A comprehensive 6-lesson course originally presented at the Institute for AI in Medicine (IKIM), University Hospital Essen on October 8, 2024, organized by the Department of Applied Computational Cancer Research. The course materials have been updated for SeuratExtend v1.1.0 and are now freely available online. Starting with fundamentals of R and Seurat, the course progressively builds to cover enhanced visualization, functional analysis, quality control, and cutting-edge methods including trajectory analysis, regulatory networks, and cell-cell communication. Perfect for beginners while providing depth needed for advanced applications.
Essential R programming fundamentals tailored for scRNA-seq analysis. Covers basic data types, data structures (vectors, matrices, data frames), file operations, and package management. Perfect for beginners starting their journey in bioinformatics.
Comprehensive walkthrough of the standard Seurat workflow, from raw count matrix to cell type annotation. Learn about data normalization, dimensionality reduction, clustering, and visualization through hands-on analysis of PBMC data.
Master advanced visualization techniques using SeuratExtend's enhanced plotting functions. Explore DimPlot2, FeaturePlot3, Heatmap, and other tools to create publication-ready figures. Includes practical examples of customizing plots and color schemes.
Master functional enrichment analysis using GO and Reactome databases through SeuratExtend's integrated GSEA pipeline. Learn to perform custom gene set analysis, interpret enrichment scores, and utilize helpful utility functions for gene naming conversions and cell proportions.
Elevate your scRNA-seq analysis with advanced quality control, doublet removal, data integration using Harmony, cell cycle analysis, and alternative normalization methods like SCTransform. Understand key considerations for processing and analyzing multi-sample datasets.
Explore cutting-edge techniques including trajectory analysis with scVelo/Palantir, cell-cell communication using CellChat/NicheNet, regulatory network inference with SCENIC, and specialized analyses for TCR/BCR data and copy number variations.