Clustering of tumor samples can help identify cancer types and discover new cancer subtypes, which is essential for effective cancer treatment. Although many traditional clustering methods have been proposed for tumor sample clustering, advanced algorithms with better performance are still needed. Low-rank subspace clustering is a popular algorithm in recent years. In this paper, we propose a novel one-step robust low-rank subspace segmentation method (ORLRS) for clustering the tumor sample. For a gene expression data set, we seek its lowest rank representation matrix and the noise matrix. By imposing the discrete constraint on the low-rank matrix, without performing spectral clustering, ORLRS learns the cluster indicators of subspaces directly, i.e., performing the clustering task in one step. To improve the robustness of the method, capped norm is adopted to remove the extreme data outliers in the noise matrix. Furthermore, we conduct an efficient solution to solve the problem of ORLRS. Experiments on several tumor gene expression data demonstrate the effectiveness of ORLRS. Tumor is a group of cells that have undergone unregulated growth and often form a mass or lump. It is critical to reveal the pathogenesis of cancer by analyzing tumor gene expression data. The advances of various sequencing technologies have made it possible to measure the expression levels of thousands of genes simultaneously . IncreasMany traditional clustering methods, such as hierarchical clustering (HC) , 13, selHowever, real data rarely can be well represented by a single subspace. A more reasonable model is to assume that the data are lying near multiple subspaces . Subspace clustering (or segmentation) has been proposed to improve clustering accuracy. It is assumed that the data points are drawn from the combination of multiple low-dimensional subspaces. The goal of subspace clustering is to obtain such multiple low-dimensional subspaces with each subspace corresponding to a cluster. Subspace clustering has obtained promising results in previous studies, and subspace clustering methods have been found widespread applications in many areas, such as pattern recognition , image pWhen the data are clean, i.e., the samples can be strictly drawn from multiple subspaces, several existing methods, such as sparse subspace clustering (SSC) , low-ranL2,1-norm based objective functions since the nonsquared residuals of L2,1-norm can reduce the effects of data outliers. In [L2,1-norm is adopted in robust PCA (RPCA) for detecting outliers. In [L2,1-norm for subspace clustering. Although the L2,1-norm is robust to outliers, it still suffers from the extreme data outliers. The L2,1-norm just reduces, not completely removes, the effects of the outliers. Capped norm is a more robust strategy than L2,1-norm due to the fact that it can remove the effects of the outliers. It has been recently studied in many applications [As pointed out in , one majiers. In , 35, theiers. In , Liu et ications , 37.X ∈ ℝm×n with m genes and n samples, a low-rank representation matrix A ∈ ℝm×n and a noise matrix E ∈ ℝm×n, i.e., X=A+E, are being sought. The low-rank representation of the i-th subspace Ai can be denoted as rank(Ai). Here, we impose the discrete constraint on a diagonal matrix Ii ∈ ℝn×n to obtain the low-rank representation rank(XIi), where Ii⊆{0,1} and ∑i=1cIi=I . The indicators of the i-th cluster are included in Ii. In contrast to traditional low-rank based models, we can directly learn the cluster indicators. To avoid trivial solutions and approximate the low-rank constraint, the rank of all subspace simultaneously can be minimized as ∑i=1c(‖AIi‖Spk)2, where ‖•‖Spk denotes the Schatten p-norm which has a better relaxation than the nuclear norm [E, capped norm is used to improve the robustness. We define θ as a thresholding parameter for choosing the extreme data outliers, and then the capped norm of E can be formulated as ‖E‖Capped=minE∑i=1nmin{‖Ei‖2, θ}. This function treats Ei equally if ‖Ei‖2 is smaller than θ. Hence, it is more robust to outliers than L2,1-norm. Meanwhile, we derive an efficient optimization algorithm to solve ORLRS with a rigorous theoretical analysis.In this paper, a one-step robust low-rank subspace segmentation (ORLRS) method via the discrete constraint and capped norm is proposed for clustering tumor sample. For a data set ear norm . For the  ① Compared with traditional low-rank representation-based methods, ORLRS can obtain the clustering result directly by learning a subspace indicator matrix from the low-rank representation matrix without spectral clustering. This avoids the graph construction process in spectral clustering and makes the clustering process simpler.  ② We introduced the capped norm into our model and formed a novel objective function for the gene expression data clustering task. Capped norm is used to constrain the noise matrix to improve the robustness of ORLRS.  ③ Optimizing the objective function of ORLRS is a nontrivial problem, thus we derive a new optimization algorithm to solve the problem. Furthermore, we have also given a rigorous convergence analysis of ORLRS.The main contributions of our paper are given as follows:The remainder of the paper is structured as follows. In We start with a brief introduction of several classical clustering methods. Then, the proposed ORLRS is presented, and the optimal solution and convergence analysis of ORLRS is provided.X ∈ ℝm×n as a data set with m features and n samples. LRR can be defined asA‖∗=∑iσi(A), i.e., the nuclear norm of A [D is a dictionary, and λ > 0 is a balance parameter.Denote orm of A , E2,1=∑jA∗ to equation (A∗|+|(A∗)T|)/2, where |A∗| is the absolute value function. Finally, the data are clustered by using spectral clustering [A brief explanation of LRR subspace clustering process is provided as follows. Firstly, the low-rank problem is solved by equation . Then, tequation  is used ustering .In this paper, we propose the one-step robust low-rank subspace clustering (ORLRS) method via discrete constraint and capped norm. Different from LRR, ORLRS was proposed for clustering the data by learning the indicators.X has c subspaces {X1, X2,…, Xc}, the low-rank representation of each subspace needs to be optimized. In the clustering task, we want each subspace to belong to its own cluster. To obtain a low-rank representation of each subspace, the following formula should be computed: rank(Xi), which has trivial solution. Therefore, we need to solve the problem in another way. We define a cluster indicator matrix as C ∈ ℝc×n: C=1 if the j-th sample belongs to the i-th subspace, and C=0 otherwise. And, the c diagonal matrices are defined as I1, I2,…, Ic ∈ I, where the diagonal elements of Ii (1 ≤ i ≤ c) are formed by the i-th row of C and I is the identity matrix. Then, XIi can be represented as the i-th subspace of X. That is, rank(Xi) can be rewritten as rank(XIi). We can get the clustering label in one step by directly optimizing Ii [Suppose the data matrix izing Ii .XIi‖Spp is the Schatten p-norm of XIi. The clustering indicators of each subspace can be obtained from the optimized diagonal matrix Ii|i=1c directly.Finally, the problem of the one-step low-rank subspace clustering method can be defined asX ∈ ℝm×n with m genes and n samples as the addition of low-rank representation matrix A ∈ ℝm×n and the noise matrix E ∈ ℝm×n, i.e., X=A+E, which is the same strategy as in RPCA. Our one-step low-rank subspace clustering problem can be written asλ > 0 is a balance parameter and ‖•‖L indicates certain regularization strategy. Note that Schatten p-norm is used to approximate the low-rank problem in equation (p-norm (0 < p < ∞) of a matrix G ∈ ℝm×n was defined as ‖G‖Sp=min(σip)p1/, where σi is the i-th singular value of G. In [p-norm with 0 < p ≤ 2 is proved. Here, we set 0 < 2k ≤ 2 to guarantee the convergence of first term in equation  algorithm is used to optimize equation  encodes the constraints of Ii|i=1c. We rewrite equation .Equation  can be sP, Q ∈ ℝn×n, q > 0, the following inequality holds when 0 ≤ h < 1:. Foh ≥ 1, the inequality is reversed.While for k ≤ 1, ‖G‖Spp=Tr((GTG)p/2) [i=1cTr(Ii(ATA)k)=∑i=1c(‖AIi‖Spk)2 since Ii|i=1c⊆{0,1}n×n. According to ∑i=1cIi=I, we convert the first term in equation (Following 0 < GTG)p/2)  and Lemmequation  to(9)∑i=Then, equation  can be rA and setting to zero, the above formula becomesH=k(ATA)k−1. So, we can achieve the optimal A:Taking derivative w.r.t F=X − A+(Y/μ). It can be easily verified that the derivative of equation mimi(13)minO is a diagonal matrix with Oii=oi. The problem of equation J3Ii|i=1c and setting to zero, the above