scanpy

To analyze single-cell RNA-seq data, load your h5ad file into an AnnData object and apply standard preprocessing workflows including quality control, normalization, and clustering to interpret the results.

Standard scRNA-seq clustering involves loading AnnData files, running QC and normalization, clustering cells using Leiden or Louvain methods, and performing differential expression to discover marker genes.

Yes, you can visualize single-cell clusters using UMAP and t-SNE to map the relationships between cell groupings identified by Leiden or Louvain clustering algorithms.

Yes, you need to load an AnnData object, typically from an h5ad file, to structure your scRNA-seq counts and metadata before running normalization and differential expression workflows.

To identify marker genes in scRNA-seq experiments, perform differential expression analysis across cell groups after completing quality control, normalization, and clustering steps on your AnnData.

The best way to handle quality control and normalization for scRNA-seq is applying standard preprocessing pipelines to your AnnData files before clustering and downstream marker-gene discovery.