PCA, t-SNE, and UMAP are key techniques in bioinformatics and machine learning that simplify complex biological data and reveal hidden patterns. 🔍 PCA: Reduces dimensionality by identifying key features and trends, ideal for data exploration. 📍 t-SNE: Preserves local similarities to uncover clusters and subgroups in non-linear data. 🌐 UMAP: Balances speed and accuracy, maintaining both local and global structures for large, interpretable datasets. These techniques are essential for AI and ML

UMAP - FlowJo Documentation

AI | Machine Learning | Tech on Instagram: "UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique used to simplify complex, high-dimensional data—making it easier to visualize in just 2 or 3 dimensions. Imagine you have a dataset that is difficult to understand since there are many features (such as height, age, weight, etc.). UMAP projects this data for visualization, in a 2D or 3D plot for instance. UMAP works by maintaining key spatial features and hiera