## 3 Results and Discussion  
We evaluated the classification performance of our scEFS with the three main feature selection methods using expression profiles from a single-cell RNA-seq analysis. All methods were applied to the GSE103334 dataset from Gene Expression Omnibus [22]. This study tracks the microglia activation in neurodegeneration, examining their phenotypic heterogeneity and transcriptional dynamic. The experimental approach utilized a neurodegeneration mouse model with Alzheimer's-like pathology and phenotypes (CK-p25 model), discovering novel microglia cell states and uncovering the underlying transcriptional programs. In our analysis, this case is considered a classification task for the accurate prediction of selected datasets derived from two different timepoints (0 weeks and 2 weeks) to assess microglia activation during the progression of neurodegeneration between CK-p25 animals and controls. The implementation process involved calculating the \(p\)-value of each feature using the BSPC package and retaining only the top 200 features with the lowest \(p\)-values. Additionally, the varImp function from the random forest classifier was used to determine the significance of each variable in predicting the class attribute of the dataset. Only the top 200 most important variables were kept. Similarly, the DUBStepR algorithm was executed by isolating the same number of the most dominant genes. A Venn diagram was used as a visual approach to organize and compare differentially expressed genes obtained from the various applied feature selection methods. It comprises a useful tool for illustrating the relationship and overlap between different datasets as well as highlighting the percentage of genes selected by each method. As depicted in Figure 1, the results of each technique yielded a set of characteristics that exhibited a maximum of 31% unique representation. There was minimal overlap between the feature selection methods, with a total of 2% overlap observed among all the different methods, and even the overlap between pairs of methods did not exceed 8%. Given this limited overlap, an ensemble method was proposed to identify potential gene markers by utilizing a ranking of all of these distinct gene groups. This limited overlap can be attributed to the dissimilar perspectives of each technique, since each feature selection method utilizes a distinct strategy to find the most dominant genes. Figure 1: A Venn diagram depicting the three main future selection methods, highlighting their low rate of overlap. The kNN classifier was used to provide a comparison between the three different techniques and the initial data. The k-nearest neighbor (kNN) method is a well-known classification approach in the fields of data mining and statistics due to the ease with which it can be implemented and the high classification performance it offers [23]. In each case study, we initially trained each classification algorithm by providing it with the set of 200 informative genes selected using each gene selection method. The goal was to assess how well the aforementioned groups can predict the class feature and compare them based on metrics such as accuracy, sensitivity, specificity, and F1 score, as Table 1 shows. In Figure 2, the receiver operating characteristics (ROC) curve is provided and the area under the curve (AUC) is used as a measure of performance between all groups. In order to understand the high-level functions and utilities of the biological system according to the isolated genes, we conducted an enrichment analysis using Enrichr [[https://maayanlab.cloud/Enrichr/](https://maayanlab.cloud/Enrichr/)]([https://maayanlab.cloud/Enrichr/](https://maayanlab.cloud/Enrichr/)) (accessed on 6 November 2022) [24; 25]. GO function and Reactome [[https://reactome.org/](https://reactome.org/)]([https://reactome.org/](https://reactome.org/)) (accessed on 6 November 2022) [26] pathway enrichment analyses were performed for the twelve DEGs, as Figure 1 shows. Following enrichment analysis, we concluded that AD is proven.