In terms of the main characteristics and predictive performance, local modeling was better than global modeling. The accuracies of five local models  obtained by the Adaptive Kennard–Stone algorithm were 91.30%, 95.65%, 91.30%, 100%, and 91.30%, respectively. The accuracies of the five local models obtained by an approach of PCA–Full distance based on DD–SIMCA were 91.30%, 91.30%, 91.30%, 100%, and 95.65%, respectively. The accuracies of the five global models were 91.30%, 91.30%, 91.30%, 100%, and 91.30%, respectively. Stable isotope ratio analysis combined with local modeling can be used as an effective indicator for protecting PGI Sunite lamb.For the protection of Protected Geographical Indication (PGI) Sunite lamb, PGI Sunite lamb samples and lamb samples from two other banners in the Inner Mongolia autonomous region were distinguished by stable isotopes (δ Allium mongolicum regel, Allium polyrhizum turcz, and Stipa capillata. It is this good ecological environment and primitive and extensive feeding mode that afford Sunite lamb with excellent quality and flavor [Sunite sheep were formed in the special ecological environment of Sunite grassland through long-term natural selection and artificial selection. They enjoy natural herbage and pure water in the ecological environment of natural grassland without pollution, and feed on more than 400 kinds of natural herbage, such as d flavor ,2. Sunitd flavor .2H and δ18O can be used to distinguish altitude, δ15N can be used to determine the type of grazing vegetation, and δ13C can determine the type of animal feed [34S is related to rainfall in the geological environment and traditional industrial emissions, so it indicates the geographical characteristics of animal food. Sr is obtained from the decay of 87Rb, and its stable isotope abundance is mainly affected by geological conditions and rock ages. Sr has good applications in plant-derived-food tracing [In order to protect the PGI products, researchers have put forward a fingerprint tracing method, that is, using chemical parameters to build the fingerprint of geographical indication products  and compmal feed . In addi tracing , but it 13C, δ15N, δ2H, δ18O, and δ34S in crude lamb protein from 13 European regions, and achieved correct classification rate of original grouping and cross validation of 78.7% and 77.6%, respectively. This indicated the feasibility of using stable isotopes to distinguish the geographical origin of lambs. However, the information of the samples Camin et al. [Thus, the stable isotope ratios can be used to distinguish PGI Sunite lamb from different origins. Stable isotopes have been applied to determine the origin of different animal-origin foods, such as beef , lamb 1,16, milkn et al.  tested wIn previous research on food traceability, the global modeling method was used to establish the discriminant model, that is, to create a model from all data sets that cover the whole space . However13C, δ15N, δ2H, and δ18O) and machine learning, we proposed two local modeling approaches to optimize the sample set. It is worth mentioning that this is the first exploration of the protection of PGI Sunite lamb, and also a new application of local modeling in origin identification. The two local modeling approaches were (a) the Adaptive Kennard–Stone (AKS) algorithm and (b) an approach of PCA–Full Distance (FD) based on Data-Driven Soft Independent Modeling of Class Analogy (DD–SIMCA). The AKS algorithm was used to select the most representative data subset based on a uniform design principle, and the approach of PCA–FD based on DD–SIMCA was used to select the local model data set based on spatial similarity. It should be emphasized here that global modeling and local modeling in this study refer to the selection of data set coverage space, and the establishment of the discriminant model still depends on machine learning. The machine learning methods used in this work are linear discriminant analysis (LDA), random forests (RF), support vector machine (SVM), back-propagation neural network (BPNN), and k-nearest neighbor (KNN) classification. Based on the confusion matrix, we compared the predictive performance of the traceability models established by the five machine learning methods.In order to better discriminate PGI Sunite lamb from other origins using stable isotopes , and principal component analysis Mahalanobis distance (PCA–MD). Both distances can be calculated in the original variable space and in the principal component space. In the principal component space, the correlation between variables is eliminated, simplifying the data information and making it superior to the original variable space. The Euclidean distance is the straight line distance between two points, and it is affected by the data distribution, noise, and characteristic metrics. Unlike the Euclidean distance, the Mahalanobis distance introduces a covariance matrix, and implements coordinate rotation and data compression, which makes Mahalanobis distance not affected by data distribution and feature dimensions . HoweverNh and Nq are the degrees of freedom (DOF) for SD and OD, and SD0 and OD0 are the means of SD and OD of all the samples [1 and FD2, have certain rules. Samples between the two yellow lines are the screened samples.Each element of the data cloud can be presented as a sum of two vectors: a vector that lies in the subspace (a projection) and a vector transversal to the hyperplane . The lengths of these vectors are important indicators that characterize a sample position with respect to the subspace (model). These statistics are often referred to as the leverage and the residual variance. In DD–SIMCA, they are termed as the score distance (SD) and the orthogonal distance (OD) correspondingly, which are used to define the critical limits of the classification model . SD is e samples , respectAs a measure of similarity, FD was used to select samples similar to one testing sample from the original sample set to solve the nonlinear problem of large-sample data modeling. This approach greatly simplifies the threshold selection process, and part of the process is visualized.The predictive performance of the model in our work based on the confusion matrix includes the sensitivity, specificity, accuracy, and kappa coefficient . The confusion matrix summarizes the results of a classification method. For a binary classification, when we determine that class 1 is positive, the schematic table of the confusion matrix is shown in In this study, instead of negative and positive, the classes were “non-PGI lamb” and “PGI Sunite lamb”, respectively. For example, TN is the number of non-PGI lamb in the test data set correctly classified as non-PGI, and FN is the number of PGI Sunite lamb incorrectly classified as non-PGI. The sensitivity, specificity, accuracy, and kappa coefficient are defined as follows:Sensitivity is the proportion of actual PGI Sunite lamb that is correctly classified as PGI Sunite lamb. Specificity is the proportion of actual non-PGI lamb that is correctly classified as non-PGI lamb. Accuracy is the ratio of true positive and true negative samples to the total number of testing samples, which reflects the overall accuracy. If the proportion of one class of samples is not dominant in all classes of samples, then its high error rate has little influence on the accuracy. In this case, the accuracy does not carry much meaning, and the Kappa coefficient can better reflect the discrimination effect. The Kappa coefficient can better reflect the consistency of actual classification and predict classification. The evaluation result of the Kappa coefficient is divided into three grades : excellen = 116) were collected from 4 banners in two cities of China’s Inner Mongolia autonomous region (v/v) solution at 1:5, vortexed for 10 min, and centrifuged at 5000 rpm for 5 min, and the supernatant was discarded [Lamb samples  were weighed into tin capsules (5 × 8 mm) and then introduced into an elemental analyzer , converting the entire material into carbon dioxide and nitrogen gas analyzed by an isotope ratio mass spectrometer . The calibration of δ13C and δ15N was analyzed with USGS40 , USGS43 , and USGS62 .For the stable isotope analysis of δ2H and δ18O, DDM and international reference materials  were weighed into silver capsules (4 × 6 mm) along with other international reference materials and introduced into the elemental analyzers . The reactor packing was a glassy carbon reactor and silver wool. The elements hydrogen and oxygen in the samples were converted into H2 and CO at 1380 °C via pyrolysis with glass carbon. The gas was transferred to an isotope ratio mass spectrometer . The calibration of δ2H and δ18O was analyzed with CBS , KHS , and B2205 .For the stable isotope ratio analysis of δ13C, δ15N, δ2H, and δ18O were Vienna–Pee Dee Belemnite (V–PDB), Air, Standard Mean Ocean Water (SMOW), and SMOW, respectively.The results of the isotope analysis were expressed as δ (‰), and the formula wasN = 116) were divided into a training set and a testing set (4:1). Due to the uneven sample size in the four regions, stratified random sampling was adopted to avoid contingency, and samples in each region were divided into 4:1. The training set samples (N = 93) were used for modeling, and the testing set samples (N = 23) were used to evaluate the prediction ability of the model. The training set data were imported into R Studio and the training set subset (N < 93) was obtained by the AKS algorithm. After that, the training set subset used five machine learning methods  to establish the geographical origin discriminant model, and finally used the confusion matrix of testing set samples to evaluate the predictive performance of the model.All of the samples  in the testing set (N = 23) and the training set samples (N = 93) was imported into DD–SIMCA in Microsoft Excel (SIMCA template-xlsb) to obtain the FD of all samples (N = 93), and the training subset was appropriately selected centering on the FD of Pi. Then, import the training set subset into R Studio and use the 5 machine learning methods to build a one-time local model for Pi. Repeat the above operation 23 times, and obtain 5 confusion matrices. The evaluation method of the model was consistent with the evaluation method of the training set subset obtained by the AKS algorithm.One sample  were imported into R Studio, and 5 machine learning methods were used to establish the geographical origin discriminant model. The evaluation method of the model was consistent with the evaluation method of the training subset obtained by the AKS algorithm.All of the training set samples (N = 93), we analyzed the main characteristics of the global lamb isotope libraries and local lamb isotope libraries obtained by the AKS algorithm. SPSS was used to conduct an independent-samples T-test to analyze the significance between the two groups (PGI Sunite lamb and non-PGI lamb), produce box diagrams to intuitively see the significance, conduct exploratory analysis , and produce the 3-dimensional scatter plot to observe the spatial distribution of the sample set. Furthermore, to know the difference of lamb between different regions, we performed a descriptive analysis of all data (N = 116) (In order to compare the changes before and after screening the training set samples (N = 116) . AdditioN = 116) .The statistical software packages R, SPSS 25.0 , and a chemometric tool employed in Excel were used. AKS was written by our laboratory using R language.In this work, the training subset was obtained by two local modeling approaches: (a) AKS and (b) the approach of PCA–FD based on DD–SIMCA. Based on the AKS algorithm, a line chart  was drawn et al.  used AKSn et al.  continuen et al. . This shPi (N = 93), the discriminant effect of the model established by the data set with a small sample size (20 ≤ N < 35) was better, and that when the FD of Pi (N < 50) was better. This may be related to the principle of screening. This method selected samples within the linear range of Pi for modeling based on similarity [Pi in the FD of all of the data. In the future, this finding will continue to be verified in order to summarize the screening rules.According to the approach of PCA–FD based on DD–SIMCA, 23 targeted training subsets were obtained with a sample size between 20 and 47. When screening data, we found that, when the FD of Pi  deviatedFD of Pi  close tomilarity . The linTo sum up, during sample screening, it is necessary to follow the screening principles and consider the data characteristics to select data with an appropriate sample size. In this work, with two local modeling methods, half or less of the original sample size could be used to obtain the same model effect as the original data.N = 40) obtained by AKS and the training set (N = 93) as an example, the main characteristics of the local and global lamb isotope libraries were compared. 