scanpy

Single-cell RNA-seq clustering and UMAP visualization in Python can be performed using a standard Scanpy workflow to execute normalization, dimensionality reduction, Leiden clustering, and generate publication-ready plots from AnnData objects.

scRNA-seq quality control and normalization involve selecting highly variable genes and preparing an AnnData object, enabling reproducible downstream dimensionality reduction, clustering, and marker identification for exploratory datasets.

Yes, Scanpy supports multi-sample scRNA-seq workflows by integrating with the scverse ecosystem, utilizing scvi-basic for batch correction and scvi-scanvi for annotation transfer across different datasets.

To identify marker genes and score gene signatures in scRNA-seq data, you can use rank_genes_groups for marker identification and score_genes for evaluating specific gene signatures within your AnnData object.

The best way to generate publication-ready plots for scRNA-seq clustering results is through the scverse-style visualization functions, producing dotplots, heatmaps, violin plots, and UMAP embeddings directly from clustered data.

Yes, you need an AnnData object to run this standard scRNA-seq analysis workflow, as it requires Python and the scverse ecosystem to execute end-to-end quality control, clustering, and visualization.