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.
📚 Foundational Literature & Methodology Benchmarks
Single-Cell Ecosystem & Tools
- Hao Y, Stuart T, Kowalski MH, et al. (2024). Dictionary learning for integrative, multimodal and scalable single-cell analysis (Seurat v5). Nat Biotechnol 42:293–304. doi:10.1038/s41587-023-01767-y
- Wolf FA, Angerer P, Theis FJ. (2018). SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19(1):15. doi:10.1186/s13059-017-1382-0
- Zheng GX, Terry JM, Belgrader P, et al. (2017). Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049. doi:10.1038/ncomms14049
Batch Correction & Dynamics
- Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. (2018). Deep generative modeling for single-cell transcriptomics (scVI). Nat Methods 15(12):1053–1058. doi:10.1038/s41592-018-0229-2
- La Manno G, Soldatov R, Zeisel A, et al. (2018). RNA velocity of single cells. Nature 560:494–498. doi:10.1038/s41586-018-0414-6
- Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ. (2020). Generalizing RNA velocity to transient cell states through dynamical modeling (scVelo). Nat Biotechnol 38:1408–1414. doi:10.1038/s41587-020-0591-3
Signaling & Spatial Transcriptomics
- Jin S, Guerrero-Juarez CF, Zhang L, et al. (2021). Inference and analysis of cell-cell communication using CellChat. Nat Commun 12(1):1088. doi:10.1038/s41467-021-21246-9
- Moses L, Pachter L. (2022). Museum of spatial transcriptomics. Nat Methods 19(5):534–546. doi:10.1038/s41592-022-01409-2
- Ståhl PL, Salmén F, Vickovic S, et al. (2016). Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353(6294):78–82. doi:10.1126/science.aaf2403