It is now possible to analyze cellular heterogeneity at the single-cell level thanks to the rapid developments in single-cell sequencing technologies. The clustering of cells is a fundamental and common step in heterogeneity analysis. Even so, accurate cell clustering remains a challenge due to the high levels of noise, the high dimensions, and the high sparsity of data.Here, we present SCEA, a clustering approach for scRNA-seq data. Using two consecutive units, an encoder based on MLP and a graph attention auto-encoder, to obtain cell embedding and gene embedding, SCEA can simultaneously achieve cell low-dimensional representation and clustering performing various examinations to obtain the optimal value for each parameter, the presented result is in its most optimal form. To evaluate the performance of SCEA, we performed it on several real scRNA-seq datasets for clustering and visualization analysis.The experimental results show that SCEA generally outperforms several popular single-cell analysis methods. As a result of using all available datasets, SCEA, in average, improves clustering accuracy by 4.4% in ARI Parameters over the well-known method scGAC. Also, the accuracy improvement of 11.65% is achieved by SCEA, compared to the Seurat model. Cells can be differentiated based on the expression of their genes. In contrast to bulk RNA sequencing, which measures gene expression across a large number of samples , single-The rapid development of single-cell sequencing technologies makes it possible to analyze cellular heterogeneity at the single-cell level . RecentlAddressing aforementioned goals, Seurat , as a poBased on the pros and cons of analyzing models presented so far, exploring new methods to improve and increase the accuracy of clustering has been considered in this field of research recently. It is worth noting that although the scGAC model has improved clustering accuracy due to the use of graph attention architecture, adjustment of its various parameters, including the number of head attentions, has not been well investigated. On the other hand, while PCA, as a linear dimension reduction method, is based on the simple assumptions for data analysis, its adoption by scGAC may not be suitable considering the biological data with uncertain distribution . In thisIn this research area, an accurate clustering method should be able to extract important information, such as boundaries and different characteristics among cells, to produce a valid result. In this regard, the SCEA method advances the implementation in four steps, as follows. SECA introduces: a) input data preprocessing, b) graph construction and denoising, c) dimension reduction, d) data clustering by K-means . As depiIn the proposed method, data preprocessing begins with a raw-count matrix of gene expressions, and so, the SECA tries to filter out the data with poor quality. For this purpose, SECA measures the quality of cells and genes based on their gene expression levels. Once preprocessing is performed, each gene must be expressed in three or further cells. For each cell, SECA determines the expression levels of the appropriate number of genes, as well as the amount of Unique Molecular Identifier)UMI. For Example, we excluded genes that were expressed in very few cells  as they may be due to the technical noise or poor sample quality, rather than biological signal. Similarly, we removed genes that had too much expression in most cells, such as housekeeping genes that are constitutively expressed across all cell types and do not contribute to the variance of the data. We determined such genes as having expression values above 75th percentile of the dataset.Once preprocessing is performed, an auxiliary cell graph is constructed to facilitate the information sharing between cells. To construct an initial similarity matrix, the Pearson correlation coefficient  between As the first step of data dimension reduction, SECA uses an MLP-based Encoder. Reducing the dimensions of the data means to condense the data with a large number of features into smaller dimensions. Dimensionality reduction techniques allow us to reduce the complexity and size of datasets, while preserving the variance and discriminative features of the samples. Generally, the single-cell data contains a large number of features (i.e. genes). Encoding, as a nonlinear dimension reduction technique, can reduce data dimensions while preserving its information content. For this purpose, the MPL extracts informational features of the raw data in a low-dimensional space to feed the graph attention autoencoder. The proposed encoder has three layers to reduce data dimension to 4096, 2048, and finally, 1024. Additionally, tanh, as a non-linear activation function , is utilGAT  are usedij is the total number of cells that are assigned to the ith cluster according to the model prediction, and assigned to the jth cluster according to the true label. ai is the total number of cells that are assigned to the ith cluster based on the prediction and aj is the total number of cells that are assigned to the jth cluster based on the true label, and finally, n is the total number of clusters.To evaluate the clustering outputs of the proposed method, we use two well-known metrics, Adjusted Rand Index (ARI)  and NormI (X. Y) calculate the mutual information between X and Y, as formulated in Eq. 3We also use the NMI  measure,https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k), Mouse [https://figshare.com/s/865e694ad06d5857db4b), Biase [https://www.ebi.ac.uk/arrayexpress/exp), Neurons_5K [https://cf.10xgenomics.com/samples/cell-exp/6.0.0/SC3_v3_NextGem_DI_Neurons_5K_SC3_v3_NextGem_DI_Neurons_5K/SC3_v3_NextGem_DI_Neurons_5K_SC3_v3_NextGem_DI_Neurons_5K_web_summary.html), Mouse Brain [https://www.10xgenomics.com/resources/datasets/mouse-tissue-microarray-in-3x3-layout-with-2-mm-edge-to-edge-spacing-ffpe-2-standard)) that range from humans to mice, as listed in Table We evaluate SECA on a diverse set of challenging single-cell datasets (Klein  (GSE6552), Mouse  (https:/), Biase  (GSE5724), Biase  , on the SCEA’s performance.To determine the accuracy of clustering, we use two parameters, ARI  and NMI s and SCEAns, respectively. It is worth noting that aside from the NMI and ARI reports, we have also disclosed the p-value results of our model for the datasets accessible through our project link. All datasets have generated p-values below 0.05, thereby establishing the credibility of both modes.Using standardization to contain data values in the fixed range can improve their applicability in many data analyzing applications. Therefore, in this section, we assess the impact of standardization, as defined in Eq. While using a multi-head attention mechanism  in our aAs shown in Figs. When working with large datasets, dimension reduction is inevitable to facilitate runtime management. Dimension reduction refers to the transformation of high-dimensional data into low-dimensional data to retain some meaningful properties of the original data, ideally close to its intrinsic dimensions . In scGAConsidering that the biological data have an indeterminate and complex distribution, and the relationships among features may not have a linear factorization, it is more appropriate to use nonlinear dimension reduction techniques. In this manner, we proposed an encoder network with layers of MLP and reduced the data dimensions to 1024, as shown in Fig. To reduce dimensions in the first step, we use an encoder based on Tensorflow.During the dimension reduction process, we also use a Graph attention Autoencoder  with a sWe use the standardization option for SCEA.In the next step, based on the results of the previous sections, we use the most optimal possible conditions for SCEA. In other words, SCEA accuracy refers to the accuracy obtained from the best experience including:As reported in Figs. TPUs are Google's custom-developed Application-Specific Integrated Circuits (ASICs) used for accelerating machine learning workloads. Researchers, developers, and businesses can leverage TensorFlow computing clusters that use Cloud TPU for maximum performance and flexibility. It should be clarified that TPU is not included in our algorithm. Instead, since a portion of our model was constructed using the TensorFlow package, we explored the potential of utilizing TPU to enhance the computational speed. Typically, when dealing with extensive matrix operations, executing the code on a TPU can greatly enhance the speed of computation compared to a CPU. However, it should be noted that not all types of code can be optimally accelerated through TPUs. In instances, where the code has minimal computational intensity or contains numerous branches or conditional statements, the TPU may not offer notable gains in terms of computational speed compared to a CPU or GPU. Furthermore, if the code necessitates significant memory bandwidth, TPU may not be the optimal choice, owing to their focus on computation as opposed to memory access. Based on our code's description, it exhibits exceptional performance when executed on TPU, highlighting its developmental benefits.At the first step of dimension reduction, as shown in Fig. As shown in Fig. To evaluate our proposed model's efficiency, we compared it to the scGAC model across all datasets with respect to the execution time. We conducted these comparisons to determine whether our method produced results more quickly than scGAC, which is considered to be one of the standard baseline methods. The comparison process was performed using a range of datasets, of varying sizes and complexities. We recorded the time taken by both models to process these datasets and drew insights from the results. The findings are visually represented in Fig. When examining the results, it was observed that our approach was indeed faster than scGAC. Our method was able to generate results in all datasets more quickly, thus providing a more efficient method for processing large datasets. Additionally, we observed that our proposed model was able to complete the processing task while using significantly fewer computational resources, making it a more cost-effective option for processing large-scale datasets.To identify cell identities and functions using scRNA-seq data, it is necessary to cluster different cells according to their gene expression. In this study, using the scGAC  tool, weFuture improvements can be made in several directions. Efficient attention-based models, such as transformers instead of GAT , which a