13C, δ2H, and δ18O, the local lamb isotope library was smaller than the global lamb isotope library. For the standard deviation of δ13C, δ15N, δ2H, and δ18O, the local lamb isotope library was larger than the global lamb isotope library. The mean reflected the overall average and the degree of data concentration, while the standard deviation reflected the degree of data dispersion. This meant that the local lamb isotope library was more centralized and more dispersed, and was the ideal training set. The histogram  in the δ13C, δ15N, δ2H, and δ18O values between PGI Sunite lamb and non-PGI lamb before and after sample screening. This meant that PGI Sunite lamb and non-PGI lamb always had a characteristic stable isotope ratio profile. 13C, δ15N, δ2H, and δ18O values. The regional disparity of the δ13C and δ15N of lamb samples was a consequence of the feeding systems [13C value in animal products was based on C3 and C4 plants in the animal diet. One study showed that the δ13C value of C3 plants ranged from −20‰ to −35‰, and that the δ13C value of C4 plants ranged between −9‰ and −17‰ [3 and C4 plants fed to PGI Sunite lamb was higher than that fed to non-PGI lamb. In another aspect, the value of δ15N reflects the nitrogen cycle in soil. Compared with other C3 plants, leguminous plants can directly utilize atmospheric nitrogen, resulting in a lower δ15N value [15N value of lambs (non-PGI lamb) from high-altitude regions, such Siziwang Banner  analysis combined with local modeling was used to discriminate PGI Sunite lamb from other origins, and the accuracy rate reached 100%, which could be used as an effective indicator system for protecting PGI Sunite lamb. A good traceability model requires that the sample set should cover as wide a range as possible and avoid the appearance of samples with basically the same chemical information as much as possible. Therefore, local modeling is very necessary for the traceability of agricultural products, but it has not been reported. In this paper, two local modeling approaches were first proposed for the protection of PGI Sunite lamb, and the identification effect of models was better than that of global modeling, which could be used for the optimization of the training set and the traceability of agricultural products. We found that the sample set with less than or equal to half of the original sample size in this study could achieve a better predictive effect. However, the ratio of the screened sample size to the original sample size will not always be 1:2, and the screened sample size is related to the information contained. The information  of the lamb samples collected in this paper was similar. This may account for the small changes in the data characteristics before and after screening. In the future, while increasing the sample size, we will try our best to make the sample set cover a wide range of differences, such as geographical origin, feeding system, breed, age, and gender. At that time, there will be a more obvious nonlinear relationship between the classification response and the isotope ratio, and the application of a local modeling method is more necessary.In this work, stable isotope ratio (δ


Several fusion genes are directly involved in the initiation and progression of cancers. Numerous bioinformatics tools have been developed to detect fusion events, but they are mainly based on RNA-seq data. The whole-exome sequencing (WES) represents a powerful technology that is widely used for disease-related DNA variant detection. In this study, we build a novel analysis pipeline called Fuseq-WES to detect fusion genes at DNA level based on the WES data. The same method applies also for targeted panel sequencing data. We assess the method to real datasets of acute myeloid leukemia (AML) and prostate cancer patients. The result shows that two of the main AML fusion genes discovered in RNA-seq data, PML-RARA and CBFB-MYH11, are detected in the WES data in 36 and 63% of the available samples, respectively. For the targeted deep-sequencing of prostate cancer patients, detection of the TMPRSS2-ERG fusion, which is the most frequent chimeric alteration in prostate cancer, is 91% concordant with a manually curated procedure based on four other methods. In summary, the overall results indicate that it is challenging to detect fusion genes in WES data with a standard coverage of ∼ 15–30x, where fusion candidates discovered in the RNA-seq data are often not detected in the WES data and vice versa. A subsampling study of the prostate data suggests that a coverage of at least 75x is necessary to achieve high accuracy. Fusion genes represent an important class of genomic alteration contributing to the tumorigenesis for both solid and hematological cancers. The hybrid genes are often produced by recurrent chromosomal rearrangements, such as translocation, deletion and insertion . In the de novo assembly-first method  represents another primary type of sequencing application which has been frequently utilized in cancer studies . The expIn our previous study, we have developed a method named Fuseq to identify fusion genes from RNA-seq data, which provides an accurate and fast prediction of the fusion aberration . In thisReads mapping is the process to infer which region in the reference sequence that a read can originate from. In recent years, a wide range of computational tools have been developed and implemented for reads mapping, e.g., BWA, HISAT2 and Bowtie2 . The staAll the discordant and split reads are first annotated with the information of mapping position, chromosome, gene names and transcript names. In this step, a GTF  file recording the characteristics of genome/transcriptome structure is provided as input . In our According to the definition, split reads overlap with the fusion breakpoints, thus provide direct evidence supporting a potential fusion event . For eacIn this step, several statistical tests and filters are applied to exclude the false positives. For example, in terms of the general features, the chromosomes of fusion genes are limited to autosome 1–22 and chromosome X and Y. The constituent genes should be protein-coding genes and the distance between the two genes from the same chromosome must be large enough (10 kilo bases in this study). Also, we set the minimum number of supporting read pairs of a fusion event to be one. Apart from the filtering criteria regarding the general characteristics, another primary factor causing false positives is paralog genes with extremely similar sequences. To address this issue, we implement an RNA-seq simulation to detect paralogs genes/transcripts. The simulation scheme is similar with what we did in our previous study . We applIn this study, we employ our method to several real WES datasets including the BeatAML cohort, TCGA-AML cohort and the Prostate Biomarkers cohort . All of The BeatAML cohort includes a total of 672 tumor specimens representing 562 unique patients, which gives a comprehensive genomic landscape of AML. Specimens from bone marrow and peripheral blood were obtained from patients with informed consent . The exohttps://tcga-data.nci.nih.gov/tcga) for public access.The TCGA-AML cohort represents another primary project investigating the genomic and epigenomic landscape of AML . In thisProstate cancer is a genetically heterogeneous disease which remains the most common and deadliest malignancy among Swedish men . The metWe first apply the Fuseq-WES method to validate the fusion genes in BeatAML dataset. PML-RARA is a well characterized fusion gene in acute promyelocytic leukemia (APL), which is a clinically and biologically unique subtype of AML . The fusThe inversion involving p13 and q22 segment on chromosome 16 leads to the fusion of CBFB and MYH11. The CBFB-MYH11 is consistently observed in patients with AML subtype M4Eo . SeveralThe formation of RUNX1-RUNX1T1 fusion is due to the balanced translocation between chromosome 8 and 21, which can be identified in 5–12% of AML cases . In the We apply the same analysis scheme on the TCGA-AML data to validate the fusion genes. The PML-RARA fusion is detected in 16 samples from RNA-seq data. We obtain six samples with matched exome sequencing data and the fusion event is identified in three of them. For CBFB-MYH11, the RNA-seq data shows that a total of 11 samples carry the fusion and there are six of them having exome sequencing data. Surprisingly, unlike the high validation rate in BeatAML data, none of the six samples having CBFB-MYH11 detected. The RUNX1-RUNX1T1 is identified in seven samples using RNA-seq and is confirmed in two out of four samples with WES data available.The Prostate Biomarkers (ProBio) project implements a targeted deep-sequencing approach to characterize the genomic profile of selected genes. TMPRSS2-ERG (TE) is a predominant fusion gene with a 55% prevalence in prostate cancer patients. From the project we obtain 65 samples with targeted sequencing data. We first use four separate tools for fusion gene detection, which include SvABA , LUMPY , GRIDSS The IGV call identifies 37 patients to be carrying the TE fusion, while the remaining 28 are TE negative. Using the Fuseq-WES approach, the TE fusion is identified in 41 samples and absent in 24 samples. Overall, the results are concordant in 36 TE positive and 23 TE negative cases, indicating a 91% agreement between the two analyses. The detailed number of mapped and split reads supporting the TE fusion is given in We further investigate how sequencing depth impacts on the accuracy of Fuseq-WES. First, we randomly select 10 samples for which the TE fusion is validated manually. Then, for each sample we randomly subsample the reads from original data (1500X coverage) to generate five lower-coverage samples including 150x (10%), 75x (5%), 30x (2%), 15x (1%) and 7.5x (0.05%). Finally, we perform Fuseq-WES for all 10 samples and report in In this study, we aim to exploit the potential value of whole exome sequencing data in the context of fusion gene detection. We develop a new method named Fuseq-WES and apply it to several representative cancer datasets. In two of the most comprehensive cohorts of AML, i.e. BeatAML and TCGA-AML, we identify and validate three fusion genes, PML-RARA, CBFB-MYH11 and RUNX1-RUNX1T1, which play important roles in the development and progression of AML. In the Prostate Biomarker dataset, we validate the most recurrent fusion gene, TMPRSS2-ERG, and achieve a 91% concordance using two detection schemes.Compared with the identification results using RNA-seq data, we find that only a fraction of the fusion genes could be detected in the exome sequencing data. For example, from the BeatAML dataset, we obtain 11 samples with exome-seq data for PML-RARA, and four of them are successfully identified to carry this chimeric