Epigenomics is the branch of biology concerned with the phenotype modifications that do not induce any change in the cell DNA sequence. Epigenetic modifications apply changes to the properties of DNA, which ultimately prevents such DNA actions from being executed. These alterations arise in the cancer cells, which is the only cause of cancer. The liver is the metabolic cleansing center of the human body and the only organ, which can regenerate itself, but liver cancer can stop the cleansing of the body. Machine learning techniques are used in this research to predict the gene expression of the liver cells for the liver hepatocellular carcinoma (LIHC), which is the third biggest reason of death by cancer and affects five hundred thousand people per year. The data for LIHC include four different types, namely, methylation, histone, the human genome, and RNA sequences. The data were accessed through open-source technologies in R programming languages for The Cancer Genome Atlas (TCGA). The proposed method considers 1,000 features across the four types of data. Nine different feature selection methods were used and eight different classification methods were compared to select the best model over 5-fold cross-validation and different training-to-test ratios. The best model was obtained for 140 features for ReliefF feature selection and XGBoost classification method with an AUC of 1.0 and an accuracy of 99.67% to predict the liver cancer. The pathogenesis of cancer stems from gene regulations responsible for unwanted cellular growth and division, which undergoes changes and mutations. Gene sets called oncogenes transform normal cell types into cancerous ones. Individual cancers have specific genetic anomalies ranging from epigenetic alterations, copy numbers (CNs), somatic mutations, and profiles. Disruption of gene expression (GE) can arise from a genotypic inheritance, cellular division, or environmental factors. GE changes lead to protein derangements that alter standard cellular behavior. Transformed cells begin abnormal proliferation, increasing in size to a tumor. Subsequently, certain tumors evolve to become cancers, and unique profile molecules identify individual types.The advent of next-generation sequencing has paved the way for mapping out of entire genome data that single out variations and mutations specific to individual tumors. Copy number variations (CNVs) identify regions on genes that appear in different numbers in various individuals or separate cells in the same individual. A significant percentage of phenotypic variation is attributable to CNVs. Gene expression is altered by distorting code sequences, disrupting long-range regulations, or breaking gene dosages. Liver cancer growth is the second most regular reason for disease-related cancer around the world. It is one of the only neoplasms with a steadily increasing incidence and mortality . Liver cLiver hepatocellular carcinoma is touted as the most aggressive form of liver cancer with etiologies ranging from chronic infections (HBV and HCV), diabetes, and metabolic diseases to autoimmune hepatitis . GenomicThe latest computation techniques and novel bioinformatics have been continuously used in new oncogenic research studies, and high-throughput technologies  are playing a major role in identifying novel tumor markers by the researchers for cancer diagnostics and targeted treatment. Distinct epigenetic signatures catalyzed by various epigenetic modifiers control developmental regulation of gene expression. Epigenetic control of chronic liver diseases advances our understanding of critical roles of DNA methylation/demethylation, histone acetylation/deacetylation, histone phosphorylation and mRNA, and micro RNA involved in these alternative pathways.In tumor development and differentiation, the Wnt signaling pathway exhibits a vital role. Accumulation of β-catenin in HCC patients indicates the activation of the Wnt signaling pathway. Almost 62% of HCC patients displayed an excessive collection of β-catenin . β-CatenTo represent the cycle of information investigation of a cellular breakdown in the liver, we constructed a stream scorch to decipher the means of this investigation. Four distinct kinds of datasets of epigenomics data incorporate CpG methylation data, histone modification data, human genome data, and RNA-Seq data are utilized for information examination. The datasets were downloaded from the RTCGA library, and a few preprocessing steps were performed by R and necktie programming . By usinData processing is performed by developing custom R Script whereas classification, as well as feature selection, is performed using data mining software—Weka .We used the R and open-source library RTCGA to download all the necessary datasets from The Cancer Genome Atlas (TCGA) Data Portal. TCGA data portal provides a platform for academic researchers and scientists to search, download, and analyze datasets generated by TCGA. It contains clinical information, genomic characterization data, and high-level sequence analysis of the tumor genomes from DNA methylation, RNA, and mRNA sequences to histone modification data with epigenomics, human genome data, and clinical information of the patients for the integration of all the data. The key is to understand genomics to improve cancer care. RTCGA is an open-source R package which is available and provided through Bioconductor. It also works as an interface for the integrative analysis of RNA-seq or microarray-based gene transcription and histone modification data obtained by ChIP-seq. The package provides methods for data preprocessing. Other packages were also used, such as TCGAbiolonks and SummarizedExperiments, ShortRead, Rsubread, BSgenome.Hsapiens. UCSC.hg19, EnsDb.Hsapiens.v75, and DESeq2  Morgan  Morgan  Liao et Team, 2 Rainer, Love et. The RTCFrom TCGA, methylation data from Illumina’s Infinium HumanMethylation450 Bead Chip (Illumina 450 k) were obtained. The addition of a methyl group to the fifth position of genomic cytosine forms 5-methylcytosine (5 mC), often called the fifth base and is a widely studied epigenetic mark. In mammals, 5 mC predominantly occurs in the CpG context. Still, in other organisms, it can occur in CHG and CHH contexts where H is an A, C, or T. Also, 5 mC is prevalent throughout various tissues with 60%–80% of CpGs being methylated. CPG’s genomic instructions and coding of exons were acquired from the code provided by http:/genome.ucsc.edu. Data from the large were downloaded from Raw ChIP-seq. The data were processed using R efficiently to ensure the accuracy of all system standardization using a custom R script. After the arrangement of crude histone marker information, the adjusted histone marker peruses were converged with the portions of every record utilizing the multicov work from the BEDTools bundle . The data were chosen from the UCSC genome browser, which is a joint effort of the ENCODE project through the UCSC Chrome genome to s bundle . The hisWe extracted the nucleotide composition data from the hg19 genome through Bioconductor’s BSgenome.Hsapiens.UCSC.hg19 open-source library , which wp-value < 0.05, FDR cut off of 0.01 after having an absolute value of log2 fold change or logFC <0.5. As a result, 6,794 genes were selected as “differentially expressed” genes.RNA-Seq gene expression data from liver cancer samples with coupled CpG methylation data were already downloaded from TCGA Research Network using RTCGA for hepatocellular carcinoma. The data are selected from TCGA, which is produced by clinicians. Differential expression analysis was performed with the DESeq2 package in R . The exp(a) Filter methods involve the methods that perform feature selection before building the classifier and do not incorporate learning.(b) Wrapper methods incorporate machine learning in measuring the quality of the subsets of features without incorporating knowledge about the particular structure of the classification or regression function.(c) Embedded methods are different from filter and wrapper methods in that the learning part and the feature selection part cannot be separated in the embedded methods.In the last decade, feature selection has received a tremendous amount of attention from machine learning researchers. Feature selection or feature engineering aims at finding the best subset of features from the total number of features that can represent the input data efficiently and can still provide good prediction results for machine learning modelling. Feature selection uses a search algorithm to find one or more informative subsets of features that indicate to predefined criteria. Feature selection should be able to extract the important or relevant features . If a problem has N features, the number of all subsets of features is equal to 2 N. Therefore, the optimal set of features is one (or could be more) of an exponential number of possible subsets and comparing all of these subsets will locate the best intractable for N > 20 . FeatureDifferential articulation of the methylated CpG locales was prepared utilizing the limma library in R . In partExtraordinary exertion was paid to process separations of CpG tests to exon-intersections. Given that at least one CpG destination may exist on the individual exon fragments of a record (counting the first and last exons), transcript-level most extreme, least and normal separations of any hyper/hypo-methylated test to the closest 5′ or 3′ exon-exon intersection were processed .The histone peruses were then standardized per 1,000 bp length of each fragment per 1 million adjusted read library. Similar to the CpG methylation, the histone marker adjustment highlights were extricated on a fragment by-section basic. The numbers addressing particular histone H3 methylation markers are four for H3k4me3, 27 for H3k27me3, and 36 for H3k36me3. Accordingly, highlights are named as a segment cell type and histone change type . To analyze histone adjustment between the disease and non-malignant growth cell types, the distinctions of the peruses between them were isolated by the normal of the two .In the