scanpy

To handle large-scale genomic datasets efficiently, this pipeline utilizes the AnnData structure to manage high-dimensional expression matrices and metadata. It processes large single-cell RNA-seq datasets by embedding quality control and clustering workflows within scalable Python data structures.

Yes, you can generate publication-quality UMAPs and heatmaps for marker gene expression directly within this workflow. The pipeline integrates visualization capabilities to produce dot plots and UMAPs that visually represent distinct cell populations and differential expression results.

You need Python environments with scanpy, pandas, numpy, and matplotlib installed to execute this scRNA-seq analysis workflow. These dependencies provide the foundational data structures and plotting libraries required to manage expression matrices and render publication-quality visualizations.

The best way to manage expression matrices and metadata for reproducible genomic analysis is using the AnnData structure. This pipeline leverages AnnData to efficiently handle large-scale single-cell count matrices and associate metadata throughout the entire clustering and visualization workflow.