alteration. In the TCGA-AML cohort, there are six exome-seq samples and we validate three of them carrying the PML-RARA fusion. High-confidence detection of fusion genes mainly depends on split reads. Thus, we also summarize the number of split reads in the WES and RNA-seq data . In the An obvious limitation of WES is that only the exome region is targeted from genomic DNA for sequencing. In this case, if a fusion junction is located in an intronic region that is out of the targeted enrichment of exonic regions, the exome sequencing is unable to capture that break point. In contrast, the RNA-seq holds an inherent advantage over exome-seq, by which the non-coding regions are removed to produce mature messenger RNA (mRNA), so that the chimeric junction is detectable by subsequent sequencing. Therefore, a fusion gene can be identified using WES data only if the break point is located in or close to the exonic regions. Another limitation of WES is the short read length, especially the length of valid split reads covering the fusion junction. We have drawn a conceptual figure in We also find that the validation rate is highly associated with gene-exon structure and the read coverage in WES data. A gene with dense exon distribution and the sequencing data with high coverage could facilitate the fusion detection. For the CBFB-MYH11 fusion, in the BeatAML data we identify a total of 15 samples with the mutation among 24 WES cases. The high validation rate could be due to that the MYH11 has 43 exons with the gene length of ∼154,000 bp, which represents a high density covering the whole region of the gene. However, using the TCGA-AML data, we cannot validate the fusion gene in any of the six cases with WES data available. From the BAM file we notice that the median read-depth of TCGA-AML and BeatAML samples are 15x and 40x, respectively, which is lower than the 75x threshold as indicated in This observation is confirmed by the analysis using prostate cancer data. The ProBio project applies a targeted sequencing method including the exons and introns, and provides ultra-deep sequenced data with read-depth up to 1500x. We achieve an overall agreement in 91% of the 65 cancer patients. A subsampling study of the prostate data suggests that at least ∼75x is needed for Fuseq-WES to get high sensitivity and accurate results. We have summarized the coverage of WES data of 23 primary cancers in The Cancer Genome Atlas (TCGA) database, which is presented in


The vast majority of goat skin processed by traditional tanneries comes from small rural producers. Thus, with the predominance of rustic creation, slaughter, and skinning methods, the batches of hides processed by tanneries have a very heterogeneous quality. Thus, there is a need to categorize the samples according to the quantity and location of defects. The categorization process is subjective and strongly influenced by the experience of the professional classifier, causing a lack of homogeneity in the composition of the goat hide lots for sale. Aiming to reduce failures in the categorization of goatskin samples, the authors investigate the application of computer vision and artificial intelligence on a set of previously categorized wet blue goatskin photographic samples. That said, is analyzed the capacity of different classifiers, with different paradigms, in detecting defects in goatskin samples and in categorizing these samples among seven possible quality levels. A hit rate of 95.9% was achieved in detecting defects and 93.3% in categorizing quality levels. The results suggest that the proposed methodology can be used as a decision aid tool in the qualification process of goat leather samples, which can reduce sample labeling errors. Fault detection in leather; Qualification of leather parts; Decision support system Gutterres Gomes Leal, Penna Rocha and Rocha Junior As commented by Aslam et al. S. Liong et al. 2Amorim, et al. E.Q. Santos Filho, G. Barreto, and J. Daniel Santos Nogueira de Aquino Renato F. Pereira, Cláudio M. S. Medeiros, and Pedro P. R. Filho Liong, S. et al. 3The methodology applied for the elaboration of the decision support system is represented in The authors used the structure developed by Edmilson Q. S. Filho, José D. A. Santos and Guilherme A. Barreto To make the study feasible, the authors used samples of goatskin available in the tannery stock, which were previously categorized by a very experienced expert, he has over 30 years of experience in this type of work, here called expert A. Were made 312 photographic records in the JPEG (Joint Photographics Experts Group) format.These samples were coded, mixed, and subsequently recategorized by another specialist, also quite experienced, here denoted as specialist B. It can be seen that, among the 312 samples, expert A categorized 21 samples as quality 1, while expert B categorized 19 samples. This difference is acceptable. However, for the lower quality samples, there are great divergences. As an example, expert A categorized 50 samples as quality 7 , while expert B categorized 89 samples, which attests to task difficulty. This result can be due to the fact that the process occurs by hand, and variables such as tiredness, inadequate lighting, among others, can lead to erroneous qualifications.In this table, the main diagonal indicates the number of samples that were equally categorized by the two experts. As can be seen, the coincidence percentage in the categorization of the two experts is only 41.9%There is a greater degree of caution on the part of Expert B, note that he has taken a slightly more pessimistic approach to the qualification given by Expert A.While identical skill levels among experts may be considered low, this difficulty can be remedied at a later stage, called finishing. In it, the leather undergoes several processes such as cutting, painting, among others, which greatly reduces this inequality between them, as it greatly reduces the number of failures in the pieces.It should also be noted that there is no standardized quantity of quality levels. The tannery can define its own standard based on the leathers purchased, where they will be applied or sold. For example, E.Q. Santos Filho, G. Barreto and J. Daniel Santos It is important to inform that despite the divisions presented, the fault detection process and definition of the quality level are based on the same methodologies presented in this work, that is, based on the number of faults and their locations.From here, the authors form the dataset for the classifier design only with samples that had coincident categorization by the two experts. Thus, the degree of uncertainty in the sample labeling is lower, which can give the properly trained classifier greater reliability.It should be noted that this article can be divided into two steps: the detection of defects and the qualification of wet-blue goat leather. The first one considers the acquisition of samples, the elaboration of data sets, and the use of classifiers for failure detection. For the second stage, it is considered the acquisition of data sets based on fault-free regions and the use of classifiers to obtain their quality level.4After acquiring the photographic records of the goat leather samples, the authors open the images in image editing software and, with the participation of professional classifiers, identify and locate each defect found. A file is generated containing the type of each defect, the identification of the image file in which the defect is found and the coordinates X and Y of the location of the defect in the image. This procedure is illustrated in Thus, 2250 defect images and 1669 defect-free leather images were cataloged. X and Y, most appropriate for the application of attribute extraction methods, seeking the best compromise between execution time and sample characterization.Then, the authors carried out an exhaustive investigation to define the window size, centered on the coordinates For each window size, the authors evaluated the performances of several combinations of extracting attributes methods and classifiers based on three different paradigms.Renato Pereira 4.1R.C. Gonzalez and R.E. Woods The attribute extraction methods evaluated are the Local Binary Pattern - LBP, presented by T. Ojala, M. Pietikäinen, and T. Mäenpää D. Huang et al. W.R. Schwartz and H. Pedrini For each attribute extraction method, various window sizes were tested. In T pixels on each side, each centered on the X and Y coordinates, which represent the center pixel of the regions saved in the report. The tested window sizes are 11, 21, 31, 41, 51, 75, and 101 pixels.Square windows are considered with 4.2According to R. Kruse et al. A. Burkov Perceptron is inserted in order to verify a possibility of linear separability between the classes of the data sets. It was implemented using learning rates with values of 0.01, 0.05, 0.1, and 0.5, with the possibility of linear or exponential reduction of them and signal activation function.The Logistic Regression is a very simple algorithm, with a better possibility of converging in non-linear problems, it is implemented with penalty parameter C assuming values like 0.01, 0.1, 1.0 and 10.0.From the data sets obtained during feature extractions, the authors noted clusters of samples, which makes it a good indicator for the use of one of the simplest alternatives for classification, the K-Nearest Neighbors (KNN). The classifier is implemented using Euclidean distance and K values equal to 3, 5, 7, 9, and 11.Unlike KNN, which proves to be quite slow when using a large amount of data, the Fisher's classifier based on Bayes rule is a “probabilistic classifiers” and is easily adaptable to different data set sizes. Its parameterization and project process can be carried out in linear time. A simpler version of Fischer's classifier, which assumes strong independence between features, is the Gaussian Naïve Bayes. Both are investigated in this article.For the MLP classifier, several parameters were evaluated: Initial learning rates are tested, ranging from 0.1 to 0.5; linear or exponential learning rate decay; hyperbolic tangent or logistic function as activation functions; and several numbers of hidden neurons, ranging 2 and 100 neurons.Regarding to SVM classifier, 4 types of kernel are used: linear, RBF, polynomial and sigmoid. The value of the penalty parameter C is evaluated from the following values 0.01, 0.1, 1.0 and 10.