Program Structure

Module 1: RNA and Transcriptomics Foundations

• Gene expression and transcription regulation overview.
• Bulk vs single-cell transcriptomics: when to use which.
• Experimental design basics: replicates, controls, confounders.
• Key outputs: counts, TPM/FPKM concepts, differential expression.

Module 2: Bulk RNA-seq Workflow (End-to-End)

• Data formats: FASTQ, count matrix, metadata.
• QC concepts: read quality, mapping/quantification checks.
• Normalization concepts and batch effect awareness.
• DE analysis outputs and interpretation.

Module 3: Bulk RNA-seq Results + Pathway Analysis

• Volcano plots, MA plots, heatmaps.
• Gene set enrichment basics (GO/KEGG/Reactome concepts).
• Functional interpretation: up/down pathways and caveats.
• Reporting: methods, results tables, and reproducibility.

Module 4: Single-Cell RNA-seq Concepts

• Barcodes, UMIs, and cell-by-gene matrices.
• Dropout and sparsity; quality metrics.
• Ambient RNA and doublets (concepts).
• Common preprocessing decisions and their impact.

Module 5: scRNA-seq QC, Normalization, and Clustering

• Filtering cells/genes using QC thresholds.
• Normalization and scaling concepts; variable genes.
• Dimensionality reduction: PCA and UMAP (workflow).
• Clustering and cluster stability checks (intro).

Module 6: Cell Type Annotation and Marker Discovery

• Marker genes: finding and validating markers.
• Dot plots, feature plots, violin plots for markers.
• Reference-based annotation concepts (intro).
• Common mistakes: over-labeling and batch-driven clusters.

Module 7: Beyond Clusters (Intro Applications)

• Differential expression between clusters/conditions.
• Trajectory/pseudotime concepts (intro).
• Dataset integration and batch correction concepts.
• Cell-cell communication concepts (overview).

Module 8: Reproducible Reporting + Best Practices

• Organizing analysis: scripts, notebooks, metadata.
• Quality and validation: sanity checks and sensitivity analysis.
• Visualization checklist for publication-ready figures.
• Writing a clear transcriptomics report.

Final Project

• Analyze a provided bulk RNA-seq dataset and a scRNA-seq dataset.
• Deliverables: DE results + pathway summary + annotated UMAP + marker panel.
• Submit: notebook + figures + short report with biological interpretation and limits.