MMP3 gene decreases in stage IV of colorectal cancer compared to other stages . Also, the expression level of the WNT11 gene was observed to increase significantly in this stage . It was also found that the expression of WNT5a, TNFSF11, and MMP3 is significantly lower, and the expression level of WNT11 is significantly higher in liver metastasis samples compared to primary tumors. In summary, this study has identified a set of potential biomarkers for CRC metastasis using ML algorithms. The findings of this research may provide new insights into identifying biomarkers for CRC metastasis and may potentially lay the groundwork for innovative therapeutic strategies for treatment of this disease.Colorectal cancer (CRC) liver metastasis accounts for the majority of fatalities associated with CRC. Early detection of metastasis is crucial for improving patient outcomes but can be delayed due to a lack of symptoms. In this research, we aimed to investigate CRC metastasis-related biomarkers by employing a machine learning (ML) approach and experimental validation. The gene expression profile of CRC patients with liver metastasis was obtained using the GSE41568 dataset, and the differentially expressed genes between primary and metastatic samples were screened. Subsequently, we carried out feature selection to identify the most relevant DEGs using LASSO and Penalized-SVM methods. DEGs commonly selected by these methods were selected for further analysis. Finally, the experimental validation was done through qRT-PCR. 11 genes were commonly selected by LASSO and P-SVM algorithms, among which seven had prognostic value in colorectal cancer. It was found that the expression of the  An estimated 90% of all cancer-related deaths are caused by cancer metastasis, making it a significant obstacle to effective cancer care2. Nearly 20% of CRC patients present with metastatic disease at initial diagnosis, and the liver is the most general metastatic site for CRC, accounting for almost 50% of all cases3. Metastasis of CRC leads to a poor prognosis4. Despite receiving standard treatments such as surgical removal, radiation therapy, and systemic chemotherapy, many patients with CRC liver metastasis still experience high rates of recurrence and less favorable clinical outcomes5. Hence, early diagnosis of liver metastases of CRC is crucial for improving patients’ prognosis and clinical outcomes6. Imaging examinations and focal biopsies are necessary for diagnosing CRC liver metastasis. However, the sensitivity of imaging techniques for CRC liver metastasis is still insufficient to accomplish the benefit of early diagnosis7. Incorporating biomarkers alongside imaging methods can significantly enhance the accuracy of detecting CRC liver metastasis8. Various biomarkers are utilized for detecting CRC liver metastasis, including but not limited to CEA, CA19-9, CA125, and others10. On the other hand, while serum markers such as CEA are helpful in diagnosing CRC, their limitations in terms of sensitivity and specificity decrease their reliability in identifying hepatic metastases particularly6. Thus, it is imperative to explore novel biomarkers to improve patients' diagnostic accuracy and clinical outcomes. Several types of biomarkers are used for cancer screening, including DNA, protein, and RNA biomarkers11. Transcriptional biomarkers are a promising class of biomarkers reflecting changes in the levels of RNA molecules produced from DNA in cells, including mRNAs, micro RNAs, long non-coding RNAs, and circular RNAs. They are non-invasive and highly sensitive, making them a valuable tool for the early detection and monitoring of various cancers12. Cancer initiation, development, and metastasis are influenced by complex processes and alterations at the transcriptome levels13.Despite numerous research efforts aimed at identifying strategies for cancer prevention, colorectal cancer (CRC) is still the second cause of cancer mortality worldwide, with about one million deaths in 202014. The analysis of transcriptome data can be complex and time-consuming, requiring expertise in bioinformatics and statistics. Traditional methods for analyzing transcriptome data involve manual curation and interpretation, which can be error-prone and may not be beneficial in handling the large amounts of data generated by modern sequencing technologies15. Artificial Intelligence (AI) algorithms such as machine learning (ML) can identify patterns and relationships in the data that would be difficult or impossible to detect using manual methods16. ML is a branch of AI that applies statistical methods and algorithms to achieve data parsing, categorization, and pattern recognition. This obtained information later enables computers to learn from data processing experiences and make more accurate predictions17. It is anticipated that ML will have a preeminent impact in the therapeutic context for detecting and treating cancer in the near future. It is worth mentioning that ML has been progressively utilized for the screening, diagnosis, and therapy of CRC over the course of the past five years18.Healthcare digitalization and the development of high-throughput technologies, such as microarray and next-generation sequencing (NGS), have enabled the collection of large amounts of transcriptome data in comprehensive databases such as The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database, which can be used to understand the underlying mechanisms of diseases which in turn developing precision medicineWe used a machine learning-based feature selection technique to explore liver metastasis-related biomarkers in CRC. These methods are particularly beneficial for handling high-dimensional data and complex interactions between features and improving predictive models' performance which, results in identifying relevant features based on their predictive power. In the present study, features commonly selected by two feature selection algorithms were investigated through in-silico and experimental validation.http://www.ncbi.nlm.nih.gov/geo) to identify any relevant available datasets based on these criteria: (1) datasets contain primary CRC and metastatic liver tumor samples; (2) comprise more than 20 samples in each group; (3) more than 10,000 genes in each dataset. Based on these considerations, three microarray datasets, GSE41568, GSE41258, and GSE68468, were included in the present study  to obtain RNA-seq data and clinical details of 644 CRC patients (89 metastatic (M1) and 555 non-metastatic (M0) samples) as well as another validation set in our study.A schematic representation of the research process is illustrated in Fig. riteria:  datasetshttps://www.r-project.org/)19. Raw data was also evaluated to contain logarithmic fold change values, and if necessary, logarithm 2 of values was obtained. The probe IDs were converted into gene expression symbols on each annotation platform. Multiple probes related to a single gene were averaged to give the gene expression value. Also, probes with a vacancy were removed.On each platform, raw data was retrieved for these three datasets. All datasets were normalized if needed in R software to conduct this analysis to uncover the pathways most relevant to all expressed genes between primary tumors and liver metastases of CRC patients in the GSE41568 dataset by setting FDR criteria to < 0.05.20 in R. |log2Fold Change |≥ 1 and False Discovery Rate threshold (FDR) < 0.05 were considered as cut-off criteria.To screen DEGs between primary tumors and liver metastasis samples in the GSE41568 dataset, we carried out differential gene expression analysis using the limma packagehttp://www.Kegg.jp)) and GO enrichment analysis of selected DEGs was carried out using ClusterProfiler21, and GOplot22 packages in R. Statistical significance was assigned in terms of Benjamini < 0.05.To investigate the biological properties of these DEGs, KEGG . Random Forest (RF), 2). Penalized Support Vector Machine (P-SVM) with two penalties of Smoothly Clipped Absolute Deviation (SCAD) and Least Absolute Shrinkage and Selection Operator (LASSO).ML methods are tools to develop and evaluate classification and prediction algorithms. Data collection, model selection, training the model, and testing the model are the four steps that make up the foundation of machine learning25. The "randomForestSRC" R package was utilized in our investigation to determine the best features.Random Forest, proposed by Breiman (2001), is a well-known technique that belongs to the ensemble algorithms used for classification and regression issues. This method forecasts an outcome by averaging the results of hundreds or more decision trees. RF is also employed as a variable selection strategy to identify informative variables26. However, a significant shortcoming of this method is that it cannot perform automated gene selection. To handle this problem, the " P-SVM" method was introduced, including two wrapper feature selection techniques for SVM classification utilizing the penalty function27.Support vector machine (SVM) classification is one of the most popular and effective classification approachesLet us consider The "wj" is the regression coefficient of the obtained hyperplane, and "b" stands for its intercept.Then the assignment rule for the test dataset to each class is given as: In the above problem, the finding of the optimal hyperplane is conducted by convex optimization. In the penalized version, maximizing the margins or optimization is achieved by the following penalized problem:Here the 28. So, the outcome, which variable takes y = 1 or y = 0, is considered to have a Bernoulli distribution with The logistic regression model considers a linear relationship between predictors  with a binary (dichotomous) outcome Then, the parameter estimation is conducted by the maximum likelihood estimation by considering the following log-likelihood function:Like the P-SVM, to handle the high dimension problem (having a greater number of predictors than the sample size), the penalized likelihood is used for variable selection. The penalty terms can have different forms. Here, we considered Lasso and SCAD (Smoothly Clipped Absolute Deviation).29: .The LASSO approach imposes a constraint on the total of the absolute values of the model parameters; the sum must be smaller than a predetermined value30. SCAD penalty, suggested by Fan and Li in 200131, is used to reduce the bias while estimating large regression coefficients32:In terms of variable selection, LASSO and SCAD have similar performancesa > 2. In this study, we set the tuning parameter α's value at 3.7. However, the lambda parameter was tuned by the cross-validation method.The tuning parameters in this equation are λ ≥ 0 and In order to assess the efficiency of genes selected by each method in differentiating primary samples from metastatic tumors, we applied the Artificial Neural Network (ANN) method using the Multilayer Perceptron procedure in SPSS 24.0. Algorithms with an area under the ROC curve (AUC) > 0.9 were opted, and commonly selected features by these algorithms were considered as the main DEGs for further analysis.33. This algorithm distinguishes sample type by estimating the degree of a sample that belongs to a specific class34. As a part of this study, We constructed an SVM classifier based on selected features for the GSE41568 training set using the "e1071" package in R35. The SVM classifier's efficacy was assessed on the training and three independent validation sets .SVM is a supervised ML algorithm that is mainly used for data categorizationDisease-free survival (DFS) and overall survival (OS) analyses were carried out to identify genes with prognostic significance. In this regard, for OS analysis, 644 TCGA COAD-READ samples were categorized into high-expression and low-expression groups based on the optimal cut-off points determined by the "survminer" and "maxstat" R packages. Using the "survival" package and P value < 0.05, we conducted a Kaplan–Meier survival analysis with a log-rank test to determine which genes are associated with overall survival.36. NetworkAnalyst is a web-based application for comprehensive gene expression profiling and meta-analysis using network-based visual analytics36. To construct the TF-gene network, the final DEGs were submitted to NetworkAnalyst to collect information on TF-gene and microRNA-gene interactions, and the resultant list of datasets was exported to Cytoscape software (version 3.7.1) for additional analysis.Toward identifying the transcription factors (TFs) of the key genes, we utilized the NetworkAnalyst online toolhttps://www.dgidb.org/) was utilized to find the potential drugs that target the final genes. This database is linked to 22 different databases. To find drug–DEG interactions in the current investigation, only empirically verified interactions were examined.The Drug Gene Interaction Database (DGIdb) . Then Yekta Tajhiz cDNA Synthesis Kit  was used to convert the extracted RNA into cDNA. The quantitative reverse transcription PCR (RT-qPCR) was carried out on each sample in duplicate using SYBR Green Master Mix Kit  in a LightCycler 96 Real-Time PCR detection system  in accordance with the manufacturer’s guidelines. The primer sequence of four evaluated genes in this study is presented in Table The data were analyzed utilizing the R programming language, SPSS 24.0, and the GraphPad Prism 9.0 software. The differences between expression values of the three sample groups were evaluated through a one-way ANOVA test. Statistical significance was determined using the thresholds of *P value < 0.05, **P value < 0.01, and ***P value < 0.001 for all statistical tests.Ethical issues  have been completely observed by the authors.The ethical protocol of this study was approved by the Ethics Committee of Hamadan University of Medical Sciences.  