−5.For the Logistic Regression algorithm, the training process stops when achieves 2000 iterations and/or the error rate achieves 10The authors used 1335 samples from the “normal” class and 1335 samples from the “defect” class to compose the training set. The samples were randomly selected from 1669 and 2250 samples, respectively from the “normal” and “defect” sets. The authors believe that this balance in the number of samples can avoid trends in the binary classification. The rest of the data, that is, 334 samples of the “normal” class and 915 samples of the “defect” class, make up the test set.For all classifiers the K-Fold cross-validation technique is used, with a value for K equal 10. The application of this method consists of dividing the total set of training data into K subsets of the same size. Thus, each subset is used for tests, while the rest of the set is applied to estimate the parameters.4.3During the defect detection process, the authors realized that by setting very small fragment size, several classification errors were obtained. As shown in The best results were achieved with the data set formed by applying the GLCM attribute extraction method to windowed images with 51 pixels.The average results and the standard deviation for the hit rates in 50 runs of the three best results obtained can be seen in Referring to the two best results in test set, the configuration of the MLP networks is as follows: Initial learning rates of the hidden and output layers of 0.5 and 0.3, respectively; linear decay in learning rates; 65 hidden neurons; logistic activation function; and rejection range of 0.1.In the case of SVM, the best kernel was RBF. The mean of support vectors is 368 and parameter C equals 10. With this configuration it was possible to obtain the best model for fault detection, its result is shown in When analyzing In From here, defect detection will be performed only by a Support Vector Machine, on attributes generated by GLCM on image fragments of 51 pixels.5For the goat leather qualification, firstly it is necessary to perform a preprocessing on the sample image, performing procedures for filtering and removing the background. Then, the leather image is fragmented into several 51 pixels windows and a matrix is constructed, in which each element represents a specific window. Each element of the matrix is assigned the value “0”.Each window is subjected to the defect detection process presented in Section 4, in which an SVM classifier assigns the value “0” to the window classified as belonging to the class “defect” and the value “1” to the window classified as belonging to the “normal “class.Then an algorithm is applied to find the defect-free convex regions in the matrix. The detection of these defect-free areas is based on a dynamic programming problem explained by Dhaval Dave Seven attributes are generated based on defect-free areas. These attributes are listed in From here, each goat leather sample image is characterized by the 7 attributes defined in the 5.1In this research, only the leather samples that obtained the same categorization by the two experts are considered to compose the data set. The data set was composed according to the undersampling technique. Thus, considering the class with the smallest number of samples , 75% of them were used to compose the training set. The other classes contribute an equal number of samples to the composition of a balanced set with a total of 42 samples. The test data set is composed by the rest of samples, that is 89 samples.The authors use the follow classifiers to perform the qualification task: Perceptron, Logistic Regression, K-Nearest Neighbors (KNN), Fisher's classifier, Gaussian Naïve Bayes, Multilayer Perceptron (MLP) and Support Vector Machine (SVM). However, the authors present in The three classifiers have average hit rates above 95% in the training set. However, the Gaussian Naïve Bayes classifier stands out from the other two by achieving an average hit rate in test set of 89.56%. As shown in In It is noticed that the samples with the best qualities are those with few defects in the central region. This is in accordance with the qualification methodology employed by the experts, as the number of defects found in the sample may not be as relevant in determining quality if they are located at the edges of the sample.5.2As commented in Section The coincidence percentage in the classification of the classifier and expert A is 64.74%, while between the classifier and expert B it is only 43.59%. It is important to point out that in this analysis we are also considering all the samples used for training and testing the classifier.It was expected that there would be a greater coincidence between the classifier and the expert A, as he is more experienced and is the instructor in the internal training programs in the tannery. It is noticed that only for qualities 1 and 6 do experts qualify similar numbers of samples. For all others there is great disparity. Expert B seems to be more rigorous, as it tends to qualify more samples in the lower quality categories.6As seen, there is a great difficulty in qualifying wet blue leather pieces, as currently a very artisanal process is used, which provides several wrong qualifications. It was found that one of the greatest difficulties of this research was to obtain a reliable set of images since the identical qualification level between two specialists was only 41.9%. Thus, to guarantee this reliability, only leather pieces with the same quality level defined by the two specialists were used for the machine learning process, which reduced the number of samples from 312 to just 131.The qualification process consists of two steps: the detection of defects and, from there, the qualification of wet blue goatskin. Although simpler, it is essential that the first step is well executed so that the entire process takes place satisfactorily. In this way, the occurrence of incorrect fault detection can lead to incorrect qualification of goat skins. Thus, it can be said that the second stage depends entirely on the results obtained in the first.To carry out the first step, that is, for the detection of failures, seven sets of images were prepared, from windows of 11, 21, 31, 41, 51, 75, and 101 pixels, each composed of 1669 samples of classes without failures and 2250 with defects. Attribute extraction was performed using the LBP and GLCM methods and, for the process of classification of failure or non-failure, Perceptron, Logistic Regression, KNN, Fisher classifier, Gaussian Naïve Bayes, MLP, SVM were used. In this first step, the best result was obtained from the combination of the image window size of 51 pixels, GLCM extractor, and SVM classifierThe second stage, that is, the qualification of the leather pieces, is based on the detection of defects. In this, the largest fault-free regions are considered, and from these, seven attributes are acquired for the formation of the dataset. To define the quality level, the same machine learning algorithms mentioned above are used. As the best result, an accuracy rate was obtained, from the Gaussian Naïve Bayes classifier, of 93.3%.As presented in this work, an index of 93.3% of correctness was observed when the wet blue leather pieces that obtained the same quality level by the two specialists are classified. However, there is a reduction in the success rate when a classification is made based on the distinction of the quality level given by each one of the professionals. It is noteworthy that these results were expected, given the great difference between the qualifications they attributed.Finally, it is believed that the proposed system becomes an effective instrument to be used as a decision support system, as it is built from a set of images that obtained the same level of quality by two specialists, and from this, was obtained an accuracy of 93.3%, which makes it suitable for application in this type of industry.C.E.B. Sousa: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.C.M.S. Medeiros: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.R.F. Pereira: Conceived and designed the experiments.A.A. Neto, M.A.V. Neto: Contributed reagents, materials, analysis tools or data.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.


More specifically, we leverage variational neural networks (VNNs), which produce high-confidence  predictions only for inputs that closely resemble the training data. We use the inverse of this confidence measure to select the samples that the oracle should label. Intuitively, unlabeled samples that the VNN is uncertain about contain features that the network has not been exposed to; thus they are more informative for future training. We carried out an extensive evaluation of our novel, probabilistic formulation of active learning, achieving state-of-the-art results on MNIST, CIFAR-10, CIFAR-100, and FashionMNIST. Additionally, unlike current active learning methods, our algorithm can learn even in the presence of out-of-distribution outliers. As our experiments show, when the unlabeled pool consists of a mixture of samples from multiple datasets, our approach can automatically distinguish between samples from seen vs. unseen datasets. Overall, our results show that high-quality uncertainty measures are key for pool-based active learning.In many applications, data is easy to acquire but expensive and time-consuming to label, prominent examples include medical imaging and NLP. This disparity has only grown in recent years as our ability to collect data improves. Under these constraints, it makes sense to select only the most informative instances from the unlabeled pool and request an oracle  to provide labels for those samples. The goal of active learning is to infer the informativeness of unlabeled samples so as to minimize the number of requests to the oracle. Here, we formulate active learning as an open-set recognition problem. In this paradigm, only some of the inputs belong to known classes; the classifier must identify the rest as  Supervised deep learning has achieved remarkable results across a variety of domains by leveraging large, labeled datasets  only those informative samples, rather than the entire dataset. The challenge, of course, is that the true usefulness of a sample can only be established a posteriori, after we have used it to train our model.Fortunately, the relationship between a model's performance and the amount of training data is not linear. There often exists a small subset of highly active learning (AL) is concerned with automatically predicting which samples from an unlabeled dataset are most worth labelingThe growing field of open-set recognition (OSR) problem, a generalization of the standard classification paradigm. In OSR, only some of the test inputs are from the trained-upon distribution; the classifier must label the remaining inputs as out-of-distribution (OOD), meaning that they do not match the types of inputs it was trained on. For example, if a network was trained on digit recognition, e.g., using MNIST, then images of animals or vehicles, such as those of CIFAR-10, would be OOD. Here, we view the labeled pool as the training distribution. The unlabeled samples which are similar to the labeled pool are deemed as in-distribution, while the unlabeled samples that are very different from the labeled pool are marked as OOD. Our hypothesis is that the samples most worth labeling are those that are most different from the currently labeled pool  because they contain features which the network has not yet been exposed to. Thus, training on these samples will allow the network to learn these features that are underrepresented in the existing training data. In short, our AL selection mechanism consists of picking unlabeled samples that are OOD relative to the labeled pool.In this paper, we formulate active learning as an least confident about because this implies that the existing training set does not contain items with similar features to them. As we detail in section 4, our OSR-based approach achieved state-of-the-art results in a number of datasets and AL variations, far surpassing existing methods.The rest of this paper is organized as follows. In section 2, we provide a brief overview of current active learning and open-set recognition methods. In section 3, we present our proposed approach, then detail our experiments in section 4. Finally, we discuss avenues for future work in section 5.acquisition function that chooses the best data points for which a label should be requested from a large, unlabeled pool of data  refers to the ability of a system to distinguish between types of data it has already seen (the training distribution) from types to which it has not yet been exposed (out-of-distribution (OOD) data). Standard deep neural networks are not suitable for OSR because they often yield high confidence values for inputs which are significantly different from the training classes. This vulnerability has been exploited for adversarial attacks on deep networks, specifically to change a classifier's labels based on imperceptible changes to the input image  , where C indicates the number of classes, Dtrain is the training set, and Dtest is the test set, s.t. Dtrain ∩ Dtest = ∅. Let N i.i.d. data points