formulation can be written asLi=k‖AIi‖Spk(AIi2AT)k − 2)/2minIIi can be updated according to the optimal solution to equation 32), we hThat is to say,A in t when 0 < k ≤ 1.Thus, the updating rule for matrix E while fixing others will monotonically decrease the objective value in equation 38) at boE while fixing others will monotonically decrease the objective value in equation 43), we cIi|i=1c while fixing others will monotonically decrease the objective value in equation d,  (d > 2) (Li in dk/2)(‖AIi‖Spk)d−1(AIi2AT)k − 2)/2, the convergence is also observed [Equation  indicateequation  during tequation  is changobserved .A, E, and Ii|i=1c according to Theorems As a result, the objective of equation  is noninLi=k‖AIi‖Spk(AIi2AT)k − 2)/2. (AIi2AT)k − 2)/2. So, computing Li takes O(m3) and computing Li|I=1c takes O(m3c), where c is the number of clusters. For Zi, we only need to compute the diagonal elements, which takes O(m3). And, computing Zi|I=1c takes O(m3c). In summary, the computational complexity of O(m3ct), where t is the iteration number.In We test ORLRS on six publicly available gene expression data sets, i.e., Leukemia , DLBCL , Colon cxi, suppose Ni as the target label and Ti as the truth label. ACC can be denoted as [φ=0 if x ≠ y and φ=1 if x=y, map(Ni) maps Ni to the equivalent label from the raw data and n is the number of tumor samples.Following , 45–47, M is the mutual information function between the true class label C and the clustering label S and H(·) is the entropy function. The larger the NMI value is, the better the clustering result is.We also evaluate the clustering performance by normalized mutual information (NMI) . NMI is   Leukemia data contain 25 cases of AML and 47 cases of ALL. It is packaged into a 7129 × 72 matrix .  DLBCL data consist of 5469 genes and 77 samples. These samples include 58 patients of diffuse large B-cell lymphoma (DLBCL) and 19 patients of follicular lymphomas (FL) .  The colon cancer data  consists  Brain_Tumor1 data set consists of 5920 genes in 90 patient samples. These samples contain 5 types of histological diagnoses, i.e., 60 cases of medulloblastoma, 10 cases of malignant glioma, 10 cases of atypical teratoid/rhabdoid tumors (AT/RTs), 4 cases of normal cerebellum, and 6 cases of primitive neuroectodermal tumors (PNETs).  The Brain_Tumor2 data set contains 10367 genes in 50 samples. It contains 4 types of malignant glioma, i.e., classic glioblastomas (CG), classic anaplastic oligodendrogliomas (CAO), nonclassic glioblastomas (NCG), and nonclassic anaplastic oligodendrogliomas (NCAO) .  9_Tumors data set integrates 9 tumor types to develop a genomics-based approach to the prediction of drug response. It contains 5726 genes in 60 samples. The number of samples of 9 tumor types is shown as follows: 9 samples of non-small-cell carcinoma (NSCLC), 7 samples of colon cancer, 8 samples of breast cancer, 6 samples of ovary cancer, 6 samples of leukemia, 8 samples of renal cancer, 8 samples of melanoma, 2 samples of prostate cancer, and 6 samples central nervous system cancer (CNS).A brief introduction of six gene expression data sets is presented, and the detailed information of these datasets is summarized in K-means is the most commonly used clustering method and is embedded into many methods including PLRR, robust LRR, and LatLRR to achieve better performance. Since our proposed method is a novel one-step robust low-rank subspace clustering model, we choose these methods as our comparison algorithms.We compare LRS , Ext-LRRIi|i=1c in the proposed ORLRS. Here, three parameters, i.e., threshold parameter θ, balance parameter λ, and low-rank constraint parameter k, need to be determined. In the experiment, we investigated one parameter by fixing the other two parameters. Since the initialization of Ii|i=1c will bring some uncertainty, the proposed ORLRS method run 100 times, and the average of the accuracies of 100 times is reported. The choices of parameters in the following are heuristic and might not be the best for tumor clustering.Since gene expression data have the characteristics of high-dimensional and small samples, we use PCA to perform dimensionality reduction. And, we use the K-means method to initialize θ for each data set.In the ORLRS model, data outliers are not heuristically determined based on the magnitude. They are selected during the optimization process. The data outliers may be distinct at different iterations (with the same thresholding parameter), while we iteratively optimize the objective function of ORLRS method. When the algorithm converges, likely correct extreme data outliers can be found. So, we just need to determine one value of θ. Since the gene expression levels in different data are very different, the values of extreme data outliers are also very different. So, the value of θ has a large range in six data sets. From θ={103, 105, 40,90,400, 106} in Leukemia data, DLBCL data, Colon cancer data, Brain_Tumor1 data, Brain_Tumor2 data, and 9_Tumors data, respectively. The results indicate that the value of θ should be determined appropriately. If the value of θ is too large, we will miss some extreme outliers. If the value of θ is too small, some important information may be removed, thereby affecting the clustering performance.λ. ORLRS can obtain the best results in the case of λ={0.6, 1,1,1,0.9, 1.1} in Leukemia data, DLBCL data, Colon cancer data, Brain_Tumor1 data, Brain_Tumor2 data, and 9_Tumors data, respectively. According to the experimental results in each data set, before reaching the best results, the clustering accuracies showed an overall upward trend when λ increases; after achieving the best results, the clustering accuracies showed an overall downward trend when λ increases. So, we suggest a rough range 0.1 ≤ λ ≤ 2 on the choice of λ.p-norm parameter 0 < k ≤ 1, we determine the value of k in this range. k. ORLRS can achieve the best performance in the case of k={1,1,1,1,0.7, 0.4} in Leukemia data, DLBCL data, Colon cancer data, Brain_Tumor1 data, Brain_Tumor2 data, and 9_Tumors data, respectively. So, a general guidance is given 0 < k ≤ 1 on the choice of k.Since the algorithm is converged for the Schatten K-means to initialize the indicator matrix Ii|i=1c. PLRR, robust LRR, and LatLRR use the normalized cuts method to segment data, which cluster data points by using the K-means method. For robust NMF method, we initial the coefficient matrix and basis matrix randomly. To avoid randomness, we run all methods 100 times, and the mean and standard error results of the clustering accuracies of 100 times are shown in In this section, experimental results of our proposed method and six comparison algorithms, i.e., LRS, Ext-LRR, RPCA, PLRR, robust LRR, LatLRR, robust NMF, and K-means, are reported. ORLRS, LRS, and Ext-LRR use p-norm to better approximate the low-rank representation. Compared with traditional clustering methods, RPCA, robust NMF, and K-means, ORLRS achieves outstanding results on all of the six data sets.Based on the results reported in The NMI results on five gene expression data sets are shown in We plotted the convergence curves of our ORLRS on different datasets. The convergence curves can be found in p-norm and discrete constraint, low-rank representation of each subspace can be well obtained. Different from traditional low-rank-based methods, such as LRR and LatLRR, ORLRS learns indicators directly and perform clustering process in one step by using the discrete constraint. Capped norm is used to improve the robustness of ORLRS since it can effectively remove the extreme data outliers in the noise matrix. Furthermore, we propose an efficient algorithm to solve the proposed subspace clustering model, and the convergence of the proposed algorithm is proved. We thus can discover the clusters of tumor data depending on the optimal cluster indicators. We tested the proposed ORLRS method on six tumor data. The results are proved that ORLRS is an excellent method for clustering tumor sample.In this paper, a novel one-step robust low-rank subspace clustering method (ORLRS) is proposed for tumor clustering, where the gene expression data set is represented by a low-rank matrix and a noise matrix. By using the Schatten There remain several interesting directions for future work. First, it might be better to learn a dictionary for ORLRS since some low-rank subspace segmentation methods achieve significant improvements by learning a dictionary. Second, ORLRS may be extended to solve other problems, such as matrix recovery and classification. Third, ORLRS may be employed in other applications, such as gene clustering and coclustering.


Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease that presents a great challenge for treatment and prevention.. This study aims to implement a machine learning approach that employs such datasets to identify potential biomarker targets. We developed a pipeline to identify potential biomarkers for NAFLD that includes five major processes, namely, a pre-processing step, a feature selection and a generation of a random forest model and, finally, a downstream feature analysis and a provision of a potential biological interpretation. The pre-processing step includes data normalising and variable extraction accompanied by appropriate annotations. A feature selection based on a differential gene expression analysis is then conducted to identify significant features and then employ them to generate a random forest model whose performance is assessed based on a receiver operating characteristic curve. Next, the features are subjected to a downstream analysis, such as univariate analysis, a pathway enrichment analysis, a network analysis and a generation of correlation plots, boxplots and heatmaps. Once the results are obtained, the biological interpretation and the literature validation is conducted over the identified features and results. We applied this pipeline to transcriptomics and lipidomic datasets and concluded that the C4BPA gene could play a role in the development of NAFLD. The activation of the complement pathway, due to the downregulation of the C4BPA gene, leads to an increase in triglyceride content, which might further render the lipid metabolism. This approach identified the C4BPA gene, an inhibitor of the complement pathway, as a potential biomarker for the development of NAFLD. Non-alcoholic fatty liver disease (NAFLD) is a form of chronic liver disease that affects 20–30% of the western population and approximately 25% of the global population ,2,3 NAFLNAFLD consists of a spectrum of hepatic abnormalities ranging from steatosis or non-alcoholic fatty liver to the various levels of necrotic inflammation leading to non-alcoholic steatohepatitis (NASH) . The minUnderstanding the pathogenesis of NAFLD will cater a better understanding of the pathophysiological processes underlying the disease and will likely highlight potential therapeutic interventions . While tIn this study, we analyse various types of omics datasets, such as transcriptomics and lipidomics, in an effort to gain a better understanding of the NAFLD’s underlying pathophysiologic processes. Initially, we analysed transcriptomics data to identify potential gene biomarkers involved in the development of NAFLD and then proceeded to analysing lipidomics data so as to identify potential lipid biomarkers, as well as the pathways which are perturbed by these biomarkers..The schematic diagram presents the pipeline developed for the biomarker ,17 identThe datasets, GSE151158, GSE58979, GSE63067, GSE89632 and GSE33814, employed in this study were downloaded from the Gene Expression Omnibus (GEO) repository on 21 January 2021. In total, these datasets consisted of 146 samples, 81 of which were steatosis-related and 65 were control. The data were split into training, testing and validation sets and were subjected to pre-processing, normalization, data integration, batch-effect correction, PCA analysis, differential gene expression analysis, identification of common significant genes, as well as supervised analysis using random forest and biological interpretation.GEOquery package (version 2.58.0) [edgeR package (version 3.32.1) [cpm function with a True log parameter.Each GEO dataset was downloaded and loaded into R (version 4.0.3) by using the getGEO function in the  2.58.0) . All dat 3.32.1)  cpm funcprcomp in the stats package and the PCA plots were visualized using ggbiplot (version 0.55) [ComBat [Due to the number of control and steatosis samples, in each GEO dataset, being low , GSE1511on 0.55) . The bat [ComBat , where blmFit and eBayes functions, available within the limma package (version 3.46.0) [p value. Significant genes with an adjusted p value less than 0.05 were then extracted. This gene list was further filtered to only include genes which were common between the derived cohort and validation set.The differential gene expression (DGE) analysis was performed using the  3.46.0) . The appThe derived cohort was split into a testing  and a training set . The validation set consisted of 13 control and 19 steatosis samples.expand.grid method for the factors mtry, (12), ntree (55) and maxnode (6) using the training data and train function from the caret package (version 6.0.86) [The random forest was set for repeated 10-fold cross validation with 5 repeats. The parameters of the random forest were tuned by the  6.0.86) . The modpROC package (version 1.17.0.1) [The accuracy of the model in classifying the steatosis samples for the testing and validation sets was evaluated based on the receiver operating characteristics (ROC) analysis. The area under the curve (AUC), sensitivity and specificity for both datasets were calculated by using the .17.0.1) .enrichR (version 3.0) [corrplot (version 0.84) [The Wilcoxon test was conducted on the training and validation datasets to investigate the genes which were upregulated and downregulated between the control and the steatosis. A further pathway and GO enrichment analysis was performed using ion 3.0) . The coron 0.84) .Two cohorts  were collected from Sanders et al., for identifying the biomarkers for NAFLD . Both thThe dataset was loaded into R (version 4.0.3) and was separated into clinical data and lipid expression data. Each of the expression data row was annotated according to the lipid names. The dataset was then scaled and the values of the missing features were imputed according to the feature mean.lmFit and eBayes functions present in the limma package (version 3.46.0) [limma obtained Benjamini–Hochberg (BH)-corrected p values and the logFC change of significant lipids between the steatosis0 and steatosis1 samples. A Volcano plot was plotted using the ggplot2 (version 3.3.3) [p values.A differential lipid expression analysis was performed on the Italian cohort using the  3.46.0) . The samn 3.3.3)  for all expand.grid for the factors mtry (12), ntree (150) and maxnode (6) using the training data and train function from caret package (version 6.0.86) [The first random forest model was formed using the Italian cohort as the training set and the Fenland data as the test set. A stratified k fold cross validation approach was implemented, where the fold value was set to 5. The parameters of the random forest were tuned by using  6.0.86) . The modexpand.grid for the factors mtry (2), ntree (55) and maxnode (6) using the training data and train function from caret package (version 6.0.86) [Then, a second random forest model was generated using the Fenland data as the training set and the Italian cohort as the test set. A stratified k fold cross validation approach was implemented, where the fold value was set to 5. The parameters of the random forest were tuned by  6.0.86) . The modpROC package (version 1.17.0.1) [The AUC, sensitivity and specificity for both the models was calculated by using the .17.0.1) .rstatix package (version 0.7.0) [p values from this test, for each of the significant features, were extracted and stored in a new data frame. A boxplot was then generated to understand the significant difference between the steatosis0 and steatosis1 samples.A Wilcoxon test was conducted using the n 0.7.0) . Since tComplexHeatmap (version 2.6.2) [Two heatmap were created using the n 2.6.2)  package p values, is then generated.The LIPEA lipid pathway enrichment analysis  employs qgraph package (version 1.6.9) [A network analysis was performed to obtain potential interactions between the various lipid classes. The R n 1.6.9)  was usedBy merging GSE151158, GSE58979, GSE63067 and GSE89632, new NAFLD transcriptomics data were derived. limma. A Benjamini–Hochberg (BH) correction was implemented on the differential lipids and the lipids with adjusted p value lesser than 0.05 were extracted. The number of significant lipids identified for the different batches were as follows: batch 1, 191; batch 2, 189; batch 3, 171; batch 4, 129; batch 5, 42; batch 6, 57. There were 11 significant lipids which were identified in all six batches ,DG(34:1),DG(36:2),TG(52:2),TG(52:3),TG(53:2),TG(53:3), TG(53:6),TG(53:7),TG(54:2)) and, of those 11 significant lipids, 2 lipids ) were upregulated and the remaining 9 lipids were downregulated (DG(34:1), DG(36:2),TG(52:2),TG(52:3),TG(53:2),TG(53:3),TG(53:6),TG(53:7) and TG(54:2)) in the steatosis0 vs. steatosis1 samples.There were two lipid datasets, the Italian cohort and the Fenland cohort. Within the Italian cohort, 120 steatosis0 and 21 steatosis1 samples were present, indicating a class imbalance. The steatosis0 samples were separated into six batches and a differential expression analysis was conducted six different times using The area under the receiver operating characteristic curve (AUC) was calculated for the random forest model to be 0.91. The prediction accuracy decreased within the validation data, with an AUC value of 0.73 B. We hypThere were seven , DG(36:2), TG(52:2), TG(52:3), TG(53:2) and TG(54:2)) lipids common to the and Italian and Fenland cohorts. These lipids were part of the first random forest model. The Italian cohort was split into training and testing sets and the Fenland cohort was kept as the validation set. There were 36 steatosis0 and 6 steatosis1 samples in the test set and 633 steatosis0 and 222 steatosis1 in the validation set.The resulting AUC value of random forest 1 for the test dataset is 0.63. An accuracy increase was reported for the validation dataset, with an AUC value of 0.67 A.The process was repeated by alternating the Italian cohort as the validation set and the Fenland cohort as the training and testing set. There were 190 steatosis0 and 67 steatosis1 samples in the test set and 120 steatosis0 and 21 steatosis1 in the validation set.Once the parameters were tuned by using the training set, the second random forest model was validated using the test and validation set. The AUC values for the test and validation datasets are 0.74 and 0.72, respectively B.A pairwise correlation among 18 genes, within the training dataset, was identified within the control and the steatosis samples . The colA pathway enrichment analysis of the transcriptomics data identified the following pathways: complement and coagulation cascades , cytokine-cytokine receptor interaction , herpes simplex virus 1 infection , B cell receptor signaling pathway (BTK and PTPN6), Toll-like receptor signaling pathway (TLR1 and TOLLIP), JAK–STAT pathway (IL15 and PTPN6) and Human immunodeficiency virus 1 infection and primary immunodeficiency pathways (BAX and B2M) . Among tp values were plotted. Among the seven lipids, TG (52.3) had the highest −log10 . Further boxplots were generated based on these lipids, revealing that triglycerides were upregulated in the steatosis1 samples and downregulated in the steatosis0 samples .The NAFLD pathogenesis is complex and unhealthy lifestyle trends have substantially increased its health burden over the past few years ,13. OmicThe biomarkers identified by our transcriptomics analysis are primarily involved in immune-related pathways. Previous research studies have shown that NAFLD is related to an excessive activation of the immune system . C4BPA, The complement system pathway is poorly characterised in NAFLD and NASH . It is iThe activation of the classical and lectin pathways leads to the apoptosis of hepatocytes which, in turn, renders the lipid metabolism; the improvement in controlling the apoptosis process might help in controlling NASH .It is indicated that there is an increased level of c3 in obese individuals and the action of c3 convertase produces c3a and c3b. c3a has a short half life but is later converted into desArg c3a which has a longer half life . desArg Our differential lipid expression analysis identified 11 significant lipids. The most common class of those lipids was triglycerides. We observed a triglyceride upregulation between the steatosis0 and steatosis1, which is in agreement with recent reports of hypertriglyceridemia being common in NAFLD patients ,58,59. DThe pathways associated with the identified significant lipids are involved in adipocyte lipolysis. It has been previously reported that elevated body mass causes fat cell lipolysis . This miIn conclusion, our findings suggest that the downregulation of C4BP results in an activation of the lectin pathway in the complement system triggering the conversion of c3 to c3a and c3b by the action of c3 convertase, thereby increasing the triglyceride levels, as shown in our study. This indicates that C4BP could be a potential biomarker linked to the complement system pathway, that would aid in the treatment of NAFLD.ComBat might have led to the loss of information. Further studies with large sample sizes should be further conducted to validate our findings.This study has several limitations. Firstly, a small number of transcriptomics samples were used to train and validate the model. Then, the dataset merging using We identified C4BPA, which activates the complement and coagulation pathway that renders lipid metabolism, as a potential NAFLD biomarker.


Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson’s disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug–gene interactions. We performed automated ML on multimodal data from the Parkinson’s progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson’s Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data . Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available. For progressive neurodegenerative diseases, early and accurate diagnosis is key to effectively developing and using new interventions. This early detection paradigm aims to identify, analyze, and prevent or manage the disease before the patient recognizes signs and symptoms while the disease process is most amenable to intervention.1. This report also describes the application of an open-source auto-ML, GenoML, in the context of facilitating production scale analyses of multimodal genomics and clinical data in a democratized manner.Here we describe work that facilitates accurate and early diagnosis using cost-effective methods in a data-driven manner2. Biomedical researchers are currently at the convergence of two scientific advances that will allow progress in early detection and remote identification of potentially high-risk individuals: first, the availability of substantial clinical, demographic, and genetic/genomic datasets, second, advances in the automation of machine learning (ML) pipelines and artificial intelligence, to maximize the value of this massive amount of readily available data3.The most recent strategic vision published by the National Human Genome Research Institute stated that the features of epigenetics and transcriptomics will be incorporated into predictive models of the effect of genotype on phenotype routinely by the year 20304. Previous biomarker studies, particularly in neurodegenerative diseases, have focused on widely known statistical approaches and linear models, using a single metric or handful of metrics for predictions5. Over the last few years, multiple studies have investigated a number of different modalities using ML, such as CSF biomarkers6, imaging8, RNA10, or include movement-related metrics11, even wearable sensor data12. While many of these efforts perform well at classification, we sought to build models based on relatively low cost and easily accessible data modalities that can be generated remotely or using existing biobank data and do not require additional clinic visits or expensive protocols. Our goal for developing a better predictive model for early detection is to identify, analyze, and prevent or manage the disease before the patient recognizes signs and symptoms while the disease process is likely most amenable to intervention in a cost-effective manner13. We aim to contribute to the field with nonlinear and ML-based approaches and leverage rapidly growing publicly available data to build these models. We also extend these models, providing not just disease prediction but also biological insight.A correct clinical diagnosis at the first visit is estimated to be accurate in only 80% of pathologically-confirmed Parkinson’s disease (PD)14 that automatically compares the consistent top dozen ML algorithms from the 2020 executive summary Kaggle has put together derived from the performance across data challenges (https://www.kaggle.com/kaggle-survey-2020) [last accessed 11 August 2021]. We surveyed the list of algorithms used in the executive summary and triaged algorithms that are fundamentally similar to get the best cross-section of machine learning methods for supervised prediction in biomedical research after a qualitative review by an expert panel. Twelve top-performing algorithms were chosen to reduce runtimes and compute the overhead needed for analysis. These were compared to one another to build an accurate peri-diagnostic model to predict disease risk. We also used the features nominated by our workflow to build unbiased networks of genes related to the onset of PD that highlight biological pathways of interest and therapeutic targets. This work leveraged clinico-demographic and multi-omic data produced and curated to build and validate models in publicly available datasets that may impact both trial recruitment and drug development. The models we have developed here improved performance over previous related efforts, with performance metrics at current cross-validation in withheld samples being equivalent, or in some cases, better than the training phase of earlier work1. The data came from the Accelerating Medicines Program—Parkinson’s Disease (AMP PD) program [https://amp-pd.org/] and the code used to carry out analyses comes from open-source automated ML software, all of which have been made publicly available to support reproducibility, transparency, and open science16.We have used publicly available multimodal PD data and GenoML, an automated ML open-source Python packageWe have shown that integrating multiple modalities improved model performance in predicting PD diagnosis in a mixed population of cases and controls. For a summary of basic clinical and demographic features, please refer to Table 1. A model based on only UPSIT might have a high AUC, but with limitations. A decline in smell identification is not PD-specific, but can also be used as a general marker of neurodegeneration and/or the effects of aging and environmental factors. A strength of using multimodal approaches is that some modalities may better predict case or control status than others . Notably, this improved balanced accuracy is of particular importance in binary classifiers where one of the predicted classes is much rarer than the other, like PD, which is relatively infrequent in the general population. Special attention was given to validate the model, interpreting and visualizing the top features aiding in the prediction of classification, and further investigation into optimizing the model, developing hypothesis-free transcriptomic communities, and exploring potential drug–gene interactions.In previous work done by Nalls and colleagues, the UPSIT-only model in the same training set as this study (PPMI) was a strong classifier (AUC 90.1%), but their integrative model was more informative based on DeLong’s testrs Table . Here, wOne benefit of the ML approach we have used is its ability to tune model parameters and accommodate nonlinear associations compared to more commonly used regression-based approaches to disease prediction. The best performing tuned model leveraging the strength of the AdaBoostclassifier that included all data showed an AUC distribution of 88.06 to 92.70% at fivefold cross-validation with a mean of 90.20% and a standard deviation of 2.3% in PPMI (see Table 17. The Shapley Additive exPlanation (SHAP) plots in Fig. 20. Looking closer at these features, we can also observe that the directionality of different genetic features is not uniform. This signifies that overexpression of some genes corresponds to healthy controls while for some features it is in the opposite direction. While sex was included in the features of the dataset, coded as “MALE”, there was a balanced distribution of sex between cases and controls in our training dataset, PPMI, at baseline. This balance might explain why sex was excluded at feature selection during data munging and ultimately did not contribute to the final predictive accuracy of the model with a calculated Shapley value of zero. We have also created a website that allows readers to further explore feature contributions to model accuracy in various scenarios [https://share.streamlit.io/anant-dadu/shapleypdpredictiongenetics/main]. The final SHAP values for all the predictive features in the top-performing model can be found in Supplementary Table Our model build included 51 SNPs and 418 protein-coding transcripts in addition to expected features like the demographics, age, family history, olfactory function, and previous genome-wide significant polygenic risk estimates in the form of PRSGene expression network communities were constructed using RNA sequencing data extracted from positive PD cases. These genes were nominated by the feature selection process. These communities of genes represent PD-specific networks derived from whole blood RNA sequencing. Consider the network itself to be conceptually similar to a pathway, composed of genes whose expression was strongly correlated in the case-only transcriptomics dataset, and the communities being subgroups of closely related genes within the larger set. We identified 13 network communities consisting of 300 genes with an Erdos-Renyi modularity score of 0.794 (a modularity score closer to 1 indicates better model fit). A link to the methods, full community annotations, and a graphical summary can be found in Supplementary Fig. N = 300) to those selected for inclusion as features in the case:control model build (N = 598), we noted enrichments of genes connected to fostamatinib  and copper  from the DrugBank annotations. The same query in the GLAD4U database resulted in a significant overrepresentation of l-lysine annotated genes . All other variations of our drug target enrichment analyses yielded no significant drugs overrepresented after multiple test correction, although the top-ranked result was consistently gamma-hydroxybutyric acid .For genes in our network communities, we evaluated the potential overrepresentation of known drug target genes across the identified communities. When comparing the genes defined as part of the network communities  of peri-diagnostic PD while also generating de novo network communities of genes correlated in PD cases, providing further data for potential therapeutic development. A strength of using this multimodal approach is that the different modalities compensate for one another, with some modalities better at predicting case status while others are better at classifying controls. All of this was accomplished in a completely transparent and open science framework, from the underlying data to code and resulting models.While other studies that look at PD risk and onset use data types such as analyzing gait25. At this prevalence for our optimized PPMI model described in Table While we do not suggest this as a replacement for current diagnostic screening methods, it can be a potential adjunct screening that could aid in identifying high-risk individuals, especially on a large biobank or study recruitment scale. Additional studies will need to be conducted to ascertain the model’s ability to distinguish very early PD cases from other diseases within high-risk cohort studies. The estimated prevalence of PD in an aging population is about 2%26. Its other strength is its applicability and utility across datasets, detailed information on transfer learning highlighted in Supplementary Note 27.The strength of our work is its high balanced accuracy in delineating cases and controls. While PDBP might more realistically model the distribution of males:females who are diagnosed with PD, PPMI realistically models most of the patients in what we believe are of greater interest . PPMI and PDBP also have distinct differences