Driver event discovery is a crucial demand for breast cancer diagnosis and therapy. In particular, discovering subtype-specificity of drivers can prompt the personalized biomarker discovery and precision treatment of cancer patients. Still, most of the existing computational driver discovery studies mainly exploit the information from DNA aberrations and gene interactions. Notably, cancer driver events would occur due to not only DNA aberrations but also RNA alternations, but integrating multi-type aberrations from both DNA and RNA is still a challenging task for breast cancer drivers. On the one hand, the data formats of different aberration types also differ from each other, known as data format incompatibility. On the other hand, different types of aberrations demonstrate distinct patterns across samples, known as aberration type heterogeneity. To promote the integrated analysis of subtype-specific breast cancer drivers, we design a “splicing-and-fusing” framework to address the issues of data format incompatibility and aberration type heterogeneity simultaneously. To overcome the data format incompatibility, the “splicing-step” employs a knowledge graph structure to connect multi-type aberrations from the DNA and RNA data into a unified formation. To tackle the aberration type heterogeneity, the “fusing-step” adopts a dynamic mapping gene space integration approach to represent the multi-type information by vectorized profiles. The experiments also demonstrate the advantages of our approach in both the integration of multi-type aberrations from DNA and RNA and the discovery of subtype-specific breast cancer drivers. In summary, our “splicing-and-fusing” framework with knowledge graph connection and dynamic mapping gene space fusion of multi-type aberrations data from DNA and RNA can successfully discover potential breast cancer drivers with subtype-specificity indication. Breast cancer has become the most prevalent cancer in women worldwide, where 7.8 million women were diagnosed with breast cancer during the past 5 years at the end of 2020 . In cancetc. In consideration that genes have functional interactions with other genes, some recent approaches for driver discovery also expand the information of genomics from gene mutations to gene interactions  project, a well-curated database including DNA sequencing data and RNA expression data of cancer samples . The canIn addition to aberration data in DNA and RNA, we can also introduce side information to prompt the subtype-specific breast cancer driver discovery task in our integration analysis. For subtype-specificity indication task in our integration, the TCGA database also provides the subtype annotations of the breast cancer samples, including five widely-accepted intrinsic molecular subtypes: Luminal A, Luminal B, HER2-enriched, Triple-negative, and Normal-like subtypes . The subSince the RNA alternation and DNA mutations are distinct types of aberrations of genes, their integration faces the challenges of data format incompatibility and aberration type heterogeneity. Therefore, we design a “splicing-and-fusing” framework to address the two challenges step by step. After the data collection , we firsP, aberration type T, gene G) if gene G occurs with a G type aberration in sample T , when the investigated breast cancer sample P (subject) belongs to (predicate) the certain subtype S (object)  . The incG, is D)  can indicate that the gene G in this triplet is an experimental validated known cancer driver, but the absence of a driver annotation triplet of a gene cannot indicate its irrelevance to cancer driver  . These friver D) . Accordir driver . Actuallr driver . The conTo preserve the aberration type heterogeneity in the integrated representation, we further propose a dynamic mapping gene space integration approach, known as the “fusing-step”. Unlike the previous scalar based methods such as statistical variation approaches  or simili.e., the fusing strategy should be information preserving . If the loss score D). The recovery loss scores are inducted to yield small values for all the triplets of known drivers in the knowledge graph data, and therefore the scores of potential cancer driver genes are also expected to be small for unobserved triplets including the genes to be tested , But we can borrow the known information from the subtype belongingness of samples with the triplet form . Here the fused representations in gene space are expected to reflect the subtype information due to the inclusion of subtype related triplets in knowledge graph. Like the idea of cancer driver discovery above, we can also adopt the recovery loss scores to indicate the subtype-specificity of the investigated gene. For a potential driver gene G discovered by the fused representations, we further utilize the recover loss score function G, belong to B, subtype S). Accordingly, we can yield the scores of the investigated gene G across all the subtypes Since only inferring whether a gene to be tested is a potential driver is not enough for the indication of subtype-specific drivers for breast cancer, therefore we should also indicate the subtype-specificity of the discovered driver genes . Note thcificity . Based oTo examine the effectiveness for subtype-specific cancer driver discovery of our multi-type aberration integration approach, we conduct several experiments to analyze our approach from multiple perspectives: (1) comparing our approach with widely-used existing cancer driver approaches, (2) testing the effect of integrating multi-type aberrations by data ablation, (3) applying ablation study for gene space by removing dynamic mapping strategies, (4) indicating subtype-specificities of genes by data visualization, and (5) unscrambling gene functions by enrichment analysis. The first three experiments are conducted for examining the cancer driver discovery power from the gold standards of known benchmarking driver genes, in order to investigate the advancement of our approach, the effect of different type information, and the effect of data fusing strategies. Specifically, we compare our approach with existing interaction based driver methods including HotNet2 , DawnRanTo provide the experiment design details of our model in driver identification task, here we also demonstrate two aspects on our used breast cancer samples. (1) The first aspect is collecting breast sample data containing both DNA and RNA aberrations. Regarding the first aspect, the reason of using DNA and RNA aberrations contained in the same samples is that we can investigate the integration of two types of aberrations only if both DNA and RNA aberrations are collected from the same samples. With the integration of two types of aberrations, our model can achieve a better performance on driver gene identification. (2) The second aspect is splitting the same cohort of breast cancer samples into two independent datasets. To avoid the ideal situation that all the used breast cancer samples in model training and performance testing are all the same from the TCGA database, we adopt the five-fold cross validation so that the testing breast cancer samples are separated from the dataset before the model training process. Through the five-fold cross validation, one-fifth of the breast cancer data are excluded from the model training stage, and these data only serve as the performance testing. Thus, although all the breast cancer data are collected from the same TCGA database, we can successfully avoid the situation that the same sample is used in driver identification.In the evaluation of cancer driver discovery, there are gold standards of known benchmarking driver genes, and consequently we easily examine whether a discovered gene is truly a cancer driver. Note that during the “splicing-step” of our approach, the information of the known cancer driver is a part of triplets of the facts include in the integrated knowledge graph as the supervision information. Thus, we employ the idea of cross validation for cancer driver discovery performance evaluation . SpecifiTo demonstrate the cancer driver discovery performance of our dynamic mapping gene space integrated multi-type aberration information, we adopt our approach on the TCGA breast cancer data  by consti.e., only considering whether the genes are with aberrations but excluding the difference of aberration types between DNA and RNA. In the knowledge graph, the relations of different aberration types are confused as only one type of aberration, but the other types of relations such as interaction or subtype belongingness are not confused. As shown in i.e., no aberration type confusing. These phenomena indicate that the difference between multi-type aberrations are informative for the