Objective: Long non-coding RNAs (lncRNAs) have been shown to be associated with the pathogenesis of different kinds of diseases and play important roles in various biological processes. Although numerous lncRNAs have been found, the functions of most lncRNAs and physiological/pathological significance are still in its infancy. Meanwhile, their expression patterns and regulation mechanisms are also far from being fully understood. Methods: In order to reveal functional lncRNAs and identify the key lncRNAs, we develop a new sparse independence component analysis (ICA) method to identify lncRNA-mRNA-miRNA expression co-modules based on the competitive endogenous RNA (ceRNA) theory using the sample-matched lncRNA, mRNA and miRNA expression profiles. The expression data of the three RNA combined together is approximated sparsely to obtain the corresponding sparsity coefficient, and then it is decomposed by using ICA constraint optimization to obtain the common basis and modules. Subsequently, affine propagation clustering is used to perform cluster analysis on the common basis under multiple running conditions to obtain the co-modules for the selection of different RNA elements. Results: We applied sparse ICA to Liver Hepatocellular Carcinoma (LIHC) dataset and the experiment results demonstrate that the proposed sparse ICA method can effectively discover biologically functional expression common modules. Conclusion: It may provide insights into the function of lncRNAs and molecular mechanism of LIHC. Clinical and Translational Impact Statement–The results on LIHC dataset demonstrate that the proposed sparse ICA method can effectively discover biologically functional expression common modules, which may provide insights into the function of IncRNAs and molecular mechanism of LIHC. I.Long noncoding RNAs (lncRNAs) refer to RNA transcripts with a length of more than 200 nucleotides and no significant protein-coding ability In addition, lncRNA is also believed to be involved in the pathogenesis of many diseases, including liver cancer There are many known mechanisms by which lncRNA can play a role. Among them, more and more information show that lncRNA is involved in regulating tumor progression and tumor biological behavior through interaction with miRNAs or mRNAs Although thousands of lncRNAs have been found and documented in public databases such as GENCODE, NONCODE and LNCipedia, the functional characterization of lncRNAs is still in its infancy. So far, only a few lncRNAs have been well functional commented In this study, we proposed a new sparse ICA method to identify the co- modules of three RNAs, including mRNA, miRNA and lncRNA on the same set of samples. Firstly, the expression matrix of the three RNA combined together was approximated sparsely to obtain the corresponding sparsity coefficient, and then it was decomposed by using ICA constraint optimization to obtain the common basis and modules. Subsequently, affine propagation clustering (APC) was used to perform cluster analysis on the common basics under multiple running conditions to obtain the co- modules for the selection of different RNA elements. Finally, we evaluated the performance of this method and the results demonstrated its validity for the three RNA data analysis, which had a high correlation between the decomposition results and the original RNA expression data.Liver Hepatocellular Carcinoma (LIHC) is one of the most common cancers in the world. Although great progress has been made in the research of liver cancer in recent years II.In this section, the relevant knowledge and detailed calculation process for sparse ICA and module elements selection will be described, respectively.A.https://cancergenome.nih.gov/) and then isolated lncRNA and mRNA data from the transcript data. Considering the method used in this study required that the three types of RNA data had the same dimensionality, that is, the number of samples corresponding to the three types of RNA data was the same. We systematically collected 20060 mRNAs, 1448 miRNAs and 7305 lncRNAs across 374 tumor samples and 50 control samples, which were denoted as three expression matrices. The detailed clinical information is showed in The LIHC transcript data and miRNA sequencing data were downloaded from The Cancer Genome Atlas (TCGA) database  mRNAs and miRNAs with P value less than 0.05 and The enrichment analysis revealed that the identified co- module is significantly enriched in a variety of KEGG pathways and GO terms, some of them have been reported to be involved in liver cancer. The single most striking observation to emerge from the figures is that both mRNA and miRNA are enriched in the pathways or biological processes related to fatty acid, such as fatty acid degradation, fatty acid biosynthesis, long-chain fatty acid metabolic process, etc. The previous study has found that cholesterol biosynthesis supports the growth of hepatocarcinoma lesions depleted of fatty acid synthase in humans C.So far, we know that many lncRNAs have been found during tumor development, but it is not clear what its function is. This study aims to use the ceRNA network to help researchers better understand the interaction mechanisms between lncRNAs and two other RNAs. The ceRNA network mainly explores the regulation and competition relationship of differential molecular composition. We first calculate significantly differentially expressed lncRNAs, mRNAs and miRNAs using the Deseq2 R package In this study, ceRNA includes 46 lncRNAs, 3 mRNAs, 3miRNAs, where all lncRNAs, mRNAs and 1 miRNA are significantly differentially expressed. Previous studies suggested that transfer of miR-25-3p by CHB-PNALT-Exo promoted the development of liver cancer by inhibiting the co- expression of TCF21 and HHIP D.The above analysis has revealed that the identified ceRNA co- module is closely associated with liver hepatocellular carcinoma. Hence, the co- module can be looked upon as a signature to inquire into the prognostic value of lncRNAs. The LnCeVar website By multivariate Cox proportional hazards regression analysis, we find that the high expression of NEAT1 and the low expression of LINC00311, C5orf17 are associated with the poor overall survival of LIHC patients. Interestingly, there is no significant association between the expression of three miRNAs and two mRNAs in ceRNAs, but the high expression of ceRNAs is related to poor overall survival of LIHC patients. This evidence illustrates that these lncRNAs may affect the expression of related miRNA and mRNA and then affects the expression of downstream molecules. It also suggests that these lncRNAs or ceRNAs may be regarded as prognostic-related signatures for LIHC. These above observations indicate that the ceRNA co- module identified by sparse ICA may be potential biomarkers for the survival of LIHC patients.E.Various algorithms have been developed for CeRNA network construction methods, mainly based on the jNMF model, such as SNMNMF algorithm The number of components of JNMF in IV.As a classical method for blind source separation, ICA is based on higher-order statistical moments, and its purpose is to decompose the observed multivariate data into the sources which are assumed statistically independent and non-Gaussian, and it has been successfully applied to the data analysis in various fields and to find the underlying independent sources. But as far as we know, it has not yet been used for the analysis of RNA expression data. Therefore, this is the first time for us to apply this method to the analysis of RNA expression, and the evaluation results show that the performance of this method is effective, and some significant results have obtained through it in the analysis of RNA data in patients with liver cancer.Since the regulatory relationship between mRNA, miRNA and lncRNA in patients with liver cancer has its own specificity, thus presents a certain degree of independence, which is the reason that ICA is adopted in this paper. In addition to independence, current ICA studies also tend to consider sparsity. For liver cancer, the number of RNA elements regulated between mRNA, miRNA and lncRNA is small compared with the respective total number of RNA, so the module matrix obtained by the proposed sparse ICA method also presents certain sparsity. Therefore, the wavelet sparse analysis method is first used to conduct sparse approximation for the expression matrices of three types of RAN in the sparse ICA method.Furthermore, in order to reduce the redundant information contained in the sample data and the size of the sample dimensions, as well as the complexity of subsequent calculations, PCA is first used to conduct dimensionality reduction processing on the data before the sparse approximation expression of sample data is carried out in this paper, and the dimension size after dimensionality reduction is determined according to the contribution rate of principal components in the PCA. In this study, the number of components with a contribution rate of more than 90% is selected as the dimension size after dimension reduction, and 40 principal components are selected in the results.In addition, the APC algorithm was adopted to obtain the final co- modules of the three RNA from the modules under multiple running conditions in our study, and then using them for the selection of different RNA elements. Different from the traditional clustering methods, such as K-means which need to determine the number of clustering in advance, APC can determine the number of clusters adaptively. Therefore, it overcomes the problem of pre-determining the number of clustering in the traditional clustering method and reduces the adverse influence brought by human subjective factors. Two co- modules were obtained from the results of multiple runs by APC method in this study. In the process of selecting the significant expression elements of the three RNAs, the row co- modules corresponding to the module matrix S obtained by APC are z-scored by putting these three types of RNAs together in this study. In addition, they can also be z-scored separately, and show the same results when the three types of RNA are z-scored together. Therefore, only the results of one situation are presented in this study.From the experimental results of the ceRNA construct, we found that most of the lncRNAs competed with mRNA to bind three miRNAs. This suggests that these miRNAs play a key role in the development of liver cancer. This is consistent with some existing descriptions in the literature. For example, among miRNAs, miR-25 is highly expressed at early stages and plays an important role in the progression of HCC, which may be of prognostic value and facilitate the development of novel therapeutic approaches for HCC In addition, there are still some shortcomings in this study, including the lack of data collection, the construction of ceRNA network not only includes lncRNA but also other RNA molecules such as circrna, and we need to include circrna molecules to improve the ceRNA network in future studies. At the same time, due to the limitations of the experimental equipment, biological validation of the experimental results was not performed in this paper, which needs to be done later. Finally, the method in this paper does not take into account the benefit of a priori knowledge to the extent that it cannot be fairly compared with current state-of-the-art ceRNA construction methods. At a later stage, we will investigate a priori knowledge-driven ICA methods to compare the current experimental results and improve the reliability and biological interpretability of the algorithm.V.In this paper, a new sparse ICA method was proposed to identify the co- module information contained in mRNA, miRNA and lncRNA, and applied it for the three RNA data analysis of LIHC patients. The results revealed that ceRNA includes 46 lncRNAs, 3 mRNAs, 3miRNAs, where all lncRNAs, mRNAs and 1 miRNA are significantly differentially expressed, and the elements of ceRNA constructed by co- module are closely associated with LIHC. Therefore, the ceRNA co- module identified by sparse ICA may be potential biomarkers for the survival of LIHC patients.


