Aim
Transcriptomics: RNA to Single Cell Applications teaches end-to-end transcriptomics workflows from bulk RNA-seq to single-cell RNA-seq. Learn experimental concepts, data formats, QC, differential expression, clustering, cell-type annotation, pathway analysis, and reporting using real datasets and notebooks.
Program Objectives
- Transcriptomics Basics: RNA biology, expression quantification, experimental design.
- Bulk RNA-seq Workflow: QC → counts → differential expression → pathways.
- Single-Cell Concepts: barcodes/UMIs, dropout, batch effects, cell heterogeneity.
- scRNA-seq Analysis: filtering, normalization, clustering, marker genes.
- Annotation: cell type labeling using markers and references.
- Advanced Topics: trajectory/pseudotime concepts, integration, cell-cell communication (intro).
- Visualization: PCA/UMAP, heatmaps, volcano plots, dot plots.
- Capstone: analyze a bulk + single-cell dataset and report insights.
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.
Participant Eligibility
- UG/PG students and researchers in Biotechnology, Genetics, Bioinformatics, Life Sciences
- Basic molecular biology required; basic R/Python helpful
- Anyone starting RNA-seq or single-cell analysis
Program Outcomes
- Understand bulk and single-cell transcriptomics workflows.
- Interpret QC metrics, clustering outputs, and marker genes.
- Perform DE and pathway interpretation with clear reporting.
- Deliver a portfolio-ready transcriptomics capstone.
Program Deliverables
- e-LMS Access: lessons, datasets, notebooks.
- Toolkit: QC checklist, DE template, marker annotation worksheet, report outline.
- Capstone Support: feedback on analysis and figures.
- Assessment: certification after capstone submission.
- e-Certification and e-Marksheet: digital credentials on completion.
Future Career Prospects
- Transcriptomics Analyst (Entry-level)
- Single-Cell Bioinformatics Trainee
- Genomics Research Assistant
- Bioinformatics Associate (Omics Data)
Job Opportunities
- Genomics Labs/CROs: RNA-seq and scRNA-seq analysis support.
- Biotech/Pharma: biomarker discovery and omics analytics teams.
- Universities/Research Centers: omics research projects.
- Hospitals/Clinical Research: translational studies (research-focused analytics).
Reviews
There are no reviews yet.