driver discovery of our integration approach. Another way to change the integrated data is to exclude the information of gene interactions from the knowledge graph. By this way, we can observe the effects of performance from the interaction information. Through To further investigate the effects of information integrations from multi-type data, we further conduct the data ablation on the multi-type data. Here, data ablation is a strategy for observing the performance effects when the data are changed . When weIn addition to data ablation that excluding part of the multi-type data, we also conduct the ablation study for gene space mapping by excluding part of the strategy during the knowledge graph representation. In our approach, the relation-specific space mapping is based on a dynamic strategy , denotedG, belong to B, subtype S) and obtain the loss score for subtype S. By switching the triplet tail across all subtypes in the triplet, we can compute the scores corresponding to the investigated gene across all the subtypes. Finally, we selected the subtype with minimizing recovery loss as the subtype-specificity of the gene to be tested. For discovered driver genes of our approach in the five-fold cross validation, we randomly select a fold and display the subtype-specificities of these genes. Since the dimension of gene space is usually larger than three and thus is difficult to display, we utilize the t-distributed neighbor embedding (t-SNE) on our gene space representation  . Since ti.e., false positives, are in As for the novel driver candidates surprisingly in top hits, we also investigate the identified new drivers that are surprisingly in top hits and never been identified in other existing methods. It should be noted that, in our experiments, the true positives are identified genes that are also included in benchmarking database , and theDiscovering subtype-specific drivers is an inevitable demand in breast cancer precision medicine. The existing widely-used computational tools for driver discovery mainly focus on exploiting the information from DNA aberrations or gene interactions. However, recent studies have demonstrated that expect DNA aberrations, RNA alternations also play important roles in cancer driver events , but theThe main advantages of our study can be summarized as three points. The first advantage is that the multi-type aberrations can contain more information beyond the scope of breast cancer genomic data. By adding the RNA alternations, interactions between genes, the subtype annotations of samples, and the supervision information of known benchmarking drivers, the discovery task of subtype-specific cancer drivers has a richer connotation than those of most of the existing computational tools. The second advantage is that the knowledge graph structure can successfully overcome the issue of data format incompatibility. We can redescribe the existing data with a series of triplets of facts, and splice them together by connecting common entities and relations into an integrated knowledge graph of multi-type aberrations of breast cancer. The third advantage is that the idea of dynamic mapping strategy can fuse the triplet data into relation-specific gene space, and the low-dimensional fused gene space can describe the aberration type heterogeneity as many-sided representations. Through the advantages above, our approach of multi-type aberration integration can not only discover potential driver genes from breast cancer data, but also indicate the subtype-specificities of these potential drivers by the integration of both DNA and RNA data of breast cancer samples.With the advancement of artificial intelligence in biomedical research  and manyDespite the achievements of our integrated approach, there are also some limitations in our study. An inevitable limitation is that our approach relies on a strong restriction of the alignment of the DNA and RNA data samples. If some breast cancer samples only contain DNA data or RNA data, these samples cannot be used in our integration analysis . Actuall10.7717/peerj.14843/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.14843/supp-2Supplemental Information 2(A) and (B) show the front view of the 3D visualization results with interaction source of STRING and iRefIndex, respectively. (C) and (D) show the top view of the 3D visualization results with interaction source of STRING and iRefIndex, respectively. (E) and (F) show the side view of the 3D visualization results with interaction source of STRING and iRefIndex, respectively.Click here for additional data file.


The clinical diagnosis of neurodegenerative diseases is notoriously inaccurate and current methods are often expensive, time-consuming, or invasive. Simple inexpensive and noninvasive methods of diagnosis could provide valuable support for clinicians when combined with cognitive assessment scores. Biological processes leading to neuropathology progress silently for years and are reflected in both the central nervous system and vascular peripheral system. A blood-based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. In this study, we set out to discover a small set of blood transcripts that can be used to distinguish healthy individuals from those with Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, Friedreich’s ataxia, or frontotemporal dementia. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood and discovered small sets of transcripts that distinguish a number of neurodegenerative diseases with high sensitivity and specificity. We validated the usefulness of blood RNA transcriptomics for the classification of neurodegenerative diseases. Information about features selected for the classification can direct the development of possible treatment strategies. Neurodegenerative diseases such as Alzheimer’s disease (AD) are complex and require expensive diagnostic procedures. In addition to the expense, a recent study found AD has a misdiagnosis rate of about 1 in 4 even at established dementia centers . The useNeurodegenerative diseases are associated with changes in specific molecular pathways and involve metabolomic and biological processes active in both the central nervous system (CNS) and vascular peripheral system . The stuBlood biomarkers are especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. The incorporation of blood screening tests in clinics around the world, which, ideally, could also classify neurodegenerative diseases, could provide valuable support for clinicians when combined with cognitive assessment scores, thereby reducing health care costs and ruling out or streamlining additional tests such as imaging. Another ideal utility of an inexpensive blood panel is the ability to monitor disease progression, for example, marking the advancement beyond mild cognitive impairment (MCI). Identifying blood-based biomarkers for AD is highly attractive and will prove essential for screening, diagnosis, differentiating, and monitoring or predicting disease progression and therapeutic outcome. Screening tests are already available for various disorders, such as gestational diabetes, cancers, and depression to name a few, but the majority are limited to simply ruling out disease having low positive predictive power ,8,9,10. Our previous blood transcriptome work was able to distinguish AD, PD, and control brains and was found to be reproducible across multiple replicates and platforms . The traHere we aim to validate our previous transcript picks using new datasets and we find that the complete set of transcripts performs equally well or better at discriminating between clinical AD and age/sex-matched nondemented individuals, providing again proof of concept of the practical use in a blood screening test. We expanded our transcript discovery using a random forest algorithm to derive supervised predictors of different neurodegenerative diseases with the aim of generating transcriptional clusters able to make clinical group discriminations that are unique to each disease. RNA expression analysis gives us the advantage of using the vast knowledge of gene expression to classify disease.https://www.ncbi.nlm.nih.gov/geo/) in June, September, November, and December 2020. The datasets were chosen using the following criteria: (1) RNA extracted from whole blood collected in Paxgene tubes; (2) dataset must contain at least one hundred human subjects; and (3) data were generated on the GPL15988nuID (Illumina HumanHT-12 V4.0 expression beadchip), GPL10558 (Illumina HumanHT-12 V4.0 expression beadchip [The gene expression data used here were sourced from the publicly available repository Gene Expression Omnibus (GEO) , beadchip ,23), or beadchip ) microarbeadchip , GSE6306beadchip , GSE1126beadchip ,23, GSE1beadchip ,23, GSE1beadchip ) with a beadchip ,23,24. CBlood RNA expression datasets were downloaded and processed in normalized form. Within each dataset we used each sample’s respective GUSB (accession NM_000181.2) expression level to ensure normalization of samples was identical to that used for normalization in the previous blood work. GUSB is commonly used in laboratory experiments to normalize data and is found to be most consistently unchanged in neurodegenerative disease ,28,29. TThe normalized data values for the predicting sets of transcripts were analyzed by multivariate discriminant analysis GB-STAT v.10 . Linear discriminant analysis is a generalization of Fisher’s linear discriminant and maximizes the separability among the two categories. A linear discriminant function (weighted sums of the transcripts) was computed for maximum separability of the two groups where the coefficients or discriminant scores are used to classify as disease or controls.n random samples (with replacement) from N available samples referred to as bootstrap sampling. Predictions gathered from a collection of trees using bootstrap sampling is called bagging, thus the Matlab function treebagger. The bootstrap sampling selection procedure results in a random exclusion of approximately a third of the samples from the training set. These samples, referred to as ‘out-of-bag’ samples, are used later to calculate an estimate of the generalization error. All of the remaining two-thirds of the training samples called ‘in-bag’ samples are randomly selected with replacement to grow each new tree with at least one observation per tree leaf. The maximum depth of a tree is set to 20. Blood RNA transcripts’ or features’ expression values for a small subset A random forest classifier is a machine learning algorithm that builds an ensemble of many classifier trees, where the prediction for a test sample is made by majority vote tallied on a combination of all trees ,32. We dfication ,33. Thisfication . Many trRandom forest classifications for individual datasets were made by random 80/20 separation of a training and test set where 20% of the samples from each class were removed prior to analysis and used for the validation test step after random forest classification and selection of transcript predictors. For the selection of AD and ALS predictors, we were able to use whole datasets  with GSE63061 (AD2) and GSE112676 (ALS1) with GSE112680 (ALS2) whole blood expression data), one set was used for training, while the other set for the independent test data and vice versa.Our overall objective was to discover a small set of transcript predictors in whole blood that are reliable low-cost identifiers of AD. We also sought to compose from our meta-analysis, a set of classifying biomarkers that distinguish between neurodegenerative diseases, even those that may have similar clinical symptoms. We extended our evaluation of a group of prediction transcripts or features compiled previously where the feature selection was guided by evidence-based empirical data and the AD literature available at that time ,35. TwenE is the determinant of a matrix of variations within classes and H the determinant of a matrix of variations between the two classes of disease samples and controls. A number close to zero with a significant p-value suggests the LDA made an excellent separation of the classes distinguishing disease samples and controls. We found that for the groups of transcripts that were able to classify disease from controls more than 70% of the time  that the Wilks’ Λ was in general less than 0.8 with F-ratio significant p < 0.05 in agreement with successful AUC values reported. An overview of LDA analysis statistics across the six neurodegenerative diseases tested here for the three functional groups of previously selected transcripts is tabulated in  were selected in both training sessions of random forest analysis on the AD blood datasets. These six transcripts selected consistently for AD classification represent the electron transport chain and ribosomal complexes.The top twenty predicting transcripts, matching probe IDs, and classification scores were saved for one hundred rounds of the four-step random forest classification scheme described in ‘Materials and Methods’ . SelectiEach of the two groups of twenty RF-selected transcripts, which discriminate between AD and controls, were further analyzed for classification power in other diseases using linear discriminant analysis (LDA). Whole blood gene expression datasets from ten cohorts spanning six neurodegenerative diseases including AD, PD, ALS, FRDA, FTD, and HD were analyzed. We first measured a baseline prediction probability for the LDA by the random number generator selection of twenty transcripts in both AD1 and AD2. We found that randomly selected transcripts can classify AD from healthy controls (HC) (AUC ≤ 71%), further justifying that good classification should be well above AUC > 71% . The grop < 0.05 and resulted in at least 75% of the case samples being classified correctly. The LDA statistics for random forest AD transcript predictors are documented in Inspection of the significance and separation between classes (disease and control) measured by the Wilks’ Λ was in good agreement with the AUC probabilities. Again, a Wilks’ Λ less than 0.8 represented good class separation combined with an F-ratio We further sought to select sets of RF classifiers for each of the other neurodegenerative diseases. We acquired two datasets each of ALS and PD; however, the two PD datasets were collected and generated on different manufacturer platforms by different research groups and we found it difficult to pair up and match transcripts between the cohorts due to different lengths and binding sites of oligomer probes and subsequently chose to treat the two PD sets individually. The random forest classifier method was applied to the blood RNA expression datasets to distinguish between disease and healthy controls. In the case of ALS, the expression datasets were first matched and sorted against each other to ensure each contained the same set of features.The random forest classification algorithm generated one hundred models in a four-step process as described in ‘Materials and Methods’ , and theTraining on ALS1 and test data ALS2 resulted in a set of 20 transcripts with sensitivity 74%, while training on ALS2 and testing on ALS1 generated a set of 20 transcripts . There wFor each disease cohort dataset, the respective group of twenty transcripts selected by RF to discriminate between disease and controls were further validated using LDA on the test samples set aside for the validation step. The results for AD groups (AD1 and AD2) are plotted together in p > 0.05 for each indicates LDA separation may be due to chance only. Additional testing of the selected transcripts will be required for validation.An inspection of separation statistics  suggestsAn inexpensive and minimally invasive blood test is ideal for earlier diagnosis of people at risk of neurodegeneration and for gaining early intervention for testing and treatment strategies. Considerable time and resources are required to evaluate patients for neurodegenerative diseases or to screen research participants using current expensive and invasive methods such as PET scans and spinal taps. An accurate, inexpensive, noninvasive blood test can rapidly screen large and diverse groups from clinics around the world for diagnosis or recruit volunteers for studies . PromisiPreviously, we described success for LDA to distinguish AD from healthy controls and PD using the 22 empirically-selected transcripts . We exteMRPL51, NDUFA1, NDUFS5, two Illumina probes for RPL36AL, and LOC646200 ) were selected in both sessions, highlighting the importance of mitochondrial and ribosome malfunction in Alzheimer’s disease. The lack of complete agreement for transcript picks by RF for AD1 and AD2 is probably the result of heterogenous disease progression, mixed pathologies, sample and data collection protocols, technical platforms, and processing, highlighting the importance of the standardization of protocols.We then used a novel random forest ensemble machine learning algorithm to look for a new set of transcripts in blood that may prove more successful in classifying disease from healthy controls and applied it to all six neurodegenerative diseases. The RF algorithm builds multiple trees to choose the best tree as if assembling a group of weak learners to form a strong learner. We used AD1 and AD2 datasets for training and test sets in both directions and found six transcripts  and Algorithm to distinguish multiple neurodegenerative diseases on the basis of transcripts in blood .


