Brian Cleary

This paper investigates the impact of cell growth on RNA velocity methods and shows systematic errors in existing approaches when growth is not accounted for.

This paper introduces SOCS, a trajectory inference method for time-series spatial transcriptomics that preserves structural coherence of biological units over time, improving accuracy compared to existing approaches.

The paper develops a framework to simulate metabolic evolution, identifies evolutionary collective modes as key drivers of simplicity in metabolic network dynamics, and validates these modes using E. coli data.

This study maps the spatiotemporal profile of ovulation in mice using single-cell RNA-seq and spatial transcriptomics, identifying cell state markers and interactions involved in the process.

This paper introduces Perturb-FISH, a method combining spatial transcriptomics and CRISPR screening to study transcriptional circuits, showing intracellular and intercellular effects in immune and neurological contexts.

The paper introduces JSIT, an optimization-based method for decoding imaging spatial transcriptomics data with lower magnification, improving throughput and recovery performance over standard methods.

Compressed Perturb-seq reduces cost and complexity of CRISPR screens by using random composite perturbations and computational decomposition, achieving similar accuracy to conventional methods with greater power to detect genetic interactions.

The paper introduces a method to compress phenotypic screens using computational deconvolution, demonstrating increased efficiency and applicability to complex multicellular models.

The paper maps coronary artery disease risk loci to endothelial cell gene programs, identifying convergence on the CCM pathway and a new CAD gene, TLNRD1.

The paper uses Perturb-CITE-Seq to identify novel immune evasion mechanisms in cancer by analyzing multi-modal single-cell data from patient-derived models.

The paper introduces CISI, a method that uses compressed sensing to efficiently recover spatial gene expression data from composite measurements, achieving high efficiency and scalability.

LAMP-Seq combines three-step isothermal RT-LAMP amplification with combinatorial barcoding to pool and sequence SARS-CoV-2 clinical samples en masse for scalable diagnostics.

This paper introduces composite measurements and molecular compressed sensing to efficiently recover gene expression profiles with fewer measurements than genes.

Optimal transport converts a 65,781-cell reprogramming time course into probabilistic fates and trajectories, exposing alternative cell fates and a gene, Obox6, that improves reprogramming efficiency.