in their recruitment styles, where PPMI is unmedicated within one year of diagnosis (confirmed by DatScan), while PDBP recruits those within 5 years of receiving a diagnosis regardless of medication status (and without DatScan confirmation). This key difference is reflected in both the mean age and mean UPSIT score reported in Table We have created an interactive web-based application for others to investigate the driving factors in our best model that incorporates all data modalities; it also gives users the flexibility to explore variations like transcriptomics-only models or a new model with none of the clinico-demographic features present 29. Another top-ranking transcriptomic feature, OTOL1, has been suggested before as a putative genetic modifier of familial PD age at onset30. CHFR has been associated in previous studies with rotenone-related PD risk31. CASP7 is potentially biologically interesting due to its expression being implicated in apoptosis and neuroprotection as well as rare missense mutations in the gene being associated with late-onset familial Alzheimer’s33. The genetic variant, rs4238361 is a potential PD risk modifying variant whose nearest coding gene is VPS13C, a gene that harbors both rare and common PD variants of interest35. The gene PHF14 has been suggested to be downregulated in neurodegenerative diseases; this potential effect mirrors that suggested in Fig. 436. Recent single-cell sequencing work has provided evidence for a connection between SQLE and dopamine stress responses in neurons relating to PD risk37. Additionally, MMP9 overexpression has been suggested to be associated with neuronal cell death in neurodegeneration38. Please see the Genes and variants affecting the model’s performance shown in Fig. 40. Here we can push therapeutic and biomarker research by identifying communities of connected genes in the blood transcriptome of PD patients. Nodes in these networks suggest shared effects in genetically targeted drugs, informing development cycles and benefitting developers as drugs connected to genetic or genomic data often have a higher level of success in trials compared to those without similar evidence42.Another strength of this work is that feature selection from model building easily segues into network community analyses to build relatively low bias networks, compared to those with potential bias taken from literature and text miningZBTB4 and FCF1. Potentially more interesting are the enriched drug targets within the nominated genes from our analyses of the transcriptomic data. Given our network communities are based on genes highly correlated in cases only this is unsurprising, aimed to build clusters of genes connected by similar expression patterns among cases. More interestingly are the findings from the network communities recognizing the overrepresentation of genes targeted by known drugs, please see Supplementary Note SLC16A7, SLC16A3, and GABBR1 genes. The SLC16A7 and SLC16A3 genes are a part of a family of drug transporter genes known as monocarboxylate transporters43. Drug transporters have a role in almost every part of the therapeutic process, from absorption, distribution, and elimination of drug molecules. The GABBR1 gene encodes a receptor for gamma-aminobutyric acid (GABA) expressed throughout the brain; defects in this gene underlie several neurobehavioral diseases44. Gamma-hydroxybutyric acid acts as an agonist, activating GABA-B receptors to exert its sedative effects. Identification of drug metabolism and receptor gene/drug interactions may lead to drug discovery, thereby helping us optimize drug therapy.Modeling exercises like these not only have the potential to build useful classifiers, they may also identify drug targets. This can happen at the feature selection, and network build phases. In our network community built from case-only expression data, only two quantitative trait loci in blood from Mendelian randomization in the previously published PD GWAS were included. These two genes are 45. With subsequent iterations of this work being facilitated by the Global Parkinson’s Genetics Program (GP2) program over the next 5 years47, we hope to expand this modeling effort into a diverse set of genetic ancestry groups and generally in larger sample series. We also acknowledge that no optimal dataset to validate the findings from PPMI exists because of the inherent study design of PPMI focusing on unmedicated recently diagnosed PD cases. Additionally, important known predictors such as constipation and REM sleep behavior disorder (RBD) were not included in this analysis. In our previous efforts, constipation did not pass feature selection , and therefore was not included in this manuscript. While RBD is an established predictor of PD, one comparable with the smell identification test, data for RBD was available as part of a questionnaire for only a subset of samples, and that would have introduced sparse data, smaller sample sizes, and issues with data harmonization across the two studies. Previously, the RBD questionnaire has been shown to be insufficient for screening idiopathic PD48. Hopefully, the ongoing extension of the PPMI study will facilitate further work.Our main weakness in this research is the lack of diversity in available sample series. Current research suggests that genetic predictive models have mixed results when being applied across genetic ancestry groupsOverall, we believe this work represents a significant conceptual and scientific advance past previous efforts. This classifier has improved performance, is more broadly applicable, and is highly reproducible. Further, the transparency of this approach and the contributions of data types move the field away from black-box predictors of disease. A further strength of this work is the use of open-source automated ML software thoughtfully designed for scientists focusing on genomic and clinical data, developing models and validating them on public controlled-access datasets, visualizing the top contributing features, and providing all the code and software publicly. For readers not specialized in ML, we have included sections on model selection and interpretation in Supplementary Note This work has helped to push past the previous paradigm of focusing on a single biomarker or class of biomarker in biomedical research to maximize data value for clinical and computational scientists by leveraging ML algorithms that explore complex relationships between features. We have provided a model(s) to improve risk prediction in PD to help with interventional and prospective studies as well as healthcare resource prioritization. We have also integrated additional analyses and data resources that may aid in developing and/or refining future interventions.https://www.amp-pd.org. All subjects provided written informed consent for their participation in the respective cohorts. Details on how the data from the respective cohorts were acquired can be found in the Acknowledgements section. The study design and data-sharing agreement was a collaboration between AMP PD and NIH and is in accordance with NIH standard ethical approval. All study participants involved in the AMP PD initiative have provided their informed consent for their data to be used for studies to their respective cohorts.This study was done in collaboration with the Accelerating Medicines Partnership in Parkinson’s disease (AMP PD) initiative as well as the Global Parkinson’s Genetics Program (GP2) initiative. Data used in the preparation of this article were obtained from the AMP PD Knowledge Platform. For up-to-date information on the study, please visit 1 and *omic data that could be passively collected due to prior precedent and the a priori genomics focus of the report. While RNA signatures are subject to change depending on disease stage, RNA sequencing at baseline was chosen for this analysis as it is the closest time point to diagnosis as possible. PPMI was chosen to be the training cohort given the recruitment design, recruiting unmedicated individuals within 1 year of diagnosis. Since our model is retrospective, we aimed only to analyze refined Parkinson’s disease diagnosis, by excluding any samples with conflicting diagnostic data within a decade of post-enrollment follow-up. We excluded any cases whose medical history included an additional neurological disease diagnosis or retraction of their PD diagnosis during follow-up. We also excluded controls developing PD or another neurodegenerative disease(s) after enrollment. Additionally, a subset of Parkinson’s disease cases and controls from the PPMI study were excluded as they came from a targeted study recruitment design purposely enriching for known genetic risk mutation carriers (LRRK2 and GBA mutation carrier focused recruitment). Analysis was done on unrelated individuals of European ancestry. AMP PD sample PRS weights were based on the most recent European ancestry GWAS data, excluding these cohorts’ contributions to the allele weightings.Clinical, demographic plus genome-wide DNA and RNA sequencing data were taken at baseline visits from the Parkinson’s progression marker initiative (PPMI) and the Parkinson’s disease biomarkers program (PDBP) in cases with PD and control unaffected by neurologic diseases. We prioritized keeping features that were available for at least 80% of the training and validation cohorts available on the AMP PD platform. Additionally, we prioritized data that met the modeling inclusion criteria in the previous effortsParticipants with required clinical, demographic, and genomic (DNA and RNA sequencing) data were identified for inclusion, with excessive missing data (>15% per feature) as exclusion criteria. Each contributing study abided by the ethics guidelines set out by their institutional review boards, and all participants gave informed consent for inclusion in both their initial cohorts and subsequent studies.50. Sex was clinically ascertained but also confirmed using X chromosome heterozygosity rates. The University of Pennsylvania smell inventory test (UPSIT) was used in modeling51. For a summary of basic clinical and demographic features, please refer to Table Clinical and demographic data ascertained as part of this project included age at diagnosis for cases and age at baseline visit for controls. Family history (self-reporting if a first or second-degree relative has a PD diagnosis) was also a known clinico-demographic feature of interest. Ashkenazi Jewish status was inferred using principal component analysis comparing those samples to a genetic reference series, referencing the genotyping array data from GSE23636 at Gene Expression Omnibus as previously described elsewhere52. Jointly genotyped sequencing data using the standard GATK pipeline from AMP PD was used. This process is described in detail, from sample prep to variant calling, in a separate manuscript detailing the AMP PD whole-genome DNA sequencing effort [under review]53.DNA sequencing data were generated using Illumina’s standard short-read technology, and the functional equivalence pipeline during alignment was the Broad