseq analysis for the same subjects with the same calibrator, showing that the two methods yield comparable results with very low variability between each other. Previous studies compared gene expression measurements generated by in silico RNAseq and on wet qPCR assays, showing consistent results between the two methods for most genes, with the only exception of some genes generally characterized by small size and low expression levels. None of the ISGs is included in the list of genes with inconsistent estimation of expression according to the Authors . Our resThe results of our study support the choice of pooling twenty healthy controls for the normalization of the assay, allowing to express results as relative to a “standard set of controls”. Of note, we validated this sample size only for the selected set of six ISGs in peripheral blood cells. The expression of other genes included in larger panels, may present higher variability among donors and between different affected tissues. However, the same procedure that we have described can also be used to define the optimal sample size for other transcription profiles, for interferonopathies or for other rheumatologic disorders. The performance of the proposed twenty-controls-sized standard could be improved by analyzing all the donors separately before pooling, and by performing cluster variance analysis, which can enable excluding rare donors with outlier variance.


Reliable identification of Inflammatory biomarkers from metagenomics data is a promising direction for developing non-invasive, cost-effective, and rapid clinical tests for early diagnosis of IBD. We present an integrative approach to Network-Based Biomarker Discovery (NBBD) which integrates network analyses methods for prioritizing potential biomarkers and machine learning techniques for assessing the discriminative power of the prioritized biomarkers. Using a large dataset of new-onset pediatric IBD metagenomics biopsy samples, we compare the performance of Random Forest (RF) classifiers trained on features selected using a representative set of traditional feature selection methods against NBBD framework, configured using five different tools for inferring networks from metagenomics data, and nine different methods for prioritizing biomarkers as well as a hybrid approach combining best traditional and NBBD based feature selection. We also examine how the performance of the predictive models for IBD diagnosis varies as a function of the size of the data used for biomarker identification. Our results show that (i) NBBD is competitive with some of the state-of-the-art feature selection methods including Random Forest Feature Importance (RFFI) scores; and (ii) NBBD is especially effective in reliably identifying IBD biomarkers when the number of data samples available for biomarker discovery is small. Inflammatory bowel disease (IBD) refers to disorders that involve chronic inflammation in the gastrointestinal tract. The two main types of IBD are ulcerative colitis (UC), which is characterized by continuous ascending inflammation from the rectum into the colon and periods of relapse and remittance , and CroIBD, particularly in children, fails to be correctly diagnosed, or diagnosed in a timely fashion, because of the frequency of nonspecific symptoms at the onset of the disease , 8. AlthIdentification of disease microbiomarkers from metagenomics data requires effective computational and statistical methods for determining, from a very large number of candidate biomarkers, a minimal subset of biomarkers that can accurately discriminate between two or more phenotypes . This task presents several challenges in practice : curse oRecent analysis of microbial ecology networks (MEN)  derived from healthy and type 2 diabetes (T2D) groups has shown topological differences between the two networks at the global, module , and node levels and found that the differences in cluster membership of the nodes in the two networks can serve as biomarkers for T2D . MotivatIn this study, we build on and extend the results of Abbas et al.  in two ahttp://biom-format.org) and meta-data  for a large cohort IBD data set [https://qiita.ucsd.edu/) (Study ID: 1939). The data set consists of 1359 metagenomics samples including rectal tissue biopsy and fecal samples and each sample has 786 OTUs at the genus level that were extracted using the summarize_taxa.py QIIME script. We filtered the data by discarding fecal samples and samples corresponding to patients with age greater than 18 years. The resulting data set consists of metagenomic biopsy samples for 657 IBD and 316 healthy control cases, respectively. Thus, each sample (which correspond to a row in the table), is encoded by a tuple of values that represent the relative abundances of the various microbial taxa (indexed by the columns) in the sample. To evaluate our models, we randomly split the data into training and test sets, named DS400 and DS573, such that the training data has 200 healthy and 200 IBD samples, and the test data has 457 healthy and 116 IBD samples. It should be noted that predictive models are often tested on a data distribution that reflects the natural distribution of the different classes. However, in this case, the available IBD and Healthy samples do not reflect the natural distribution of IBD and Healthy cases in the pediatric population. The prevalence of IBD worldwide has been reported to be close to 0.3% of the population [all of the methods in our comparison to substantially overestimate the true performance of the predictive models were they to be deployed in a real-world setting. However, this should not impact the validity of the overall conclusions from our study.BIOM files . In our experiments, we examined the effect of using a small fraction of the training data for performing feature selection. Specifically, we experimented with the following choices of data for feature selection, which we call the feature selection data sets (FSDS): k highest scoring nodes provide the k features used to train and evaluate binary classifiers for predicting whether or not a given metagenomic sample belongs to a healthy of IBD individual.We summarize the Network-based Biomarker Discovery (NBBD) framework below: . Given We evaluated the NBBD framework using five network construction methods and nine node importance scoring methods summarized below.We experimented with several widely used