Chapter 11 of Bioinformatics

Omics data soon

Gene expression and single-cell analysis: normalisation, dimensionality reduction, clustering and differential expression with scanpy.

soon This chapter is planned and not yet written. The outline below is the current plan; it may change.

What it will cover

• Count matrices, normalisation and log transforms
• Quality control and batch effects
• PCA, UMAP and clustering of cells
• Differential expression and multiple testing
• Cell-type annotation with markers and classifiers
• Lab 11: a single-cell workflow on a small public dataset

Planned learning outcomes

1. Run a standard single-cell analysis pipeline with scanpy and interpret each step.
2. Recognise batch effects and confounding in omics data.
3. Train a cell-type classifier and evaluate it across batches.

In the meantime

Everything here builds on Part I, in particular the evaluation habits of Chapter 2. Episode 2.6 already introduces the group splits that become sequence-identity splits here.

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