Richard K. Perez

This paper introduces scITD, a method to identify multicellular gene expression patterns that distinguish disease individuals, showing its application in SLE and COVID-19 datasets.

This study examines neuronal and behavioral responses to naturalistic texture images in macaque monkeys, finding that V2 neurons better predict texture perception than V1 neurons.

This paper introduces SURGE, a method to discover context-specific eQTLs from single-cell RNA-seq data, showing its relevance in disease through colocalization analysis.

This study shows that low-coverage single-cell RNA sequencing of more samples increases statistical power for cell-type-specific eQTL analysis compared to high-coverage sequencing of fewer samples.

The paper develops a computational method to correct off-target effects in CRISPR-Cas9 essentiality screens, improving specificity while preserving known gene essentialities.

The paper examines laminar differences in V1 and V2 responses to naturalistic textures, finding stronger sensitivity in V2 and feedback-driven activity in V1.