DNA methylation signatures are usually based on multivariate approaches that require hundreds of sites for predictions. Here, we propose a computational framework named CimpleG for the detection of small CpG methylation signatures used for cell-type classification and deconvolution. We show that CimpleG is both time efficient and performs as well as top performing methods for cell-type classification of blood cells and other somatic cells, while basing its prediction on a single DNA methylation site per cell type. Altogether, CimpleG provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution.The online version contains supplementary material available at 10.1186/s13059-023-03000-0. DNA methylation (DNAm) is intrinsically related to chromatin structure and cell differentiation and controls the expression of neighboring genes . The stat-test [A common approach for the detection of DNAm signatures is the use of statistical tests, which characterize differential methylated cytosines (DMC) between samples in two biological conditions . Such apt-test  or on fot-test . Elastict-test , 23 is at-test . Neural t-statistics for the selection of the top 100 DNAm sites per cell type (50 hypo- and 50 hyper-methylated). It then explores a nonlinear random effects model, to determine which coefficients are used as predictors for relative proportions of cells. This approach is implemented within the minfi package [t-test to select large cell specific DNAm signatures. These signatures are used to build models on a training mixture data set. IDOL iteratively removes DNAm sites, based on their predictive importance, until an optimal signature for the cell deconvolution problem is found. Finally, EpiDISH performs cell deconvolution with a robust partial correlation based method. It uses for this DNAm signatures selected by a There are several methods focusing on the problem of reference-based cell deconvolution, i.e., estimating the proportions of cells in a mixture. Jaffe et al. uses  package . The ENm package  is simil package , however package  expands n) sites . Despitet-statistic measure with the Area under the Precision-Recall Curve (AUPR) [We have recently described studies on the use of few CpG sites for cell-type deconvolution , 28. Amoe (AUPR)  to select-statistic score to pre-select active features followed by the area under the precision-recall curve (AUPR) for feature selection. The use of precision-recall curves instead of the usual ROC curves for feature selection [CimpleG is a computational framework for the selection of DNAm signatures for cell-type classification Fig. A. It proelection  was adopelection , Elasticelection , and Booelection ).Moreover, CimpleG provides two curated and pre-processed DNAm datasets with a compendium of DNAm arrays with 14 somatic cell types and eight different leukocytes  or the t-statistic (CimpleG Score), which is equivalent to the approach used by state-of-art DNAm analysis and cell deconvolution methods. This was to ensure that the combination of these metrics, was stronger than their individual use. Notably, some models  could not cope with the high dimensional feature size of the datasets. This was mainly due to very large memory usage or days of execution time. Therefore, for these models, as a pre-training step, we have performed an unsupervised feature selection considering variance and co-variance of the DNAm sites . Next, wWe observed that Elastic Net, CimpleG, and CimpleG (score) have the highest median AUPR for the somatic cells and the leukocyte dataset Fig. A–B, indiAnother relevant point is the number of CpGs that are implemented in the signatures derived by the different methods. Elastic Net selected the largest number of features with 3378 unique features across all six models for the leukocyte cells  or the model prediction scores  to build reference matrices for each model vs. cell type. We used these as input for the deconvolution method, the non-negative least squares (NNLS) algorithm , which iConsidering the problem of deconvolution of major (5–6) leukocyte cells, we observe that IDOL had highest ranking when considering the lowest prediction error (lowest RMSE). IDOL was first in 2 out of the 3 data sets followed by CimpleG with 10 CpGs (second in 2 data sets) and minfi (one time first and another time third) Fig. . If we oWe also evaluated a selection of methods on the two artificial mixture data sets (Leukocytes 1 and 2). Here, the deconvolution problem is on the more granular annotation, with 12 different leukocyte cell types  or specific (12 cell types) leukocyte cells. Our results confirm the previously reported superior performance of the IDOL algorithm . IDOL isWhile the deconvolution of hematopoietic subsets is just one example for epigenetic signatures, it also addresses a relevant clinical need. Current flow cytometric measurements are only applicable for fresh blood samples and do not facilitate analysis of dried blood spots, coagulated material, or retrospective analysis. Furthermore, it is not trivial to standardize flow cytometry settings. These clinical needs can be met by epigenetic deconvolution of leukocyte subsets , 40, 41.R-squared  and the ment}R2) , 25, 27.mentclass2pt{minimmentclass2pt{minimterature , 43. In Altogether, the CimpleG framework can be seen as a general pipeline for DNAm signature selection. It is implemented in R and provides functionalities to estimate both small DNAm signatures (using CimpleG), but also to derive Elastic Net and other machine learning methods evaluated here. It provides two large, manually curated and normalized datasets for the testing and benchmarking of methods. Moreover, cell-type classifiers can be integrated with the NNLS method for cellular deconvolution, which makes it the first end-to-end pipeline for the estimation of cell-specific small DNAm signatures to cellular deconvolution. We are not aware of any other computational package addressing these.There are further aspects to be explored in the future. One technical problem usually encountered in array based DNA methylation data sets is missing values, which usually increase with data set size. Currently, none of the evaluated deconvolution workflows, except for CimpleG, supports predictions with missing values via imputation . FurtherWe propose a computational framework named CimpleG for the detection of small CpG methylation signatures used for cell-type classification and deconvolution. Small signatures are important due to their potential application in clinics. Our extensive benchmarking for both cell-type detection and cell deconvolution indicates that CimpleG is fast and can perform as well as state-of-the-art approaches, which require large DNA methylation signatures. This works also provide two large benchmarking data sets with either leukocytes and epithelial cells, which represents a useful benchmarking resource for future approaches. Taken together, CimpleG provides a valuable tool for clinician scientists as an easy-to-use stand-alone framework for the identification of CpG sites that are best suited as a biomarker for targeted DNAm analysis.i in observation j, n is the number of observations and p is the number of DNAm sites. This essentially describes a matrix of beta-values. The beta-value is the ratio of methylated probe intensity versus the overall intensity in the Illumina methylation assays  [M-values, these are the log2 ratio of intensities of methylated versus unmethylated probes [j is of a cell type of interest and zero otherwise (others).CimpleG receives as input a matrix  assays  , 48. Altd probes , a metrid probes . CimpleGi, for the cell type of interest, by the difference in means  is used to generate a threshold below which sites with a value of a are selected. By default, we select CimpleG first selects active features such that f active features for each fold. CimpleG sets y. CimpleG adopts an AUPR curve instead of AUC as an accuracy metric, as it makes the procedure less affected by class imbalance [CimpleG performs a balanced stratified K-fold cross-validation loop on the training data and uses the previous procedure to find the sikowski . In shormbalance .i, which minimizes the score i denotes a given feature. Finally, a and the AUPR values will have on the final score. We set these as 0.8 and 0.2, respectively. We then use this score to rank all active features for a given cell type.After evaluation of all folds, we compute a final metric by considering DNAm sites We have compiled and curated two human cell-type DNA methylation datasets that were measured with Illumina Infinium Human Methylation 450k and EPIC BeadChip arrays and deposited in the Gene Expression Omnibus (GEO) . Cell-tyWe have searched for DNAm array data with an emphasis on purified, well-characterized, non-malignant cells in GEO. After manual curation, we had 576 samples, spanning over 14 different cell types, from 46 distinct studies  was downloaded from GEO. If .IDAT files were not available, the tabular data with probe intensities was used. For datasets where IDAT files were available, the SeSAMe pipeline (sesame R package v1.16.1 ) was useCimpleG also allows the user to train classifiers using a number of different machine-learning algorithms to generate complex methylation signatures. For this, we explore methods exposed by the tidymodels framework  and provElastic Net  is a reg package  as the uDecision Trees are simple models that are defined by a set of if/else statements creating a tree-based structure. These models are very easy to understand, but they tend to be inaccurate and unstable and also have several hyper-parameters to tune. We used the rpart package as the uRandom Forests is an ensemble based method that builds predictions by combining decision trees in a bagging fashion. In essence, random forests create a large ensemble of independent decision trees, which are based on a random and small selection of features, and then builds its predictions using a combination of the predictions of all the individual decision trees. We used the package ranger  as the uBoosted Trees are also models that are built on ensembles of decision trees. While boosted trees also typically create a large number of decision trees, the selection of samples and features are based on the predictive performance of the previously trained decision trees. To perform predictions all the decision trees in the ensemble are combined to produce a result. Here, we used the package xgboost  as the uNeural Networks are models that work with the concept of combining layers of interconnected computational neurons (perceptrons) to produce a prediction. Here we focus on a simple, feed-forward neural network with three layers, the input layer, a single hidden layer, and the output layer. We used the nnet package  as the uThe overall design is shown in Additional file Next, we performed model training for every evaluated approach and for every relevant cell type within the somatic cells and leukocyte datasets Tables .V be a matrix with DNAm levels of mixture samples, W is the cell-type specific methylation values (our reference matrix) and H would be the unknown cell-type proportions per sample. Cell deconvolution can be posed as a matrix factorization problem, that is:Let m (NNLS) .W in two distinct ways depending on the type of model the signatures originated from. If the signatures were generated from one of the simple methods (CimpleG or brute-force), then we take their DNAm site selection and build a reference matrix using the average DNAm values of each CpG per class label. Otherwise, for each machine learning method, we build the reference matrix by using the average classification score per classifier, per cell type. To perform cellular deconvolution on the matrix V, we again use two different approaches. For the simple methods, we take the methylation level of the DNAm signature sites. However, for the complex methods, we use the classification score of each classifier for the different samples. Taken together, the W and V matrices are fed to the NNLS algorithm to reconstruct H.For our signatures, we generate