and written informed consent was obtained from all patients to participate in the study.Details of selected databases are summarized in Table 38. The DEGs were most strongly related to the BP of “humoral immune response” (GO:0006959), CC of “blood microparticle” (GO:0072562), and MF of “peptidase regulatory activity” (GO:0052547), according to the analysis of the GO terms. Remarkably, most of the top enriched gene ontology BP, CC, and MF terms were related to a lipid metabolic process, including the “fatty acid metabolic process” and “peptidase activity”. Moreover, the screened DEGs were considerably enriched in the “complement and coagulation cascade pathway” , biological process (BP), and cellular component (CC) GO:005257, accordMachine learning algorithms of RF, P-SVM, and logistic regressions with LASSO and SCAD penalties were employed for selecting features associated with metastasis among 496 screened DEGs. The LASSO method applied by the “glmnet”, P-SVM method by the “penalizedSVM” package, and SCAD by “grpreg” package in R selected 20, 32, and 6 features, respectively, as the most relevant features. Also, 43 features were selected by random forest algorithm. Genes selected by each algorithm are listed in supplementary table MMP3, TNFSF11, WNT5A, EPHA3, WNT11, CXCR4, MAP2, MAB21L2, FOXC1, TMEM158, PDE4D. These 11 genes may have the potential to be considered as a diagnostic panel for colorectal cancer metastasis.A multi-layer perceptron artificial neural network was utilized to compare the accuracy of feature selection methods. In this regard, ANN was trained with 70% of the data based on features selected by each algorithm, a hold-out validation technique was applied in SPSS, and the performance of the algorithms was evaluated with respect to the area under the receiver operating characteristic (ROC) curve. The AUC results revealed that P-SVM and LASSO are the most accurate models, with an AUC of 0.94 and 0.9, respectively. Roc curves of different algorithms are presented in Fig. The 11 mentioned (previous paragraph) genes from the GSE41568 training dataset were used to construct an SVM predictive classification model. The C-Classification SVM method was applied with Radial Based Function (RBF) kernel and tenfold cross-validation. The AUC in the GSE41568 was 1, standing for sensitivity and specificity of 100% , TNFSF11 , MMP3 , and MAP2  were the most significant genes predicting poor OS in CRC patients. The findings show that OS is lower in patients with low expression of MMP3, WNT5a, and TNFSF11. Also, patients with high expression of WNT11 had lower OS. The association between the expression of these genes with OS of CRC patients is presented in Fig. Survival analysis was conducted by the optimal cut-off value. The results showed that seven of eleven feature genes were significantly related to the poor prognosis of CRC patients. In this case, To uncover other underlying TFs regulating the selected genes, the TF–DEGs network was constructed using the networkanalyst tool and ENCODE database. According to this database, a total of 65 TFs were found to be related to the feature genes. The constructed network was visualized by Cytoscape and is depicted in Fig. MMP3, EPHA3, MAP2, TNFSF11, CXCR4 and PDE4D had approved targeting drugs. A total of 30 drugs were found, nine of which were antineoplastic, including Lenalidomide, Anastrozole, Letrozole, Colchicine, Plerixafor, Bevacizumab, Cisplatin, and Vandetanib. The drug-gene network was illustrated using Cytoscape and is represented in Fig. The list of selected genes was imported into the DGIdb database to investigate any FDA-approved drugs related to these genes. Among feature genes MMP3, WNT5a, WNT11, and TNFSF11 genes. Analysis between three research groups revealed that expression of the MMP3 gene was significantly lower in the liver metastasis group compared to other groups. The expression of this gene was also lower in the stage IV CRC group compared to CRC samples from other stages. WNT11 was the other gene that showed a significant expression alternation in different groups. The results showed that this gene overexpressed significantly in liver metastases compared to stage 4 and stages 1,2,3 samples. This gene was also expressed in higher levels in stage 4 CRC samples compared to stages 1,2,3 CRC group. The gene expression levels of WNT5a and TNFSF11 were also significantly lower in liver metastases compared to stage 4 and stages 1,2,3 CRC groups but showed no significant expression alternations between stage 4 and stages1,2,3 CRC samples  was first found to have a role in bone dissolution and lymph node formation, majorly via the RANK/RANKL/OPG pathway51. Current studies have proved the critical role of RANK/RANKL/OPG in cell migration and invasion. Additionally, it has been observed that TNFRSF11(RANK) is engaged in the development of several types of cancers, including lung cancer52, prostate cancer53, renal cancer54, breast cancer55, and melanoma56. On the other hand, several investigations have shown that the RANKL/RANK system promotes both primary carcinogenesis and metastasis through osteoclast-independent mechanisms57. Furthermore, Ahern et al. found that TNFSF11 knockdown enhances the anti-metastatic effect of antibodies targeting PD1/PD-L1 and suppresses the growth of the subcutaneous tumors of colon cancer animal models58.MMP3, also known as stromelysin-1, is a member of matrix metalloproteinase (MMP). MMP3 participates in several cellular biological processes, including cell differentiation and inflammation. This gene contributes to the onset and progression of several disorders. MMP3 is capable of degrading ECM, which facilitates tumor invasion and metastasis. Different studies have revealed MMP3 expression alternation and its role in metastasis in various cancers, such as osteosarcoma59 and ovarian cancer60. Moreover, MMP3 expression was also discovered to be substantially elevated in malignant colorectal tumors compared with normal tissue61.MMP3 is downregulated in metastatic lesions compared with primary tumors of different cancers. Maiti et al. revealed that MMP3 downregulates significantly in metastatic sites vs. primary breast tumors using qPCR . In addition, they discovered a correlation between MMP3 and the prognosis of breast cancer patients62. Another study on this issue was conducted by wang et al. to figure out essential DEGs in CRC metastasis using NGS profiling on primary colorectal tumor samples from CRC patients with and without liver metastases and validated their findings by qPCR and immunostaining. The results of this research indicated that MMP3 significantly downregulates in samples with liver metastasis63. The current study found clear support for these findings. We observed that the MMP3 expression level was significantly lower in high-stage tumor samples compared with low-stage samples. This is in contrary to the findings claiming that the expression level of this gene is not associated with the tumor stages64.Interestingly, very few studies have shown that 65. This protein takes part in the formation of heterochromatin structure, which causes gene silencing66. Various studies have been conducted to indicate EZH2 involvement in different cancers' progression and metastasis. In a study by Zheng et al., they represented that this transcription factor plays a direct role in breast cancer bone metastasis through the TGF-B pathway. They observed that EZH2 knockout in mice prevented bone metastasis67. Also, Chen et al. reported that EZH2 is responsible for poor prognosis in CRC. They proved that EZH2 is upregulated in colorectal tumor tissues by qRT-PCR and western blot analysis. Additionally, they observed that high EZH2 expression was substantially linked with tumor stage, tumor size, histological differentiation, and lymph node metastasis68. Nevertheless, additional studies are required to investigate the role of this TF in CRC metastasis.The other part of our study was identifying vital transcription factors involved in CRC metastasis. We found that EZH2 modulates five interacting DEGs and could be considered an essential TF in this process. EZH2 is an inhibitory transcription factor that plays a role in the histone methylation process69. Lenalidomide and Plerixafor have also shown potential for metastatic CRC treatment. In a study by Galustian et al., they proved that Lenalidomide can inhibit metastatic CRC in vivo and in vitro70. On the other hand, Plerixafor has completed phase one trials for metastatic CRC71. Other drugs in the drug-DEGs network in this study have also found to be effective in the treatment of different metastatic cancers, such as breast cancer72 and thyroid carcinoma73. These drugs may have the potential to be repurposed as a treatment for metastatic CRC, providing new options for patients and physicians.In the last part of this study, we investigated the DGIdb database for possible drugs targeting our final genes. In this regard, nine antineoplastic drugs were found. Among them, Bevacizumab, sold under the brand name Avastin is an approved drug for the treatment of metastatic CRC74. This imperative is underscored by a substantial decrease in the 5-year survival rate, notably in instances of distant metastases, where the survival rate plummets to 10%76. The necessity to identify precise genes and pathways is increased aiming for timely diagnosis and personalized therapeutic strategies to effectively confront the intricacies of cancer progression and metastasis77. Importantly, the identification of stage-specific biomarkers in colorectal cancer is of great importance in this context, as it considerably enhances our ability for early detection and targeted interventions, thereby contributing significantly to addressing the challenges associated with colorectal cancer79.The mortality rate of cancer is closely linked to the stage of cancer progression, highlighting the possibility of reducing mortality through early detection and management80. this advanced computational approach not only supports the necessity of early detection and intervention but also improves the accuracy and effectiveness of the biomarker selection process81. The application of machine learning, including algorithms like LASSO and P-SVM, introduces a nuanced and targeted methodology, augmenting our capability to discern key biomarkers associated with different stages of colorectal cancer83. This innovative approach represents an important step toward refining our understanding of cancer progression, establishing a foundation for the development of more effective diagnostic and therapeutic strategies tailored to specific stages of the disease84.The integration of ML algorithms into the analysis of existing datasets holds promising potential for identifying stage-specific biomarkers in colorectal cancerAlthough further investigations are needed, the present study contributes to a better understanding of CRC metastasis. It is crucial to consider the limitations of this study when interpreting its findings, including low liver metastases sample size due to the scarcity of liver metastasis samples of CRC from participating hospitals. This constraint may have affected the study's ability to detect statistically significant differences and limits the generalizability of the results.We employed two machine learning algorithms to identify biomarkers associated with CRC metastasis. Through these methods, a total of 11 biomarkers were identified, and four of them were experimentally validated. Also, the SVM model based on these 11 feature genes showed the optimal classification performance in identifying CRC liver metastasis samples. The joint application of these genes could be considered as a diagnostic panel for metastasis assessment, further augmented by the development of an innovative AI predictive model based on these genetic signatures. These findings make a significant contribution to the continuous pursuit of deeper comprehension regarding the intrinsic molecular mechanisms driving CRC metastasis, with potential implications for the advancement of more efficacious diagnostic and therapeutic strategies tailored to this affliction. Additionally, the use of machine learning approaches in this study highlights the potential of this method for identifying biomarkers in complex biological systems.Supplementary Table 1.Supplementary Figure 1.


