Scanpy provides a comprehensive suite of tools that are essential for the analysis of scRNA-seq data in oncology. Some of the key functionalities include:
Data Preprocessing: Scanpy offers tools for normalization, filtering, and scaling of scRNA-seq data, which are critical steps for accurate downstream analysis. Dimensionality Reduction: Techniques such as PCA, t-SNE, and UMAP are implemented in Scanpy to reduce the complexity of high-dimensional data, making it easier to visualize and interpret. Clustering: Scanpy can cluster cells into distinct groups based on their gene expression profiles, helping to identify different cell types within a tumor. Differential Expression Analysis: This feature allows researchers to identify genes that are differentially expressed between different cell clusters or conditions. Trajectory Inference: Scanpy supports trajectory inference to study the differentiation pathways of cancer cells and understand their evolution.
