Seurat offers several key features beneficial for cancer research, including:
Data Integration: Integrates multiple scRNA-seq datasets to provide a comprehensive view of cellular diversity. Dimensionality Reduction: Techniques like PCA, t-SNE, and UMAP to visualize complex datasets in reduced dimensions. Clustering: Identifies distinct cell populations based on gene expression profiles. Differential Expression Analysis: Determines genes that are differentially expressed between cell types or conditions. Trajectory Inference: Maps the developmental trajectories of cells, which is crucial for understanding cancer progression.