With the advances in high-throughput sequencing technology, an increasing amount of research in revealing heterogeneity among cells has been widely performed. Differences between individual cells’ functionality are determined based on the differences in the gene expression profiles. Although the observations indicate a great performance of clustering methods, manual annotation of the clusters of cells is a challenge yet to be addressed more scalable and faster. On the other hand, due to the lack of enough labelled datasets, just a few supervised techniques have been used in cell type identification, and they obtained more robust results compared to clustering methods. A recent study showed that a complementary step of feature selection helped support vector machine (SVM) to outperform other classifiers in different scenarios. In this article, we compare and evaluate the performance of two state-of-the-art supervised methods, XGBoost and SVM, with information gain as a feature selection method. The results of the experiments on three standard scRNA-seq datasets indicate that XGBoost automatically annotates cell types in a simpler and more scalable framework. Additionally, it sheds light on the potential use of boosting tree approaches combined with deep neural networks to capture underlying information of single-cell RNA-Seq data more effectively. It can be used to identify marker genes and other applications in biological studies. In living organisms, there are a great variety of cells that can be distinguished with the help of single-cell RNA sequencing (sc-RNA sequencing) technology. Single-cell RNA sequencing (scRNA-seq) is a novel sequencing technology that involves individual cell information and can be used in cell heterogeneity studies. Studying different types of cancer, detecting unknown tumours and tumour heterogeneity, drug discovery, diagnosis, and prognosis are a few numbers of the new opportunities for research in this scope.Identifying cell type heterogeneity is one of the first fundamental steps in an in-depth analysis of single-cell RNA sequencing data. Hidden diversity and characteristics of a particular cell type can be found via deferentially expressed genes (DEGs). Machine learning approaches can be effectively used to identify hidden differentiation in the expression profiles of the genes with high probability. scRNA-seq data comes with a variety of limitations. The highlighted one is the lack of annotation for most of the data which are publicly available. In a general single-cell RNA-seq downstream analysis, clustering techniques are widely used to reveal groups of cells and cell types. However, setting up the parameters, including the number of clusters, is a challenging point . For insK-NN, and SVM and feature selection methods, including Analysis of Variance (ANOVA) F-value, Information Gain, and Chi-squared considered complementary of the classifiers. One of the challenges covered in this study was selecting cell-specific genes in the feature selection step. A benchmark study was performed based on the number of selected features. However, it remains for a exploration of general-purpose feature engineering techniques against domain-specific ones, and in particular pattern-aware techniques to be done. When reviewing different approaches, including supervised and unsupervised, for cell type annotation based on scRNA-seq data, there is no comparison with the XGBoost method. Precisely, the power of XGBoost and SVM was proven in the previous studies and XGBoost performs in a faster and simpler way. Moreover, SVM together with a general-purpose feature selection had been shown as a high-performance method in the supervised cell-type annotation. In this study, inspired by the recent works completed in cell-type classification, we compared two forefront approaches; the general-purpose model, a SVM classifier with information gain feature selection method and XGBoost tree with its inherent feature selection strategy. This paper guides users and practitioners to select the most proper model based on the inherent features of their datasets.Additionally, the ensemble learning schema combines weak learners’ voting for an accurate final vote on similarity search space. For instance, EDGE  has utilThe schematic view of To validate our models, we considered the most commonly used evaluation metrics, namely accuracy, precision, and recall, to systematically estimate and compare the performance of our models. To this end, we used 10-fold cross-validation to test and train the model. Additionally, we tuned XGBoost parameters as follows: (1) the regularization parameter value to create a new split in trees, We used Scikit-learn  in PythoTo evaluate the performance of the model, we used public, annotated scRNA-seq datasets with accession numbers GSM2230757, GSM2230758, and GSM2230762 under series GSE84133  extracteRaw read count matrices contain low-quality RNA sequencing information based on differential expression levels. Data pre-processing is performed to ensure removing any weakly expressed genes or low-quality cells, including damaged, dead, or degraded during sequencing, and are represented by a low number of expressed genes in the read count matrices. We followed the standard pre-processing pipeline in scRNA-seq data analysis . Based oHyperparameter tuning, also known as hyperparameter optimization or model selection, is the process of systematically searching for the best combination of hyperparameters to optimize the performance of a machine learning model. Hyperparameters are parameters that are not learned from data but are set before training. Examples of hyperparameters for XGBoost include the learning rate, max depth, gamma, minimum child weight, and column sample by the tree. In this research, the process of tuning these parameters has been completed automatically using Bayesian optimization. Bayesian optimization is a method for efficiently searching for the best set of hyperparameters of a model. The basic idea is to use a probabilistic model, such as a Gaussian process, to model the function that maps from hyperparameters to the performance of the model on a given task. Mathematically, Bayesian optimization can be formulated as an optimization problem in which we want to find a set of hyperparameters that maximize the expected performance of the model, given the current state of the probabilistic model. The expected performance is given by the mean of the model, and the uncertainty in the model is represented by the variance. The acquisition function, such as the expected improvement, is used to balance the exploration and exploitation of the search space. To optimize the acquisition function, we optimize the hyperparameters of the probabilistic model to maximize the expected improvement. This is done by using gradient-based optimization algorithms, such as L-BFGS, or using more sophisticated methods, such as Hamiltonian Monte Carlo. In summary, Bayesian Optimization is a powerful method for tuning a machine learning model’s hyperparameters by using a probabilistic model to guide the search for the best set of hyperparameters and balancing exploration and exploitation using an acquisition function.X in the classification process, as shown in Equation  model  uses a skNN, and RF) with a group of genes extracted from a pioneer general-purpose feature selection method, information gain. The second scenario was defined with genes obtained using the inherent approach in the XGBoost tree, which uses the latent pattern in scRNA-seq data. We calculated the average of all measurements when comparing the results. The number of features is determined based on the one with the highest final predictive accuracy. Results are presented in The first objective of this work was to evaluate the accuracy of the main classifiers  with 200 features selected from IG, RF, and IG combined, achieving high accuracy (96.06%) with 400 features. Lastly, SVM achieves high accuracy with 400 features (98.09%) selected from the IG feature selection method. XGBoost coupled with IG provides the best performance, with 99.67% accuracy.To explore the effect of the selected feature genes as a form of prior knowledge, we evaluated the classifiers’ performance based on the different numbers of selected features. The optimal value of features,  methods . k-NN clFor Data3, SVM outperformed the other two classification methods and achieved the highest precision of 84.91% with 300 features selected from the IG feature selection method. Regarding misclassification rate and recall, the results are very close to XGBoost. In general, Data3 has fewer features, comparatively speaking. Hence, as mentioned earlier, XGBoost is less effective when it comes to small-scale datasets.We performed an extra step of biological evaluation for detecting cell types using highly-ranked features identified in the feature selection phase. Among a wide range of gene set enrichment analysis (GSEA) databases, we chose the C8 collection of MSigDB, which includes cell type signature’s gene sets . We sepaThis study compares two recently reported pioneer classification models, XGBoost and SVM, for discovering cell types using a list of marker genes. One with a blind feature selection method, pure information gain, and the other one with data sparsity-aware inherent feature selection, GXBoost feature splitting algorithm. It is shown that considering the data with its latent sparsity pattern significantly enhances the overall accuracy of the predictive models. Since the high degree of sparsity in scRNA-seq data arises from false technical zeros and true biological zeros, exploiting the patterns of existing and non-existing values for selecting biomarkers makes it more precise, faster, and more meaningful. Our study particularly demonstrates the effectiveness of ensemble tree models with an inherent sparsity-awareness feature selection approach in the cell-type automatic annotation problem. Biological validation of the results confirmed the overall accuracy of the prediction.Moreover, the lack of canonical biomarkers for certain cell types makes it more complicated to find rare cell types using the existing genes in the list of top-ranked ones. In this case, following a manual lookup in the gene set repositories of related genes in gene sets could support the study and the results. Biologically speaking, the relation among genes is defined by structural, functional or evolutionary information. This work provides a guideline for researchers to select and apply the well-suited tool in annotating cell types using associated genes or uncovering homogeneous markers.