Cancer molecular subtyping plays a critical role in individualized patient treatment. In previous studies, high-throughput gene expression signature-based methods have been proposed to identify cancer subtypes. Unfortunately, the existing ones suffer from the curse of dimensionality, data sparsity, and computational deficiency.L1 and L2 regularization and dropout layers are added.To address those problems, we propose a computational framework for colorectal cancer subtyping without any exploitation in model complexity and generality. A supervised learning framework based on deep learning (DeepCSD) is proposed to identify cancer subtypes. Specifically, based on the differentially expressed genes under cancer consensus molecular subtyping, we design a minimalist feed-forward neural network to capture the distinct molecular features in different cancer subtypes. To mitigate the overfitting phenomenon of deep learning as much as possible, For demonstrating the effectiveness of DeepCSD, we compared it with other methods including Random Forest (RF), Deep forest (gcForest), support vector machine (SVM), XGBoost, and DeepCC on eight independent colorectal cancer datasets. The results reflect that DeepCSD can achieve superior performance over other algorithms. In addition, gene ontology enrichment and pathology analysis are conducted to reveal novel insights into the cancer subtype identification and characterization mechanisms.DeepCSD considers all subtype-specific genes as input, which is pathologically necessary for its completeness. At the same time, DeepCSD shows remarkable robustness in handling cross-platform gene expression data, achieving similar performance on both training and test data without significant model overfitting or exploitation of model complexity.The online version contains supplementary material available at (10.1186/s13040-022-00295-w). The gene expression-based consensus molecular subtyping has been demonstrated as a promising paradigm for patient stratification and therapy . MoleculIn the past, application-specific supervised cancer diagnosis methods have been developed to address the previously mentioned challenges; for instance, Yang et al.  construcRecently, deep learning has been proposed to deal with those problems. It automatically generates informative features from low-dimensional features to discover the essential data representations . SeveralL1 and L2 regularization are added into the network architecture to enhance the robustness of DeepCSD. Finally, to demonstrate the performance, we collected eight independent datasets to train and validate DeepCSD. The experimental results reveal that DeepCSD can achieve competitive performance over the current state-of- the-art cancer-subtype diagnosis methods including Random Forest (RF), deep forest (gcForest), support vector machine (SVM), XGBoost, and DeepCC. In addition, we directly applied the DeepCSD model to independent TCGA data and can characterize differential gene expressions among diverse marker genes. Meanwhile, gene ontology enrichment, and KEGG pathology analysis are conducted to reveal novel insights into the cancer subtype identification and characterization mechanisms with validations.At the beginning, to emphasize the pathological importance of different genes in identifying cancer subtypes, differential gene expression analysis is computed to identify different subtype-specific genes in each pair of subtypes. Secondly, we proposed a novel deep learning framework for cancer-subtype diagnosis, named DeepCSD. To avoid overfitting to the maximum extent possible, the dropout layer, https://www.synapse.org/#!Synapse:syn2623706/wiki/) (downloaded on 1 Oct 2020).In this study, we collected eight independent colorectal cancer datasets  includinSince the tumor microenvironment makes an important contribution to gene expression , we idenP value . Mathematically, the fold-change can be defined as follows: In this study, we focus on the well-established consensus molecular subtypes (CMS), i.e. CMS1-CMS4. Each subtype is characterized by its own unique feature: CMS1 (microsatellite instability immune), CMS2 , CMS3 (metabolic), and CMS4 . The definitions can be found in . Such hiA and B denote the genes expression values of the same gene in different subtypes, A and B. In common, the log2 fold-change is widely used to normalize the range of fold-change. The log2 fold-change can be defined as follows: while However, the fold change has a drawback that the misclassification of differentially expressed genes with large differences may result in poor identification of changes in high expression levels.P value is implemented as an alternative to the rejection point and provided the minimum level at which the initial hypothesis will be rejected. The P value we used is adjusted by the Benjamini-Hochberg (BH) program [Q value. In this study, we take the T-test to compute every gene’s P value. More formally, the T-test is defined as follows: To address it, the  program , also caP value is calculated according to the T distribution to measure the significance of this difference. Next, we took BH to obtain a Q value. More formally, the Q value in this study is defined as follows: Then the significance Q value for each CMS group. Then, the genes with ∣log2 fold-change ∣>1 and Q value <0.05 are retained and identified. Finally, we input those subtype-specific genes into deep learning and then construct our DeepCSD model.After that, we calculated the fold-change and Our deep neural network (DNN) is a feed-forward neural network assembled by a sequential layer-by-layer structure that realizes a series of functional transformations. Each layer is fed with the previous layer’s outputs as its inputs is to execute its transformation function. Specifically, every layer consists of multiple “neurons”. The input data will pass every layer by the “connections” with the weight parameters between “neurons”. The basic layers in DeepCSD are the representation layers and dropout layer  is the (l−1)-th layer’s output, θl is the l-th layer’s threshold, and αl is the l-th layer’s activate function. It is worth noticing that R0 does not exist. In other words, the dropout layer is not employed in DeepCSD’s first layer.while L2 regularization and dropout layers are employed in our model. For the dropout layer, it is to abandon a part of neurons to mitigate the overfitting. The brief implementation step is defined as follows: The natural method is to process the original inputs layer by layer to obtain “deep features”. In other words, every layer in the framework of deep learning is to extract deep features from the previous layer’s outputs. The more layers the DNN has, the deeper features the model can extract. In theory, the addition of layers and parameters can increase the model expressiveness for addressing complex learning tasks. However, the possibility of overfitting is also increased . To redup, f(x) is the input of dropout layer, and where the Bernoulli function is to randomly generate a binary mask vector with Bernoulli trial probability e output .L1 and L2 regularization to representation layers 1 and 2 by adding a penalty term to the empirical loss. It is worth to be noticed that the output layer in DeepCSD does not employ such regularization.After that, DeepCSD applies For such a multi-class classification task, we minimized the objective function of DeepCSD defined as follow: n is the number of samples, t-th neuron’s output of i-th sample, λ is the learning rate, L1,and L2. With one-hot label vector encoding, the objective function can be simplified as follow: while i-th sample.while The Adam algorithm has been chosen as our model’s optimizer . The updwhile gt, the Adam algorithm automatically updates the corresponding parameter. Next, we take the update process of W3 as a sample to explain the detail of our model update process. Mathematically, the output is defined as follow: We can see that if we compute the parameter of W3. The Adam algorithm makes some adjustment to W3 if the gt is computed. The gt for W3 is calculated as follow: We focus on the parameter updating process of while W3R2f2(x)−Θ3). Firstly, the partial derivative calculation of softmax activation function for k-th neuron is calculated as follow: Next, we focus on partial derivative calculation of activation function for T (expand matrix W3 by row). Then, the partial derivative calculation of activation function for (W3R2f2(x)−Θ3) is divided in two situations as follows: Therefore, we view gt is given by the following formula: The final gt appeared. The wk will be strengthened if “ k=t”; it will be punished if “ k≠t”.Combining those column vectors into a matrix in order, the −5. For SVM and RF, the parameter setting follows the default parameter values of Python Scikit-learn 0.21.2. For gcForest, we fixed this model to a multi-class mission with the literature default setting [gamma=0.1, maxdepth=12, subsample=0.7, colsamplebytree=0.7, earlystoppingrounds=15. For DeepCC, all parameter setting follows the