Jeffrey F. Quinn

CellConsensus provides a consensus cell typing method by aggregating marker genes from curated atlases and literature, improving accuracy and reproducibility across tissues and platforms.

ALARMIST identifies multicellular communication motifs in spatial transcriptomics data, revealing microenvironmental drivers of tumor progression in lung and brain cancers.

MultiTME integrates spatial and single-cell data using a cycle-consistent deep generative model, revealing a spatially coherent tumor axis in colorectal cancer.

STAPLE automates spatial transcriptomics analysis by integrating multiple tools into a modular framework and provides AI-driven interpretation of results.

The paper presents a pan-cancer ex vivo drug screen atlas with 2.1M experiments, identifying tissue-specific drug sensitivities and demonstrating their predictive value for clinical profiles.

BATCHIE efficiently identifies effective drug combinations through adaptive experimentation, reducing the number of required experiments while accurately predicting synergies.

BayesTME is a Bayesian method for spatial transcriptomics that unifies analysis goals, outperforms existing methods, and identifies spatial transcriptional programs in human and zebrafish melanoma tissues.