last decade, feature selection has received a tremendous amount of attention from machine learning researchers. Feature selection or feature engineering aims at finding the best subset of features from the total number of features that can represent the input data efficiently and can still provide good prediction results for machine learning modeling. Feature selection uses a search algorithm to find one or more informative subsets of features that indicate to predefined criteria. One significant issue while applying a massive epigenetic dataset onto the unmistakable classifier is excess features.A couple of sorts of examinations exhibited that the use of a component choice procedure can somehow improve the precision and besides decline the excess. In this research, feature selection methods were applied to reduce the features and include the following: PCA , CFS (correlation-based feature selection), chi-squared, ReliefF, MI score , RFE (recursive feature elimination), EMLR (embedded method with logistic regression), EMRF (embedded method with random forest), and SelectK.Chi-squared χ2 is a filter method that evaluates features individually by measuring their chi-squared statistic with respect to the classes. In statistics, the χ2 test is applied to test the independence of two events, where two events A and B are defined to be independent if (AB) = (A) (B) or, equivalently, P (A|B) = P(A) and P(B|A) = P(B). In feature selection, the two events are the occurrence of the term and circumstance of the class. We then rank terms with respect to the following quantity:ncorrect .Mutual information (MI) is another filter-based method that measures the amount of information that one random variable has about another variable. MI between two random variables is a non-negative value, which measures the dependency between the variables. It is equal to zero if and only if two random variables are independent, and higher values mean a strong dependency/association. Mutual information “is not concerned” with whether the univariate association is linear or not.Recursive feature elimination (RFE), a wrapper method, is used to select features by recursively considering smaller and smaller sets of features based on an external estimator that assigns weights to features . First, the estimator is trained on the initial set of features, and the importance of each feature is obtained either through a coefficient attribute or through feature importance attribute. Then, the least important features are pruned from the current set of features. That procedure is recursively repeated on the pruned set until the desired number of features to select is eventually reached .Algorithms such as logistics regression-based SelectKBest features or decision trees and random forest tree-based algorithms can also be used to extract the features from high dimensional features. The methods are usually based on the following approaches. Permutation-based variable importance: this algorithm consists of permuting the variables at test time and looking at the accuracy loss. This technique is part of the wrapper algorithms. It can thus be used with any learning algorithm.Dimensionality reduction is the process of reducing the number of features used in machine learning algorithms. This can be used to increase the accuracy and the performance of machine learning algorithms. One form is to perform data compression. For example, transform 3D data into 2D data and eliminate a feature or dimension. It can also be used to reduce dimensions to be able to visualize data efficiently. Dimensionality reduction can be used to speed up the time it takes for other learning algorithms to learn. By using dimensionality reduction, the number of features or the amount of training samples is reduced, which reduces the running time of the training, but the compressed data still retain the same information as the uncompressed data. The algorithm is formulated as a minimalization problem. When given N-dimensional data and N-1 dimensional data are preferred, the algorithm tries to find the correct N-1 dimensional value so that the projection is the closest to the original data. Before this algorithm is run, the features of the data should be scaled so that all features are on a similar scale. This can be carried out by using mean normalization. In order to reduce the dimension from n to k, the covariance matrix should be computed. From this matrix, the eigenvectors need to be computed using singular value decomposition. From these values, only the first k values are going to be used and be multiplied with the training data.After applying feature selection for dimensionality reduction, classification algorithms are applied to quantify and look at the qualification between different feature selections. Any grouping methodology uses a ton of parameters to portray each object. These highlights are noteworthy to the information being inspected. Classification is a supervised learning technique where labels are present on the information and the classification model predicts the gene expression of the dataset. In this research, eight classifiers were applied and their performance was evaluated, XGBoost (XG), random forest (RF), naïve Bayes (NB), K-nearest neighbor (KNN), multilayer perceptron (MLP), Decision tree (DT), and radial and linear support vector machines (SVMs).XGBoost is a choice tree-based gathering machine learning calculation that utilizes a slope boosting system. In expectation issues including unstructured information , artificial neural organizations will, in general, beat any remaining calculations or systems. In any case, with regards to little-to-medium organized/plain information, decision tree-based calculations are viewed as top tier at this moment.Random forests are utilized for grouping and relapse by developing decision trees during preparations. It in this way settles on the choice tree dependent on the subsets that are chosen randomly from the dataset.Naïve Bayes classifiers are the probabilistic classifiers that work on Bayes’ theorem principle. The algorithm has independent assumptions among the features. On an abstract level, given a vector representing some features, the naive Bayes will assign a probability to all possible outcomes  given this vector. It is a conditional probability model. Based on Bayes’ theorem, a reliable and computable model can be constructed for all the possibilities that need to be generated. From the naive Bayes probability model, we can then construct a classifier. The naive Bayes classifier usually combines the probability model with a decision rule. This rule will define which hypothesis is to be picked. A common rule is simply picking the one with the highest probability, which is also called the “maximum posterior” (MAP) decision rule  Rainer,.It is a non-parametric model of classification, which can, likewise, be utilized for regression. It stores all the potential cases and uses them to order the new cases dependent on the comparability record. The K-nearest neighbors or the KNN algorithm is a supervised learning algorithm that computes the classification by looking at the classes of the training data’s K-nearest neighbors. When training the algorithm, the input data and classes are stored. There are multiple methods to compute the distance between data. Euclidean distance can be used for continuous data. For discrete variables, another metric can be used, such as the Hamming distance. There are many different methods for computing the distance. A significant drawback of the KNN algorithm is the weakness of skewed data. Skewed data are data that belong to a class that is underrepresented in the complete dataset. Since the class is chosen based on the most popular nearest class, these popular classes may dominate the prediction. A straightforward solution is to gather more data. The problem can be overcome more realistically by taking the distance between the input data and the neighbors into account. If this does not work, it is possible to use learning algorithms to select the best data from the dataset so that no class is underrepresented. These methods are far more advanced and have a higher computational cost.A multilayer perceptron (MLP) is a feedforward artificial neural organization that creates a bunch of yields from a bunch of data sources. An MLP is described by a few layers of information hubs associated with a coordinated diagram between the information and yield layers. MLP utilizes backpropagation for preparing the organization. MLP is a deep learning method. Artificial neural networks work on a simplified model of how human brains work. They generate layers of nodes, each having certain input and output values. An activation function triggers the nodes . This function can take many shapes and be triggered by a combination or a series of inputs of a neuron. Once the activation function has been triggered, the neuron will send its output signal throughout the outgoing channels.A decision tree is a simple representation for classifying examples. It is supervised machine learning where the information is continuously part as per a specific parameter. A decision tree consists of nodes: test for the estimation of a specific trait. Edges/branch: correspond to the result of a test and interface with the following hub or leaf. Leaf hubs: terminal hubs that foresee the result (speak to class names or class circulation).Linear SVM is a quick data mining calculation for managing multiclass classification issues out of an incredibly enormous arrangement of data. The arrangement of numerical capacities called kernel is applied to achieve the cycle. Normally support vector machines, as well as general logistic regression work with linear classifiers. A classifier can be linear or non-linear. A linear classifier has a decision boundary, which is a linear function. This can be seen by visualizing that there is a straight line drawn through the data. On one side of this line, the data belong to one class and the data on the other side of this line belongs to another class .Cross-validation