D path associations and important CNVs. The experimental results on both simulation and prostate cancer data show that IHI-BMLLR is significantly better than two state-of-the-art CNV detection methods  under false-positive control. Furthermore, we applied IHI-BMLLR to prostate cancer data and found significant path associations. Three new cancer-related genes were discovered in the paths, and these genes need to be verified by biological research in the future.Copy number variation (CNV) may contribute to the development of complex diseases. However, due to the complex mechanism of path association and the lack of sufficient samples, understanding the relationship between CNV and cancer remains a major challenge. The unprecedented abundance of CNV, gene, and disease label data provides us with an opportunity to design a new machine learning framework to predict potential disease-related CNVs. In this paper, we developed a novel machine learning approach, namely, IHI-BMLLR (Integrating Heterogeneous Information sources with Biweight Mid-correlation and L1-regularized Logistic Regression under stability selection), to predict the CNV-disease path associations by using a data set containing CNV, disease state labels, and gene data. CNVs, genes, and diseases are connected through edges and then constitute a biological association network. To construct a biological network, we first used a self-adaptive biweight mid-correlation (BM) formula to calculate correlation coefficients between CNVs and genes. Then, we used logistic regression with L1 penalty (LLR) function to detect genes related to disease. We added stability selection strategy, which can effectively reduce false positives, when using self-adaptive BM and LLR. Finally, a weighted path search algorithm was applied to find top  Copy number variations (CNVs) contribute to a substantial fraction of human genetic variation and are increasingly involved in disease associations and genome evolution . Many evWith the increase in the number of verified CNV–disease associations, several databases have been published, such as DGV , DGVa L, dbVar , CNVD Q, and DECin silico analyses of tumor-gene expression. The calculation methods can mainly be categorized into statistical classification-based and machine learning-based methods. People use statistical classification methods to develop innovative solutions to identify disease-related CNVs. For example, Recently, many researchers are committed to using machine learning-based methods to study the complex mechanisms between CNVs and diseases. Overall, the results of existing machine learning-based methods show that integrating diverse CNV-related information, disease-related information, and machine learning methods can boost the prediction accuracy of the CNV–disease association. However, most existing methods are limited to CNV and disease data. The prediction results contain many false-positive results  due to lack of consideration of the role of gene in CNV–disease association mechanism. In addition, most methods calculate the CNV/disease similarities only on those that have at least one known CNV–disease association.D path associations and important CNVs. To address the aforementioned issues (or limitations), based on our previous work , we put Compared with the traditional CNV–disease association analysis methods, our proposed approach has the following advantages. Firstly, compared with single CNV analysis, IHI-BMLLR can detect weighted associations, and consider all CNVs and genes simultaneously. Secondly, IHI-BMLLR uses three kinds of data , which can use more information about CNV–disease mechanisms and provide insight into CNV–disease complex association mechanisms. Thirdly, the self-adaptive BM and weighted path search algorithm can help IHI-BMLLR accurately identify disease-related CNVs. Finally, because IHI-BMLLR does not require prior information, it is more suitable for large-scale data lacking complete prior information.https://github.com/nathanyl/IHI-BMLLR.In the experiment section, we first compared the receiver operating characteristic (ROC) performance of IHI-BMLLR with two state-of-the-art CNV detection methods (CCRET and DPtest) using four kinds of simulation data. The experimental results show that IHI-BMLLR can significantly improve the detection performance of disease-related CNVs using gene data. From the results of the boxplots, we can see that the stability of IHI-BMLLR is better than CCRET and DPtest. Prostate adenocarcinoma (PRAD) is the most common cancer for males and the second death rate caused by cancer in men . IHI-BMLN ∈ {200, 500, 800, 1100}. The simulation data were generated as follows. Firstly, we generated 100 causal CNVs that are related to disease. The state label of the data is an equal number of diseases or normal states . Secondly, we used a three-layer fully connected neural network to generate 10 gene expression data. In the three-layer fully connected neural network, the input layer contains 100 nodes, the middle layer contains 10 nodes, and the output layer contains 1 node. The nodes in the input layer represent CNVs, the nodes in the middle layer represent genes, and the nodes in the output layer represent disease. Thirdly, we used the TensorFlow with the back propagation (BP) algorithm to train the three-layer fully connected neural network until the neural network can correctly predict more than 95% of disease state label nodes  to the data.In the “Results and Discussion” section, we compared IHI-BMLLR with CCRET and DPtest on simulation data and prostate cancer data. In this section, we will introduce simulation data set and prostate cancer data. Simulation data set contains four kinds of data sets with the same number of CNV–gene true associations, the same number of features, and different sample sizes . In simulation data set, the number of CNV–gene true associations is 100 CNVs–10 genes associations, the number of samples el nodes . We usedhttps://xenabrowser.net/  element of CNV data matrix. Gene expression matrix is represented by Y ∈ RN×Q with N sample size and Q gene traits, and disease state label matrix is represented by Z ∈ RN×K with K diseases.In this paragraph, we introduce the notations used in this article. We used boldface uppercase to represent matrices, boldface lowercase for vectors, and lowercase letters for scalars. We denote the CNV genotype data matrix by D path associations and important CNVs. In the following paragraphs, we introduce the methods in the machine learning framework of finding the CNV–disease path associations. We also introduce how to discover CNVs affecting genes, identify genes affecting diseases, construct a biological network, and define a mathematical formula to calculate the scores of the path associations. Finally, we show how to use a weighted path search algorithm to discover top We constructed a biological association network, the nodes in the network are used to represent CNV, genes, and diseases. We describe how to establish a connection between two nodes using self-adaptive BM coefficient and LLR under SS strategy. Self-adaptive BM and LLR are powerful techniques to find correlations between CNVs and genes or correlations between genes and diseases, and the SS method is used to effectively control the number of false-positive results.Based on the self-adaptive BM coefficient, we computed correlation coefficients between CNVs and genes :α is often set to 9 empirically. However, this setting does not consider the characteristic of the data. In this paper, we set α to the data-driven parameter (mad(x)+med(x))/2. The range of self-adaptive BM values is from −1 to 1. If the correlation between a pair of elements is stronger, the absolute value of BM is larger.The parameter y as follows:Next, Equations (2) and (3) are used to detect associations between diseases and genes, the logistic loss function is applied to measure the gap between predicted disease state and true disease state . Given a gene expression vector RQ represents coefficient value vector of logistic regression model in Equation (2), and σ(⋅) represents the sigmoid function; thus, logistic regression formula with L1 penalty function can be defined as follows:where θ ∈ Z ∈ RN×1. The regularization parameter can affect the performance of model; the regularization parameter λ is determined by cross-validation technique. However, we tend to get many false-positive results when only using the cross-validation technique . Secondly, in M times experiments, if the number of non-zero absolute value of coefficient between CNV and gene or gene and disease is greater than or equal to M⋅ϕ times, then the CNV or gene will be retained. ϕ is a predefined parameter used to effectively control the number of false-positive results. People have done a lot of in-depth research on the choice of M and ϕ values. Meinshausen’s research show that when M is greater than or equal to 100 times, it is sufficient to control false positives  represents the expected value of false-positive CNVs associated with the qth gene, and parameter c represents the number of non-zero associations found by the IHI-BMLLR method. From Equation (4), we can see that the upper limit of false-positive results is inversely proportional to the parameter ϕ. When we apply IHI-BMLLR to detect the association between genes and diseases, the same situation exists for the relationship between false positives and parameter ϕ.where qth gene trait and the pth CNV, the significance score of the association can be defined as follows:After self-adaptive BM and LLR combined with the SS method is used in the data set, and after obtaining the result, we can calculate the significance scores of the connections in the biological association network. When detecting the relationship between the pth CNV and qth gene. Nb represents the number of data sets in which Nb/M is in the range 0–1. mean) represents the average value of the accurately identified correlation coefficients. The larger the value of score  . In the D high-score path associations. The weighted path score formula can be defined as follows:In a biological association network, a weighted path search algorithm can be defined as follows. Firstly, in the biological network, we choose genes that are simultaneously associated with CNV and disease. We can find all existing association paths by selecting these genes. Secondly, we can obtain the score of paths by summing the weighted scores [Equation (5)] of each part of the path. Finally, we sort all paths in descending order of scores and select the top In order to evaluate the performance of methods fairly, we used the ROC curve to observe the performance of methods and compared the performance of methods using the area under receiver operating characteristic (AUROC). We calculated TPR and FPR based on the confusion matrix .In the disease-related CNV true labels test, FaST-LMM-EWASher , which iIn the designed real data and fake data test, three methods  were applied to real CNV–disease data and obtained a set of “real” results; then, these three CNV detection methods were applied to “fake” data that exchange samples between two category conditions and obtained a set of “fake” results. Compared with the expected biologically significant “real” results, the “fake” results have no biological significance. An excellent CNV detection method should find as many CNVs as possible in the “real” results, while reporting as few CNVs as possible in the “fake” results. In addition, when methods find the same amount of CNVs on the “fake” data set, the method that detects more CNVs on the “real” data set has better performance.p-value of the path by calculating the proportion of the path score in 5,000 random data that is less than the path score in the original data.In order to measure the statistical significance of the 10 paths, we used a method of calculating statistical significance by comparing the original path score and the random path score. In bioinformatics research, this is a widely used statistical significance calculation method . Firstlyp-value of score is 0.001, this means that there are random path scores less than the original path score under null hypothesis.The null hypothesis in this paper is that path scores from random data are randomly distributed scores. The alternative hypothesis is that the path score is related to the structure of the sample data. Assuming that the Our study firstly compared performance of IHI-BMLLR with two state-of-the-art methods  using AUC in the simulation data. The results show that IHI-BMLLR performs clearly better than other methods. Then, we compared the stability performance of these three methods using boxplots. The results show that the stability of IHI-BMLLR is better than the other two methods. IHI-BMLLR also achieved a better performance in test for real and fake data. In order to find the path-related information in cancer, we applied IHI-BMLLR to PRAD data from TCGA. The results contained 212 path associations. We used disease-related CNV true labels test to calculate the statistical significance of the top 10 high-scoring path associations, and further analyzed the statistical significance of CNVs and genes in the top 10 paths.M = 100, and D = 2,000. N ∈ {200, 500, 800, 1100}). For the parameters in method IHI-BMLLR, we set ϕ 0.7 and 0.8, It can be seen that in the ROC and AUC results, the IHI-BMLLR achieved higher AUC values regardless under both values of the ϕ parameter. The results suggest that when the pathogenesis mechanism of the disease is complex, for example, when CNVs affect the disease through a complex transmission mechanism, two biological factors association  analysis may not accurately find the causal CNVs that affect the disease. Each kind of simulation data set was randomly generated 100 times. We calculated the AUC value of each method 100 times and then generated the corresponding boxplot. PRAD is the most common cancer for males and the second death rate caused by cancer in men. Research shows that CNV makes an important contribution to the proliferation of PRAD malignant cells . CNV–disThe main limitation of using real data sets to test disease-related CNV analysis methods is the lack of experimentally verified CNV data. The lack of verification data makes it difficult to effectively evaluate the performance of a method. In order to effectively compare the performance of various methods, true labels test was applied to three methods. Firstly, FaST-LMM-EWASher was used to identify the PRAD-related CNVs; then, these CNVs are defined as true labels. Secondly, we selected the top 100 CNVs from the PRAD-related CNVs. Because these 100 CNVs are closely related to the development of PRAD, the method should discover as many CNVs as possible. Finally, we compared the performance of methods in discovering these true labels. The experimental results are shown in We also used real data and fake data test to evaluate and compare the performance of three disease-related CNV detection methods. When the ground truth is unknown, the test method is widely used in bioinformatics research . As showM = 100. The parameter ϕ is set to 0.7 to ensure that as many biologically meaningful paths as possible are included in the result. A path contains a CNV, a gene, and the disease. We found 212 paths in the PRAD data results. It should be noticed that the maximum path score is 2. We studied and analyzed the top 10 high-score paths, which contain three prostate oncogenes PCGEM1  can be found in the article/ LY conceived the method, conducted the experiments, and wrote the main manuscript text. LY and ZS designed the method. TS and JZ prepared the 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.