reference [In this study, those eight molecular datasets are employed for experimental comparisons. For the DeepCSD, the parameters of Adam follows the default setting  and the  setting . For XGBTo comprehensively demonstrate the DeepCSD performance, five state-of-the-art cancer-subtype diagnosis methods including Random Forest (RF), multi-Grained Cascade Forest (gcForest), support vector machine (SVM), XGBoost, and DeepCC are employed in this study. Firstly, two traditional frameworks were constructed based on RF and SVM. The fundamental idea of SVM is to find a hyperplane in a given sample space to distinguish samples with different classes . AlthougFour evaluation metrics have been measured in this study. For each dataset, according to the relationship between the real subtype and the diagnosis of DeepCSD, each diagnosis of DeepCSD was divided into true positive (TP), true negative (TN), false positive (FP), and false negative (FN). These four-measure metrics are defined as follow: yi is the sample’s true label, I(x) is the indicator function, and n is the samples of each molecular data.which Firstly, DeepCSD provides competitive performances in specificity, precision, sensitivity, and accuracy: 1) the accuracy of DeepCSD is higher than other classifiers in GSE13067, GSE17536, and GSE20916 by 5%, while the specificity and sensitivity is not less than 94%. 2) although the performance of DeepCSD is not the best in GSE39582 and GSE2109, the difference in accuracy is only lower than the champion by ∼1% while the sensitivity is higher than 92% and specificity is higher than 96%. 3) the average accuracy, precision, specificity, and sensitivity are higher than the second place by 2% ∼5%.Secondly, 1) gcForest and RF provide good performance since those methods are boosting methods. Its accuracy, specificity, and sensitivity are higher than 90% in 5 out of 8 datasets. The accuracy of RF is higher than gcForest on GSE20916, GSE14333, and GSE39582. However, the specificity of RF is lower than gcForest for those datasets. 2) gcForest, RF, SVM, XGBoost and DeepCSD can obtain better results than other compared algorithms on GSE39582 due to the influence of subtype-specific genes.Thirdly, the average accuracy of DeepCSD, gcForest, and RF are above 90% while SVM and XGBoost cannot provide such performance.Last but not least, we can find that its accuracy on GSE20916 and GSE2109 is not bad at all compared with other datasets. Moreover, we also computed the distance correlations  between To comprehensively demonstrate the performance of DeepCSD, all datasets  are adopted for model training with 10-fold cross-validation. We identified the differential genes as the inputs of SVM, GCF, RF, and XGBoost by subtype-specific gene analysis. The experimental results are summarized in Fig. To explore the influence of subtype-specific genes, GSE39582 was chosen as an example to reveal the subtype-specific genes’ contributions.In GSE39582, 2917 subtype-specific genes were extracted and visualized as red and blue dots in Fig. To demonstrate the distinctiveness of those 2917 subtype-specific genes, we employed spectral clustering on all samples as depicted in Fig. To further demonstrate the subtype-specific genes’ performance, we compared our proposed DeepCSD with DeepCC. DeepCC combines samples’ gene expressions with public data to process gene set enrichment analysis (GSEA) to select some gene expressions which the authors called Functional Spectrum . As mentThe experimental results are shown in Fig. It can be found that the performance of DeepCSD is higher than EDT and SKB under the same condition. Meanwhile, we also compared DeepCSD with and without differential genes. The results reveal that the differential genes analysis is very important in DeepCSD. Therefore, we can conclude that the subtype-specific differential gene expression analysis is an inevitable step towards cancer subtype diagnosis improvement.In DeepCSD, the dropout parameter is 0.5. It indicates that all inputs delivered from previous layers will be abandoned for half of the neurons. Traditionally, the parameter is chosen in the range from 0.2 to 0.5. However, to reduce the influence of overfitting to the utmost extent and to strengthen the generalization ability, dropout=0.5 is chosen in DeepCSD , 20.L1 and L2 can strengthen the important features and reduce the weights of redundant features. Therefore, if the parameter is too large, the learning ability of DeepCSD will be limited. On the opposite, if the parameter is too small, the phenomenon of overfitting may not be prevented. To find a suitable parameter to strike a balance, we trained DeepCSD with the learning rate ranging from 0.1 to 0.9. The analysis is shown in Fig. The real challenge lies in the parameter setting of the regularizer. It is well known that the regularizer of We can summarize some conclusions from Fig. https://www.synapse.org/#!Synapse:syn2623706/wiki/). The TCGA dataset has 512 patients with explicit CMS labels in total. Meanwhile, each patient sample in TCGA has 20293 features. In the first, the TCGA dataset was split randomly into a training set and test set with the 9:1 ratio. Next, we trained DeepCSD on a training set with 10-fold cross-validation and took the corresponding test set to demonstrate the performance of DeepCSD. To identify the differential genes, we applied the subtype-specific differential gene expression analysis, resulting in 4881 genes for such TCGA data. From SupplementaryTo further demonstrate the performance of DeepCSD, we applied it to annotate subtypes on an independent colorectal cancer dataset from The Cancer Genome Atlas (TCGA) , 24. TheFrom In the previous section, we have demonstrated the competitive performance of DeepCSD based on subtype-specific genes. In this section, we investigated the biological significance of the subtype-specific genes.p-values. After that, we randomly took the 9-th neuron as example; for instance, the top three enriched GO biological processes in 9-th neuron are mucosal immune response (GO:0002385), prostaglandin secretion (GO:0032310), and regulation of secretion by cell (GO:1903530). Meanwhile, as depicted in p-values of the enriched term are clustered in the second figure of Fig. Firstly, we identified the top 100 genes with the largest weights in each of the first 10 neurons in the representation layer 1 (the detail of those selected genes can be found in Supplementaryhttps://www.kegg.jp). As shown in Fig. In addition, to further analyze the molecular pathways behind the layer of DeepCSD, those selected genes were taken to the molecular pathway analysis with KEGG (Given the central importance of colorectal cancer subtyping, the consensus molecular subtype classification is always desirable for patient stratification. Specifically, the most important component to the consensus molecular subtyping is that the underlying classifier can diagnose every subtype correctly. In this study, we proposed a deep learning framework based on differential gene expression analysis to diagnose cancer subtypes.Based on the results, we observe that DeepCSD can achieve better colorectal cancer subtype identification than RF, SVM, gcForest, XGBoost, and DeepCC. We also highlighted the significance of the differential gene expression analysis compared with other possible algorithms as shown in Fig. In the future, we are optimistic that our minimalist approach can demonstrate its own value and applicability to cancer genomics in the face of high-throughput medical data.Additional file 1 Supplementary figures and tables.


Kinship analysis from very low-coverage ancient sequences has been possible up to the second degree with large uncertainties. We propose a new, accurate, and fast method, correctKin, to estimate the kinship coefficient and the confidence interval using low-coverage ancient data. We perform simulations and also validate correctKin on experimental modern and ancient data with widely different genome coverages (0.12×–11.9×) using samples with known family relations and known/unknown population structure. Based on our results, correctKin allows for the reliable identification of relatedness up to the 4th degree from variable/low-coverage ancient or badly degraded forensic whole genome sequencing data.The online version contains supplementary material available at 10.1186/s13059-023-02882-4. Kinship analysis is a method for determination of the familial relationship between individuals from genome data. The kinship coefficient is defined as the probability that two homologous alleles drawn from each of two individuals are the result of identity by descent (IBD). This is a classic measurement of relatedness , 2. SeveRecently huge genomic datasets have been generated from ancient samples in order to uncover the genetic relations of ancient and modern populations. From this data, it is also of high interest to study the family organization of ancient populations. However, analyzing ancient DNA (aDNA) poses additional difficulties due to the widely different but generally