For all methods within CimpleG, in the likely scenario that the data we are trying to deconvolve includes missing values, CimpleG will perform mean-imputation so that deconvolution can take place and it will warn the user of how many probes were missing and if applying this procedure makes sense for their data. Although the main focus of CimpleG is on the of use small signatures, before training, the user can also choose to perform deconvolution using the default CimpleG procedure but with a higher number of probes  compares to other state-of-the-art methods, we selected a number of methods widely used for reference-based deconvolution of DNAm data. These were IDOL , 27, EpiIDOL  , 27 is aCandidateDMRFinder.v2, it selects a panel of candidate probes. This selection is based on t-tests run across the contrasts, in this case cell-types, in the data. The dataset used here was the Purified 12 leukocytes data (see below) with 56 different cell-specific samples, covering 12 different cell-types. Under our experimental setup, this step reduces the number of probes available for consideration from 689,105 down to 3540. Then it uses its iterative approach, with the IDOLoptimize function by exploring a training mixture data set to optimize the selection of probes to solve the cell mixture deconvolution problem. This process is guided through the use of the RMSE and R²(fit) metrics to determine if a given subset of probes, provides a better result than the previous iteration. We use the same mixture data set  as used in [projectWBCnew function. The deconvolution method itself being based on the often called Constrained Projection (CP) algorithm [IDOL’s training process consists of two different steps. First using its function  used in . Finallylgorithm .As mentioned above, for the refining of DNAm signatures, we use the same mixture training dataset as in . Six samIDOLoptimize step can take a rather long amount of time, with IDOL taking exactly 2 h to execute, while IDOLmin took a little over 9 min to execute. This was achieved while running in parallel on a 6-core setup, with otherwise default parameters, except for the libSize parameter which was set to 13 for IDOLmin  and 1200 for IDOL to reflect the optimal procedure in described in its most recent study [We should note that the nt study . For comcentDHSbloodDMC.m dataset. In this dataset, EpiDISH leverages the information of DNAse Hypersensitive Sites along with other purified blood cells data, from the Reinius et al. study [EpiDISH   is a metl. study , generatestimateCellCounts [RGChannelSet, a specific object from this package that is basically devoid of any pre-processing. By default, during computation, estimateCellCounts will normalize the data, applying quantile normalization. This step normalizes the user data along with minfi’s reference data. It uses quantile normalization, by default, for the type of data that we are analyzing, blood cells. However to make normalization more consistent with other data sets evaluated here, we opted to set the parameter controlling this, “processMethod,” to preprocessNoob. This approach was used in all of minfi’s related benchmarking analysis.The R package minfi (v1.44.0) is an all encompassing package for the analysis of DNAm data generated with the Illumina Infinium Human Methylation BeadChips . For thellCounts . It is iTo perform its predictions, minfi uses by default a pre-generated reference dataset for deconvolution, “FlowSorted.Blood.450k.” This dataset is based on samples assayed as part of the Reinius et al. study  as well.n et al. .estimateCellProp. It uses a pre-generated reference dataset “FlowSorted.Blood.450k” and uses the same DNAm selection procedure and deconvolutuion algorithm (CP) as minfi. It is more flexible than minfi, as it allows the use of pre-processed DNAm data  and it allows the user to choose if quantile normalization should be applied or not. We have set the parameter controlling normalization  to “FALSE”. More importantly, it allows the user to pick the number of probes with the highest magnitude in effect (hyper- and hypo-methylated) to be used for deconvolution. We should note that although ENmix allows you to set this parameter (nProbes) to 1, their implementation makes it so that it will always select 1 probe which is hyper-methylated and 1 probe which is hypo-methylated, effectively doubling the number of probes it is actually selecting. If nProbes is set to 50, the selection of probes should mirror the selection done by minfi.ENmix (v1.34.0) is, similarly to minfi, an all encompassing package for the analysis of DNAm data from the Illumina Infinium Human Methylation platform . For theTo evaluate the performance of the different methods for deconvolution we used data compiled in Salas et al. . This inThis dataset is a subset of the data with the GEO accession number GSE167998. Comprising 56 different purified leukocyte samples spanning across 12 different leukocyte subtypes , setting the pre-processing code argument to “QGCDPB.” With this setting, the pipeline will perform the following tasks in this order: mask probes of poor design, mask all but cg-probes, infer channel for Infinium-I probes, apply non-linear dye-bias correction, perform detection p-value masking using out-of-band probes and finally perform background subtraction using out-of-band probes . After pre-processing, we removed the masked probes from our data. Further, using Illumina’s EPIC platform annotation, we have removed probes associated with sexual chromosomes. In the end we have a matrix with beta-values for 56 samples across 689,105 probes.To process these data, we have acquired the raw data (.IDAT files) from GEO. We have pre-processed the data with the data processing pipeline from SeSAMe   of the more refined subsets into the combined groups. We have combined neutrophils, eosinophils and basophils into granulocytes (gran); B naive cells and B memory cells into B cells (bcells); CD4 T naive cells, CD4 T memory cells and T regulatory cells into CD4 T cells (cd4t); and finally, CD8 T naive cells and CD8 T memory cells into CD8 T cells. Monocytes and NK cells were unaffected by this change. For simplicity, we refer to this dataset as Leukocyte dataset 1.This dataset is a subset of the data with the GEO accession number GSE167998. It includes 6 samples of artificially mixed leukocytes , CD8 T cells (cd8t), granulocytes (gran) and monocytes (mono). The last cell type was the CD3- fraction of lymphocytes and it is represented as being the sum of B and NK cells (nont_b_and_nk). Given this, for the predictions made for these samples, we have summed up the cd4t, cd8t and gran groups as described before (see Leukocyte dataset 2). Furthermore, we have summed up bcells and nk into the nont_b_and_nk group.a priori to create a deconvolution reference matrix . We train CimpleG, CimpleG.10, elasticnet (ElasticNet), boostedtree (XGBoost), neuralnet , randforest (Random forests) and decisiontree (Decision tree) with the “Purified 12 leukocytes data” with default parameters. The exception being the trainNext, we performed the cell deconvolution predictions by using default settings. We have capped predicted proportions to 0 in case negative values were present. We always generate predictions for the more granular classes (12 leukocyte subsets) for the methods that can do so (CimpleG and IDOL). We then sum up these inferences to accommodate the broader cell-type groups, 5 or 6 major leukocyte groups depending on the dataset.We should note that, for most methods, the same signatures were used across deconvolution. The exceptions are minfi and ENmix, which perform feature selection at each deconvolution run and might select distinct signatures. Taking CimpleG as an example, for the deconvolution problem, CimpleG was trained once on the expanded leukocyte reference. Then this trained model was used to evaluate the three different leukocyte datasets as well as their more broadly annotated, simplified versions. ENmix and minfi however, due to the nature of using pre-generated reference datasets, and the implementation of their deconvolution algorithms, only really select which probes will be used for deconvolution at the time of deconvolution. This means for example, that ENmix.min can use two distinct sets of signatures when trying to deconvolve Leukocyte dataset 1 and Leukocyte dataset 2. Thus, with that caveat .We compute In this study we also refer to these metrics as ults see . To rankAdditional file 1: Supplementary Figures S1toS11.Additional file 2: Supplementary Tables S1toS7. This work includes 7 supplementary tables. Table S1 has information regarding CimpleG signatures for classification. Table S2 has information of data sets used for the somatic cells data set. Table S3 has information of data sets used for the leukocyte cells data set. Table S4 has information of data sets used for the purified 12 Leukocyte Data. Table S5 has information of data sets used for the mixture leukocyte dataset 1. Table S6 has information of data sets used for the mixture leukocyte dataset 2. Table S7 has information of data sets used for the mixture leukocyte dataset 3.Additional file 3. Review history.


Cell composition deconvolution (CCD) is a type of bioinformatic task to estimate the cell fractions from bulk gene expression profiles, such as RNA-seq. Many CCD models were developed to perform linear regression analysis using reference gene expression signatures of distinct cell types. Reference gene expression signatures could be generated from cell-specific gene expression profiles, such as scRNA-seq. However, the batch effects and dropout events frequently observed across scRNA-seq datasets have limited the performances of CCD methods.We developed a deep neural network (DNN) model, HASCAD, to predict the cell fractions of up to 15 immune cell types. HASCAD was trained using the bulk RNA-seq simulated from three scRNA-seq datasets that have been normalized by using a Harmony-Symphony based strategy. Mean square error and Pearson correlation coefficient were used to compare the performance of HASCAD with those of other widely used CCD methods. Two types of datasets, including a set of simulated bulk RNA-seq, and three human PBMC RNA-seq datasets, were arranged to conduct the benchmarks.HASCAD is useful for the investigation of the impacts of immune cell heterogeneity on the therapeutic effects of immune checkpoint inhibitors, since the target cell types include the ones known to play a role in anti-tumor immunity, such as three subtypes of CD8 T cells and three subtypes of CD4 T cells. We found that the removal of batch effects in the reference scRNA-seq datasets could benefit the task of CCD. Our benchmarks showed that HASCAD is more suitable for analyzing bulk RNA-seq data, compared with the two widely used CCD methods, CIBERSORTx and quanTIseq. We applied HASCAD to analyze the liver cancer samples of TCGA-LIHC, and found that there were significant associations of the predicted abundance of Treg and effector CD8 T cell with patients’ overall survival.https://github.com/holiday01/HASCAD.HASCAD could predict the cell composition of the PBMC bulk RNA-seq and classify the cell type from pure bulk RNA-seq. The model of HASCAD is available at The online version contains supplementary material available at 10.1186/s12920-023-01674-w. RNA sequencing (RNA-seq) is a next-generation sequencing-based technology to target whole transcriptome of a sample . This te. analyzed the ICOS+ FOXP3+ regulatory T cells (Tregs) in 57 HCC patients by immune-histochemistry (IHC), and reported that the infiltration of ICOS+ FOXP3+ Tregs showed a negative correlation with patient survival [Notably, certain types of immune cells infiltrating in the tumor microenvironment (TME) may actively interact with cancer cells and thus promote malignant phenotypes such as enhancing survival of cancer cells and supporting their metastasis , 3. The Thus, to further investigate the biological roles played by various immune cells in complex tissues, a few computational methods have been developed to perform cell composition deconvolution (CCD) –15. CCD To mitigate the influence caused by the technical biases in the data, quite a few methods, such as DWLS, CIBERSORTx, and MuSic, have chosen to build their refGEP with only a single scRNA-seq dataset, although this approach might limit the performance of CCD. Evidence suggests that leveraging heterogeneity across multiple reference datasets may reduce technical and biological biases in the data and thus increase the accuracy of CCD . TherefoOn the other hand, deep neural network (DNN)-based approaches, such as Scaden , have reIn addition, we argue that the preparation of the huge training set should take the removal of batch effects across different reference datasets into consideration. Above all, training data should be derived from multiple sources and each may contain within-sample technical and biological biases. Although SCDC appears to address the issue of batch effects by using an ENSEMBLE approach, their results imply that the best performance of SCDC can be achieved only when the reference data and the bulk samples were derived from the same source. The ENSEMBLE approach adopted by SCDC did not really remove batch effects in the reference dataset.. assessed 14 batch-effect correction methods for scRNA-seq data, and they recommend that “Harmony” is the first choice to try [Batch effects pose a great challenge to the analysis of high-throughput datasets, since such technical variations may confound the biological variations of interest in the down-stream statistical analysis. Tran et ale to try . Harmonye to try . HarmonyHence in this study, Harmony, an algorithm that has been designed to normalize the scRNA-seq data, was used to remove the batch bias in the preparation of the training set . This stThis study used the harmonized expression data of scRAN-seq to develop a CCD model, HASCAD (HArmonized ScRNA-seq Cell Assisted cell Deconvolution model). Our model is a deep neural network (DNN) model, which is inspired by the work of Scaden. Notably, our CCD model is designed to predict from the bulk RNA-seq data the fractions of 15 cell types . These GEPs of single cells were used to simulate the training data, with the cell fractions being randomly assigned.We carried out the procedures of Harmony to perform a PCA-based dimension reduction of the input data, and then the low-dimension embedding was iteratively adjusted . The itegenessamples output by Symphony).Finally, in terms of the cell fraction prediction of new bulk RNA-seq data, the expression values will be preprocessed by using the functions provided by Symphony, which is an extension tool of Harmony that is designed to integrate new datasets into reference atlases . Hence, 2(CP10K + 1) using R package Seurat [2(CP10K + 1) is a standard transformation used in scRNA-seq data analysis [In order to prepare the training data, three PBMC scRNA-seq datasets, PBMC6K (3′ v1), PBMC8K (3′ v2), and PBMC5GEX (5′), were obtained from the GitHub repository of Symphony. These data were initially transformed by loge Seurat , 20. CP1analysis . The datanalysis . As thesU is the PCA loading, the coefficients/weights of the liner combinations. For batch variation removal, the corrected embedding Z' would be calculated by iterating between the step of a maximum diversity clustering and the step of a mixture model based linear batch correction. The convolution of U with Z’ was taken as the corrected gene expression profiles of scRNA-seq, which can then be used for the downstream cross-sample analyses.Before carrying out the Harmony-based normalization, variance stabilizing transform (VST) was used to select the genes and scale the gene expression for a dataset, which is the default setting of Harmony. Then, the functions implemented in Harmony were used to remove the batch-specific variations from the embedding, ment}ΣVT , 20. U iTo allow an integrated analysis of new scRNA-seq datasets with the batch effect-removed reference scRNA-seq, we used the query-projection strategy that was provided by Symphony . A non-rentclass1pt{minimaTo assess the benefit of using Harmony-Symphony batch-corrected data (H–S corrected data) in the training of our CCD model, we also prepared non-harmonized scRNA-seq data. Non-harmonized data were pre-processed using a log transformation of the total count per-million (TPM) as follows:On the other hand, we also assessed if the Harmony-Symphony batch-corrected data could somehow be analyzed by other CCD methods, in a way to improve the performances. Since input data required for running CIBERSORTx and quanTIseq must be TPM values, we prepared the H–S corrected scRNA-seq data and transformed them back to TPM-like values Table .Table 3The architecture of HASCAD is an ensemble of three parallel DNN modules Fig. , which is refers to each cell type; truths refers to the ground-truth cell proportion of cell s; PCC loss refers to Pearson’s correlation coefficient.The loss function used in the training of HASCAD was defined as follows:Sn is a vector of n1, n2, …, where each element corresponds to the cell fraction assigned to each cell type. The cell fractions for various cell types in the samples are randomly chosen, and the gene expression values of the simulated bulk RNA-seq data correspond to the mean values of gene expression obtained from these samples. The ground truth Sn was normalized to sum to one:We prepared the bulk RNA-seq data by using the Harmony-Symphony corrected scRNA-seq profiles. By following the strategy that has been used by Scaden, we simulated bulk RNA-seq data by combining the genes expression values from multiple single cells. In the simulated bulk RNA-seq data, In the first benchmark we used the simulated gene expression profiles based on scRNA-seq data. ScRNA-seq gene expression profiles were used to generate in silico bulk RNA-seq data. To assess if Harmony-Symphony correction (H–S correction) could improve the performance of CCD, the model predicted the cell fractions in H–S corrected gene expression profiles and in non-H–S corrected gene expression profiles, respectively. Two scRNA-seq datasets were used to generate the training and validation data, and another dataset was used to make testing data.The second benchmark used a set of bulk RNA-seq data of pure type immune cells retrieved from NCBI GEO (accession number GSE141498) . There aexp denotes raw count reads mapped to transcript.Next, HASCAD was assessed by using human PBMC RNA-seq dataset with experimentally estimated cell fractions. The dataset was GSE107572, downloaded from NCBI GEO . The rawTo apply HASCAD to predict cell fractions, the gene expression values for input were further transformed by using Harmony-Symphony correction. The performance metrics is the mean squared error (MSE) between the ground-truths and the predicted cell fractions. Two widely used CCD methods, CIBERSORTx and quanTIseq, were also assessed by using the original TPM values provided in the dataset, as suggested by the manuals of these two methods.https://gdc.cancer.gov/). R package biomaRt was used to map the Ensembl transcript ID to HGNC gene symbols, and the HTseq-count values were transformed using TPM normalization. By following the H–S correction procedure as described in Sect. 2.2, the TCGA-LIHC bulk RNA-seq data were deconvolved using HASCAD by adopting the three PBMC scRNA-seq datasets as the reference data. Kaplan–Meier plotter was utilized to investigate the association between immune cell abundance and the prognosis of LIHC patients. The data used in the survival analysis included the overall survival time of patients, and the predicted high/low abundance of various immune cell types.To predict the cell composition of HCC samples and to perform survival analysis of the patients, 414 RNA-seq samples of the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset  were downon-harmonized  and mono16 (CD16+) cells were clustered. Naïve CD8 T cells and memory CD8 T cells were clustered. Hence, we show that the Harmony-Symphony approach used in this study could remove to a certain extent the batch variations across scRNA-seq datasets, and thus we took the H–S corrected scRNA-seq data to prepare for the training and validation of our HASCAD model.Three scRNA-seq datasets, PBMC6K PBMC8K, and PBMC5GEX, were used to prepare the training data, and the numbers of cells for each dataset is provided in Table zed Fig. A, whereazed Fig. B. It canzed Fig. A, where zed Fig. B. For exThus, with the pre-processed scRNA-seq data, it was ready to train our HASCAD. To assess if the normalization of scRNA-seq data by using the Harmony-Symphony strategy can truly benefit the training of our HASCAD model, this study compared the performance metrics of the model trained by using H–S corrected scRNA-seq data, with that of the model that was trained by using the non-corrected scRNA-seq data. In the following text, the previous type of models will be referred to as Harmony-corrected model, and the latter ones will be referred to as non-Harmony-corrected models. A leave-one-dataset-out cross-validation was carried out as described in . The HarIt was noted that all of the HASCAD models converged in approximately 80 epochs and no significant overfitting was noticed Fig. . As whatIn addition, a 10-fold cross validation was conducted to further assess the performance of the HASCAD models. The result shows that, for the Harmony-corrected models, the Pearson’s correlation coefficients (PCCs) between the predicted cell fractions and the ground-truth were over 0.79, whereas the best PCC of all non-Harmony-corrected models was 0.63 Fig. . This reIn addition, we also assessed if CIBERSORTx and quanTIseq could predict the cell fractions in the simulated bulk RNA-seq data that were generated using the batch variation-removed scRNA-seq datasets. To follow the usage suggestions of these two tools, we prepared the input data by transforming the simulated data of bulk RNA-seq to TPM values. It turns out that by using CIBERSORTx and quanTIseq the PCCs of the predictions with the ground truths were better when the simulated bulk RNA-seq data were derived from non H–S corrected scRNA-seq data  was used as the performance metrics, it is obvious that the performance of CIBERSORTx was worse than those of HASCAD and quanTIseq . Using the HASCAD predicted cell proportions as the features, the distribution of the samples was visualized by PCA. Clearly there are two distinct clusters, the normal cluster and the tumor cluster, suggesting that the immune cell compositions of tumor samples are very different from those of normal samples  are involved in tumor development and progression by inhibiting antitumor immunity . To explHASCAD is a cell composition deconvolution (CCD) method we designed to predict the immune cell fractions in bulk RNA-seq samples. HASCAD has been implemented by using a deep neural network architecture, and it has been trained by using the reference cell-specific gene expression signatures derived from scRNA-seq data. Unlike regression-based CCD methods that have assumed a linear relation between bulk gene expression profiles and the fractions of various cell types, HASCAD takes advantage of the non-linear property of neural network, which might improve the robustness for the prediction of immune cell fractions. Compared to a previous DNN model designed for CCD, Scaden, which could analyze only five immune cell types, HASCAD could predict the cell fractions of as many as 15 immune cell types. To reduce the overfitting that might be caused by using scRNA-seq data containing batch variations, the training and validation data for optimizing HASCAD have been prepared by applying a batch-effect removal approach, the Harmony-Symphony correction. We note that by using the training data in which the batch variations have been removed to a certain extent, HASCAD is likely to have robust performance. Besides, the performance of HASCAD might be further improved if the input RNA-seq data could be pre-processed by using the Harmony-Symphony correction strategy.In the benchmark using human PBMC RNA-seq datasets, the performance of HASCAD is similar to that of