It can be applied to model and non-model organisms and can be run modularly in R; it can also be run from the command line, allowing scalability with large datasets. Code and reports for the studies are available from https://github.com/fmjabato/ExpHunterSuiteExamples.High-throughput gene expression analysis is widely used. However, analysis is not straightforward. Multiple approaches should be applied and methods to combine their results implemented and investigated. We present methodology for the comprehensive analysis of expression data, including co-expression module detection and result integration via data-fusion, threshold based methods, and a Naïve Bayes classifier trained on simulated data. Application to rare-disease model datasets confirms existing knowledge related to immune cell infiltration and suggest novel hypotheses including the role of calcium channels. Application to simulated and spike-in experiments shows that combining multiple methods using consensus and classifiers leads to optimal results. ExpHunter Suite is implemented as an R/Bioconductor package available from  However, different experimental designs, sequencing protocols and technologies mean that the properties of the output data can vary greatly. A single analysis package is rarely sufficient to ensure robust analysis2.RNA sequencing (RNA-seq) is widely used across molecular biology and biomedicine, including rare disease research4. DE methods are based on different assumptions and analysis procedures, making it impossible to know the most appropriate method for a given dataset5. This has led to the appearance of workflows that include multiple differentially expressed gene (DEG) detection packages6. Previous studies have looked at combining results of multiple DEG methods to improve DEG detection, using consensus and ranking based strategies as well as p-value integration9. However, to our knowledge no previous work has applied machine learning based classification methods to this problem. Detected DEGs serve as input for functional enrichment analysis, in which lists of genes are converted into biological knowledge10. Although protocols suggest using functional enrichment in addition to DE analysis, few packages implement both3. Fewer still combine multiple annotation databases and custom term sets. Co-expression analysis, which searches for groups of co-expressed genes (CEGs) that correlate with phenotypic data11, is also often overlooked in RNA-seq data analysis, despite its potential for better understanding molecular processes and disease12. It can be used as an alternative to DEG detection, or as a complementary analysis technique. Here we present a comprehensive methodology for the analysis of transcriptomic data. We provide a collection of tools, the ExpHunter Suite, implemented as an R/Bioconductor package including auxiliary scripts for assessing performance and simulating RNA-seq data. It incorporates the DEgenes Hunter pipeline13, in addition to co-expression analysis, multiple reports related to quality control and result interpretation, and provide ways to compare and combine results. It can be used with and without reference genomes and has been applied to a range of species19, with annotation being provided through orthologous translation to perform functional analysis of non-model organisms, as demonstrated in previous work involving our group20. It is also possible to specify multifactorial experimental designs and control for additional factors. An overview of the methodology is given in Fig. Various workflows exist for initial RNA-seq data analysis to produce a table of counts, which serves as input for downstream processes such as differential expression (DE) analysisWe apply it to simulated and spike-in and real datasets, showing that some widely-used expression analysis methods can behave quite differently depending on the properties of the data, potentially over-predicting or estimating inaccurate values. We provide novel methodology to combine results, including a Naïve Bayes classifier approach, that lead to robust DEG detection. The real datasets are derived from two experiments modelling rare diseases related to carbohydrate metabolism. We use the package to confirm existing knowledge related to immune cell-infiltration in Lafora disease and calcium channel involvement in PMM2-CDG, and suggest related genes for further study. Through co-expression analysis, we find examples of divergent expression patterns between mRNA transcript and protein levels for the same gene, detect genes related to the extracellular matrix with a potential role in PMM2-CDG and modules of genes including triggers of NK-A. thaliana dataset from the R package TCC21.DE analysis was performed on a range of simulated expression datasets, to evaluate how different properties of the dataset can affect the performance of DEG detection and combination methods. Multiple expression datasets were simulated based on the two rare disease RNA-seq experiments described in this article and an For the 108 combinations of parameters described in Supplementary Methods, Table 22.All packages show similar performances in most situations, with no single method performing the best in all scenarios Fig. . ImportaOur methodology allows the use of a consensus based threshold to ensure robust results, based on the number of methods detecting a given gene as DE. This minimum-vote threshold is further described in “A. thaliana experiments. In terms of F-measure, defined as the harmonic mean of precision and recall, also known as F1-score,, this gradually increased with larger minimum-vote thresholds for Lafora disease, whilst the opposite was true for A. thaliana. This shows the importance of taking the properties of the dataset into account in DE analysis. It also suggests that there is no single best strategy for all experiments.Better results are observed for the Lafora disease experiment derived simulations across most measures, with the exception of the vote system with a minimum-vote threshold of 1. As with the individual package analysis, this is followed by the PMM2-CDG and We applied the methodology to a publicly available RNA-seq dataset derived from samples to which known quantities of endogenous RNA had been added. Three groups of mice received different mixes corresponding to different quantities of transcripts of known genes; a fourth group of samples received no mix. For DEG detection, we focused on the comparisons between samples receiving mixes, as these represented more subtle changes in gene expression than those involving the no mix receiving samples.We calculated F-measure in order to compare the performance of the DEG detection methods and combination strategies Fig. . Full deFor the vote system, a threshold of only one DEG detection method leads to the highest recall, as expected Table . This coIn terms of the estimated logFC, all methods showed similar correlation with the known values Table . Here weWhen applied to the rare disease datasets, the DEG detection methods showed remarkable differences to each other in terms of estimated logFC in a number of situations. This is illustrated in Fig. The methodology was applied to the PMM2-CDG dataset. Full details of the analysis are given in Supplementary Results—Case Studies and the ExpHunter Suite generated report in Supplementary Report 24. Figure CACNA1H, highly underexpressed in the disease samples, which encodes a calcium channel involved in various developmental disorders25. Other calcium channels, such Cav2.1, have been reported to be involved in PMM2-CDG cerebellar syndrome and proposed as a therapeutic target26.The functional enrichment results point to the alteration of the formation and composition of basement membranes in the disease, due in part to extracellular matrix and collagen related processes, including impaired collagen IV network formation, associated with PMM2-CDG related symptoms, intra-cerebral haemorrhages and stroke like episodesThe utility of our package is further emphasized by the results of its application to the Lafora disease dataset, full details of which are given in 27. Further analysis of the clusters highly correlated with the mutant animals found similar results, including immune system and inflammatory processes known to be important in the disorder and suggesting potential targets for further study and helping better understand its underpinning mechanisms .To assess data quality, the package runs principal component analysis (PCA), calculates correlation between samples and produces expression heatmaps, before and after normalisation. More advanced quality control is also implemented for the DEG and CEG analysis packages.37 using default parameters.ExpHunter Suite can launch one or several DEG detection packages. Currently, edgeR, limma, NOISeq and DESeq2 are includedDEG detection packages require a table of counts and an indication of which samples are controls and which are treated, specified in the target file or by input arguments. Overexpressed and underexpressed genes in treated samples will have positive or negative base 2 logarithmic fold change  values, respectively. The target file can also contain additional factors to include in the DE models, such as pairing and control for external factors. Columns in this file can also be included in multifactorial experimental designs, to look for group-specific changes and interactions between factors.Genes are tagged as prevalent/possible DEGs, based on package results, using a user-specified threshold: if a gene is detected as DE by at least as many packages as the threshold, it is considered a prevalent DEG according to this vote system. Conversely, if a gene is detected as DE by at least one method but fewer than the threshold, it is considered a possible DEG. By default, the threshold comprises an adjusted p-value < 0.05 and absolute logFC The package also performs score integration to obtain combined logFC and adjusted p-value/FDR values for each gene across all packages. LogFC values are combined using the arithmetic mean and the FDR values are combined using Fisher’s method. The combined-FDR can also be used to decide whether a gene is DE, instead of the vote system described above.The results of the different DE detection packages can also be combined using a Naïve Bayes classifier. For this approach, we train the model using vectors of p-values calculated by each package alongside labels indicating whether the vector represents a DEG or non-DEG. These vectors are derived from simulated datasets described below in the Study datasets sections. Multiple datasets were created including different numbers of genes changing between case and control samples and different magnitudes of fold change. The model, once trained on these datasets, can