Introduction to Single-Cell RNA-seq Available Beginner
The biological motivation for scRNA-seq, how droplet microfluidics works, barcodes and UMIs explained, the count matrix structure, and a tour of the scverse ecosystem.
Setting Up Your Python Environment Available Beginner
Three setup paths — local conda/mamba, Google Colab, and VS Code. Install the complete scverse stack, run the verification script, and apply the seven best practices that keep your project reproducible.
The AnnData Object Explained Available Beginner
Build AnnData from scratch, load all 12 real PBMC samples from CSV.gz files, concatenate them into one 107k-cell object, master indexing and slicing, understand layers and embeddings, and save your first checkpoint.
Loading Raw Data and Quality Control Metrics Coming Soon Beginner
Read 10x Genomics data into AnnData, compute QC metrics (total counts, n_genes, mitochondrial fraction), and visualise distributions.
Filtering Cells and Genes Coming Soon Beginner
Apply QC thresholds to remove low-quality cells, doublets, and uninformative genes. Best practices for threshold selection.
Normalization, Log-Transformation & Feature Selection Coming Soon Intermediate
Library-size normalisation, log1p transformation, and highly variable gene selection to prepare data for dimensionality reduction.
PCA and the Neighborhood Graph Coming Soon Intermediate
Run PCA on highly variable genes, choose the number of components, and build a k-nearest-neighbor graph for downstream analyses.
UMAP and t-SNE Visualisation Coming Soon Intermediate
Generate UMAP and t-SNE embeddings, interpret scatter plots, and understand the strengths and limitations of each method.
Clustering with Leiden and Louvain Coming Soon Intermediate
Apply community-detection algorithms to identify cell clusters, tune resolution, and evaluate clustering stability.