methods for constructing MEN from metagenomic data. We used the default parameters of each tool, unless noted otherwise. Each of these methods is briefly described as follows.SparCC: Sparse Correlations for Compositional data (SparCC)  infers aMB: The Meinshausen and Bühlmann (MB) method  is anothhttp://ieg4.rccc.ou.edu/mena. We used the default parameters except for the parameter controlling the number of OTUs that build the network. An OTU was used if it is expressed in at least 25% of the samples. The default value of that parameter is 50% of the samples, and with the parameter set to 50% the method failed to construct the network.RMT: Random Matrix Theory (RMT) method uses the Pearson correlation coefficient to add an edge between two OTUs if their correlation is higher than a threshold. Instead of using a user-defined threshold, RMT utilizes a procedure based on the Random Matrix Theory to automatically detect a reliable threshold. The method is implemented in the Molecular Ecological Network Analysis Pipeline  availablCoNet: This method infers the association network by combining two complementary approaches to evaluate the significance of the associations . The fird. In our experiments, we set the number of neighbors to seven and used the absolute value of Pearson’s Correlation between two vectors (subtracted from one) as the distance function between two vectors.Proxi: Proxi  is a PytWe considered two approaches for scoring nodes  based on: (i) differences in the topological properties of the nodes in the two networks ; (ii) coGi and Gj be two graphs constructed using two groups of metagenomics samples . The Node Topological Property Scoring (NTPS) method scores each node v ∈ Vi ∩ Vjwith respect to a node topological property P as follows: scoreP(v) = |fP − fP|, where fP is the value of the property P for a node v in a graph G. In this work, we experimented with the following node properties computed with NetworkX software [Let software :v is defined as σ is the total number of shortest paths between u and w and σ is the number of shortest paths between u and w passing through v.Betweenness Centrality (btw): Betweenness centrality of a node v is given by d is the shortest path distance between u and v and n is the number of nodes that can reach v.Closeness Centrality (cls): Closeness centrality of a node v is given by N(v) denotes the set of neighbors of node v and ku is the degree of node u ∈ N(v).Average Neighbor Degree (and): The average neighborhood degree of a node v is given by T(v) is the number of triangles that include node v and deg(v) is the degree of v.Clustering Coefficient (cc): For unweighted graphs, the clustering coefficient of a node v is the size of the largest maximal clique containing v. A clique is a subset of nodes such that there is an edge between every pair of distinct nodes.Node Clique Number (ncn): The node clique number of a node v is the largest value k of a k-core containing v, where a k-core is a maximal subgraph that contains nodes of degree k or more.Core Number (cn): The core number of a node Critical Attack Set Scoring (CASS) is based on a node resilience clustering algorithm, NBR-Clust , 30. We Node-based resilience measures quantify the resilience of a network in terms of the extent of damage (as measured by disruption of connectivity between otherwise connected components or clusters of nodes) caused to the network by the removal of a set of critical nodes  . BecauseG =  is defined as [S is an attack set and Cmax(V − S) is the largest connected component in V − S. The goal is to identify small attack sets that consist of nodes that are most cricial in preserving network connectivity.The VAT of an undirected, connected graph fined as , 33: VATCmax, which means that attack set sizes will tend to be larger than with VAT. Generally, the attack set for integrity SI will include the most crucial nodes (as generated by VAT), plus additional nodes that if removed, make the graph disconnected.Integrity is defined as : I(G)=miω(V − S) is the number of connected components in V − S. This measure identifies nodes that, if removed,result in partitioning the graph into a large number of components. Thus, the tenacity attack set ST will include almost all nodes that if removed, can make the graph disconnected.Tenacity is defined as : T(G)=miSV ⊆ SI ⊆ ST.In order to calculate these resilience measures, we utilized a heuristic known as Greedy betweenness centrality (Greedy-BC) . For a gGH) and IBD (GD) samples. We then select the nodes that are shared by the critical attack sets of both graphs.To select features for training IBD classifiers, we apply the NBR-Clust algorithm separately to the the IBD and Healthy networks to obtain the critical attack sets for healthy  [Given a training data set DS400, a feature selection data set , a test data set DS573, a feature selection method (FSM), and the number of selected features est (RF)  classifiest (RF)  with theIn addition to our proposed network-based feature selection methods, we considered the following traditional and commonly used feature selection methods: (i) Filter-based feature selection using Information Gain (IG) and F-Statistic (FStat); (ii) Recursive Feature Extraction (RFE) that uses LASSO  estimatoWe report the predictive performance of all IBD classifiers considered in this study as measured using Accuracy (ACC), Sensitivity (Sn), Specificity (Sp), Matthews Correlation Coefficient (MCC), and Area Under ROC Curve (AUC) .We observed that some feature selection methods  are sensitive to changes in the FSDS. For example, the best subset of features returned using the IG filter is with DS300. On the other hand, with DS400 , the IG filter fails to determine a good subset of selected features. We suspect that the biomarkers identified using such unstable feature selection methods are likely to be unreliable.Results in k identified biomarkers for k ∈ {10, 20, 30, 40, 50, 60}. A total of 900 experiments were conducted and are reported in Tables A-E in in First, for each of the five FSDS considered in our experimented and using graphs generated by five NIMs, we evaluated our NBBD framework using six topological properties for NTPS approach and Second, we repeated the experiments described in the preceding paragraph but using CASS based on three node resilience measures as the Node Importance Scoring module in our NBBD framework. The performance of the resulting