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Phase 4 — Single-cell RNA-seq
Module 27

Scanpy — Core Workflow

Scanpy is the Python equivalent of Seurat and is increasingly preferred for large datasets. Reproduce the exact same PBMC analysis in Python — having both Seurat and Scanpy versions shows bilingual competence that few candidates demonstrate.

Weeks 35–36Timeline
~14 hrsStudy time
FREEAlways
Start Lesson 1 → View in curriculum →
What you'll learn

Topics covered in this module

AnnData
sc.pp.normalize_total
PCA · UMAP
Leiden clustering
rank_genes_groups
.h5ad export
Curriculum

10 lessons in this module

1Introduction to Scanpy & AnnDataWhat is Scanpy, AnnData object structure, installing Scanpy, loading 10x PBMC dataLive
2Quality Control in Scanpysc.pp.calculate_qc_metrics, filtering cells & genes, mitochondrial %, violin plotsSoon
3Normalisation & Log-transformationsc.pp.normalize_total, sc.pp.log1p, why normalise, counts per cellSoon
4Highly Variable Genessc.pp.highly_variable_genes, HVG plot, subsetting to HVGsSoon
5Scaling & PCAsc.pp.scale, sc.tl.pca, elbow plot, variance explainedSoon
6Neighbourhood Graph & UMAPsc.pp.neighbors, sc.tl.umap, interpreting UMAPSoon
7Clustering with Leidensc.tl.leiden, resolution parameter, cluster UMAP overlaySoon
8Marker Gene Detectionsc.tl.rank_genes_groups, dotplots, violin plots per clusterSoon
9Cell Type AnnotationManual annotation, adata.obs['cell_type'], annotated UMAPSoon
10Saving, Exporting & Reproducibilityadata.write_h5ad, figures, scripts, session infoSoon

📋 Not sure where this fits? Module 27 is part of the full bioinformatics curriculum — a structured 42-week learning path from Bash to single-cell RNA-seq.

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Lesson 1 is live. The rest are on their way.

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Module 27 of 31