Single-Cell Genomics & Spatial Biology
Master droplet microfluidics, Cell Ranger, STARsolo, Seurat v5, Scanpy, scVI deep generative batch correction, RNA velocity dynamics, ligand-receptor networks, and subcellular spatial transcriptomics (10x Visium, Xenium & MERFISH).
Curriculum Modules
Click on any chapter below to explore complete code, mathematical principles, and benchmarks.
1: Single-Cell Technologies, Droplet Microfluidics & Chemistries
Microfluidic droplet physics, 10x Chromium 3' vs 5', SMART-seq3 full-length sequencing, Split-pool barcoding, cell barcode and UMI architectures, and Poisson loading dynamics.
2: Raw Read Processing, STARsolo & Cell Ranger Count
Demultiplexing BCLs, FASTQ structure, Cell Ranger count CLI, STARsolo ultra-fast alignment, GTF transcriptome filtering, and sparse count matrix formats.
3: Quality Control, Mitochondrial Outliers & Doublet Scrubbing
Filtering dying cells by mitochondrial percentage, ribosomal gene checks, adaptive Median Absolute Deviation (MAD) thresholds, DoubletFinder in R, and Scrublet in Python.
4: Normalization, Feature Selection & Variance Stabilization
Global scaling normalization (LogNormalize, CPM), Pearson residuals via regularized negative binomial regression (SCTransform), and Highly Variable Gene (HVG) selection.
5: Dimensionality Reduction: PCA, t-SNE & UMAP Geometry
Linear dimensionality reduction with PCA, JackStraw / Elbow inflection heuristics, non-linear embeddings (Barnes-Hut t-SNE vs. UMAP topology), and parameter optimization.
6: Graph-Based Clustering (Louvain & Leiden) and Marker Annotation
K-nearest neighbor (k-NN) and shared nearest neighbor (SNN) graphs, Louvain vs Leiden modularity algorithms, marker gene catalogs, and automated cell typers (SingleR, Azimuth).
7: Marker Gene Discovery & Single-Cell Differential Expression
Wilcoxon rank-sum testing, MAST hurdle modeling for dropouts, ROC analysis, pseudobulk aggregation across biological replicates with DESeq2, and false discovery control.
8: Batch Effect Correction & Multi-Sample Integration
Technical batch variation vs biological heterogeneity, Mutual Nearest Neighbors (MNN), Seurat CCA integration, Harmony ridge regression, and scVI deep generative autoencoders.
9: Trajectory Inference & Lineage Branching (Pseudotime)
Continuous biological transitions vs discrete clusters, graph learning, diffusion pseudotime (DPT), Monocle 3 Principal Graph, Slingshot cluster MST, and lineage bifurcations.
10: RNA Velocity Dynamics with Velocyto & scVelo
Intron retention, spliced vs unspliced mRNA counting, dynamical transcription rates, phase portraits, velocity vector fields on UMAP, and directional differentiation forecasting.
11: Cell-Cell Communication & Ligand-Receptor Networks
Intercellular signaling networks, CellChat mass action law modeling, CellPhoneDB permutation testing, signaling axes, and circos communication plots.
12: Single-Cell Multi-Omics: CITE-Seq, SHARE-Seq & Single-Cell ATAC-Seq
Simultaneous surface protein and RNA profiling (CITE-seq ADT normalization), single-cell chromatin accessibility (scATAC-seq), Signac/ArchR, and TF motif accessibility.
13: Spatial Transcriptomics: 10x Visium, Xenium, MERFISH & Stereo-seq
Sequencing-based vs imaging in situ transcriptomics, histological alignment, cell-type deconvolution (RCTD, cell2location), spatial autocorrelation (Moran's I), and tissue neighborhoods.