## 1 Introduction  
Alzheimer's (AD) disease is a chronic degenerative disease of the brain and is the most common cause of dementia. Its main clinical manifestation is memory impairment, but other brain functions are also affected, and thus, the patient's social life is seriously affected [1]. Exploring the molecular characteristics of blood cells using single-cell RNA-sequencing (scRNA-seq), data have emerged as a powerful approach to unravelling the molecular basis of AD at the cellular level [2]. It has enabled the identification of transcriptional changes in individual cells, providing unprecedented resolution and sensitivity to detect early changes in disease progression. Furthermore, scRNA-seq can capture the heterogeneity of cell types within the peripheral immune system, which is crucial for understanding the immune response to AD [3, 4]. Due to these advantages, scRNA-seq analysis of liquid biopsies, such as blood, is considered a first choice to address the challenges of AD discovery as well as non-invasive blood-based sampling [5]. Although this area may offer promising results, it is in its infancy, and limited studies have been published [6, 7, 8]. It is worth mentioning that machine learning (ML) analysis has not yet been applied to scRNA-seq data for the purpose of extracting valuable knowledge and identifying potential AD biomarkers. Therefore, there is a significant need for novel approaches to extract insights from such data and advance our understanding of AD. One major challenge in scRNA-seq data lies in the fact that these are often generated in multiple batches, which can introduce batch effects that confound downstream analyses [9]. In order to address this issue, deep learning (DL) techniques have been developed to integrate scRNA-seq data from multiple batches [10; 11; 12]. These approaches can effectively remove batch effects, producing a more accurate representation of underlying biological processes. In recent years, there has been a significant advance in the application of deep learning methods for the integration of single-cell RNA sequencing (scRNA-seq) data [13; 14]. These methods have shown remarkable progress in addressing the challenges posed by batch effects in scRNA-seq datasets, enabling the integration of data from multiple batches and producing a more accurate representation of underlying biological processes. During single-cell RNA-sequencing studies, the initial FASTQ file output is transformed into a count's matrix that provides a summary of the number of molecules for each gene that can be detected in each cell in the dataset [15]. After going through preprocessing procedures, the resulting counts matrix contains tens of thousands of gene expression profiles (features) for each sample, leading to an extremely high-dimensional dataset. Two approaches are commonly employed to handle high-dimensional datasets: dimensionality reduction methods and feature selection [16]. The first entails transforming the initial high-dimensional space into a lower-dimensional space while maintaining pairwise distances between points below a specified threshold. The latter aims to identify the most relevant features that encapsulate relevant information in the dataset. Concentrating on feature selection preserves the gene structure, thus lending interpretability to the studied model. In this context, we selected the study conducted by [PERSON] et al. (2021) [6] as it provides the most indicative scRNA-seq approach for peripheral five subtypes of immune cells in AD, offering gene expression profiles for tens of thousands of selected cellular samples. The authors employed a variety of analytical processes, including statistical tests, network-based analysis and functional enrichment analysis, and promising results were obtained, revealing abnormal changes in the immune cell composition and transcriptional state of AD. These findings suggest a disturbance in the immune infiltration of the peripheral immune environment at a single-cell level.