Performs comprehensive single-cell RNA-seq data analysis and visualization using the Scanpy Python framework.
This skill provides a specialized bioinformatics workflow for analyzing single-cell RNA sequencing data within your development environment. Built on the scalable Scanpy toolkit and AnnData structure, it streamlines the entire analytical pipeline from raw data ingestion to advanced biological interpretation. It enables researchers to perform rigorous quality control, normalization, dimensionality reduction, and clustering while providing the tools necessary for marker gene identification and trajectory inference, ensuring reproducible and publication-quality results.
Key Features
01End-to-end scRNA-seq pipeline including QC, normalization, and scaling
02Advanced dimensionality reduction using PCA, UMAP, and t-SNE
03Trajectory inference and pseudotime analysis with PAGA and DPT
04Automated marker gene identification and cell type annotation support
05Publication-ready visualization templates for heatmaps, dot plots, and UMAPs
0616 GitHub stars
Use Cases
01Modeling developmental lineages and cellular transitions through trajectory inference
02Identifying distinct cell populations and their specific marker genes in complex tissues
03Analyzing large-scale transcriptomics datasets from 10X Genomics or H5AD files