where only m of them are labeled (m << N). Each sample d-dimensional feature vector, and yi ∈ {1, 2, …, C} represents the target label. At the start, m labeled data points, and an unlabeled pool N − m data points with unknown target labels. We will update both t ∈ {0, 1, …}. At any given iteration, the budget b is defined as total number of samples from the unlabeled pool . Note that, while the above formulation is similar to continual learning, in active learning we assume that samples from all classes are present in both the labeled and unlabeled pools during all iterations. We do not learn new classes in an incremental fashion.In active learning, we first train a classifier multiple datasets. As we show in our experiments, our OSR-based AL method can automatically ignore samples that do not belong to the target dataset.Importantly, unlike other formulations of AL, here we allow for the unlabeled pool entanglement penalty on the latent vector. This penalty forces the dimensions of the latent space to be as uncorrelated as possible, making them easier to interpret. A VNN combines the encoder-decoder architecture of a β-VAE with a probabilistic linear classifier (see pϕ(x|z) and pξ(y|z) are the reconstruction and classification terms. The last term is the entanglement penalty, which is given by the Kullback-Leibler divergence between the latent vector distribution and an isotropic Gaussian distribution.Variational neural networks (VNNs) , which uses the loss function in Equation (1), and a simplified version (M1) without the reconstruction error:M2 achieves better results overall.As in Mundt et al. , we evalinformation bottleneck. These networks are based on a variational approximation . Specifically, our aim is to select xi based directly on how uncertain the VNN is about it. Specifically, we rank all unlabeled samples by the value of the most likely class label and select the b samples with the lowest maximum values. Since the sum of class likelihoods is normalized, the value of the maximum class probability will approach one for highly certain samples and approach C| is the number of classes, for highly uncertain samples. In other words, the class likelihoods of uncertain samples have higher entropy than those for which the VNN is certain about.Here, we select a data point As our experiments show, uncertainty sampling is suitable for active learning problems in which all unlabeled samples belong to known classes. However, for the case where the unlabeled pool also contains samples from unknown classes, we need a more robust way to exclude outliers. For this latter case, we employed the sampling procedure defined in Mundt et al. , which lnot belong to any previously known class. In other words, the lower this value, the more likely that the sample is an outlier.For completeness, here we will briefly outline the methodology proposed in Mundt et al. . IntuitiWe performed experiments on four image classification datasets—MNIST   with SGD optimizer.M1 as shown in Equation (2) with Adam optimizer.M2 as shown in Equation (1), with SGD optimizer.M2 as shown in Equation (1) with Adam optimizer.b images from the unlabeled pool. These were added to the labeled pool and used in the subsequent learning stages.We defined a learning stage  as lasting 150 epochs on CIFAR-10 and CIFAR-100 and 10 epochs on MNIST and FashionMNIST. At the completion of a stage, we requested labels for Our results were comparable with the state of the art on MNIST. However, as For this dataset, we compared our approach against the existing state of the art, including BatchBALD, WAAL, CoreSet, and DBAL. As shown As b) was 1,000. Under this setting, we compared our method against BatchBALD . VAAL, by comparison, came in second, followed by Core-set, exhibiting accuracies  for c=20 and  for c=20, respectively. Random sampling achieved an accuracy of 45.33% for c = 10 and 45.87% for c = 20.We first investigated the effect of bias that may be present in the initial labeled pool, a et al. , bias caa et al. , we remob on performance. Specifically, we investigated the effect of budgets of size b = 5% and b = 10%, referring to percentage of samples taken from b = 10 and  for b = 20.In this section, we tested the effect of different budget sizes across all noise levels. In other words, our M1 model with 30% noise was more accurate than VAAL, etc. with 10% noise.Next, we investigated the performance of our approach in the presence of noisy data caused by an inaccurate, or noisy oracle. As in Sinha et al. , we assuWe also replicated the sampling time analysis put forth in Sinha et al. . Specifiin-distribution data samples from out-of-distribution samples. Whenever our model selected an OOD sample, the oracle discarded the sample, thus reducing the overall budget size. The discarded samples were placed back in the unlabeled pool .Finally, we also tested an extreme case of active learning in which data samples from other datasets are mixed into the current unlabeled pool. We used CIFAR-10 for these experiments. Here, we intentionally added 20% data  from other datasets to the unlabeled pool; thus, the network must distinguish not only between informative and non-informative samples but also distinguish M2 method's performance on CIFAR-10 when the unlabeled pool contained images from either SVHN, KMNIST, or FashionMNIST. Here, we used Weibull sampling (section 3.4) due to its better outlier rejection compared to uncertainty sampling. Specifically, we grouped the Weibull probabilities of the samples in the unlabeled pool into three categories: (1) high-confidence samples, which resemble the labeled pool; (2) middle-confidence samples, which are mostly samples from the target dataset that do not resemble the labeled pool; and (3) low-confidence samples, which are mostly samples from other datasets (OOD). Here, we want to sample middle confidence samples and ignore the rest. We empirically determined that samples with Weibull probabilities in the range of 0.4 to 0.8 corresponded to this middle confidence range and thus selecting only samples in this range yielded the best results. For comparison, we also tested random sampling as a baseline. Impressively, despite the presence of 20% OOD samples, our method significantly outperformed existing state-of-the-art methods trained on the regular unlabeled pool  can be found below: JM and RE conceived of the presented idea. JM and BC carried out the experiments. JM, BC, and RE wrote the manuscript. All authors contributed to the article and approved the submitted version.This research was funded in part by NSF Award 1849946 and a grant from GoodAI Research.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


RNA-seq Normalization (RSeqNorm), that offers a simple workflow to compute normalized RNA-seq data for both FFPE and FF samples. It includes SMIXnorm and MIXnorm for FFPE RNA-seq data, together with five commonly used normalization methods for FF RNA-seq data. Users can easily upload a raw RNA-seq count matrix and select one of the seven normalization methods to produce a downloadable normalized expression matrix for any downstream analysis. The R package is available at https://github.com/S-YIN/RSEQNORM. The web-based tool, RSeqNorm is available at http://lce.biohpc.swmed.edu/rseqnorm with no restriction to use or redistribute.RNA-sequencing (RNA-seq) provides a comprehensive quantification of transcriptomic activities in biological samples. Formalin-Fixed Paraffin-Embedded (FFPE) samples are collected as part of routine clinical procedure, and are the most widely available biological sample format in medical research and patient care. Normalization is an essential step in RNA-seq data analysis. A number of normalization methods, though developed for RNA-seq data from fresh frozen (FF) samples, can be used with FFPE samples as well. The only extant normalization method specifically designed for FFPE RNA-seq data, MIXnorm, which has been shown to outperform the normalization methods, but at the cost of a complex mixture model and a high computational burden. It is therefore important to adapt MIXnorm for simplicity and computational efficiency while maintaining superior performance. Furthermore, it is critical to develop an integrated tool that performs commonly used normalization methods for both FF and FFPE RNA-seq data. We developed a new normalization method for FFPE RNA-seq data, named SMIXnorm, based on a simplified two-component mixture model compared to MIXnorm to facilitate computation. The expression levels of expressed genes are modeled by normal distributions without truncation, and those of non-expressed genes are modeled by zero-inflated Poisson distributions. The maximum likelihood estimates of the model parameters are obtained by a nested Expectation-Maximization algorithm with a less complicated latent variable structure, and closed-form updates are available within each iteration. Real data applications and simulation studies show that SMIXnorm greatly reduces computing time compared to MIXnorm, without sacrificing the performance. More importantly, we developed a web-based tool,  The application of next-generation sequencing (NGS) on measuring transcript abundance is widely known as RNA-seq. RNA-seq works by sequencing a library of cDNA fragments in a high-throughput manner in order to provide a comprehensive quantification of transcriptomic activities in biological samples , etc., are based on scaling factor estimation, where the normalized expression is obtained by dividing the raw count by an estimate of the sample-specific scaling factor. For example, RPM   tissue biospecimens are considered the gold standard in molecular analysis using gene expression, as freezing preserves RNA well. A number of normalization methods have been well-studied on FF bulk RNA-seq data  tissues, as they are widely available from routine diagnostic sample preparation  (http://lce.biohpc.swmed.edu/rseqnorm/), to conduct normalization for both FF and FFPE RNA-seq data. It offers seven normalization methods, with accompanying diagnostic plots for users to visually examine the RNA-seq data quality. Based on this platform, we compared different normalization methods using both comprehensive simulation studies and real data applications. These results, together with the RSeqNorm web portal, will facilitate users to select the best normalization method for their application.More importantly, there is a lack of platforms that integrate existing methods and produce normalized data by different methods. Evans et al.  mentioneRSeqNorm is given in section 2.2. Section 3 reports simulation studies and real data analyses. Finally, a brief concluding discussion is made in section 4.The paper is structured as follows. In section 2.1, we present the SMIXnorm method. The statistical model of SMIXnorm is simplified from that of MIXnorm. An efficient nested Expectation-Maximization (EM) algorithm is designed for model fitting. We further justify the simplifications by comparing SMIXnorm to MIXnorm from a technical point of view. An introduction of the web portal CI× J, where Cij is the number of reads in sample i for gene j. We adopt a similar latent variable framework as in MIXnorm to address the zero inflation of such data. That is, the binary latent variable Dj = 1 indicates gene j is expressed and Dj = 0 indicates gene j is not expressed in the study for j = 1, ..., J. Then we model the count data as a mixture of zero-inflated Poisson (ZIP) and normal distributions,Assume the RNA-seq count data from FFPE samples can be summarized by a matrix Lij = log(Cij+1) denotes the log transformed count, 0 ≤ πj, ϕ ≤ 1, δ, μi, σi≥0 for i = 1, ..., I and j = 1, ...J. The model assumes an unobserved variable Dj which follows a Bernoulli distribution with parameter ϕ. Genes with Dj = 0 are considered not expressed. These include low-expression