quanTIseq, better than CIBERSORTx. HASCAD is superior to quanTIseq in that it can prediction the fractions of more cell types. With the fast and wide application of the scRNA-seq technology in various studies, it can be expected that there will be more scRNA-seq datasets that can be used to optimize CCD methods in the future. HASCAD, a DNN based method, could be readily tuned by using new datasets.On the other hand, we notice that analyzing the RNA-seq sample with a highly skewed distribution of cell fractions can be very challenging to CCD methods. For example, HASCAD usually gave a predicted cell fraction very deviated from 100% when analyzing the RNA-seq samples consisting of only the pure-type immune cells. Other two CCD methods, CIBERSORTx and quanTIseq also have such a limitation. We therefore adopted an alternative approach to assess the three methods, where the cell type predicted to have the highest fraction value was assigned as the major cell type of such pure-type RNA-seq samples. In the 209 pure-type RNA-seq samples, HASCAD is able to correctly identify the major type of immune cells, whereas CIBERSORTx and quanTIseq does not perform well in the cases of at least one cell types.et. al. reported that glioblastoma patients with higher levels of HSCs had poor prognosis [We used HASCAD to estimate the immune cell compositions in TAGA-LIHC samples, and performed survival analysis to explore the association between immune cell abundance and patients’ prognosis. Interestingly, the log-rank tests revealed significant associations with patients’ overall survival in the immune-cell abundance groups of hematopoietic stem cells (HSC) and three subtypes of CD8 T cells  of bulk RNA-seq data. HASCAD has been trained by using the synthetic bulk RNA-seq data that were simulated by using three scRNA-seq datasets consisting of 15 immune cell types. To mitigate the batch-specific variations that may cause model overfitting, the scRNA-seq datasets are preprocessed by using a novel normalization approach, the Harmony-Symphony correction. We show that, in the benchmarks, the HASCAD model that is trained based on the Harmony-Symphony normalized scRNA-seq datasets can really achieve a better cross-dataset performance. HASCAD has a performance that is at least comparable to those of two widely used CCD methods, and it can predict more cell types than the other methods that were built based on the RNA-seq or scRNA-seq data.Additional file 1:Figure S1. Venn diagram showing the number of genes in the reference and GSE data. Figure S2. The scatter plots of the ground-truth and predicted cell abundance made by HASCAD for the nine human PBMC bulk RNA-seq samples. Each point corresponds to a cell type in each sample. R^2 refers to Pearson’s correlation coefficient (PCC). Figure S3. The scatter plots of the ground-truth and cell abundance predictions made by CIBERSORTx for the nine human PBMC bulk RNA-seq samples. Each point corresponds to a cell type in each sample. R^2 refers to Pearson’s correlation coefficient (PCC). Figure S4. The scatter plots of the ground-truth and cell abundance predictions made by quanTIseq for the nine human PBMC bulk RNA-seq samples. Each point corresponds to a cell type in each sample. R^2 refers to Pearson’s correlation coefficient (PCC). Figure S5. The Bland-Altman plots (BA plots) of the cell type-specific differences between the ground- truth and the predictions made by HASCAD for the nine PBMC bulk RNA-seq samples. Each point corresponds to one of the nine PBMC bulk RNA-seq samples. Figure S6. The BA plots of the cell type-specific differences between the ground-truth and the predictions made by CIBERSORTx for the nine PBMC bulk RNA-seq samples. Each point corresponds to one of the nine PBMC bulk RNA-seq samples. Figure S7. The BA plots of the cell type-specific differences between the ground-truth and the predictions made by quanTIseq for the nine PBMC bulk RNA-seq samples. Each point corresponds to one of the nine PBMC bulk RNA-seq samples. Figure S8. The scatter plots of the ground-truth and cell abundance predictions made by xCell for the nine human PBMC bulk RNA-seq samples. R^2 refers to Pearson’s correlation coefficient (PCC). Each point corresponds to one of the nine PBMC bulk RNA-seq samples. Figure S9. The Venn diagram showing the number of genes reference scRNA-seq data and TCGA-LIHC bulk RNA-seq data. Figure S10. The barplot showing HASCAD-predicted proportions of various cell types in 364 TCGA- LIHC bulk RNA-seq samples. Figure S11. A Kaplan–Meier plot showing the difference in overall survival of TCGA-LIHC patients between the high and the low groups of HASCAD-predicted proportions of effector CD8 T cells. Figure S12. A Kaplan–Meier plot showing the difference in overall survival of TCGA-LIHC patients between the high and the low groups of HASCAD-predicted proportions of memory CD8 T cells. Figure S13. A Kaplan–Meier plot showing the difference in overall survival of TCGA-LIHC patients between the high and the low groups of HASCAD-predicted naïve CD8 T cells. Figure S14. A Kaplan–Meier plot showing the difference in overall survival of TCGA-LIHC patients between the high and the low groups of HASCAD-predicted proportions of hematopoietic stem cells. Table S1. Cell-type mapping of xCell to the cell types investigated in GSE107572.


Defects in DNA damage repair (DDR) pathways lead to genomic instability and oncogenesis. DDR deficiency is prevalent in esophageal squamous cell carcinoma (ESCC), but the effects of DDR alterations on mutational processes and tumor immune microenvironment in ECSS remain unclear.Whole‐exome and transcriptome sequencing data of 45 ESCC samples from Taizhou, China, were used to identify genomic variations, gene expression modulation in DDR pathways, and the abundance of tumor‐infiltrating immune cells. Ninety‐six ESCC cases from The Cancer Genome Atlas (TCGA) project were used for validation.HFM1 and NEIL1, were downregulated in ESCC tumor tissues and had an independent effect on the infiltration of mast cells. In the Taizhou data, increased expression of HFM1 was associated with a poor prognosis, and the increased expression of NEIL1 was associated with a good outcome, but no reproducible correlation was observed in the TCGA data.A total of 57.8% (26/45) of the cases in the Taizhou data and 70.8% (68/96) of the cases in the TCGA data carried at least one functional impact DDR mutation. Mutations in the DDR pathways were associated with a high tumor mutation burden. Several DDR deficiency‐related mutational signatures were discovered and were associated with immune cell infiltration, including T cells, monocytes, dendritic cells, and mast cells. The expression levels of two DDR genes, HFM1 and NEIL1 could play a crucial role in ESCC progression and may also serve as prognostic markers.This research demonstrated that DDR alterations could impact mutational processes and immune cell infiltration in ESCC. The suppression of  DNA damage repair (DDR) deficiency leads to genomic instability and oncogenesis and is prevalent in esophageal squamous cell carcinoma (ESCC). Using whole‐exome and transcriptome sequencing data from Taizhou and TCGA, we demonstrated that DDR alterations could impact mutational processes and immune cell infiltration in ESCC. The suppression of HFM1 and NEIL1 could play a crucial role in ESCC progression and may also serve as prognostic markers. Tumor cells with DDR deficiency tend to have a high TMB, which is believed to be a key driver in the generation of immunogenic neoantigens displayed on the major histocompatibility complexes (MHC) on the tumor cell surface that influence patient response to immunotherapy.Nevertheless, only a few of the mutations in cancers that are expected to be immunogenic are recognized by T cells. Thus, only a small number of patients identified by these biomarkers can benefit from ICB.Several DDR pathways have been implicated in the infiltration of immune cells in some solid tumors, but limited data exist detailing the impact of DDR deficiency on immune cell infiltration in ESCC.HFM1 and NEIL1 and revealed their effects on mast cells and patient outcomes. These findings highlight the importance of the DDR in the tumorigenesis of ESCC and show its potential applications for predicting the response to tumor immunotherapy.In the present study, we investigated the genomic and transcriptomic alterations of 160 DDR genes from nine DDR pathways in 45 Taizhou ESCC cases and 96 TCGA ESCC cases. We identified ubiquitous deficiencies in the DDR in ESCC and showed their essential role in mutational processes. We then evaluated the profiles of immune cell infiltration utilizing the CIBERSORT algorithm and confirmed the association between mutations and immune cell infiltration. Finally, we discovered the suppression of 22.1Tumors and matched normal tissues were obtained from patients who had been diagnosed with ESCC at Taixing People's Hospital in Taizhou city in China. Tissues were collected during surgery and immediately stored at −80°C. All samples were collected from ESCC patients who received surgery as their primary treatment from 2014 to 2017 at Taixing People's Hospital. Two pathologists checked the samples independently to confirm the tumor type and purity. Finally, 45 tumors (composition >50% tumor cells) and matched normal tissues were selected for sequencing. The clinical features of the patients are summarized in Table 2.2Using the exome capture procedure, DNA was extracted from the collected samples using SeqCap EZ Human Exome Probes v3.0 kits . The captured DNA samples were sequenced using the HiSeq X Ten system. The paired‐end 150 bp sequencing mode was employed to yield an average of 139.8 million high‐quality reads of 21.0 Gb bases for each of the 90 samples.Total RNA (>18 nucleotides) was extracted from the tissues using the miRNeasy Mini Kit with DNase treatment (Qiagen). All RNA samples had the RNA integrity numbers (RINs) > 7.5. Libraries for tumor and normal tissues were prepared using Ribo‐Zero rRNA Removal Kit coupled with the Illumina RNA‐sequencing (RNA‐seq) library protocol. One of the tumor RNA libraries failed to pass our quality control due to RNA degradation . The remaining 89 RNA samples were sequenced using the Illumina HiSeq 2500 platform.2.3The high‐quality reads were mapped to the human reference genome (GRCh38) using Burrows–Wheeler Aligner (BWA v0.7.17).sigminer (v2.1.4) based on nonnegative matrix factorization (NMF) with default options.The tumor mutation burden (TMB) was defined as the number of mutations (excluding synonymous mutations) in the tumor tissue. The mutational signatures were extracted using the R package According to the literatures, a set of 160 DDR genes from 9 DDR pathways was summarized  and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway.The raw sequencing reads were processed with Trimmomatic (v0.35) to remove the sequencing adapter and the low‐quality data.2.5TCGAbiolinks.https://jhubiostatistics.shinyapps.io/recount/) that processes and unifies raw sequencing data from both platforms after uniform realignment, gene expression quantification, and batch effect removal.The somatic mutations and relevant clinical data of the additional 96 ESCC samples were obtained from The Cancer Genome Atlas (TCGA) project. The clinical features of the patients are summarized in Table 2.6https://cibersort.stanford.edu/) and leucocyte signature matrix 22 (LM22) were used to quantify the relative fraction of 22 immune‐cell types in the samples.p value reflects the statistical significance of the results, and a threshold less than 0.05 is recommended. Finally, samples with CIBERSORT p values less than 0.05 were included in the following analyses.CIBERSORT . Clinical variables were summarized using percentages and descriptive statistics . We estimated the correlation between two quantitative data with Pearson's correlation analysis. The Kruskal–Wallis test was performed to evaluate the difference between groups. Linear regression analysis was conducted to identify the correlation between the DDR gene expression and the proportions