then be used to predict DEG status for a novel gene, given a vector of p-values. Full details are given in 38 is employed to locate modules of genes showing correlated expression across samples. This process is automated, but produces various graphics that can indicate problems that require user intervention. The expression values for the genes in each module are also summarised to produce a single value per sample 11. Correlation between the eigen-gene values for each module and the additional factors in the target file are also calculated. For quantitative values, correlation is calculated directly using Pearson’s correlation coefficient. Qualitative variables are first converted into binary vectors using the WGCNA package. Full details in Weighted gene co-expression network analysis (WGCNA)The functional analysis module is aimed at interpreting gene lists by looking for enrichment of sets of functionally related genes, i.e. sets of genes involved in the same biological process/pathway, with shared function or similar cellular location. These sets can be predefined, or the user can supply his own set using the Gene Matrix Transposed file format (*.gmt). Functional analysis using a non-model organism can be performed using an orthologue dictionary to borrow information from another species. This module integrates directly with the DEgenes Hunter module, searching for enrichment within the lists of identified DEGs/CEGs.39, KEGG40 and Reactome Pathway Knowledgebase41. Over-Representation Analysis is used, based on significant overlap between the input DEG/CEG gene list and the different sets of functionally related genes42. Gene Ontology (GO) is analysed using both topGO43 and clusterProfiler42, KEGG using clusterProfiler and Reactome using ReactomePA44.Multiple annotation systems have been integrated: Gene OntologyTables of results are generated for each of the DEG detection methods specified. A general output table is also created, which contains all DEG and CEG related results. HTML reports that allow the user to inspect the results and identify potential problems are also produced. The DE analysis report contains multiple plots for checking the quality of the samples, including Venn diagrams and bar charts of the results of the DEG detection methods. A section containing the co-expression results is also added, if applicable.The functional analysis module produces a set of tables showing the enriched categories. There is a general report to help the reader interpret the results and identify the most relevant enriched terms, as well as connections between terms. If co-expression analysis is performed, a report is also created for each module, containing additional information such as plots of gene expression and the relationship between eigen-gene values and phenotypic data.Simulated datasets The ExpHunter Suite includes methodology to simulate an RNA-seq counts table, based on the R package TCC21. Full details are given in Arabidopsis thaliana count table from TCC and the real disease datasets. In total, 24,300 simulated datasets were produced, investigating a range of parameters  to the samples before sequencing46. These transcripts correspond to endogenous mouse genes whose expression could not be detected in these samples. Real study cases We applied our methodology to two rare disease datasets. A minimum-vote threshold of 3 was used to determine prevalent DEGs for downstream analysis. The first disease, PMM2-CDG is a heterogeneous, multi-systemic disease caused by the deficiency of the PMM2 enzyme, for which there is no effective treatment48. The dataset was derived from skin fibroblast cell lines from patients and controls, and distinct groups of samples were derived before and after the addition of a molecular chaperone. The second, Lafora disease is a neurodegenerative disorder that leads to progressive myoclonus epilepsy, characterised by the accumulation of insoluble poorly branched glycogen deposits in the brain and peripheral tissues49. The dataset consisted of three groups of mice: two mutant groups which exhibited disease symptoms (Epm2a and Epm2b) and a control group.We show the utility of our methodology by applying it to simulated datasets, publicly available spike-in data and real RNA-seq rare-disease datasets. Full details including experimental design and how the packages were used are given in The study was approved by the Ethics Committee of the Universidad Autonoma de Madrid (CEI-105-2052) and conducted according to the principles of the Declaration of Helsinki. All participants gave informed consent.Supplementary Legends.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Report 1.Supplementary Report 2.Supplementary Report 3.Supplementary Report 4.Supplementary Report 5.Supplementary Report 6.


Clustering and feature selection act major roles in many communities. As a matrix factorization, Low-Rank Representation (LRR) has attracted lots of attentions in clustering and feature selection, but sometimes its performance is frustrated when the data samples are insufficient or contain a lot of noise.To address this drawback, a novel LRR model named TGLRR is proposed by integrating the truncated nuclear norm with graph-Laplacian. Different from the nuclear norm minimizing all singular values, the truncated nuclear norm only minimizes some smallest singular values, which can dispel the harm of shrinkage of the leading singular values. Finally, an efficient algorithm based on Linearized Alternating Direction with Adaptive Penalty is applied to resolving the optimization problem.The results show that the TGLRR method exceeds the existing state-of-the-art methods in aspect of tumor clustering and gene selection on integrated gene expression data. In most countries, cancer is the first or second cause of death ]. Thus, . developed a new PCA in [Data dimension reduction aims to get a significant low-dimensional representation of high-dimensional data, remove redundant features and prevent overfitting. Therefore, it has achieved great successes in many areas, such as characteristic gene selection , image aw PCA in , called w PCA in , RPCA is. introduced a low-rank matrices recovery approach for removing the noise of data. Then, PCA is applied to the low-rank matrix. The robustness of data processing is enhanced significantly through the approach in [In , Wright roach in .X are usually obtained from a union of multiple subspaces . proposed a low-rank representation (LRR) model that can excavate the global distribution between data points to study X [The experimental data al space –15. Sinc study X . LRR str study X , 17. How study X , Laplaci study X , Locally study X , and gra study X .i.e. data manifold for data analysis. Contrasted with LLRR, Latent LRR (LatLRR) model adds another view, i.e. feature manifold to do image processing [X, the nuclear norm means that the sum of all singular values belonging to X. Since the nuclear norm minimizes the sum of all singular values for accomplishing the minimization problem, all non-zero singular values have different influences for the rank [A graph-Laplacian regularized LRR (LLRR) model  was deveocessing . For solthe rank . Thus, tthe rank  and attrthe rank , 25, 26.L1-norm is used for realizing the sparse constraints of outliers. The main contributions of our paper are showed as follows. Firstly, compared with the popular LRR model regularized by the nuclear norm, our TGLRR method can obtain a better performance by the TNN, and solve the non-convex and discontinuous issues. Secondly, the TGLRR method can seize the valuable information lying in data manifold and feature manifold simultaneously. Finally, the TGLRR method can capture the internal similarity information and some underlying affinity among data points by incorporating a graph regularization term and utilizing a linear association of some bases to represent each data point.To strengthen the efficiency and robustness of the model, in our paper, a novel LRR method is developed, named Truncated nuclear norm and graph-Laplacian regularized Low-Rank Representation model (TGLRR). In the objective function of TGLRR, the nuclear norm is replaced by the TNN for reaching the robust approximation of rank function, a graph-Laplacian regularization is imposed to find the local manifold structure, and the The remainder of this article is organized as follows. In the Results section, TGLRR is exploited for clustering and feature selection on integrated gene expression data. In Conclusions section, conclusions and the future work are given. In Methods section, our TGLRR method is put forward and the optimization problem is resolved through an efficient framework based on LADMAP .https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga). The pertinent information about the seven integrative datasets is listed in Table To validate the performance of TGLRR model, six clustering experiments and a feature selection experiment are conducted. The experimental data are integrative cancer gene expression data instead of single cancer data for avoiding sample imbalance problem. Seven different datasets are produced via integrating five different single gene expression data downloaded from The Cancer Genome Atlas , normalized mutual information (NMI) and F-measure. As a widely used metric in machine learning field, ACC can be defined as follows:To measure the performance of our approach, three quantity metrics are adopted in this paper, n indicates the total number of tumor samples in an integrated data, i-th sample, and where The second index of NMI is defined byt) is the marginal probability distribution function, namely, the probabilities that a tumor sample arbitrarily chosen from an integrated dataset belongs to cluster T. In addition, where T and F-measure is the comprehensive evaluation index considering both precision and recall, and written as:where To prove the effectiveness of TGLRR, the detailed clustering results of these methods on integrative tumor gene expression data are listed by three tables. In tables, the values about ACC, NMI and F-measure are the average of 100 clustering results of each approach, and the values on the right of ± are the variance of 100 results.Table The clustering results on two integrated datasets containing three types of tumors are exhibited in Table From Table Cancers are commonly relevant to gene mutation or abnormal expression of genes. Thus, in this subsection, the TGLRR method is used to identify co-feature genes of PAAD, ESCA and HNSC from PAAD-ESCA-HNSC dataset.X via TGLRR scheme. From the formula , a minimmutation . It is e−2, 103 and 4 are assigned to r.Similar to the subsection of Parameters Selection, 10https://www.genecards.org/). These genes are most likely to lead to PAAD, ESCA and HNSC simultaneously.Table