genes that have abundance below detection limit, or biologically non-expressed genes that are absent from the biological sample of interest, or genes that should have been expressed but suffer from high-level mRNA degradation. The observed counts from non-expressed genes are due to background noise and are modeled by a zero-inflated Poisson distribution with gene-specific probability of extra zeros πj and a common expected Poisson count δ. Genes with Dj = 1 are expressed genes and we model the log counts of those genes by a normal distribution with sample-specific location and scale parameters. Compared to the model of MIXnorm, we use the normal distribution in (2) instead of a truncated normal distribution and a common Poisson mean δ rather than the sample-specific mean δi. As will be discussed in section 2.1.3, these would greatly facilitate the computation while not hurting much the performance. Note that the normal distribution assigns positive densities to negative log counts Lij, which never occur in real data. However, it is reasonable to assume that the negative values only take a negligible portion of the density in modeling expressed genes as the mean (log) counts of such genes are usually well above zero. Further, SMIXnorm is directly applicable to FF or like samples based on the same argument in Yin et al.  denote the set of all parameters in the simplified model. The observed data likelihood as a function of Θ is defined as follow:Let Θ =  is the probability density function (pdf) of Cij for expressed genes such that log(Cij+1) follows the normal distribution in (2), and p is the zero-inflated Poisson probability mass function (pmf) for non-expressed genes as described in (1). Note that a continuous distribution is used to approximately model the discrete random variable log(Cij+1). See Yin et al.  as the complete data and update the parameter estimates iteratively by an EM algorithm. However, direct implementation of the EM algorithm requires Newton-Raphson type maximization to update the parameters within the ZIP component, which may cause the EM algorithm fail to converge due to numerical instability. By a similar approach in Yin et al. . Let Ycom =  denotes the complete data. The complete-data log-likelihood with latent variables D and Z is given byA common approach to obtain the maximum likelihood estimate (MLE) of Θ is to treat ( = (μt) denote the set of current parameter estimates after t iterations of the algorithm. The distribution of D given the observed data and Θt) as observed data, where j, the kth cycle of the inner E step involving Z is reduced to calculating the conditional expectation of Zij given Note that in (5), the complete-data log-likelihood is linear in C) in two consecutive iterations which is trivial to obtain via (4). We set the number of inner EM cycles K = 5 within each iteration in our implementation as in Yin et al.  through the conditional expectation of Dj that can be computed by Equation (7). We observe in practice that after the nested EM algorithm converges, the conditional expectation i to δ has little effect in the normalization step.There are two major simplifications when comparing SMIXnorm to MIXnorm. First, SMIXnorm assumes a common Poisson mean δ for the non-expressed genes, where MIXnorm allows the sample-specific Poisson means δX =  by a truncated normal distribution TN, where θ and τ are the mean and standard deviation of the corresponding normal distribution before truncation. To estimate the mean of the truncated normal distribution, which is a function of θ and τ, say m, one common approach is to first obtain the maximum likelihood estimates m is simply the sample mean 2). Under the large sample situation, as is often the case in RNA-seq data involving many genes from whole-genome experiments, both m. The use of a normal distribution without truncation is appealing since it has closed-form parameter updates within the nested EM algorithm, while a truncated normal distribution requires additional latent variable structures and data augmentation to obtain closed-form parameter updates  provides a set of analysis routines for normalization of RNA-seq data from either FF or FFPE samples. The workflow is illustrated in C. The web portal can compute the normalized expression as well as diagnostic information for download. We implement SMIXnorm, MIXnorm and five commonly used normalization methods, including Reads Per Million (RPM), Upper-Quartile (UQ), DESeq, Trimmed Mean of M-values (TMM) and PoissonSeq (PS). Though developed for FFPE data, SMIXnorm and MIXnorm are directly applicable to FF data normalization. For FF data normalization, there seems to have no unanimously best normalization method. We suggest using the methods offered by RSeqNorm and evaluating the normalization performance using prior information or known biological knowledge. For example, users may conduct a differential expression (DE) test following the normalization step and select the method that detects more genes that are known to be differentially expressed in the literature.The RSeqNorm accepts the raw read count matrix in a comma-separated values (CSV) file. The  element of the count matrix records how many reads have been assigned to gene j in sample i. An example input file is downloadable from the RSeqNorm website. Detailed file requirements are shown in I(·) is the indicator function and cw is set to 0.5 in RSeqNorm. In practice, we observe that the choice of cw is not sensitive in identifying expressed genes. The conditional probability of being expressed  over all input genes. High frequencies near 0 indicate that most biologically expressed genes are actually expressed in all samples in the experiment and so the data are of high quality. Ideally, one would expect that a gene is either expressed among all samples or not expressed in any of the samples. In this ideal case, the histogram shows frequencies only at 0 and 1 on the horizontal axis.We conducted simulation study to show that SMIXnorm greatly reduces computing time and maintains performance comparable to MIXnorm that is better than all FF RNA-seq normalization methods. Our simulation study evaluates the computing time of SMIXnorm and MIXnorm with different sample sizes. Note that the SMIXnorm model cannot be used to simulate the data since it permits negative log transformed counts that do not exist in practice. Here, a modified MIXnorm model was used to generate synthetic data sets  data are available in the repository Gene Expression Omnibus from a published study  > 2] from FF and FFPE samples based on normalized expression levels and summarize results in The paired design of the ccRCC study allows us to conduct differential expression (DE) analysis between ccRCC and normal tissues. Specifically, two ccRCC and two adjacent normal tissues were obtained from each of the 16 patient. The two pairs were then stored in FFPE and RNAlater, respectively. We identified DE genes [Benjamini–Hochberg adjusted We have developed an efficient normalization method, named SMIXnorm, for FFPE RNA-seq data normalization. Modified from the MIXnorm statistical model, we use a similar two-component mixture model to separately model the expressed and non-expressed genes. The simplifications of the statistical model avoid the complex likelihood function and the need of a complicated latent variable structure to invoke the nested EM algorithm. We have shown through real data applications and simulation studies that SMIXnorm greatly reduces the complexity of the likelihood function and the computing time without sacrificing the performance. Though other FF normalization methods take only about 1 s to normalize each dataset in our three data applications, their performance is not comparable to that of SMIXnorm and MIXnorm for FFPE samples. Some FF normalization methods perform even worse than the use of original data without any normalization.We mentioned in the soft tissue sarcomas application that MIXnorm failed to converge at the default maximum number of iterations and tolerance level. Due to the severely different RNA degradation levels among the 41 soft tissue sarcomas FFPE samples, 10 FFPE samples have more than 10, 000 zero counts and 9 FFPE samples have less than 3, 000 zero counts. Consequently, MIXnorm gives negative estimated sample-specific location parameters of the truncated normal distributions for those samples with a higher proportion of zero counts, which blurs the distinction between expressed and non-expressed genes. SMIXnorm models the expressed genes by normal distributions without truncation, which naturally constrains the location parameters to be non-negative in all the EM iterations as sample means are used for estimation. Thus, SMIXnorm seems to be more robust and converges faster than MIXnorm.Recently, single-cell RNA sequencing (scRNA-seq) becomes an important technology in molecular analysis. While bulk RNA-seq measures the expression in the population level across cells, scRNA-seq allows for the cell level resolution and therefore, reveals heterogeneity of cell subpopulations. Similar to FFPE RNA-seq data, a prominent feature of scRNA-seq data is the sparsity. The high proportion of zero count arises for both biological reasons and technical reasons  for FF or like RNA-seq data. Though MIXnorm and SMIXnorm are specifically designed for FFPE RNA-seq normalization, they can be applied to FF data directly. However, it is generally inappropriate to implement other existing methods on FFPE data. The input for all methods is a read count matrix at the gene level. The output is an expression matrix after normalization with the same dimension as the input data. The R package, RSEQNORM, which implements SMIXnorm and MIXnorm is downloadable from the website (http://lce.biohpc.swmed.edu/rseqnorm).With the rapid adaption of FFPE samples in RNA-seq analysis, it is important for users to realize that different normalization procedures should be used for FFPE vs. FF data. We offer The original contributions presented in the study are included in the article/RSeqNorm. All authors contributed to the article and approved the submitted version.SY and XW proposed and implemented the SMIXnorm method. SY, XZ, XW, and GX performed data analysis and wrote the manuscript. XZ, GX, and YX provided resources and helpful discussions. SY, XZ, and BY developed the web portal The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cellular and molecular aberrations contribute to the disparity of human cancer incidence and etiology between ancestry groups. Multiomics profiling in The Cancer Genome Atlas (TCGA) allows for querying of the molecular underpinnings of ancestry-specific discrepancies in human cancer. Here, we provide a protocol for integrative associative analysis of ancestry with molecular correlates, including somatic mutations, DNA methylation, mRNA transcription, miRNA transcription, and pathway activity, using TCGA data. This protocol can be generalized to analyze other cancer cohorts and human diseases.For complete details on the use and execution of this protocol, please refer to   •Protocols for ancestry associations with TCGA molecular data•Protocols for ancestry associations with oncogenic pathways•Statistical and power analysis for determining significant associations•Key considerations of potential confounding factors Cellular and molecular aberrations contribute to the disparity of human cancer incidence and etiology between ancestry groups. Multiomics profiling in The Cancer Genome Atlas (TCGA) allows for querying of the molecular underpinnings of ancestry-specific discrepancies in human cancer. Here, we provide a protocol for integrative associative analysis of ancestry with molecular correlates, including somatic mutations, DNA methylation, mRNA transcription, miRNA transcription, and pathway activity, using TCGA data. This protocol can be generalized to analyze other cancer cohorts and human diseases. Timing: 0.5–1 h1.https://gdc.cancer.gov/node/1248) . The ancestry calls include patient classification into European (EUR), East Asian (EAS), African (AFR), Native/Latin American, and (South) Asian, using genotyping calls from SNP6.0 array and exome sequencing data  or small indels 3.Exclude ancestry-admixed samples (20%<non-EUR ancestry<80%) and genes with less than 30 altered samples in the AFR group.Note: We chose the threshold for ancestry-admixed samples to ensure we have enough non-European ancestry samples while excluding samples with a high-degree of ancestry mixture to allow a clear comparison between populations. We found two samples with more than 20% ancestry in more than two ancestry groups  in TCGA. We included those two samples by associating the percentage of each ancestry with genomic alterations.4.Combine the above file with the TCGA ancestry assignments, clinical data, subtype annotation 5.