low genome coverage and postmortem damage (PMD) observed in these samples. The aDNA databases usually contain sequence data between 0.05 and 3× average genome coverage –12, sincIn the present study, we wanted to address the difficulties of low-coverage aDNA data and dissect the main factors that affect kinship calculations. To overcome typing bias, random sampling of one allele per site  was used successfully in aDNA studies –22. In oHere we offer guidelines and a list of the necessary bioinformatics tools required to calculate the corrected kinship coefficient. These guidelines overcome the technical limitations of generally low genome coverage, postmortem damage, genotyping uncertainties, and the partial overlapping of genetic markers between samples. As a proof of concept, we validated our proposed methodology on both experimental modern and ancient data with widely different genome coverages, using samples with known family relations and known or unknown population structure.To use marker counts similar to aDNA data, all modern dataset were downsampled to the autosomal marker positions of the 1240K SNP set of the AADR dataset  in all oSince the selected kinship methodology, PC-Relate, applies principal component analysis (PCA) to identify population structure, we first tested the effect of RPsH on PCA. We selected the British (GBR), Toscani (TSI), Iberian (IBS), and Finnish (FIN) populations from the 1000 Genome Project Phase 3 (1KG phase 3) dataset (404 samples) and randomized the diploid dataset with three different seeds. We performed smartpca analysis on the original diploid and the three random pseudo-haploidized dataset. According to our results, RPsH does not alter the PCA calculations significantly , FIN (HG00356.SG), CEU , and TSI (NA20798.SG) populations. This allowed us to overcome the interference of other effects, such as skewed recombination/segregation, differences in sequence alignment, genotyping, genome composition, or the population structure between the relatives. This idealized experimental setup is the equivalent of the monozygotic twin kinship relation, in forensics referred to as sample matching, while the maximal expected kinship coefficient (0.5) allows for the most sensitive analysis.To study the effect of RPsH on true first/second-order relatives, we also selected samples with known family relations from the 1KG phase 3 dataset. (a) HG00702-HG00657 a parent-child relation from a Han population, where exactly 50% of genome is shared between the two samples, (b) NA20526-NA20792 siblings from a TSI population, where 50% of the genome comes from the same parents; however, a different subset of the markers are found in the sibs due to segregation and recombination, and (c) HG00124-HG00119 second-order relatives from a GBR population, where statistically 25% of genomes are shared. We calculated the kinship coefficient for each of the selected relatives (kin1/kin2) using their own reference population for the 100 different randomization and calculated the mean and the standard deviation of the estimated kinship coefficients. Knowing the expected kinship coefficients , we were able to validate that RPsH does not significantly alter the calculated kinship coefficient in these settings .Using the 100 random pseudo-haploidized fully typed dataset of the previous experiment, we randomly depleted the markers between the selected sample dups and true 1KG relatives to a marker overlap fraction between 5 and 100% using different random seeds. We calculated the mean and SD of the estimated kinship coefficients between the selected sample pairs of the different randomizations for each overlap fraction  only post mortem damage, 2) only endogenous contamination , 3) only exogenous contamination, and (4) equal combination of the first three sources of genotype errors. In each scenario, we had approximately twice as many genotyping errors per individual introduced as in a typical aDNA dataset (see “Methods”). We calculated the mean and SD of the corrected kinship coefficients  was seen in case of the exogenous contamination. We speculate that it was likely due to the fact that in this scenario  the states of the markers were uniformly set to the homozygote major state leading to higher bias than that of random flip of minor/major state in different samples in the other scenarios. In our simulations even at the applied relatively high error rates , the largest effect was still significantly smaller than the 50% difference of expected kinship coefficients between different degrees of relations thus the estimated degree of relatedness for the analyzed relatives remained the same. However, we have to note that skewed IBD sharing and high genotyping error in the test individuals could lead to false (one-degree higher) classification of the analyzed relation.In this analysis, we wanted to investigate the scenario in which proper reference population is unknown or unavailable which is often the case for ancient samples. Using the same public 1KG phase 3 dataset and the three known relatives HG00702-HG00657 parent-child of Han population, NA20526-NA20792 siblings of TSI population, and HG00124-HG00119 second-order relatives of GBR population, we investigated the effect of the reference population on the calculated kinship coefficients. We tested three different scenarios: (1) the reference population was the same as that to which the selected individual belonged to; (2) the reference population was from a different super-population (AFR); (3) the reference population was from the same super-population as the selected individual  , we analyzed a complex admixed Cabo Verdean-Hungarian family with known pedigree. In this family, we had multiple old as well as recent admixes resulting in various admix component ratio individuals. WGS data was available for siblings (1st order), differently admixed half-sibs (2nd order), and 5th-order relatives as shown in Additional file We tested two scenarios: the reference population was (1) only African (AFR) representing the majority of the admix sources in the tested samples; (2) both African and European (EUR) populations were included. Additionally, we performed marker depletion to investigate the effect of coverage in this complex scenario Fig. .Fig. 4ThThe results in Fig. To test whether random haploidization alters the kinship coefficient calculation compared to the better phased diploid data in this complex admixed case, we also performed this analysis from the original diploid dataset  and estimated the kinship coefficients between these individuals. Although there are a few true relatives in the selected individuals, the overwhelming majority of pairwise relations are expected to be unrelated, thus representing the variance of technical error of the whole analysis. To test the effect of overlapping genotyping fraction on the mean and standard error of the corrected kinship coefficient, we randomly depleted the marker set in these individuals between 100,000 markers and the fully typed marker count (~1.2M), amounting to 10–100% of the marker count of the original dataset. Using RPsH, we also created a pseudo-haploid dataset for comparison. We calculated the pairwise kinship coefficient matrix and corrected the estimated kinship coefficients by the marker overlap fraction. Using the pairwise matrix of 1020 individuals, we plotted the 519,690 kinship coefficients between all combinations of individuals for the diploid and haploid dataset  could be used as a criteria to differentiate relatives from unrelated with the same sensitivity and specificity independent of the marker overlap fraction. Since in this experiment we could not exclude errors due to missing genome components from the reference populations, we used a conservative N=6 sigma threshold to identify biological differences. As expected, the haploid dataset resulted in higher variance due to random information loss especially at very low (<5%) marker overlap fractions. Although the marker overlap fraction correction differs two magnitudes (0.007–0.996) between very low and high marker overlap fraction sample pairs, the analysis shows that similar to the corrected kinship coefficient between relatives  compared to the correlation with the uncorrected estimates  from these two ancient individuals. Accordingly, we assessed the robustness of our correction method on the 6 combinations of these datasets. We present the uncorrected and corrected kinship coefficients calculated from this data in Table To show that our methodology is also suitable for ancient data, we analyzed low-coverage ancient sequences with known family relations. In the first example, we present the analysis of a known father-son (first degree) relation of two Medieval samples. From both remains, we had two types of biological samples, bone powder taken from the teeth and from the es 0.87×–1.9× fromIn the second example, we reanalyzed a published group of five related males from the Corded Ware Culture (2500–2050 BCE) with first-, second-, third-, and fourth-degree