is a statistical method of evaluating and comparing learning algorithms by repeatedly partitioning the given dataset into two disjoint subsets: the training and the test subsets. The training subset is used to build the classifier model, and then the samples belonging to the test subset are used to test the trained model. The process is repeated with several partitions and gives an estimate of the classification performance. The most common form of cross-validation is k-fold cross-validation.K-fold cross-validation: the k-fold cross-validation partitions the given dataset into k equally sized subsets. Then, training is performed on k−1 subsets and testing is performed on the remaining subset. This process is repeated k times (folds) with each subset taken as a test set in turn .Leave-one-out cross-validation: in this method, we use k-fold cross-validation, where k is equal to the number of samples in the dataset. In each “fold,” n−1 samples are used as a training set, and a single sample is used for testing. This procedure is repeated for all samples. This method is computationally expensive as it requires the construction of n different classifiers. However, it is more suitable for smaller datasets .The training and testing of the models were conducted on various assumptions. There were total nine methods of the feature selection. The models were trained on 5-fold cross-validation as there are lesser observations in the data, the 5-fold cross-validation will allow the models to train on all data as well as testing on all data utilizing the all datasets and providing us the average results for each fold. The models were trained with 5-fold cross-validation to ensure overfitting does not happen. The evaluation metrics used to evaluate the performance of the classifiers include accuracy, sensitivity, specificity, and area under receiver operating characteristics (AUC). We considered the accuracy score, confusion matrix, sensitivity score, and specificity score as the evaluation metrics for all the machine learning models. The percentage ratio for feature selection is raw, 95%, 90%, 85%, 80%, 75%, 70%, 65%, 60%, 55%, and 50%. Furthermore, the results are concluded using eight different classifiers. Also, 5-Fold cross validations are performed for different training and testing ratios . The epigenomics data were compiled using four sources, namely, methylation data, histone H3 marker ChIP-seq data, human genome data, and RNA-Seq gene expression data. In total, 1,000 features were calculated. Nine feature selection methods from the three categories of feature selection algorithms  were applied.As it can be seen from As per aforementioned As per aforementioned Overviewing the best number of features with feature selection and classifiers, which were obtained with AUC and accuracy.The next step with model building and evaluation was to use grid-search-based hyper-parameter tuning for finding the best combination of parameters to find the best model. For this approach, a custom R script was written. The training was conducted using 80% data and 20% data were used for testing of the model. SMOTE oversampling was also used in this approach, which was also used with the model comparison strategy. SMOTE balances the data and the imbalanced classes. The best hyper-parameter corresponds to the best model of same kind with a number of different values supplied for the respective model.• KNN: (K = 9)• XGBoost: n = 10)• MLP: • Random forest: • Decision tree: (max depth = 1)• SVM linear: (L2 and C = 1)• SVM radial: (sigma = 1)The following best parameters were found for different models for the CFS method.• KNN: (K = 7)• XGBoost: • MLP: (n = 7)• Naive Bayes: • Random forest: (m_try = 200)• Decision tree: (max depth = 1/2)• SVM linear: (L and C = 200)• SVM radial: (sigma = 1/2/3/4)The following best parameters were found for different models for the chi-squared method.• KNN: (K = 9)• XGBoost: n = 1)• MLP: • Random forest: (m_try = 200)• Decision tree: (max depth = 1)• SVM linear: (L2 and C = 2)• SVM radial: (sigma = 0.5)The following best parameters were found for different models for the CFS method.• KNN: (K = 5)• XGBoost: • MLP: (n = 10)• Naive Bayes: • Random forest: (m_try = 40)•Decision tree: (max depth = 3)• SVM linear: (L1 and C = 0)• SVM radial: (sigma = 0.5/1)The following best parameters were found for the different models for the PCA method.• KNN: (K = 4)• XGBoost: • MLP: (n = 7)• Naive Bayes: • Random forest: (m_try = 200)• Decision tree: (max depth = 1/2)• SVM linear: (L1 and C = 1)• SVM radial: (sigma = 1)The following best parameters were found for different models for the Relief method.• KNN: (K = 5)• XGBoost: n = 7)• MLP: • Random forest: (m_try = 0)• Decision tree: (max depth = 1/2)• SVM linear: (L1 and C = 2)• SVM radial: (sigma = 0)The following best parameters were found for different models for the RFE method.• KNN: (K = 5)• XGBoost: • MLP: (n = 7)• Naive Bayes: • Random forest: (m_try = 100)• Decision tree: (max depth = 1/2)• SVM linear: (L1 and C = 4)• SVM radial: (sigma = 0)The following best parameters were found for different models for the EMLR method.• KNN: (K = 5)• XGBoost: • MLP: (n = 1)• Naive Bayes: • Random forest: (m_try = 0)• Decision tree: (max depth = 1/2)• SVM linear: (L1 and C = 2)• SVM radial: (sigma = 1)The following best parameters were found for different models for the EMRF method.• KNN: (K = 8)• XGBoost: n = 8)• MLP: • Random forest: (m_try = 200)• Decision tree: • SVM linear: (L2 and C = 1)• SVM radial: (sigma = 4)The following best parameters were found for different models for the SelectK method.p-value < 0.05 ), a conclusion can be made out of the assumptions that the observed effect reflects the characteristics of the entire population, on the other hand, if the p-value is greater than 0.05, the observed effect does not reflects the characteristics of the entire population.Machine learning models are statistical and probabilistic which work on the basis of calculations and statistical methods and identifying patterns in the data to make predictions. There are chances that observations which consist of drawing samples from a population indicates an effect which can occur because of sampling errors. If the observed effect indicates a p < 0.05) is found to exist. One-way ANOVA is an omnibus test and needs a post hoc study to identify the specific ensemble methods demonstrating these statistically significant performance differences. In this study, a Tukey post hoc test  normal distribution, 2) homogenous variance, 3) absence of significant outliers, and 4) independence of observations . Shapirohoc test  was alsoThe normality check was conducted on the AUC scores represented in the The results below show the output of the Bartlett’s test.Bartlett test of homogeneity of variances.Data: AUC by model.p-value < 2.2e-16.Bartlett’s K-squared = 207.82, df = 7, p-value is less than the significance level of 0.05. This means we can reject the null hypothesis that the variance is the same for all models. This means that there is good evidence to suggest that the variance in AUC is different for the different models. The following results show the output for the Shapiro’s test.From the output we can see that the Shapiro–Wilk normality test.Data: df$AUC.p-value = 8.91e-15.W = 0.46391, p-value which is less than 0.05 implying that the distribution of the data is quite different from normal distribution. We also performed the Levene’s test. In this test again, the null hypothesis H0 is that all variances are equal.From the output, the The following output shows the one-way ANOVA for models and feature selection methods. One-way ANOVA for models and AUC is shown in the results below.Df Sum Sq Mean Sq F value Pr (>F).Model 7 0.9147 0.13067 29.56 <2e-16 ***.Residuals 64 0.2829 0.00442.---Signif. codes: 0 “***” 0.001 “**” 0.01 “*” 0.05 “.” 0.1 “ ” 1p-value is lower than the usual threshold of 0.05. So, we are confident to say that there is a statistical difference between the models. One-way ANOVA for the feature selection method and AUC is shown in the results below.The Df Sum Sq Mean Sq F value Pr (>F)Feature.Selection.Method 8 0.0348 0.004344 0.235 0.983.Residuals 63 1.1628 0.018457.p-value is greater than the usual threshold of 0.05. So, we are confident to say that there are no statistical differences between the feature selection methods.The In this research, a methodology was proposed to predict liver cancer using epigenomics data, feature extraction methods, and machine learning methods. Using the Illumina Infinium HumanMethylation450 K Bead chip CpG methylation range, this technique utilizes information from combined pulmonary disease and neighboring ordinary organs in the Cancer Genome Atlas (TCGA) and histone alteration marker ChIP-seq from the ENCODE initiative. It sees a comprehensive list of characteristics covering the four classes of CpG methylation, histone H3 methylation alteration, human genome, and RNA-Seq data. Different techniques and choices of features selection methods  and eight different classifiers  were implemented, and classification was contrasted in the training-to-testing ratios and cross-validation methods to select the best model. The results show that a selection of 140 features gave the best model. XGBoost with ReliefF feature selection methods performed as the best model with 100% of AUC and 99.67% of accuracy toward the correct prediction of liver cancer.As a future work, other cancer cell diagnoses such as breast cancer, bone cancer, blood cancer, and oral cancer can be used for predicting the gene expression. The accuracy level and methodology of this research and analysis results can help to work more on such datasets. More profound choices can be made on merging the datasets, using all histone modifications, and collecting and building capacity to harness more data for more patients and develop a predictive model that is highly accurate as this model and can be used in real-time predictions. A predictive model can be built with multiple types of epigenomics data obtained from the same samples in the ideal setting.


