scanpy

End-to-end scRNA-seq analysis is performed by loading data into an AnnData object to compute QC metrics, normalize counts, identify highly variable genes, construct neighborhood graphs, and execute Leiden clustering for cell-type annotation.

The best way to run quality control and normalization on scRNA-seq data is to compute QC metrics, filter cells and genes, normalize counts, log-transform, and identify highly variable genes before downstream clustering.

Yes, you can use Scanpy to analyze scRNA-seq datasets stored in h5ad, 10x, or CSV formats, loading them directly into an AnnData object for quality control, dimensionality reduction, and trajectory inference.

Trajectory inference in single-cell analysis works by leveraging the neighborhood graph constructed from normalized highly variable genes, allowing Scanpy to map developmental progressions after Leiden clustering identifies distinct cell populations.

Yes, you need matplotlib alongside Scanpy to generate publication-ready plots visualizing dimensionality reductions, Leiden clustering results, and identified marker genes in scRNA-seq workflows.

Limitations of using Scanpy for single-cell RNA-seq trajectory inference include relying on Python ecosystem dependencies like matplotlib for visualization and requiring properly filtered, normalized AnnData inputs to accurately infer developmental progressions.