Transcriptomics for Biomedical Research
Unlocking the Secrets of Gene Expression: Empower Your Biomedical Research with Practical RNA-seq Data Analysis Skills
Master modern transcriptomic analysis from physical sequencing principles to single-cell resolution. Covers study design, public data mining (GEO/TCGA), expression normalization, exploratory PCA, DESeq2 modeling, publication visualizations, GSEA pathway enrichment, STRING network biology, WGCNA co-expression, unsupervised machine learning, and complete 10x Genomics Seurat v5 single-cell workflows.
Curriculum Modules
Click on any module below for deep-dive production code, mathematical derivations, reproducible pipelines, and figures.
Chapter 1: Next-Generation Sequencing Technology: Types, Principles & RNA-Seq File Formats
Illumina SBS, DNBSeq, PacBio HiFi & ONT direct RNA sequencing, Poly(A) vs Ribo-Zero library prep, and FASTQ, BAM, GTF & count matrix structures.
Chapter 2: Biomedical Literature Mining, Experimental Study Design & Statistical Power
Designing robust transcriptomic experiments: biological vs technical replicates, confounding batch control, and statistical power modeling with RNASeqPower.
Chapter 3: Open Biomedical Repositories: GEO, TCGA, GDC & SRA Data Extraction
Programmatic retrieval of public RNA-Seq cohorts using GEOquery and TCGAbiolinks across clinical oncology and disease datasets.
Chapter 4: Clinical Metadata Handling, Patient Cohort Filtering & Pivot Table Exploration
Wrangling clinical annotations, sample sanitization, factor level recoding, cohort contingency tables, and count-matrix alignment.
Chapter 5: Expression Count Normalization, Library Sizing & Log Transformation
Mathematical formulations of CPM, RPKM, FPKM, TPM, DESeq2 median of ratios, edgeR TMM, and variance-stabilizing transformations (VST).
Chapter 6: Exploratory Data Analysis & Dimensionality Reduction: PCA, MDS & t-SNE
High-dimensional transcriptomic exploration: Principal Component Analysis (PCA), Scree plots, Multi-Dimensional Scaling, and non-linear t-SNE.
Chapter 7: Preliminary Statistical Tests & Spreadsheets: t-Tests, FDR & Volcano Screening
Student/Welch t-tests, Mann-Whitney U, multiple testing correction with Benjamini-Hochberg FDR, and building differential expression spreadsheets.
Chapter 8: Differential Gene Expression Analysis with DESeq2: Negative Binomial GLMs
Dispersion shrinkage, Empirical Bayes estimation, complex multi-factor design formulas, Wald tests, LRT, and apeglm log2 fold change shrinkage.
Chapter 9: Publication-Quality DEG Visualizations: Enhanced Volcano, MA Plots & ComplexHeatmap
High-impact journal graphics: customized EnhancedVolcano plots, MA diagnostics, and ComplexHeatmap clustering with multi-track clinical annotations.
Chapter 10: Pathway Annotation & Functional Over-Representation: DAVID, Reactome, KEGG & iLINCS
Hypergeometric over-representation testing, Gene Ontology (BP/MF/CC), Reactome/KEGG signaling cascades, and drug repurposing signatures with iLINCS.
Chapter 11: Gene Set Enrichment Analysis (GSEA) & clusterProfiler Workflows
Subramanian GSEA algorithm, MSigDB Hallmark signatures, running enrichment scores, multi-pathway dotplots, ridgeplots, and cnetplot network graphs.
Chapter 12: Molecular Interaction Networks: Cytoscape, STRING, GeneMANIA & X2K
Protein-protein interaction (PPI) topology, hub bottlenecks, Cytoscape programmatic styling with RCy3, and upstream kinase/TF discovery with X2K.
Chapter 13: Correlation & Regression Analysis in RNA-Seq: WGCNA Co-Expression Modules
Pairwise gene correlations, scale-free topology soft thresholding, Topological Overlap Matrix (TOM), Dynamic Tree Cut, and clinical trait association.
Chapter 14: Unsupervised Machine Learning: Hierarchical Clustering & Consensus Subtyping
Dissecting patient heterogeneity with ConsensusClusterPlus, PAM50 validation, silhouette scoring, and Kaplan-Meier clinical survival association.
Chapter 15: Introduction to Single-Cell RNA-Seq: 10x Genomics, Cell Ranger & Quality Control
Droplet-based scRNA-Seq microfluidics, Cell Barcode/UMI deduplication, Cell Ranger count pipelines, and empty droplet vs doublet filtering.
Chapter 16: Single-Cell Analysis Workflow: Seurat v5, SCTransform, Clustering & UMAP
Modern Seurat v5 layer architectures, SCTransform variance stabilization, HVG detection, SNN graphs, Louvain/Leiden modularity, and 2D UMAP projections.
Chapter 17: scRNA-Seq Differential Expression, Marker Prioritization & Label Assignment
Cell-level differential expression with Wilcoxon & MAST, cellular detection rate adjustments, marker ranking, and multi-gene DotPlot/Violin visualizations.
Chapter 18: Automated Cell Type Annotation (SingleR, Azimuth, CellTypist) & Functional Analysis
Reference-based automated label transfer with SingleR and Azimuth, prediction scores, pseudobulk differential expression, and cell-cell communication modeling.