[TMB] ~ [ancestry] + [age] + [gender] + [subtype]Code EUR ancestry as 0 and AFR ancestry as 1. Code the somatic alteration status into a binary variable that represents Tumor Mutation Burden (TMB). Samples with one or more mutations have TMB 1, otherwise 0. Perform logistic regression of somatic alteration status on ancestry, controlling for age, gender, and subtype. One could use the following model in R:6.Apply Benjamini-Hochberg FDR correction to all the p-values7.Repeat steps 3–6 to analyze somatic alterations between EUR and EAS by coding EUR as 0 and EAS as 1.8.Visualize ancestry-associated genes using a Q-Q plot see )9.Repeat steps 3–8 for each cancer type and perform cancer-type-specific analysisNote: Ancestry-specific somatic alterations can be tissue-type specific. For certain cancer types, a large set of sample size for non-European samples is needed to reach sufficient detection power . Calculate the Spearman’s correlation between their mutation frequencies and the fraction of AFR or EAS ancestry per individual obtained from ADMIXTURE  see anc. Use the11.[mutational signature] ~ [ancestry] + [age] + [gender] + [subtype] + [TMB]Download the mutational signature file see . Correla12.Apply the Benjamini-Hochberg FDR correction to all the p-values from the mutation signature correlations.13.[ancestry] ~ [arm-level SCNA] + [aneuploidy] + [genome doubling] + [age] + [gender] + [subtype]Download TCGA arm-level somatic copy number alteration calls (SCNAs), genome doubling, aneuploidy scores, and leukocyte fraction or immune infiltration scores (IMS) calculated based on methylation see . Correla14.Apply the Benjamini-Hochberg FDR correction to all these p-values.15.Validate genes with ancestry-specific somatic alterations identified from cancer-type-specific analysis (FDR < 0.1) by comparing alteration frequencies in AFR vs. EUR and EAS vs. EUR using Fisher’s exact test, in an independent cancer cohort CRITICAL: Inclusion of cancer subtype in the TCGA pan-cancer analysis is necessary to eliminate mutations enriched in specific subtypes due to uneven distribution of ancestry in the subtypes . Other clinical characteristics may also be considered potential confounders in the somatic mutation-ancestry correlations, e.g., disease stage and smoking history.Note: For validation using external data sets, it is crucial to ensure that the disease ontology terms are harmonized with TCGA data sets. In some cases, external datasets will not have as detailed subtype information. The correlation between somatic alteration frequency and the fraction of AFR or EUR ancestry per individual is performed using binomial logistic regression. It is also important to use the same procedure of ancestry calling for independent cancer cohorts. See our companion protocol Timing: 3 hTiming: 1 h for step 16Timing: 1 h for step 17Timing: 1 h for step 1816.a.Note: The obtained beta value file is a masked matrix with NA indicating either lack of signal or design flaws. More detailed information about masking can be found in  to stabilize the slope coefficient in different runs.Create a matrix file with rows corresponding to the sample and columns corresponding to CpGs, and the sample covariates : [DNA methylation β-value] ~ [ancestry] + [cancer subtype] + [gender] + [purity] + [age]ii.Reduced (without ancestry): [DNA methylation β-value] ~ [cancer subtype] + [gender] + [purity] + [age]For each probe, fit two linear regression models, with and without ancestry, while accounting for cancer subtype, gender, age, and tumor purity, i.e.,b.anova function.Test the F-statistics and p-value for ancestry association by contrasting the two regression models. In R, one can use the c.Apply the Benjamini-Hochberg FDR correction to all the p-values.d.Identify ancestry-associated methylation as CpGs with FDR-corrected q < 0.05. One may optionally filter CpGs with a small effect size. See e.Repeat steps 16c–17c for the seven cancer types with more than ten non-EUR samples .Test DNA methylation for ancestry associationNote: One can validate the effectiveness of the fitting by inspecting the fitted slope coefficient for cancer subtypes against known biology. For example, in TGCT one will expect an excessive number of CpGs with loss of DNA methylation due to its cell of origin, and in IDH-mutated gliomas one expects a global gain in DNA methylation.18.a.Note: The Infinium HumanMethylation450 BeadChip has 65 built-in SNP probes (probes with ‘rs’ in the probe name). One can use these standard SNP probes as positive controls for ancestry association since these SNPs reflect the genetic background and differ between ancestry groups. For example, in our pan-cancer analysis, 63 of these 65 SNP probes were significantly associated with ancestry . One care 3A in ). The saFor both pan-cancer analysis and cancer type-specific analysis, tally the number of ancestry-associated CpGs.b.Note: It is essential to use a comprehensive gene model to capture all biotypes. Many CpGs are associated with noncoding RNAs, pseudogene transcription, and endogenous viral elements. Our annotation uses GENCODE v22. One should use a more updated gene model if available. A significant fraction of the ancestry-associated CpGs are not identified as associated with genes. CpGs at potential regulatory enhancer elements can be identified using the latest cis-regulatory element definition, such as  of SPATC1L . In addition to sex, age is a major determinant of DNA methylation of CpGs of specific sequence context in late-replicating DNA and polycomb targeted CpG islands (gain of DNA methylation). Tissue-specific DNA methylation also causes tissues of different cell composition to have shifts in average DNA methylation readouts. Therefore, it is critical to include sex, age, and tumor purity or other cell type estimates as explicit covariates in the model searching for ancestry-associated DNA methylation.CRITICAL: Sample size and residual tumor heterogeneity affect the ability to find statistically significant ancestry associations .CRITICAf.Use a Wilcoxon signed-rank test to test whether a local ancestry Z-score is associated with ancestry-specific DNA methylation level.  in pan-cancer analyses. Only subtype (where applicable) is used in cancer-specific analyses. If subtype is not taken into account, subtype-specific gene expression may dominate ancestry associations.Code .txt file with samples as rows and columns as variables . Batch information can be extracted from the plate ID portion of the TCGA barcode.c.Code ancestry calls, such that EUR ancestry is 0 and EAS or AFR ancestry is 1.d.Sort the mRNA matrix so that it matches the sample and order from the preceding step.e.Note: we excluded genes that show a lack of expression (zero read count) in over 20% of samples since zero read count is highly unlikely in practice and indicates low quantification. We excluded 4,262 genes, leaving 16,269 genes in the final matrix.Log2-transform the gene expression matrix and filter for genes expressed in >80% of samples.f.Transpose the mRNA expression data matrix so that samples are in rows and genes are in columns A Bottom.Retrieving and filtering TCGA Data20.a.i.Reduced (without ancestry): [mRNA expression] ~ [tumor type / subtype] + [batch]ii.Full (ancestry): [mRNA expression] ~ [ancestry] + [tumor type / subtype] + [batch]Perform two independent linear regressions on the gene expression data, correcting for tumor type/subtype and batch (use glm function in R):b.anovaFor each gene, perform an ANOVA comparing the reduced model and full model's coefficients and output the beta-statistics and p-values. In R, one can use c.Use the Benjamini-Hochberg FDR adjustment on ANOVA p-values to correct for multiple comparisons.d.Note: we used 0.001, a stringent FDR cutoff, to focus on sizeable effect-size ancestry association discoveries .f.The same analysis is also performed on a per-tumor type basis as above.Identification of ancestry associated genesNote: This procedure was performed independently for AFR vs. EUR and EAS vs. EUR. After generating the output files, it is useful to spot-check results for known genes associated with the variables to ensure the sample file was coded correctly and matched the gene expression matrix. For example, one can plot median expression over ancestry for GSTM1  data  see key.b.Log2-transform RPM values, using y = log2(x+1).Preprocess input reference mature strand dataNote: These data include RPM expression levels of 743 miRNA mature strands (miRs) for 10,824 TCGA samples, batch-corrected to enable pan-cancer analyses. The consolidated dataset, for which both miRNA expression data and ancestry calls were available, included 8,180 samples across 32 tumor types. No miRNA sequence data were available for GBM, a pilot-phase TCGA project that used microarrays rather than sequencing. Among the 32 tumor types, 12 had subtype annotations. If batch corrected, pan-cancer atlas miRNA data are used, set negative miRNA reads-per-million (RPM) values to 0.22.a.Apply a linear regression model with a binary design matrix based on the subtype calls as predictors to explain the normalized RPM expression of mature strands across the samples of each tumor type and subtype.b.i.The sample size of each group to be tested should be greater than or equal to five.ii.The coefficient-of-variation across the expression levels of the union of both sample groups should be 0.1 or larger.iii.For the union of samples of both groups, at least five samples had an RPM value of 25 or higher (4.7 in log2 space).iv.miRNAs that were flagged as having ancestry-specific SNPs (identified in somatic mutation association above) were discarded. See below for more details about the ancestry-specific SNPs.Perform Wilcoxon rank-sum tests between each pair of ancestry groups on the output of this model, applying the following pre-filtering criteria:Quantify ancestry associations for mature strandsCRITICAL: In TCGA miRNA-seq data, only reads aligned with exact matches were counted towards expression (pression . Given tpression  lists th23.a.Download a GFF file giving miRNA annotations from miRBase v22.1 see . Read thb.Download a GFF file giving gene and transcript annotations from Ensembl see . Read thc.i.same strand (ignore.strand=FALSE).Apply the iRanges mergeByOverlap function to the miR and gene GRanges objects. It is required that gene and miR be on the ii.opposite strand from the gene, using mergeByOverlap while ignoring strand, then taking the difference between these miRs and the same-strand hosted miRs calculated above.Identify miRs that are overlapped by a gene but are on the iii.intergenic miRs as the difference between all miRBase miRs and the ignore-strand miRs calculated above.Identify Identify hosted miRs and host genes by seeking overlap of gene and miR transcripts above see . In R, od.i.Download reference miRBase mature strand sequences as ‘mature.fa’ from miRbase see .ii.Reorganize the file so that each mature strand has only one row in the file. Read the new file into R.iii.