Molecular subtyping of triple-negative breast cancer (TNBC) is essential for understanding the mechanisms and discovering actionable targets of this highly heterogeneous type of breast cancer. We previously performed a large single-center and multiomics study consisting of genomics, transcriptomics, and clinical information from 465 patients with primary TNBC. To facilitate reusing this unique dataset, we provided a detailed description of the dataset with special attention to data quality in this study. The multiomics data were generally of high quality, but a few sequencing data had quality issues and should be noted in subsequent data reuse. Furthermore, we reconduct data analyses with updated pipelines and the updated version of the human reference genome from hg19 to hg38. The updated profiles were in good concordance with those previously published in terms of gene quantification, variant calling, and copy number alteration. Additionally, we developed a user-friendly web-based database for convenient access and interactive exploration of the dataset. Our work will facilitate reusing the dataset, maximize the values of data and further accelerate cancer research.  This feature leads to the ineffectiveness of the main targeted therapeutic treatments for TNBC patients. Meanwhile, the high heterogeneity of TNBC makes it possible for each subtype to find its specific etiology and optimal treatment, suggesting the requirement of precise patient subtyping5. Therefore, molecular profiling-based subtyping is an important strategy for the study of TNBC to achieve precise drug administration9.Triple-negative breast cancer (TNBC) is the most aggressive form of breast cancer and is characterized by the lack of expression of estrogen receptor (ER) and progesterone receptor (PR), and the lack of amplification of the human epidermal growth receptor 2 (HER2)10. A unique dataset was established including gene quantification, variant calling, and copy number alteration (CNA) data of 465 primary Chinese TNBCs. This research helped discover actionable targets for different subtypes and explore connections of multiomics features with clinical information, especially in East Asian TNBC patients12. Since its initial publication in 2019, this dataset has been utilized extensively by the scientific community, deepening the understanding of the heterogeneity about the immune microenvironment in TNBC, facilitating the discovery of multiple molecular biomarkers, and the development of prognostic models for TNBC patients27.To provide a broader molecular profile of TNBC, we performed a large single-center multiomics study of TNBC at the Fudan University Shanghai Cancer Center (FUSCC)However, most of the follow-up studies utilized only a part of the rich dataset, suggesting that the full potential of the dataset has not been realized, presumably due to a lack of detailed description and user-friendly toolsets to access the datasets. Specifically, the dataset has not been sufficiently described and details of the analysis pipelines were not fully available in our previous article and other follow-up studies. Moreover, there have been updates of mainstream databases (e.g. the adoption of the human reference genome hg38 over hg19) and software packages previously used. Importantly, the reproducibility of results with the updated pipelines has not been evaluated. Therefore, a detailed description of the dataset with special attention to data quality as well as reanalyzing this dataset using the updated pipelines are urgently needed for facilitating reusing this TNBC dataset.http://fudan-pgx.3steps.cn/cdataportal/study/summary?id=FUSCC_BRCA_2022) to help with data exchange, interpretation, and reuse. The updated dataset will allow researchers to better conduct TNBC studies, thereby facilitating the development of precision medicine in breast cancer. A visual summary of the study design and workflow is shown in Fig. In this Data Descriptor, we performed various rigorous quality control (QC) to ensure the data quality from RNA sequencing (RNAseq), whole-exome sequencing (WES), and OncoScan copy number variations (CNV) assay. Additionally, we updated the dataset with a detailed description of the processing steps. By comparing the updated and previously published results, we found good concordance of gene quantification, variant calling, and CNA, demonstrating the excellent reproducibility of the analysis results and pipelines. Eventually, we stored the updated profiling files in the data portal . RNA library preparation was performed from 0.3–1 µg of total RNA as described in the Illumina TruSeq Stranded Total RNA LT sample preparation kit with Ribo-Zero Gold . All processes involved were implemented according to the manufacturers’ manuals. After using an Agilent 2100 Bioanalyzer (Agilent Technologies) with the DNA chip to test quality and using a Qubit® 3.0 fluorometer  to conduct further quantification, the libraries were sequenced on the Illumina HiSeq platform , and 150 bp paired-end reads were generated as FASTQ files. For each library preparation from tissue, 12 samples were loaded in a single lane. Additional batch information was also collected for the subsequent elimination of batch effects.28 to remove adapter sequences. In the process, fastp v0.19.628 was used to check the quality of reads before trimming and FastQC v0.11.5 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) after trimming. Then, we used FastQ Screen v0.12.029 to extract the first 10,000 reads from the clean FASTQ files to detect whether the raw data were contaminated with other species, junction primers, etc. Qualimap v2.0.030 was used to calculate the quality of the mapping process. We randomly selected 10% bam files for quality testing for efficiency and cost-effectiveness purposes.Preliminary processing of raw FASTQ reads was performed using fastp v0.19.631, SAMtools v1.3.132, StringTie v1.3.433 and Ballgown v2.14.14934. Genome Reference Consortium human genome build 38 (Genome version: GRCh38_snp_tran) and gene models from Ensembl (version: Homo_sapiens.GRCh38.93.gtf) were used for read mapping and gene quantification. Also, to remove batch effects and other unwanted variations in the dataset, we applied the RUVg method of the RUVSeq package35 to preprocess raw read count data. The RUVg read counts were used for sex check and differential expression analyses. Moreover, transcripts per kilobase of exon model per million mapped reads (TPM) were used to normalize the gene expression. To choose genes with accurate expression values, we removed genes with TPM values equivalent to zero in more than 30% of samples and applied the log2 ratio to TPM values prior to subsequent analyses. To avoid infinite values, a value of 0.01 was added to the TPM value of each gene before the log2 ratio. TPM values were used to perform principal component analysis (PCA).We conducted read alignment and quantification using HISAT v2.1https://github.com/fudan-tnbc/rnaseq-qc-directional).Detailed parameters of data processing and quality control pipelines of the RNAseq can be obtained on GitHub  analyses were conducted between female and male samples in each of 22 tissue types that were not sex-specific tissues. A gene was identified as sex-specific when satisfying the criteria of Student’s t-test p < 0.05 and fold change ≥2 or ≤0.5. Sex-specific genes were then identified and used for subsequent sex check, including five male-specific genes  and two female-specific genes (XIST and TSIX).Sex-specific genes were identified using transcriptomic expression profiles from GTEx v7.0https://www.r-project.org, R development core team). Hierarchical clustering analysis (HCA) was performed using Ward linkage based on a distance matrix using the Euclidean method to measure the distance of the samples and genes, using R package pheatmap v1.0.12.PCA was conducted with the univariance scaling, using the prcomp function of R v4.1.2 . The purity and quantity of total DNA were estimated by measuring the absorbance at 260 nm (A260) and 280 nm (A280) using a NanoDrop 2000 spectrophotometer . The extracted DNA was considered to be of good quality and suitable for subsequent experiments if the A260/A280 ratio was within the range 1.6–1.9.® 3.0 fluorometer , the libraries were sequenced on an Illumina HiSeq platform , and 150 bp paired-end reads were generated as FASTQ files. For each library preparation from tissue, 12 samples were loaded in a single lane. And, for each library preparation from blood, 20 samples were loaded in a single lane. Finally, we obtained a WES dataset of 279 matched tumor/normal pairs of samples. The batch information was also collected to differentiate the QC metrics of samples.Qualified genomic DNA from tumor tissues and matched white blood cell  samples were prepared for WES. A total of 300 ng of each DNA sample fragmented on a Bioruptor Plus sonication system was used to perform end repair, A-tailing, and adapter aligation with an Agilent SureSelectXT Library Prep Kit  according to the manufacturer’s protocol. An approximate 750 ng of prepared DNA in a volume of 3.4 μL was then captured using Agilent SureSelect Human All Exon V6 (Agilent Technologies) probes, followed by the amplification of the captured library with indexing primers. Quality control was performed using the Agilent 2100 Bioanalyzer (Agilent Technologies) with a DNA chip. After being quantified with a Qubit37 was used to identify whether the raw FASTQ files were from the same individual. To ensure the accuracy of the WES data matching in the TNBC cohort, we performed a pairwise analysis of all WES data using a modified version of NGSCheckMate v1.0.0 software37. We first calculated the variant allele frequency (VAF) of approximately 10,000 inherent SNPs in each input FASTQ file to obtain a VAF file. Then, the correlation scores between all VAF files were calculated. The presence of mislabeling was determined based on whether the WES data associated with the same patient ID had significantly higher correlations