Single-Cell Omics and Multi-Omics Integration

Overview

Single-cell technologies have revolutionized biology by allowing measurement of gene expression, chromatin accessibility, and other molecular features in individual cells. Computational analysis is essential for extracting meaningful biology from these high-dimensional, sparse datasets.

Key Topics

• scRNA-seq quality control, normalization, and clustering
• Cell type annotation and marker gene identification
• Pseudotime and trajectory analysis
• Cell-cell communication inference
• Spatial transcriptomics data analysis
• Multi-modal single-cell data integration (CITE-seq, ATAC-seq)

Applications

• Tumor microenvironment characterization
• Developmental biology and lineage tracing
• Immune cell profiling
• Drug response heterogeneity