kinship relations . In TablThese individuals were analyzed in the original READ manuscript  where reN=26), and joint datasets (N=30) of the same sample were identified. Curiously, we identified sample duplicates with different master IDs in the AADR dataset published in different manuscripts  where all four sample pairs were from the same geological site and belonged to the same population and haploid typing was identical or nearly identical as some branch defining markers were likely missing due to coverage differences. We also identified a likely sample mix [We performed kinship analysis on 2136 ancient Eurasian individuals from the AADR 1240K dataset that had more than 100K genotyped markers. Since no manual curation or additional matching reference population was used, we filtered potential relatives above 0.046875 corrected kinship coefficient (~3rd–4th degree of kinship). Our analysis identified 410 related individuals in 184 kin groups . To date, the best analysis tools were able to indicate mainly first- and second-degree relatedness from very low-coverage ancient samples , 28–30. zed data . In the zed data , the majDiploid variant calling and genotype likelihood-based methods with the extra information of rare alleles allow better phasing and identification of IBD fragments leading to improved kinship coefficient estimations from deeply genotyped WGS data. Accordingly, some methods attempt to infer genotype likelihoods or diploid genotype calls from low-mid genome coverage (2–4×) data , 28, 31.To overcome these difficulties and mitigate the main genotyping biases in case of low-coverage ancient samples, we used a combination of strategies to account for the effects caused by PMD and varying low genome coverage. We used random allele sampling that is the gold standard methodology when performing PCA and other population genetic analyses on ancient samples, as it leads to statistically equal genotype likelihoods of genotyped markers regardless of the genome coverage. To avoid excessive, variable amounts of false positive variants due to the variable rate of PMD, exogenous DNA contamination, and technical errors , we restricted our analysis to the already known biallelic, high-frequency, and population-informative SNPs of the 1240K AADR dataset. This strategy perfectly aligned with our choice of kinship analysis method since the PC-Relate algorithm uses PCA to differentiate between IBD/IBS fragments.We have demonstrated that random pseudo-haploidization of data in our analysis pipeline does not affect the result of kinship analysis  between 3.72 and 3.46% of the analyzed sample pairs (ancient and modern respectively). Our analysis shows that when proper reference population is available, the applied method is suitable to identify relations up to the 4th degree from low to high coverage mixed samples  of experimental AADR data is 0.1274  bias in the analyzed 1KG dups and first-degree relatives and also in the validated first-degree ancient samples. However, this downward bias is also present in the kinship coefficient estimation of fully typed diploid 1KG relatives suggesting that the original PC-Relate algorithm and not the applied correction or pseudo-haploidization is accountable for this bias. This is also supported by our simulations on the corrected kinship coefficient calculated from marker depleted pseudo-haploid data and the original fully typed diploid data  . For marTo test the effect of genome coverage on the estimated kinship coefficients from real ancient data, we used our unpublished 1240K genotype data of a known medieval parent offspring. The dataset contains low-coverage partially typed pseudo-haploid genotype data from 3+2 separate library preparations from different biological samples of the analyzed individuals. We deposited the unpublished medieval datasets in PLINK 1240K binary format presented in this manuscript at Zenodo .N=2810). We excluded samples older than 8000BC (N=216) as older samples were very few and were lacking proper number of samples as a reference population  [The tools work with the main genotype data formats . We documented the usage and options of the new tools with command examples in Additional file rectKin) , 36.We defined the overlapping marker fraction between two samples as the number of markers typed in both samples divided by the number of all markers in the dataset.pseudoHaplo” tool, we created 100 randomly pseudo-haploidized datasets from the fully typed modern diploid dataset using different random seeds. In all of the presented examples, we used our own tool “markerOverlap” to calculate the pairwise overlapping marker fraction matrix of samples used for the kinship coefficient correction [Using our “rrection , 36.inbreed: YES” option. We used the R (version 4.0.5) [We selected the GBR, TSI, IBS, and FIN populations from 1KG dataset 404 samples) and randomized the diploid dataset with three different seeds. We performed smartpca , 38 analn 4.0.5)  and the 4 samplesdepleteMarkers” to randomly deplete markers from a fully typed  dataset, resulting in the desired percentage of marker overlap between two samples [To study the effect of coverage and the resulting lower genotyping percentage on the kinship coefficient calculation in a controlled fashion, we used “ samples , 36. UsidepleteIndivs” to create a random, partially typed sample cohort with marker count between 100,000 and the full 1,150,639 markers. From the partially typed diploid dataset, we also created a pseudo-haploidized dataset using the “pseudoHaplo” tool [To assess the technical error of low/variable coverage data on the whole workflow, we selected 1020 fully typed diploid Eurasian samples  of the 1KG phase 3 dataset. We applied “lo” tool , 36. We PLINK and EIGENSTRAT data format were designed for biallelic markers. There are only 4 possible allelic states , thus any other nucleotide that is different than the minor or major allele cannot be represented and the allelic state of samples with invalid alleles are set to the “missing” state at such marker positions.Post mortem damage; if the C->T or G->A conversion leads to different nucleotide than the minor or major allele, the state is set to “missing,” otherwise, if the minor and major alleles are C/T, T/C, G/A or A/G, the homozygote minor and major states are flipped.Exogenous (non-human DNA) contamination; since the exogenous DNA consist of mainly DNA of microorganisms , it leads to excessive homozygote major allele, thus random subsets of markers are set to the homozygote major allele state.Endogenous (human DNA) contamination; random subsets of markers are set to the state of the same markers genotyped from another sample .Based on the data format restriction, the three typical aDNA-related genotype errors can be simulated in the following ways for pseudo-haploid PLINK dataset:ANGSD X contamination  0.02–0.05="QUESTIONABLE", >0.05="QUESTIONABLE_CRITICAL" or "FAIL”.mtcontam <0.8 is "QUESTIONABLE_CRITICAL", 0.8-0.95 is "QUESTIONABLE", and 0.95–0.98 is recorded but "PASS", gets overridden by ANGSD X contamination.From most population genetic analyses, highly contaminated samples are excluded. In the comprehensive AADR ancient dataset, the following criteria is used to mark bad-quality sequences:n=3589 ancient samples) 157 is marked CRITICAL/FAIL (>5% error rate), while the mean of the X contamination rate of all ancient samples is 1.28%.Accordingly, the 1240K v42.2 AADR dataset . Accordingly, each sample had random 0–5% genotyping error, leading to an overall ~2.5% genotype error rate of the whole dataset that is roughly the double of the genotyping error rate of the experimental AADR aDNA dataset. In each simulation, we used 100 different randomizations with different random seed and calculated the mean and SD of the corrected kinship coefficients.-inbreed 1 -kinship” parameters.Kinship coefficient estimation was performed by the PCAngsd  softwareAdditional file 1: Figure S1. Comparison of PCA using diploid and pseudo-haploid data. Figure S2. Effect of reference population (same super-population) on kinship coefficient. Figure S3. Pedigree of complex admixed modern family. Figure S4. The date distribution of ancient AADR individuals. Figure S5. Geographical distribution of ancient AADR individuals.Additional file 2: Table S1. Effect of RPsH and marker overlap on kinship coefficient.Additional file 3: Table S2. Effect of genotyping errors on kinship coefficient.Additional file 4: Table S3. Comparison of kinship analysis of a complex admixed family using pseudo-haploid and diploid data.Additional file 5: Table S4. Comparison of kinship coefficient correction using experimental 1KG Phase III data.Additional file 6: Table S5. correctKin analysis of 2126 ancient AADR individuals.Additional file 7: Table S6. Comparison of correctKin with READ software.Additional file 8: Note S1. Installation requirements and a step-by-step method description including detailed usage examples to perform correctKin analysis from various data sources.Additional file 9. Review history.


