Simplify complex data sets with our dimensionality reduction tools. Utilize PCA, UMAP, and t-SNE to uncover patterns and relationships in high-dimensional data, facilitating intuitive sample clustering.

Pluto's suite of Dimensionality Reduction tools, including PCA (Principal Component Analysis), UMAP (Uniform Manifold Approximation and Projection), and t-SNE (t-Distributed Stochastic Neighbor Embedding), empowers researchers to tackle the challenge of high-dimensional data. These powerful algorithms transform complex datasets into more manageable, lower-dimensional spaces, while preserving the essential relationships and patterns. This process not only aids in revealing hidden structures within the data but also enhances the interpretability and visualization of complex biological datasets. Whether you're analyzing gene expression profiles, proteomics data, or multi-omics datasets, dimensionality reduction in Pluto serves as a foundational step in exploratory data analysis, enabling scientists to identify clusters, outliers, and pathways of interest with unprecedented clarity and intuition. By leveraging these tools, researchers can accelerate discovery and gain deeper insights into their data, opening up new avenues for hypothesis generation and testing.