than the other pairs.NGSCheckMate29, FastQC and Qualimap30. After the QC process, the WES reads were aligned using BWA-mem38 and the BAM files were generated using Samtools32. Next, the BAM files were further preprocessed with the Sentieon Genomics tools version 202010.0239, which sequentially included (1) duplicates marking; (2) calculating data quality metrics; (3) conducting base quality score recalibration (BQSR); and (4) performing variant calling with TNseq and TNscope.The quality of WES data was assessed using FastQ Screen40, TNseq39, and TNscope39. Specifically, for the raw VarScan2 results, processSomatic and somaticFilter were used to extract high-confidence somatic mutations and to remove clusters of false positives and single nucleotide variants (SNV) calls near indels. For the other two callers, a filtering procedure based on a panel of normal (PoN) samples was used to screen out expected germline variation and artifacts. This PoN panel was based on 279 normal samples, from which each VCF file was created corresponding to the sites identified as mutations by TNseq and TNscope, respectively. In addition, the location of the population germline resource containing the population allele frequencies obtained from gnomAD41 was used to filter the raw TNseq results.Three callers were used to identify the somatic mutations, including VarScan210. First, we removed any spurious variant calls arising as a consequence of sequencing artifacts and then made use of consensus mutations in at least two out of three callers to identify somatic mutations . Secondly, additional filtering based on Bam-readcount42 was performed to reduce false positive calls: (1) variant allele frequency (VAF) ≥ 8%; (2) sequencing depth in the region ≥ 8; (3) sequence reads in support of the variant call ≥ 2. Only variants with the following functional classification were considered in this study, i.e, missense mutation, nonsense mutation, nonstop mutation, RNA mutation, silent mutation, variants at splice site or translation start site, insertion and deletion .To obtain the final set of mutation calls, we also used a two-step approach as in the previous publicationhttps://github.com/fudan-tnbc/. The different analysis phases are encapsulated in separate repositories, including ngscheckmate_fastq and vaf_ncm for paired assays, cbcga-wes-qc and bam-readcount for quality control, and variant-calling for the detection of somatic mutations. The detailed parameters can also be accessed.The above process has been packaged into integrated pipelines which can be accessed at Genome-wide copy number analysis was performed using an OncoScan CNV Assay Kit  according to the manufacturer’s recommendations. The arrays were washed and stained using a GeneChip Fluidics Station 450  and were scanned using a GeneChip Scanner 3000 7 G . The fluorescence of clusters was measured to generate a DAT file. Cluster intensity values were automatically calculated using a built-in algorithm from DAT files using the GeneChip Command Console software , and a CEL file was generated. Apart from the 401 tumor samples, we also added 23 randomly selected white blood cell samples from these patients as a reference cohort of DNA to assess the level of recurrent germline/potential false-positive calls.43. ndSNPQC measures how well genotype alleles are resolved in the microarray data but are only applicable to normal diploid markers. Higher ndSNPQC values indicate better identification of each genotype.Chromosomal Analysis Suite (ChAS) v4.3 software (Affymetrix) was used to perform quality control of Affymetrix OncoScan CNV SNP probe assays. Common QC metrics for OncoScan CNV data were collected, including the Median of the Absolute values of all Pairwise Differences (MAPD), Normal Diploid Waviness Standard Deviation (ndWavinessSD), and SNP Quality Control of Normal Diploid Markers (ndSNPQC). MAPD and ndWavinessSD represent short-range and long-range noise levels, respectively, and these values are negatively related to the quality of CNA estimationMoreover, we used R software to pick regions altered in 12 or more of the 23 WBC samples. These regions were doubled in range and were defined as recurrent germline/potential false-positive calls for subsequent removal. The probe-level output from the ChAS v4.3 software was filtered by the log2 ratio of segment-level output. The filtered probe-level file was then analyzed using ASCAT v2.4.3 to obtain segmented copy number calls. These segments from ASCAT v2.4.3 that overlap with previously described recurrent germline/potential false-positive calls were removed.44 that was run with the same parameter settings as the previous publication10 .The filtered segments were subsequently used to produce log2 ratios by dividing the total copy number . These segments were used as the input of GISTIC2.0 v2.0.2344 for further analysis. The former file provided CNA information on chromosome peaks and the latter provides the CNA profile of genes. We regarded the old and new different peaks as the same when the following conditions were met (1) both peaks are identical (2) both peaks contain the most identical genes (3) the band range of different peaks fluctuates within 1. Then, we used the Jaccard Index, which represented the number of observations in both sets divided by the number in either set (intersection set divided by union set), to measure the consistency of gene CNA profiles across different pipelines.The files “all_lesions.conf_95.txt” and “all_thresholded.by_genes.txt” were collected from the results of GISTIC2.010. For determining the impact of the new analysis on the molecular subtyping results, we selected the unfiltered FPKM expression profiles and the SD top 2000 genes (only 1889 genes were consistent with the old pipeline because of the shifted genome version) used in the old analysis to subtype classification.The expression profiles of 2,000 protein-coding genes which had the highest SDs (Standard Deviation) in tumor samples from the old expression matrix (Fragments Per Kilobase of exon model per Million mapped fragments (FPKM)) were selected to perform k-means clustering with maximum 1,000 iterations  in the old analysis. Four subgroups corresponding to the four subtypes of FUSCC TNBC could be obtained, including a luminal androgen receptor (LAR) subtype, an immunomodulatory (IM) subtype, a basal-like and immune-suppressed (BLIS) subtype, and a mesenchymal-like (MES) subtype. The details could be seen in the previous publicationhttp://www.biosino.org/node) by pasting the accession (OEP000155)45. OncoScan CNV data and sequence data have also been deposited in the NCBI Gene Expression Omnibus 46 and Sequence Read Archive 47. The updated profiling files can also be seen at Figshare (10.6084/m9.figshare.19783498.v5)48 in parallel to the Data Portal .All the omics data and attached metadata can be viewed in the National Omics Data Encyclopedia ; (5) the average length of insert size was approximately 200 bp  the49 Fig. ; (5) the bp Fig. ; (6) the bp Fig. .Fig. 2QuRPS4Y1, DDX3Y, EIF1AY, KDM5D, TXLNGY) and two female-specific genes (XIST and TSIX). All samples passed the sex check because they were all from female patients with high expression of female-specific genes and little expression of male-specific genes was observed in our results  clustering based on tumor and paired normal tissue classification. For sex check, we used seven sex-specific genes, including five male-specific genes  of patients were confirmed to have matched tumor-normal samples. Two tumor samples  did not match the labeled paired samples, and they were not matched with any samples in the cohort. In addition, one tumor sample (FUSCCTNBC030) failed to match the labeled paired sample and matched well with another tumor sample (FUSCCTNBC032) in the cohort. Overall, sample ID mismatch problems occurred in the WES data of three samples, which were labeled as FUSCCTNBC044_WBC, FUSCCTNBC140_WBC, and FUSCCTNBC030_TT. We recommend that researchers remove them in subsequent studies. Detailed information on sample mismatch can be found in Supplementary Table We then ensured the reliability of the WES data by calculating the key quality metrics in both the raw data and the mapped data Fig. . The ove) and generated a series of QC metrics  and new  pipelines on the results, we compared the profiling results generated about five years apart by two versions of the pipelines of the FUSCC TNBC dataset.Many changes have occurred in the data analysis pipelines after the publication of the FUSCC TNBC articlep) by comparing tumor samples with normal samples in the RNAseq expression profiles (read counts) of the old and new analyses through limma software50. The results showed a great consistency rate  of these differentially expressed genes  in the two analyses  and p-value , and the final set of mutation calls . For each mutation set, we calculated the concordance of mutated genes annotated in the old and new results for each patient separately and represented the consistency rate using the Jaccard Index. The analysis showed that Final Mutation exhibited the highest consistency, followed by 1st Filter Mutation. For individual variant callers, the concordance of TNseq, TNscope, and VarScan gradually decreased, with the Jaccard Index even below 0.25 for VarScan. In this case, the performance of the two filtered mutation sets was significantly affected by VarScan  at the peak level51 Fig. . The red51 Fig. . Moreove51 Fig. .http://fudan-pgx.3steps.cn/cdataportal/study/summary?id=FUSCC_BRCA_2022) for easy reuse. Meanwhile, online visualization exploration is made available in this portal, and overall statistical results of multi-omics data with clinical information are provided in the form of interactive charts. In addition, users can also query specific samples or genes according to their interests, and the results displayed in the portal will timely change.The updated profiling files can be downloaded at Data Portal (Table S1.Table S2.Table S3.Table S4.Table S5.Table S6.