From Table All in all, the TGLRR method is successful in identifying co-characteristic genes on the integrative gene expression datasets.The TGLRR method is applied to the tumor clustering and gene selection, and superior to the other methods. Based on above results, it can be affirmed that the TNN could capture more valuable information existed in data than the nuclear norm from data. By comparing the results of DGLRR, a conclusion can be drawn that the graph Laplacian regularization imposed on feature manifold may cause adverse effects for clustering on our integrative datasets. The TGLRR method has some limitations. For example, on HNSC-PAAD-CHOL-ESCA dataset, the variance values of TGLRR is larger than LRR. It may be caused by the integrated datasets and its stability needs to be improved in future.In a word, these improvements to the prevenient LRR model can help TGLRR catch more useful information concealed in the low-dimensional manifold structure.The paper proposes a Low-Rank Representation approach called TGLRR. It can capture the global and local geometric structures in data manifold via using the raw data matrix as the dictionary matrix and introducing the graph-Laplacian regularization term. Furthermore, TGLRR can gain a better approximation to the rank operator than the approaches regularized by the nuclear norm. The objective function of TGLRR is perfectly resolved through an iterative algorithm based on LADMAP framework. The efficiency and robustness of our TGLRR method are testified through the encouraging experimental results.X are sampled from a union of several low-dimensional subspaces Based on the assumption that the observation data aised in . If dataX via LRR. where P and the reconstruction fidelity of data matrix X damaged by errors matrix P. 0-norm of matrix P, which indicates the number of non-zero elements in matrix P.where L0-minimization is non-convexity and intractable. Usually, solving the problem (5) is NP-hard [L0-norm is replaced with the L2,1-norm [Since the rank function is discrete, the problem (5) may have multiple solutions and the  NP-hard . To bett2,1-norm . The coni-th largest singular value of Z) denotes the nuclear norm of matrix Z, and L2,1-norm of matrix P. To get a self-expression model, the observation data X are generally installed as the dictionary matrix [where y matrix , 14, 22.i.e. the similarity between the data point Z can be seen as an affinity matrix [For low-rank matrix y matrix . LRR is y matrix .X that are used as the basis. First, LRR method requires that the basis contains adequate data samples from the subspaces so as to possess the capacity of representing the underlying subspaces. Second, LRR method demands that noise of data X is little, i.e. only a part of X is corrupted. To remedy these two shortcomings of LRR, Liu et al. proposed the following convex optimization LRR problem [Nevertheless, LRR method has to face two issues owing to the raw data  problem :8\documeL1-norm of matrix P and G is the feature matrix separated from the original X. Equation  is put forward. Considering that some data may exist nonlinear geometric structure  and the where X.Essentially, TGLRR can get a more precise approximation to the rank function with the help of the TNN than the nuclear norm. And the underlying low-dimensional structures of data could be captured by the aid of the graph-Laplacian regularization and the basis matrix To correctly solve the optimization problem of 14), an efficient iterative algorithm based on LADMAP framework is designed. The algorithm (Algorithm 1) is implemented via alternating two iterative procedures till Eq. , the SVD  of The second step is to resolve the following convex optimization problem:Step 2: To achieve the separation of objective function (15), an auxiliary variable function , an auxiY1 and Y2. Thus, the augmented Lagrangian function can be defined asEquation  can be swhere Z, F, G and P. In the iterative procedure, Z, F, G or P is updated when the other three variables are fixed, respectively.Next, the alternating minimization strategy is adopted to compute Updating ZZ, the below minimization objective w.r.t.Z needs to be solved.To get the solution of Equation  has a clwhere Updating GZ, the SVT operator is employed in computing G. The optimal solution Similar to the solution of where Updating Fw.r.t.F isThe below sub-problem Z and G, we can differentiate Eq. . For the same activity, the time complexity of SVD decomposition of G is F can be obtained in P, P and In this subsection, the time complexity about TGLRR is discussed. Clearly, the main running time of TGLRR is expended on calculating the matrices


Triple-negative breast cancer (TNBC) is a highly aggressive cancer with poor prognosis. The lack of effective targeted therapies for TNBC remains a profound clinical challenge. Fusion transcripts play critical roles in carcinogenesis and serve as valuable diagnostic and therapeutic targets in cancer. The present study aimed to identify novel fusion transcripts in TNBC.We analyzed the RNA sequencing data of 360 TNBC samples to identify and filter fusion candidates through SOAPfuse and ChimeraScan analysis. The characteristics, including recurrence, fusion type, chromosomal localization, TNBC subgroup distribution, and clinicopathological correlations, were analyzed in all candidates. Furthermore, we selected the promising fusion transcript and predicted its fusion type and protein coding capacity.MFGE8-HAPLN3 as a novel biomarker and further validated it in TNBC samples using PCR and Sanger sequencing. Further, we successfully identified three types of MFGE8-HAPLN3  and predicted the ORF of E6-E2, which could encode a protein of 712 amino acids, suggesting its critical role in TNBC.Using the RNA sequencing data, we identified 189 fusion transcripts in TNBC, among which 22 were recurrent fusions. Compared to para-tumor tissues, TNBC tumor tissues accumulated more fusion events, especially in high-grade tumors. Interestingly, these events were enriched at specific chromosomal loci, and the distribution pattern varied in different TNBC subtypes. The vast majority of fusion partners were discovered on chromosomes 1p, 11q, 19p, and 19q. Besides, fusion events mainly clustered on chromosome 11 in the immunomodulatory subtype and chromosome 19 in the luminal androgen receptor subtype of TNBC. Considering the tumor specificity and frameshift mutation, we selected MFGE8-HAPLN3 as a novel biomarker with promising clinical application in the future.Improved bioinformatic stratification and comprehensive analysis identified the fusion transcript  Triple-negative breast cancer (TNBC) lacks the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) and accounts for 10% to 20% of the newly diagnosed breast cancer cases . ChemothZNF384 have been identified in B-cell precursor acute lymphoblastic leukemia; eight fusion partners have been reported for the ZNF384 gene. Moreover, the clinical features of patients depend on the functional defect of the fusion partner gene of ZNF384  chromosomal translocation  and The National Omics Data Encyclopedia (NODE) (OEP000155) (http://www.biosino.org/node). The RNA-seq data of 360 tumor tissues and 88 adjacent normal breast tissues were obtained. In addition, the corresponding clinicopathological characteristics, including age, histological type of the tumor, tumor size, lymph node status, histological grade, clinical stage and ER, PR, HER2, and Ki67 status, were collected (RNA-seq data used in the current study were downloaded from the Gene Expression Omnibus (GEO) GSE118527)  recurrent fusion transcripts; (2) fusion transcripts with different functions formed by promoter swapping in the non-coding regions or frameshift in the coding regions; (3) fusion transcripts whose partner genes are associated with tumor; (4) fusion transcripts overexpressed in tumor tissue but unexpressed or expressed in low amounts in the adjacent normal breast tissue. We further predicted the open reading frame (ORF) and protein-coding capacity of the fusion transcripts according to the nucleotide sequence.t-test or one-way analyses of variance (ANOVA), while the variables that did not meet the normal distribution were analyzed using the Mann-Whitney Wilcoxon test or Kruskal-Wallis test; Pearson’s Chi-square test or Fisher’s exact test was used to compare the categorical variables. In addition, the correlations were analyzed using the Pearson or Spearman test according to the normality of the distribution. All tests were two-sided, and P<0.05 indicated statistical significance. All statistical analyses were performed using SPSS 25.0 or R Studio .The data distribution was characterized by frequency tabulation and summary statistics. The data were examined for normality using the Shapiro-Wilk test. The continuous variables with normal distribution were assessed using the vs. 1.807, P<0.05, To detect fusion transcripts, RNA-seq data from a set of 448 frozen samples  were analyzed  was screened out. Milk fat globule-EGF factor 8 protein (MFGE8) and Hyaluronan And Proteoglycan Link Protein 3 (HAPLN3) were both located on the long arm of chromosome 15  that could encode a protein of 712 amino acids. Collectively, these findings suggested that MFGE8-HAPLN3 fusion exists in TNBC samples and plays a critical role in TNBC. The predicted ORF of MFGE8-HAPLN3 and its function in vivo have yet to be unambiguously characterized and need to be verified in further exploration.According to PCR and Sanger sequencing, three types of MFGE8-HAPLN3 as a potential biomarker in TNBC.Gene fusions/transcripts are important driver events in neoplasia and serve as valuable diagnostic biomarkers and therapeutic targets in cancer. Aberrant fusions have been widely described in multiple tumor types, such as non-small-cell lung cancer  and lympMYB-NFIB gene fusion promoted the aggressive behavior in adenoid cystic carcinoma  can be found in the article/Our study was approved by the independent ethics committee/institutional review board of Chongqing University Three Gorges Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.M-YW: conception of the work, data collection, data analysis and interpretation, drafting the article, critical revision of the article, and final approval of the version to be published. MH and C-YW: conception of the work, data collection, critical revision of the article, and final approval of the version to be published. X-YT, J-GW, Y-DY, XX, and C-WG: data collection, critical revision of the article, and final approval of the version to be published. All authors contributed to the article and approved the submitted version.This work was supported by the Natural Science Foundation Project of Chongqing, China (cstc2016jcyjA0338).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.


