Wesley Tansey

CellConsensus: An agent-curated atlas for automatic cell typing

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

Decoding Multicellular Communication Motifs from Spatial Transcriptomics with ALARMIST

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

Cycle-consistent deep generative modeling unifies cellular states across unpaired spatial and single-cell modalities

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

STAPLE: automating spatial transcriptomics analysis and AI interpretation

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

A Pan-Cancer Ex Vivo Drug Screen Atlas for Functional Precision Oncology

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.

A Bayesian active learning platform for scalable combination drug screens

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

Acquisition of discrete immune suppressive barriers contributes to the initiation and progression of preinvasive to invasive human lung cancer.

This study identifies immune suppressive barriers in preinvasive lung cancer progression using single-cell analyses, revealing key cellular and spatial changes that could inform therapeutic strategies.

BayesTME: A unified statistical framework for spatial transcriptomics

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.