Acute cellular stress is known to induce a global reduction in mRNA translation through suppression of cap dependent translation. Selective translation in response to acute stress has been shown to play important roles in regulating the stress response. However, accurately profiling translational changes transcriptome-wide in response to acute cellular stress has been challenging. Commonly used data normalization methods operate on the assumption that any systematic shifts are experimental artifacts. Consequently, if applied to profiling acute cellular stress-induced mRNA translation changes, these methods are expected to produce biased estimates. To address this issue, we designed, produced, and evaluated a panel of 16 oligomers to serve as external standards for ribosome profiling studies. Using Sodium Arsenite treatment-induced oxidative stress in lymphoblastoid cell lines as a model system, we applied spike-in oligomers as external standards. We found our spike-in oligomers to display a strong linear correlation between the observed and the expected quantification, with small ratio compression at the lower concentration range. Using the expected fold changes constructed from spike-in controls, we found in our dataset that TMM normalization, a popular global scaling normalization approach, produced 87.5% false positives at a significant cutoff that is expected to produce only 10% false positive discoveries. In addition, TMM normalization produced a systematic shift of fold change by 3.25 fold. These results highlight the consequences of applying global scaling approaches to conditions that clearly violate their key assumptions. In contrast, we found RUVg normalization using spike-in oligomers as control genes recapitulated the expected stress induced global reduction of translation and resulted in little, if any, systematic shifts in the expected fold change. Our results clearly demonstrated the utility of our spike-in oligomers, both for constructing expected results as controls and for data normalization. Advances in high throughput gene expression profiling technologies, such as microarrays and next generation sequencing technologies, have revealed many technical challenges in making seemingly straightforward comparisons. Oftentimes, despite great care in implementing experimental procedures, systematic shifts in quantification range are observed between samples, and sometimes even between samples that are otherwise considered identical. In response to these technical challenges, many procedures have been developed to identify and to remove these unwanted variations –9. ApproIn a scenario where a global reduction in gene expression is expected, a normalization approach alternative to global scaling is needed to accurately quantitate gene expression. External standards designed for quality control to facilitate cross platform comparison, such as ERCC RNA spike-in control mixes , 17, couTo provide a feasible alternative, Risso et al. contemplated between different approaches of using spike-in controls for data normalization and demonstrated potential issues with spike-in based global scaling approaches and a spike-in based local regression approach , 2, 18. While ERCC spike-in oligos are available for RNA-Seq studies, they are not easily adaptable to ribosome profiling experiments, an approach enabling transcriptome-wide profiling of mRNA translation . ConsequTo meet this apparent need, a few recent studies have explored the utility of different spike-in formulations as external standards for data normalization , 27. HerOur spike-in oligomer design follows Lutzmayor et al. oligomer design for small RNA-Seq . The keyUsing this design, we generated 993 core sequences each in combination with 65,536 different permutations of flanking sequences. We selected 16 oligo sequences, one from each category of core sequence, based on their free energy profile similarity to the endogenous miRNA  with and without 30 minutes of Sodium Arsenite induced Oxidative stress. A 30 minute treatment with Sodium Arsenite at a final concentration of 134 μM is sufficient to induce phosphorylation of eIF2α in LCL . PhosphoWe generated a total of 873 million sequencing reads, which, after filtering out the rRNA, tRNA, and snoRNA reads, resulted in an average of ~2 million ribosome footprint reads uniquely mapped to the GRCh38 human genome and ~32K reads mapped to the spike-in oligo sequences . Using Spearman’s Rho, we found all spike-in oligos used in this study were monotonic. We next evaluated the Pearson correlation between the expected, based on concentration, and the observed quantification level. Overall We found strong correlations between the expected and the observed . On the other hand, for spike-in oligos used in the lower concentration range, we observed a decrease in correlation between the observed and the expected . This deOf note, we found oligo A2_752 to be an outlier of the observed trend . A PearsNext, for each spike-in oligomer we compare the observed between-sample fold change to the expected fold change, which is calculated based on our modified Latin square pooling design . OverallIt has previously been shown that a standard-curve-based-normalization approach using control oligos is technically challenging . ConsistThe first, and often challenging, step of applying RUVg for data normalization is to determine the number of unwanted variables (k) to remove. As a factor analysis method, RUVg approaches the data normalization issue by identifying unknown factors that contribute to the inter-sample distribution differences in quantification level. These nuisance variables, such as variation in sequencing depth, technical differences between batches or between sequencing centers, that contribute to unwanted variation are termed unwanted variables (k). While RUVg provides a formal procedure for estimating these unwanted variables, it is not always clear how many of these unwanted variables should be removed. Removing too many k could end up inadvertently removing biological variations and, in some designs, overfit the data.2 = 0.73 to 0.83. This choice of k is robust against the exact composition of spike-in oligomers used for normalization . In such a scenario, these differences in discoveries between TMM normalized data and unnormalized data were resulted from biased effect estimates instead of a difference in statistical power. In other words, we do not expect relaxing significance cutoffs to recover these mismatches. Consistent with our expectations, for differentially translated genes found in TMM normalized data (at 5% FDR) but not in unnormalized data, we found limited recovery from relaxing significance cutoffs for unnormalized data  we found the observed fold changes to have a tight range of values across subsampling iterations  and the CV decreases steadily as a function of quantification levels  that resembles endogenous miRNAs, which allows us to evaluate the correlations between minimum free energy and the quantitative properties of the spike-in oligomers. We found no significant association between oligomer minimum free energy and the correlation between the observed and the expected oligomer quantification. A lack of correlation observed between spike-in performance and minimum free energy is reassuring, in that the quantification level is in general not biased by the minimum free energy of RNA fragments. On the other hand, we found the outlier A2 oligomer to have the highest minimum free energy of zero among the 16 spike-in oligomers. Unfortunately, because A2 is the only spike-in oligomer with a minimum free energy of zero, we were unable to conclude if a minimum free energy of zero led to the lower correlation between the observed and the expected.Our analyses on the quantitative property of spike-in oligomers revealed two major unwanted effects, a manufacturing batch effect and a ratio compression effect.Based on the observed pattern of manufacturing batch effect, we postulate that the between batch variations in either the quantification process from the manufacturer or variations in preparing the stock solution when we first received the oligomers  were the likely culprit. The progressive nature of ratio compression led us to postulate that a consistent pipetting error could have resulted in the observed compression, which is more pronounced at the lower concentration range. These unwanted technical effects will clearly have a negative impact on a study relying on a standard-curved-based absolute quantification approach. On the other hand, their potential impact on results from a factor-analysis-based relative quantification approach is expected to be limited: unwanted effects that are not shared by endogenous genes, especially the ones that are properly randomized across the effect of interest, such as the oligomer manufacturing batch effect and ratio compression effect observed here, will have limited contribution to the test results when fitted as covariates in the linear model for differential expression.In contrast to features that are unique to spike-in oligomers, features that are shared between spike-in oligomers and endogenous genes are the critical attributes forming the basis of spike-in-based RUVg normalization. RUVg has previously been used for spike-in-based normalization . Risso eApplying RUVg normalization to our stress response ribo-seq dataset, using our 16 spike-in oligomers as control genes, preserved the expected global reduction in mRNA translation in response to stress treatment and slightly increased power for detecting differences. Although the power increase was rather limited, RUVg normalization increased accuracy in fold change estimates, as was indicated by the increase in regression coefficient  of a linear model fit between the observed and the expected fold changes for control spike-in oligomers. This regression coefficient increase was observed consistently across subsampling from groups of spike-in oligomers. As the first study evaluating the panel of spike-in oligomers, we paid careful attention to avoid introducing biases. Importantly, we did not attempt to increase sequencing coverages only for libraries that appear to have lower coverages (a rather common practice for sequencing studies). This is especially relevant for our study, because most  of the low coverage libraries are from the treated group; by submitting only libraries from the treated group for additional rounds of sequencing we will introduce bias in the data. Our careful experimental design and operation in conjunction with the large effect from stress treatment likely resulted in the majority of differentially translated genes readily detectable without normalization, which could explain the limited power increase from RUVg normalization. On the other hand, deliberately introducing sequencing coverage differences between samples in future studies will be useful for evaluating the robustness of our spike-in-based RUVg normalization approach against such bias.A fundamental feature of our study design is the expected fold change of the same oligomer between samples receiving different pools of spike-in oligomers. This expected fold change (or the lack of it) is determined during spike-in pool preparation and this determination is independent of the samples and the downstream experimental conditions. We used the expected fold changes to construct true negatives and true positives, which we used as the yardstick to evaluate our results from quantitating spike-in oligomers. An underlying assumption of this approach is an unbiased  loading of spike-in oligomer pools between samples. An important decision we made for this study is therefore the loading reference used to determine the amount of spike-in pools to add to each sample. Given that we are working with cultured cells and are testing for differences in cellular response to acute stress, a straightforward choice for loading reference would be the number of cells in each sample. However, our preliminary results indicated high variability between cell counts. Instead, we used the amount of digested macromolecular RNA as our loading reference, which assumes no stress-induced macromolecular RNA turnover within the 30-min treatment time window. Our