KDM5B and LAPTM4B, two genes previously associated with the biology and prognostication of myeloid neoplasms. This classifier achieved high concordance indexes in the training and validation sets , and predictions were particularly accurate in patients at the highest risk of death. Additionally, ST-123 provided significant prognostic improvements in patients with high-risk mutations. Our results indicate that survival of patients with AML can be predicted to a great extent by applying machine learning tools to transcriptomic data, and that such predictions are particularly precise among patients with high-risk mutations.Acute Myeloid Leukemia (AML) is a heterogeneous neoplasm characterized by cytogenetic and molecular alterations that drive patient prognosis. Currently established risk stratification guidelines show a moderate predictive accuracy, and newer tools that integrate multiple molecular variables have proven to provide better results. In this report, we aimed to create a new machine learning model of AML survival using gene expression data. We used gene expression data from two publicly available cohorts in order to create and validate a random forest predictor of survival, which we named ST-123. The most important variables in the model were age and the expression of  European Leukemia Net (ELN) risk classification (Acute Myeloid Leukemia (AML) is a heterogeneous neoplasm characterized by cytogenetic and molecular alterations that drive patient prognosis. Currently established AML risk stratification guidelines, like the fication , are basIn the last years, the emergence of artificial intelligence has brought new expectations to the field of medicine, particularly for disease diagnosis and prognostication. Machine learning (ML) is a field of artificial intelligence that performs outcome prediction based on complex interactions between multiple variables by making little assumptions about the relationship between the dependent and independent variables . In ML, NPM1, CEBPA, FLT3, IDH1, IDH2, KIT, WT1 and RUNX1 . The GSE68833 database was used for validation, and it contains gene expression data from 137 adult patients with AML included in The Cancer Genome Atlas (TCGA) cohort, with a median age of 59 years . Both databases used partially overlapping microarray chips, so that we rank-normalized the log2-transformed expression estimates and selected a set of 44,366 common gene expression probes between both cohorts.Two databases available in the Mclust algorithm . Briefly, the Mclust algorithm determines the most likely set of clusters according to geometric properties . An expectation-maximization algorithm is used for maximum likelihood estimation, and the best model is selected according to Bayes information criteria. The association of each of these probe-level clusters with overall survival was calculated using cox regression. Thereafter, those probes whose clusterization was significantly associated with survival  were selected for multivariate clusterization using the same Mclust algorithm. Cluster prediction was performed on the test set using parameters estimated in the training cohort, and cox regression was used to verify the association of this clusterization with overall survival. The Shoenfeld’s test was used to assess the proportional hazards assumption.Briefly, the lgorithm  was usedWe analyzed gene expression association with overall survival using cox regression implemented in R. Assumption of proportional hazards was checked with Schoenfeld’s method.We started our analysis by testing the association of each probe with overall survival in the training set using multivariate cox regression. Schoenfeld’s method was used to assess the proportional hazards assumption.rfsrc function implemented in the randomForestSRC package in R  was calculated as the integrated Brier score divided by time, and represents the average squared distances between the observed survival status and the predicted survival probability at each time point. CRPS is always a number between 0 and 1, being 0 the best possible result. Survival prediction on the test cohort was performed using the predict.rfsrc function with default parameters. Harrel’s concordance index (c-index) was used to assess model discriminative power on the bootstrapped training set and on the test set. C-index reflects to what extent a model predicts the order of events  in a cohort. C-indexes below 0.5 indicate poor prediction accuracy, c-indexes near 0.5 indicate random guessing and c-indexes of 1 represent perfect prediction.Random forest survival models were created with the age in R . We decivimp function, and we iteratively removed those samples with negative or low weight (importance < 1 × 10−4). We replicated the models in the independent set.Variable selection was selected by fitting age-adjusted cox regression models of overall survival in the training set. We initially analyzed different sets of genes in order to select the best input sets. Afterwards, we selected different sets of genes according to their multiple testing adjusted p-values; either False Discovery Rate (FDR) or Bonferroni-adjusted p-values below 0.01, 0.05, 0.1. Variable reduction was performed by iteratively removing those variables with low importance. Variable importance was calculated with the Stellae-123 (ST-123). ST-123 achieved c-indexes of 0.7228 in the training set and 0.6988 in the test set, indicating a high reproducibility of the personalized risk prediction  , RUNX1 mutation (14 patients) and ASXL1 mutation (6 patients)  . We selected cases harboring Sherve et al.  , which se et al. , report e et al. . On the e et al. .It has been hypothesized that a better risk stratification based on the integration of different layers of clinical and biological complexity could, for example, reduce the number of allogeneic stem cell transplants performed in patients with AML by 20-25% while maintaining the same overall survival rate as that with the current treatment recommendations . In the KDM5B and LAPTM4B. Not surprisingly, both genes are deeply vinculated with carcinogenesis. KDM5B (Lysine Demethylase 5B) encodes a master epigenetic regulator of H3K4 methylation that regulates the expression of several oncogenes and tumor suppressors during carcinogenesis  plays several roles in carcinogenesis, such as promoting tumor growth and metastasis, inhibiting apoptosis, initiating autophagy and driving multidrug resistance mechanisms  can be found in the article/AM had the idea and performed the research. AM, AP, MC, JD, MG, and BA analyzed the data and wrote the paper. NA, LB, RF, AA, MA, and EF analyzed the manuscript and made suggestions. CC, GM, PM, MM, and JL gave final consent for publication. All authors contributed to the article and approved the submitted version.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.


Glaucoma is a generic term of a highly different disease group of optic neuropathies, which the leading cause of irreversible vision in the world. There are few biomarkers available for clinical prediction and diagnosis, and the diagnosis of patients is mostly delayed.Differential gene expression of transcriptome sequencing data (GSE9944 and GSE2378) for normal samples and glaucoma samples from the GEO database were analyzed. Furthermore, based on different algorithms (Logistic Regression (LR), Random Forest (RF), lasso regression (LASSO)) two diagnostic models are constructed and diagnostic markers are screened. GO and KEGG analyses revealed the possible mechanism of differential genes in the pathogenesis of glaucoma. ROC curve confirmed the effectiveness.LR-RF model included 3 key genes , and the LASSO model outputted 5 genes , both algorithms have excellent diagnostic efficiency. ROC curve confirmed that the three biomarkers ADH1C, ENO2, and NAMPT were effective in the diagnosis of glaucoma. Next, the expression analysis of the three diagnostic biomarkers in glaucoma and control samples confirmed that NAMPT and ADH1C were up-regulated in glaucoma samples, and ENO2 was down-regulated. Correlation analysis showed that ENO2 was significantly negatively correlated with ADH1C (cor = -0.865714202) and NAMPT (cor = -0.730541227). Finally, three compounds for the treatment of glaucoma were obtained in the TCMs database: acetylsalicylic acid, 7-o-methylisomucitol and scutellarin which were applied to molecular docking with the diagnostic biomarker ENO2.In conclusion, our research shows that ENO2, NAMPT, and ADH1C can be used as diagnostic markers for glaucoma, and ENO2 can be used as a therapeutic target.The online version contains supplementary material available at 10.1186/s12886-022-02350-w. Glaucoma is a group characterized by progressive degeneration of retinal ganglion cells of optic neuropathy, which is the main cause of blindness and raises the prevalence , 2. TherBiomarkers make it more convenient to explore the ocular matrix. Repeated sampling and evaluation of biomarkers can show the biological process of disease progression and drug treatment . StudiesCurrent used treatment includes drugs, laser surgery and minimally invasive surgery for glaucoma . GlaucomThe discovery and application of glaucoma biomarkers can be difficult, the marker selection is limited by patients with individual differences, techniques for the use, and analysis of therapeutic drugs . At presThis study obtains glaucoma clinical data from the public database GEO database. a large number of data analysis were used which including the obtained of database sample genes and the prediction of targeted biomarkers and their transcription factors in the online database. In addition, through the molecular docking between the protein crystal encoded by the diagnosed biomarker and the small molecule drug, it is tested that the targeted drug can be successfully docked with the marker protein. It provides a reference for biomarkers to become the key target of targeted drug therapy in the future.The GEO database dataset GSE9944, GSE2378 were used to download the gene sequencing data and sample information of glaucoma and normal samples. GSE9944 selected data from the GPL8300 platform with a large number of samples, including 13 glaucoma samples and 6 normal samples. GSE2378 selected the data measured by the GPL8300 platform with a large number of samples, including 7 glaucoma samples and 6 normal samples.P < 0.05 was significant) by the Cluster Profiler (version 3.18.0) package. GO plot was used to structure the chord diagram of GO items, and Ggplot2 package was used to create the volcano diagram  and bubble diagram (KEGG pathway) , 7-O-Methylisomucronulatol, Scutellarin [PMID: 34414202], and molecular docking was carried out with the diagnostic biomarker ENO2 . The molecular docking of Acetylsalicylic acid  1, and Forkhead Box O (FOXO) transcription factor 1 and 3a ( FOXOs 1 –3a), which is the key to the pathogenesis of glaucoma or optic nerve degeneration , 30. FurTo explore whether the three diagnostic molecules can be used as therapeutic targets of glaucoma. Three compounds with glaucoma therapeutic effects were selected by the TCMSP database, which was used for molecular docking with diagnostic markers. 7-O-Methylisomucronulatol was key compound in the compound-target network of Qing Guang An Granule (QGAG) to the treatment of glaucoma . ScutellOur research shows that ENO2, NAMPT, and ADH1C as diagnostic markers have been proved to be effective in the diagnosis of glaucoma, and ENO2 is a potential therapeutic target.Additional file 1.Additional file 2.Additional file 3.


