Applied Sciences, Vol. 13, Pages 2353: An Ensemble Feature Selection Approach for Analysis and Modeling of Transcriptome Data in Alzheimer’s Disease

Applied Sciences, Vol. 13, Pages 2353: An Ensemble Feature Selection Approach for Analysis and Modeling of Transcriptome Data in Alzheimer’s Disease

Applied Sciences doi: 10.3390/app13042353

Authors: Petros Paplomatas Marios G. Krokidis Panagiotis Vlamos Aristidis G. Vrahatis

Data-driven analysis and characterization of molecular phenotypes comprises an efficient way to decipher complex disease mechanisms. Using emerging next generation sequencing technologies, important disease-relevant outcomes are extracted, offering the potential for precision diagnosis and therapeutics in progressive disorders. Single-cell RNA sequencing (scRNA-seq) allows the inherent heterogeneity between individual cellular environments to be exploited and provides one of the most promising platforms for quantifying cell-to-cell gene expression variability. However, the high-dimensional nature of scRNA-seq data poses a significant challenge for downstream analysis, particularly in identifying genes that are dominant across cell populations. Feature selection is a crucial step in scRNA-seq data analysis, reducing the dimensionality of data and facilitating the identification of genes most relevant to the biological question. Herein, we present a need for an ensemble feature selection methodology for scRNA-seq data, specifically in the context of Alzheimer’s disease (AD). We combined various feature selection strategies to obtain the most dominant differentially expressed genes (DEGs) in an AD scRNA-seq dataset, providing a promising approach to identify potential transcriptome biomarkers through scRNA-seq data analysis, which can be applied to other diseases. We anticipate that feature selection techniques, such as our ensemble methodology, will dominate analysis options for transcriptome data, especially as datasets increase in volume and complexity, leading to more accurate classification and the generation of differentially significant features.