Institute’s implementationP > 1E-4, missingness by haplotype P > 1E-4, Hardy–Weinberg p value >1E-4, minor allele frequency in cases >5% (in the latest Parkinson’s disease meta-GWAS)17. As an a priori genetic feature to be included in our modeling efforts, we also used the basic polygenic risk score from the latest Parkinson’s disease meta-GWAS (genome-wide significant loci only) that did not include our testing or training samples as weights17.Quality control for these samples based on genetic data output by the pipeline included the following inclusion criteria: concordance between genetic and clinically ascertained genders, call rate >95% at both the sample and variant levels, heterozygosity rate <15%, free mix estimated contamination rate <3%, transition:transversion ratio >2, unrelated to any other sample at a level of the first cousin or closer (identity by descent <12.5%), and genetically ascertained European ancestry. For inclusion of whole-genome DNA sequencing data, the variants must have passed basic quality control as part of the initial sequencing effort (PASS flag from the joint genotyping pipeline) as well as meeting the following criteria: non-palindromic alleles, missingness by case-control status 54. For this study, we focused on blood withdrawn at baseline. Variance stabilized counts were adjusted for experimental covariates using standard limma pipelines55. Gene expression counts for protein-coding genes were extracted, then differential expression p values were calculated between cases and controls using logistic regression adjusted for additional covariates of sex, plate, age, ten principal components, and percentage usable bases. While there is literature on the effects of non-coding RNA on PD have been researched elsewhere56, protein-coding genes were chosen for this analysis. Given the quality of data for known protein-coding genes, their relevance in drug development and the robust annotations available we focused on this subset that is more suitable for applications in downstream analyses such as looking at known drug–gene interactions.RNA sequencing data from whole blood on the same samples was generated by the Translational Genomics Research Institute team using standard protocols for the Illumina NovaSeq technology2 < 0.1 with any other variants in ±1 MB. As a note, these variants were not p value filtered based on recent GWAS, but they do exclude regions containing large tracts of linkage disequilibrium57. Our genetic data pruning removed SNPs in long tracts of high LD such as in the HLA region (we excluded any SNPs within r2 > 0.1 within a sliding window of 1 MB), while retaining known genetic risk SNPs within the region. For RNA sequencing data, all protein-coding genes’ read counts per sample were used to generate a second set of ten principal components. All potential features representing genetic variants  from sequencing were then adjusted for the DNA sequence-derived principal components using linear regression, extracting the residual variation. This adjustment removes the effects of quantifiable European population substructure from the genetic features prior to training, this is similar in theory to adjusting analyses for the same principal components in the common variant regression paradigm employed by GWAS models. The same was done for RNA sequencing data using RNA sequencing derived principal components. This way, we statistically account for latent population substructure and experimental covariates at the feature level to increase generalizability across heterogeneous datasets. In its simplest terms, all transcriptomic data were corrected for possible confounders, and the same is done for genotype dosages. After adjustment, all continuous features were then Z-transformed to have a mean of 0 and a standard deviation of 1 to keep all features on the same numeric scale when possible. Once feature adjustment and normalization were complete, internal feature selection was carried out in the PPMI training dataset using decision trees (extraTrees Classifier) to identify features contributing information content to the model while reducing the potential for overfitting prior to model generation59. Overfitting here is defined as the over-performance of a model in the training phase with minimal generalizability in the validation dataset due to the inclusion of potentially correlated or unimportant features. The implementation of decision trees for feature selection helps remove redundant and low-impact features, helping us to generate the most parsimonious feature set for modeling. Feature selection was run on combined data modalities to remove potentially redundant feature contributions that could artificially inflate model accuracy. Export estimates for features most likely to contribute to the final model in order of importance were generated by the extraTrees classifier for each of the combined models, and are available on the Online Repository. By removing redundant features, the potential for overfitting is limited while also making the models more conservative. Additionally, if a variant provided redundant model information, such as being in strong linkage with a PRS variant, it would be removed from the potential feature list.As part of the initial data preprocessing, principal components summarizing genetic variation in DNA and RNA sequencing data modalities are generated separately. For the DNA sequencing, ten principal components were calculated based on a random set of 10,000 variants sampled after linkage disequilibrium pruning that kept only variants with r60. Among the top 5% of features ranked in the Shapley analysis, the mean correlation between features was r2 < 5%, with a maximum of 36%. By removing redundant features using correlation-based pruning and an extraTrees classifier as a data munging step, the potential for overfitting is limited while also making the models more conservative. We then compared how each algorithm performed on identical training and testing data. Once the best performing algorithm had been selected, a thorough hyperparameter tuning of the algorithm with fivefold cross-validation  was performed. While Z-transformations were done on the entire dataset prior to splitting, the results from the cross-validation were stable over iterations, suggesting minimal bias. Additionally, the training and validation sets were not transformed together, with PPMI and PDBP kept separate. The model was exported to enable external validation and transfer learning in the readers’ own data. This hyperparameter tuning and cross-validation phase was carried out to both improve performance and reduce bias61. We validated the models built by taking the trained and tuned models from PPMI and fitting them to the external validation dataset, PDBP. Details on study design, participants, and raw data processing from PPMI and PDBP can be found in Supplementary Note Figure After the data preprocessing process  described above, data from PPMI was randomly split into 70% training and 30% testing. Training of the algorithms was performed on the training set, and validation of the algorithms was performed on the testing set. A total of 12 well-performing ML algorithms were competed to identify which algorithm could maximize AUC across the two classes (cases and controls).These algorithms were chosen due to their success in other domains, execution in Python’s scikit-learn package, and their ability to export probability-based predictions, allowing the training, testing, and interpretation of the model more straightforward. The algorithms included are: logistic regression (LogisticRegression), random forests (RandomForestClassifier), adaptive boosting (AdaBoostClassifier), gradient boosting (GradientBoostingClassifier), stochastic gradient descent (SGDClassifier), support vector machines (SVC), multi-layer perceptron neural networks (MLPClassifier), k-nearest neighbors (KNeighborsClassifier), linear discriminant analysis , quadratic discriminant analysis , bagging (BaggingClassifier), and extreme gradient boosting (XGBClassifier). These algorithms are, broadly, a departure from standard linear models used in genetic prediction analyses, employing tree-based methods (boosting), kernel-based methods . Linear models are traditionally used in the biological space because of their power and ease of interpretation. They excel at correlative modeling, and information such as co-occurrence probabilities can inform the model. When more flexibility is needed, however, such as when the order of events matters and a better fit to the data can be found, nonlinear machine learning models are preferred.62 to remove potentially redundant feature contributions that could overfit the model. An in-depth comparison of the top features identified by Shapley values and were ultimately selected by the best performing model can be found in Supplementary Data Fig. Feature selection was carried out using the extremely randomized trees classifier algorithm (extraTrees)The algorithm with the highest AUC and balanced accuracy in the withheld 30% of PPMI was selected for tuning and cross-validation. The top competing algorithm was then selected to undergo a computationally intensive hyperparameter tuning phase in the entire PPMI dataset, no longer split into training and testing once, instead of undergoing cross-validation each time parameters were iterated. In this analysis, the top-performing algorithm (AdaBoostClassifier) was tuned for several potential predictors (estimators) between 1 and 1000 for 25 random iterations at fivefold cross-validation per iteration.p values based on feature inclusion thresholds. We iterated across all possible combinations of p value thresholds  for genetic data from the most recent published GWAS and for transcriptomic data from our differential expression work also described above17. Genetic and transcriptomic data are structurally different and analyzed using different methods, and genetic data is wider than transcriptomic data. Given the differences in modalities, each modality went through separate feature selection phases. Each of these combinations were then used as inputs, and during the munging stage underwent feature selection using the extraTrees classifier. During training, at each of the 49 combinations of thresholds, the best model determined by maximizing the AUC metric was chosen. The final best performing model was chosen based not only on maximizing AUC, but by using a combination of the best-balanced accuracy, sensitivity, and specificity metrics reported to best account for case-control imbalance. Further details on ML metrics and interpretation can be found in Supplementary Note p values identified in the largest meta-analysis of 17 datasets from PD GWAS available from European ancestry samples17 prior to training the data. The PPMI training set is only a minuscule portion of the most recent GWAS study (less than 0.1% of the sample size); additionally, the algorithmic feature selection described above is generally much more conservative and excluded the majority of features reaching the p value thresholds