High dimensional multi-omics data integration can enhance our understanding of the complex biological interactions in human diseases. However, most studies involving unsupervised integration of multi-omics data focus on linear integration methods. In this study, we propose a joint deep semi-non-negative matrix factorization (JDSNMF) model, which uses a hierarchical non-linear feature extraction approach that can capture shared latent features from the complex multi-omics data. The extracted latent features obtained from JDSNMF enabled a variety of downstream tasks, including prediction of disease and module analysis. The proposed model is applicable not only to sample-matched multiple data  but also to feature-matched multiple data , and therefore it can be flexibly applied to various cases. We demonstrate the capabilities of JDSNMF using sample-matched simulated data and feature-matched multi-omics data from Alzheimer’s disease cohorts, evaluating the feature extraction performance in the context of classification. In a test application, we identify AD- and age-related modules from the latent matrices using an explainable artificial intelligence and regression model. These results show that the JDSNMF model is effective in identifying latent features having a complex interplay of potential biological signatures. With the development of high-throughput technology, large amounts of omics data have been produced. These resources enable us to understand disease- and patient-specific variations. Each omics datapoint contains different forms of information and is complementary to other data. By integrating multi-omics data, we can capture more complex interactions of the biological system.Motivated by this, many studies using various machine learning methods have been performed to integrate multi-omics data, including MOFA  based onTo enhance the understanding of complex relationships from multi-omics data, we developed a novel method called the joint deep semi-NMF model (JDSNMF) A. This m1.It introduces a hierarchical non-linear unsupervised learning model for capturing complex information from multi-omics data.2.It introduces a flexible integration model for multiple data which does not require matching samples.3.We compared the feature extraction performance of JDSNMF using sample-matched simulated data and feature-matched AD data.4.We identified the interpretability of JDSNMF through module analysis using an explainable artificial intelligence (xAI) D and regIn summary, the main contributions of this work include:In this section, a brief review of the most relevant works, i.e., NMF, joint NMF, semi-NMF, and deep semi-NMF, is provided.NMF ,11 is a I matrices ith coefficient matrix, and the rank Z is a feature extracted matrix in which multiple data are integrated, and a coefficient matrix i dataset. It is useful for integrating heterogeneous data such as multi-omics data.jNMF is an advanced NMF model that decomposes multiple data into a common basis matrix and multiple coefficient matrices . Given nH matrix. Matrix X and matrix Z may have mixed signs. Semi-NMF decomposes with Z matrix as the cluster centroid, and the H matrix can be viewed as the cluster indicator.Semi-NMF  is a relX matrix into m layers as follows:Deep semi-NMF  is a mulImplicit representations of layers are suitable clusters according to the corresponding attributes. The multi-layer model helps learning multiple low-dimensional representations of the original matrix.I is the number of multiple datasets, C is the number of samples (or features), and M is the number of individual features (or samples). JDSNMF decomposes the given original matrices by minimizing the following objective function.g such as a sigmoid activation function and a ReLU activation function to make the decomposed H matrix non-negative with non-linearity.Our proposed model, JDSNMF, has advantages of non-linear dimensionality reduction and heterogeneous data integration. It adapts several advanced NMF approaches for feature extraction to learn complex non-linear manifolds from complex heterogeneous data. JDSNMF adapts joint NMF  to integThe feature latent matrix and the sample latent matrix of the first layer are used in module construction. In the application of NMF, researchers have used two different approaches to construct modules. One is to assign each feature (or sample) to a module with a maximum value ,14, and U is a feature latent matrix, and U, U are smaller than a negative threshold for each module. We assumed that genes with values larger than a positive threshold (positive genes) increase GE or DM values of corresponding samples in the original data matrix and that genes with values less than a negative threshold (negative genes) decrease GE or DM values of corresponding samples in the original data matrix. In the K3 module, AD samples from the GE data and NL samples from the DM data were selected by thresholds; Gene 1 and Gene 4 are positive and negative genes, respectively. This result indicates that Gene 1 is more likely to be overexpressed and hypermethylated, and Gene 6 to be underexpressed and hypomethylated, in some of the AD samples. We conducted four experiments to validate the effectiveness of the proposed JDSNMF. First, we evaluated the feature extraction performance in the context of classification on sample-matched simulated data. Second, we evaluated the feature extraction performance on feature-matched multi-omics data.The performance of JDSNMF using the GE data of the Addneuromed (ANM) and DM data of the MRC London Brainbank cohort as feature-matched data was compared with models using only GE data of the ANM cohort. We provide details of the comparison of the performances of AD/normal (NL) classification and mild cognitive impairment (MCI)/NL classification on the GE data of the ANM cohort. Third, we compared the performance of AD/NL classification according to the reduced dimension K on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Fourth, we highlight AD- and age-related modules and match the module to the known database, indicating that JDSNMF is effective in module analysis.n = 20,017). We normalized ANM Cohorts 1 and 2 to reduce the batch effect using the Combat method from the ‘SVA’ R package [We used a blood GE profile from the ANM consortium, a large cross-European AD biomarker study, and a follow-on Dementia Case Register (DCR) cohort in London. We downloaded the normalized RNA profile from the GEO: ANM Cohort 1 (GSE63060) and ANM Cohort 2 (GSE63061). The ANM Cohort 1 contains blood GE data of 145 patients with AD, 80 MCIs, and 104 NL, and the ANM Cohort 2 has those of 140 patients with AD, 109 MCIs, and 135 NLs after removing three borderline MCI samples and one other sample. We averaged the expression levels of probesets with the same RefSeq IDs, and then filtered out the uncommon RefSeq IDs between two cohorts . We filtered out the uncommon RefSeq IDs between the GE profiles and the DM profile. We also mapped RefSeq IDs to gene symbols and averaged the values of the same gene symbols in the GE and DM profiles .We used a peripheral whole blood DM profile from the MRC London Brainbank for Neurodegenerative Disease . We downhttp://adni.loni.usc.edu (accessed on 1 September 2019). Both GE and DM profiles were obtained from whole blood. Because the GE profile did not contain diagnostic information, we obtained diagnosis of GE samples using the subject ID and visit code of the diagnostic information file (DXSUM_PDXCONV_ADNIALL.csv). The GE profile from ADNI consisted of 362 participants . We averaged the expression values of the same gene symbols . Because ADNI has longitudinal data, there are samples with different diagnosis for each participant. Thus, we selected the DM samples examined on similar dates with GE samples for each participant. We obtained 294 participants , and we converted the IDAT file into a beta-value format using a “minifi” R package [n = 21,099). We filtered out the uncommon genes between the GE profiles and the DM profile .For the ADNI database, we downloaded robust multichip average normalized GE and raw DM profiles (IDAT format) from In this section, the hyperparameters and settings of JDSNMF, NMF, deep semi-NMF, and classification models are described.Our JDSNMF model has four key hyperparameters: the number of layers, the reduced dimensions of each layer, a layer to use for classification, and an L2 norm parameter. In the NMF method, the hyperparameters were the updated algorithm, the number of reduced dimensions, and the initialization method. The hyperparameters in the deep semi-NMF method were the same as those in the JDSNMF model.For JDSNMF and deep semi-NMF, the Adam Optimizer  and earlhttps://github.com/dmcb-gist/Joint-deep-semi-NMF, accessed on 30 January 2020. The scikit-learn python library [We used an NMF method in the scikit-learn library  and impl library  for the  library  was usedWe evaluate the robustness performance and feature extraction performance of our proposed JDSNMF model on sample-matched simulated datasets.sth sample and jth feature. The class label probability jth feature, and Three datasets with small sample sizes and high dimensionality are simulated, while most of the features are irrelevant to the objective. We generated the predictor matrix  follows :(7)ys=exWe evaluate the feature extraction performance with NMF, deep semi-NMF, and JDSNMF on simulated data . We measure the classification performance of the output from each feature extraction model using SVM. The optimal hyperparameters of each feature extraction model are searched by validation data. The hyperparameters of SVM were fixed. We repeated the analysis 20 times with different simulated datasets. To evaluate our model’s ability to identify latent information predictive of AD from feature-matched omics data, we compared it with a feature selection approach based on differentially expressed genes, NMF, and a deep semi-NMF. The GE data of the ANM cohort were used for all models. Additionally, the DM data of the MRC London Brainbank cohort were used for JDSNMF.p-value. For the baseline, we randomly selected the same number of genes selected in the feature selection model  was employed to evaluate performance. In AD/NL classification, we divided samples into training, validation, and test sets (3:1:1 ratio) in each fold. The optimal hyperparameters were searched by validation data. In MCI/NL classification, we used optimal hyperparameter sets that had the best performance in the AD/NL classifications . The classification performances of each of the three classifiers are shown in In this section, we attempt to interpret the clinical and biological meanings of the latent matrices from the GE data of the ANM cohort and the DM data of the MRC London Brainbank cohort. We identified AD- and age-related modules and matched the module to the known database. We used the GE sample latent matrix, DM sample latent matrix, and feature latent matrix from the JDSNMF model with the best performance in the “Feature-matched real dataset experiments” section . We constructed the 60 modules from the GE sample latent matrix, DM sample latent matrix, and feature latent matrix.We used a locally interpretable model-agnostic explanation (LIME)  as the xq-value < 0.0001) in module 7. Among them, the “Alzheimer disease” pathway was statistically significant  were positive genes and 12 were negative genes . We surveyed these genes from previous studies for relevance to AD. SNCA , NADH-ubiquinone oxidoreductase, and ubiquinol-cytochrome c oxidoreductase, which are components of the mitochondrial respiratory chain. Previous studies reported that mitochondrial dysfunction is related to AD [Module 7 had the highest sum of LIME values for the three classifiers . At the ed in AD . The samal cells . In a pral cells , althouged to AD . Reduceded to AD .Other pathways are known to be directly or indirectly related to AD. For