Single-Cell RNA Sequencing Data Analysis Using R and Python

• 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.

📅 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

📅 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

📅 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

• 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.