SCRAM
Hybrid cell annotation for tumor and non-tumor cells in single-cell datasets, including the identification of hybrid cell states.
Research
The Serin Lab develops AI and single-cell genomic methods to define brain tumor cell states, model therapeutic response, and prioritize clinically actionable interventions. Our long-term goal is to build predictive frameworks that can help make brain tumors treatable with greater precision.
Representative methods from the lab
Our research program combines method development with biological and translational questions in neuro-oncology.
COORS
Computational annotation of developmental-like cell states in brain tumors using human brain developmental reference atlases.
CLIPPR
Integrative cancer subtyping that combines bulk transcriptional measurements with single-cell-derived features and RNA-inferred CNV signals.
CaSpER and XCVATR
Methods for RNA-based genomic profiling, copy-number inference, and expression-linked variant analysis in bulk and single-cell data.
We are especially interested in collaborative projects that connect AI, single-cell genomics, and translational neuro-oncology, as well as students who want to work at that interface.