The study of Gene Expression (GE) data is important for understanding genetic transcription, revealing disease mechanisms, improving diagnostics, and guiding targeted therapies. However, the high dimensionality of GE data presents challenges. Dimensionality Reduction (DR) techniques address this by reducing computational complexity and simplifying data processing. This study aimed to develop an Autoencoder (AE) model for GE data DR and compare its feature weighting with that of Principal Component Analysis (PCA). A command-line interface (CLI) tool for processing high-dimensional data was also created. This study used a gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis to compare the biological functions of genes identified by PCA and AE. A hypergeometric test evaluated the overlap between features selected by PCA and AE, and network analysis provided a comprehensive comparison, identifying hub genes. This study also assessed the predictive potential of PCA and AE-reduced datasets, determining which method better preserved the original data's information. The findings highlighted that PCA and AE select and prioritise features differently, each capturing unique aspects of the data. Despite these differences, both methods consistently identified similar biological processes and functions, as evidenced by GO terms and KEGG pathways analysis. Interestingly, PCA and AE reduced data retained almost identical amounts of information from the original data. The developed tool, named AutoGeneReducer, can reduce high-dimensional data in GE analysis. Future work will explore additional DR techniques such as t-SNE, UMAP, and Variational Autoencoders (VAEs) to enhance understanding of complex dataset structures and further advance the AutoGeneReducer tool.