RF classifiers are reported in Table F in In summary, our results suggest that the five NIMs, except MB , can be k ∈ {10, 20, 30, 40, 50, 60} biomarkers in Tables G-K in Preliminary results reported in an early version of this work  show thTable H in DS50 ⊂ DS100 ⊂ FSD200 ⊂ DS300 ⊂ DS400), we expect feature selection methods to return the same or better subset of features as we increase the size of the FSDS used. Our expectation is almost realized using the RFFI method, except that there is a drop in AUC score when DS300 is used. On the other hand, our expectation is violated using the hybrid feature selection methods. The highest AUC score is observed using DS50, and increasing the size of the FSDS leads to a drop in classifier performance. This suggests that NIMs such as MB and RMT might be highly unstable to changes in the input data. In other words, networks constructed from DS50 and DS400 (as an example) are substantially different. For instance, Fig A in Sensitivity analysis of Kruskal-Wallis and Mann-Whitney nonparametric tests against the number of samples analyzed has been conducted using all variants of FSDS. The complete results of this analysis is reported in Figs B-K in We show that interesting biological findings can be derived from comparative analysis of the IBD and healthy ecological networks inferred from the DS400 training set using the RMT program . Our choFor module level comparisons, we used the MCODE clustering algorithm , implemeWe also analyzed the differences between IBD and healthy networks at the node level. Following Abbas and El-Manazalawy , we limiThe past decade has witnessed a revolution in microbiology and microbiome research. Advances in sequencing technologies and computational techniques coupled with large scale collaborative efforts such as Human Microbiome Project (HMP)  and Amerk top-scoring nodes are used as the set of selected features to train and test classifier using machine learning. We conducted extensive experiments to evaluate the NBBD framework, configured using five different network inference tools and nine different node importance scoring methods, using a large data set from a cohort of 657 IBD and 316 healthy healthy pediatric metagenomics biopsy samples, respectively.To address this challenge, we have developed NBBD, a novel metagenomics system biology framework for microbial biomarker discovery. The NBBD framework integrates network analysis and machine learning approaches for reliable identification of biomarkers from metagenomics data. Given two OTU tables corresponding to two phenotypes, NBBD uses any existing tool for constructing phenotype specific networks from the data. Depending on the tool used, these networks model the interactions, the correlations, or the proximity relationships between microbes. Next, the nodes are scored using different scoring methods that quantify the extent to which the nodes contribute to differences in the topological properties of the nodes in the two networks. The Although several tools for constructing microbial ecology networks from metagenomics data have been developed, they leave considerable room for improvement , 66. ForIn this study, we performed experiments to examine the sensitivity of classifiers to the number of samples in the feature selection data set. To facilitate fair comparison between classifiers, we used the entire training data for training the classifiers using the features determined based on different subsets of the training data. Our results suggest that traditional feature selection methods fail to determine a minimal subset of discriminative features from small feature selection datasets. Interestingly, we found that several network-based feature selection methods returned a minimal subset of discriminative features using the smallest feature selection data set, DS50.In this work, we showed how to develop network-based feature selection methods using arbitrary topological properties of vertices in the network  It would be interesting to explore other network based feature selection methods, e.g., using vertex similarity algorithm  to be reliable, we need to ensure the feature selection methods have a high degree of stability with respect to changes in the underlying network. Note that the stability of feature selection algorithms is a function of both the properties of the algorithm itself as well as the data supplied to the algorithm. Hence, improvements are needed on both fronts.Fundamentally, constructing microbial ecology networks from metagenomic data requires determining the correlation or similarity between (abundances of) microbial taxa from a relatively small number of metagenomic samples. This problem is not fundamentally different from the problem of determining gene co-expression networks from gene expression data , or thatWe have proposed a novel Network-Based Biomarker Discovery (NBBD) framework for detecting disease biomarkers from metagenomics data. NBBD consists of two major customizable modules: A network inference module, for constructing microbial ecology networks from OTU tables extracted from the metagenomic data for the phenotypes of interest; and a node importance scoring module, which compares the resulting phenotype-specific networks and scores the nodes based on different measures of the node’s contribution to the differences between the networks.We have evaluated the proposed NBBD framework, using five different network construction methods, in combination with nine different node importance scoring methods, on a large data set from a cohort of 657 IBD and 316 healhy pediatric metagenomics biopsy samples. Our results show that NBBD, when used to train predictive models for IBD diagnosis from metagenomic data, is very competitive with some of the state-of-the-art feature selection methods including the widely used method based on random forest feature importance scores. Our results further show that a hybrid approach that combines NBBD scores and the random forest feature importance scores yields further improvements in performance. Furthermore, the proposed method is able to achieve its best observed performance using only 50 samples for feature selection. Work in progress is aimed at further improving the two key components of NBBD, e.g., by incorporating recent advances in high dimensional correlation matrix estimation –76 to imS1 File(XLSX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.