- 🫁 Respiratory disease and COPD
- 🫧 Single-cell transcriptomics
- 🧪 Multi-omics integration
- 🤖 Interpretable machine learning for biological discovery
- 🧫 Disease and cancer genomics
| Area | Methods & Tools |
|---|---|
| Programming | R · Python · Bash |
| Single-cell | Seurat · Scanpy · SingleR · CellChat · SCENIC · Monocle |
| Bulk RNA-seq | DESeq2 · WGCNA |
| Microbiome | DADA2 · phyloseq · MMUPHin · MaAsLin2 |
| Metagenomics | MetaPhlAn · HUMAnN — bioBakery |
| Machine Learning | Random Forest · XGBoost · SHAP |
| Deep Learning | PyTorch · CNN |
| Dimensionality Reduction | PCA · PCoA · UMAP |
| Statistics | PERMANOVA · differential abundance · survival analysis |
| Workflow / HPC | Snakemake · SLURM |
| Cancer Genomics | TCGA · GTEx · Xena · MAF |
| Visualization | ggplot2 · ComplexHeatmap · Cytoscape |
Biological Question
│
▼
Data Generation
│
▼
┌─────────────────────┐
│ Multi-Omics Data │
│ │
│ RNA-seq │
│ scRNA-seq │
│ Microbiome │
│ Metagenomics │
└─────────────────────┘
│
▼
Data Processing & QC
│
▼
Statistical Analysis
│
▼
┌─────────────────────┐
│ Computational │
│ Modeling │
│ │
│ ML · Network │
│ Regulatory Biology │
└─────────────────────┘
│
▼
Biological Interpretation
│
▼
Insight
|
