Building computational methods at the intersection of immunology, cancer biology, and single-cell omics.
I develop analytical tools and reproducible workflows for multiomics data, from benchmarking cell type annotations to integrating hundreds of patient samples across cohorts. My work spans research in cancer systems immunology with a focus on turning complex biological data into actionable insights for biomarker and target discovery.
- 🔬 Single-cell & spatial omics — cell state modeling, annotation, integration, and benchmarking across large cohorts
- 🧬 Cancer immunology — tumor microenvironment characterization, T cell biology, translational oncology
- 🕸️ Gene regulatory networks — graph-based gene prioritization and influence scoring for target discovery
- 📦 Open-source tools — reproducible pipelines and R/Python packages for the single-cell community
| Project | Description |
|---|---|
| HiTME | Hierarchical tumor microenvironment cell type classifier for scRNA-seq |
| scTypeEval | Framework for evaluating annotation quality through inter-sample consistency |
| PSlab | Multiomics analyses of T cell differentiation programs |
- Xu, Y. et al. Proteasome-guided haem signalling axis contributes to T cell exhaustion. Nature, 2026
- Andreatta, M., Garnica, J., Carmona, S. J. Identification of malignant cells in single-cell transcriptomics data. Communications Biology, 2025
- Tzeng, S.-F. et al. PLT012, a Humanized CD36-Blocking Antibody, Is Effective for Unleashing Antitumor Immunity. Cancer Discovery, 2025
- Garnica, J. et al. Context-dependent regulation of T-follicular helper cell formation. Cell Reports, 2025
- Garnica, J. et al. Epigenetic events underpinning the transdifferentiation of Tfh cells into Tr1 cells in vivo. eLife, 2024
