Single-Cell RNA Sequencing Data Analysis Using R and Python
Decode Cellular Heterogeneity Through Advanced Single-Cell Transcriptomics and Computational Analysis
About This Course
Single-cell RNA sequencing (scRNA-seq) has transformed genomics by enabling researchers to investigate gene expression at the resolution of individual cells, uncover cellular heterogeneity, identify rare cell populations, and understand developmental and disease mechanisms. This international workshop provides comprehensive training in scRNA-seq data analysis using R and Python through industry-standard computational workflows. Participants will gain hands-on experience with widely used bioinformatics tools, perform analyses on real-world public datasets, and learn best practices for interpreting single-cell transcriptomic data for research and precision medicine applications.
Aim
To equip participants with the theoretical knowledge and practical skills required to analyze single-cell RNA sequencing (scRNA-seq) data using R and Python. The workshop focuses on modern computational workflows for quality control, clustering, cell type annotation, differential expression analysis, and biological interpretation using state-of-the-art bioinformatics tools and publicly available datasets.
Workshop Objectives
- Understand the principles and applications of single-cell RNA sequencing.
- Perform preprocessing and quality control of scRNA-seq datasets.
- Analyze and visualize single-cell transcriptomic data using Seurat and Scanpy.
- Identify cellular subpopulations through clustering techniques.
- Annotate cell types using computational approaches.
- Perform differential gene expression and functional enrichment analyses.
- Conduct trajectory and data integration analyses for advanced biological studies.
- Interpret single-cell datasets for disease biology and precision medicine research.
Workshop Structure
📅 Day 1: Foundations of Single-Cell Transcriptomics and Data Preprocessing
- Introduction to Single-Cell RNA Sequencing technologies and experimental workflows
- Comparison of Bulk RNA-Seq and Single-Cell RNA-Seq
- Overview of scRNA-seq platforms (10x Genomics, SMART-Seq, Drop-seq)
- Data formats, count matrices, and metadata structure
- Quality control metrics and identification of low-quality cells
- Normalization, scaling, and identification of highly variable genes
- Introduction to Seurat (R) and Scanpy (Python) analysis frameworks
🛠️ Hands-on
Hands-on 1: Import and explore publicly available scRNA-seq datasets using Seurat and Scanpy.
Hands-on 2: Perform quality control, filtering, normalization, and feature selection using R and Python.
📅 Day 2: Cell Clustering, Annotation, and Differential Gene Expression
- Dimensionality reduction using PCA, t-SNE, and UMAP
- Graph-based clustering and identification of cellular subpopulations
- Marker gene identification and biological interpretation
- Automated and reference-based cell type annotation
- Differential gene expression analysis between cell populations
- Functional enrichment and pathway analysis
- Best practices for reproducible scRNA-seq analysis
🛠️ Hands-on
Hands-on 1: Perform clustering, visualize cell populations, and identify marker genes.
Hands-on 2: Annotate cell types and conduct differential gene expression analysis using Seurat and Scanpy.
📅 Day 3: Advanced Single-Cell Analysis and Biological Interpretation
- Cell trajectory and pseudotime analysis
- Cell-cell communication and interaction analysis
- Batch correction and data integration across multiple datasets
- Multi-sample and multi-condition comparative analysis
- AI-assisted biological interpretation of single-cell datasets
- Case studies in cancer, immunology, developmental biology, and precision medicine
- Current trends in spatial transcriptomics and multi-omics integration
🛠️ Hands-on
Hands-on 1: Perform trajectory analysis and integrate multiple scRNA-seq datasets.
Hands-on 2: Generate a complete single-cell analysis report with publication-quality visualizations and AI-assisted biological interpretation.
Who Should Enrol?
This workshop is designed for Undergraduate and Postgraduate Students, Ph.D. Scholars and Research Fellows, Faculty Members and Academicians, Bioinformatics and Computational Biology Professionals, Genomics, Transcriptomics, and Biomedical Researchers, Clinical Researchers, Biotechnology and Pharmaceutical Industry Professionals, Data Scientists working in Healthcare and Life Sciences, and Researchers interested in Precision Medicine and Systems Biology who wish to enhance their knowledge of modern computational approaches and next-generation therapeutics in diabetes research and drug discovery.
Important Dates
Registration Ends
October 1, 2026
IST 3:30 PM
Workshop Dates
October 1, 2026 – October 3, 2026
IST 4:00 PM
Workshop Outcomes
- Independently perform complete scRNA-seq data analysis workflows using R and Python.
- Apply internationally accepted computational pipelines for single-cell transcriptomics.
- Generate high-quality visualizations suitable for scientific publications.
- Interpret cellular heterogeneity and identify biologically relevant cell populations.
- Integrate multiple single-cell datasets for comparative studies.
- Utilize AI-assisted approaches for biological interpretation and reporting.
- Apply single-cell analysis techniques in biomedical research, translational medicine, and precision healthcare.
Fee Structure
Student Fee
₹1999 | $65
Ph.D. Scholar / Researcher Fee
₹2999 | $75
Academician / Faculty Fee
₹3999 | $85
Industry Professional Fee
₹4999 | $100
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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