Model systems are an essential resource in cancer research. They simulate effects that we can infer into humans, but come at a risk of inaccurately representing human biology. This inaccuracy can lead to inconclusive experiments or misleading results, urging the need for an improved process for translating model system findings into human-relevant data. We present a process for applying joint dimension reduction (jDR) to horizontally integrate gene expression data across model systems and human tumor cohorts. We then use this approach to combine human TCGA gene expression data with data from human cancer cell lines and mouse model tumors. By identifying the aspects of genomic variation joint-acting across cohorts, we demonstrate how predictive modeling and clinical biomarkers from model systems can be improved. A joint dimension reduction approach integrates gene expression data from human tumor cohorts, human cancer cell lines, and mouse model tumors to improve prediction of clinical biomarkers from model systems. This presents a pressing need to better identify the aspects of model system genomics that can be confidently translated to humans.In cancer genomics research, model systems such as cell lines and mouse models are used to permit experimentation that is impossible to do in humans. However, a major concern in utilizing these models is the translational barriers that exist when inferring effects from model system to the human in vivo context. These barriers are one of the sources of variation that contribute to ~91% of preclinical drugs failing to reach the market due to low efficacy in humans3. Studies have sought to combine the various data types within a single cohort  to better understand the interactions between the biological systems and robust subtype tumors5. While results from these multi-omic integration studies are promising for investigating more complex genetics, in the context of translating model system effects, the inherent differences between the model and human are not considered.To address these challenges, the application of large, public repositories of high-dimensional multi-omics data has become an essential resource for translational studies. Specific to cancer biology, The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) provide invaluable multi-omics profiling across thousands of tumor samples and human cancer cell lines respectivelyhorizontal integration) have been presented as a means to address these differences. The joint analysis techniques used in these studies come from a variety of statistical and machine learning domains7. From these, dimension reduction-based methods have been shown to be successful in both clustering and capturing relevant correlation structure9. Joint dimension reduction (jDR) approaches decompose input data blocks into lower dimensional spaces that minimize redundant variation and tend to eliminate spurious sampling noise10. Correlations among the low-dimension representations are then determined using a range of statistical techniques to determine shared components of variation. Generally, any non-shared variation is removed from the downstream analysis as noise. However, jDR methods such as Joint and Individual Variation Explained (JIVE)11, Multi-study Factor Analysis12, and Multi-Omic Factor Analysis13, parse non-shared variation from noise, isolating variation that acts in a specific manner to a cohort. This cohort-specific variation can then be leveraged to allow a more informed analysis for identifying the drivers behind genomic effects.More recently, methods to integrate data across cohorts  have shown success in clustering model systems and tumor biopsy pairings. These methods have successfully shown how integration can be used to generate more informed hypotheses from model system datasets. However, only TRANSACT has demonstrated an improvement in drug response prediction, but these predictive models have not been validated on clinical datasets outside of the integration . As a result, the ability for jDR to be used for clinical translation remains uncertain.Specific to translating cell line or mouse variation to human datasets, methods such as CellignerAngle-based Joint and Individual Variation Explained (AJIVE)17, to identify the shared and individual behavior of genes across model systems and humans. We integrate cell line and mouse gene expression with TCGA to demonstrate how jDR can separate joint-acting and cohort-specific variation, and be employed to isolate cell line and mice gene expression signals pertinent to human biology. Clinical trial cohorts, independent of the integration and training, provide evidence supporting how jDR can significantly improve response prediction from cell lines, in addition to improving biomarker discovery from mouse models.Here, we present an approach utilizing the jDR technique OA, OB) through these subspace bases, we can create joint  and individual  representations of the original inputs. These matrices contain only the shared or unique-acting variation, respectively.AJIVE is an extension of JIVE that employs the use of thresholded Singular Value Decomposition (SVD) to create low-dimension approximations of input data matrices Fig. . Then, tOTCGA-BRCA, OCCLE) into the resulting joint  and individual  approximation matrices. We calculated the proportion of joint variation in OCCLE to estimate how much of the variation structure can be captured by OTCGA-BRCA, thus giving us a measure for how well observations can be translated from cell lines to primary tumors.To determine if we could discriminate joint and individual gene expression effects, we conducted a gene-wise integration of RNA-seq data from 1102 human primary breast tumor samples from TCGA (TCGA-BRCA) with RNA-seq from 935 cancer cell lines from CCLE. We evaluated how well the datasets represent one another Fig.  by compaJCCLE = 50.33%) with a smaller portion acting specifically to the cell lines (ICCLE = 12.15%). Grouping cell line measures by tissue reveals that breast-tumor-derived cell lines have, on average, the highest proportion of JCCLE relative to ICCLE, making them the most well-represented population of cell lines by OTCGA-BRCA according to this proportion metric .We found the majority of variation across all CCLE cell lines to be shared with TCGA-BRCA  expecting a stronger joint component. Integrating OTCGA-BRCA with only breast cell lines resulted in more joint variation (JCCLE-BRCA = 64.68%), as well as a significant decrease in cell line individual variation  of mammary cancers21 to compare how well GEMMs represent human tumors against cell lines , but also a significant increase in IGEMM variation (28.82%). This indicates that the GEMMs dataset may be capturing more aspects of variation within TCGA-BRCA. However, it also suggests that there is a large portion of genomic variation uniquely behaving in mice that, without AJIVE integration, could interfere with an analysis.In addition, we integrated TCGA-BRCA with a collection of 290 mouse specimens from multiple genetically engineered mouse models   Fig. . We expeOCCLE-BRCA to ICCLE-BRCA with the majority of variation persisting into JCCLE-BRCA . This indicates most the molecular subtype variation in the cell lines is shared with human tumors. However, in TCGA-BRCA, we found a larger proportion of luminal (25.5%) and basal-like (30.6%) variation remained in ITCGA-BRCA, suggesting there are primary tumor-specific aspects of subtype variation that the cell lines do not fully capture. Genes involved in immunoglobulin and immune signatures had variation predominantly present in ITCGA-BRCA (74.7%) with only 1.1% of immune-related variation present in JTCGA-BRCA, confirming our expectation that immune-related tumor variation would not be captured by the cell lines.We observed a small fraction of the luminal (3.2%) and basal-like (3.3%) subtype gene variation persisted from JTCGA-BRCA: 24.0%, luminal JTCGA-BRCA: 12.2%). This agrees with previous research finding these GEMMs do not well represent luminal breast tumors19.Integrating the TCGA-BRCA data with GEMMs Fig. , reveale24, we set an FDR threshold to determine significantly joint-behaving genes between the integrated datasets . Integrating TCGA-BRCA with GEMMs  and EGFR, correlate to the joint, individual, and original versions of the CCLE-BRCA expression data . However, ERBB2 had an opposite correlation within ICCLE-BRCA (p = 0.003), suggesting potential aspects of cell line-specific variation may mask tumor sample effects. This masking effect can be better seen in our full CCLE analysis .To investigate how AJIVE integration captures known genetic associations of drug response, we evaluated how known gene targets of lapatinib, a tyrosine kinase inhibitor of ata Fig. . We founERBB2-targeting response using the original, joint, and individual approximations of CCLE-BRCA when integrated with TCGA-BRCA.Investigation of the joint and individual effects supports the notion that tumor expression signatures and drug response associations are contained within the joint component. We then hypothesized that AJIVE integration could benefit predictive modeling of cell line drug response data by removing nuisance variation. We developed three elastic net models of 27, and pathological complete response (pCR) was used as the primary outcome. Combining all trial arms together, we predicted ERBB2-targeting response and pCR status for each sample and generated a receiver operating characteristic (ROC) curve  our predictive performance over training over the full, unintegrated CCLE-BRCA dataset ; this neoadjuvant trial of HER2 + breast cancer encompassed three arms treated with a combination of paclitaxel + trastuzumab, paclitaxel + lapatinib, and