Analyzing high dimensional data involves several approaches: - Dimensionality Reduction: Techniques like [[Principal Component Analysis]] (PCA) and [[t-Distributed Stochastic Neighbor Embedding]] (t-SNE) reduce the number of variables while preserving essential information. - Machine Learning: Algorithms such as [[random forests]], support vector machines (SVM), and deep learning models can identify patterns and make predictions based on high-dimensional data. - Cluster Analysis: Methods like [[hierarchical clustering]] and [[k-means clustering]] group similar data points, helping to identify distinct cell populations or tissue regions.