choice of loading reference is supported both by the literature and by our results from quantitating the between-pool fold change for each spike-in oligomer, which, as mentioned above, is independent of the choice of loading reference. Although, in theory, the amount of loading control used could be determined in absolute scale  and the between sample loading variation could then be estimated as unwanted factors by RUVg. In reality, how such an approach would perform requires further investigation with a different study design.Our spike-in oligomers, when used as control oligos, clearly demonstrated the dramatic consequences of using a global scaling normalization approach to transform ribo-seq data collected from an acute cellular stress response study. When evaluating the expected true negatives constructed based on our spike-in pooling design, we found an extremely high proportion of false discoveries in TMM normalized data. In contrast, in data without normalization or normalized using the spike-in-based factor analysis approach we found the proportion of false discoveries to consistently fall slightly above the expected. When evaluating the expected fold changes of true positives, we found TMM normalization to shift fold changes based on differences in sequencing coverage between libraries. In the current dataset, such shifts ended up creating both false positives and false negatives. We want to emphasize here that the troubling results observed from TMM normalization does not indicate problematic behaviors of the TMM normalization method per se. Instead, it reflects the consequences of violating an important underlying assumption for all global scaling normalization approaches, i.e. any systematic shifts were assumed to be experimental artifacts. In contrast, a factor-analysis-based normalization approach doesn’t stipulate such an assumption. In our analysis, using spike-in oligomer quantification as controls genes, RUVg normalization maintained the expected global reduction of ribosome occupancy level in the treated samples, despite heavily transforming the data .In order to keep the linear model parameterization consistent across comparisons, in our differential expression tests, we extracted normalized counts from RUVg as input data. While we designed our study this way to keep a fair comparison between normalization approaches, for studies focusing on identifying genes that are differentially translated, we suggest following RUVg authors’ advice to fit these unwanted factors as covariates. By extracting the normalized counts , one risks potentially removing biologically relevant variations. These considerations are not unlike the ones to contend with when using a typical linear modeling approach for adjusting batch effects. Similarly, when deciding on the number of k to fit as covariates, the degrees of freedom available from the dataset could put some constraints on the available options. The costs and benefits of including additional samples should therefore be carefully considered during study design in order to enable effective factor-analysis-based normalization.While our study aimed specifically to address the unmet need of external standards for transcriptome-wide profiling of translational regulation in stress response, the exceedingly high proportion of false positives observed as a result of applying TMM normalization to our oxidative stress response ribo-seq dataset and the almost ubiquitous use of global scaling normalization approaches in the field of genomics prompted the following unsettling question: How common have such error occurred without the researchers acknowledging it? It is easy to envision scenarios of subtler global shifts in gene expression for studies comparing between developmental stages or testing for drug treatment effects. Without appropriate use of external standards, these subtle shifts could escape researchers and the subsequent application of global scaling normalization will lead to distortion of effect size, which, as we have shown, results in both false positives and false negatives. On the other hand, while an appropriate application of external standards could safeguard against such pitfalls, further studies to identify effective approaches for determining the amount of spike-in oligomers to use for each sample is needed. Nonetheless, a collective consciousness in reducing experimental biases and a wide adoption of external spike-in oligomers could enable future retrospective meta-analyses to provide insight.2 in RPMI media supplemented with 15% FBS, 2 mM L-glutamate, 100 IU/ml penicillin, and 100 μg/ml streptomycin, in accordance with instructions provided by Coriell. Of note, cell cultures were vigilantly maintained at a cell density between 600,000 to 700,000 cells/ml to avoid inadvertent induction of stress response. To induce oxidative stress, cells were treated with 134 μM  Sodium Arsenite , a heavy metal oxidative stressor, for 30 minutes in otherwise the same cell culture conditions. For the control group, nuclease free water was used in place of the Sodium Arsenite solution. After treatment, cells were pelleted by centrifugation at 100g for 10 min and washed twice with cold PBS (4°C). Cell pellets were flash frozen in liquid nitrogen and stored at -80°C.Three lymphoblastoid cell lines (LCLs) , each derived from a separate Yoruba people from Nigeria, were purchased from Coriell Institute for Medical Research (NIGMS Human Genetic Cell Repository). The cells were maintained at 37°C with 5% COProteins were prepared from flash frozen cell pellets using M-PER Mammalian Protein Extraction Reagent  following vendor’s instructions. Protein concentration was estimated using bicinchoninic acid (BCA) assay  and 5 μg of proteins from each sample were used for western blot. All antibodies  used were purchased from Cell Signaling Technologies . Seven  miRBase , we deteRibosome footprint profiling experiments were performed following the ligation free protocol described in Hornstein et al.  with a fCRISPR/Cas9 mediated rRNA removal was performed following Han et al.  and MitoSequence reads were processed and mapped to the human genome following a modified procedure used by Ingolia et al. . Before To focus our analysis on sufficiently quantitated genes, we only analyzed genes containing at least one sequencing read from each sample, which resulted in a dataset including 12,357 genes Data . For spiStatistical analyses were performed in the R statistical computing environment (version 4.0.4). Linear regression analyses were performed using the lm function. Spearman and Pearson correlations and corresponding tests were performed using cor and cor.test. Student’s t-Test were performed using t.test. Wilcoxon Rank Sum tests were performed using wilcox.test.TMM normalization was performed using the cpm function from the edgeR package  with effRUVg normalization was performed using the RUVSeq package . Factor μ is the intercept term and ε is the residual. To identify differentially expressed genes, we tested the null hypothesis of β1 is zero using the empirical Bayes moderated t-statistics from the eBayes function. False discovery rates were calculated using the Benjamini-Hochberg procedure.Differential expression analyses were performed using the limma package . After pS1 File(PDF)Click here for additional data file.S2 File(PDF)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis.We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance.iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.The online version contains supplementary material available at 10.1186/s12859-023-05432-8. Recent advances in droplet-based single-cell RNA sequencing (scRNA-seq) technology have enabled investigators to assess transcriptome-wide differences at single-cell resolution –3. InsteDespite the great potential of scRNA-seq technology, challenges remain in the corresponding data analysis. Specifically, one common task in scRNA-seq data analysis is to identify cell type specific differentially expressed (DE) genes between two groups of subjects , which cTechnical batch effect is one possible reason for the large variation across subjects because many studies processed cells and cDNA libraries from different subjects in different batches due to the requirement of sample freshness in certain tissue types and early-stage scRNA-seq protocols. This may lead to batch effect in the data so that cells of the same type from different subjects have different expression profiles. However, in the scRNA-seq dataset from patients with idiopathic pulmonary fibrosis (IPF), the large variation across subjects was still present after adjusting for batch effect using scVI . In addiMany DE analysis methods for scRNA-seq data have been developed and compared –25. TheyWithin the category of methods that ignore subject effect, there are methods specifically designed for scRNA-seq data and methods adopted from bulk RNA-seq DE analysis. Among the methods designed for scRNA-seq data, BASiCS  and TASCAll the methods mentioned above treat cells from the same subject as independent, which may be efficient for identifying cell type marker genes, but inappropriate for DE analysis to identify disease or phenotype associated genes due to the presence of dominant subject effect confounded with disease effect as described above. One simple and straightforward solution is to aggregate expression levels of cells from the same subject by averaging and then to compare the aggregated sample-level “pseudo-bulk” expression levels between two groups of subjects using Student’s t test. We denote this method as subject-t-test (subT). Furthermore, two recent studies proposed the following three DE analysis methods to consider subject effect in scRNA-seq data. Zimmerman et al.  developeIn this article, we develop a new statistical model to identify DE genes in scRNA-seq data with multiple subjects, named iDESC. A zero-inflated negative binomial mixed model is used to consider both subject effect and dropouts. iDESC models dropout events as inflated zeros by pooling information across genes and assuming that genes with similar expression share similar dropout rates. In addition, iDESC allows dropout rate to be subject/batch specific. The non-dropout events are modeled by a negative binomial distribution. In the negative binomial component, a random effect is used to separate subject effect from disease effect. Wald statistic is used to assess the significance of disease effect. We compared iDESC with 11 existing DE analysis methods based on type I error, statistical power, between-dataset consistency and validation using both simulated and real datasets.Previous studies reported that the dropout rate of a gene in a given cell depends on the expected expression level of the gene in the cell and dropout events are more prevalent for genes with lower expression . As expeWe evaluated and compared the performance of iDESC and eleven existing DE analysis methods Table  using boWe compared method performance based on type I error, statistical power, and consistency of the identified DE genes in three independent scRNA-seq datasets on the same disease, the Kaminski dataset , the Krop value < 0.05 and compared it to the nominal level 0.05. Figure To assess type I error, we permuted the group labels of subjects in both Kaminski and Kropski datasets 500 times. All twelve methods were applied to the permuted datasets to identify DE genes in macrophages. For each gene, we calculated the empirical type I error as the proportion of permuted datasets with a To compare power, we simulated scRNA-seq data with 150 true DE genes and 300 non-DE genes under a wide range of parameter settings estimated from the Kaminski macrophage dataset. Expression data of the DE genes was simulated to have a fold-change of Figures rge Fig. b. When wmentclass2pt{minimmentclass2pt{minimWe used two cell types, macrophage and fibroblast, from three independent scRNA-seq datasets  of whole lung tissue from IPF patients and normal controls , 18, 42 p value < 0.01, iDESC identified 5,577 and 1,124 DE genes in Kaminski and Misharin macrophage datasets, respectively, and 417 and 534 DE genes in Kaminski and Kropski fibroblast datasets, respectively. The top upregulated DE genes in