## 3 Results and Discussion  
To achieve a more robust outcome, we utilized three distinct machine learning-based feature selection methods in this study, each with its unique procedure and significance. Regarding the first method, the objective of conducting differential expression analysis was to discern the genes that exhibited differential expression levels across specific conditions. The identification of such genes could provide valuable biological insights into the underlying mechanisms affected by the conditions of interest. Logistic regression is a simple yet powerful algorithm that proves particularly useful for scRNA-seq analyses [19]. This is due to the substantial number of cells typically available in scRNA-Seq experiments, as well as the ability to incorporate transcript information in gene-level testing. By utilizing logistic regression, the contribution of individual transcripts (isoforms) to the gene-level differential analysis could be identified, which enhanced the interpretability of results. Rather than adopting the conventional method of using cell labels to study gene expression, logistic regression was utilized for each gene to predict cell labels using constituent transcript quantification. This approach capitalizes on the ability of logistic regression to highlight the optimal linear combination of the number of isoforms for differential analysis. In contrast to other methods, fitting a linear logistic regression was relatively and computationally efficient, which was particularly important as the scale of scRNA-Seq experiments continued to grow. Triku is a feature selection algorithm that belongs to the second category. It selects genes that have a localized expression and are expressed in neighboring cells that share similar characteristics (i.e., functions). The idea behind triku is that biologically important genes have localized expressions in groups of similar cells. It distinguishes between genes that are expressed in a few unrelated cells (uninteresting) and genes expressed in a set of similar cells (interesting) by looking at the expression of nearby cells. Triku calculated the distribution of gene expression values considering the expression of neighboring cells, and this helped distinguish between the two cases. Finally, the expected distribution of gene expression values was calculated using a random sample of k cells. Variable importance (VI) in random forests is a well-known feature selection method that estimates the importance of each feature in a dataset by measuring how much each feature contributes to the accuracy of the model [21]. In the random forest algorithm, multiple decision trees are built on different subsets of the data, and the VI score of each feature can be calculated based on how much the accuracy of the model decreases when that feature is randomly permuted. Features with high VI scores are considered to be more important, as they have a larger impact on the accuracy of the model. Regarding our outcomes, the tSNE plots clearly indicated a significant change in the scatter pattern of the data after integration with SCALEX, suggesting that the use of SCALEX effectively corrected the batch effects, making the data appropriate for subsequent analyses. Our study capitalized on the results of three well-established feature selection methodologies, each designed to identify relevant genes in a distinct manner. By exploiting the heterogeneity across these methods, we were able to highlight a diverse set of important genes, offering a more comprehensive understanding of the underlying biological mechanisms at play in the analyzed dataset. Specifically, we utilized a differential expression-based approach, the nearest neighbor search strategy, and an embedded method employing the variable importance measure of a random forest algorithm, each contributing unique insights to the overall analysis. It is important to point out that our analysis had five distinct single-cell RNA-seq studies (three AD and two normal control).