of interest thus reducing any impact caused by potential data leakage. In this report, we only focused on a model with a 1E-5 maximum p value for genetic data inclusion and a 1E-2 maximum P for transcriptomic data inclusion; however, all potential models were exported and saved for public use in transfer learning for similar datasets for the scientific community (Online Repository).This process detailed in the paragraphs above was carried out 49 times, at varying thresholds of 63. This post hoc optimization was done again after fitting the tuned model to the external validation cohort. Here we generate the probabilities for being a case in the PDBP validation cohort using the same model with the same features and parameters as in the training and tuning in PPMI, except here the probability threshold for discerning case status is specific to PDBP allowing us to better address imbalance specific to PDBP. This is a post hoc probabilistic optimization that does not include any reweighting or triaging of features in either dataset, allowing for a stand-alone validation phase. We have constructed a web app to allow the user to evaluate the contribution of different features at validation to an individual sample’s classification [https://share.streamlit.io/anant-dadu/shapleypdpredictiongenetics/main], showing the interplay between clinical and omic data on a more granular level.After training, we refit the model to the withheld samples using an optimized threshold for case probability based on Youden’s J calculation to better account for case-control imbalance and subsequently increase balanced accuracy and related metrics65. A surrogate xgboost model was trained in 70% of the data and later tested in the 30% of withheld data to evaluate the model’s contributing features. The interactive website (https://share.streamlit.io/anant-dadu/shapleypdpredictiongenetics/main) was developed as an open-access and cloud-based platform for researchers to investigate the top features of the model developed in this study and how these may influence the classification  of a particular sample. In its simplest description, the Shapley values are similar to standard regression derived relative importance measures with regard to interpretation.The Shapley additive explanations (SHAP) approach was used to evaluate each feature’s influence in the ML model. Shapley values are a game theory-based approximation of a feature’s impact on a model relative bidirectional change in that feature as relative to all other features in the model. Shapley explanations enhance understanding by creating accurate explanations for each observation in a dataset. The SHAP package was used to calculate and visualize these Shapley values seen in the figures in the manuscript and the interactive websiteFurther information on research design is available in the Supplemental MaterialSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Reporting Summary


The growth of abnormal cells in the brain causes human brain tumors. Identifying the type of tumor is crucial for the prognosis and treatment of the patient. Data from cancer microarrays typically include fewer samples with many gene expression levels as features, reflecting the curse of dimensionality and making classifying data from microarrays challenging. In most of the examined studies, cancer classification  accuracy was examined without disclosing biological information related to the classification process. A new approach was proposed to bridge the gap between cancer classification and the interpretation of the biological studies of the genes implicated in cancer. This study aims to develop a new hybrid model for cancer classification (by using feature selection mRMRe as a key step to improve the performance of classification methods and a distributed hyperparameter optimization for gradient boosting ensemble methods). To evaluate the proposed method, NB, RF, and SVM classifiers have been chosen. In terms of the AUC, sensitivity, and specificity, the optimized CatBoost classifier performed better than the optimized XGBoost in cross-validation 5, 6, 8, and 10. With an accuracy of  Brain cancer is the leading cause of death in women under the age of 20 and men under 40 ,2. MoreoMicroarray-based gene expression profiling has proven useful for cancer detection, prognosis, and treatment . In addiCancer microarray data typically contain a small number of samples with many gene expression levels as features, which leads to the curse of dimensionality, making the classification of microarray data a difficult task. The bioinformatics community uses a variety of approaches to classify microarray data using machine learning systems. Most of the examined studies on cancer classification using microarray data sets take cancer classification accuracy into account without disclosing any biological information of the cancer classification process. Few studies have investigated the biological interpretation of microarray data sets in addition to the model classification accuracy. This research aims to bridge the gap between cancer classification and biological interpretation by improving accuracy performance, and the selected significant genes agree with the findings of relevant biomedical studies. This paper proposes a hybrid model based on three different machine learning techniques, including the commonly used ensemble classification methods of gradient boosting , an extrThe experiments show that the proposed model substantially reduces the number of genes required for classification and improves classifier accuracy. Additionally, the proposed hybrid model’s selected genes (features) are biologically interpreted, and the biological interpretation coincides with the findings of relevant biomedical studies. The proposal of a novel ensemble classifier to ensure that the genes selected in our model are biologically interpreted. On top of that, the results are also satisfactory and in line with pertinent biomedical studies. The identification of relevant and non-redundant genes for the biological context by ensemble mRMRs, allowing for enhanced biological interpretations.The analysis of a brain cancer microarray dataset on high-dimensional data using Catboost and XGboost.The optimization of the hyperparameters of the two classifiers using the hyperboot optimizer.The outperformance of Catboost compared to XGboost with regard to the AUC, sensitivity, specificity, and accuracy.The main contributions of this work are:The remainder of the paper is structured as follows: In recent years, multiple pieces of research have used a wide range of machine learning methods for the classification, diagnosis, and treatment of cancer disease. BU-Net was developed by Rehman  and otheBashir et al.  used a fTo overcome the dimensionality problem, Kumar et al.  introduck-nearest neighbor, and decision tree on the diffuse large B cell lymphoma (DLBCL) dataset. Experiments confirmed that the proposed model could successfully compete, with excellent predictions concerning the accuracy, precision, sensitivity, F-measure, and ROC value.Pradana et al.  introducSampathkumar et al.  developeGiven all that has been mentioned so far, all of the algorithms listed above address two major issues: brain cancer classifiers and the curse of dimensionality. However, there is no biological interpretation of the microarray data set discussed in the literature. To the best of our knowledge, this paper is the first paper to provide a consolidated biological interpretation of the results of the proposed work. This section discusses ensemble classification and how to set the hyperparameter values and the minimum redundancy maximum relevance (mRMR) feature selection. The three main components of the proposed method are ensemble classification, hyperparameter optimization, and minimum redundancy maximum relevance (mRMR). First, the ensemble methods use multiple learning algorithms to achieve better predictive performance than if they were used by themselves. Secondly, we have hyperparameter optimization, in which the parameters of the classifier are tuned to find the optimal setting. The third one is minimum redundancy maximum relevance (mRMR), which is a filter-type feature selection method that obtains the best feature set by minimizing the similarities between features and classified variables and by maximizing their correlationsEnsemble learning methods use multiple machine learning algorithms to generate weak predictive results based on the features extracted from various data projections. Following this, the results are fused with various voting mechanisms to achieve better results than any constituent algorithm alone ,33. FiguThe gradient boosting method, which constructs the solution stagewise and solves the overfitting problem by optimizing the loss functions, is one of the most commonly used ensemble classification methods. The main concept of a gradient boosting model is depicted in XGBoost is an efficient gradient-boosted decision tree algorithm . GradienHyperopt is a Python library that implements sequential model-based optimization (SMBO) . HyperopHyperparameter search spaces are typically large multi-dimensional spaces. Hyperopt outperforms grid and random searches, particularly as the search space grows. Within the framework of our proposed model, Hyperopt is used to optimize the settings for the XGBoost and CatBoost hyperparameters. It aims to identify the optimal genes for microarray data analysis and to improve the classification of cancer microarrays.Minimum redundancy maximum relevance (mRMR) is a filter-type feature selection method that obtains the best feature set by maximizing the correlation between the features and the classified variables and by minimizing the correlation between features. The classic function enables the collection of appropriate and non-redundant features with ease . In set Ensemble (mRMR) feature selection implements two ensemble approaches: exhaustive and bootstrap ensemble mRMR. The exhaustive variant of the mRMR heuristic extends it by starting multiple feature selection procedures, with the This section describes the model (as shown in (i)Avoid features in greater numeric ranges dominating those in smaller ranges;Avoid numerical difficulties during calculation;Ensure that each feature is scaled to the range .Preprocessing the dataset (brain cancer microarray). This step is vital to(ii)The data were partitioned into two sets: The training set is used for the training. The testing set is used to test final model 3, initializing CatBoost with specific solution parameters. (iii)CatBoost is used as a feature selector with 8-fold cross-validations . CatBoost calculates the means for each fold.