IPF macrophage , followed by muscat-PB . In contrast, MAST-RE, muscat-MM and subT did not achieve significant between-dataset overlap in macrophage. They had significant overlap in fibroblast while their p-values were much larger than iDESC and others. In addition, iDESC had the largest Jaccard Index in both cell types , followed by muscat-MM .We further evaluated method performance based on the consistency of DE genes between datasets. For each method in each cell type, we overlapped the identified DE genes from the two chosen datasets Fig. a, b. FisLastly, we used a list of 83 IPF-related genes in the Harmonizome database  to validp < 0.01) by iDESC in the Kropski and Misharin macrophage datasets. We also examined the expression distribution of the top 3 DE genes, FN1, CCL18 and SPP1, identified in the Kaminski macrophage dataset, in the three IPF macrophage datasets. Additional file p < 0.01, Wilcoxon rank sum test) in the three IPF macrophage datasets.To validate the top DE genes identified in the Kaminski macrophage dataset, we used the Kropski and Misharin macrophage datasets. Table In summary, we evaluated method performance using real datasets based on consistency of results across different datasets and validation using previously reported IPF associated genes in public database and literatures. Our method, iDESC, achieved the best performance based on both evaluation criteria. The top DE genes identified by iDESC were highly biologically relevant, well supported by literatures, and validated by two other independent datasets.The runtime of iDESC is 33.3 and 24.6 min to analyze the Kaminski and Kropski fibroblast datasets, respectively, using a 10-core, 100 GB RAM, Intel Xeon 2.6 GHz CPU machine. The computation time of iDESC is relatively long because of the mixed model fitting for subject effect and the consideration of dropouts. For a dataset of 50–100 subjects and ~ 2000 cells per sample, iDESC took about 50 h using a 10-core, 100 GB RAM, Intel Xeon 2.6 GHz CPU machine. To provide more information on computation time, we recorded the time of the five methods that consider subject effect, iDESC, MAST-RE, muscat-MM, muscat-PB and subT, on 10 genes in the Kaminski fibroblast dataset that includes 2,290 fibroblasts. Additional file We have developed a new method, iDESC, to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC fits a zero-inflated GLMM assuming dropouts to have zero count and captured expression to have a negative binomial distribution. Information across genes were pooled to model the dependency of dropout rate on gene expression level. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. Wald test is used to assess the group mean difference in captured transcripts. We compared the performance of iDESC with elevent exiting DE analysis methods using both simulated data and real datasets. Permutation analysis using real data demonstrated that the type I errors of methods that consider subject effect were well calibrated, whereas the type I errors of methods that ignore subject effect were highly inflated. Using simulated data based on parameters estimated from real datasets, we showed that iDESC achieved comparable or higher power among methods that consider subject effect. In three independent scRNA-seq datasets of IPF patients and healthy controls, several of the top DE genes identified by iDESC were well supported by literatures regarding their important roles in IPF pathogenesis. Moreover, iDESC achieved the most consistent and validated results between independent datasets and using public database, respectively. These results demonstrated superior performance of iDESC over the other existing methods, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.Like most DE analysis methods for scRNA-seq data, iDESC requires accurate cell type annotation, which is a key step to ensure the validity and biological relevance of the downstream DE analysis. Cell clustering and cell type annotation are commonly performed with the removal of technical (batch) or biological (subject) effects through data integration. During this step, group/disease effect will be removed along with subject effect. Therefore, the downstream DE analysis will be performed on the original count or normalized data in each annotated cell type instead of the integrated data. Inaccurate cell typing may lead to data distribution deviated from the negative binomial distribution or with multiple modes. Possible remedies to reduce the negative impact of inaccurate cell type annotation include the following strategies. First, we strongly recommend examining the cell distribution through data visualization using UMAP and/or t-SNE plots and performing cell clustering to detect potential incorrect cell annotations. Second, a goodness-of-fit test for iDESC and/or replacing the negative binomial distribution with a multi-modal distribution or a mixture model may improve model fitting.Despite the advantages of iDESC over the other DE analysis methods shown in this article, iDESC can be improved in several directions. First, in some cell types, when the cell-to-cell heterogeneity of certain genes is high or heterogeneous cell subtypes exist, negative binomial distribution may not fit the data well. Especially when heterogeneous cell subtypes are present in the data, the distribution of expression may be multi-modal. Data transformation, a goodness-of-fit test for iDESC and/or replacing negative binomial distribution with a multi-modal distribution or a mixture model may improve the model fitting. Second, estimation of dispersion parameter in a negative binomial distribution has been shown to be challenging. Multiple dispersion correction approaches , 58–60 tThe computational speed of iDESC is relatively slow due to its consideration of dropouts and subject effect, which are critical for iDESC to achieve significant improvement in performance. Besides implementing parallel computing on high performance computers, the following two future work can potentially reduce the runtime of iDESC. First, the “glmmTMB” package uses the Template Model Builder (TMB) framework to calculate the first and second order derivatives of the likelihood function by automatic differentiation (AD). It is possible to speed up the algorithm by specifying the calculation of first and second order derivatives for the quasi-likelihood function of our model to skip this process. Second, the objective function of iDESC was optimized using the "nlminb" optimizer in an iterative scheme, which is an unconstrained quasi-Newton method optimizer. Replacing nlminb with a more efficient algorithm such as stochastic gradient descent (SGD) may further reduce the runtime.We developed iDESC, a zero-inflated negative binomial mixed model that considers both subject effect and dropouts, to identify cell type specific differentially expressed genes in scRNA-seq data with multiple subjects. iDESC had well-calibrated type I error and comparable or higher power than other existing DE methods. When applied to three independent scRNA-seq datasets with IPF patients and healthy controls, iDESC achieved the highest between-dataset consistency and validation rate based on genes found to be associated with IPF in public database.To identify cell type specific DE genes between two groups of subjects, iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropout events in scRNA-seq data with multiple subjects. The model includes two components: a zero component representing dropouts and a negative binomial component representing captured expression.Suppose cells are collected from Previous research found that the dropout rate of a given gene in a given cell depends on the expression level of the gene in the cell. Genes with lower expression level tend to have a higher dropout rate . In addiTo test if gene glmmTMB’ .We evaluated the performance of iDESC and other methods using three scRNA-seq datasets of whole lung samples from three independent IPF studies generated using the 10X Genomics Chromium platform. All datasets included IPF patients and healthy controls. In this article, we chose to focus on macrophage and fibroblast because both cell types have been recognized to play a significant role in IPF pathogenesis –50.Kaminski refers to the scRNA-seq dataset of frozen distal lung parenchyma samples from 32 IPF and 28 control donor lungs in Adams et al. . The rawKropski refers to the scRNA-seq dataset of fresh whole lungs from 10 IPF patients and 8 healthy donors in Habermann et al. . The rawMisharin refers to the scRNA-seq dataset of fresh lung tissues from 4 IPF patients and 8 transplant donors in Reyfman et al. . The rawIGF1 and MFAP5, and these cells do not express ITGA8 or MYLK as the myofibroblasts in the Kaminski dataset. However, a good portion of the fibroblasts in the Misharin dataset and the PLIN2+ fibroblasts in the Kropski dataset expressed ITGA8 and MYLK, suggesting that they are similar to the myofibroblasts in the Kaminski dataset but not the fibroblasts. The difference in cell type nomenclature may lead the DE analysis results to be invalidated across the three datasets. To ensure cells of the same type were compared across the three datasets so that DE analysis results were comparable, we conducted integration analysis across the three datasets using Seurat [The raw UMI count matrices of all three datasets were downloaded from the links provided in the publications. We integrated the three datasets for data visualization and noticed that the cell type nomenclature in these three datasets were quite different. For example, the fibroblasts in the Kaminski dataset were distinguished by expressing g Seurat . The grag Seurat  was applp value < 0.05 were considered significant. The empirical type I error for each gene was calculated as the proportion of permuted datasets having a p value < 0.05 for the given gene.To assess type I error, we randomly permuted the group labels of subjects in both Kaminski and Kropski datasets so that no model assumptions were made in data generation and the within-subject cell-to-cell correlation structure was preserved in the data. The permuted datasets were not expected to show transcriptomic difference between the two groups. We performed 500 permutations on each dataset and applied all DE analysis methods to the permuted datasets. Genes with a To compare the statistical power of all methods, we simulated single-cell expression data with ground truth to mimic the real datasets. Macrophages in the Kaminski dataset were used for this analysis. First, for each gene, we fit the following ZINB model on the macrophage data from all subjects in the Kaminski dataset to estimate gene-level dispersion We applied all methods to the simulated datasets to identify DE genes under different p-value threshold. By comparing the identified DE genes to the ground truth, the area under an ROC curve (AUC) score as well as the sensitivity and specificity under the p-value threshold of 0.05 were calculated for each method to evaluate method performance.p value < 0.01 were considered to be differentially expressed. The cell-level effect coefficient [https://maayanlab.cloud/Harmonizome/). The proportion of validated genes was calculated as the percentage of 83 genes that were the between-dataset overlapping DE genes.We applied the DE analysis methods that consider subject effect to macrophage and fibroblast separately from the three real datasets. Method performance was assessed based on the consistency of the identified DE genes between datasets. Genes with a fficient  was usedAdditional file 1: Figure S1. Power comparison of iDESC and 7 methods without considering subject effect in simulated datasets. Evaluation criteria including (a) sensitivity and (b) specificity under the p-value threshold of 0.05, and (c) area under an ROC curve (AUC) to measure the accuracy of identified DE genes under three levels of capture efficiency (δ) and number of cells per subject (m). (d) ROC curves and the corresponding AUC scores when β = 0.4, δ = 1, m = 50.Additional file 2: Figure S2. Boxplots showing the expression distribution of the top 3 DE genes, FN1, CCL18 and SPP1, in the three IPF macrophage datasets.Additional file 3: Table S1. Runtime of five methods using a single core.