′ end of each sequence, in R, using substr.Isolate bases 2 to 8 from the 5iv.′ or 3′ mature strand contains each seed sequence. Associate seed sequences with miRs. See notes below on isomiRs and miRNA targeting.Tabulate distinct seed sequences and which miRNA's 5Seed sequence familiesCompile a general miRNA-host-gene data resource24.a.Download pan-cancer, batch-corrected, normalized RSEM GRCh37 expression data for 20531 RSEM genes and 743 expressed reference miRBase mature strands  see key.b.For a cancer type, given the host gene/hosted miRNAs , calculate distributions of Spearman’s correlations between RPMs for hosted a miR and RSEM normalized expression for the miR’s host gene see .Calculate expression correlations between miRNAs and host genesCRITICAL: Reference genome assembly and gene annotations. The latest Ensembl gene annotations are based on the recent GRCh38 assembly, but TCGA and PanCancer data are GRCh37/hg19. While we recognized that the GRCh37/hg19 TCGA RSEM gene expression data would be unavailable for some Ensembl v94/GRCh38 genes, we prioritized using near-current miRNA annotations and biotypes . To get MIMAT mature strand IDs and reference sequences , we clicked on the miRBase ‘Get sequence’ button for a mature strand.To add the read pileup graphics, we went to each miRNA’s miRBase v22.1 web page and copied the read pileup graphic . Member9-2, 9-3  was not Note: isomiR variation and targeting. A multi-step biogenesis process generates mature strands. The ‘trimmed’ locations of 5′ and 3′ ends of a mature strand may differ from the locations given for a reference miRBase strand  data matrix see b.i.Create a merged ancestry/subtype annotation file with samples as rows and variables  as columnsii.Create a PARADIGM pathway matrix file with samples as columns and pathway features as rowsCreate input files for association analysisc.Filter and sort the above two input files for overlapping samples (rows of the merged ancestry/subtype file and columns of the IPL data file) by patient ID so that they have matching orderd.Exclude ancestry-admixed samples (20%<non-EUR ancestry<80%)Retrieve and preprocess pathway activity dataNote: The PARADIGM algorithm integrated platform-corrected expression, gene-level copy number, and pathway interaction data and was used to infer activities of ~19K pathway features  > 0.05ii.>10% of samples have non-zero IPLsiii.Cross-sample standard deviation of IPLs > 0.05For each cancer type, filter pathway features by the following minimum variation criteria:c.i.Apply a linear regression model with a binary design matrix based on the subtype calls as predictorsii.Calculate the residuals from the regression as IPLs “not explained” by subtypeIf the cancer type is annotated with subtypes, first regress IPLs in each sample on its subtype membership. Use the residual IPLs as the IPL of each sample in the following analysis.d.Compare EUR and each non-EUR ancestry group using 1) t-test and 2) Wilcoxon Rank sum test.e.i.each cancer type without subtype adjustmentii.each cancer type with subtype adjustmentiii.each cancer subtypeConduct both tests withinf.Apply Benjamini-Hochberg FDR correction to all the p-values.g.i.both tests (t-test and Wilcoxon rank-sum test)FDR corrected p < 0.05 from ii.abs(difference in mean IPLs) > 0.05Identify pathway feature that meets the following criteria as significantly associated with ancestryIdentification of ancestry associated pathway featuresNote: Among the 33 tumor types, 24 have ≥5 patients of non-European ancestry and were considered in the analysis. 10 of the 24 included tumor types have subtype annotation, and the subtype-adjusted and within-subtype analyses (described in steps 27b–27d) were performed.28.a.Note: Each line in the pathway structure file is an interaction between an (upstream) parent node in the first data column and its downstream (child) node in the third data column. The middle column denotes the type of interaction. Interactions can be of the following 6 types: "-t>" = transcriptional activation ; "-t|" = transcription repression ; "-a>" = activation ; "-a|" = repression ; "component>" = component of a complex ; "member>" = member of a family.Download and read in the pathway interaction dictionary containing ~45K interactions linking pathway features within a superimposed pathway structure , Data S2b.For each within tumor type, subtype adjusted, and within subtype comparison, identify ancestry-associated pathway features that are the parent nodes in >10 regulatory interactions  within the network structure as key regulatory nodes.c.Select the comparisons that yielded key regulatory nodesd.Obtain the differences in means of the key regulatory nodes for selected comparisons e.Merge differences in means into a single data matrix for display . For instance, only 143 of the ~19K pathway features have > 10 downstream targets and have the potential of being identified as a key regulatory node in our analysis. However, if one were to require a lower number of downstream targets , 376 features would potentially be identified.29.Note: Each line in the pathway structure file is a pathway feature and has two columns containing the feature name and feature type. The dictionary uses the term 'protein' instead of 'gene' (as different isoforms of the same gene may exist as separate features in the SuperPathway)a.Read in pathway feature dictionary for ~19K pathway features within the superimposed pathway structure see Note: Eacb.i.cancer driver gene set (Table S1 of )ii.curated cancer pathway gene set (Table S3 of )iii.curated DNA damage repair gene set (Table S1 of )Download the following gene sets see i.cancer c.Create a single merged .gmt file from the three gene sets above as an input to the enrichment analysisd.i.Number of cancer pathway/driver genes identified as having ancestry-associated IPLsii.Number of cancer pathway/driver genes that can be mapped to the pathway feature dictionaryiii.Number of 'protein' features identified as having ancestry-associated IPLsiv.Number of 'protein' features in the pathway feature dictionaryFor each comparison, use a hypergeometric test to identify over-represented gene sets in the merged input file based on the following counts (over-representation is assessed only if the odds ratio is > 2):e.Apply the Benjamini-Hochberg FDR correction to the hypergeometric test p valuesf.Identify ancestry-associated pathways as those with FDR-corrected p values < 0.05 in the subtype-adjusted analysis , which may otherwise be missed.See  Figure 2See  Figure 3A bona fide ancestry association should show a clear difference between ancestries. For example, if one were to plot DNA methylation from different ancestry groups, one should see a clear distinction in the distribution of the DNA methylation level A. DNA meSee  Figure 4See  Figure 5CCPG1. The figure also shows a miRBase v22.1 read pileup profile, which indicates that, for this miRNA, we expect the 5p strand to be more highly expressed than the 3p strand. miRBase read pileups also suggest the level of 5′ and 3′ isomiR variation to expect for each mature strand. Figure S6H-J of .ands in : In TCGA marker papers, for location-specific calculations of the relationship between DNA methylation and miRNA expression, we used DNA methylation probes that were close to miRNA TSS’s reported from FANTOM deep CAGE sequencing, e.g., Table S2 from  and the odds ratio (slopes).3.For each mutation with a specific frequency at a different odds ratio, simulate 1000 times generalized linear regression between somatic mutation and ancestry assignment, controlling for the subtype. Power is defined as the percentage of tests returned with a p-value less than 5e-05 .4.Plot mutation frequency with different odds ratio against power.5.a.Simulate DNA methylation levels for each CpG included on the HM450 platform using a beta distribution Beta for gender-specific methylation differences and a normal distribution of N  for subtype-specific methylation differences.b.For each given methylation level, compute the DNA methylation readout as a binomial distribution Binom(N=40), modeling the average bead number for each probe in the HM450 microarray.c.Simulate methylation readouts for all HM450 CpGs 1000 timesd.Perform the same regression analysis for each simulation using the real ancestry annotation.e.Compute the statistical power as the fraction of inferred significant ancestry-specific differences over all the simulated methylation differences. Plot power against simulated methylation differences.f.Repeat that process for pan-cancer and each cancer type.The following protocol details the simulation procedure to estimate the power to detect ancestry-associated differential DNA methylation.CRITICAL: The TCGA dataset is not balanced with equal representation of patients of different gender, subtype, and ancestry backgrounds. To faithfully reflect the dataset's limitations, power analyses should maintain the population structure and only permutate the ancestry label. It is expected that greater simulated methylation differences and more samples, especially of the less-represented ancestry, will result in greater power to detecting ancestry-associated differential methylation. For example, EAS-EUR differences are better captured in LIHC, where Asians are well-represented, than in BRCA, where they are not. Conversely, AFR-EUR differences show the opposite effect because African ancestry is better represented in the TCGA BRCA cohort . Therefore, we focused on pan-cancer ancestry association and cancer type-specific association only in eight cancer types with sizable non-European patients. While this limits our capability to detect more subtle association in underrepresented ancestry groups, the methodology can be used to analyze more balanced cohorts and cohorts with better coverage of the non-European ancestries.In TCGA, non-European samples are underrepresented except for a few cancer types .CpG methylation might not be indicative of transcript expression. Typically, DNA methylation is associated with transcriptional silencing only at gene promoters and cis-regulatory elements. These transcription-associated CpG can be hard to identify and vary by cell type context. Besides, DNA methylation does not fully determine gene expression. Other activating and repressive epigenetic modifications can co-regulate gene transcription. Complementary epigenetic silencing can confound the absence of gene expression. For example, polycomb-repressive complex (PRC)-mediated suppression of gene expression is a more reversible parallel mechanism of epigenetic silencing . It typiAncestry-associated miRNA has too many target genes. It is hard to prioritize the most disease-relevant targets.Typically, miRNA targeting can lead to a targeted RNA transcript being degraded; for coding genes, targeting can also interfere with protein translation . For ranA lack of correlation between ancestry-associated miRNA transcription and host gene expression .One can check whether the miRNA and host gene are transcribed on the same strand. Moreover, if the miRNA and host gene transcription happen on different strands, the miRNA might not be hosted by the host gene. If the miRNA has its promoter, it can also cause de-coupling of the miRNA expression and the host gene. The miRNA transcription will then be driven by the promoter independent of the host gene transcription.achernia@broadinstitute.org).Further information and requests for resources and reagents should be directed to and fulfilled by the lead contact, [Andrew D. Cherniack] (This study did not generate new unique reagents.https://gdc.cancer.gov/about-data/publications/CCG-AIM-2020).Data used and generated are listed in the