Single-cell Omics
Prognostic Marker Discovery Pipeline
- Python
- Metadata Harmonization
- Survival Analysis
Overview
Developed a semi-automated pipeline to standardize heterogeneous public single-cell datasets, enabling the discovery of prognostic markers across multiple tissue types.
Key highlights
- Curated and standardized 67 public scRNA-seq datasets across 11 tissue types.
- Implemented a metadata harmonization module to resolve inconsistencies in public data reporting.
- Applied Gene Set Variation Analysis (GSVA) and statistical testing to link cell type abundance with patient prognosis.
