scrna-preprocessing-clustering

scRNA Preprocessing And Clustering

Version Compatibility

Reference examples assume:

• scanpy 1.10+
• anndata 0.10+
• pandas 2.2+
• matplotlib 3.8+

Before using code patterns, verify installed versions match the environment:

• Python: python -c "import scanpy, anndata; print(scanpy.__version__, anndata.__version__)"
• If signatures differ, inspect the installed API and adapt the pattern instead of retrying unchanged.

Overview

Use this skill to turn raw or minimally processed scRNA-seq data into an analysis-ready object with:

• QC-filtered cells and genes
• normalized expression values
• highly variable genes
• PCA and UMAP embeddings
• Leiden clusters
• saved h5ad artifact for annotation, DE, integration, or trajectory analysis

When To Use This Skill

• raw 10x matrices, filtered count matrices, or h5ad inputs need standard preprocessing
• the user wants UMAP, clustering, or marker discovery
• downstream tasks depend on a stable single-cell object rather than ad hoc plots

Quick Route

• If the input is already a processed h5ad, inspect adata.raw, embeddings, cluster columns, and QC columns before rerunning preprocessing.
• If the input is raw counts, do QC first and only normalize after filtering obvious low-quality cells.
• If multiple batches are present, preprocess cleanly first, then consider integration instead of hiding batch effects with aggressive filtering.

Progressive Disclosure

• Read technical_reference.md for QC decision rules, assay caveats, and integration branching.
• Read commands_and_thresholds.md for concrete Scanpy code, default thresholds, and output conventions.

Default Rules

• Keep raw counts recoverable. Prefer adata.raw = adata.copy() before regression or scaling.
• Report thresholds explicitly. Do not silently drop cells or genes.
• Show QC distributions before applying hard filters.
• Use vector outputs such as .pdf or .svg for final figures when possible.