## 1 Introduction  
High throughput molecular biology technologies, such as whole-genome sequencing, have been used extensively in a research capacity to investigate disease mechanisms, allowing a deeper analysis at the cellular level and more reliable results [1]. In the era of personalized medicine there is a constant need to develop robust computational approaches for big data analysis in order to explore molecular entities and hidden patterns in a more realistic manner [2]. Towards overcoming these challenges, remarkable progress has been observed through different generations of sequencing technology providing explosive growth in omics data [3]. Single-cell sequencing, as an emerging technique, has revolutionized the way diseases are studied at the cellular level. This technology can accurately study individual cells and explore pathological mechanisms at the single-cell level to indicate diagnostic biomarkers or potent therapeutic targets [4]. The single-cell RNA sequencing (scRNA-seq) technique allows detection and quantitative analysis of mRNA molecules at a single cell resolution instead of bulk RNAseq studies which investigate global gene expression. It is a complex process, which involves single cell isolation and capture, lysis of cells, reverse transcription, amplification, and library preparation [5, 6]. However, although experimental approaches are rapidly increasing, in silico pipelines for handling raw data files remain limited. A typical scRNA-seq dataset includes thousands of cells and their corresponding transcripts, therefore, from a computational perspective, we have to deal with a large amount of complex data (features) for each cell with ultra-high dimensionality and huge volume [7]. To effectively analyze these complex data, feature selection is a critical aspect of machine learning data preprocessing, which has gained significant importance in recent years due to the exponential growth in the size of real-world datasets [8]. The feature selection process aims to identify and select relevant features to improve model performance and efficiency. By removing irrelevant features, it can also improve the efficiency of the algorithm. In the context of scRNA-seq data, feature selection is particularly important as it can help identify the most important genes and cellular pathways, leading to a better understanding of the cellular functionality [9]. Machine learning (ML) processes are the first choice for mining information from such datasets as they can tackle part of this data complexity. In terms of high dimensionality due to the large number of feature spaces (genes), ML approaches to feature extraction can address this challenge by removing as much noisy and redundant information from the extracted features as possible [10]. In some cases, feature importance scores generated by ML methods may be unreliable, which can lead to instability and make it challenging to distinguish between important and unimportant features [11]. While filter and wrapper feature selection methods are commonly used, they also have some limitations. One common issue with filter approaches is that they tend to overlook how the classifier interacts with each feature and evaluate each feature independently, neglecting feature correlations. Additionally, it is difficult to determine the appropriate threshold for selecting only relevant features and excluding noise [12]. Wrapper feature selection also has several drawbacks, including a greater risk of overfitting, longer training times, reliance on a specific classifier, and higher computational cost and discriminatory power [13]. Ensemble feature selection frameworks provide a dependable method to decrease the dimensionality of high-dimensional data and pinpoint the most meaningful features in a case study [14; 15]. An ensemble method that combines the results of multiple feature selection techniques can overcome the limitations of using single feature selection methods. This approach can lead to improved performance and robustness compared to any individual method.