of infiltrating immune cells. Differences in overall survival (OS) were assessed with Kaplan–Meier curves, and statistical significance was calculated using the log‐rank test. The thresholds for the different prognostic risk groups were calculated using the R package 33.1A total of 57.8% (26/45) of ESCC cases in the Taizhou dataset had at least one functional impact somatic mutation Table . FA 20.. The mosIn the TCGA dataset, 70.8% (68/96) of cases carried at least one functional impact DDR mutation Table . The mos3.2We compared the TMB between the DDR mutation and DDR wild‐type groups. In both datasets, we observed that the TMB was relatively high in the DDR mutation group. We excluded benign mutations from the DDR mutation group, and the results remained significant Figure . When weFive de novo signatures (Signature A/B/C/D/E) were extracted from the Taizhou and TCGA ESCC samples and matched to the COSMIC reference signatures Figure . SignatuFurthermore, we analyzed whether DDR mutations contribute to these signatures. In the Taizhou data, the expression of Signature A and Signature C were relatively high in the DDR mutation group, while the expression of Signature E was relatively low in the DDR mutation group Figure . In term3.3The relative fraction of infiltrating immune cells in all esophageal samples was calculated using the CIBERSORT algorithm with their gene expression data. In both datasets, the infiltration of activated CD4 memory cells, regulatory T cells, and M0/M1 macrophages in the tumor samples increased significantly, while the infiltration of monocytes and resting mast cells was significantly reduced Figure .We then analyzed the correlation between genomic alterations and the infiltration of immune cells Figure . In the p = 0.0054, HR = 0.31, 95% CI, 0.13–0.74). The TCGA data showed no difference in OS between the high TMB and low TMB groups. When we analyzed the mutational signatures, the high expression group of Signature B exhibited a good prognosis in the Taizhou data. The high expression groups of Signature A, Signature B, and Signature C exhibited a good prognosis in the TCGA data, while the high expression group of Signature D was associated with a worse prognosis.  and BER (NEIL1) pathways were downregulated in ESCC tumor tissues, while genes in HR , FA , NER (RPA3), NHEJ (PRKDC), and TLS (MAD2L2) pathways were upregulated in ESCC tumor tissues  and between TCGA ESCC tumor samples and·GTEx normal esophageal mucosa. A total of 1753 upregulated genes and 2335 downregulated genes were found in Taizhou ESCC samples, and 2820 upregulated genes and 3124 downregulated genes were found in the TCGA ESCC samples. Among them, 30 DEGs were DDR genes in Taizhou samples, and 26 were DDR genes in the TCGA samples. Eighteen DDR genes were differentially expressed in both datasets. Among them, genes in the MMR , and the expression of NEIL1 was significantly correlated with the infiltration of activated mast cells  and resting mast cells .The correlation between the relative fractions of several infiltrating immune cells and the DEGs of DDR was evaluated using the Kruskal–Wallis test. The results are shown in Figure on Table . The dat3.5HFM1/NEIL1 in tumor samples with the median value as a cutoff in both datasets. And 531 repeated DEGs were confirmed for HFM1 . Regarding NEIL1, high expression of NEIL1 was associated with a good prognosis . However, there was no reproducible significant overall survival correlation in the TCGA data. ; methodology (lead); resources ; software ; visualization (lead); writing – original draft (lead); writing – review and editing (lead). Tao Qing: Conceptualization ; methodology ; writing – review and editing . Sibo Zhu: Methodology ; supervision ; writing – review and editing . Xiaorong Yang: Investigation ; resources ; writing – review and editing . Weicheng Wu: Methodology ; resources ; writing – review and editing . Kelin Xu: Conceptualization ; writing – review and editing . Hui Chen: Investigation ; writing – review and editing . Yanfeng Jiang: Investigation ; resources ; writing – review and editing . Chengkai Zhu: Software ; visualization ; writing – review and editing . Ziyu Yuan: Investigation ; resources ; writing – review and editing . Tiejun Zhang: Supervision ; writing – review and editing . Li Jin: Project administration ; supervision ; writing – review and editing . Chen Suo: Conceptualization ; supervision ; writing – review and editing . Ming Lu: Investigation ; supervision ; writing – review and editing . Xingdong Chen: Conceptualization ; funding acquisition ; project administration ; supervision ; writing – review and editing . Weimin Ye: Conceptualization ; methodology ; writing – review and editing .This work was supported by the National Natural Science Foundation of China , the National Key Research and Development Program of China , the Innovation Grant from Science and Technology Commission of Shanghai Municipality, China (grant number: 20ZR1405600), three‐Year Action Plan for Strengthening Public Health System in Shanghai (grant number: GWV‐10.2‐YQ32).The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.The protocols for sample collection were approved by the Institutional Ethics Committee of the School of Life Sciences, Fudan University, and Qilu Hospital of Shandong University. All participants or their proxies provided informed consent.Figure S1Click here for additional data file.Figure S2Click here for additional data file.Figure S3Click here for additional data file.Figure S4Click here for additional data file.Figure S5Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.


SETD7) was sensitive to all six drugs on the shortlist, while two others (SRARP and YIPF5) were sensitive to both radiation and drugs. Furthermore, we did not find any radioresistance markers for the TNBC. The proposed biomarkers and drug sensitivity analysis will provide potential candidates for future clinical investigation.Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type of tumor. Several staging and classifying systems have been developed due to the variability of different types of breast cancer. Due to high heterogeneity, personalized treatment has become a new strategy. Out of all breast cancer subtypes, triple-negative breast cancer (TNBC) comprises ∼10%–15%. TNBC refers to the subtype of breast cancer where cells do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptors . Tumors in TNBC have a diverse set of genetic markers and prognostic indicators. We scanned the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases for potential drugs using human breast cancer cell lines and drug sensitivity data. Three different machine-learning approaches were used to evaluate the prediction of six effective drugs against the TNBC cell lines. The top biomarkers were then shortlisted on the basis of their involvement in breast cancer and further subjected to testing for radion resistance using data from the Cleveland database. It was observed that Panobinostat, PLX4720, Lapatinib, Nilotinib, Selumetinib, and Tanespimycin were six effective drugs against the TNBC cell lines. We could identify potential derivates that may be used against approved drugs. Only one biomarker ( Triple-negative breast cancer (TNBC) is an aggressive tumor that accounts for ∼10%–15% of all breast cancer (BC) subtypes and has a bad prognosis . PharmacIn simpler terms, drug sensitivity can be defined as the amount of activity achieved on a target . It is measured using various methods including area above the curve (AAC) and IC50 values, whereas drug resistance refers to the resistance of a target to a drug or specific compounds, which may be triggered by mutations or overdosing. This is correlated to biomarkers, which are entities that play a critical role in tumor survival. Radiotherapy is the most effective cancer treatment . For quiNumerous methods and approaches have been developed to solve the drug response prediction problem, including normalized regression techniques , elastic net, and ridge regression) , supportFirst, the sensitivity data for the drugs and cell lines data were retrieved from the two datasets [Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC)] and drug activity was examined using the IC50 and the area above the curve (AAC) graphs by us. This was done for the 16 shortlisted drugs considered in this study. Then we used the multivariate machine learning models to predict the accuracy of drug sensitivity on the cancer cell lines and shortlisted the best-performing drugs. We manually searched for potential biomarkers after shortlisting biomarkers from molecular data. Furthermore, for the radiation sensitivity data, the Cleveland database was used because we wanted to find biomarker signatures that were sensitive to both chemotherapy and radiation. Identifying signatures associated with radiosensitivity or radioresistance was possible with the RadioGx package.We curated a collection of cancer cell line screens from two different data sources for this study: Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) . Each daWe used two packages, PharmacoGx  and RadiThe drug dose-response curve function can be used to plot drug dose-response data from PharmacoSet objects . In eachIn pharmacogenomic studies, cancer cell lines were also tested for their dose-dependent response by increasing concentrations of various compounds from which IC50 values , the areWe examined the consistency of the GDSC and CCLE databases and discovered that the names of the cells and drugs utilized in the datasets were not identical. However, we used PharmacoGx to overcome these disparities and conducted a comparison analysis between the two datasets. The hgu133a platform was used to profile GDSC, whereas the more comprehensive hgu133plus2 platform was used to profile CCLE. While the hgu133a platform is essentially a subset of the hgu133plus2 platform in this case, Ensemble Gene IDs summarise the expression information in PharmacoSet objects, making it possible to compare datasets from different platforms.The downloadPSet utility was used to import the datasets from storage for testing consistency between them. To obtain the common intersect among the datasets, the intersectPSet was used. We created a breakdown of the gene expression and drug sensitivity metrics for both datasets, leaving one gene expression pattern and one sensitivity profile per cell line within each dataset. The Pearson correlation coefficient was then used to compare the gene expression and susceptibility metric between the datasets.We used the robust Concordance Index (rCI) to examine the concordance of multiple pharmacogenomic data sets (rCI) . The robRidge, elastic net, RF, LASSO, and SVM are some of the commonly used multivariate machine learning algorithms employed in our study. Multiple metrics have been used to evaluate the performance, including the Pearson correlation coefficient, concordance index, and robust concordance index.L2 regularization is used in ridge regression, which introduces the given penalty element to the OLS equation.The L2 term is directly proportional to the square of the coefficients’ magnitude. If lambda (λ) is 0, the formula is the basic OLS, but if it is bigger than zero, a restriction is added to the coefficients. This restriction leads to reduced coefficients , which tend to zero as lambda increases. Reduced coefficients result in a reduced variance and, as a result, a smaller error value. As a result, while ridge regression reduces the intricacy of a model, it does not lower the number of variables; rather, it reduces the influence of those variables.The L1 penalty term is used in lasso regression, which refers to the least absolute shrinkage and selection operator. The penalty for L2 is equivalent to the absolute amount of the coefficients’ magnitude:A lambda value of zero, like ridge regression, spits out the fundamental OLS equation; however, with the right lambda value, lasso regression can push several coefficients to zero. The greater lambda is, the more characteristics are reduced to zero. This can completely exclude some