(iv)By setting a threshold, irrelevant features are then removed. Suppose the score of a gene is above the threshold. In that case, the gene will be selected (as seen in (v)The importance value of each gene is registered using a voting process. For example, the gene with index 1 in fold 0 receives an importance value of 1 if the same gene is present in the next fold; then, the gene importance is +1, and so on, for all of the 8-fold cross-validations. After this was applied, voting is conducted 50 genes  Core (TM) i5-7500 CPU with a 32-bit operating system, 4 GB RAM, and the Windows 7 operating system as well as the NumPy, SciPy, Pandas, Keras, and Matplotlib frameworks and Python 2.7 programming language.Molecular profiles from 28 patient samples were analyzed . In addition, an analysis of frozen specimen RNA with oligonucleotide microarrays containing probes for 1070 genes was conducted. Gene expression data are available in the Supplementary Information .k-fold tests were used in the majority of previous experiments. k-fold verification works to find crossed validity by randomly dividing data into k subsets of (approximately) equal size and k-times. As a result, it will run several times, with one subset serving as a test group and the other subset, k-1, serving as a training group  as non-diseased . It is described as AUC shows the area under the receiver operating characteristics (ROC) curve, calculated as Sensitivity, as calculated by This experiment compares optimized (CatBoost and XGBoost) classifiers in the proposed hybrid model to achieve the best hyperparameter of the two classifiers. The Hyperopt optimizer is used. From the data in Number of non-zero genes importance (every fold)..The number of genes selected by embedded SVM (with Redundant), 980 genes.The number of genes selected by embedded SVM (Unique), 671 genes.The final number of genes after we applied voting was 50 genes.In this experiment, CatBoost and optimized CatBoost were compared with the original brain cancer microarray data . Based on the performance metrics in The classic learning algorithms random forest (RF), naive Bayes (NB), and support vector machines (SVM) were used to assess the gene classification accuracy of selected optimal genes by the proposed hybrid model. These learning algorithms were applied to the newly collected dataset, which only included the best genes, and the overall accuracy was calculated. In A subset of genes (features) from the brain cancer data set was biologically interpreted to demonstrate the proposed model’s efficacy in improving critical items such as classification accuracy and for selecting genes with important biological backgrounds. A few classes of important genes derived from microarray technologies were used to diagnose and to provide the prognostic purposes of brain cancer after using the proposed hybrid model’s biological portrait.https://david.ncifcrf.gov/list.jsp (accessed on 18 June 2020) [http://www.geneontology.org/GO.tools.microarray, (accessed on 18 June 2020) are generally considered to be the most inclusive and fastest-growing public repository for grouping functionally related genes. Following that, it can be shown that the proposed approach is the most effective way to pick a large group of genes for brain cancer pathway detection and prognosis.The proposed hybrid model aims to determine crucial gene subsets with the maximum amount of accuracy needed to treat a brain cancer patient. In this segment, the selected group of probe sets could be studied by using the web tool DAVID  ne 2020) ,39. TablIn Brain disorders are becoming a major issue, particularly malignant brain tumors, which significantly impact people’s lives. The brain cancer microarray data have proven to be a complicated classification task due to the small number of samples that have a large number of gene expression levels as features. As part of brain cancer microarray data analysis, the present study proposed an effective and powerful technique for the selection of significant and relevant genes with biomedical relevance. Three distinct techniques were used for classification and prediction . We used the same dataset and three different algorithms to evaluate the performance of the proposed model . The experimental results demonstrate that the proposed hybrid model significantly improves critical items such as classification accuracy and that the selected genes have an important biological background.Furthermore, selecting optimal genes (features) with biological significance can assist biological researchers in brain cancer treatment. The major contributions of this paper are: (a) The application of Catboost and XGboost on high-dimensional microarray data to create a cancer microarray dataset; (b) the use of the hyperboot optimizer to optimize the hyperparameters of the two classifiers, and the outperformance of the Catboost on XGboost in terms of AUC, Sen, Spec, and accuracy; (c) the selection of genes that are non-redundant and relevant to the biological context using ensemble mRMR, which leads to more detailed biological interpretations. Later, the output of the gene subset was combined with the Catboost-selected features. Then, a voting process was applied to obtain unique, informative genes (features) with high relevance and minimum redundancy; (d) the selected genes in our proposed model were biologically interpreted, and the results agree with the findings of relevant biomedical studies. Developing robustness should be a priority for future work.


Most hepatocellular carcinoma (HCC) patients occur on a background of liver cirrhosis, the molecular mechanisms of liver cirrhosis and its progression to HCC remain to be fully elucidated. Single cell differentiation trajectory analysis has been used in cell classification and tumor molecular typing, which correlated with disease progression and patient prognosis. Here we use cell differentiation trajectory analysis to investigate the relevance of liver cirrhosis and HCC. Single-cell RNA sequencing (scRNA-seq) data of liver cirrhosis and bulk RNA-seq and clinical data of HCC were downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) for analysis. HCC samples were divided into three subtypes, based on differentiation-related genes (DRGs) of liver cirrhosis, each with a different expression profile and overall survival (OS). A two- DRGs (CD34 and RAMP3) based prognostic risk scoring (RS) signature was established which could differentiate OS between high-risk and low-risk groups. And expression levels of CD34 and RAMP3 were predominantly high in endothelial cells. By integrating the RS and clinicopathological features, a nomogram was constructed and can accurately predicted the 1-year, 3-years, and 5-years OS. In conclusion, cell differentiation trajectory of liver cirrhosis can predict the prognosis of HCC, and provides new perspectives on the mechanisms of progression of liver cirrhosis to HCC. Hepatocellular carcinoma (HCC) is the most common primary liver cancer and accounts for 4.7% of newly diagnosed cancer cases and 8.2% of cancer related deaths . Most paSingle-cell RNA sequencing (scRNA-seq) enables the analysis of gene expression at single-cell resolution. Relative to traditional bulk sequencing, scRNA-seq can identify different cell types based on cell marker genes, which provides a new approach in defining functional biomarkers . Recentlhttps://www.ncbi.nlm.nih.gov/gds). The data was then processed on R using the Seurat package. The proportion of mitochondrial genes was then calculated and its relationship with total gene numbers and sequencing depth determined by correlation analysis. Cells in which < 50 genes were identified and with a mitochondrial gene proportion of >5% were excluded from analysis. Upon normalization of the scRNA-seq data by the LogNormalize method, the top 1,500 genes with significant differences across cells were identified using variance analysis. Next, dimensionality was reduced using principal component analysis (PCA)  were downloaded from Gene Expression Omnibus (GEO) (dataset GSE136103) (is (PCA) . The t-S heatmap . Cluster heatmap . Intracehttps://portal.gdc.cancer.gov).Bulk RNA-seq data and survival data from 233 HCC samples were obtained from GEO. These data belonged to datasets GSE10186 (118 samples) and GSE76427 (115 samples). 50 normal liver samples and 374 HCC samples with transcriptomic data and 377 HCC samples with clinical data  were dowBased on the expression of DRGs in GEO samples, R’s ConsensusClusterPlus package was used for HCC consensus clustering using a K-means algorithm . The proSample stromal, immune, and tumor purity scores were assessed using ESTIMATE. A total of 30 DGCs  and 38 Ip < 0.05. The filtered genes were then subjected to multivariate Cox regression analysis to produce a for DRGs based on the prognostic risk scoring (RS) signature. Next, patients were divided into the high and low risk groups based on mean RS signature value. For the prognostic model, after exclusion of samples with unavailable survival information, 370 HCC samples from TCGA cohort and 195 HCC samples from the GEO cohort were used as training and validation sets, respectively  was then carried out using the WGCNA package and correlation between key modules associated with HCC differentiation determined. The threshold for differential expression of genes in key modules was |log2 (FC)| >1 and FDR < 0.05, for univariate analysis was ectively . The effThe construction of nomogram based on the TCGA cohort. RS and clinical variables such as, age, grade, and stage were considered. Univariate and multivariate analyses were applied and a nomogram constructed for predicting the 1-year, 3-years, and 5-years overall survival. The nomogram was assessed using calibration curves.2 tests were used to compare dichotomous variables. Survival analyses were done using Kaplan-Meier analysis and compared using log-rank tests. p = < 0.05 indicated statistically significant differences.All statistical analyses were done on R (version 4.1.1) and Perl (version 5.30.1). Continuous variables are presented as mean ± SD. T-test or analysis of variance was used to compare continuous variables. χThe workflow of this study is shown in p = < 0.05) were selected for further analysis. Using the tSNE algorithm, 6,461 liver cirrhosis cells were classified into 17 clusters .DRGs-based consensus clustering was done on GEO datasets, and HCC samples grouped into three molecular subtypes . Areas under ROC curve of the TCGA cohort to predict 1-year, 3-years, and 5-years OS were 0.739, 0.719, and 0.640, respectively (https://www.proteinatlas.org)  .p = < 0.001). The two prognostic factors were used to construct a nomogram for predicting 1-year, 3-years, and 5-years OS  that were used for constructing the RS signature. CD34 is a transmembrane glycosylated protein first described in hematopoietic stem cells . It is eHere, using cell differentiation trajectory of scRNA-seq data from liver cirrhosis, we stratified HCC into three distinct molecular subtypes that differ with regards to expression profiles, clinical features, and outcomes. These findings highlighted the close relationship between liver cirrhosis and HCC and provided an effective complementary strategy for HCC molecular typing. Our data show that single-cell transcriptomics offer an effective avenue for elucidating the mechanisms underlying liver cirrhosis and its progression to HCC. We find that CD34 and RAMP3 were considered important molecular markers.