example, differential expression of oxidative phosphorylation genes is observed in AD . In wildFor each module, we performed linear regression analysis and calculated the Pearson correlation coefficient between the LIME importance score in the AD classification and the number of genes overlapped with the AD pathway in KEGG pathway analysis. i in module j, i with coefficient i with coefficient i with coefficient k-th principal component of the sample latent matrix for the i-th sample with coefficient N is the number of PCs that are not related to age. p-value = 4.58 To study other modules further, we used age information and found age-related modules by applying the following linear regression model to the GE and DM sample latent matrix.d to age . We focuq-value < 0.05) in module 1 . We selected 750 ADNI2 samples with both data types. The numbers of NL, significant memory concern (SMC), early MCI (EMCI), late MCI (LMCI), and AD are 180, 102, 170, 156, and 142, respectively.As brain imaging data are also important for the diagnosis of AD, we evaluated the feature extraction performance of our proposed JDSNMF model on sample-matched bioimaging datasets. The experiments were performed on magnetic resonance imaging (MRI) regions of interest (ROIs) and AV-45 amyloid positron emission tomography (PET) ROI datasets from ADNI-TADPOLE ( K of each model was selected by a validation set from each fold. The AUC values for one-vs-rest were calculated for multiclass classification. We measured the classification performance of extracted features from each feature extraction model using SVM. The reduced dimension We introduced a novel JDSNMF model—a non-linear feature extraction model—that can capture shared latent representations from complex multi-omics data. The proposed model is applicable to the existing sample-matched multi-omics integration and the new feature-matched multi-omics integration. JDSNMF demonstrated significant improvements in multi-omics feature extraction tasks on sample-matched simulated data and feature-matched multi-omics data. In AD experiments, we identified AD-related and age-related modules through the recent xAI model and traditional regression approaches. We observed that many AD-related genes were included in modules important for AD prediction and that genes in the age-related modules were significantly enriched with known aging-related genes. These results suggest that it is applicable not only to analysis of large cohorts with multi-omics data but also to integration analysis between single omics cohorts. Furthermore, feature-matched integration analysis can be applied to cross-species comparison, cross-disease comparison, and cross-cohort comparison. We also confirmed that our model is applicable to bioimaging data by further experiments.However, this method has some limitations. Our model involves simple multi-task learning with uniform loss weights. This can lead to an optimization conflict while learning to reconstruct each omic datum. In the future, we will apply advanced multi-task learning methods  to the J


Prostate cancer (PCa) is the most common malignancy among men worldwide. However, its complex heterogeneity makes treatment challenging. In this study, we aimed to identify PCa subtypes and a gene signature associated with PCa prognosis. In particular, nine PCa-related pathways were evaluated in patients with PCa by a single-sample gene set enrichment analysis (ssGSEA) and an unsupervised clustering analysis . We identified three subtypes with differences in prognosis . Differences in the proliferation status, frequencies of known subtypes, tumor purity, immune cell composition, and genomic and transcriptomic profiles among the three subtypes were explored based on The Cancer Genome Atlas database. Our results clearly revealed that the Risk_H subtype was associated with the worst prognosis. By a weighted correlation network analysis of genes related to the Risk_H subtype and least absolute shrinkage and selection operator, we developed a 12-gene risk-predicting model. We further validated its accuracy using three public datasets. Effective drugs for high-risk patients identified using the model were predicted. The novel PCa subtypes and prognostic model developed in this study may improve clinical decision-making. Prostate cancer (PCa) is highly intractable and incurable after metastasis. It is the leading type of noncutaneous cancer in males globally . ConventNEUROI and NEUROII gene expression levels  and The Tumor Fusion Gene Data Portal (https://www.tumorfusions.org/) (RNA sequencing (RNA-seq) data (Workflow type: HTSeq-Counts), single nucleotide variants (SNV) (Workflow type: MuSE Variant Aggregation and Masking), copy number variants (CNV) (Data type: Masked Copy Number Segment), and clinical phenotypes for patients with PCa in TCGA were downloaded. RNA-seq data were normalized using the DESeq2 R package . Tumor mns.org/) .Data sets from Gene Expression Omnibus (GEO) and cBioPortal for Cancer Genomics were used to validate the accuracy of the prognostic model . GSE7076n = 484) were evaluated using the ssGSEA algorithm and enrichment scores were obtained for each gene set.Based on a literature review, nine gene sets associated with PCa were selected . HALLMARn = 484) using the ConsensusClusterPlus R package ” was selected as the gene signature.xCell is a web-tool for cell type enrichment analyses of gene expression data for 64 immune and stroma cell types . AccordiThe ESTIMATE algorithm can estimate tumor purity by calculating the ratio of stromal to immune cells based on gene expression data . The EstA weighted correlation network analysis (WGCNA) can be used find phenotype-associated gene modules . RNA-seqLASSO regression was performed on data for patients in training group using the glmnet R package . Genes mThe accuracy of the prognostic model was determined by a tdROC analysis using the survivalROC R package. The endpoints were follow-up times of 1, 3, and 5°years. The area under the curve in the tdROC analysis was used to quantify accuracy. AUC values were obtained for the training group, internal validation group, and three external validation groups .The log-rank test and Cox regression for survival analyses were completed using the survival R package. The survival curve was plotted using the survminer R package. DFS, PFI, and biochemical recurrence-free survival were used as clinical outcomes. Follow-up time was evaluated in units of years. Finally, univariate and multivariate Cox regression analyses were used to explore whether the prognostic model is an independent predictor of prognosis.Based on CTRP version 2 and PRISM databases, drug sensitivities of high-risk patients identified using the model were predicted by ridge regression based on gene expression levels. The analysis was implemented in the pRRophetic R package . Componep < 0.05 was considered significant.All statistical analyses were completed in R version 3.6.3. Chi-squared tests and Fischer’s exact tests were used to compare discrete variables between or among groups. Continuous variables were compared using the Wilcoxon test (two groups) and the Kruskal–Wallis test (three or more groups). p < 0.05). Since these subtypes were identified based on ssGSEA scores, the levels of nine PCa-associated gene sets in the three subtypes were displayed in a heatmap in p < 0.001). Furthermore, the PSA, Gleason score, residual tumor, pathological T, and survival outcome were associated with the subtype status  and relative change in the area under the CDF curve are shown in e status . Collectp < 0.01; MYC_V1 score: rho = 0.49, p < 0.01; MYC_V2 score: rho = 0.35, p < 0.01), as shown in MKI67 and ssGSEA scores was also explored. The levels of MKI67 were positively correlated with ssGSEA scores for the E2F and MYC gene sets , as shown in MKI67 levels were highest in Risk_H among the three subtypes , as shown in We found that the ssGSEA scores for the E2F and MYC gene sets were positively correlated with proliferation scores . More single-copy deletion events were observed in the Risk_H subtype for RHOBTB2 and TNFRSF10C (chi-squared test p < 0.05). As shown in RHOBTB2 and TNFRSF10C were associated with lower expression levels . Consistent with these findings, lower expression levels of RHOBTB2 and TNFRSF10C were detected in the Risk_H subtype . These data suggest that SNVs in SPOP and CNVs in RHOBTB2 and TNFRSF10C are more common in the Risk_H subtype than in other subtypes. Low expression levels of RHOBTB2 and TNFRSF10C in Risk_H could be associated with single deletion CNV events.As shown in p < 0.001; MYC_V1: rho = 0.52, p < 0.001; MYC_V2: rho = 0.35, p = 0.0011). Taken together, we identified a single group of genes that effectively reflects the Risk_H subtype.As shown in via LASSO. In the training group, one 12-gene combination had the highest cross-validated C-index , internal validation group , MSKCC2010 data set , DKF2018 data set , and GSE70769 data set , as shown in p < 0.001), internal validation group (Wilcoxon test p < 0.001), MSKCC2010 data set (Wilcoxon test p < 0.001), DKF2018 data set (Wilcoxon test p < 0.05), and GSE70769 data set (Wilcoxon test p < 0.001). According to univariate and multivariate Cox regression analyses . As shown in p < 0.001, and Wilcoxon test p < 0.001). Collectively, we identified ten target drugs predicted to be effective for high-risk patients, and leptomycin B, LY2606368, and vincristine showed particularly high effectiveness.Using compounds from the CTRP and PRISM databases, we predicted drug sensitivity for patients with high risk scores. As shown in The mechanism underlying PCa progression is complex and cannot be explained by a single pathway. Accordingly, in this study, we used gene expression information for eight PCa-related pathways  extracted from Molecular Signatures Database v7.2 and data for PCa cohorts from multiple platforms  to identify three PCa subtypes . These subtypes were then used to construct a risk-predicting model and drug sensitivity prediction was performed for the high-risk group.For patients with PCa, an elevated hypoxic status is related to a more aggressive and advanced disease; hypoxia reduction could increase immunity and the response to specific immunotherapies . AdditioMKI67 gene expression level support the highly proliferative feature of the Risk_H cohort. Additionally, the proportions of immune and stromal cells were highest in the Risk_L cohort. NK cells, which possesses important anti-cancer functions , which was associated with the worst clinical outcomes. Further analyses of the proliferation scores and unctions , were mog cancer . Howevergnancies . We deteAfter establishing the prognostic value and properties of the subtypes, we constructed a twelve gene-based risk-prediction model. This model could supplement current strategies for clinical decision-making and prognostic predictions. Finally, we filtered twelve drugs expected to show high sensitivity in high-risk patients with PCa, 3-CI-HPC, CD-437, CR-1-31B, leptomycin B, SR-II-138A, YM-155, elesclomol, LY2606368, obatoclax, topotecan, VE-822, and vincristine.As a complex and heterogeneous disease, PCa is difficult to manage by a universal treatment approach. In this study, we divided PCa into three clusters based on eight pivotal pathways, allowing for more innovative and objective results than those obtained by analyses of single pathways. Moreover, we translated the results into a clinically useful tool and identified potentially effective drugs for high-risk patients, providing direct guidance for clinical strategies aimed at precision medicine. However, our study had limitations. First, the results are based on retrospective investigations of cohorts from multiple platforms; prospective explorations are needed to validate our results. Further clinical studies of the drug candidates are needed. Despite these drawbacks, our results provide novel ideas for PCa management.