paclitaxel + lapatinib + trastuzumab. Measurements of mRNA-seq expression were collected from the 305 patientsrve Fig. . By compset Fig. . Examiniset Fig.  revealedset Fig. . By addi16. We found applying the TRANSACT-trained model had no significant change in predictive performance over using the original, unintegrated CCLE-BRCA data  sensitivity in human melanoma patients, and predictive of response to chemotherapy and/or trastuzumab therapy in human breast cancer patientsNCT 00770809)28 and MADRID (NCT 01560663)29 29 Fig. . We foun30, the signature was enriched in responsive samples. We built elastic net models of anti-PD1, anti-CTLA4 therapy response from the integrated and unintegrated versions of the GEMM dataset with TCGA-BRCA. These models were then used to predict ICI response in the human melanoma study , suggesting drug prediction models could be made more clinically relevant by applying jDR integration with an external dataset that represents the target clinical population. To reinforce this notion, we modeled immunotherapy response from breast GEMMs and applied these models to a human melanoma study. Although performance remained the same after jDR integration with TCGA-BRCA, the choice of integrated dataset had a significant effect on the model’s predictive performance and could potentially be improved by selecting datasets that better represent both the model system population and clinical population. In scenarios where representative datasets are not available, our findings using jDR to integrate pan-cancer cell lines from CCLE and all tumor types of TCGA , CCLE (GEO: GSE36139), CALGB 40601 (dbGaP Study Accession: phs001570.v2.p1 and GEO: GSE116335), and (dbGaP Study Accession: phs001427.v2.p1)30 gene expression data were obtained and processed through an identical pipeline aligning to human reference genome hg38 using STAR and quantified with Salmon. Expression values were upper quantile normalized and log-transformed. Hematopoietic and lymphoid tissue cell lines were removed from CCLE. Genes with no expression in any dataset were removed and an intersecting gene list across datasets was used. Genes were mean-centered as required by AJIVE.TCGA-BRCA .All analysis and plotting were done in Jupyter using python 3.6.7 with NumPy v1.18.5, SciPy v1.5.2, pandas v1.1.2, matplotlib v3.3.1, seaborn v0.9, and scikit-learn v0.23.2 packages. AJIVE was run using the py_jive package , JIVE decomposes each matrix into joint (J), individual (I), and residual noise (E) structures:Joint and Individual Variation Explained (JIVE)rk low-rank approximations, i.e., U and V are orthogonal across rk and D is a diagonal matrix. Right singular vectors  and Principal Angle Analysis. An initial SVD step decomposes each matrix into for each rk), we implemented a bootstrap approach on each input dataset to find the optimal ranks at which AJIVE correctly identifies joint variation. Samples within one input data matrix were randomly divided into two equal halves. AJIVE was repeatedly applied on the two halves while increasing the initial rank parameter. Percentage of variation (sum of squares) explained by joint and individual structure were calculated on the output matrices of each rank. Residual variation was defined as any variation not explained by joint or individual structure. The process was repeated (n = 10) with joint and individual percentages being collected and plotted  were horizontally integrated across genes using AJIVEFor integrating TCGA-BRCA with breast cell lines, we repeated the same integration process using the 50 cell lines from CCLE derived from breast tissue (CCLE-BRCA). AJIVE was run with CCLE and TCGA-BRCA setting initial signal ranks of 150 and 275 respectively. For integrating CCLE-BRCA and TCGA-BRCA, initial signal ranks were set to 35 and 135.To quantify how the variation of each sample was divided into joint, individual, and residual components, we multiplied the right singular vectors of the final reprojected SVDs of each data block by the diagonal matrix. This maintains orthonormality such that we can calculate percentage of explained variation by the sum of squares with each matrix from the original. We evaluated how well represented a group of cell lines was in an integration Fig.  by calcug, variance in the joint matrix s, then the statistic is set to zero. This threshold is determined through observing the distribution of summed variances and prevents artifacts due to low-variance genes. For our analysis, s was set to 0.5.We developed a statistic to determine the “joint behavior” of a gene. This is represented by the log-ratio of each gene P values were computed from an expected distribution through random permutations of combining the joint and individual matrices and shuffling joint/individual labels. FDR values were calculated according to the permutation approach described by ref. 24.To evaluate if human-relevant aspects of cell line drug response biology can be captured in joint structure, we calculated Spearman rank correlation coefficients of each gene in the original, joint, and individual matrices to lapatinib response AUC (area under the drug response curve) data for the cell lines with response data. In cases where multiple AUC values were provided for a single cell line, the median was used.34. We trained a linear elastic net model on the cell lines that had lapatinib dose-response AUC data from the original, joint, and individual CCLE (n = 551) and CCLE-BRCA (n = 32) matrices. The following procedure was conducted on each matrix using scikit-learn.ElasticNet parameter definitions:We chose to use Elastic Net due to a long history of success in the drug response prediction literature, demonstrating equivalent success to complex models100 alpha values were calculated at each l1_ratio of 0.1, 0.5, 0.7, 0.9, 0.95, and 1 by computing the elastic net path (sklearn.linear_model.enet_path).We split the data into 20 iterations of training (67%) and test (33%). In CCLE, splits were stratified according to cell line tissue of origin.For each iteration, we fit elastic net models on the training set using an exhaustive grid search over all alpha and l1_ratio combinations.2.We applied each model to its respective test set and calculated an R2 across the 20 iterations.Final model was selected using the best mean RThe final original, joint, and individual elastic net models were applied to CALGB 40601 gene expression data for samples in each arm of the trial. In each arm, a ROC curve was computed using pathological complete response (pCR) as the positive class. Performance was measured by area under the ROC curve (ROC-AUC).16 is a nonlinear implementation of PRECISE15 that utilizes kernel-PCA to derive nonlinear principal components that are then geometrically compared into principal vectors. Vectors that best balance the contributions of the input datasets are called consensus features and are used to project the datasets into a reduced-dimension joint space. To compare with our AJIVE approach, we repeated our experiment integrating CCLE-BRCA and TCGA-BRCA using TRANSACT and applied the trained predictive model on CALGB 40601. Code to run the integration was used from the TRANSACT package: https://github.com/NKI-CCB/TRANSACT. TRANSACT elastic net models were trained on consensus features using the same modeling procedure as detailed above.TRANSACThttp://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt.Mouse and TCGA-BRCA RNA-seq data were analyzed through an identical AJIVE pipeline using initial rank selections of 60 and 135, respectively. Prior to integration, genes were matched by capitalizing gene labels and translating any homologous genes according to the Jackson Laboratory human-mouse homolog map available at 30.Predictive models of anti-PD1/anti-CTLA4 response were trained from the GEMMs dataset using logistic regression with elastic net penalty (sklearn.linear_model.SGDClassifier). Four models were trained using the GEMMs samples integrated with TCGA-BRCA, TCGA-SKCM, TCGA-GBM, and the unintegrated GEMMs dataset . An identical cross-validation procedure was implemented as detailed above. Final selected models from cross-validation were tested on a human melanoma immunotherapy study as described in ref. R2 values were calculated from the linregress function in SciPy.All information on the statistical analyses performed in this work has been included in the related figures, figure legends, results, and methods. All statistical tests used to evaluate significance in addition to error bar definitions are described in the figure legends or methods. Statistical calculations were performed using the related functions in the pandas, SciPy and scikit-learn python packages. Further information on research design is available in the Peer Review FileSupplementary InformationReporting Summary-New


