How to Build a Single-Cell RNA-seq Analysis Pipeline with Scanpy for PBMC Clustering, Annotation, and Trajectory Discovery
This tutorial details how to perform advanced single-cell RNA sequencing analysis using the Scanpy library. The process involves loading and cleaning a PBMC dataset, identifying and removing low-quality cells and potential doublets, and then normalizing and transforming the data. The analysis includes dimensionality reduction, cell clustering with the Leiden algorithm, and annotation of cell populations using marker genes. Finally, the workflow explores cell trajectory structures and calculates custom scores before saving the analyzed data. AI
IMPACT Provides a practical guide for researchers to analyze single-cell RNA-seq data, potentially accelerating biological discovery.