Developing an understanding of how microbial communities vary across conditions is an important analytical step. We used 16S rRNA data isolated from human stool samples to investigate whether learned dissimilarities, such as those produced using unsupervised decision tree ensembles, can be used to improve the analysis of the composition of bacterial communities in patients suffering from Crohn’s disease and adenomas/colorectal cancers. We also introduce a workflow capable of learning dissimilarities, projecting them into a lower dimensional space, and identifying features that impact the location of samples in the projections. For example, when used with the centered log ratio transformation, our new workflow (TreeOrdination) could identify differences in the microbial communities of Crohn’s disease patients and healthy controls. Further investigation of our models elucidated the global impact amplicon sequence variants (ASVs) had on the locations of samples in the projected space and how each ASV impacted individual samples in this space. Furthermore, this approach can be used to integrate patient data easily into the model and results in models that generalize well to unseen data. Models employing multivariate splits can improve the analysis of complex high-throughput sequencing data sets because they are better able to learn about the underlying structure of the data set.IMPORTANCE There is an ever-increasing level of interest in accurately modeling and understanding the roles that commensal organisms play in human health and disease. We show that learned representations can be used to create informative ordinations. We also demonstrate that the application of modern model introspection algorithms can be used to investigate and quantify the impacts of taxa in these ordinations, and that the taxa identified by these approaches have been associated with immune-mediated inflammatory diseases and colorectal cancer. The analysis of the composition of ecological communities is essential to determine their function within the broader environment and within a host. The statistical analysis of differences between communities, beta diversity, is often an important aspect of statistical pipelines used to investigate ecological and metagenomic data sets. Central to the analysis of beta diversity is the calculation of pairwise distances, or dissimilarities, between samples. This calculation is carried out by carefully choosing and then applying a particular dissimilarity that is known to measure an important characteristic of the data. For example, some methods (such as correlation-based distance methods) perform well at measuring environmental gradients, while others, such as the Jaccard distance, can perform well when one is interested in clustering data –3. When –3–11–Machine learning algorithms are uniquely suited to address these challenges. Unlike statistical models, machine learning models tend not to assume anything about the underlying distribution of each feature , 5. Furt14–large-scale nonparametric discovery of markers (LANDMark), as an alternative to RFs  was able to identify significant differences (P ≤ 0.001) between groups found in the positive controls across each cross-validation fold  to demonstrate class separation in the projection is not enough. Each ordination method should also be able to place new data of the same class into a similar location in the ordination space. When running this experiment, however, it quickly became apparent that creating ordinations using principal-coordinate analysis (PCoA) or robust principal component analysis (RPCA) resulted in an important limitation. Specifically, both methods were unable to transform and project new data into the space created using the training data. To do this, we would have needed to use all the available data (train and test), and this would have resulted in data leakage, since the test data would influence the final projection. Therefore, to prevent data leakage and the reporting of overly optimistic and potentially misleading results, the generalization performance of the RPCA and PCoA ordination methods was excluded   transformation consistently resulted in large  ≤ 0.05) . Except rmations . The crormations . No statP ≤ 0.0001), and the LANDMark and TreeOrdination ASV MMT models performed particularly well in this test, with average receiver operating characteristic-area under the curve (ROC-AUC) scores for each model of 0.953 and 0.953, respectively. These performed significantly better than the Extra Trees and random forest classifiers (P ≤ 0.0001), and the ROC-AUC score from both models is comparable to that from Baxter et al. (2016) (0.952) (P ≤ 0.0001). Since the TreeOrdination and LANDMark models performed best, we used them to calculate the discrimination thresholds that maximized the detection of colorectal cancer while minimizing the detection of healthy tissue. These thresholds were 0.30 for LANDMark and 0.43 for TreeOrdination. When using these thresholds, both ASV MMT models discovered a substantial fraction of additional colorectal cancers  . However (0.952)  and that cancers , with th cancers .P ≤ 0.0001) and random forest (P ≤ 0.0001) classifiers trained on the same data. Both models resulted in a mean balanced accuracy score of 0.63. Once again, we calculated the optimal discrimination threshold for distinguishing lesions  from healthy tissue. This threshold was found to be 0.69 for LANDMark and 0.66 for TreeOrdination. Compared to FIT alone, the ASV MMT models resulted in better detection of additional colorectal cancers and adenomas . Most of the variation between samples, approximately 87%, can be explained by the first principal component, which appears to be associated with disease status. The variation along this component appears to be predominantly driven by FIT scores , with variation between these groups being driven by FIT score . Nearly all of the variation between samples, approximately 84%, could be explained by the first principal component. This strongly suggested that disease status was associated with the variation along this component. The ASVs that were predominantly associated with this variation belonged to Fournierella spp., Staphylococcus spp., Dialister spp., and Ruminococcaceae  , and UMAOur work presents results that attempt to address a gap ecology and microbial ecology by using metric learning to model and understand community structure. These approaches to measuring pairwise dissimilarity have been applied to the analysis of genomic and transcriptomic data sets , 22, 41.Fig. 4F statistics and high reconstruction losses between distances in the ordination and the original space are observed. While UMAP represents an important improvement, since it is a graph-based method that can model these nonlinearities, we lose access to the loading scores and interpretability if it is used  has on the ordination. In ordination approaches like PCoA, this information is exposed as loading scores along each principal component. However, these methods are limited in their ability to project data when the pattern separating groups of samples is nonlinear. Furthermore, they are sensitive to outliers due to their reliance on a distance measure . Assumin is used , 44, 45. is used , 47.Ruminococcaceae were important in refining the locations of samples in FIT-negative adenomas and colorectal cancers and determining the locations of samples in the Crohn’s disease data set in TreeOrdination models. The identification of this taxon is important, since there is evidence to suggest that the depletion of this family is associated with colorectal cancer and gut inflammation  and TreeOrdination models can learn effective dissimilarity matrices. When paired with modern dimensionality reduction approaches, such as UMAP, the global structure of the data set is preserved. These representations can then be combined with existing matrix factorization and model introspection algorithms to create informative ordinations. Additional work should be conducted on larger data sets to understand when metric learning should be used. Furthermore, by using larger data sets, the effect of individual differences will be reduced, since a larger sample of the population will be used in each test set . This wiTwo human microbiome data sets were selected for use as case studies. The first data set was chosen because it contained samples from patients who suffered from immune-mediated inflammatory diseases (IMID) . The micPRJNA450340 and SRP062005) (https://github.com/terrimporter/MetaWorks)  , 28. AlltaWorks) . The defequences . Finallyequences . ASVs thtih and jth samples in X. Distances were calculated using the pairwise_distances function from scikit-learn (version 1.1.2)  .(1)JaccN  algorithms , 44. Thehttps://github.com/cameronmartino/deicode-benchmarking/blob/master/simulations  were extracted for further analysis  and 5% of training samples (in the case of the control data sets) were identified. These ASVs were then removed from both the training and testing samples. This filtration step was taken since a reduction in the number of features can often lead to a more generalizable model , 62, 63.analysis . RandomiF statistics associated with significant P values (defined as P ≤ 0.05) provide evidence that differences exist between groups, and larger F statistics provide evidence for a better separation between groups. PCoA projections were created using scikit-bio, while UMAP projections were created using the umap package  were created. These ordinations were also used as input into a PERMANOVA analysis.The transformed training data were used as the input for a PERMANOVA (using 999 replications). The training data were also projected using principal coordinate analysis (PCoA)/robust principal component analysis (RPCA) and UMAP to create two-dimensional projections, which were used as input into another PERMANOVA analysis (using 999 replications). In this experiment, high  package , 66. In Where appropriate, the transformed and projected training data were also used to train an Extra Trees and LANDMark classifier. The high-dimensional TreeOrdination embedding was used to train an Extra Trees classifier. The generalization performance of each model was measured using the balanced accuracy score (from scikit-learn) . This waTo investigate which ASVs were different between sample types, we constructed a TreeOrdination model (as described above) using the Crohn’s disease data. For this model, 70% of the samples were used for training data, while the remaining 30% were used for testing. Shapley scores for each group were calculated to determine which features impacted the location of samples in ordination space , 46, 47.It was important to investigate how TreeOrdination and LANDMark behaved when faced with data where the differences between classes were not as clear. To carry out this investigation, we used a large colorectal cancer data set consisting of 490 samples . Since tFor each test , we measured the ability of LANDMark and TreeOrdination to identify colorectal cancers and lesions. This was done by first determining the probability threshold that maximized the balanced accuracy score of the model concerning the prediction of lesions. To do this, we split each subset into training and testing folds using 5-fold stratified cross-validation with five repeats as described above. After training each model, the probability of colorectal cancer was determined using the test set. These probabilities were then averaged across folds. A list of probability thresholds (spaced evenly from 0 to 1.0 using increments of 0.01) was then used to determine the best possible cutoffs for the colorectal cancer versus healthy and healthy versus lesion models. Finally, we investigated which ASVs were different between sample types by constructing a TreeOrdination model (as described above). For this model, 80% of the samples were used for training data, while the remaining 20% were used for testing. Prediction probabilities were calculated using TreeOrdination. The subset of adenoma and colorectal cancer samples at or above this threshold were extracted, and healthy samples below this threshold in the test set were identified. These samples were then grouped into FIT-positive and FIT-negative samples and plotted. Shapley scores for each group were calculated to determine which features impacted the location of samples in ordination space , 46, 47.https://github.com/jrudar/Unsupervised-Decision-Trees. The raw sequences that were used in this study can be obtained from the Sequence Read Archive (accession numbers PRJNA450340 and SRP062005)  , 28. TheetaWorks . The Tre