Single-cell RNA sequencing (scRNA-seq) reveals the transcriptome diversity in heterogeneous cell populations as it allows researchers to study gene expression at single-cell resolution. The latest advances in scRNA-seq technology have made it possible to profile tens of thousands of individual cells simultaneously. However, the technology also increases the number of missing values, i. e, dropouts, from technical constraints, such as amplification failure during the reverse transcription step. The resulting sparsity of scRNA-seq count data can be very high, with greater than 90% of data entries being zeros, which becomes an obstacle for clustering cell types. Current imputation methods are not robust in the case of high sparsity. In this study, we develop a Neural Network-based Imputation for scRNA-seq count data, NISC. It uses autoencoder, coupled with a weighted loss function and regularization, to correct the dropouts in scRNA-seq count data. A systematic evaluation shows that NISC is an effective imputation approach for handling sparse scRNA-seq count data, and its performance surpasses existing imputation methods in cell type identification. Single-cell RNA sequencing (scRNA-seq) is designed to profile gene expression at the single-cell level, making it possible to study the heterogeneity among individual cells . Howevervia data diffusion to predict the true gene expression level. SAVER is a Bayesian-based imputation method that imputes dropout values and generates a substitution for each gene. DrImpute is a clustering-based method that generates estimations using cluster priors and distance matrices. ALRA is an adaptively-thresholded low-rank approximation method that rescales the scRNA-seq expression matrix using randomized singular value decomposition. VIPER is a statistical method that fits a linear model for each cell by cell-cell interaction.Many methods have recently been developed for modeling and processing scRNA-seq count data, including scVI , VASC W, scSVA , scVAE , and scABasically, these methods impute dropouts by leveraging information on similarities between cells/genes using the correlation structure of the scRNA-seq data. For example, current imputation approaches, including scImpute and DrImpute, identify similar cells/genes based on clustering and then impute the missing data by averaging the gene expression values for each detected cluster. The accuracy of these imputation methods highly relies on clustering analysis. EnImpute combines the imputation results obtained from eight different imputation methods and calculates the expected values. scDoc imputes dropout events by leveraging information for the same gene from highly similar cells. However, current methods may fail to capture the nonlinearity and the count structure of the scRNA-seq data. Moreover, it becomes more challenging for the traditional imputation methods to handle datasets with increasing size .Recently, some deep learning-based imputation methods have been developed for efficiently handling the higher dimensional scRNA-seq data, such as DCA , DeepImpIn this study, we develop a novel imputation method, Neural Network-based Imputation for scRNA-seq data (NISC) to improve cell type clustering. It is based on neural networks with a novel weighted loss function, coupled with regularizations. Through a series of simulation studies and real data analysis, NISC is compared with the other imputation methods, including AutoImpute, DCA, DeepImpute, LATE, SAVER, MAGIC, ScImpute, DrImpute, EnImpute, ALRA, VIPER, scDoc, scIGAN, and scGNN. The results show that NISC outperforms the existing imputation methods as it can recover the gene expression more correctly and distinguish the cell types more precisely, particularly for scRNA-seq data with high sparsity/noise.It is evident that the process of imputing the dropouts for scRNA-seq data is similar to the process of outlining a noisy image, so autoencoder is utilized to impute the sparse scRNA-seq data . AutoencPij for the gene i in cell j using Michaelis-Menten kinetics (MMK)  ,Pij=1−Sconstant . We use We propose a novel loss function with the mean square error weighted by the dropout probability estimated through Michaelis-Menten kinetics.L2 regularization (ridge regression) and dropout regularization to reduce the complexity of the autoencoder network (note: this is different from the term “dropout” event in scRNA-seq data). It is the first time that these two regularization techniques have been combined with an autoencoder network for imputation of scRNA-seq data. We define the regularization term L2 norm of the weight matrix, that is, the sum of all squared weight values of the matrix . α is defined as the value of the regularization rate, which determines how powerful the effect of the regularization term will be. The regularization term α and the regularization term will be excluded if α is zero. If α is too large, the neural network model will be less sensitive therefore increase the risk of underfitting. Conversely, if α is too small, the complexity of the model will be increased, so the risk of overfitting will be high. An appropriate value of α can be determined through cross-validation suggested by The loss function will be minimized through the autoencoder learning process. Note: the function “log” is the natural logarithm. The intuition behind this is that the estimated dropout probability L2 regularization, dropout regularization is also used in NISC as it is a strategy to turn off neurons of the neural network with certain probability during training, which then further reduces the model’s complexity  plots or t-SNE (t-distributed stochastic neighbor embedding) plots  dependinFour evaluation metrics are calculated to evaluate the accuracy of the cell type clusters in the visualization plots, including Adjusted Mutual Information (AMI) , Adjuste(1) Two cell types for 800 genes and 1,000 cells.(2) Four cell types for 800 genes and 1,000 cells(3) Four cell types for 2,000 genes and 10,000 cellsTo evaluate the performance of our imputation method, we compare it with existing methods on simulated scRNA-seq count data, which are generated by the widely used simulator, Splatter . Both raFor each scenario, two sparsity levels are examined, i.e., approximately 80 vs 90%. In the Splatter simulation setting, the differential rate of 0.2 is used, indicating that 20% of the total genes are marker genes. As substantial noise is added to input data to mask cell type identities through simulation, our purpose is to predict the imputed values for the dropouts accurately and therefore identify cell types.Our deep learning framework in NISC consists of three hidden layers with 1600, 400, and 1600 neurons, respectively, for the simulation data of 800 genes. For the case of 2,000 genes, the number of neurons for three hidden layers are 4,000, 1,000, and 4,000, respectively. A widely used active function, rectified linear unit , is emplFour evaluation metrics, including AMI, ARI, FMI, and SS are calculated on the visualization result for the simulated data in High accuracy in cell type visualization does not necessarily mean the imputed values are close to the true values. We calculated RMSE  between the ground truth value and the corresponding imputed value by each method. A direct comparison in gene expression values among the ground truth, raw data, and imputed data can be found in the heatmap plot . It showA consistent conclusion can be obtained from UMAP plot  for thisFor the case of 4 cell types with 10,000 cells, we only compared the deep-learning-based methods . We notiComputational time: Among the deep-learning-based methods, LATE is the fastest, and scIGAN is the slowest. Specifically, the order of the computational time for seven deep learning-based methods is: LATE < DeepImpute < DCA < NISC < AutoImpute < scGNN < scIGAN. We used High Performance Computer systems with 2894 MHz CPU, 5 cores, and 36 GB memory on each core. For a simulation dataset with 2,000 genes and 10,000 cells, it took about 10 min for LATE, 12 h scIGAN, and 50 min for NISC.We apply NISC and the compared methods on mouse lung scRNA-seq data (GSE52583) with 201 cells . Figure The evaluation matrices on the clustering for this dataset are also calculated . Though We also apply NISC and the compared methods on scRNA-seq data of 92 mouse embryonic cells and 22,936 genes (GSE29087). The sparsity of the data is 83.04%. The cell types of this data set are reported in the original publication . We visuThe above real scRNA-seq datasets do not have ground truth, since usually it is challenging to obtain the ground truth for real scRNA-seq data. Alternatively, it will be convincing to evaluate the performance of the imputation approaches if we use a real scRNA-seq dataset with low sparsity and distinct cell types and set it to be the ground truth data for evaluations. For this purpose, we apply the imputation methods on lung adenocarcinoma data (GSE69405) that profiles the gene expression of single cancer cells with TPM  measurements . These cT-SNE plots  of the sNISC is a data-driven method and does not require any prior knowledge. Real data and simulated data show that NISC can impute the dropouts in the scRNA-seq data, improving the accuracy of cell type clustering. Four performance measures were calculated to evaluate the clustering accuracy for the imputed data by various imputation methods. RMSE, which measures the distance between true (if available) and imputed values, was also calculated. Generally, compared with other existing estimation methods, NISC has a lower RMSE and a higher score in the evaluation measures of clustering accuracy.NISC is an unsupervised neural network-based imputation method with autoencoder techniques implemented. Compared with other neural network-based methods, we investigated how different loss functions affect the imputation results. We developed a novel loss function weighted by Michaelis-Menten kinetics (MMK) and investigated its difference and standard mean square error (MSE) loss. Fig. S1 shows that the MMK loss can achieve more effective imputation under the sparse simulation setting, while by regular MSE the loss function is less effective. In addition, we add L2 regularization and dropout regularization to the model  to avoidAn effective neural network for imputation requires sufficient neurons in the network. Due to many genes in scRNA-seq studies, GPUs are recommended for NISC to speed up the training process of the autoencoder network. NISC imputation is not suitable for some types of data which lose Michaelis-Menten kinetics, such as 10x Genomics data , and som