Triple-negative breast cancer (TNBC) is one of the more aggressive subtypes of breast cancer. The prognosis of TNBC patients remains low. Therefore, there is still a need to continue identifying novel biomarkers to improve the prognosis and treatment of TNBC patients. Research in recent years has shown that the effective use and integration of information in genomic data and image data will contribute to the prediction and prognosis of diseases. Considering that imaging genetics can deeply study the influence of microscopic genetic variation on disease phenotype, this paper proposes a sample prior information-induced multidimensional combined non-negative matrix factorization (SPID-MDJNMF) algorithm to integrate the Whole-slide image (WSI), mRNAs expression data, and miRNAs expression data. The algorithm effectively fuses high-dimensional data of three modalities through various constraints. In addition, this paper constructs an undirected graph between samples, uses an adjacency matrix to constrain the similarity, and embeds the clinical stage information of patients in the algorithm so that the algorithm can identify the co-expression patterns of samples with different labels. We performed univariate and multivariate Cox regression analysis on the mRNAs and miRNAs in the screened co-expression modules to construct a TNBC-related prognostic model. Finally, we constructed prognostic models for 2-mRNAs (IL12RB2 and CNIH2) and 2-miRNAs (miR-203a-3p and miR-148b-3p), respectively. The prognostic model can predict the survival time of TNBC patients with high accuracy. In conclusion, our proposed SPID-MDJNMF algorithm can efficiently integrate image and genomic data. Furthermore, we evaluated the prognostic value of mRNAs and miRNAs screened by the SPID-MDJNMF algorithm in TNBC, which may provide promising targets for the prognosis of TNBC patients. Triple-negative breast cancer (TNBC) is a specific subtype of breast cancer (BC) . CompareMicroRNAs (miRNAs) can regulate gene expression, cancer development, and metastasis by binding to target messenger RNAs (mRNAs) . PreviouImaging genetics is a non-invasive method that correlates genomics and imaging data to discover significant disease-related modules and explain the pathogenesis of the disease. Most of the previous imaging genetics research has been done on Alzheimer’s disease. Due to the characteristics of small samples and high dimensions of imaging genetics, a variety of effective penalty terms are needed to enable the algorithm to perform effective feature selection in high-dimensional data. In recent years, matrix decomposition technology has been widely used in biological multi-omics analysis and has made significant progress. However, few studies have used this type of technology to integrate cancer imaging genetics data and explore the impact of genetic data on imaging phenotypes. Deng et al. proposed a multi-constrained joint non-negative matrix factorization (MCJNMF) algorithm that integrated PET images and DNA methylation data of sarcomas and successfully discovered co-expression modules associated with lung metastasis. Furthermore, they extended the modality to three dimensions by extending the MCJNMF algorithm. They proposed a multidimensional non-negative matrix factorization (MDJNMF) algorithm that integrated pathological images, DNA methylation data, and copy number variation data from sarcoma data. The mechanism of interaction of the three data in sarcoma patients was successfully discovered .This paper proposes a sample prior information-driven multidimensional joint non-negative matrix factorization (SPID-MCJNMF) algorithm, which adds a diagnostic information constraint to the basis matrix based on the MDJNMF algorithm. Specifically, we add the clinical stage information of triple-negative breast cancer patients into the algorithm through the Laplace constraint, which is used to make the samples of the same stage closer in the feature space, and the samples of different stages further in the feature space, allow the algorithm to identify expression patterns across samples of different stages. The results show that compared with several other competitive algorithms, the SPID-MCJNMF algorithm has better reconstruction performance and obtains a significant co-expression module with biological significance. Then, to further mine the biomarkers related to the prognosis of TNBC, we performed the prognosis analysis based on the modules screened by the SPID-MCJNMF algorithm and constructed the mRNA and miRNA-related prognosis models, respectively. Prognostic analysis of external datasets further validated the predictive accuracy of the prognostic model. Our study may provide new targets for the treatment and prognosis of TNBC.Non-negative matrix factorization (NMF) is a robust dimensionality reduction algorithm widely used in bioinformatics to ensure the non-negativity of the original data. Joint non-negative matrix factorization (JNMF) is evolved from NMF, which solves the disadvantage that NMF can only decompose single-modal data, and its objective function is shown in Among them, The MDJNMF algorithm is proposed by Deng et al. Based on the JNMF algorithm, they added orthogonal constraints to the coefficient matrix to prevent redundant features from affecting the results. In addition, they added the absolute value of the Pearson correlation coefficient between WSI image and genetics as prior information to the algorithm, and its objective function is shown in Eq. A can be obtained, and its element Aij can be defined as:Aij is each element of the matrix A∈Rn×n, which is used to embed the disease course information of the sample. Furthermore, we introduce a degree matrix D, which is a pair whose diagonal elements are angle matrix To improve the model’s generalization ability and identify markers associated with breast cancer, this subsection introduces a diagnosis-guided penalty term. By treating each sample as a node in an undirected graph, connections between nodes are used to embed clinical information about the patient. In this paper, we embed the clinical stage information of triple-negative breast cancer patients. If any two nodes are selected, and their diagnosis is the same, then there is a connection between them; if the diagnosis is different, there is no connection. Then the adjacency matrix ,Others Where thThen Among them, the elements of Finally, the update rules for W and According to the continuously updated  studies . In addip-values less than 0.05 were considered significant (Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to explore the biological processes in which key mRNAs are involved. In addition, in order to further explore the pathways in which key miRNAs are involved, this paper is based on miRDB, TargetScan, and miRTarBase three databases for key miRNA target gene prediction, and KEGG enrichment analysis for their target genes. Pathways with nificant .p-value less than 0.05 were reserved as input for multivariate Cox regression analysis. Next, multivariate Cox regression analysis was used to construct mRNA and miRNA-related prognostic models. We then calculated a risk score, which can be used to classify patients in the training cohorts and validation cohorts into high and low-risk groups. The formula for calculating the risk score is as follows:In the prognostic survival analysis, only TNBC patients with a survival time greater than 90 days were retained. Univariate Cox regression analysis was used to identify genes (mRNAs or miRNAs) associated with the prognosis of TNBC patients, and mRNAs or miRNAs with a https://www.cancer.gov). In this study, the mRNA expression data , miRNA expression data , WSI image data (69 cases), and clinical data (116 cases) of TNBC patients were obtained from the TCGA database. Finally, 69 TNBC samples with mRNA expression data, miRNA expression data, WSI image data, and clinical data were retained. The GEO cohort  was used to validate the accuracy of the prognostic model constructed from mRNA expression data. The miRNA data of the TCGA-BRCA cohort (1000 cases) were utilized to validate the accuracy of the prognostic model constructed from the miRNA expression data.The data used in this paper are from the TCGA database . WSI images are from 69 patients with sarcoma. Feature extraction for each WSI image consists of three steps, nuclear segmentation, cell-level feature extraction, and aggregating cell-level features into patient-level features . The miRNA-related KM curve showed that the OS rate of high-risk patients in the training dataset was significantly lower than that of low-risk patients over 5 years (p = 0.003). To verify the predictive accuracy of the prognostic model, we plotted the 1-, 3-, and 5-year ROC curves of TNBC patients in the mRNA as well as miRNA-related training datasets. The mRNA-related ROC curve showed that the 2-mRNAs prognostic model we constructed could predict the 1-year (AUC = 0.849), 3-year (AUC = 0.752), and 5-year (AUC = 0.802) survival rates of TNBC patients with high accuracy . The miRNA-related KM curve showed that the OS rate of high-risk patients in the test dataset was significantly lower than that of low-risk patients over 5 years (p = 0.025). The mRNA correlation ROC curve showed that the 2-mRNAs prognostic model we constructed could predict the 1-year (AUC = 0.788), 3-year (AUC = 0.591), and 5-year (AUC = 0.569) survival of TNBC patients in the test set with certain accuracy rate  and 2 miRNAs (hsa-miR-203a-3p and hsa-miR-148b-3p). Moreover, the mRNA-related and miRNA-related prognostic models we constructed can predict the overall survival of TNBC patients with high accuracy. Epigenetic changes in IL12RB2 play an essential role in the plastic behavior of T Helper 17 (Th17) Cells . Treg anin vitro . Therefoin vitro . Xu et ain vitro . Breast g TRIM59 . In breag TRIM59 .In conclusion, this paper proposes a SPID-MDNMF algorithm that can effectively integrate image data, mRNAs expression data, and miRNAs expression data. Compared with other similar algorithms, the SPID-MDNMF algorithm has better reconstruction performance. Based on module 4 screened by the SPID-MDNMF algorithm, we constructed 2-mRNAs (IL12RB2 and CNIH2) and 2-miRNAs  by performing a prognostic survival analysis on the TCGA-TNBC cohort -148b-3p) prognostic model. The prognostic model can better predict the prognosis of TNBC.In summary, we proposed the SPID-MDJNMF algorithm to integrate the imaging genetics data of TNBC patients and obtain the co-expression patterns of TNBC patients in different stages. For the significant co-expressed modules, a variety of bioinformatics analyses were performed to construct a prognostic model for TNBC patients. Multiple genes with prognostic value obtained from screening may be potential biomarkers for TNBC.