Fgf-signaling is compartmentalized within the mesenchyme and controls proliferation during salamander limb development. Published 20, September 201910.1126/science.aaq0681). After contacting the authors of the published dataset to clarify an ambiguity in their Supplementary Materials, we discovered that we treated the counts in their scRNA-seq dataset as raw counts when in fact they were already normalized and logged (log2(TPM)). Thus, the analysis we published used values that were normalized twice. After re-analysing the data and comparing the outcome to our original data we found that it did not affect any of our conclusions. However, the re-analysed data does slightly affect the visual representation of data presented in Figure 4 and in the two associated Suppl. figures.In the original version of our manuscript, we used a previously published scRNA-seq dataset .W = 0.95551, p<2.2e-16).The sigmoidal curve follows a normal distribution . Color scale legends reflect log2(TPM) values.The sigmoidal curve follows a normal distribution .Expression was defined as one or more RNA-seq reads mapped to the gene in question.To:transcripts per million.Expression was defined as one or more T and p statistic in the Main text for the Welch Two Sample t-test changed from: T = −4.1588, p = 0.0038, TO: T= -3.8104, p = 0.0064In addition to the figures, a Lastly, we have updated our methods section to reflect the single normalization.Meis2 and Hoxd11) were segregated on opposite ends of principle component 3 in the PCA of cells from stage 44, but not 40. Due to the low expression levels of most genes of interest, raw read counts were used for visualizing gene expression levels on PCA plots by rescaling the log2 of the read count to span between 0 and 14. To determine co-expression of Fgf ligands and receptors, expression was defined as the presence of one or more reads mapping to the gene in question.Single-cell RNA-seq data from embryonic limb buds  were analyzed as follows: read counts from stages 40 and 44 (as reported in Gerber), and which correspond to our stages 44 and 45  were normalized and used as input for principle component analysis. The top twenty loadings of the positive and negative axes of the first five principle components were inspected to identify principle components that segregated developmental axes markers on separate ends. No reliable anterior-posterior, or dorsal-ventral markers segregated in this manner in the current dataset. However, proximal-distal markers (To:Gene expression (log2(TPM)) from stages 40 and 44 (as reported in Gerber), and which correspond to our stages 44 and 45  were used as input for principal component analysis. The top twenty loadings of the positive and negative axes of the first five principal components were inspected to identify principal components that segregated developmental axes markers on separate ends. No reliable anterior-posterior, or dorsal-ventral markers segregated in this manner in the current dataset. However, proximal-distal markers (Meis2 and Hoxd11) were segregated on opposite ends of principle component three in the PCA of cells from stage 44, but not 40. In order to orient PC3 so that Hoxd11 expression is on the right and Meis2 on the left , PC3 values were multiplied by -1. To determine co-expression of Fgf ligands and receptors, expression was defined as the presence of one or more transcript per million.Analysis of single-cell RNA-seq data Single-cell RNA-seq data from embryonic limb buds  were analyzed as follows: Also, the methods listed the incorrect concentration of EdU.Larvae were additionally treated with 0.1 ug/ml of EdU at stage 46 for 24 hrTo:0.1 mg/ml of EdU at stage 46 for 24 hrLarvae were additionally treated with The article has been corrected accordingly.


Copy number aberrations (CNA) are one of the most important classes of genomic mutations related to oncogenetic effects. In the past three decades, a vast amount of CNA data has been generated by molecular-cytogenetic and genome sequencing based methods. While this data has been instrumental in the identification of cancer-related genes and promoted research into the relation between CNA and histo-pathologically defined cancer types, the heterogeneity of source data and derived CNV profiles pose great challenges for data integration and comparative analysis. Furthermore, a majority of existing studies have been focused on the association of CNA to pre-selected “driver” genes with limited application to rare drivers and other genomic elements. In this study, we developed a bioinformatics pipeline to integrate a collection of 44,988 high-quality CNA profiles of high diversity. Using a hybrid model of neural networks and attention algorithm, we generated the CNA signatures of 31 cancer subtypes, depicting the uniqueness of their respective CNA landscapes. Finally, we constructed a multi-label classifier to identify the cancer type and the organ of origin from copy number profiling data. The investigation of the signatures suggested common patterns, not only of physiologically related cancer types but also of clinico-pathologically distant cancer types such as different cancers originating from the neural crest. Further experiments of classification models confirmed the effectiveness of the signatures in distinguishing different cancer types and demonstrated their potential in tumor classification. Copy number variations (CNV) are a class of structural genomic variants, in which the regional ploidy differs from the normal state of the corresponding chromosome. Germline copy number variations constitute a major part of genomic variability within and between populations and are an important contributor to genetic and inherited diseases , which presents a potential for early-stage and non-invasive cancer detection, attracts the attention of both the academia and the industry   represents an initiative from the International Cancer Genome Consortium (ICGC) for the uniform analysis of a representative set of cancer samples using Whole Genome Sequencing (WGS)  to perform a uniform normalization  using All copy number data can be represented in a platform-independent format of genomic segments, where each segment represents a continuous region on a chromosome. As the segments vary dramatically in platform-dependent resolution, reported size, and biologically supported thresholds  to perform a feature extraction, which generated a panel of cytobands with high separation power. In the second phase, the selected cytoband features were denormalized to the protein coding genes from each band. Finally, another iteration of the feature selection process was performed to arrive at a selection of genes that contribute to the distinctiveness of individual sample profiles.Another straightforward approach is to use the coverage of protein coding genes to represent each segment. This method provides inherent connections to genetic functions; however, direct mapping to protein coding genes overlooks the structural impact of CNA and also results in a large number of features, which are often difficult for machine learning methods to perform in optimal. In this study, we designed a two-phase approach to generate a refined gene panel as the feature representation, so that the noise and redundancy of the feature space could be reduced with consideration to functional significance. In the first phase, CNA segment data was mapped to cytobands, which represent “genomic phenotypes” correlated to gene density and chromosome structure and provided a good balance between feature number and genomic resolution. The cytobands features were then added to a hybrid model of An autoencoder is an unsupervised neural network that can derive abstracted representations from data. It usually consists of two parts, an encoder and a decoder, and both are also neural networks on their own. The input is first transformed to an encoding, then restored to its original by the decoder. The aim of an autoencoder is to reconstruct the input as exact as possible, and in the process, it learns a representation (encoding) of the input. It's typically used for dimensionality reduction and to remove noise from signals  were compared on their abilities of input reconstruction. The denoising and contractive autoencoders showed the best performance in the restoration of CNV data, and the denoising autoencoder was chosen for its efficiency in implementation. The details of the comparison are elaborated in Layer-wise Relevance Propagation (LRP) is a technique to scale the importance and contribution of input features in a deep neural network. It utilizes the network weights and activation functions to propagate the output backward until the input. The basic propagation rule is illustrated as the following:j and k are two neurons from two adjacent layers. a is the activation of a neuron, and w is the weight between two neurons. The initial R is known from the output, and this formula is iterated to compute R for each neuron in the previous layer until reaching the input layer. LRP provides a solution to the “black-box” problem of deep neural networks, and is especially useful in tracing the network attentions and understanding the network behavior. In our hybrid model, we exploited LRP's ability to quantify the importance of input features in distinguishing different samples. When an autoencoder can reconstruct a large number of inputs with high accuracy, it provides an encoding that carries the information to recognize the differences between samples. If we apply LRP to this encoding, we would be able to measure the importance of each feature in the encoding.Here, A primary goal of the research was to investigate the similarity and differences of CNA between cancers, through CNA feature dimension reduction toward an optimal representation of the uniqueness of each sample. In our hybrid model, we combined autoencoder's ability to recognize differences and the LRP's ability to scale feature relevance. First, the transformed feature matrix was used as the input data for the training of a denoising autoencoder. Then, the encoding generated by the autoencoder was used as the input of LRP to compute the weightings of each feature in the initial feature matrix. Next, the weightings of each feature were summed and normalized for all samples. Finally, by applying a threshold, we obtained a panel of high weighting features representing the uniqueness of each input sample.cytobands with high weighted CNA coverage; then, the hybrid model was utilized on the new feature matrix (CNA coverage on protein coding genes of the high-weighting cytobands), to calculate a panel of high-weighting genes as a representation of each sample's feature space. In the two-phased feature extraction, the hybrid model first returned a panel of Progenetix and arrayMap resources . In general, spatial and annotation analysis suggest that some feature genes reflect structural and functional alternations in samples; the signatures of different subtypes show high preference in several genomic regions that suffer frequent CNAs in many cancer types; and some subtypes, which have distinct disease codes, exhibit high similarity in their signatures.In the majority of copy number studies, analyses of tumor samples are focused on identifying the driver genes or the focal regions. However, in this study, the frequent drivers such as TP53 and PTEN, and the common aberrations such as CDKN2A/B and MYC are removed due to their prevalence. The genes in the signatures rather reflect the uniqueness of each sample or each cancer subtype. It is importance to note that the feature genes are not intended as the only nor the optimal representation. The fundamental objective of the study is to explore the potential driver mutations that are infrequent but relevant to specific cancer types. Although the signatures do not imply pathogenic causation, we can instead reveal their potential implications and correlations by investigating the signatures and the feature genes  The immunoglobulin heavy chains related genes showed the highest significance. Although the mutations of these genes are known to play causal roles in B-cell related malignancies  gene families are defined based on evolutional and functional homologies . High-level deletions are frequently observed and selected as features in many subtypes. Different studies have shown similar observations of frequent deletions on chromosome 22, however, the mechanism of the deletion is still obscure and the functional impact remains putative  were removed in the process. For future studies, complementary strategies should be developed to include these samples without detrimental effects on data normalization and quality. With respect to sample annotation for histologic and diagnostic classifications, extensive efforts went into the curation of the data with the aim to use a unified set of classifications. Here, future analyses may make use of emerging cancer specific ontologies for a better integration of varying annotation granularities using hierarchical terms and concepts. Currently, our research group is finishing a parallel study on ontology mappings of cancer samples, and we are expecting a considerable improvement in the disease annotation of samples in future studies.In summary, this study presents a systematic pipeline for integrative and comparative analyses of a large amount of copy number data. The resulting CNA signatures offer new perspectives on the understanding of common foundations in cancers and show promising potential in applications of tumor classification.https://www.progenetix.org;Data from TCGA is available at: https://portal.gdc.cancer.gov;Data from PCAWG is available at: https://dcc.icgc.org; Segmentation data and metadata of open-access samples used in the study are available at https://progenetix.org/progenetix-cohorts/gao-2021-signatures/.Data from Progenetix is available at: BG developed the method and implemented the analysis. MB conceived the original concept and supervised the project. All authors contributed to the writing and revisions of the manuscript.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.