Innate lymphoid cells (ILCs) are a unique type of lymphocyte that differ from adaptive lymphocytes in that they lack antigen receptors, which primarily reside in tissues and are closely associated with fibers. Despite their plasticity and heterogeneity, identifying ILCs in peripheral blood can be difficult due to their small numbers. Accurately and rapidly identifying ILCs is critical for studying homeostasis and inflammation. To address this challenge, we collect single-cell RNA-seq data from 647 patients, including 26,087 transcripts. Background screening, Lasso analysis, and principal component analysis (PCA) are used to select features. Finally, we employ a deep neural network to classify lymphocytes. Our method achieved the highest accuracy compared to other approaches. Furthermore, we identified four genes that play a vital role in lymphocyte development. Adding these gene transcripts into model, we were able to increase the model’s AUC. In summary, our study demonstrates the effectiveness of using single-cell transcriptomic analysis combined with machine learning techniques to accurately identify congenital lymphoid cells and advance our understanding of their development and function in the body. These hThe accurate classification of ILCs is of great medical significance, as the functions and behaviors of various ILCs in the body can vary greatly. Abnormalities in the functions of ILCs can impact the onset and progression of various conditions, including inflammation, autoimmune diseases, metabolic disorders, and allergies . To effeIn recent years, computational methods have been widely applied to mine biological information using omics data . The expIn this paper, we obtained genes associated with innate immune cells and used their expression levels to predict immune typing. Differential gene expression levels can directly reflect the developmental state of cells, and changes in gene expression can also affect the levels of proteins and metabolites. This work has yielded a precise and low-cost approach to classifying ILCs.In this section, we provided a detailed overview of the implementation of this work, which includes the framework, data preprocessing, feature compression, and evaluation. To construct our machine learning model, we needed three main components: data points, features, and labels . In thisFirstly, we obtained single-cell transcriptome data associated with innate lymphoid cells from the GEO database , and theFor our study, we utilized the Bjroklund dataset , which c• Related GenesThis section presents a comparative analysis of three commonly used feature compression methods. For each method, we provide detailed information about its implementation.• PCATo reduce the dimensionality of features using the related gene method, we followed a specific process. First, we obtained a gene set related to the disease from the DisGeNet website. Then, we intersected this gene set with the gene set of the original data to obtain a new, reduced gene set.n × p data matrix X, where n is the number of samples and p is the number of variables. We first need to center X by subtracting the mean of each variable from the entire variable, resulting in a new matrix Principal component analysis (PCA) is a commonly used data dimensionality reduction technique, which aims to transform high-dimensional data into lower-dimensional data for better data processing and analysis . The basS of Next, we compute the covariance matrix S to obtain the eigenvalues λ1 ≥ λ2 ≥⋯ ≥ λp and their corresponding eigenvectors v1, v2, …, vp. Finally, we select the top k eigenvectors v1, v2, …, vk and project the data X onto these eigenvectors to obtain a new n × k matrix Y, where n × p data matrix X into a new n × k matrix Y, where k is the number of variables we choose.• LASSOThen, we perform eigendecomposition on Lasso (Least Absolute Shrinkage and Selection Operator) is a widely used linear regression technique for feature selection and sparse modeling . Lasso cβ is the model parameter vector, X is the feature matrix, y is the response variable vector, and λ is the regularization parameter. The L1 regularization term ‖β‖1 shrinks some parameters to zero, achieving feature selection and model sparsity. By adjusting the regularization parameter λ, we can control the degree of sparsity of the model.Here, i consists of a linear transformation zi)( = Wi)(hi−1)( + bi)( and a nonlinear activation function gi) = x as the input and hi)( = gi)((zi) as the output of layer i. The weights and biases of each layer are denoted by Wi)( and bi) can be defined as a function In the last layer, we apply the softmax function, as below:zL), and the learning rate was set to 0.001.In this section, we conducted a thorough analysis of the sample size to ensure its appropriateness. Subsequently, we evaluated various methods for feature dimensionality reduction and machine learning classification, and selected the optimal model. Finally, we examined the impact of incorporating hub genes and validated their effectness.Our analysis involved raw data from the GEO database, which included properties of immune typing. To ensure clear labeling, we assigned the labels 0, 1, 2, and 3 to NK cells, ILC1, ILC2, and ILC3, respectively. This resulted in a dataset containing four types of samples, consisting of 74 NK cells, 126 ILC1 cells, 139 ILC2 cells, and 308 ILC3 cells. Importantly, the sample was relatively balanced among the different types of cells.To assess the statistical performance of our data, we used an online web server called SSizer . And theTo provide a comprehensive evaluation of our method, we compared it with several other methods. Since there is no consensus on the best feature selection method, we simultaneously compared three different feature selection methods. The feature selection process is described in detail in To assess the accuracy of our method, we performed 5-fold cross-validation. This is a common machine learning model evaluation method where the dataset is randomly divided into five mutually exclusive subsets. Each subset is used once as a validation set while the remaining four subsets are used for training. This process is repeated five times, with each subset used once as the validation set, to evaluate the performance of the model. The results, as shown in Although other feature dimensionality reduction methods seem to achieve better performance, the related gene method provides an intuitive characterization. We further verified the performance of the model on the related gene method. The confusion matrices of different machine learning classification models are presented in We performed a literature investigation and identified four genes, BCL11B, BATF, RUNX3, and ZBTB46, that are associated with Innate Lymphoid Cells. To investigate whether transcripts of these genes could improve the prediction of immune typing, we incorporated them into our model. We conducted several experimental comparisons to fully evaluate the impact of these transcripts on the model’s accuracy, and the results are shown in Our experiments demonstrated that the addition of these transcripts significantly enhanced the accuracy of the model. Among all the methods tested, LASSO-DNN showed the best performance. These findings suggest that incorporating the transcripts of these four genes, particularly in combination with LASSO-DNN, has the potential to improve the performance of the immune typing model. Therefore, our study highlights the importance of these genes in immune typing and provides a framework for future research in this field.Cellular immunoassay detection currently relies on sequencing technology and biological experiments. With the continuous development of medicine, immunotherapy has significantly improved the survival rate of patients with advanced cancer. Therefore, it is of utmost clinical and basic research significance to predict immune typing in advance. To achieve this, two critical steps are necessary, namely, identifying key transcripts and building effective machine learning models to accurately predict immune typing based on these transcripts.In this study, we identified genes associated with Innate Lymphoid Cells and obtained their corresponding transcription expression levels. We used three feature extraction methods for feature dimension reduction and designed a DNN model for predicting immune typing. We compared the performance of the DNN method with several other methods and found that the combination of DNN and LASSO provided the best classification performance. Moreover, we tested whether the information of four genes found in literature research could improve the accuracy of our model. These four genes provided valuable information and effectively enhanced the accuracy of the model in predicting immune typing.While our method was developed specifically for the classification of ILCs, it has the potential to be applied in other contexts. In general, the machine learning model we have developed processes numerical matrices based on biological data from cells, and can produce results for any such dataset. However, one important factor to consider is the similarity between ILCs and other cell types. If other cell types share similar gene expression patterns with ILCs, our approach may also be effective for these cell types. Conversely, if the gene expression patterns are significantly different, our approach may not be suitable.In summary, our study presents a novel method for predicting immune typing and demonstrates the accuracy of genes previously identified in literature research. Our findings contribute to the advancement of immune typing prediction and provide a framework for future research in this field.