characteristics and leave us with a selection of predictors that can assist reduce the multi-collinearity and complexity of the model. Predictors that haven't shrunk to zero are crucial, therefore L1 regularization permits for selecting features (sparse selection).The elastic net is a third regularly used regression model that contains penalties both from L1 and L2 regularization:Elastic net allows us to tweak the alpha parameter in addition to specifying and picking a lambda value, where 𝞪 = 0 refers to the ridge and 𝞪 = 1 to lasso. Simply expressed, if alpha is set to 0, the penalty function reduces to an L1 (ridge) term, and if alpha is set to 1, the penalty function lowers to the L2 (lasso) term. As a result, we may improve the elastic net by selecting an alpha value between zero and 1. For sparse selection, this effectively shrinks certain coefficients and sets others to 0. Finally, in each category, SVM and random forests are effective strategies .The RF method is an ensemble approach that employs a large number of classification and regression trees (CART) . The booic class . Each trThe support vector machine technique can be used for both linear and non-linear data for classification as well as regression problems. Each data point is first projected onto an n-dimensional subspace, with n being the variety of attributes. The hyperplane that divides the data into two groups is then found, with the minimal proximity for both categories maximized and categorization mistakes reduced .We considered the GDSC data as input which contains RNA data. There was no need to split the data into training and testing because we wanted to use different databases for training and testing. The intersect function was used to interest the GDSC and CCLE data. To efficiently select potential features, we used the RStudio package maximum relevance minimum redundancy (mRMR) . Keeping1. Similarities search for Tanespimycin: First, we did data preparation or data labeling where we added a column for activity with a pIC50 of >= 6.0, and we found the number of active compounds was 210 while the number of inactive compounds was 198. Molecule encoding was done using the MACCS Method and we applied three classical machine learning approaches to classify our molecules namely, SVM, RF, and ANN, and the performance of the models where we fitted classical machine learning models on a train-test split of the data was observed. Splitting the data was reused for the two other classical models. We used test (x) and train (x) for the respective fingerprint splitting and test (y) and train (y) for the respective label splits, where the training data size was 326 and the test data size was 82.2. Similarities search for Selumetinib: First, we did data preparation or data labeling where we added a column for activity with a pIC50 of >= 6.0, and we found the number of active compounds was 93 while the number of inactive compounds was 51. Molecule encoding was done using the MACCS Method and we applied three classical machine learning approaches to classify our molecules namely, SVM, RF, and ANN, and the performance of the models where we fit classical machine learning models on a train-test split of the data was observed. Splitting the data was reused for the two other classical models. We used test (x) and train (x) for the respective fingerprint splitting and test (y) and train (y) for the respective label splits, where the training data size was 115 and the test data size was 29.3. Similarities search for Lapatinib: First, we did data preparation or data labeling where we added a column for activity with a pIC50 of >= 6.0, and we found the number of active compounds was 735 while the number of inactive compounds was 542. Molecule encoding was done using the MACCS Method and we applied three classical machine learning approaches to classify our molecules namely, SVM, RF, and ANN, and the performance of models where we fit classical machine learning models on a train-test split of the data was observed. Splitting the data was reused for the two other classical models. We used test (x) and train (x) for the respective fingerprint splitting and test (y) and train (y) for the respective label splits, where the training data size was 1,021 and the test data size was 256.Data retrieval for shortlisted targets, pre-processing of the bioactivity compounds, labeling of active compounds with the rule of five (Lipinski), descriptors calculation, and clustering of the molecules based on their fingerprint similarity were done for the three shortlisted compounds . Cell lines of interest can be chosen by any researcher, and a molecular signature that correlates to specific molecular features along with a given sensitivity profile can be computed. The identification of signatures associated with radiosensitivity or radioresistance can be performed thereafter.in vitro model systems, RadioGx allows one to compute the correlation signatures of molecular features with the response to treatment A natural question is how the signature for gamma radiation will compare to the signatures for the six shortlisted drugs on three biomarkers. This can be used to generate hypotheses for combination therapies or to learn more about the mechanism of drugs in the body.For The association between molecular features and response to a given drug is modeled using a linear regression model adjusted for tissue source:t-test) is used. The false discovery rate (FDR) technique is then used to fix p-values for multiple testing. With biomarker discoveries across pharmacogenomic research from CCLE and GDSC data, we can predict the significance of the link between medications and associated reported biomarkers.Where We have considered two types of cell lines data which include the drug sensitivity and the radiation sensitivity from the respective databases. RNA, RNA-Seq, copy number variation (CNV), mutational, and drug response data are examples of this data type. The details of the datasets are given in In pharmacogenomic studies, cells were also evaluated for their reaction to increasing doses of various substances in pharmacogenomic research, and the minimum inhibitory concentration and AAC were calculated as a result . These pTo plot the drug-dose analysis results included in PharmacoSet objects, the drug dose response curve function was used. The AAC curves for all of the shortlisted drugs on 10 cell lines are included in the supporting documentation .The AAC calculation of the chosen drugs is summarized in  and number cost function were optimized for a split. We observed that the sensitivity for RF was 0.79, the specificity for RF was 0.82, and the AUC for RF was 0.89. The support vector classifier was applied where the set model parameters for the SVM kernel were rbf, C value of 1, gamma value of 0.1, and the probability was True. We observed that the sensitivity for SVM was 0.79, the specificity was 0.90, and the AUC for SVM was 0.88. A neural network classifier was applied where the set model parameters for ANN hidden layer sizes were 5 and 3, and the random state was seed We observed that the sensitivity for ANN was 0.74, the specificity was 0.82, and the AUC for ANN was 0.89, as shown in 2. Similarities search for Selumetinib: The random forest classifier was applied where the set model parameter for random forest estimators was 100, and the number of trees to grow criterion (entropy) and number cost function were optimized for a split. We observed that the sensitivity for RF was 0.88, the specificity for RF was 0.54, and the AUC for RF was 0.74. The support vector classifier was applied where the set model parameters for the SVM kernel were rbf, C value of 1, gamma value of 0.1, and the probability was True. We observed that the sensitivity for SVM was 0.94, the specificity was 0.46, and the AUC was 0.75. The neural network classifier was applied where the set model parameters for ANN hidden layer sizes were 5 and 3, and the random state was SEED. We observed that the sensitivity for ANN was 0.88, the specificity was 0.54 and the AUC was 0.75, as shown in 3. Similarities search for Lapatinib: The random forest classifier was applied where the set model parameter for random forest estimators was 100, and the number of trees to grow criterion (entropy) and number cost function were optimized for a split. We observed that the sensitivity for RF was 0.93, the specificity was 0.81, and the AUC was 0.92. The support vector classifier was applied where the set model parameters for the SVM kernel were rbf, C value of 1, gamma value of 0.1, and the probability was True. We observed that the sensitivity for SVM was 0.93, the specificity was 0.73, and the AUC was 0.90. The neural network classifier was applied where the set model parameters for the ANN hidden layer sizes were 5 and 3, and the random state was SEED. We observed that the sensitivity for ANN was 0.91, the specificity was 0.73, and the AUC was 0.89, as shown in Data retrieval for shortlisted targets, pre-processing and labeling of the bioactivity compounds, fingerprint descriptors calculation, and clustering of the molecules based on their fingerprint similarity were done for all three shortlisted molecules . In breast cancer cell lines, primary breast tumors, and metastatic breast cancer, SRARP has been shown to be highly co-expressed with AR , lower grade, and lobular histology  and androgen response element reporter activity  with rows addressing the radiation type and columns addressing cell lines, representing values that sum up the viability measurements. Sensitivity measures can be specified using the sensitivity measure function.Radiogenomics is designed similarly to pharmacogenomics. The only difference is in the method of cell treatment. The only available clinical database which holds YIPF5 (which stands for Yip1 domain family member 5) plays a role in transport between the endoplasmic reticulum and Golgi. YIPF5 is a prognostic marker in head, neck, liver, and breast cancers.The SETD7 was not observed to be sensitive to gamma radiation while the other two biomarkers (SRARP and YIPF5) were highly sensitive to the radiation  patients in the early stages as well in the late stages of the progression of the disease. TNBC patients have a poor prognosis, as a result, a considerable effort has been made so that we can find responsive molecular targets to treat these malignancies. Although the accessibility of data has been increased due to the high throughput sensitivity of drug testing, effective drug response still remains a challenge. Understanding the interaction between a cell line and a specific drug will eventually allow for tailored treatment for specific cancer patients . These rSETD7, SRARP, and YIPF5, were identified. SETD7 was not radiosensitive, while SRARP and YIPF5 showed sensitivity to all the drugs and gamma radiations from the Cleveland database. Additionally, no radioresistant biomarkers were found for TNBC. The main limitation was the accuracy limit, which is insignificant because of the low availability of data. Accuracy can be further improved when more data become available. Here we are specifically focusing on the TNBC data only. In future studies, we can improve model performance by considering more data and including single-cell data for drug and biomarker screening.In this study, we predicted drug sensitivity on breast cancer cell lines, out of which three main biomarkers were shortlisted by evaluating their response to the drugs and exposure to radiation. In our case of predicting drug sensitivity, the highest accuracy was found for PLX-4720 drugs using a random forest approach. Three main biomarkers, In our study, we attempted to provide a solid groundwork for machine learning-driven prediction of drug sensitivity for TNBC, which has not been reported previously at this level, and the shortlisted markers could be potential therapeutic targets. Future research will likely focus on computational and experimental molecular modeling of shortlisted drugs and biomarkers. This understanding will bring the era of personalized cancer medicine closer to reality.