This project explores various dimensiona reduction and clustering techniques applied to high-variance gene expression data. The primary objective is to identify meaningful patterns in gene expression by reducing dimensions while preserving key structures. The analysis begins with data preprocessing, scaling, and feature selection, followed by Principal Component Analysis (PCA) and Factor Analysis (FA) to determine the optimal number of components or factors. Clustering methods, including K-Means, Hierarchical Clustering, and t-SNE with K-Means, are then applied to uncover potential subgroups within the data.