## 3 Results and Discussion
Given a dataset with ultra-high dimensionality we have two main pillars to deal with its complexity: dimensionality reduction and feature selection methods. The first pillar transforms features into a lower dimension trying to keep the pairwise sample distances as efficiently as possible based on the original feature space. The latter pillar includes a simpler method that isolates a list of features that are the most dominant features in the entire dataset. In this study, we focus on the second way since we are also interested in identifying dominant genes in scRNA-seq data that can improve the performance of various supervised learning tasks such as the classification process. An indicative feature selection categorization includes three main strategies: filter-based, wrapper-based, and ensemble-based strategies [46]. More specifically, filter methods select features based on a performance measure independent of the data modeling algorithm used, while they can classify individual features or evaluate entire subsets of features. Indicative measures for selecting the best features through such methods are information, distance, consistency, similarity, and statistical measures. The reason is that it is computationally more feasible and is performed by selecting the variables that have a higher value in a predefined numerical function that estimates the weight of the variable for the classification task. Functions incorporate various criteria such as information gain, mutual information, chi-square, odds ratio, relevance score, and correlation coefficient [47]. Wrappers are feature selection algorithms that assess a subset of features based on the accuracy of a predictive model developed with them. The evaluation is carried out with the assistance of a classifier that provides an estimate of the importance of a certain group of attributes [48]. This class of approaches has proven effective; however, their high computational cost limits their use. In terms of ensemble methods, we refer to a compilation of subsets of features derived from a variety of different base classifiers. The list of ranked genes obtained by the present approach includes all values from the variable importance scores derived from the three feature selection methods. Using Borda count, the aim of this score is to identify genes that have high scores across all feature selection methods, so as to obtain robust and remarkable gene markers that drive the structure of the given transcriptomics dataset [49]. A gene will rank low, for example, if it presents the best value in one feature selection method and is somewhere in the middle of the ranking list for the other methods. On the other hand, a gene will earn a high Borda score if it ranks third in all scores (even it is not in the top positions) because its function was significant in all feature selection methods. According to this process, we ensure that we obtain a fairer genes hierarchy and we do not miss any important gene due to the limitations and weaknesses of each individual method. Population heterogeneity, spatial heterogeneity, and temporal heterogeneity are three distinct groups of biologically relevant heterogeneity. Cellular heterogeneity provides insights into the network connectivity and plays an important role in regulating intrinsic cell fate decisions. Data distribution derives the selection of the optimal visualization tool such as methods for classification and pathway modeling [50]. Dimensionality is the typical problem of maintaining several features in a heterogeneous dataset with a number of instances from an analytics perspective, such as in scRNA-seq [51]. The main purpose of this research is to develop an ensemble feature selection approach for transcriptome data using established feature selection techniques. The evaluation of the results is based on both how effectively the classification models respond and how adequately the enrichment analysis works.